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kwinkunks/rainbow | notebooks/Guessing_colourmaps-MPLIMAGE-DUPECOL.ipynb | 1 | 4242843 | null | apache-2.0 |
Seek/ValueTrackerQT | Untitled.ipynb | 1 | 5336 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sqlalchemy import Column, Integer, String, ForeignKey\n",
"from sqlalchemy.orm import relationship, backref\n",
"\n",
"from sqlalchemy.ext.associationproxy import association_proxy\n",
"from sqlalchemy.ext.declarative import declarative_base\n",
"\n",
"Base = declarative_base()\n",
"\n",
"class User(Base):\n",
" __tablename__ = 'user'\n",
" id = Column(Integer, primary_key=True)\n",
" name = Column(String(64))\n",
"\n",
" # association proxy of \"user_keywords\" collection\n",
" # to \"keyword\" attribute\n",
" keywords = association_proxy('user_keywords', 'keyword')\n",
"\n",
" def __init__(self, name):\n",
" self.name = name\n",
"\n",
"class UserKeyword(Base):\n",
" __tablename__ = 'user_keyword'\n",
" user_id = Column(Integer, ForeignKey('user.id'), primary_key=True)\n",
" keyword_id = Column(Integer, ForeignKey('keyword.id'), primary_key=True)\n",
" special_key = Column(String(50))\n",
"\n",
" # bidirectional attribute/collection of \"user\"/\"user_keywords\"\n",
" user = relationship(User,\n",
" backref=backref(\"user_keywords\",\n",
" cascade=\"all, delete-orphan\")\n",
" )\n",
"\n",
" # reference to the \"Keyword\" object\n",
" keyword = relationship(\"Keyword\")\n",
"\n",
" def __init__(self, keyword=None, user=None, special_key=None):\n",
" self.user = user\n",
" self.keyword = keyword\n",
" self.special_key = special_key\n",
"\n",
"class Keyword(Base):\n",
" __tablename__ = 'keyword'\n",
" id = Column(Integer, primary_key=True)\n",
" keyword = Column('keyword', String(64))\n",
"\n",
" def __init__(self, keyword):\n",
" self.keyword = keyword\n",
"\n",
" def __repr__(self):\n",
" return 'Keyword(%s)' % repr(self.keyword)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Keyword('new_from_blammo'), Keyword('its_big')]\n"
]
},
{
"data": {
"text/plain": [
"[Keyword('new_from_blammo'), Keyword('its_big')]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
">>> user = User('log')\n",
">>> for kw in (Keyword('new_from_blammo'), Keyword('its_big')):\n",
"... user.keywords.append(kw)\n",
"...\n",
">>> print(user.keywords)\n",
"[Keyword('new_from_blammo'), Keyword('its_big')]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<__main__.UserKeyword at 0x21de64d1908>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"UserKeyword(Keyword('its_wood'), user, special_key='my special key')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[Keyword('new_from_blammo'), Keyword('its_big'), Keyword('its_wood')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"user.keywords"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Keyword' object has no attribute 'special_key'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-5-743e46fd84aa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0muser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeywords\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspecial_key\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m: 'Keyword' object has no attribute 'special_key'"
]
}
],
"source": [
"user.keywords[2].special_key"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
alvaroing12/CADL | session-2/lecture-2.ipynb | 3 | 2760808 | null | apache-2.0 |
mne-tools/mne-tools.github.io | dev/_downloads/8b7a85d4b98927c93b7d9ca1da8d2ab2/compute_mne_inverse_volume.ipynb | 1 | 3014 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Compute MNE-dSPM inverse solution on evoked data in volume source space\n\nCompute dSPM inverse solution on MNE evoked dataset in a volume source\nspace and stores the solution in a nifti file for visualisation.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Alexandre Gramfort <[email protected]>\n#\n# License: BSD-3-Clause"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from nilearn.plotting import plot_stat_map\nfrom nilearn.image import index_img\n\nfrom mne.datasets import sample\nfrom mne import read_evokeds\nfrom mne.minimum_norm import apply_inverse, read_inverse_operator\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nmeg_path = data_path / 'MEG' / 'sample'\nfname_inv = meg_path / 'sample_audvis-meg-vol-7-meg-inv.fif'\nfname_evoked = meg_path / 'sample_audvis-ave.fif'\n\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = \"dSPM\" # use dSPM method (could also be MNE or sLORETA)\n\n# Load data\nevoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0))\ninverse_operator = read_inverse_operator(fname_inv)\nsrc = inverse_operator['src']\n\n# Compute inverse solution\nstc = apply_inverse(evoked, inverse_operator, lambda2, method)\nstc.crop(0.0, 0.2)\n\n# Export result as a 4D nifti object\nimg = stc.as_volume(src,\n mri_resolution=False) # set True for full MRI resolution\n\n# Save it as a nifti file\n# nib.save(img, 'mne_%s_inverse.nii.gz' % method)\n\nt1_fname = data_path / 'subjects' / 'sample' / 'mri' / 'T1.mgz'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot with nilearn:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_stat_map(index_img(img, 61), str(t1_fname), threshold=8.,\n title='%s (t=%.1f s.)' % (method, stc.times[61]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
tomchor/pymicra | publications/agu2017/pprog/all_poster.ipynb | 2 | 193277 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"\\begin{exampleblock}{Preamble}"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pymicra\n",
"import pandas as pd\n",
"from glob import glob\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fconfig = pymicra.fileConfig('tij_pr_qc.config')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"\\end{exampleblock}\n",
"\n",
"\\begin{exampleblock}{Basic quality control example}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20110224-1220.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1220.out\n",
"\n",
"20110224-1250.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1250.out\n",
"\n",
"20110224-1340.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1340.out\n",
"\n",
"20110224-1410.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1410.out\n",
"\n",
"20110224-1440.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1440.out\n",
"\n",
"20110224-1510.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1510.out\n",
"\n",
"20110224-1610.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1610.out\n",
"\n",
"20110224-1640.out\n",
"Passed all tests\n",
"Re-writing mydata/20110224-1640.out\n",
"\n",
" control percent\n",
"total 8 100.0\n",
"failed lines test 0 0.0\n",
"failed replacement test 0 0.0\n",
"passed all tests 8 100.0\n",
"Runs with replaced nans 0 0.0\n",
"Runs with replaced bound 0 0.0\n",
"Runs with replaced spikes 8 100.0\n",
"20110224-1220.out\n",
"20110224-1220.out : !FAILED failed maxdif test test!\n",
"\n",
"Failed variable(s): theta_v, u \n",
"\n",
"20110224-1250.out\n",
"20110224-1250.out : !FAILED failed maxdif test test!\n",
"\n",
"Failed variable(s): theta_v \n",
"\n",
"20110224-1340.out\n",
"Passed all tests\n",
"Re-writing out1/20110224-1340.out\n",
"\n",
"20110224-1410.out\n",
"20110224-1410.out : !FAILED failed maxdif test test!\n",
"\n",
"Failed variable(s): theta_v \n",
"\n",
"20110224-1440.out\n",
"Passed all tests\n",
"Re-writing out1/20110224-1440.out\n",
"\n",
"20110224-1510.out\n",
"20110224-1510.out : !FAILED failed maxdif test test!\n",
"\n",
"Failed variable(s): theta_v \n",
"\n",
"20110224-1610.out\n",
"20110224-1610.out : !FAILED failed maxdif test test!\n",
"\n",
"Failed variable(s): theta_v \n",
"\n",
"20110224-1640.out\n",
"Passed all tests\n",
"Re-writing out1/20110224-1640.out\n",
"\n",
" control percent\n",
"total 8 100.0\n",
"failed STD test 0 0.0\n",
"failed maxdif test 5 62.5\n",
"passed all tests 3 37.5\n"
]
},
{
"data": {
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"<div>\n",
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" .dataframe thead tr:only-child th {\n",
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" <td>20110224-1510.out</td>\n",
" <td>NaN</td>\n",
" <td>20110224-1510.out</td>\n",
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"</table>\n",
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],
"text/plain": [
" passed all tests total failed STD test failed maxdif test\n",
"0 NaN 20110224-1220.out NaN 20110224-1220.out\n",
"1 NaN 20110224-1250.out NaN 20110224-1250.out\n",
"2 20110224-1340.out 20110224-1340.out NaN NaN\n",
"3 NaN 20110224-1410.out NaN 20110224-1410.out\n",
"4 20110224-1440.out 20110224-1440.out NaN NaN\n",
"5 NaN 20110224-1510.out NaN 20110224-1510.out\n",
"6 NaN 20110224-1610.out NaN 20110224-1610.out\n",
"7 20110224-1640.out 20110224-1640.out NaN NaN"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fnames = sorted(glob('mydata/*.out'))\n",
"# Prints reports on screen and writes further info to file\n",
"pymicra.util.qc_replace(fnames, fconfig,\n",
" file_lines=36000,\n",
" lower_limits=dict(theta_v=10, mrho_h2o=0, mrho_co2=0),\n",
" upper_limits=dict(theta_v=45),\n",
" spikes_test=True,\n",
" max_replacement_count=360,\n",
" chunk_size=1200,\n",
" outdir='out1',\n",
" replaced_report='rrep.txt')\n",
"\n",
"fnames2 = sorted(glob('out1/*.out'))\n",
"# Prints reports on screen and writes further info to file\n",
"pymicra.util.qc_discard(fnames2, fconfig,\n",
" std_limits = dict(u=0.03, v=0.03, w=0.01, theta_v=0.02),\n",
" dif_limits = dict(u=4.0, v=4.0, w=1.0, theta_v=2.0),\n",
" chunk_size=1200,\n",
" outdir='out2',\n",
" summary_file='discard_summary.csv',\n",
" full_report='frep.txt')\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"\\end{exampleblock}\n",
"\n",
"\\begin{exampleblock}{Pre-processing and calculation of fluxes example}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Beginning of pre-processing ...\n",
"Rotating data with 2d method ... Done!\n",
"Converting theta_v to kelvin ... Done!\n",
"Didn't locate mass density of h2o. Trying to calculate it ... Done!\n",
"Moist air density not present in dataset\n",
"Calculating rho_air = p/(Rdry * theta_v) ... Done!\n",
"Calculating dry_air mass_density = rho_air - rho_h2o ... Done!\n",
"Dry air molar density not in dataset\n",
"Calculating dry_air molar_density = rho_dry / dry_air_molar_mass ... Done!\n",
"Calculating specific humidity = rho_h2o / rho_air ... Done!\n",
"Calculating h2o mass mixing ratio = rho_h2o / rho_dry ... Done!\n",
"Calculating h2o molar mixing ratio = rho_h2o / rho_dry ... Done!\n",
"Thermodynamic temperature not found ... trying to calculate it with theta_v ~ theta (1 + 0.61 q) relation ... done!\n",
"Didn't locate mass density of co2. Trying to calculate it ... Done!\n",
"Calculating co2 mass concentration (g/g) = rho_co2 / rho_air ... Done!\n",
"Calculating co2 mass mixing ratio = rho_co2 / rho_dry ... Done!\n",
"Calculating co2 molar mixing ratio = mrho_co2 / mrho_dry ... Done!\n",
"Pre-processing complete.\n",
"\n",
"Beginning Eddy Covariance method...\n",
"Fluctuations of theta not found. Will try to calculate it with theta' = (theta_v' - 0.61 theta_mean q')/(1 + 0.61 q_mean ... done!\n",
"Calculating fluxes from covariances ... done!\n",
"Applying WPL correction for water vapor flux ... done!\n",
"Applying WPL correction for latent heat flux using result for water vapor flux ... done!\n",
"Re-calculating cov(mrho_h2o', w') according to WPL correction ... done!\n",
"Applying WPL correction for F_co2 ... done!\n",
"Re-calculating cov(mrho_co2', w') according to WPL correction ... done!\n",
"Beginning to extract turbulent scales...\n",
"Data seems to be covariances. Will it use as covariances ...\n",
"Calculating the turbulent scales of wind, temperature and humidity ... done!\n",
"Calculating the turbulent scale of co2 ... done!\n",
"Calculating Obukhov length and stability parameter ... done!\n",
"Calculating turbulent scales of mass concentration ... done!\n",
"Done with Eddy Covariance.\n",
"\n"
]
},
{
"data": {
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"<style>\n",
" .dataframe thead tr:only-child th {\n",
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" .dataframe tbody tr th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>tau</th>\n",
" <th>H</th>\n",
" <th>Hv</th>\n",
" <th>E</th>\n",
" <th>LE</th>\n",
" <th>F_co2</th>\n",
" <th>u_star</th>\n",
" <th>theta_v_star</th>\n",
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" <td>-0.012771</td>\n",
" <td>0.180323</td>\n",
" <td>-0.029847</td>\n",
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" <td>21.794729</td>\n",
" <td>-0.070824</td>\n",
" <td>83.302161</td>\n",
" <td>0.022208</td>\n",
" <td>0.000369</td>\n",
" <td>-0.000003</td>\n",
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"</div>"
],
"text/plain": [
" tau H Hv \\\n",
" <kilogram / meter / second ** 2> <watt / meter ** 2> <watt / meter ** 2> \n",
"0 0.034587 -18.135628 -5.744854 \n",
"\n",
" E LE \\\n",
" <millimole / meter ** 2 / second> <watt / meter ** 2> \n",
"0 3.930086 173.166173 \n",
"\n",
" F_co2 u_star theta_v_star theta_star \\\n",
" <millimole / meter ** 2 / second> <meter / second> <kelvin> <kelvin> \n",
"0 -0.012771 0.180323 -0.029847 -0.094221 \n",
"\n",
" mrho_h2o_star mrho_co2_star Lo zeta \\\n",
" <millimole / meter ** 3> <millimole / meter ** 3> <meter> <dimensionless> \n",
"0 21.794729 -0.070824 83.302161 0.022208 \n",
"\n",
" q_star conc_co2_star \n",
" <dimensionless> <dimensionless> \n",
"0 0.000369 -0.000003 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fconfig = pymicra.fileConfig('tij_pr.config')\n",
"sconfig = pymicra.siteConfig('tij_pr.site')\n",
"fname = 'out2/20110224-1340.out'\n",
"\n",
"data, units = pymicra.timeSeries(fname, fconfig, parse_dates=False)\n",
"# Prints reports showing which calculations are being done\n",
"data = pymicra.micro.preProcess(data, units, solutes=['co2'])\n",
"fulldata = data.detrend(units=units, ignore=['p'], join_data=True)\n",
"\n",
"# Prints reports showing which calculations are being done\n",
"results = pymicra.micro.eddyCovariance(fulldata, units, \n",
" site_config=sconfig, \n",
" wpl=True, solutes=['co2'])\n",
"print(results.with_units(units))"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"\\end{exampleblock}\n",
"\n",
"\\begin{exampleblock}{Visualization is easy}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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n6n+fWsCzCKHC4MHu/RICq4IKc2uC8aSX/KJbXOg8l+ennk2De3dl3odX1moj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\nDk4kQAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e79141828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data[['u', 'v', 'w']].plot(figsize=(15,9), grid=True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"image/png": 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iAwCFWamKWvHxByS8/u1BAEA1zx4RERHRMMPgoxBWmVZ8bF3JE3xK84w40ulB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\nmDAERnaFt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EkqbNnlYcADsHVzzk5vm35M+CnSMvhIkiRJKmH2bATHj4RPXoOplya8vMFH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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e70358f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"uw_spectra = pymicra.spectra(fulldata[[\"u'\", \"w'\"]], \n",
" frequency=20, anti_aliasing=True)\n",
"uw_spectra.binned(bins_number=100).plot(loglog=True, \n",
" grid=True, figsize=(14,10))\n",
"plt.show()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"\\end{exampleblock}"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
JasonSanchez/w261 | week11/MIDS-W261-HW-11-Sanchez.ipynb | 1 | 327022 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MIDS - w261 Machine Learning At Scale\n",
"__Course Lead:__ Dr James G. Shanahan (__email__ Jimi via James.Shanahan _AT_ gmail.com)\n",
"\n",
"## Assignment - HW11\n",
"\n",
"\n",
"---\n",
"__Name:__ *Jason Sanchez* \n",
"__Class:__ MIDS w261 (Section *Fall 2016 Group 2*, e.g., Summer 2016 Group 1) \n",
"__Email:__ *jason.sanchez*@iSchool.Berkeley.edu \n",
"__Week:__ 11"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"<a name=\"1\">\n",
"# 1 Instructions\n",
"[Back to Table of Contents](#TOC)\n",
"\n",
"MIDS UC Berkeley, Machine Learning at Scale\n",
"DATSCIW261 ASSIGNMENT #11\n",
"\n",
"Version 2016-07-27 (FINAL)\n",
"\n",
"\n",
" === INSTRUCTIONS for SUBMISSIONS ===\n",
"Follow the instructions for submissions carefully.\n",
"\n",
"https://docs.google.com/forms/d/1ZOr9RnIe_A06AcZDB6K1mJN4vrLeSmS2PD6Xm3eOiis/viewform?usp=send_form \n",
"\n",
"# TYPE-1.5 Fun option: Complete HW11.8 only (no need to complete the rest of the questions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I am choosing to do the Type 1.5 fun option. Mainly because this will give me more time to perform Spark experiments without focusing on the exact requirements of each HW problem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"HW11.8\"><h2 style=\"color:darkgreen\"> HW11.8 [OPTIONAL] Create an animation of gradient descent for the Perceptron learning or for the logistic regression </h2>\n",
"[Back to Table of Contents](#TOC)\n",
" \n",
"Learning with the following 3 training examples. Present the progress in terms of the 2 dimensional input space in terms of a contour plot and also in terms of the 3D surface plot. See Live slides for an example.\n",
"[Back to Table of Contents](#TOC)\n",
"Here is a sample training dataset that can be used:\n",
"-2, 3, +1\n",
"-1, -1, -1\n",
"2, -3, 1\n",
"\n",
"Please feel free to use \n",
" + R (yes R!)\n",
" + d3\n",
" + https://plot.ly/python/\n",
" + Matplotlib\n",
"\n",
"I am happy for folks to collaborate on HW11.8 also.\n",
"\n",
"It would be great to get the 3D surface and contours lines (with solution region and label normalized data) all in the same graph\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I chose to implement the solution for a perceptron.\n",
"\n",
"In this first chart, I show the decision boundary of the solution after each iteration. The darker the line, the higher the iteration count.\n",
"\n",
"In implementing the solution, there were a few design decisions I made: \n",
"* I presumed an intercept term was not allowed (otherwise the problem would be too each considering there would be three features and only three data points to classify. This meant that the decision boundary must pass through the origin.\n",
"* A decision boundary that passes throught the origin on this dataset is particularly pernicious because a line that passes through the positive labels passes directly through the origin. This means that how we deal with predictions of exactly 0 is important. To make the problem more difficult, I wrote the code to classify points that fall on the decision boundary as always being incorrect classifications. The effect of this is that the decision boundary never passes through a point, but instead tries to get very, very close to each point.\n",
"* The next issue is that we are guaranteed to misclassify one of the two positive points with every iteration. The weights continually decrease in an effort to classify this single point correctly and never converge to a single solution. To solve this issue, I adjusted the gradient to include a term that forces the weights to sum to 1 over time. This forces the algorithm to converge towards a single point.\n",
"* Also, I added a decaying learning rate to prevent oscillations. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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aa67h6NGjrF+/nrq6OgICAsjLy2PWrFmcPHmSTZs20d7efl4A6OnTpy9LAKhz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\nXDDMzMwsdy4YZmZmlrv/AAKXIWeq1Wj9AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe7817a940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from random import choice, seed\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"import numpy as np\n",
"from ipywidgets import interact\n",
"\n",
"seed(333)\n",
"\n",
"data = [((-2, 3), 1),\n",
" ((-1, -1), -1),\n",
" (( 2, -3), 1)]\n",
"\n",
"weights = [1, 1]\n",
"\n",
"n_iterations = 101\n",
"w1_vals = []\n",
"w2_vals = []\n",
"data_loss = []\n",
"\n",
"def predict(X, weights, y=None):\n",
" return sum(w*x for w, x in zip(weights, X)) or -y\n",
"\n",
"def error_rate(prediction, y):\n",
" return max(0, -y*prediction)\n",
"\n",
"@np.vectorize\n",
"def all_points_error(w1, w2):\n",
" data = [((-2, 3), 1),\n",
" ((-1, -1), -1),\n",
" (( 2, -3), 1)]\n",
" total_error = []\n",
" for X, y in data:\n",
" y_hat = predict(X, (w1, w2), y)\n",
" loss = error_rate(y_hat, y)\n",
" total_error.append(loss)\n",
" average_error = sum(total_error)/len(total_error)\n",
" return average_error\n",
"\n",
"for t in range(n_iterations):\n",
" X, y = choice(data)\n",
" # Predict with current weights. If tie, predict wrong.\n",
" prediction = predict(X, weights, y)\n",
" loss = error_rate(prediction, y)\n",
" if loss:\n",
" for index, x in enumerate(X):\n",
" total_weight = sum(weights) or 1\n",
" bias = (1-total_weight)*(weights[index]/total_weight)\n",
" delta = y*x/(t+1) + bias\n",
" weights[index] += delta\n",
" if t%(n_iterations//50) == 0:\n",
" w1, w2 = weights[0], weights[1]\n",
" w1_vals.append(w1)\n",
" w2_vals.append(w2)\n",
" data_loss.append(all_points_error(w1, w2))\n",
" \n",
"plt.figure()\n",
"for index, (w1, w2) in enumerate(zip(w1_vals, w2_vals)):\n",
" x1 = list(range(-3,4))\n",
" x2 = [-w1/w2*x for x in x1]\n",
" plt.plot(x1, x2, color=\"%f\" % (1-index/len(w1_vals)))\n",
"plt.plot(-2, 3, \"o\", color=\"LawnGreen\")\n",
"plt.plot(-1, -1, \"o\", color=\"OrangeRed\")\n",
"plt.plot(2, -3, \"o\", color=\"LawnGreen\")\n",
"plt.title(\"The decision boundary from each \\niteration of SGD\")\n",
"plt.xlabel(\"x1\")\n",
"plt.ylabel(\"x2\")\n",
"plt.ylim(-5,5)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This code demonstrates that the sum of the weights approach one over time."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-0.2142857142857144,\n",
" 2.0650441518460436,\n",
" 1.7169103689261154,\n",
" 1.7169103689261154,\n",
" 0.7312983037479666,\n",
" 0.7312983037479666,\n",
" 1.0673822587223123,\n",
" 1.0673822587223123,\n",
" 1.1180876498808339,\n",
" 1.1180876498808339,\n",
" 1.1180876498808339,\n",
" 1.1180876498808339,\n",
" 0.9494781872704072,\n",
" 0.9494781872704072,\n",
" 1.0510890424353243,\n",
" 0.950301752291886,\n",
" 1.0432275310405712,\n",
" 1.0432275310405712,\n",
" 0.9604745056017685,\n",
" 1.035943177497436,\n",
" 1.035943177497436,\n",
" 1.035943177497436,\n",
" 0.9677563875765219,\n",
" 1.030107184823184,\n",
" 1.030107184823184,\n",
" 1.030107184823184,\n",
" 1.030107184823184,\n",
" 0.9732447813415748,\n",
" 0.9732447813415748,\n",
" 1.0243435880581493,\n",
" 0.9758710309609366,\n",
" 0.9758710309609366,\n",
" 1.0216457599432598,\n",
" 1.0216457599432598,\n",
" 1.0216457599432598,\n",
" 0.9791495034306519,\n",
" 0.9791495034306519,\n",
" 0.9791495034306519,\n",
" 1.0181609849994757,\n",
" 0.9813639159529808,\n",
" 0.9818740380887245,\n",
" 0.9818740380887245,\n",
" 1.0168554820916589,\n",
" 0.9832205292190515,\n",
" 0.9832205292190515,\n",
" 0.9832205292190515,\n",
" 0.9832205292190515,\n",
" 1.0149128120401925,\n",
" 1.0149128120401925,\n",
" 0.9851337734289363,\n",
" 0.9851337734289363]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[sum(total_weight) for total_weight in zip(w1_vals, w2_vals)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, here is a 3D surface chart that you can move using the sliders. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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iOcVisbwlvKmbW7AxzlQJaGaUE6Vyp5uRhGZEFGSxsGYgsR5lZWVJBwSPx5P3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\nOfqFF14gJSWF3NxchoeHee2111i1ahXbtm1jzpw5J7IUj9i7d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BKdjjLDYgIikUgICwub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+mUsuuWRS3AuOBRqNhrCwsDGfb2xsRKVSce6559ofCw4OZsaMGezbt+9kBuSj4hc9SyIiIpDJ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\nBAYj4uMWPUPAO+BIRFS7mc8sbX4DCTeS/FdBERYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe53e2c390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def convex_plot(azim, elev):\n",
" fig = plt.figure()\n",
" ax = fig.gca(projection=\"3d\")\n",
"\n",
" x1mesh, x2mesh = np.mgrid[-2:2:200j,-2:2:200j]\n",
" fmesh = all_points_error(x1mesh, x2mesh)\n",
" ax.plot_surface(x1mesh, x2mesh, fmesh, alpha=.5, linewidth=0.35)\n",
" ax.view_init(azim=azim, elev=elev)\n",
"\n",
" ax.plot3D(w1_vals, w2_vals, data_loss, color=\"r\")\n",
" plt.show()\n",
" \n",
"interact(convex_plot, azim=(0,90,10), elev=(0,90,10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And here is a contour plot. I plotted the contour of the original loss function so you could see the valley that the optimization routine would chase as well as the clear convergence of the algorithm."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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TvSHegnfJGcvgjo8gMwcmDIFb+oT/luD1wpgvYPhoaFwPfh8PZ54U/l4zM+GJ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+dQ+s/Sr683oh+XeoBMGfUsxKAaONnhSNn32aGqToUYmNrPWhFM82WE/RV/nUZODyqS/3qad8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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe7817a2e8>"
]
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],
"source": [
"fig = plt.figure(figsize=(5,5))\n",
"plt.axis(\"equal\")\n",
"x1mesh, x2mesh = np.mgrid[-2:2:200j,-2:2:200j]\n",
"fmesh = all_points_error(x1mesh, x2mesh)\n",
"CS = plt.contour(x1mesh, x2mesh, fmesh, 30)\n",
"plt.plot(w1_vals, w2_vals, \"o-\", markersize=5, linewidth=4, alpha=.5)\n",
"plt.ylim(-2,2)\n",
"plt.xlim(-2,2)\n",
"plt.clabel(CS, inline=1, inline_spacing=5, fontsize=8)\n",
"\n",
"plt.show()"
]
}
],
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| mit |
ifduyue/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb | 2 | 369658 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Probabilistic Programming\n",
"=====\n",
"and Bayesian Methods for Hackers \n",
"========\n",
"\n",
"##### Version 0.1\n",
"\n",
"`Original content created by Cam Davidson-Pilon`\n",
"\n",
"`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n",
"___\n",
"\n",
"\n",
"Welcome to *Bayesian Methods for Hackers*. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chapter 1\n",
"======\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Philosophy of Bayesian Inference\n",
"------\n",
" \n",
"> You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, *even more difficult*, test too! You are starting to believe that there may be no bugs in this code...\n",
"\n",
"If you think this way, then congratulations, you already are thinking Bayesian! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more *confident* about our code, but still not certain. Bayesian inference works identically: we update our beliefs about an outcome; rarely can we be absolutely sure unless we rule out all other alternatives. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### The Bayesian state of mind\n",
"\n",
"\n",
"Bayesian inference differs from more traditional statistical inference by preserving *uncertainty*. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving *certainty* from randomness? To reconcile this, we need to start thinking like Bayesians. \n",
"\n",
"The Bayesian world-view interprets probability as measure of *believability in an event*, that is, how confident we are in an event occurring. In fact, we will see in a moment that this is the natural interpretation of probability. \n",
"\n",
"For this to be clearer, we consider an alternative interpretation of probability: *Frequentist*, known as the more *classical* version of statistics, assume that probability is the long-run frequency of events (hence the bestowed title). For example, the *probability of plane accidents* under a frequentist philosophy is interpreted as the *long-term frequency of plane accidents*. This makes logical sense for many probabilities of events, but becomes more difficult to understand when events have no long-term frequency of occurrences. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities and saying across all these realities, the frequency of occurrences defines the probability. \n",
"\n",
"Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of *belief*, or confidence, of an event occurring. Simply, a probability is a summary of an opinion. An individual who assigns a belief of 0 to an event has no confidence that the event will occur; conversely, assigning a belief of 1 implies that the individual is absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, an individual's belief should be equal to that frequency, excluding any outside information. Similarly, under this definition of probability being equal to beliefs, it is meaningful to speak about probabilities (beliefs) of presidential election outcomes: how confident are you candidate *A* will win?\n",
"\n",
"Notice in the paragraph above, I assigned the belief (probability) measure to an *individual*, not to Nature. This is very interesting, as this definition leaves room for conflicting beliefs between individuals. Again, this is appropriate for what naturally occurs: different individuals have different beliefs of events occurring, because they possess different *information* about the world. The existence of different beliefs does not imply that anyone is wrong. Consider the following examples demonstrating the relationship between individual beliefs and probabilities:\n",
"\n",
"- I flip a coin, and we both guess the result. We would both agree, assuming the coin is fair, that the probability of Heads is 1/2. Assume, then, that I peek at the coin. Now I know for certain what the result is: I assign probability 1.0 to either Heads or Tails (whichever it is). Now what is *your* belief that the coin is Heads? My knowledge of the outcome has not changed the coin's results. Thus we assign different probabilities to the result. \n",
"\n",
"- Your code either has a bug in it or not, but we do not know for certain which is true, though we have a belief about the presence or absence of a bug. \n",
"\n",
"- A medical patient is exhibiting symptoms $x$, $y$ and $z$. There are a number of diseases that could be causing all of them, but only a single disease is present. A doctor has beliefs about which disease, but a second doctor may have slightly different beliefs. \n",
"\n",
"\n",
"This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial truths, but gather evidence to form beliefs. Alternatively, you have to be *trained* to think like a frequentist. \n",
"\n",
"To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$. We call this quantity the *prior probability*.\n",
"\n",
"John Maynard Keynes, a great economist and thinker, said \"When the facts change, I change my mind. What do you do, sir?\" This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even — especially — if the evidence is counter to what was initially believed, the evidence cannot be ignored. We denote our updated belief as $P(A |X )$, interpreted as the probability of $A$ given the evidence $X$. We call the updated belief the *posterior probability* so as to contrast it with the prior probability. For example, consider the posterior probabilities (read: posterior beliefs) of the above examples, after observing some evidence $X$:\n",
"\n",
"1\\. $P(A): \\;\\;$ the coin has a 50 percent chance of being Heads. $P(A | X):\\;\\;$ You look at the coin, observe a Heads has landed, denote this information $X$, and trivially assign probability 1.0 to Heads and 0.0 to Tails.\n",
"\n",
"2\\. $P(A): \\;\\;$ This big, complex code likely has a bug in it. $P(A | X): \\;\\;$ The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.\n",
"\n",
"3\\. $P(A):\\;\\;$ The patient could have any number of diseases. $P(A | X):\\;\\;$ Performing a blood test generated evidence $X$, ruling out some of the possible diseases from consideration.\n",
"\n",
"\n",
"It's clear that in each example we did not completely discard the prior belief after seeing new evidence $X$, but we *re-weighted the prior* to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others). \n",
"\n",
"By introducing prior uncertainty about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, we update our beliefs, and our guess becomes *less wrong*. This is the alternative side of the prediction coin, where typically we try to be *more right*. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Bayesian Inference in Practice\n",
"\n",
" If frequentist and Bayesian inference were programming functions, with inputs being statistical problems, then the two would be different in what they return to the user. The frequentist inference function would return a number, representing an estimate (typically a summary statistic like the sample average etc.), whereas the Bayesian function would return *probabilities*.\n",
"\n",
"For example, in our debugging problem above, calling the frequentist function with the argument \"My code passed all $X$ tests; is my code bug-free?\" would return a *YES*. On the other hand, asking our Bayesian function \"Often my code has bugs. My code passed all $X$ tests; is my code bug-free?\" would return something very different: probabilities of *YES* and *NO*. The function might return:\n",
"\n",
"\n",
"> *YES*, with probability 0.8; *NO*, with probability 0.2\n",
"\n",
"\n",
"\n",
"This is very different from the answer the frequentist function returned. Notice that the Bayesian function accepted an additional argument: *\"Often my code has bugs\"*. This parameter is the *prior*. By including the prior parameter, we are telling the Bayesian function to include our belief about the situation. Technically this parameter in the Bayesian function is optional, but we will see excluding it has its own consequences. \n",
"\n",
"\n",
"#### Incorporating evidence\n",
"\n",
"As we acquire more and more instances of evidence, our prior belief is *washed out* by the new evidence. This is to be expected. For example, if your prior belief is something ridiculous, like \"I expect the sun to explode today\", and each day you are proved wrong, you would hope that any inference would correct you, or at least align your beliefs better. Bayesian inference will correct this belief.\n",
"\n",
"\n",
"Denote $N$ as the number of instances of evidence we possess. As we gather an *infinite* amount of evidence, say as $N \\rightarrow \\infty$, our Bayesian results (often) align with frequentist results. Hence for large $N$, statistical inference is more or less objective. On the other hand, for small $N$, inference is much more *unstable*: frequentist estimates have more variance and larger confidence intervals. This is where Bayesian analysis excels. By introducing a prior, and returning probabilities (instead of a scalar estimate), we *preserve the uncertainty* that reflects the instability of statistical inference of a small $N$ dataset. \n",
"\n",
"One may think that for large $N$, one can be indifferent between the two techniques since they offer similar inference, and might lean towards the computationally-simpler, frequentist methods. An individual in this position should consider the following quote by Andrew Gelman (2005)[1], before making such a decision:\n",
"\n",
"> Sample sizes are never large. If $N$ is too small to get a sufficiently-precise estimate, you need to get more data (or make more assumptions). But once $N$ is \"large enough,\" you can start subdividing the data to learn more (for example, in a public opinion poll, once you have a good estimate for the entire country, you can estimate among men and women, northerners and southerners, different age groups, etc.). $N$ is never enough because if it were \"enough\" you'd already be on to the next problem for which you need more data.\n",
"\n",
"### Are frequentist methods incorrect then? \n",
"\n",
"**No.**\n",
"\n",
"Frequentist methods are still useful or state-of-the-art in many areas. Tools such as least squares linear regression, LASSO regression, and expectation-maximization algorithms are all powerful and fast. Bayesian methods complement these techniques by solving problems that these approaches cannot, or by illuminating the underlying system with more flexible modeling.\n",
"\n",
"\n",
"#### A note on *Big Data*\n",
"Paradoxically, big data's predictive analytic problems are actually solved by relatively simple algorithms [2][4]. Thus we can argue that big data's prediction difficulty does not lie in the algorithm used, but instead on the computational difficulties of storage and execution on big data. (One should also consider Gelman's quote from above and ask \"Do I really have big data?\")\n",
"\n",
"The much more difficult analytic problems involve *medium data* and, especially troublesome, *really small data*. Using a similar argument as Gelman's above, if big data problems are *big enough* to be readily solved, then we should be more interested in the *not-quite-big enough* datasets. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Our Bayesian framework\n",
"\n",
"We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a *prior* belief in event $A$, beliefs formed by previous information, e.g., our prior belief about bugs being in our code before performing tests.\n",
"\n",
"Secondly, we observe our evidence. To continue our buggy-code example: if our code passes $X$ tests, we want to update our belief to incorporate this. We call this new belief the *posterior* probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after its discoverer Thomas Bayes:\n",
"\n",
"\\begin{align}\n",
" P( A | X ) = & \\frac{ P(X | A) P(A) } {P(X) } \\\\\\\\[5pt]\n",
"& \\propto P(X | A) P(A)\\;\\; (\\propto \\text{is proportional to })\n",
"\\end{align}\n",
"\n",
"The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it to connect prior probabilities $P(A)$ with an updated posterior probabilities $P(A | X )$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Example: Mandatory coin-flip example\n",
"\n",
"Every statistics text must contain a coin-flipping example, I'll use it here to get it out of the way. Suppose, naively, that you are unsure about the probability of heads in a coin flip (spoiler alert: it's 50%). You believe there is some true underlying ratio, call it $p$, but have no prior opinion on what $p$ might be. \n",
"\n",
"We begin to flip a coin, and record the observations: either $H$ or $T$. This is our observed data. An interesting question to ask is how our inference changes as we observe more and more data? More specifically, what do our posterior probabilities look like when we have little data, versus when we have lots of data. \n",
"\n",
"Below we plot a sequence of updating posterior probabilities as we observe increasing amounts of data (coin flips)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
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R0wqEUkoppZRSKmJagVBKKaWUUkpFTCsQSimllFJKqYhpBUIppZRSSikVMa1AKKWUUkop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7P3cDerPj3EjBSbtzMxMVrWsokhkzUQwyzY8+9UVaWspYeN/Z2GspYe33M6BSb0jzLkxZuJNo+O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\nOhAiIiIiIhKaOhAiIiIiIhLa/wNELXdUsJ1f3wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f53fc284550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"The book uses a custom matplotlibrc file, which provides the unique styles for\n",
"matplotlib plots. If executing this book, and you wish to use the book's\n",
"styling, provided are two options:\n",
" 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n",
" book's styles/ dir. See http://matplotlib.org/users/customizing.html\n",
" 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n",
" update the styles in only this notebook. Try running the following code:\n",
"\n",
" import json\n",
" s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n",
" matplotlib.rcParams.update(s)\n",
"\n",
"\"\"\"\n",
"\n",
"# The code below can be passed over, as it is currently not important, plus it\n",
"# uses advanced topics we have not covered yet. LOOK AT PICTURE, MICHAEL!\n",
"%matplotlib inline\n",
"from IPython.core.pylabtools import figsize\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"figsize(11, 9)\n",
"\n",
"import scipy.stats as stats\n",
"\n",
"dist = stats.beta\n",
"n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]\n",
"data = stats.bernoulli.rvs(0.5, size=n_trials[-1])\n",
"x = np.linspace(0, 1, 100)\n",
"\n",
"# For the already prepared, I'm using Binomial's conj. prior.\n",
"for k, N in enumerate(n_trials):\n",
" sx = plt.subplot(len(n_trials)/2, 2, k+1)\n",
" plt.xlabel(\"$p$, probability of heads\") \\\n",
" if k in [0, len(n_trials)-1] else None\n",
" plt.setp(sx.get_yticklabels(), visible=False)\n",
" heads = data[:N].sum()\n",
" y = dist.pdf(x, 1 + heads, 1 + N - heads)\n",
" plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n",
" plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n",
" plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n",
"\n",
" leg = plt.legend()\n",
" leg.get_frame().set_alpha(0.4)\n",
" plt.autoscale(tight=True)\n",
"\n",
"\n",
"plt.suptitle(\"Bayesian updating of posterior probabilities\",\n",
" y=1.02,\n",
" fontsize=14)\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The posterior probabilities are represented by the curves, and our uncertainty is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line). \n",
"\n",
"Notice that the plots are not always *peaked* at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what $p$ is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased *away* from lumping around 0.5 (with no prior opinion, how confident would you feel betting on a fair coin after observing 8 tails and 1 head?). As more data accumulates, we would see more and more probability being assigned at $p=0.5$, though never all of it.\n",
"\n",
"The next example is a simple demonstration of the mathematics of Bayesian inference. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Example: Bug, or just sweet, unintended feature?\n",
"\n",
"\n",
"Let $A$ denote the event that our code has **no bugs** in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability of no bugs as a variable, i.e. $P(A) = p$. \n",
"\n",
"We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$. To use the formula above, we need to compute some quantities.\n",
"\n",
"What is $P(X | A)$, i.e., the probability that the code passes $X$ tests *given* there are no bugs? Well, it is equal to 1, for a code with no bugs will pass all tests. \n",
"\n",
"$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event $X$ occurring even though our code *indeed has* bugs (denoted $\\sim A\\;$, spoken *not $A$*), or event $X$ without bugs ($A$). $P(X)$ can be represented as:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\begin{align}\n",
"P(X ) & = P(X \\text{ and } A) + P(X \\text{ and } \\sim A) \\\\\\\\[5pt]\n",
" & = P(X|A)P(A) + P(X | \\sim A)P(\\sim A)\\\\\\\\[5pt]\n",
"& = P(X|A)p + P(X | \\sim A)(1-p)\n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have already computed $P(X|A)$ above. On the other hand, $P(X | \\sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is reduced. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\\sim A) = 0.5$. Then\n",
"\n",
"\\begin{align}\n",
"P(A | X) & = \\frac{1\\cdot p}{ 1\\cdot p +0.5 (1-p) } \\\\\\\\\n",
"& = \\frac{ 2 p}{1+p}\n",
"\\end{align}\n",
"This is the posterior probability. What does it look like as a function of our prior, $p \\in [0,1]$? "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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0e062s+dEW6S4b+dkW3fM55iSn8nCSFG/sDyPheW5TNfEmwlHPfoiIiIiY1R/P/3uE+3s\nPdnGnkj7TXNnb0zvT4tcIPtGUR8U9pN5Lv14ox59ERERkXGup8+pbmhn94mg/WZPpLjvjLGfPjvd\nmF+Wy6LyPBZOyWVReS7zSnN199hJLO5C38w+APwtcAL4Z3f/UcKiioN69CVW6qOVMJQvEivlioTx\nxJNPMXPZhQOK+v317XT3xVbUF2Sls7A8KOYXTcljUXkus4pzSE9T64284VxW9LOANcBa4H1mVubu\nX0tMWCIiIiITQ0dPH/tOtrP7RBt7Trax+0Q7WzftJX93YUzvL8/LPF3Q9xf30wqyNPVGzupcCv3j\n7t4GbAQ2mtmHExRTXNasWZPKj5dxRCtuEobyRWKlXBGAzp4+9tW3s6uujd0ngkf1qQ4GL9TnLxi+\nbplWkMXiKUH7zaIpuSwuz6M0LzMJkctEdC6F/pvM7H3Az4AngU4AMytx91OJCE5ERERkrOov6vsL\n+t0n2jjQMLSoH0llUfbpYn5RpLgv0kWykkDnkk3bgIeANwMPANPMbDUwBfhAAmILRT36Eiv10UoY\nyheJlXJlYuvq7WN/ZKV+14mg/eZAQ3tMRb0RTL5ZVJ7L4il5LJ6Sy/Edr3DdNReMetwyuZ1Lof8C\nMMvdPwt81szygWuBP09IZCIiIiIp0NvnVDd0sPNEG7vr2th5opX99R30xFDVGzCrOJvFU/JYMjWP\nxVPyWFiWS15W+oDjNu7VJBwZfQmfo29mi919d0JPGgPN0RcREZGw+tw53NjJzkhP/c66tphHWhpQ\n2V/UTwmK+kXlQ4t6kXMxpubop6LIFxERETkbd6eutZsdda3sqms7Xdy3dffF9P6ZRdksmZLLkshq\n/cLyPPJV1MsYFlehb2Y3ufvDg5+nknr0JVbqo5UwlC8SK+XK2NPU0cOuyCr9zrpWdta10dDeE9N7\np+Zncl6k9ab/z8LsxK2PKl8kGeLN2EuBh4d5LiIiIpJ0nT197DnZX9QHjyNNnTG9tyQng/OmBqv0\nSyJtOBppKRNBvIW+jfA8ZTRHX2KlFRQJQ/kisVKuJE+fOzWnOthx/I2V+v317cTQVk9eZhpLpuZx\n3pQ8zpuaz5KpeUzNz0z6zaeUL5IM8Rb6PsJzERERkYRqaOtmR10b24+3nu6vj6WvPiPNWFiey3lT\n8yKPfGYVZ5OmO8rKJJGIFf0xQT36Eiv1RUoYyheJlXIlMTp7+thzoo3tdW3sPN7Kjro2jrV0xfTe\n2cXZnFeRz/mRwn5+WS5Z6WNzjKXyRZJBt18TERGRlHB3jjR1sv14sFq//XhrzC04ZbkZp4v68yMt\nOJqAIzJQIlp3xgT16EustIIiYShfJFbKlbNr7eplZ10r24+3sSNS2Dd19p71fVnpxuIpeUFRX5HP\n+VPzqShIfl99IilfJBkmzMW4IiIiMnb0uXPwVMeA1frqho6YVgpnF2dHCvqgsJ9flktGmsoNkbDi\nLfT/Y4TnKaMefYmV+iIlDOWLxGqy50pLZ8+Aon5HXRutXWdfrS/MTmdpRf7pwv68qYmdVz9WTfZ8\nkeSI678kd9833HMRERGZ+NydQ42dbDveyrZjrWyLrNafTZrB/LJclk7NZ+m0PJZW5FNZlD2uW3BE\nxjJzT227vZndCHwRSAPud/fPD3q9CHgQmAOkA/e4+zcGn+fxxx93reiLiIgkXnt3L7vq2gYU9s0x\n9NYX52SwrCKf8yvyWFYRXDCbm6kLZkXC2Lx5Mxs2bIjrp+GU/m7MzNKALwMbgCPAS2b2U3ffEXXY\nx4DX3f0mM5sC7DSzB909tntYi4iISMzcneMt3Ww73nK6qN97sp2+s6wLphksLM9lWUU+SyOP6YVZ\nWq0XSaHQhb6ZFQCXA4uBIqAVqAWecffDIU93MbDb3asj5/4ucDMQXeg7UBh5XgicHK7IV4++xEp9\nkRKG8kViNV5zpbfP2Vffzmu1QWH/+rFWTrR1n/V9hdnpLKvIZ9m0fK3Wx2G85ouMLzEX+ma2DLgb\nyAK2EKzA7wBygTLgT82sBHjM3b8X42krgYNR24cIiv9oXwYeNrMjQAFwa6wxi4iIyEBtXb1sPx4U\n9K8fa2H78TY6es5+l9m5pTksq8hn+bRgtX5WsXrrRca6mAp9M7sVyAP+1N07z3LsRWb2SeBf3L09\nATHeALzi7tea2ULgMTNb5e4t0Qft2bOHj370o8yZMweA4uJiVq5cefqn5Y0bNwJoW9usX79+TMWj\n7bG9rXzR9njffuSxJzjQ0IHNWsHrx1rZ8tJz9DkULQzuP9O0twoYuJ2dkcall13O8mkFdNe8ypyS\nHK6/5oLT568+DrPHyN9P29qeaNtbt26lsbERgJqaGtatW8eGDRuIR0wX45rZHHevifmkZunAVHev\nPctxlwJ/7+43RrY/BXj0Bblm9jPgs+7+TGT7ceCT7r4p+ly6GFdERCa7PneqGzp4rbaF14618lpt\nC3WtZ2/DmZKfyfJp+ayYVsDyacHc+nTNrRcZE0b9YtxYinwze5O7Px05vpegb/9sXgIWmdlc4Cjw\nXuC2QcdUA9cBz5jZNGAJMGSkp3r0JVYbN6ovUmKnfJFYpSJXunv72H0i6K/fWtsS0zQcIxhxuXxa\nPium57N8WgEVBVnJCVhO0/cWSYaYCv1okUk5Mwj662dGPa4DLg1zLnfvNbO7gUd5Y7zmdjO7K3jZ\n7wP+EfiGmb0aedtfuXt92LhFRETGu7auXrYdD1bqX6ttZUddK129Z/7NfHZGGksr8lgeWa1fWpFP\nfpYumhWZDELN0Tezp4HLgC7gGMGqfTrwDLDa3a8djSBjodYdERGZaE61d/NabStbIyv2++rPPuay\nOCeDlZGV+pXTC1hQnkuG2nBExq1kztF/M/AJYJe7/xjAzD7g7t80M/3+SURE5BycaO3i1aMtkcK+\nlZpTZ7/b7IzCLFZML2DF9AJWTtedZkXkDaEK/cgUnc+b2QVm9hngfoI597j7xlGIL2bq0ZdYqS9S\nwlC+SKzC5oq7c7S5KyjqI8X90eauM77HgAXluayYVsCK6cHFs+X5mecYuaSCvrdIMoTu0Qdw91ci\nPfMfAy42swcJ2oDOfj9sERGRScjdqTnVwdb+VpyjLWe9MVVmmnHe1LzIin3QjqP+ehGJVage/WFP\nYLYAuANY4u4pu5mVevRFRGQs6R91+erRFl6tbeHVoy00dvSc8T3Z6cayafmsnB70159fkU92RlqS\nIhaRsSiZPfpDuPs+4O8i8+5FREQmpejCfkukFedshX1eZhrLpxWwakZQ2C+ekktmugp7EUmMcy70\no/xjAs8Vmnr0JVbqi5QwlC8ykv7CfsvRFl492syTTz1N+pxVZ3xPYXb66dX6VTMKWKAbU01a+t4i\nyZCwQt/dn0/UuURERMYad+dAVGH/6tEWmqJuTtXa3UfRoPcUZaezakYhq2cEhf3c0hzSNBFHRJLk\nrD36ZjYfuMTdvxvTCc3KgVvc/d8TEF/M1KMvIiKJ5O4cauxky9EWqo40syWGHvtghn2BCnsRSZhR\n7dF39/1mhpl9HjgIPAFs86ifEMwsH7gE2ACcBL4YTzAiIiKpVNvcSdWRNwr7k2eZilOck8GqGW8U\n9nNKVNiLyNgRU+uOu+8HPmlmnwBeBTCzHuBpoIfgLrlPAv/s7g2jFOsZqUdfYqW+SAlD+TKxnWjt\noupIC1uONlN1pIVjLWeeY9/firNmZmTFviTn9M2pNm7cyDzlisRI31skGcL26J8PrAIWAB8B7nb3\n6oRHJSIiMgqaOnrYcrSFVw43U3W0mUONnWc8Pi8zjVUzClgzM+izn1+WqxV7ERk3Qs3RN7O7+nvv\nzSwH+KC7f3W0ggtDPfoiIjJYR08fr9UGhf0rR5rZe7KdM/1fLycjjRXT81kzo5DVMwtYVJ6nqTgi\nklLJnKN/ulnR3TvMrCWeDxURERkNvX3Ozro2XjnSzCuHm9l+vJXuvpFL+8x0Y1lFPmtmFrJmRgFL\npuZpjr2ITBhhC/0PmFk38EzkRllnbmZMIvXoS6zUFylhKF/Gtv6Rl1VHmtl8uJmttS20dfeNeHya\nwZIpeVwws5A1lYUsr8gnK0F3nlWuSBjKF0mGsIV+C3Az8IVIwV9jZlOAXwFXu/sDiQ5QREQk2snW\nbjYfaWLz4WDVvr79zCMv55bksGZmIWsrC1k1o4D8rPQkRSoiklphe/TXufumyPNVwDWRx5VAtrvn\nj0qUMVCPvojIxNTe3cvW2hZePhys2lc3dJzx+Kn5maytLAzacWYWUp6XmaRIRUQSL2k9+v1FfuT5\nqwSjNu81szTgM/EEICIiEq23z9l9oo3NkcJ+2/FWes7QZ1+Ync6amYVcMLOQC2YWMLMo+/TISxGR\nySxs686w3L3PzB5KxLnipR59iZX6IiUM5UtyHG3u5OVDQWG/5WgzzZ29Ix6bmWYsm5bP2spCLqws\nYmF57piYjKNckTCUL5IMCSn0Adx9S6LOJSIiE1t7dy9VR1p4+XATmw41c6TpzPPs55fmsLaykLWV\nRayYnk9upvrsRUTOJlSP/limHn0RkbGrz519J9vZdLiJlw818/qxM7fjlOVlsLayiLWRi2jL1Gcv\nIpNUMufoi4iIxKShrZuXDzez6VAwIedUx8jTcbLTjVUzgqL+wlmFzC3JUZ+9iMg5mjCFvnr0JVbq\ni5QwlC+x6+7t4/Vjrbx8qIlNh4O70J7JgrIcLqwsYt2sIpZPzydrnN+oSrkiYShfJBkmTKEvIiLJ\nd7ylixcPNvHSoSaqjjTTfoabVRXnZEQuoC3kwllFGnspIjLKQvfom1kW8EFgDVAQ/Zq7vz9hkYWk\nHn0RkdHX1dvHa7UtvHQwuIi2+tTIM+3TDZZNK2DdrKCwX1SeS5racUREQkl2j/43gdXAI8CxeD5U\nRETGj9rmTl46vWrfQkfPyKv20wuzWDeriHWzClk9o1B3oRURSaF4Cv0bgfnufirRwZwL9ehLrNQX\nKWFMxnzp6unj1doWXjrUxEsHmzjUOPLoy8x0Y/WMAi6aFfTazyqevDermoy5IvFTvkgyxFPo1wDZ\niQ5ERERSp6416LV/saaJzUea6TzDqv3MoiwumlXERbOLWDWjkJyM8X0RrYjIRBVTj76ZXRu1eQHw\nbuBeBrXuuPtvExpdCOrRFxGJXW+fs+N4Ky8cbOLFg03sqx95Qk5WurF6RiEXzS7iollFVBZrrUdE\nJFmS0aN/f9RzBwz4zKBjHFgQTxAiIjL6mjp62HSoiRcONrHpUBPNnb0jHjuzKJuLI4X9qhkFZGvV\nXkRk3Imp0Hf3+f3Pzewv3f2fBh9jZn+eyMDCUo++xEp9kRLGeM4Xd2dffTsvHmzihZomdtS1MtLN\naDPSjJXTC7hkThGXzC6isjgnucFOAOM5VyT5lC+SDPH06P8dMKTQB/4WuOfcwhERkXPR1dNH1dFm\nnq9p4oWaRupau0c8tjwvk4tnF3Hx7CIumFlInibkiIhMKDEX+lF9+hlmdg1B+06/BUBzIgMLa82a\nNan8eBlHtIIiYYyHfKlv6+aFg008X9PI5sMjX0hrwNKKfC6aHazaLyzPnbQTckbDeMgVGTuUL5IM\nYVb0+/v0s4EHovY7UAt8PFFBiYjIyPpbcp6vCYr7nXVtIx5bkJXOulmFXDy7mItmF1Gcoxuii4hM\nFjF/x+/v0zezb6XyDrgjUY++xEp9kRLGWMmXrp4+thxt4fmaRl442MjxlpFbcmYVZ3PpnGIunVPE\n8mkFpKdp1T4ZxkquyPigfJFkCL20MxaLfBGRiaipo4fnaxp5vqaRTYeaR7wjbZrBimkFXDqniEvn\nFjNLF9KKiAixz9G/0t2fijy/dqTj4pmjb2Y3Al8E0oD73f3zwxxzNfD/gEygzt2vGXyM5uiLyERw\ntKmTZ6sbea66kdeOtYw4JSc/K52LZhVy6Zxi1s0qokgtOSIiE1Iy5uj/G7Ai8vz+EY4JPUffzNKA\nLwMbgCPAS2b2U3ffEXVMMfCvwJvd/bCZTQnzGSIiY5m7s/tEO89Wn+LZ6kYONHSMeOzMouxg1X5O\nMSumF5ChlhwRETmDWOfor4jafKe7b0nQ518M7Hb3agAz+y5wM7Aj6pjbgf9y98ORWE4MdyL16Eus\n1BcpYYx52WgbAAAgAElEQVRGvnT3Bv32z1Y38nx1Iyfahu+3N+D8ijwum1vM5XNKmF2SrSk5Y5i+\nt0gYyhdJhnh+1/uImeUDTwNPRh6veCw9QENVAgejtg8RFP/RlgCZZvYEUAD8i7t/O47PEhFJmdau\nXl482Miz1Y28dLCJtu7h++0z0421Mwu5bG4xl84ppiwvM8mRiojIRBHPxbhzzGwBcCVwFXA3UG5m\nG9397YkOkCDGtcC1QD7wnJk95+57og/SHH2JlVZQJIxzyZeG9m6eq27kmQONvHKkmZ4RGu4Ls9O5\nZHYRl88t4cJZheRm6sZV45G+t0gYyhdJhriu3nL3fWaWAWRFHjcCFXGc6jAwJ2p7VmRftEPACXfv\nADrM7ClgNTCg0P/hD3/I1772NebMCU5XXFzMypUrT/+HtHHjRgBta1vb2h7V7drmTh748aNsPdZC\nfen5ONC0twqAooXBgkTT3irK8jJ5x/VXc/mcYhr3VJGW1sT6+fNSHr+2ta1tbWs7tdtbt26lsbER\ngJqaGtatW8eGDRuIR0xTdwa8wex7wGUEF8/+DngKeNrdQ98Z18zSgZ0EF+MeBV4EbnP37VHHnA98\nieCHiWzgBeBWd98Wfa577rnH77zzzrAhyCS0caP6IiV2Z8sXd6f6VAfPHGjkmQOn2HOyfcRjF5Xn\ncvm8Eq6YW8y80hz1208w+t4iYShfJFbJmLoTbS3QB2yJPKriKfIB3L3XzO4GHuWN8Zrbzeyu4GW/\nz913mNmvgVeBXuC+wUW+iEgy9bmzs66NZw+c4pnqRg41dg57nAHLp+ezfl4Jl88tZnphdnIDFRGR\nSS30ij6Amc0g6NG/ElgP5AJPufuHEhte7DRHX0RGU2+f8/qxVp7ef4pnDpwacVJORppxwcxCrphX\nzGVziinVxbQiInIOkr2ij7sfNbOdwEyCvvprgLfEcy4RkbGqt895tbbldHHf0N4z7HE5GWlcPLuI\nK+YVc/HsYvKzdDGtiIikXuhC38weJljFbyYYrfkI8BfuvjvBsYWiOfoSK/VFypn09DlVR5p5en9w\nA6uDr286fRFttMLsdC6bU8wV80pYW1lIdkZaCqKVsUTfWyQM5YskQzwr+j8C/tjd9yc6GBGRVOjq\n7eOVw0Fx/1xNI82dvcMeV5qbwRVzS3jT/BJWzSggXXemFRGRMSyuHv2xSD36IhJGV08fmw43BcV9\ndeOIN7Aqz8tk/byguF8+LV/FvYiIJFXSe/RFRMajrt4+Xj7UzJP7Gni+ZuTivqIgkzfNK2H9/BKW\nVuSTpjGYIiIyDk2YQl89+hIr9UVOLt29fWw+3MyT+0/x7IFTIxb3MwqzeNP8YOV+yZS80zPulS8S\nK+WKhKF8kWSYMIW+iEi/nj7nlcPNPLW/gWcONNLSNXzPfWVRNlcuKOHK+SUsKMvVDaxERGRCUY++\niEwIvZFpOU/tP8XGA6dGvKB2RmEWVy0o5aoFKu5FRGTsS3mPvpk9AGwEvunuw//fVUQkwXr7nK21\nLTy5r4GNBxpp7Bh+zv20giyuWlDClQtKWVyu4l5ERCaHRLXuGHA78OfA8gSdMxT16Eus1Bc5vrk7\nO+ra+N3eBp7c30B92/DF/dT8TK5aUMqV80s4b2pe3MW98kVipVyRMJQvkgwJKfTd/Q4AM9O93kVk\nVOyvb+d3exv43b4GjjZ3DXvMlLxM3rSghKvml3J+RZ6m5YiIyKSmHn0RGbOONnXyu30NPLG3gQMN\nHcMeU5KTwVULSrhqQSnLpmkUpoiITCxJ7dE3swLgcmAxUAS0ArXAM+5+OJ4gRET6nWzr5qlIcb+j\nrm3YY/Kz0lk/r5irF5SyZmahbmIlIiIyjJgLfTNbBtwNZAFbgCPADiAXKAP+1MxKgMfc/XujEOsZ\nqUdfYqW+yLGnpbOHp/ef4ol9DWw50sJwv2fMTjcunVPM1QtLuWh2EVnpaUmJTfkisVKuSBjKF0mG\nmAp9M7sVyAP+1N07z3LsRWb2SeBf3L09ATGKyATU1dvHiweb+O2eel6oaaK7b2h5n26wblYRVy8s\n5bI5xeRlpacgUhERkfEpph59M5vj7jVmVuLup2I4Ph2Y6u61iQgyFurRFxn7+tx5rbaVx/fU8/T+\nU8PeyMqAVTMKuHphKW+aV0JRju7rJyIik9eo9+i7e03k6R8D/yuG43sJ+vZFRDjQ0M7jexp4Ym89\nx1u6hz1m8ZRcrllYxtULSpiSn5XkCEVERCaesE2uHzGzsuFeMLO3JSCeuFVVVaXy42Uc2bhxY6pD\nmBROtnbzw1eP8Uc/3sFH/msH39tybEiRP60gi9vWTONrv7+Uf33n+dyysmLMFfnKF4mVckXCUL5I\nMoT9nfhfAP/NzB5y97r+nWZ2FfBp4OeJDE5Expe2rl42HjjF43vqqRrhotrC7HSuml/KhkXBOEzd\npVZERGR0xDVH38w+BjwGXAV8HCgHTrr7qsSGFzv16IukRm+fU3Wkmcd21/PMgVN09g79npKZblw2\np5gNi8pYN6uQzCRNzBERERnvkjZHP9KesxWYA7wObAM+A/wQSFmRLyLJV93Qzm921/P4ngZOtA3t\nuzdg9cwCNiwqY/28EvI1MUdERCSpwrbufBvIBH4AXAosAV519x5gc4JjC0Vz9CVWml0cv8aOHp7Y\n28Bvdtez68TwN7OaV5rDdYvLuGZhKVPHWL99PJQvEivlioShfJFkCFvo/xa4y91PRrZfNrN3mVkO\nsC+W0ZsiMr509/bxwsEmHttdz4s1jQzTmUNxTgbXLirl+kVlLCzPVd+9iIjIGBCqR9/MLnL3l4bZ\n/07g0+5+QSKDC0M9+iKJ4+7sOtHGY7vreWJvA82dQ+fdZ6YZl84t5vrFZaybVURGmop7ERGRREta\nj/5wRX5k/08id88VkXGsoa2b3+yp59Fd9VSf6hj2mKUVeVy/uJyrFpRQmK2bWYmIiIxVify/9AMJ\nPFdo6tGXWKkvcqCePufFg438emc9LxxspG+YX/JVFGRy3aIyrltcxqzinOQHmULKF4mVckXCUL5I\nMiSs0Hf3xxJ1LhEZfQca2nl0Vz2/2V3PqY6eIa/nZKRx5fwSrl9cxsoZBaSp715ERGRciblH38z+\nCohlKc+Adnf/v+cSWFjq0Rc5u9auXp7Y28Cvd51kZ93wU3NWTM/nhiXlXDm/hNxMjcQUERFJpaT0\n6Ce7cBeRxOhzZ8vRFn698yQbD5yia5ixOeV5mbx5cRlvXlJG5SRrzREREZmoEnZ7SjN7U6LOFY+q\nqqpUfryMIxs3bkx1CElR19rFg5uP8oHvbeOTv9jDb/c2DCjyM9KMK+eX8I83LODB9y7njotmqsgf\nxmTJFzl3yhUJQ/kiyRC6R9/M0oAZQCUwM+pxHcFNtEQkRXr6nBdqGvnlzpNsOtQ07IW1C8pyuWFJ\nGdcuKqM4R1NzREREJqqwc/SfBi4DuoBjQC2QDjwDrHb3a0cjyFioR18msyNNnfxy50ke23WS+vah\nF9YWZqdz7cIyblhSxqIpeSmIUEREROKRtDn6wJuBTwC73P3HAGb2AXf/pplpRpRIEnX19PFM9Sl+\nufMkVUdahj3mgpmFvOW8ci6fW0xWRsI69URERGQcCPV/fndvd/fPAwfM7DNmthDwyGspbTZTj77E\narz3RR5oaOcrzx/itode47NPVA8p8svyMrht9TS++Z5lfP6ti7h6YamK/HMw3vNFkke5ImEoXyQZ\n4mrQdfdXzOxV4GPAxWb2IEEbUG9CoxMRANq7e3lq/yl+ueMk2463Dnk9zeCiWUW89fwpXDy7iPQ0\nzbwXERGZ7EL16A97ArMFwB3AEne/NSFRxUE9+jIR7a9v5+c7TvCb3fW0dfcNeX1aQRY3nlfOm5eU\nMTU/KwURioiIyGhKZo/+EO6+D/g7M/tZPO83sxuBLxK0Ed0faQ0a7riLgGeBW939R/HGKzLWdfX0\n8dT+U/x8xwlePzZ09T4jzbhsbjFvOa+ctZWFumOtiIiIDCuRjbv/GPYNkVGdXwZuAJYDt5nZ+SMc\n9zng1yOdSz36Equx2hd5qLGD+144zG0Pvcb/fbJ6SJE/qzibD188k+/ctpy/2zCfdbOKVOQnwVjN\nFxl7lCsShvJFkiFhQ7Td/fk43nYxsNvdqwHM7LvAzcCOQcd9HPghcNE5BSkyxvT0Oc9Wn+Ln20/w\nyjCTc9IN1s8r4W1Lp7B6RgGmwl5ERERidNZC38zmA5e4+3djOaGZlQO3uPu/x3B4JXAwavsQQfEf\nfb6ZwDvd/RozG/BatDVr1sQSngjr16d+Euyx5i5+sfMEv945/Nz7aQVZvPX8cm5cUk5pXmYKIpR+\nYyFfZHxQrkgYyhdJhrMW+u6+38wws88TFOVPANs86ipeM8sHLgE2ACcJeu4T5YvAJ6O2taQp41Jv\nn/PSoSZ+vv0ELx5sYvBl8GkGl8wu5m1Ly7mwUpNzRERE5NzE1Lrj7vuBT5rZJ4BXAcysB3ga6CG4\nS+6TwD+7e0OIzz8MzInanhXZF20d8F0LehamAG8xs253fzj6oHvvvZf8/HzmzAlOV1xczMqVK0//\nxNzfC6dtbUf3RSbj85o6erj3e7/kuepGemYuB6Bpb3BNSdHCNZTlZbC4Yx8Xzy7iHddfkPKvj7ZT\nmy/aHr/b/fvGSjzaHtvb/fvGSjzaHjvbW7dupbGxEYCamhrWrVvHhg0biEeo8Zpm9m/AvwILgI8A\nd/f318f14WbpwE6C3wQcBV4EbnP37SMc/3XgkeGm7txzzz1+5513xhuKTCIbN248/R/UaNpV18bD\n2+p4Yl8D3b1D/zu7sLKQty2dwqVzisnQ6v2Ylax8kfFPuSJhKF8kVskcr7nF3V8HXjezx4APAl+N\n54MB3L3XzO4GHuWN8Zrbzeyu4GW/b/BbRjqXevQlVqP5jbV/NOZPt9Wxs65tyOtF2encsKScty2d\nwsyi7FGLQxJH/yOWWClXJAzliyRD2EK/u/+Ju3eY2dAxISG5+6+A8wbtG/ZCXnfXkr2MScdbuvjZ\n9hP8cudJGjt6hry+eEouNy+bylULSsnOSORUWxEREZHhha04PmBm74vcDRegK9EBxUtz9CVW0f2R\n58Ld2Xy4ib9/bB/v/97rfHfLsQFFfmaacd2iUu69aQlfvvk83rykXEX+OJSofJGJT7kiYShfJBnC\nrui3EMy5/4KZdQM1ZjYF+BVwtbs/kOgARcaa1q5efrO7noe31XGwsXPI61PzM3n70inceF45pbka\njSkiIiKpEfZi3HXuvinyfBVwTeRxJZDt7vmjEmUMHn/8cV+7dm2qPl4mgcONnfx0Wx2P7jpJW3ff\nkNcvmFnITcuCi2s1GlNEREQSIWkX4/YX+ZHnrxKM2rzXzNKAz8QTgMhY5u5UHWnhx68f54WaobPv\n8zLTuH5xOe9YNoU5JTkpiVFERERkOAlpGHb3PuChRJwrXurRl1jF0hfZ0dPHL3ac4K4f7eCTv9zD\n84OK/DklOdx9+Sz+87YVfOzyWSryJzD10UqslCsShvJFkiFsj/6I3H1Los4lMho6e3qpbe7i4KkO\nqhvamV6YRXZG+oBj6lq7eHjbCX6x4wTNnb1DznHx7CLeuXwqF1YWEtzDTURERGRsCtWjP5apR19G\n0tHTy87jbfz49Tqeq27EAQMum1vM7y2fypKpueyr7+Anr9Xx9IFT9A36TyInI403Lynj5mVTma2V\nexEREUmiZN4wS2Rc6ejp5bFd9Xzp2UMD9jvwbHUjz1Y3UpGfyfHW7iHvnVaQxc3Lp3LjkjIKsvWf\nioiIiIwvE2aot3r0ZTg7j7cNKfKb9g7MlcFF/uoZBXz6uvl84z3LuGVlhYr8SU59tBIr5YqEoXyR\nZFAFIxNWZ08vP369LqZjDbhucSnvWlHBwvK80Q1MREREJAkSsqJvZg+Y2Z1mln72o0fHmjVrUvXR\nMkbVNnfxXHXjkP1FC4fmigPvWTVNRb4MsX79+lSHIOOEckXCUL5IMiSqdceA2wnm6oukXG+fDxmJ\neTadvRPjwnQRERERiKPQj9wcawB3v8PdrwNStqyuHn0BaO/u5cevHeeOH2zj/peODHvM4B59CH5S\nzU7XuEwZSn20EivlioShfJFkCNWjH2nNaTGzEnfvHPy6uw8dXSKSBCdau/jp63X8fMdJWrqGzr8/\nm8vnFjO9MGsUIhMRERFJjVCFvrv3mtkuoBwYfrk0RdSjPzntr2/nB1uP87u9DfQMGoBfmJ3OxbOK\neHxvw4D9w/Xo/96KqUNuniUC6qOV2ClXJAzliyRDPFN3vgP8zMzuBQ7BG23Q7v7bRAUmMhJ357Vj\nrXx/yzFeONg05PWZRdm8a8VUrl9chhksm5Y/ZMRmtE9cMYslU3URroiIiEws8RT6fxT58+8H7Xdg\nwTlFcw6qqqrQnXEntj53nq9p5PtbjrPteOuQ11dMy+f3V1Zw6Zxi0tPe6Le/fkkZc0tz+PFrdTxb\n3Ujj3iqKF67h8rnF/N6KqSyZmkeOVvNlBBs3btTKm8REuSJhKF8kGUIX+u4+fzQCERlJd28fv93b\nwA9ePU7NqY4BrxlBf/17Vk9jaUX+sO/PyUhn1YxCzpuaR21zF88+e5LLLz+f6YVZatcRERGRCcvc\nw48UNLPFwG1AJXAYeMjddyc4tlAef/xx14r+xNLW1csvdpzgR6/VcaJt4HXemWnGhkVlvHtVBbNL\nclIUoYiIiMjo2rx5Mxs2bIhrNGDoFX0zeweRPn2gGjgP2GRm73P3h+MJQiRaQ1s3P3m9jke2nxgy\nQScvM423nT+Fd62ooDw/M0URioiIiIx98dww6zPAze5+u7v/tbv/AXBzZH/KaI7++HekqZN/2XiQ\n933vdR7acmxAkV+am8GdF83gwfcu58OXVJ5Tka/ZxRKG8kVipVyRMJQvkgzxXIw7C3h60L6Nkf0i\noe2vb+e7W47x5L4GBk3IZGZRNu9eVcH1i8rIykjUjZxFREREJr7QPfpm9gTwK3f/fNS+vwLe6u5X\nJza82KlHf/zZVdfGf1bV8mx145DXlkzJ4z2rK7hibsmACToiIiIik0lSe/QJxms+YmZ/DBwEZgNt\nwDviCUAmn1ePtvBQVS0vH24e8traykJuXT2NNTMKMFOBLyIiIhKv0L0Q7r4DWAq8B7gn8udSd9+e\n4NhCUY/+2ObubDrUxJ/9bBd/8fPdQ4r8y+cW86Wbl/C5tyzigpmFo1rkqy9SwlC+SKyUKxKG8kWS\nIZ4Vfdy9h6AvX+SM+tx5trqRh6pq2X2ifcBraQZXLSjlvaunMb8sN0URioiIiExMMfXom9mV7v5U\n5Pm1Ix3n7r9NYGyhqEd/bOntc57c18BDW45R3TDwJlcZacZ1i8q4dXUFlcWagS8iIiIykmT06P8b\nsCLy/P4RjnFgQTxByMTR3dvHb3bX871Xj3GkqWvAa1npxlvOK+fdq6ZRUZCVoghFREREJoeYevTd\nfUXU5iJ3nz/MI6VFvnr0U6u7t4+fbT/BHT/Yxv/beHBAkZ+bmcZ7VlXw7VuX87HLZ6e8yFdfpISh\nfJFYKVckDOWLJEOoHn0zSwdazKzE3TtHKSYZR7p7+/j1rnq+u6WW4y3dA14rzE7n5mVTeefyqRTl\nxHU5iIiIiIjEKZ45+luAt7j7kdEJKT7q0U+urt4+Ht1Vz0NVtdS1Dizwi3MyuGVlBe9YOoW8rPQU\nRSgiIiIy/iV7jv53gJ+Z2b3AIYLefCC1F+NKcpytwH/PqgrevnQKuZkq8EVERERSKd4bZgH8/aD9\nKb0Yt6qqCq3oj56u3j5+vfMkD205xolBBX5JTgbvHkcF/saNG1m/fn2qw5BxQvkisVKuSBjKF0mG\n0IW+u88fjUBkbDpbgf+eVRW8bZwU+CIiIiKTSegefQAzux54L1Dh7u8wswuBYs3RnzjOWuCvnsbb\nl04hJyP0zZVFREREJEZJ7dE3s48Dfwx8DbglsrsD+BJweTxByNjR0+c8uusk33llaA++CnwRERGR\n8SOeau1PgOvc/XNAX2TfDuC8eAIwsxvNbIeZ7TKzTw7z+u1mtiXy2GhmK4c7j+bon5vePuc3u+v5\n0A+38cWNBwcU+aW5Gdx1SSXfeu9ybllZMe6LfM0uljCULxIr5YqEoXyRZIjnYtxC4GDkeX/fTybQ\nNfzhIzOzNODLwAbgCPCSmf3U3XdEHbYPuNLdG83sRuA/gEvjiFuG0efOMwca+dbLR6k+1THgtZKc\nDG5dPY23aQVfREREZNyJp9B/CvgU8H+i9n0CeCKOc10M7Hb3agAz+y5wM8FvCABw9+ejjn8eqBzu\nRGvWrInj4ycvd+elQ018Y9NR9pxsH/BaYXY6715Vwc3Lpk7Ii2w15UDCUL5IrJQrEobyRZIhnkL/\n48AjZvZhoNDMdgLNwNvjOFclb/x2AIK5/Bef4fgPAb+M43MkStWRZr6x6SjbjrcO2J+Xmca7VlTw\n+ysryNeNrkRERETGtXjGax41s4uAi4C5BIX6i+7ed+Z3nhszuwa4Axj2R+B7772X/Px85syZA0Bx\ncTErV648/RNzfy/cZN6ubujg1fS5vHKkhaa9wTUNRQvXkJ1urOg5wNWzS7nhwtVjJt7R2o7uixwL\n8Wh7bG8rX7Qd63b/vrESj7bH9nb/vrESj7bHzvbWrVtpbGwEoKamhnXr1rFhwwbiEXq8ppn9hbv/\n8zD7/8zdvxDyXJcCf+/uN0a2PwW4u39+0HGrgP8CbnT3vcOd65577vE777wzzMdPGntOtPHNl4/y\nwsGmAfsz04y3nj+F966ZRnleZoqiS76NG3WTEomd8kVipVyRMJQvEqtzGa8ZT6Hf5O5Fw+yvd/ey\nkOdKB3YSXIx7FHgRuM3dt0cdMwd4HHjfoH79ATRHf6hDjR18Y9NRntp/asD+NIMblpTzBxdMp6Ig\nK0XRiYiIiMjZJGWOvpldG3maHmmjif7ABQR9+qG4e6+Z3Q08SjDq8353325mdwUv+33A3wFlwL+Z\nmQHd7n6mPv5J72RbNw9uPsovd56kL+rnOAOuWVjK+9ZOp7I4J2XxiYiIiMjoi7nQB+6P/JkDPBC1\n34FjBBfphubuv2LQDH53//eo5x8GPny281RVVTHZV/Rbu3r5/pZj/Oi143T2DvxNzfp5xbxv7Qzm\nl+WmKLqxQ78ulTCULxIr5YqEoXyRZIi50Hf3+QBm9i13f//ohSRhdfX08fC2Oh7acozmzt4Br62Z\nWcAfXjST86bmpyg6EREREUmFeHr0rwEOuPt+M5sOfB7oBf7G3WtHIcaYTMYe/d4+5/E99Xzz5aMD\n7mQLsLA8lz+8aCYXVhYSdDyJiIiIyHiTlB79KP8G3BB53j9lpwe4D7gpniAkHHfn+ZomHth0hOqG\ngXeznV6YxR3rZnDVglLSVOCLiIiITFppcbyn0t1rzCyDoOD/CPBHwOUJjSykqqqqVH580rxe28Kf\n/Ww3n35s34Aivzgng49dNov7b1nKNQvLVOSfQfQMY5GzUb5IrJQrEobyRZIhnhX9JjObBqwAtrl7\ni5llAZNnEHsKHGho5+svHeW5msYB+3Mz07hlZQW/v6KCPN3NVkREREQi4in0vwS8BGQBfxLZdwWw\nI1FBxWPNmjWp/PhRU9/WzTdfPsqvdw0clZmRZrzt/CncfsE0SnP1M1YYmnIgYShfJFbKFQlD+SLJ\nELrQd/fPm9mPgd6ou9QeBj6U0MgmuY6ePn649Tjf33KMjp6+Aa9ds7CUD144gxlF2SmKTkRERETG\nunh69CGYnf8HZvbvZvY/Adx9a+LCCm+i9Oj3ufPorpPc+f1tfOvlowOK/LWVhfzbO8/jr6+ZpyL/\nHKgvUsJQvkislCsShvJFkiH0ir6ZvQP4DvAzoJrgZlcvmdn73P3hBMc3qbxypJn7XjjM3pPtA/bP\nK83hI5dUsm5WUYoiExEREZHxJp45+luBT7j7E1H7rga+7O4rEhte7MbzHP2aUx38xwuHeeFg04D9\npbkZfODCGdywpJz0NE3REREREZlskj1Hfxbw9KB9GyP7JYRT7d18e3MtP99xYsCFttnpxi2rpvHu\nlZqkIyIiIiLxiadHvwr480H7/iyyP2XGU49+V08f391Sywe/v41Htr9R5Btw/eIyHnjPMj5w4QwV\n+aNEfZEShvJFYqVckTCUL5IM8azofxR42Mz+GDgIzAbagHckMrCJqM+d3+1t4IFNRzje0j3gtdUz\nCrjrkkoWTclLUXQiIiIiMpGE7tEHiNwV91JgJnAEeMHdu8/8rtE11nv0tx9v5SvPHWJHXduA/bOL\ns/nwJZVcMrsI091sRURERCRKUnr0zSwP+B8Ed8TdDHzW3Tvj+dDJ5GRbNw+8dITHdtcP2F+ck8H7\n107nLedPIUMX2oqIiIhIgoXp0f9XgvacHcAtwD+PSkRxGms9+l29fXxvyzHu/MG2AUV+Zppx66oK\nvvGeZbxj2VQV+SmgvkgJQ/kisVKuSBjKF0mGMD36NwJr3f2omX0JeAr4+OiENX65O8/VNHLfC4c5\n0tQ14LXL5xbzkUsqmambXYmIiIjIKIu5R9/Mmty9KGq73t3LRi2ykMZCj/6Bhna++vxhNh9uHrB/\nbmkOf3RpJWsrdcMrEREREYldsuboZ5jZNQRTIIfbxt1/G08Q411TRw/f3lzLI9vrBszDL8xO5/1r\nZ/D2pVN0wysRERERSaowPfrHgQeA+yOPk4O2v5bw6EJIRY9+b5/zyLY67vzBNn667Y0iP83gpmVT\n+Pq7l3Hz8qkq8scY9UVKGMoXiZVyRcJQvkgyxLyi7+7zRjGOcafqSDNfff4Q++o7BuxfPaOAj142\ni/lluSmKTEREREQkzjn6Y1GyevSPt3Tx7y8c5un9pwbsn1aQxV2XVHLFvGLNwxcRERGRhEhWj/6k\n1t3bx49eq+PBV2rp7Ok7vT87I43b10zj91dUkJURphNKRERERGT0TJjKdDR79F853Mx//9EO7n/p\nyIhk3gUAAAtWSURBVIAi/9qFpXz93Uu5bc10FfnjiPoiJQzli8RKuSJhKF8kGbSifwYnWoM2nSf3\nDWzTmVeaw8evmM3K6QUpikxERERE5MzUoz+Mnj7nJ68d59uv1NLe/cYKfl5mGu+/cAY36Y62IiIi\nIpIE6tFPoC1Hmvnys4eoPjVwms41C0v5yCWVlOdlpigyEREREZHYTZjG8nPt0T/Z1s3nnjjAX/5i\nz4Aif25JDv/01kX89TXzVORPEOqLlDCULxIr5YqEoXyRZJj0K/q9fc5Pt9XxrZeP0hbVppObmcb7\nLpjOO1dUqE1HRERERMadSd2j/1ptC1965iD7Gwa26Vy1oIS7LqlkSn5WIkMUERGR/9/e/cdaXddx\nHH++uIjKFTFESeBiiqKiAioIEhqYpaJGc7PUDUvDkYE109GsXK25rK2a+SszTHPLaWFLskxdYyrO\nnymICSKigCC3gQh68wfguz/O9+q5l3Pu/Z4rnO853/N6bGz3fL+fz/e8D3vvfN/7ft/f8zGzirhH\nv0Jb3tvG3KfW8c/lGztsb+m/O7MntnDMkH4ZRWZmZmZmtnM0VI9+RPCvFW/yjXlLOxT5u/fuxYxx\ng7n57MNd5DcA90VaJZwvlpZzxSrhfLFqaJgr+uu2vM/1j63h32vf7rD9swf255IThrL/Xm7TMTMz\nM7P8yH2P/rYPgz8/38ofn1vPB9s//qwDm3dj9sShTDxwn2qGaWZmZmaWmnv0y3ixtY1rF67mtaKH\nbXsJpo3cj68ddwB9+zRlGJ2ZmZmZ2a6TeY++pNMkLZO0XNL3yoy5TtLLkhZJGlNqTHGP/jvvb+O6\nhWu47G/LOxT5h+y7J9d96TAuOWGoi/wG5r5Iq4TzxdJyrlglnC9WDZkW+pJ6ATcApwJHAudJOrzT\nmNOB4RFxKDATuLnUsVasWEFE8PDKTcyYt5T7lm2gvVFnj969mDl+CNdPO4wR+/XddR/I6sKSJUuy\nDsHqiPPF0nKuWCWcL5bWJ1kUNuvWneOBlyNiFYCku4BpwLKiMdOAOwAi4klJ/SUNiojW4gO1tbVx\n1YMreWrNlg5vML5lb2ZPbGFQPz9sawWbN2/OOgSrI84XS8u5YpVwvlhaixcv7vHcrAv9IcCaotev\nUyj+uxqzNtnW2mlchyJ/QN/ezDqhhUmf6Y/klW3NzMzMrLFkXejvNOvXr4ejQcCZRwzkonGDaXYf\nvpWwevXqrEOwOuJ8sbScK1YJ54tVQ9aF/lpgWNHrocm2zmNauhnD8OHDaVtyOwArlsDdL41mzJiS\nz+1agxs7dizPPvts1mFYnXC+WFrOFauE88XKWbRoUYd2nebm5h4fK9Pf0ZfUBLwEfB54A3gKOC8i\nlhaNmQrMiogzJE0Aro2ICZkEbGZmZmZWJzK9oh8R2yXNBh6k8AtAt0bEUkkzC7vjloj4h6SpklYA\nbcCFWcZsZmZmZlYPcrMyrpmZmZmZfSzzBbMqtbMW2LL86y5XJJ0vaXHyb6Gko7OI02pDmu+WZNw4\nSVslnV3N+Kx2pDwPTZb0nKQXJC2odoxWO1Kci/aWND+pWZZI+noGYVoNkHSrpFZJz3cxpqIat64K\n/Z25wJblW5pcAVYCJ0XEaOBq4HfVjdJqRcp8aR/3M+CB6kZotSLleag/cCNwZkQcBZxT9UCtJqT8\nbpkF/CcixgBTgF9KyvrHUiwbt1HIlZJ6UuPWVaFP0QJbEbEVaF9gq1iHBbaA/pIGVTdMqwHd5kpE\nPBER7SuWPEFhfQZrTGm+WwAuBeYB/61mcFZT0uTK+cA9EbEWICI2VDlGqx1p8iWAfsnf/YCNEbGt\nijFajYiIhcCmLoZUXOPWW6FfaoGtzsVZuQW2rLGkyZViM4D7d2lEVsu6zRdJg4EvR8RvKCzZYY0p\nzXfLCGCApAWSnpY0vWrRWa1Jky83ACMlrQMWA9+pUmxWfyqucX1ryBqepCkUfs1pUtaxWE27Fiju\nr3Wxb+X0Bo4FTgaagcclPR4RK7INy2rUqcBzEXGypOHAQ5JGRcQ7WQdm9a/eCv2dtsCW5V6aXEHS\nKOAW4LSI6Op2meVbmnwZC9wlScBA4HRJWyNifpVitNqQJldeBzZExHvAe5IeAUYDLvQbT5p8uRC4\nBiAiXpH0KnA48ExVIrR6UnGNW2+tO08Dh0g6UFIf4Fyg80l2PnABQLLA1lsR0VrdMK0GdJsrkoYB\n9wDTI+KVDGK02tFtvkTEwcm/gyj06X/LRX5DSnMeuheYJKlJUl9gPLAUa0Rp8mUVcApA0m89gsKP\nRVhjEuXvGFdc49bVFX0vsGVppckV4CpgAHBTcpV2a0Qcn13UlpWU+dJhStWDtJqQ8jy0TNIDwPPA\nduCWiHgxw7AtIym/W64Gbi/6ScU5EfFmRiFbhiTdCUwG9pW0GvgR0IdPUON6wSwzMzMzsxyqt9Yd\nMzMzMzNLwYW+mZmZmVkOudA3MzMzM8shF/pmZmZmZjnkQt/MzMzMLIdc6JuZmZmZ5ZALfTMzMzOz\nHHKhb2ZmZmaWQy70zcxsp5B0UNYxmJnZx1zom5nlgKQXJJ2U4fsfBIxPOXaYpK/u4pDMzBqeC30z\nsxok6TVJ/5O0RdIbkm6T1Lfc+Ig4KiIeqWaMnXwzIu5KMzAiVgN9JY3cxTGZmTU0F/pmZrUpgDMi\nYm/gWGAs8MPOgyQ19fQNKpkraaykv0t6WNJFkmZKulHSZEmjgDVdzN1T0qOdNt8JzO5h6GZmloIL\nfTOz2iWAiHgDuB84CkDSq5LmSFoMvCOpKdl2crL/CEkLJG2StETSWR8dcMe5qc4DEfEM8C4wNyJ+\nHxG/BW4C7gbOBBZ0Mf3bwAnF7xUR7wN9JO2V+n/DzMwq0jvrAMzMrGuSWoCpwLyizecCpwMbI2K7\npPaxvYH5wFzgC8CJwL2SjouIl0vM/bCCUD4HzCl6fTDwNjAOuKZM7McAy4EPgAOAtUW7FwMTgQeL\nxh8MXEzhjoaSze1/B/BERMyvIGYzs4blQt/MrHb9VdI2YDNwHx2L6V9HxLoScyYAzRHx8+T1Akn3\nAecBP+lmbllJe87WiFiZvN6DQkE+G7gsIqLEnCbgnIj4vqRWYAgdC/11wKEUFfrJ8a9MGdORFB4A\nHgk8CuwPfBARf6jks5mZ5ZULfTOz2jUtIsq1xLxeZvtgduyXX0WhyO5ublemAKslfQXoA+wFXBoR\nqyRdUWbOLODW5O/1SWzF3gJG9CCWdkMp3BWYGhFXJA8rLwJc6JuZ4ULfzKyWqYt9O1xBT6wDhnXa\nNgx4KcXcrkwB7oiIP5XYt63zhqQF53jgLUmTgCZ2LPT3BNpKzGtv3emwi06tOxHxgKQrKdztgMJD\nyxsq+VBmZnnmQt/MLF+eBNokzQF+BUyi8LDsj8tNkHQbEBFxUZn9vYCTgO+WOUSrpOaIKC7aLwQu\naH8GQNIYdiz0B1C40v+RSlp3El8E2uOeDvyigrlmZrnmX90xM6tNXV11L7UvACJiK3AWhYd3NwA3\nANOLHsQtNbcFWFjqjSSNBn4K7A5MLhPPwxSu3iNpQvJMwAiSOxLJFf3RwCmSTiyaNwp4rMwxuyWp\nGRgEnCjpYuDpiPhLT49nZpY3KvH8lJmZNQhJu1Hoax8VEdt7eIxPAVdExA8qnDc3Imb05D2T+WcB\nkyPi8p4ew8wsz3xF38ysgUXE1og4sqdFfnKMTcBGSfumnSNpHPBQT99T0qHA5cBASfv09DhmZnnm\nK/pmZvaJJX38FycLaXU3tonCHYCfdzfWzMx6zoW+mZlVlaRPA5sj4t2sYzEzyzMX+mZmZmZmOeQe\nfTMzMzOzHHKhb2ZmZmaWQy70zczMzMxyyIW+mZmZmVkOudA3MzMzM8shF/pmZmZmZjnkQt/MzMzM\nLIf+DyJv+65PsOkIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f53fc28cfd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figsize(12.5, 4)\n",
"p = np.linspace(0, 1, 50)\n",
"plt.plot(p, 2*p/(1+p), color=\"#348ABD\", lw=3)\n",
"#plt.fill_between(p, 2*p/(1+p), alpha=.5, facecolor=[\"#A60628\"])\n",
"plt.scatter(0.2, 2*(0.2)/1.2, s=140, c=\"#348ABD\")\n",
"plt.xlim(0, 1)\n",
"plt.ylim(0, 1)\n",
"plt.xlabel(\"Prior, $P(A) = p$\")\n",
"plt.ylabel(\"Posterior, $P(A|X)$, with $P(A) = p$\")\n",
"plt.title(\"Are there bugs in my code?\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the biggest gains if we observe the $X$ tests passed when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a strong programmer (I think), so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33. \n",
"\n",
"Recall that the prior is a probability: $p$ is the prior probability that there *are no bugs*, so $1-p$ is the prior probability that there *are bugs*.\n",
"\n",
"Similarly, our posterior is also a probability, with $P(A | X)$ the probability there is no bug *given we saw all tests pass*, hence $1-P(A|X)$ is the probability there is a bug *given all tests passed*. What does our posterior probability look like? Below is a chart of both the prior and the posterior probabilities. \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AviUfHTt2pEuXLixdupSlS5eybNkyVqxYwcSJEyv6VHfCZWwckZijk+Xqtm/X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\nGJ2EMS8B/g3EJtGJ/AqoKzA8gfsXEZH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"text/plain": [
"<matplotlib.figure.Figure at 0x7f53c6a13390>"
]
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"source": [
"figsize(12.5, 4)\n",
"colours = [\"#348ABD\", \"#A60628\"]\n",
"\n",
"prior = [0.20, 0.80]\n",
"posterior = [1./3, 2./3]\n",
"plt.bar([0, .7], prior, alpha=0.70, width=0.25,\n",
" color=colours[0], label=\"prior distribution\",\n",
" lw=\"3\", edgecolor=colours[0])\n",
"\n",
"plt.bar([0+0.25, .7+0.25], posterior, alpha=0.7,\n",
" width=0.25, color=colours[1],\n",
" label=\"posterior distribution\",\n",
" lw=\"3\", edgecolor=colours[1])\n",
"\n",
"plt.xticks([0.20, .95], [\"Bugs Absent\", \"Bugs Present\"])\n",
"plt.title(\"Prior and Posterior probability of bugs present\")\n",
"plt.ylabel(\"Probability\")\n",
"plt.legend(loc=\"upper left\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that after we observed $X$ occur, the probability of bugs being absent increased. By increasing the number of tests, we can approach confidence (probability 1) that there are no bugs present.\n",
"\n",
"This was a very simple example of Bayesian inference and Bayes rule. Unfortunately, the mathematics necessary to perform more complicated Bayesian inference only becomes more difficult, except for artificially constructed cases. We will later see that this type of mathematical analysis is actually unnecessary. First we must broaden our modeling tools. The next section deals with *probability distributions*. If you are already familiar, feel free to skip (or at least skim), but for the less familiar the next section is essential."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_______\n",
"\n",
"## Probability Distributions\n",
"\n",
"\n",
"**Let's quickly recall what a probability distribution is:** Let $Z$ be some random variable. Then associated with $Z$ is a *probability distribution function* that assigns probabilities to the different outcomes $Z$ can take. Graphically, a probability distribution is a curve where the probability of an outcome is proportional to the height of the curve. You can see examples in the first figure of this chapter. \n",
"\n",
"We can divide random variables into three classifications:\n",
"\n",
"- **$Z$ is discrete**: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. Discrete random variables become more clear when we contrast them with...\n",
"\n",
"- **$Z$ is continuous**: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can progressively make the values more and more precise.\n",
"\n",
"- **$Z$ is mixed**: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. it is a combination of the above two categories. \n",
"\n",
"### Discrete Case\n",
"If $Z$ is discrete, then its distribution is called a *probability mass function*, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is *Poisson*-distributed if:\n",
"\n",
"$$P(Z = k) =\\frac{ \\lambda^k e^{-\\lambda} }{k!}, \\; \\; k=0,1,2, \\dots $$\n",
"\n",
"$\\lambda$ is called a parameter of the distribution, and it controls the distribution's shape. For the Poisson distribution, $\\lambda$ can be any positive number. By increasing $\\lambda$, we add more probability to larger values, and conversely by decreasing $\\lambda$ we add more probability to smaller values. One can describe $\\lambda$ as the *intensity* of the Poisson distribution. \n",
"\n",
"Unlike $\\lambda$, which can be any positive number, the value $k$ in the above formula must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members. \n",
"\n",
"If a random variable $Z$ has a Poisson mass distribution, we denote this by writing\n",
"\n",
"$$Z \\sim \\text{Poi}(\\lambda) $$\n",
"\n",
"One useful property of the Poisson distribution is that its expected value is equal to its parameter, i.e.:\n",
"\n",
"$$E\\large[ \\;Z\\; | \\; \\lambda \\;\\large] = \\lambda $$\n",
"\n",
"We will use this property often, so it's useful to remember. Below, we plot the probability mass distribution for different $\\lambda$ values. The first thing to notice is that by increasing $\\lambda$, we add more probability of larger values occurring. Second, notice that although the graph ends at 15, the distributions do not. They assign positive probability to every non-negative integer."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
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dua5fy1rOY3lQWXpGU287fj+otzw9jZaj/X6I1qvl5Odhw4ZkYktXVxezZs1i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\nwAcAgIn8AmR7R+/mpLe2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f53c616fa58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figsize(12.5, 4)\n",
"\n",
"import scipy.stats as stats\n",
"a = np.arange(16)\n",
"poi = stats.poisson\n",
"lambda_ = [1.5, 4.25]\n",
"colours = [\"#348ABD\", \"#A60628\"]\n",
"\n",
"plt.bar(a, poi.pmf(a, lambda_[0]), color=colours[0],\n",
" label=\"$\\lambda = %.1f$\" % lambda_[0], alpha=0.60,\n",
" edgecolor=colours[0], lw=\"3\")\n",
"\n",
"plt.bar(a, poi.pmf(a, lambda_[1]), color=colours[1],\n",
" label=\"$\\lambda = %.1f$\" % lambda_[1], alpha=0.60,\n",
" edgecolor=colours[1], lw=\"3\")\n",
"\n",
"plt.xticks(a + 0.4, a)\n",
"plt.legend()\n",
"plt.ylabel(\"probability of $k$\")\n",
"plt.xlabel(\"$k$\")\n",
"plt.title(\"Probability mass function of a Poisson random variable; differing \\\n",
"$\\lambda$ values\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Continuous Case\n",
"Instead of a probability mass function, a continuous random variable has a *probability density function*. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with *exponential density*. The density function for an exponential random variable looks like this:\n",
"\n",
"$$f_Z(z | \\lambda) = \\lambda e^{-\\lambda z }, \\;\\; z\\ge 0$$\n",
"\n",
"Like a Poisson random variable, an exponential random variable can take on only non-negative values. But unlike a Poisson variable, the exponential can take on *any* non-negative values, including non-integral values such as 4.25 or 5.612401. This property makes it a poor choice for count data, which must be an integer, but a great choice for time data, temperature data (measured in Kelvins, of course), or any other precise *and positive* variable. The graph below shows two probability density functions with different $\\lambda$ values. \n",
"\n",
"When a random variable $Z$ has an exponential distribution with parameter $\\lambda$, we say *$Z$ is exponential* and write\n",
"\n",
"$$Z \\sim \\text{Exp}(\\lambda)$$\n",
"\n",
"Given a specific $\\lambda$, the expected value of an exponential random variable is equal to the inverse of $\\lambda$, that is:\n",
"\n",
"$$E[\\; Z \\;|\\; \\lambda \\;] = \\frac{1}{\\lambda}$$"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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or15PqGcxAK4hzJK7H2bl/3sMF4lkuHUiIiIi0tm0W029mU0B7nfOnR+d/hrg\nnHPfi1vna8AQ59xtZjYc+D/n3JjEbWWqpj5RXdUu1v70t9TEXTDb/6LpTPzpfQSjPfkiIiIiIqnK\nhZr6wcCGuOmN0XnxfgaMN7Ny4CPgznZqW5vk9+7B6Huup/uEsbF5W198jff+7VZqt1VlsGUiIiIi\n0plkW2nLucCHzrkzzWwk8KqZTXTO7Y1f6ZFHHiFQtZvBJf0AKO5ayJHDhnPikd548u8tXwLQLtPB\nLgVsO2sS24O1DF64DoB5H8zngzMu4/PPP0nxuBGxurNp06YBZPV0fI1cNrSno00rvv5NN87LlvZ0\npOnFixdz8803Z017OtL0Y489xoQJE7KmPR1pWp+3im+uTDc+LisrA2Dy5MnMmDGD1mrv8psHnHPn\nRaeTld+8CHzHOfdWdHoOcI9z7qDhZWbPnu0uKCltl3a3xrZ/vsOmZ16CaEzzirtxzBMPUjJ9SoZb\nlrq5c+fGTjZJP8XXP4qtfxRb/yi2/lFs/aX4+qet5TftmdQHgZXADGAz8B4wyzm3PG6dR4EK59y3\nzaw/8D5wjHPuoFqWbKmpT2bXopWsf+JZIrV13oxAgHEP3O4NhWmtfn1EREREpBPJ+pp651wYuA14\nBVgKPOOcW25mN5rZDdHVHgJOMbNFwKvAVxMT+mzXY+JYRt9zPaFe3b0ZkQgr7nuEJXc9fCDRFxER\nERFJo3Ydp94593fn3Fjn3Gjn3Hej8x53zj0RfbzZOXeuc25i9OcPybaTyXHqU9H1iIGM+cbNFI44\nIjZv0zMv8d7M27P+Atr4+i5JP8XXP4qtfxRb/yi2/lFs/aX4Zh/dUdYnoZ7FjPrKdfQ6+djYvJ3z\nFzPv3OvYtWhlBlsmIiIiIh1Nu9XUp1M219Qncs6x7dW3KX/u77ELaANdC5jw428x8OLWX9ksIiIi\nIh1X1tfUd1ZmRr9zpjLijs8R6NoFgMj+Wj668V5WfecXuHA4wy0UERERkVyXk0l9ttfUJ9P96DGM\n+caNFPTvG5u39pGn+eCzX6Fux+4MtuxgqpHzl+LrH8XWP4qtfxRb/yi2/lJ8s09OJvW5qsuAEkZ/\n40aKjx4dm7f9tXeZd+517F6yKoMtExEREZFcppr6DHCRCFv+PIetL78Rmxfoks9RP7iHwZefn8GW\niYiIiEgmqaY+h1ggwMBLz2b4rVcT6FIAQKSmjsW3P8iyr88mUlef4RaKiIiISC7JyaQ+F2vqk+kx\n6UjGfPMAVCRbAAAgAElEQVQmugzsF5tX9tTzvHfZbdRs2ZaRNqlGzl+Kr38UW/8otv5RbP2j2PpL\n8c0+OZnUdySNdfY9jj8qNm/n/MW8ffYXqJz7fgZbJiIiIiK5olU19WZ2iXPuhejjPs65St9a1oxc\nr6lPxjnHtlfeovz5/4uNZ08gwKivfJGRd16DBYOZbaCIiIiI+K69aurPM7OfRh/3N7NvtXaHkpyZ\n0e/caYy861ryirt5MyMRVn//l7x/1d3UbqvKbANFREREJGu1NqkPAMvN7BHn3DLgTB/a1KKOUlOf\nTPGRIxl73210G1Mam1f5xnzePvtaqt7x/7hVI+cvxdc/iq1/FFv/KLb+UWz9pfhmn9Ym9YOdcz8H\ntprZQ8D9PrSp0wv1LGbU3V+g/wWnx+bVbtnO/MtuZ+1Pf4uLRDLYOhERERHJNq2tqT/BOTc/+vjr\nwGLn3It+Na4pHbGmvim7F69i/a+fI7x3X2xeyYyTmfDIt8jv2yuDLRMRERGRdGuXmvrGhD76+DuA\nCr191n3CGMbeewuFI4+Izds2Zx5vzfg8lW9qdBwREREROcwhLZ1zb6erIa3RkWvqk8nv3ZPRX/kS\nJedMi82r3bqd+Z+5k1UP/4JIfUPa9qUaOX8pvv5RbP2j2PpHsfWPYusvxTf7aJz6HGF5QQZffh4j\n7rjmwOg4zrH2J0/z7sU3s299eWYbKCIiIiIZ06qa+mzRmWrqk6nftYeyXz/HnmVrYvPyirtx1A++\nysBLzs5gy0RERETkcLTXOPWSBUI9ihlx5+cZNPNcCHgvYcOeaj666X4W3/kQDXurM9xCEREREWlP\nKSX1ZvaVJubfnd7mpKaz1dQnY4EA/c49lTFfu4H8kt6x+ZuefZm3zvw8O979qE3bVY2cvxRf/yi2\n/lFs/aPY+kex9Zfim31S7am/r4n5uqNshhUOH8LYe2+h15RjYvP2l5Xz7qW3ehfR1tVnsHUiIiIi\n0h6arak3s8Y7xv4VuAiIr+8ZAdzrnBvmX/OS6+w19U3Z8d4iNv7+L4T31cTmdZ8whok/u5+iscMz\n2DIRERERSUVba+rzWlj+ZPR3F+DXcfMdsBW4vbU7FP/0OnEi3UYNpeypP7F3xVrAu3nV2+d+gTHf\nuoVh183EArqMQkRERKSjaTbDc84Nd84NB37f+Dj6M8I5d7Jz7i/t1M6DqKa+afm9ezLyrmsZdMUF\nWJ73P1ukpo4V3/ox7195F/s3bG72+aqR85fi6x/F1j+KrX8UW/8otv5SfLNPSt22zrlrzKy/mX3K\nzL5gZtc1/vjdQGk9CwTod9YpjPnWzXQZMiA2v/Jf85k7/XNs+N2fycWhTEVEREQkuZTGqTezS4Df\nAR8DRwFLgaOBuc656b62MAnV1KcuUt/Alj/PoeKVuRD3WvedfhJH/fBrdB3cP4OtExEREZF4fo9T\n/xDwBefcsUB19PcNwAet3aG0r0Aoj0Ezz2X0V6+noH/f2Pztr73LW2d8lo3//aJ67UVERERyXKpJ\n/VDn3B8T5v0GuCbN7UmJaupbr9uooYy971ZKzp4K5v3z17CnmiV3P8wHV3+Fms3bANXI+U3x9Y9i\n6x/F1j+KrX8UW38pvtkn1aS+wswa6zTWmdnJwEgg2Jqdmdl5ZrbCzFaZ2T1NrHOGmX1oZkvM7LXW\nbF+aF8gPMfgz5zPqP75Ifr8+sfnb/zmPuaddxYbfvoCLRDLYQhERERFpi1Rr6u8BVjvnnjeza4An\ngAgw2zl3b0o7MgsAq4AZQDkwH7jSObcibp0ewNvAOc65TWbW1zm3PXFbqqk/fJHaOsr/91W2//Od\ng2rte59yHEfN/hrdhg/JYOtEREREOidfa+qdc99zzj0fffw0MAY4PtWEPupE4GPn3HrnXD3wDHBx\nwjpXAc875zZF93VIQi/pESjIZ8iVFzLqK9cd1Gtf9fYC3pr+Wdb+7HdEGhoy2EIRERERSVWb7kTk\nnCtzzi1v5dMGAxvipjdG58UbA/Q2s9fMbL6ZfS7ZhlRTnz5FY4Yz7v7b6HfeqRAIsCxSTaSmjlUP\n/Zx3Lrie3UtWZbqJHYpqEP2j2PpHsfWPYusfxdZfim/2aemOsu0tDzgOOBPoBswzs3nOudXxK73x\nxhu8WfVXBpf0A6C4ayFHDhvOiUceDcB7y5cAaDrF6ffXrITxgzl68k2s+cWvWVZRCcD4RSuZd+4X\nqbroJAZffgGnnXUmcOCNPG3aNE1rOmumG2VLezrS9OLFi7OqPR1pevHixVnVHk1rWtOZ+fs1d+5c\nysrKAJg8eTIzZsygtVKqqU8HM5sCPOCcOy86/TXAOee+F7fOPUAX59y3o9O/Av7WWPrTSDX1/nHh\nMBWvvMWWv/4TV98Qm9/1iIGM/86XKTnrlAy2TkRERKRj83uc+nSYD4wys2Fmlg9cCfwlYZ0/A9PM\nLGhmhcBJQGvLfOQwWDBI//NPY9z9t1E0dnhs/v4Nm/ngs1/hwy9+g5ryigy2UEREREQSNZvUm9mA\ndO3IORcGbgNewbsj7TPOueVmdqOZ3RBdZwXwf8Ai4B3gCefcssRtqabeP42lOQX9+zLy7i9wxLWX\nEiwqjC3f+tLrvHnqVax74lldSNsGiaUikj6KrX8UW/8otv5RbP2l+GafvBaWrwK6N06Y2Z+cc//W\n1p055/4OjE2Y93jC9A+BH7Z1H5I+FgjQZ+rx9Jg4jvLn/4+qtxYAEK7ex4r7HqH8j39j/Pe+Ss/j\nxme4pSIiIiKdW7M19Wa2xzlXHDdd5Zzr3S4ta4Zq6jNj76pP2PC7v1AbvfssAGYMufpTjPn6TeT3\n6Zm5xomIiIh0AH7V1LfPVbSSE4rGDGfsfbcy8NKzsVD0Sx7n2Pi7v/Dm1Csoe+p5XDic2UaKiIiI\ndEItJfV5ZjbdzM40szMTp6Pz2p1q6v3TWFPflEBeHv0vOJ1x376D7hMOVFLV79zDsq/P5u1zr2PH\ne4v8bmbOUg2ifxRb/yi2/lFs/aPY+kvxzT4t1dRXAL+Om65MmHbAiHQ3SrJfQUlvRtzxOXZ9tIJN\nz75M3bYqAPYs+Zh3P30Tg2aex5h7b6FL/74ZbqmIiIhIx9du49Snk2rqs0ukvp5tr7zFlpfewNXX\nx+YHiwoZeefnGXb9Zwh2KchgC0VERERyg6/j1JvZ+OjQk183sxvMTMOdSEwgFKL/hWdw5IN30uP4\no2Lzw3v3ser/Pcbc065my4uvkYv/QIqIiIjkgpbGqTcz+zWwGPgG8GngW8AiM3vKzFr9X0Q6qKbe\nPy3V1Dcnv09Pht80i5F3f4EuA/vF5u8vK2fhl77Je5feyq5FK9PRzJylGkT/KLb+UWz9o9j6R7H1\nl+KbfVrqqb8BOAOY4pwb5pw72Tk3FDgZOBW40ef2SQ4qPnIkY++/lSFXfeqgG1fteGch8869jsV3\nPUzN1u0ZbKGIiIhIx9LSOPVzge86515Msuwi4OvOuak+ti8p1dTnjobq/Wx98TW2/fMdiERi84OF\nXRl+y1WU3jyLvG6FzWxBREREpPPwq6Z+PPBGE8veiC4XaVJet64MvuICbwjMY8bF5of37Wf1D5/k\nzZOvYMNvXyDS0JDBVoqIiIjktpaS+qBzbk+yBdH5KV1om26qqffP4dTUN6fLgL6MuO2zjLzrWroM\n7h+bX1tRydL/+D5vTb+GilfmdviLaVWD6B/F1j+KrX8UW/8otv5SfLNPS+PUh8xsOtDUVwAtPV/k\nIMXjRzH2vlupmvchW16YQ/3O3QBUf7yOBdd8lV5TJjH2vtvoeZy+BBIRERFJVUs19evwbjDVJOfc\n8DS3qUVz5sxxyz/ex/j8BgYEI2RmDB45XJHaOrbNmcfWv/2LSE3tQcv6X3A6o++5gaKx7X56iYiI\niGRMW2vqc/bmU19b4B1r70CEo/LrOSq/gfH59fQM5t7xdHYNe6rZ8uJrbH/9vYMupiUQYNBl5zLq\nP75E4dCBmWugiIiISDvx5UJZMys0s4fN7C9m9oCZZcVtQeNr6qsiAd6sKeAXu7txx/aefH17d367\nuysf1ISojqgLv7X8qqlvTl5xN4bMuogjH7yTnpOPPrAgEqH8j3/jzalXsOwbP6K2orLd25ZuqkH0\nj2LrH8XWP4qtfxRbfym+2aelmvhHgcnA34CZQB/gdr8blYpR7KeMAuoS/i/ZFA6yaX+QV/eD4SjN\nCzM+2os/Jr+BAuX5WaugXx9Kb7ySfeeVs/mFV9mz5GMAXH0DZb9+jk1/eJGhX7qc4bdcTX6v7hlu\nrYiIiEj2aKmmfjNwnHNus5kdAfwrEzX0iebMmeN2friJiIPN5LOOAtbRhU0un0gzBfZBHCNDDRyZ\n7/2MCjWQryQ/a+1d9Qmb//dVqleXHTQ/WFRI6fWfofTGKwn1VHIvIiIiHYcvNfVmtts51z1uuso5\n17uNbUybxqQ+UZ0zNpHP+miSv9WFcM0k+aGEJH9kqIGQkvys4pxjz5JVlP/pVWo2bjloWV5xN4bd\ncAWlN1xBqEdxhlooIiIikj5+3Xwqz8ymm9mZZnZm4nR0Xrtrapz6fHMMt1rOsN1caxXcaeX8G9s5\nnj30pf6Q9esxVtSH+N/qrjy8o5ibKnrynaoiXtjbhRV1edR1wmtuM1FT3xwzo/uEsYy99xaG3XAF\nBQNLYssa9lSzZvaveePEmaz+0VM07KnOYEtToxpE/yi2/lFs/aPY+kex9Zfim31aqqmvAH4dN12Z\nMO2AEeluVLp0MccYahhDDbCLahegjALKKGA9BVQROmj9eozl9SGW14eg+kBP/rh872dkSDX5mWKB\nAL1OmEDP449i5/zFbHnxNWq3bAegYdceVn//l6x/4hmGXX8Fw744U2U5IiIi0qnk7JCWycpvWmtP\nXJJfRgE7EpL8REEcI0JhxoXqGZvfwOhQA10zck9dcZEIO95bxJa/vkZdwqg4ecXdGHrdZZRefwX5\nfXtlqIUiIiIirdfpxqlPR1KfqDHJ3xBN8hN78hM1jq4zJr+BsaEGxuQ30D2Qe/HMZS4cZse7H7Hl\nxdep21Z10LJg1y4ccc0llN5yFV36981QC0VERERS51dNfVZqqqb+cBVbhKNsP+fZTm6wrdxKOZ+m\nkknspU+SmnyH8UlDHv+3rws/2VXEbdt6cs/27jy1u5C39uezPZx74c22mvqWWDBI71OO48gH72To\nF2dSMOBAzX14fw3rHn+Gf504k2Vf+yH71pdnsKUe1SD6R7H1j2LrH8XWP4qtvxTf7NNSTX2nVmwR\nxrOf8ewHoNoF2Eh+rDe/woUgYXSdzeEgm/cHeW2/d5+u3oEIY6K9+GNCDQzJCxNQXX7aWTBI7ymT\n6HXiRHZ9uIwtL74eGy0nUltH2X/9iQ2//TMDLp7B8FuvpvtRozPcYhEREZH0UfnNYahxxkYK2Eg+\nGyhgcwvj5AN0NceokJfgj9bFt75xzrF70Uq2vvQ6+z7ZeMjyvtOnMOL2z9Hr5ElYC6+ZiIiISHtR\nTX0WqI/eDGtDtCd/E/nUt1DhFMAxNC8cS/JHhxroHcy91yRbOefYu3wNW//+L/YuX3vI8h7HHcWI\n2z5Lv3OnYcFgBlooIiIicoBq6rNAyGCo1THV9nClbecuyrmWrZzFTsaxjyLChzwngrGuIY9X9nfh\n0V1F/Pv2nty1rTs/39mNV/YVsLY+SEM75vi5VlPfEjOjePwoRt19HWO+eTM9jj8K4t4muxYs5cPr\nvs6b02ZR9tTzNFTv97U9qkH0j2LrH8XWP4qtfxRbfym+2Uc19T4KGAygngHUMxlwDnYRjJXsbKSA\n7UlG2KmMBKmsDfJObT4A+TiGh7xe/FGhMKM0yk6bFJYOZvhNs6jdup2KV+ZS9faHuAbvH619n2xk\n2ddn8/H3f8kR11zC0OtmasQcERERyRntWn5jZucBP8b7huBJ59z3mljvBOBt4Arn3J8Sl2dr+U1b\n1DhjU7RUZyP5bE6hZAegXzDMqFADI0Pe7yPywuSpNLxV6nftYduceVS+8R7hfTUHLbNQHgMvPYfh\nN11J8fhRGWqhiIiIdDZZX1NvZgFgFTADKAfmA1c651YkWe9VYD/w646e1CcKO6ggxCbyKY/26O9O\n4QuVxt78EaEwI0MNjFJtfsrCNbVUvbWAbXPepm7bjkOW9556HMO+dDn9zlHdvYiIiPgrF2rqTwQ+\nds6td87VA88AFydZ73bgOaCiqQ1la019OgQNBlo9k62aT1sVt9gWbqWcS6jkBPYwmFqCSf4Rq8NY\nWR/ib/u68LNobf6d23rwyM5uvFhdwPK6PGoiLe+/o9XUpyLYpYCSGSdz5EN3UXrzLLqNHHrQ8qq3\nFvDhF77Ov6Z8hk8e+2/qd+5u875Ug+gfxdY/iq1/FFv/KLb+UnyzT3vW1A8GNsRNb8RL9GPMbBBw\niXNuupkdtKwzK7YI49jPuOh4+Q009uYXUE4+5eSzK8lLuSMS4IPafD6I1uYbjsF5EUbkNTAi2qs/\nRGU7MRYI0PO4o+h53FFUr9nAtlffYueHSyHi/RO1f8NmVn77Z6z+/q8YdPn5DPviTIrGDs9wq0VE\nRESy70LZHwP3xE0nTTdXr17NX+f/L/16e3cPLezSleGDh3H0yCMBWLJmOUCHnV6x1ps+IW75fheg\nx8hjKCefj9asoJIQhSOPBWD3Gu+bje4jJ7GxIciylYtj0/k4uqxfwKBgmDPGj2fE6Im8u2wRZnDi\nkUcDB3rvO930TVdSV7WTfzz3Ars/Wsm4Oq/0ZnF1JYv/63eMf/p/6X3KcWw5aQw9TzqG0844HTjQ\nezFt2rRDpqdNm9bsck1rOlunG2VLezrKdOO8bGlPR5rW563imyvTjY/LysoAmDx5MjNmzKC12rOm\nfgrwgHPuvOj01wAXf7GsmTUOJG5AX6AauME595f4bXXkmvp0iTjYTh6boz355eSz3YVwKdxoqdAi\nDA+Foz36YYaHGugVcIk3z+1UInX17HhvEdvmzIvdqTZeQf++DLn60xzx2U/TZVC/DLRQREREOoJc\nuFA2CKzEu1B2M/AeMMs5t7yJ9Z8C/prsQtnZs2e7UlfiZ3M7pDpnbCEUS/Q3J7kId/eahXQfOemQ\n5/YIRCjNa2B4KOz95DXQsxNeiOuco/rjdWyb8w67Fi6LleY0smCQfudO44jPX0qfUydjgYMvW4nv\nkZP0Umz9o9j6R7H1j2LrL8XXP21N6vNaXiU9nHNhM7sNeIUDQ1ouN7MbvcXuicSntFfbOot8cwyl\njqHUxebtdQE2RxP8LeSznORX0+6KBPioLp+PDjyVXoEIpaEGhueFKQ01UJoX7vCJvplRNGY4RWOG\nU7djN5VvzqfyX+/TsGsPAC4cZuvLb7D15TfoOmwQR3z20wy+4kIK+vXJcMtFRESkI2vXcerTReU3\n/mm8QVZjou8l+6GUxs6HaKKf10BpKOz95DXQs4OX7riGMLs+Ws72199j74q1hyy3vCD9zpnGkM9e\nTN/TT9CwmCIiItKkrC+/SScl9e3LOaiK1udvIcQW8tnqQtRbaol+j0CEYXlhhkV780tDYfoGIh0y\n0a/ZXMH2N+azY96Hh9zQCqDL4P4MufrTDL7iAroO7p+BFoqIiEg261RJvWrq/bNkzfLYKDvNiTio\nJI8tcYl+RSsS/ULzEv2hoTCl0YR/YDBCsIMk+pG6enZ+sITKN9+n+uP1sfnLItWMD3QDM/qcfgJD\nrryIfuedSrBLQQZb2zGovtM/iq1/FFv/KLb+Unz9k/U19dKxBAxKaKCEBiZE50U4ONHf2kyP/j4X\nYHl9gOX1odi8EI4j8sIMC4UZmtfA0LwwR+SF6dKet0hLk0B+iN4nH0vvk4+lZnMFlW9+QNXbH8Ke\nam8F56h8/T0qX3+PUM9iBl56DoOvvJDuE8diHfErDBEREfFVTvbUq/wmdzgHO8iLJfne7xA1pFZX\nbjj6ByMMjfbqD8tr4Ii8cE4OsRmpb2DXh8uonPsBe1esSXopePH4UQz6zPkM+rdzdHGtiIhIJ9Sp\nym+U1Oc252A3QbY29uZHE/09rfjiqMgiDA15PflDoz+Dc+juuHWVO6h6+0Oq3v6Quu07Dl0hEKDv\n6Scy6DPn0f/c0wgWdmn/RoqIiEi761RJvWrq/ZNqTb0f9rkAWwlREf3ZSj6VLi+lG2YBBHEMzItw\nRLQ3v/Enm3r131u+JHbnWgAXibD343VUzV3Azg+W4urrD3lOsKiQARdNZ9Dl59P75EmHjH0vHtV3\n+kex9Y9i6x/F1l+Kr39UUy85r9AiDKeW4dTG5tUD2+OS/MaEvy7JEJthjI0NQTY2BJkXN7+beeU7\nQ6I/R0R/Z0OtvgUCFI8dQfHYEQy56iJ2frCUqnkfUr1qXWyd8N59bHrmJTY98xJdBvVjwMVnMejf\nzqb46DGqvxcREREgR3vqVX7TuTWOpe/16h9I9He18n/UfsEDiX5jst8/GMmKEp7a7TvY8e5H7Ji3\nkNqt25Ou0230MAZeeg4DLz2bbsOHtHMLRURExA+dqvxGSb0kU+uMbYTYFk3ytxGiwoWoS3GYTfBK\neAblhRmcF/GS/Wji3zcYIZCBZN85x75PNrJj3kJ2zF9MuHpf0vV6TDqSAZecxYBPnanx70VERHJY\np0rqVVPvn0zW1PuhsVe/Mdlv/GlNrT5AfjTZHxK9IHdINPHv08qbaCXW1LeGawizZ/lqdry7iF0L\nlxOprUu6Xs8TJjDg02cy4FNn0mVA53mfqL7TP4qtfxRb/yi2/lJ8/aOaepEkzKAnYXoSZjQH7vDa\nAFRGE/zt5MWS/d1NvCXqMNY15LGu4eDlXcwxKOgl+o0/g6LJfrp79i0vSPcJY+k+YSzh2jp2L1rJ\njnc/Ys+SVbhwJLbezvmL2Tl/MSvu+wm9TprIgE/NoP+Fp3eqBF9ERKSzycmeepXfiF9qnMWS/Ypo\nwr+dEPtSHFe/UYE5BgYbk/wwg4IRBueF6edDGU9D9T52LVjGzvcXs2fFWu92v4nMvB78C8+g/wWn\n0/WIgelthIiIiKRFpyq/UVIv7W2fC8R69bfH9fCnehOtRiEcA/LCDAxGvGQ/mvAPyAuTn4Zkv2FP\nNTs/9BL8vSs+8eqPkug+cRz9LzqDAReeQbeRQw9/xyIiIpIWnSqpV029fzpaTb2fnINqAmwnRGU0\n2d8eTfb3N5Hs716zkO4jJx0y33CUBCOxZH9gMMzAaClPcaBt79H63XtjPfh7V61rMsEvGjucfuef\nRv9zT6X7pCNzdphM1Xf6R7H1j2LrH8XWX4qvf1RTL9LOzKCICEXUUho3tr5zsI9ALNGP/727iW05\njIpwkIpwkI/qQgctK7IIA/MiDAqGY738A/PClLQw/GaoexF9zziRvmecSMOeanYtXM7OBUvZu3zN\nQTX4e1d+wt6Vn7D2x7+hYGAJ/c89lX7nnUrvU44jkB9qegciIiKSNXKyp17lN5KrGmv2K8mjMtqr\nX0keu1o5Gg94w296vfthBuRFor37EQYEw3Rv5i66Dfv2s/ujFexasIzdSz/G1TckXS+vuBt9z5xC\nv7On0vfMk8nv3aO1hysiIiKt1KnKb5TUS0fT4KAqmuhXkkdV42+XR30rxtlvVGgRBkRr9Q/6HTz4\nTrrh2jr2LF3NroXL2b1oBeHq/ck3GAjQ64QJlJw9lX5nT6XbmNKcLdMRERHJZp0qqVdNvX9UU++v\n1sbXOdhNMJbwV0V79qvIY08bq+d6Brzkvn+0V39AMEL/vDAl1FO/poxdC5ez68Nl1FXubHIbXYcN\nouSsUyg582R6n3Icwa4FbWpLOqm+0z+KrX8UW/8otv5SfP2jmnqRDsgMehCmB2GGx9XtA9Q5oyqa\n4FcSYkf0cZXLa/YuujsjAXZGAqyoT9gXjl69ezPgnAn0Py/M4IpN9F2+jPyly2j4pAzi/v/fv76c\nsiefo+zJ5wh0yaf3KcdTMuNkSmZMobB0SDpDICIiIinIyZ56ld+INK1xVJ6qaBlPY+K/gzx2uDwi\nbSibKazezVEfL2XUysX0W7WCYG1t0+uOHErJ9JPoe8ZJ9Dr5WPK6dT2cwxEREelUOlX5jZJ6kbaJ\nxJXzNCb8O6I/u1wwpYt1Aw0NDFm3mtKPlzJ81TL6bNvS5LqWH6LXiRPpe/qJ9J1+EsXjR2GB1l8j\nICIi0ll0qqReNfX+UU29v7I5vmEHO6MJfmOyvzOFhL/7jkpKVy1l+KqlDF27klB9fdL1AMI9e2CT\nJ9F96vEMnn4SQ0YPJhRMT5Kv+k7/KLb+UWz9o9j6S/H1j2rqReSwBA360EAfDh3iMgzsivXqBw9K\n+Hf27M2ik05j0UmnEayvZ8i61QxbvZzS1cvpu7X84H3s3AX/eIO9/3iDld+GeX37sX3ceGqOmUD+\n8cfQb2BvBhTnM7B7AQOK8unZNU+j7IiIiKQgJ3vqVX4jkj0aS3p2xiX6O6LT9buqGbhmBaWrVzB0\n9QoK9+1tejtmVAw6gg3Dx7BhxBg2DRtJoFshA4ryGVCcz4DiAvoXe48HFufTvyifogL1S4iISMfS\nqcpvlNSL5AbnYD8BL9mPGHWbtxFa+wndV6+m7/pPyGtoulQnEgiwZfAwNgwfzYYRYykfOoKG/PyD\n1inKDzIgmuD3j/4eUFwQm1eYH/T7EEVERNKqUyX1qqn3TzbXfHcEiu8Brr6BhvUbqV1bBmvWEdpU\njjXzeRQOBtkyeBibSkexsXQU5UNHUNflwMg6u9cspPvISQc9p7gg6CX8Rfn0K85nQFE+/YoO/BNQ\nlB9UeU8KVDvrH8XWP4qtvxRf/6imXkRyioXyCI0qJTSqFDgNt78Gt24DkbXrcWvX47ZUHLR+MBxm\ncNlaBpet5cR/veKV6ww8go3DR7Fp2CiWukPvhrunNsye2v2srkx+p9zCUICSxqQ/9jsUe9yra4hg\nQEm/iIhkv5zsqVf5jUjH5/btw32ygciadbhPynAV21t8TvXAgWwbMYoNQ0eyZmApVb36enfwaqOg\nQf9peBQAABwPSURBVN9uXsLfryhEv275lMQl/iXd8ummEh8REUmjnOipN7PzgB8DAeBJ59z3EpZf\nBdwTndwD3OycW9yebRSR7GCFhdhRYwkcNRYAV73P68lfV4b7ZANu89ZDntNt82a6bd5M6Vtvcirg\neveifvxY9owaTeXwkWweeAQ7w8aumgZ27W+gPtJ8p0bYwda9dWzdW9fkOt3yg5R0a0zyQ5R0y6ek\nKPq7mzcvP09j84uIiL/arafezALAKmAGUA7MB650zq2IW2cKsNw5tyv6D8ADzrkpidtSTb1/VPPt\nL8U3fdz+Gtz6jV6Sv34jS9evZry1cPfaUB6BsaMITDiSwNHjqBs3hl1FPbwkv6aBXTXh2OPdNQ3s\nq4+kpa09uuR5CX9RPv26hejbLZ++jf8AdAvRp1uI/DSN1+8H1c76R7H1j2LrL8XXP7nQU38i8LFz\nbj2AmT0DXAzEknrn3Dtx678DDG7H9olIDrGuXbBxowiMGwVA3srF5OX38BL99Rtw6zdCbUIPe30D\nkSUriCyJfezQs38JvaPfCASOGkdg7EisS4G3ejjC7sZEv9ZL9L2EPxx7HE6hX6TxH4WmavvhQOLf\nN5r0l3QL0afQS/z7Rud3DanUR0REkmvPnvrLgHOdczdEpz8LnOicu6OJ9b8CjGlcP55q6kWkJS4S\nwW3dhivbhCvbRGTDJqjc0fITg0FsVCnBo8YSOHIMgfFjsGFDsOChCbVzjn31EXbXNLC71uvp3x3t\n5d9d6z3eUxsmXZ+yhaFArJe/b6HXw9+30PsnoPFxjy55urhXRCSH5UJPfcrMbDrwBUDf64hIm1gg\ngA3sDwP7w0nHAeD2VuM2lBMp2+gl+xs3Q0PCHXTDYdzKNTSsXAO87M0r7EpgzEgCR44mMH4MgSNH\nY4MGYGZ0yw/SLT/IQAqStiMSceytC7O7NtrDX9vAnpqDp/emmPjvq49QtrOGsp01Ta4TMOhdGE36\nC70e/t7Rx32i/wj0KQxpOE8RkQ6mPZP6TcDQuOkh0XkHMbOJwBPAec65pN1qjzzyCLvLq+jX26ur\nL+zSleGDh8VqlZesWQ6g6TZMNz7OlvZ0tGnF17/pxnnNrW9F3Via3wCjBnD0udNx4QhL3n8HV7Gd\n8XUBIhvKWba1DIDxgW4ALItUw95qxi/cT2ThEm8aGN+jP4GxI1neI4QdMZiJF34KGzyAJR+8C8CE\nyd7lQEsXxE33gMXvv0M34Ozo8sXvv4MrcJROOIE9tWEWzp/HvroIvUZPYndtmLWL3mNfXYTQ0AmE\nnTcePxAbkz9xeufqhewEtjexvHG6ZMyx9C4MUbd+Ed275DHphJPpXRhiy/IPKO4S5MzTT6N31xAf\nzZ/HkiVLuPnmmwGvjhaI1dJq+vCmH3vsMSZMmJA17elI042Ps6U9HW1a8U3fdOPjsjLv78/kyZOZ\nMWMGrdWe5TdBYCXehbKbgfeAWc655XHrDAXmAJ9LqK8/iC6U9Y8u5PSX4uufdMXW7a/BbSzHbdxM\nZNNmrzd/z97UnlzcjcCYUQTGjCAwdiSBMSOxoYOTlu60ul3RUp89tQ3R8fcPlPfsrQuzJ1ryU9OQ\nnot7G4WChtuwhDGTTqR3YR69C0P07hqiV2GIPoV5scc9VfbTJrrY0D+Krb8UX//kxB1loyPaPMKB\nIS2/a2Y3As4594SZ/RL4N2A9YEC9c+7ExO2opl5E2pPbtQe3aTORjZv5/9u7sxhLrrMO4P+vqu7a\ne/fMdE9Pz24jYpBsECSAJSCykJxEJAIhAUKK4AmxRkJCiAgpPMITIeQBIQICJBaJhyQSSViUSBFE\nOM7SjuNJQmxjG89M9/S+3qWWj4dzqm7d29vdqrur5/+TWlV16tw71cfHM99X9VWV3n8Avb8E1I4u\ngWlTKsF54pYp3/meO5Anb8O5ewtSLmdyrEEYYacZYqceYqcRmIDfJgG7jTBJCk56nGevHDE3+05X\nC5iqtIL96YqHqUqh1V4toFpwWPpDRHSEXAT1w8KgnojOkqoCG1vQB0uI7i9BHzzsLdB3HMjCPJwn\nb8N54jacJ29DnrwDuTxzKsGuqqIZaivIb5qA/7DtYQf/AFB0BVMVE+RPpZKASZsATFU8TFU8TFaY\nABDR4+exCupZfpMdlodki+ObnbMe2yTQf7iM6MES9OEy9MFy96U7gCnfuXsLzp1bkCduw7l705zV\nH6lmd+AnaAQRvvLCl7Dw1A9h157537UlP7up4L825LKfWMkVTCYBv4fJcsEG/CYBmEwlAGMlF07O\nEgCWMGSHY5stjm92LtTTb4iI8kZEgOlJyPRk8hZcwD5xxwb4+nAZ0dIjYHUdOOyEys4eosVXEC2+\n0v7dc1cgd27CuXMDzp1bcO7cgNy8njxPP0slz8F4ycPNqePLhUL7lJ+9VNAfr++lEoC9Zm9n/xuh\nnvhW31hcAjRV8TBRNgH/ZMXDZNn+2CRgwm5XeBWAiC6QXJ6pZ/kNEeWZNn3ooxXow0cm4F96BF16\ndPBlWcdxHMj8nAnwb9+Ac+s6nNs3zDP1M6rXH4a49CcO9PdSCUD6x7RFCDIo/4kVXMFk2Qb5Nvif\nKHuYiJOCeNvuZykQEZ0GnqknIsoJKRYgC/PAwnzSpqrA1jZ0acW8NGv5EaKlFWBlDYgOKW2JIujb\nDxC+/QD4YuphYSKQq1cgt2ygf3MBcvM6nFsLkInx7H+5E4gISp6g5DmYrhaO7ZtOANp/ota6bxKD\nPT9EI+gtAfBDxcqej5U9v6v+niMYL7tJsD9uz/iPl1tn/8dT+8ZLLgqu09MxERH1K5dB/eLiIm6B\nNfVZOOu65IuO45udvI+tiACTE5DJCeB7n0jaNQiha+s20F+BLq9CH60c/XZcVVPq82AZ0ZdebN83\nOQ7n5nXIzQU4Nxfg3Fgwj9ycn4V4R/9z8PJX/jt57v5p6iUBAEwJUDr43/cjuzSJwH7Hvl6vAgSR\nYn0/wPp+cHJnq1pwUkG+h4mym6yPlz289c2v4NlnnzVtTASGijXf2eL4nj+5DOqJiB4X4rmQ2cvA\nbPuJDPV96Moa9NEq9FG8XAXWNw6v1weAzW1Em68AL72CMN3uupBrc3BuXINcv2bO7l+fhyzMQy7P\nZPa7DZvrSBIcd6MZ2qC/GdnAv2Pdt4mAH6LWjPp6EtC+H2Hfb+LhzuGlVduvPcQnt15ta6sUHBv0\nu0nwP15yMXZgvbWfpUFExJp6IqILRIMAurZhAvxHq9DVNUQra8DKOhB0f4Y5USpBFq7CuT4PuT4P\nZ8EG+wtXIZemIc7jc1bZD6Mk0DfBethab4aoxW2p7dP6F9YRYKzkYazkJglBvJ1uHyu5GCvbZdFF\ntZi/JwYRXXSsqSciIojnHX5mP7I1+6tr5gz/yhp0dR26ugZsH/PYzUYD+tobCF974+C+YtEE/Nfm\nTKB/bQ7OtauQa3PmiT2Fk0tm8qTgOphwTTlNN1QV9cAkAjW/dRWgFm93JAOmvb9EIFJgqx5gqx4A\naHT9OUeA0aIJ/EdLblsSELfHbaNJm9kueY9PQkeUB7kM6llTn5281yWfdxzf7HBsjyeOAFMTkKkJ\n4Mk7bfu00TR1+6vrwKpZmu0NoF7HvWgPTzkjB7+02YS+/ibC1988uM9xIFdmIPNzkGtX4czPQq7O\nQeZn4czPAtNTF75cRERQKbioFFwAhyc4nfcrxIlAHODHwX8tFfS32mx7EMEP+7smECmw3Qix3QhP\n7tyh4ArGii5GS14S7MeBf7ptJN5XbCUGp/E4UdZ8Z4vje/7kMqgnIqLhkVIRMj8HzM8d2Kf7+3AX\nvwq3Mgld2wDW1qFrm9D1jePfoBtF5kk+SyvA117GgZCxWDRP6Zmfg3P1CuTqLGRuFnL1CpyrV4Cp\nyQsf9B+mPRHoXhAp6n581t8E++nkoB60koJ9P0TdjwZKBgDz9KD1WoD1Wu9lXY4gCfZHiibQHyl6\nNiFIt7UShZFiq53vGCA6iDX1RETUF63VWgH+2gZ0fRO6Ybe3dgb78lIJMnvZBPlzVyBzlyGzV0zb\n3GXI5ZkLV95zFsIovjIQ2uDfJgF+hFrQCv7ryb4wWR8gHxhYnBRUCzYJKLgY6UgGRgqOWcb7i+0/\nRVeYGNC5xJp6IiI6VVKpQBYqwMLVA/s0CICNrVSgb5cbW8DGJlA/oe670YC+9Tb0rbdxyFP6zfP4\nZ6Yhs5cgVy6ZYD9ezl6CXLkMmZ6EuL2d8X7cuI4kQW4vVBVBpEkikAT9QYRGKiGoBx37/QiNoL8n\nCaVFCuw0Quw0Qiwfc0vIcbzkd3dQtUF/NZUQVIsmGajaPnES0eprPuc6TAzofMhlUM+a+uywLjlb\nHN/scGyz08/YiucBl2eOfCSm1upJgK8bW9BN+7OxBWxunRz0q5qbflfXgFe+c3gf14VcnoZcvmTO\n7F+xCUCyPWMSg+LZnfE/q3cADEpEUHAFBddBP680i68Q1AMT5MfBfmcS0DiiTzfvGNh+bRHjd585\ncn8Qqb25uI9fIKXsOUmAbwJ/B5U4GSiY9mqqPe5T7ehbytmVA9bUnz+5DOqJiCjfpFKGVMrA/Oyh\n+7Veh25um7P9m1vQrW1gcxu6tQ3d3Dr+iT2xMGzV9R9nctwE+fbHuXwJMjMFuTRjkoKZaXOTMc/6\nD02/VwhiYaRtAX96Ga+/vlPFzNVRNDr2Ne32sMqH4u9dRx+PjE1xBCbAt4lAJUkCnKS9Yturncvi\nwXZeQXj8sKaeiIhyR4MQ2N4xQf7WNrBl1zfj7e3jb+TtleNApqcgl6bMk3suTZvAf2bKnO2fsW1T\nk5ByaXh/LmUmCCPUA0UzbCUCrR9FI+xoC9sTiUYQoXmWNxacoOAKqgXXXElIJQStpV33nAPtZS/V\nx3NRLjgoew4ThVPCmnoiInpsiOcC05OQ6ckj+6jv28B/B7q901rfsuvbO8Du3tFv4E2Lola5z0lG\nqibIn54ydf0zdjk9Zc7423UmAGfLcx2MugDQ/xUYVUXTBvvN0CYDQZQkBE2bHDSTtlbfut3fDE1y\n0E1JUS/8ULEVBtga4neWXEE5Dv49kwjEAX8lWZpEomz7lFOJQbxdTiUKTBaGJ5dBPWvqs8O65Gxx\nfLPDsc1OXsdWCgVgxpbPHEHDCNjdg+7sANu70O0d6PYusLML3WmtY7/W/R+8tw/d24e+dfIV5XuF\nAN935TrEBvvmXQKT5mdy3K5PQCYnTJmQl8t/ts/EadyvICIoeTKUF3GFkSaJQTNsXQmIE4IkQQjb\nk4XOpMK3SUIW1xAaoaIRmvsQTrpnoRdFV1KJQCvgL3mtwD+dBJhtt73PYZ/xHBRydq/CIPi3AxER\nPbbEdYCJMcjE2LH9NAiAnT3ozq5NAnbNetwWb+/tA9Ghz+s5XKMBvf8Qev9hd/3HRiCTJsiXqQlg\nYtwE/xPjtn3cBP8TE+Z3Gh15bAKavHMdQcVxURnCfdvx04nSAX/6qkDbMtUeJwRJIhEqfFt6NOwr\nCWnmWOKXoPlD/W5HgJLnoOSawD8d9JdSy/Z1QdlzbZu0Pu85KB7yuaIrcM7B/2esqSciIhoSjRSo\n16A7eyb4390zgf+u3d5rtfWcAPTDdYDxccjEmAn8J8ZMIjA+lrRhfBQybtowMWb2lYrZHhfljqrC\nDzVJBvzwkGTgkOTAj9LL9s/6oQ78eNPzoui2gv9SKjlItxU9B2XXQTHVXrQJQvJ5z0Fx9TXW1BMR\nEZ0lcQSoViHVKjB7fJmoqgL1OrBrynVM0N9a6u5eUsqDvf3eSoBiYWQfG7rZWzlGqQQZHwXGbZA/\nNmqC/7FR0z422mq3bTI2aq4MsEToQhIRFD1B0QMGuQ+hU/qqQhLot61HaEbaWg8VQby/o91PEgbz\nnX54ei9Ji6827Bx8f3bP/ugH+/tcLv/PY019dvJaO5sXHN/scGyzw7HNhojgmw/ewPfffceRz/NP\n0ygyT/RJBfq6X2st9+P2GnTfJgHNPksZGg3oSgNYWeu9NrtagYyOmGB/bMQG+6OQ0aoJ+sdGzf7R\nkdSyChmx60N6b0Be3wGQF8Ma39Y7D4CRISYLsSgyVwOaYWQD/VYiECcFB/ZFUVu/wH6Hn0oc4u0g\nyrY0qRe5DOqJiIgeN+I4wEjVPF2ny89oEAD7NRv011rrtbit3rFdM4nDIGVB8Z/xaLW/mzWLRWC0\nChkdsYF+FTJSBUZGzDLZVzVXRUYqZkzisRmpAtVK/8dPF4rjCErOcG5mPkp8tSEO9oN0omC3/SiV\nIISaWkZtn/NDBbDf13Gwpp6IiIgSqmrO8Ndq0P26KRHar0FrdaBWh9Zs4F+rd7Q1gEaju0eEnoZy\nyVw1sEG+xAlAtWpefNa2r9JaViqmXyXeLgOVskmqiDIWRYrxrTdYU09ERESDERGgVARKRfMozR5o\npECzaQL9er0V/Dcatq0B1BtAvQ6txcu6SQbifcNKCuz36fqmObZBvy8O7isVoGqXlbIJ/MtlSKVk\nEoFK2SQU8XqlBCnbz9ol4jcql8vmnQtEQ5DLoJ419dlh7Wy2OL7Z4dhmh2ObnYs2tuKICWjLJQh6\nSwgAe5XA91sBeb2RBPzxetLWaKb2N03iUDftaDZxL9rDU87I8H65OEHBkJKEmOeZwL9kEgCUbRJQ\nLpmXk7WtlyClUmuMyyWgZH7MejHV1rE+5CsNvGfh/MllUE9EREQXj4iYmvpi0Tx5p8/v0Ujhfedl\nFK7dNsG/TQLa1ptNqG1DowltNtvbU9vwg6H+nm2CANgJzGNQ4+PP4s8pFkxwb4N8lErm0aWd68US\npFQwyUCxYPoXzbbY70CpiOjN7yJExXxnsQiUCkDRrhcL9r9jgWVLp4g19URERETH0CgyVxAavgn6\nm01z30GcBPh23fdNQpDu6/umb9NvfS5u8/3zcw9CVjzPBvkFG/DbYL9YAAqmHYVCKxkoFICiZ94I\nXbCJSKqf6Ruve+3bxQLEtiX7km374zrn+oVsrKknIiIiyog4TlLmAqDvKwidVBUIw7ZAX+N13wf8\nwCQCfpC0mf1Bsh++bxIHPzCfT30Wge0XDP7s9L4F9jj2a21XIM4slRFJAn4UPIgXJwCeSUAKNqGw\n60kfz7V9CuY+iPg7PBfwPPN+hvg77I8UPLvf9Gv1cW0f2+a22tR1gXJ/v9qpBvUi8jyAjwJwAHxC\nVf/4kD4fA/AeAHsAfllVFzv7sKY+OxetvvO84fhmh2ObHY5tdji22cnD2IpIKwiEeQxnFueQNVIb\nXMeJQGAedxoH/HECEQSpZMAs1befC8JWWxDg3uYynipNtD4XBKZP5/p5Ez/dyb7D4bDk4syvnXzm\n43197NSCehFxAHwcwHMAHgB4UUQ+parfTvV5D4C7qvqkiLwLwJ8DOHAXxquvvopbdxnUZ+F/7795\n7v8SzDOOb3Y4ttnh2GaHY5sdjm2LOJKUwCRtA37nW1/8HJ7+8eeP7WOuREQmuA9TSUG8HrYnAZru\nF4ZJYqDxehgnC2Zd4+8OwqS/xt+ZfCZsX89BudPi4iKee+65nj93mmfq3wngu6r6JgCIyD8C+ACA\nb6f6fADA3wKAqr4gIhMiMquqy+kv2tvbA2Vjv97Ha8ipaxzf7HBss8OxzQ7HNjsc22x1M77mSoRr\nftLtWR1UFzSKTKLRFuwHQBiZhCD5SSUjbfui9j6RTSai6Mj9GnbsS/XVjm2EIV566aW+frfTDOqv\nAfi/1PbbMIH+cX3u27ZlEBERERENQBwHcBxT29657wyOp1MUhMC9L/T12Vw+Z2hpaemsD+HCerS+\nctaHcKFxfLPDsc0OxzY7HNvscGyzxfE9f07zTP19ADdS2wu2rbPP9RP64O7du/jUN/4j2X766afx\nzDPPDO9IH2M/LT+DyWeunfVhXFgc3+xwbLPDsc0OxzY7HNtscXyHZ3Fxsa3kZmSkv5emndpz6kXE\nBfAdmBtlHwL4MoBfVNVvpfq8F8BvqOr7RORHAHxUVfm6MiIiIiKiY5zamXpVDUXkNwH8G1qPtPyW\niPyq2a1/oaqfEZH3isirMI+0/JXTOj4iIiIiorzK5RtliYiIiIio5VzfKCsiz4vIt0Xkf0Tk947o\n8zER+a6ILIoIC+u7dNLYishPiMimiHzN/vzBWRxnHonIJ0RkWUS+cUwfzts+nDS2nLf9E5EFEfm8\niLwiIi+LyG8f0Y9zt0fdjC3nbn9EpCQiL4jI1+3YfuSIfpy3PepmbDlvByMijh23Tx+xv6d5e6pv\nlO3FMF9WRe26GVvri6r6/lM/wPz7awB/BvvOhU6ctwM5dmwtztv+BAB+R1UXRWQUwFdF5N/4d+5Q\nnDi2Fuduj1S1ISLvVtV9e+/ef4nIZ1X1y3Efztv+dDO2Fudt/z4E4B6A8c4d/czb83ymPnlZlar6\nAOKXVaW1vawKwISIzJ7uYeZSN2MLnI9HtuaOqv4ngI1junDe9qmLsQU4b/uiqkuqumjXdwF8C+Y9\nIWmcu33ocmwBzt2+qOq+XS3BnKzsrCvmvO1TF2MLcN72RUQWALwXwF8e0aXneXueg/rDXlbV+Zfg\nUS+rouN1M7YA8KP2ks+/iMhTp3NojwXO22xx3g5IRG4BeAbACx27OHcHdMzYApy7fbElDF8HsATg\n31X1xY4unLd96mJsAc7bfv0JgN/F4YkS0Me8Pc9BPZ2trwK4oarPwJTqfPKMj4eoG5y3A7LlIf8M\n4EP2rDINyQljy7nbJ1WNVPUHYN5t8y4GlsPTxdhy3vZBRN4HYNlewRMM6WrHeQ7qh/ayKjrgxLFV\n1d34spuqfhZAQUSmT+8QLzTO24xw3g5GRDyYoPPvVPVTh3Th3O3TSWPLuTs4Vd0G8AUAz3fs4rwd\n0FFjy3nbt2cBvF9EXgfwDwDeLSKd94r1PG/Pc1D/IoAnROSmiBQB/AKAzruDPw3ggwAg5mVVm6q6\nfLqHmUsnjm26bktE3gnz+NP10z3MXDsu8+a8HcyRY8t5O7C/AnBPVf/0iP2cu/07dmw5d/sjIpdE\nZMKuVwD8FIDOG5A5b/vQzdhy3vZHVT+sqjdU9Q5MDPZ5Vf1gR7ee5+25ffoNX1aVnW7GFsDPiciv\nAfAB1AD8/Nkdcb6IyN8D+EkAMyLyFoCPACiC83ZgJ40tOG/7JiLPAvglAC/bGloF8GEAN8G5O5Bu\nxhacu/26CuBv7FPdHAD/ZOcpY4XBnTi24LwdqkHnLV8+RURERESUc+e5/IaIiIiIiLrAoJ6IiIiI\nKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVE\nRERERDnHoJ6IiIiIKOcY1BMRERER5Zx31gdARETnk4j8MIA/BHAfQATgc6r6yTM9KCIiOpSo6lkf\nAxERnWMi8usA3qGqv3XWx0JERIfjmXoiIjqSiHwYwCwDeiKi84019UREdCgR+X0AVVX9kIg8JSIz\nZ31MRER0OAb1RER0gIj8GICXAfyriHwewM+q6toZHxYRER2BNfVERERERDnHM/VERERERDnHoJ6I\niIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5\nBvVERERERDn3/yYiCPS0Jy4rAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f53c6873cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = np.linspace(0, 4, 100)\n",
"expo = stats.expon\n",
"lambda_ = [0.5, 1]\n",
"\n",
"for l, c in zip(lambda_, colours):\n",
" plt.plot(a, expo.pdf(a, scale=1./l), lw=3,\n",
" color=c, label=\"$\\lambda = %.1f$\" % l)\n",
" plt.fill_between(a, expo.pdf(a, scale=1./l), color=c, alpha=.33)\n",
"\n",
"plt.legend()\n",
"plt.ylabel(\"PDF at $z$\")\n",
"plt.xlabel(\"$z$\")\n",
"plt.ylim(0,1.2)\n",
"plt.title(\"Probability density function of an Exponential random variable;\\\n",
" differing $\\lambda$\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### But what is $\\lambda \\;$?\n",
"\n",
"\n",
"**This question is what motivates statistics**. In the real world, $\\lambda$ is hidden from us. We see only $Z$, and must go backwards to try and determine $\\lambda$. The problem is difficult because there is no one-to-one mapping from $Z$ to $\\lambda$. Many different methods have been created to solve the problem of estimating $\\lambda$, but since $\\lambda$ is never actually observed, no one can say for certain which method is best! \n",
"\n",
"Bayesian inference is concerned with *beliefs* about what $\\lambda$ might be. Rather than try to guess $\\lambda$ exactly, we can only talk about what $\\lambda$ is likely to be by assigning a probability distribution to $\\lambda$.\n",
"\n",
"This might seem odd at first. After all, $\\lambda$ is fixed; it is not (necessarily) random! How can we assign probabilities to values of a non-random variable? Ah, we have fallen for our old, frequentist way of thinking. Recall that under Bayesian philosophy, we *can* assign probabilities if we interpret them as beliefs. And it is entirely acceptable to have *beliefs* about the parameter $\\lambda$. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"##### Example: Inferring behaviour from text-message data\n",
"\n",
"Let's try to model a more interesting example, one that concerns the rate at which a user sends and receives text messages:\n",
"\n",
"> You are given a series of daily text-message counts from a user of your system. The data, plotted over time, appears in the chart below. You are curious to know if the user's text-messaging habits have changed over time, either gradually or suddenly. How can you model this? (This is in fact my own text-message data. Judge my popularity as you wish.)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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09CjG7DKsbe1Jpd25Q+PsB0vukCZ7K3fu7X7daub+ypx7mTn3dDox+xkzZtDT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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f53c60622e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figsize(12.5, 3.5)\n",
"count_data = np.loadtxt(\"data/txtdata.csv\")\n",
"n_count_data = len(count_data)\n",
"plt.bar(np.arange(n_count_data), count_data, color=\"#348ABD\")\n",
"plt.xlabel(\"Time (days)\")\n",
"plt.ylabel(\"count of text-msgs received\")\n",
"plt.title(\"Did the user's texting habits change over time?\")\n",
"plt.xlim(0, n_count_data);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we start modeling, see what you can figure out just by looking at the chart above. Would you say there was a change in behaviour during this time period? \n",
"\n",
"How can we start to model this? Well, as we have conveniently already seen, a Poisson random variable is a very appropriate model for this type of *count* data. Denoting day $i$'s text-message count by $C_i$, \n",
"\n",
"$$ C_i \\sim \\text{Poisson}(\\lambda) $$\n",
"\n",
"We are not sure what the value of the $\\lambda$ parameter really is, however. Looking at the chart above, it appears that the rate might become higher late in the observation period, which is equivalent to saying that $\\lambda$ increases at some point during the observations. (Recall that a higher value of $\\lambda$ assigns more probability to larger outcomes. That is, there is a higher probability of many text messages having been sent on a given day.)\n",
"\n",
"How can we represent this observation mathematically? Let's assume that on some day during the observation period (call it $\\tau$), the parameter $\\lambda$ suddenly jumps to a higher value. So we really have two $\\lambda$ parameters: one for the period before $\\tau$, and one for the rest of the observation period. In the literature, a sudden transition like this would be called a *switchpoint*:\n",
"\n",
"$$\n",
"\\lambda = \n",
"\\begin{cases}\n",
"\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n",
"\\lambda_2 & \\text{if } t \\ge \\tau\n",
"\\end{cases}\n",
"$$\n",
"\n",
"\n",
"If, in reality, no sudden change occurred and indeed $\\lambda_1 = \\lambda_2$, then the $\\lambda$s posterior distributions should look about equal.\n",
"\n",
"We are interested in inferring the unknown $\\lambda$s. To use Bayesian inference, we need to assign prior probabilities to the different possible values of $\\lambda$. What would be good prior probability distributions for $\\lambda_1$ and $\\lambda_2$? Recall that $\\lambda$ can be any positive number. As we saw earlier, the *exponential* distribution provides a continuous density function for positive numbers, so it might be a good choice for modeling $\\lambda_i$. But recall that the exponential distribution takes a parameter of its own, so we'll need to include that parameter in our model. Let's call that parameter $\\alpha$.\n",
"\n",
"\\begin{align}\n",
"&\\lambda_1 \\sim \\text{Exp}( \\alpha ) \\\\\\\n",
"&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n",
"\\end{align}\n",
"\n",
"$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n",
"\n",
"$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n",
"\n",
"An alternative, and something I encourage the reader to try, would be to have two priors: one for each $\\lambda_i$. Creating two exponential distributions with different $\\alpha$ values reflects our prior belief that the rate changed at some point during the observations.\n",
"\n",
"What about $\\tau$? Because of the noisiness of the data, it's difficult to pick out a priori when $\\tau$ might have occurred. Instead, we can assign a *uniform prior belief* to every possible day. This is equivalent to saying\n",
"\n",
"\\begin{align}\n",
"& \\tau \\sim \\text{DiscreteUniform(1,70) }\\\\\\\\\n",
"& \\Rightarrow P( \\tau = k ) = \\frac{1}{70}\n",
"\\end{align}\n",
"\n",
"So after all this, what does our overall prior distribution for the unknown variables look like? Frankly, *it doesn't matter*. What we should understand is that it's an ugly, complicated mess involving symbols only a mathematician could love. And things will only get uglier the more complicated our models become. Regardless, all we really care about is the posterior distribution.\n",
"\n",
"We next turn to PyMC3, a Python library for performing Bayesian analysis that is undaunted by the mathematical monster we have created. \n",
"\n",
"\n",
"Introducing our first hammer: PyMC3\n",
"-----\n",
"\n",
"PyMC3 is a Python library for programming Bayesian analysis [3]. It is a fast, well-maintained library. The only unfortunate part is that its documentation is lacking in certain areas, especially those that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why PyMC3 is so cool.\n",
"\n",
"We will model the problem above using PyMC3. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random; it is probabilistic in the sense that we create probability models using programming variables as the model's components. Model components are first-class primitives within the PyMC3 framework. \n",
"\n",
"B. Cronin [5] has a very motivating description of probabilistic programming:\n",
"\n",
"> Another way of thinking about this: unlike a traditional program, which only runs in the forward directions, a probabilistic program is run in both the forward and backward direction. It runs forward to compute the consequences of the assumptions it contains about the world (i.e., the model space it represents), but it also runs backward from the data to constrain the possible explanations. In practice, many probabilistic programming systems will cleverly interleave these forward and backward operations to efficiently home in on the best explanations.\n",
"\n",
"Because of the confusion engendered by the term *probabilistic programming*, I'll refrain from using it. Instead, I'll simply say *programming*, since that's what it really is. \n",
"\n",
"PyMC3 code is easy to read. The only novel thing should be the syntax. Simply remember that we are representing the model's components ($\\tau, \\lambda_1, \\lambda_2$ ) as variables."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Applied log-transform to lambda_1 and added transformed lambda_1_log_ to model.\n",
"Applied log-transform to lambda_2 and added transformed lambda_2_log_ to model.\n"
]
}
],
"source": [
"import pymc3 as pm\n",
"import theano.tensor as tt\n",
"\n",
"with pm.Model() as model:\n",
" alpha = 1.0/count_data.mean() # Recall count_data is the\n",
" # variable that holds our txt counts\n",
" lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n",
" lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n",
" \n",
" tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data - 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the code above, we create the PyMC3 variables corresponding to $\\lambda_1$ and $\\lambda_2$. We assign them to PyMC3's *stochastic variables*, so-called because they are treated by the back end as random number generators."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with model:\n",
" idx = np.arange(n_count_data) # Index\n",
" lambda_ = pm.math.switch(tau > idx, lambda_1, lambda_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This code creates a new function `lambda_`, but really we can think of it as a random variable: the random variable $\\lambda$ from above. The `switch()` function assigns `lambda_1` or `lambda_2` as the value of `lambda_`, depending on what side of `tau` we are on. The values of `lambda_` up until `tau` are `lambda_1` and the values afterwards are `lambda_2`.\n",
"\n",
"Note that because `lambda_1`, `lambda_2` and `tau` are random, `lambda_` will be random. We are **not** fixing any variables yet."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with model:\n",
" observation = pm.Poisson(\"obs\", lambda_, observed=count_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The variable `observation` combines our data, `count_data`, with our proposed data-generation scheme, given by the variable `lambda_`, through the `observed` keyword. \n",
"\n",
"The code below will be explained in Chapter 3, but I show it here so you can see where our results come from. One can think of it as a *learning* step. The machinery being employed is called *Markov Chain Monte Carlo* (MCMC), which I also delay explaining until Chapter 3. This technique returns thousands of random variables from the posterior distributions of $\\lambda_1, \\lambda_2$ and $\\tau$. We can plot a histogram of the random variables to see what the posterior distributions look like. Below, we collect the samples (called *traces* in the MCMC literature) into histograms."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 10000/10000 [00:02<00:00, 4511.50it/s]\n"
]
}
],
"source": [
"### Mysterious code to be explained in Chapter 3.\n",
"with model:\n",
" step = pm.Metropolis()\n",
" trace = pm.sample(10000, tune=5000,step=step)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lambda_1_samples = trace['lambda_1']\n",
"lambda_2_samples = trace['lambda_2']\n",
"tau_samples = trace['tau']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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IXTt2aOm3f6ztG14r9FQAAABKCi03wehFi0cPfTyO83hkHo/M45F5PDLPBgp6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\niEgd04ReRERERKSOaUIvIiIiIlLH/j+iN1eauGJfmQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f53abe52dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figsize(12.5, 10)\n",
"#histogram of the samples:\n",
"\n",
"ax = plt.subplot(311)\n",
"ax.set_autoscaley_on(False)\n",
"\n",
"plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
" label=\"posterior of $\\lambda_1$\", color=\"#A60628\", normed=True)\n",
"plt.legend(loc=\"upper left\")\n",
"plt.title(r\"\"\"Posterior distributions of the variables\n",
" $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n",
"plt.xlim([15, 30])\n",
"plt.xlabel(\"$\\lambda_1$ value\")\n",
"\n",
"ax = plt.subplot(312)\n",
"ax.set_autoscaley_on(False)\n",
"plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
" label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", normed=True)\n",
"plt.legend(loc=\"upper left\")\n",
"plt.xlim([15, 30])\n",
"plt.xlabel(\"$\\lambda_2$ value\")\n",
"\n",
"plt.subplot(313)\n",
"w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n",
"plt.hist(tau_samples, bins=n_count_data, alpha=1,\n",
" label=r\"posterior of $\\tau$\",\n",
" color=\"#467821\", weights=w, rwidth=2.)\n",
"plt.xticks(np.arange(n_count_data))\n",
"\n",
"plt.legend(loc=\"upper left\")\n",
"plt.ylim([0, .75])\n",
"plt.xlim([35, len(count_data)-20])\n",
"plt.xlabel(r\"$\\tau$ (in days)\")\n",
"plt.ylabel(\"probability\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interpretation\n",
"\n",
"Recall that Bayesian methodology returns a *distribution*. Hence we now have distributions to describe the unknown $\\lambda$s and $\\tau$. What have we gained? Immediately, we can see the uncertainty in our estimates: the wider the distribution, the less certain our posterior belief should be. We can also see what the plausible values for the parameters are: $\\lambda_1$ is around 18 and $\\lambda_2$ is around 23. The posterior distributions of the two $\\lambda$s are clearly distinct, indicating that it is indeed likely that there was a change in the user's text-message behaviour.\n",
"\n",
"What other observations can you make? If you look at the original data again, do these results seem reasonable? \n",
"\n",
"Notice also that the posterior distributions for the $\\lambda$s do not look like exponential distributions, even though our priors for these variables were exponential. In fact, the posterior distributions are not really of any form that we recognize from the original model. But that's OK! This is one of the benefits of taking a computational point of view. If we had instead done this analysis using mathematical approaches, we would have been stuck with an analytically intractable (and messy) distribution. Our use of a computational approach makes us indifferent to mathematical tractability.\n",
"\n",
"Our analysis also returned a distribution for $\\tau$. Its posterior distribution looks a little different from the other two because it is a discrete random variable, so it doesn't assign probabilities to intervals. We can see that near day 45, there was a 50% chance that the user's behaviour changed. Had no change occurred, or had the change been gradual over time, the posterior distribution of $\\tau$ would have been more spread out, reflecting that many days were plausible candidates for $\\tau$. By contrast, in the actual results we see that only three or four days make any sense as potential transition points. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Why would I want samples from the posterior, anyways?\n",
"\n",
"\n",
"We will deal with this question for the remainder of the book, and it is an understatement to say that it will lead us to some amazing results. For now, let's end this chapter with one more example.\n",
"\n",
"We'll use the posterior samples to answer the following question: what is the expected number of texts at day $t, \\; 0 \\le t \\le 70$ ? Recall that the expected value of a Poisson variable is equal to its parameter $\\lambda$. Therefore, the question is equivalent to *what is the expected value of $\\lambda$ at time $t$*?\n",
"\n",
"In the code below, let $i$ index samples from the posterior distributions. Given a day $t$, we average over all possible $\\lambda_i$ for that day $t$, using $\\lambda_i = \\lambda_{1,i}$ if $t \\lt \\tau_i$ (that is, if the behaviour change has not yet occurred), else we use $\\lambda_i = \\lambda_{2,i}$. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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bwPmw/u9//8usWbN49tlnmTBhAikpKYHH79mzhxdeeIETJ06wcOFCNm7cSLdu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\niEgFaaAuIiIiIlJBGqiLiIiIiFTQ/wcvO0//8pTK2wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f53ab6616d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figsize(12.5, 5)\n",
"# tau_samples, lambda_1_samples, lambda_2_samples contain\n",
"# N samples from the corresponding posterior distribution\n",
"N = tau_samples.shape[0]\n",
"expected_texts_per_day = np.zeros(n_count_data)\n",
"for day in range(0, n_count_data):\n",
" # ix is a bool index of all tau samples corresponding to\n",
" # the switchpoint occurring prior to value of 'day'\n",
" ix = day < tau_samples\n",
" # Each posterior sample corresponds to a value for tau.\n",
" # for each day, that value of tau indicates whether we're \"before\"\n",
" # (in the lambda1 \"regime\") or\n",
" # \"after\" (in the lambda2 \"regime\") the switchpoint.\n",
" # by taking the posterior sample of lambda1/2 accordingly, we can average\n",
" # over all samples to get an expected value for lambda on that day.\n",
" # As explained, the \"message count\" random variable is Poisson distributed,\n",
" # and therefore lambda (the poisson parameter) is the expected value of\n",
" # \"message count\".\n",
" expected_texts_per_day[day] = (lambda_1_samples[ix].sum()\n",
" + lambda_2_samples[~ix].sum()) / N\n",
"\n",
"\n",
"plt.plot(range(n_count_data), expected_texts_per_day, lw=4, color=\"#E24A33\",\n",
" label=\"expected number of text-messages received\")\n",
"plt.xlim(0, n_count_data)\n",
"plt.xlabel(\"Day\")\n",
"plt.ylabel(\"Expected # text-messages\")\n",
"plt.title(\"Expected number of text-messages received\")\n",
"plt.ylim(0, 60)\n",
"plt.bar(np.arange(len(count_data)), count_data, color=\"#348ABD\", alpha=0.65,\n",
" label=\"observed texts per day\")\n",
"\n",
"plt.legend(loc=\"upper left\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our analysis shows strong support for believing the user's behavior did change ($\\lambda_1$ would have been close in value to $\\lambda_2$ had this not been true), and that the change was sudden rather than gradual (as demonstrated by $\\tau$'s strongly peaked posterior distribution). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-to-text subscription, or perhaps a new relationship. (In fact, the 45th day corresponds to Christmas, and I moved away to Toronto the next month, leaving a girlfriend behind.)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Exercises\n",
"\n",
"1\\. Using `lambda_1_samples` and `lambda_2_samples`, what is the mean of the posterior distributions of $\\lambda_1$ and $\\lambda_2$?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#type your code here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2\\. What is the expected percentage increase in text-message rates? `hint:` compute the mean of `lambda_1_samples/lambda_2_samples`. Note that this quantity is very different from `lambda_1_samples.mean()/lambda_2_samples.mean()`."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#type your code here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3\\. What is the mean of $\\lambda_1$ **given** that we know $\\tau$ is less than 45. That is, suppose we have been given new information that the change in behaviour occurred prior to day 45. What is the expected value of $\\lambda_1$ now? (You do not need to redo the PyMC3 part. Just consider all instances where `tau_samples < 45`.)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#type your code here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References\n",
"\n",
"\n",
"- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/31/n_is_never_larg).\n",
"- [2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n",
"- [3] Salvatier, J, Wiecki TV, and Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. *PeerJ Computer Science* 2:e55 <https://doi.org/10.7717/peerj-cs.55>\n",
"- [4] Jimmy Lin and Alek Kolcz. Large-Scale Machine Learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD 2012), pages 793-804, May 2012, Scottsdale, Arizona.\n",
"- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. <https://plus.google.com/u/0/107971134877020469960/posts/KpeRdJKR6Z1>."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n",
" }\n",
" div.cell{\n",
" width:800px;\n",
" margin-left:16% !important;\n",
" margin-right:auto;\n",
" }\n",
" h1 {\n",
" font-family: Helvetica, serif;\n",
" }\n",
" h4{\n",
" margin-top:12px;\n",
" margin-bottom: 3px;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" line-height: 145%;\n",
" font-size: 130%;\n",
" width:800px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
" }\n",
" .CodeMirror{\n",
" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
" }\n",
" .prompt{\n",
" display: None;\n",
" }\n",
" .text_cell_render h5 {\n",
" font-weight: 300;\n",
" font-size: 22pt;\n",
" color: #4057A1;\n",
" font-style: italic;\n",
" margin-bottom: .5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
" \n",
" .warning{\n",
" color: rgb( 240, 20, 20 )\n",
" } \n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML at 0x10f034850>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"../styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [bayes]",
"language": "python",
"name": "Python [bayes]"
},
"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 |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/image_understanding/solutions/Keras_Flowers_on_TPU.ipynb | 1 | 2298241 | null | apache-2.0 |
miykael/nipype_tutorial | notebooks/basic_iteration.ipynb | 1 | 11605 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iterables\n",
"\n",
"Some steps in a neuroimaging analysis are repetitive. Running the same preprocessing on multiple subjects or doing statistical inference on multiple files. To prevent the creation of multiple individual scripts, Nipype has as execution plugin for ``Workflow``, called **``iterables``**. \n",
"\n",
"<img src=\"../static/images/iterables.png\" width=\"240\">\n",
"\n",
"If you are interested in more advanced procedures, such as synchronizing multiple iterables or using conditional iterables, check out the `synchronize `and `intersource` section in the [`JoinNode`](basic_joinnodes.ipynb) notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Realistic example\n",
"\n",
"Let's assume we have a workflow with two nodes, node (A) does simple skull stripping, and is followed by a node (B) that does isometric smoothing. Now, let's say, that we are curious about the effect of different smoothing kernels. Therefore, we want to run the smoothing node with FWHM set to 2mm, 8mm, and 16mm."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nipype import Node, Workflow\n",
"from nipype.interfaces.fsl import BET, IsotropicSmooth\n",
"\n",
"# Initiate a skull stripping Node with BET\n",
"skullstrip = Node(BET(mask=True,\n",
" in_file='/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz'),\n",
" name=\"skullstrip\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a smoothing Node with IsotropicSmooth"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"isosmooth = Node(IsotropicSmooth(), name='iso_smooth')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, to use ``iterables`` and therefore smooth with different ``fwhm`` is as simple as that:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"isosmooth.iterables = (\"fwhm\", [4, 8, 16])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And to wrap it up. We need to create a workflow, connect the nodes and finally, can run the workflow in parallel."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create the workflow\n",
"wf = Workflow(name=\"smoothflow\")\n",
"wf.base_dir = \"/output\"\n",
"wf.connect(skullstrip, 'out_file', isosmooth, 'in_file')\n",
"\n",
"# Run it in parallel (one core for each smoothing kernel)\n",
"wf.run('MultiProc', plugin_args={'n_procs': 3})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**, that ``iterables`` is set on a specific node (``isosmooth`` in this case), but ``Workflow`` is needed to expend the graph to three subgraphs with three different versions of the ``isosmooth`` node.\n",
"\n",
"If we visualize the graph with ``exec``, we can see where the parallelization actually takes place."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Visualize the detailed graph\n",
"from IPython.display import Image\n",
"wf.write_graph(graph2use='exec', format='png', simple_form=True)\n",
"Image(filename='/output/smoothflow/graph_detailed.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you look at the structure in the workflow directory, you can also see, that for each smoothing, a specific folder was created, i.e. ``_fwhm_16``."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!tree /output/smoothflow -I '*txt|*pklz|report*|*.json|*js|*.dot|*.html'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's visualize the results!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nilearn import plotting\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotting.plot_anat(\n",
" '/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz', title='original',\n",
" display_mode='z', dim=-1, cut_coords=(-50, -35, -20, -5), annotate=False);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotting.plot_anat(\n",
" '/output/smoothflow/skullstrip/sub-01_ses-test_T1w_brain.nii.gz', title='skullstripped',\n",
" display_mode='z', dim=-1, cut_coords=(-50, -35, -20, -5), annotate=False);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotting.plot_anat(\n",
" '/output/smoothflow/_fwhm_4/iso_smooth/sub-01_ses-test_T1w_brain_smooth.nii.gz', title='FWHM=4',\n",
" display_mode='z', dim=-0.5, cut_coords=(-50, -35, -20, -5), annotate=False);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotting.plot_anat(\n",
" '/output/smoothflow/_fwhm_8/iso_smooth/sub-01_ses-test_T1w_brain_smooth.nii.gz', title='FWHM=8',\n",
" display_mode='z', dim=-0.5, cut_coords=(-50, -35, -20, -5), annotate=False);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotting.plot_anat(\n",
" '/output/smoothflow/_fwhm_16/iso_smooth/sub-01_ses-test_T1w_brain_smooth.nii.gz', title='FWHM=16',\n",
" display_mode='z', dim=-0.5, cut_coords=(-50, -35, -20, -5), annotate=False);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ``IdentityInterface`` (special use case of ``iterables``)\n",
"\n",
"We often want to start our worflow from creating subgraphs, e.g. for running preprocessing for all subjects. We can easily do it with setting ``iterables`` on the ``IdentityInterface``. The ``IdentityInterface`` interface allows you to create ``Nodes`` that does simple identity mapping, i.e. ``Nodes`` that only work on parameters/strings.\n",
"\n",
"\n",
"For example, you want to start your workflow by collecting anatomical files for 5 subjects."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# First, let's specify the list of subjects\n",
"subject_list = ['01', '02', '03', '04', '05']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can create the IdentityInterface Node"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nipype import IdentityInterface\n",
"infosource = Node(IdentityInterface(fields=['subject_id']),\n",
" name=\"infosource\")\n",
"infosource.iterables = [('subject_id', subject_list)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it. Now, we can connect the output fields of this ``infosource`` node to ``SelectFiles`` and ``DataSink`` nodes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from os.path import join as opj\n",
"from nipype.interfaces.io import SelectFiles, DataSink\n",
"\n",
"anat_file = opj('sub-{subject_id}', 'ses-test', 'anat', 'sub-{subject_id}_ses-test_T1w.nii.gz')\n",
"\n",
"templates = {'anat': anat_file}\n",
"\n",
"selectfiles = Node(SelectFiles(templates,\n",
" base_directory='/data/ds000114'),\n",
" name=\"selectfiles\")\n",
"\n",
"# Datasink - creates output folder for important outputs\n",
"datasink = Node(DataSink(base_directory=\"/output\",\n",
" container=\"datasink\"),\n",
" name=\"datasink\")\n",
"\n",
"wf_sub = Workflow(name=\"choosing_subjects\")\n",
"wf_sub.connect(infosource, \"subject_id\", selectfiles, \"subject_id\")\n",
"wf_sub.connect(selectfiles, \"anat\", datasink, \"anat_files\")\n",
"wf_sub.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can check that five anatomicl images are in ``anat_files`` directory:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! ls -lh /output/datasink/anat_files/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This was just a simple example of using ``IdentityInterface``, but a complete example of preprocessing workflow you can find in [Preprocessing Example](example_preprocessing.ipynb))."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"Create a workflow to calculate various powers of ``2`` using two nodes, one for ``IdentityInterface`` with ``iterables``, and one for ``Function`` interface to calculate the power of ``2``."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
},
"outputs": [],
"source": [
"# write your solution here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"solution2": "hidden"
},
"outputs": [],
"source": [
"# lets start from the Identity node\n",
"from nipype import Function, Node, Workflow\n",
"from nipype.interfaces.utility import IdentityInterface\n",
"\n",
"iden = Node(IdentityInterface(fields=['number']), name=\"identity\")\n",
"iden.iterables = [(\"number\", range(8))]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"solution2": "hidden"
},
"outputs": [],
"source": [
"# the second node should use the Function interface\n",
"def power_of_two(n):\n",
" return 2**n\n",
"\n",
"# Create Node\n",
"power = Node(Function(input_names=[\"n\"],\n",
" output_names=[\"pow\"],\n",
" function=power_of_two),\n",
" name='power')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"solution2": "hidden"
},
"outputs": [],
"source": [
"#and now the workflow\n",
"wf_ex1 = Workflow(name=\"exercise1\")\n",
"wf_ex1.connect(iden, \"number\", power, \"n\")\n",
"res_ex1 = wf_ex1.run()\n",
"\n",
"# we can print the results\n",
"for i in range(8):\n",
" print(list(res_ex1.nodes())[i].result.outputs)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
djvanhelmond/AdventofCode2015 | Day 14/Day 14 Reindeer Olympics.ipynb | 1 | 2723 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#!/usr/bin/env python3"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Deer:\n",
" def __init__(self, name, flyspeed, flytime, resttime):\n",
" self.name = name\n",
" self.flyspeed = int(flyspeed)\n",
" self.flytime = int(flytime)\n",
" self.resttime = int(resttime)\n",
" self.points = 0\n",
" \n",
" def distance(self, time):\n",
" full = (time // (self.flytime + self.resttime)) * self.flyspeed * self.flytime\n",
" rest = min(self.flytime, time % (self.flytime + self.resttime)) * self.flyspeed\n",
" return (full+rest)\n",
"\n",
" def receive_award(self):\n",
" self.points += 1 \n",
"\n",
" def tell_award(self):\n",
" return(self.points)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distance has the winning reindeer: 2660\n",
"Points for the winning reindeer: 1256\n"
]
}
],
"source": [
"filename = './input'\n",
"deers = []\n",
"attime = 2503\n",
"\n",
"with open(filename) as f:\n",
" for line in f:\n",
" deer = name, _, _, speed, _, _, fly, _, _, _, _, _, _, rest, _ = line.split()\n",
" deers.append(Deer(deer[0], deer[3], deer[6], deer[13]))\n",
"\n",
"print (\"Distance has the winning reindeer:\", max(i.distance(attime) for i in deers)) \n",
"\n",
"for n in range(1, attime):\n",
" furthest = max(i.distance(n) for i in deers) \n",
" for i in deers:\n",
" if i.distance(n) == furthest:\n",
" i.receive_award()\n",
"\n",
"print (\"Points for the winning reindeer: \", max(i.tell_award() for i in deers)) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/data8_notebooks/lab01/lab01.ipynb | 2 | 34839 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lab 1: Expressions\n",
"\n",
"\n",
"#### Today's lab\n",
"\n",
"In today's lab, you'll learn how to:\n",
"\n",
"1. navigate Jupyter notebooks (like this one);\n",
"2. write and evaluate some basic *expressions* in Python, the computer language of the course;\n",
"3. call *functions* to use code other people have written; and\n",
"4. break down Python code into smaller parts to understand it.\n",
"\n",
"In the middle of the lab, you'll see how to run *automated tests* we've provided to check whether your code is working properly. When you're done, **follow the instructions at the end of this notebook** to run all the tests and submit your lab.\n",
"\n",
"This lab covers parts of [Chapter 3](http://www.inferentialthinking.com/chapters/03/programming-in-python.html) of the online textbook. You should read the book, but not right now. Instead, let's get started!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Jupyter notebooks\n",
"This webpage is called a Jupyter notebook. A notebook is a place to write programs and view their results.\n",
"\n",
"## 1.1. Text cells\n",
"In a notebook, each rectangle containing text or code is called a *cell*.\n",
"\n",
"Text cells (like this one) can be edited by double-clicking on them. They're written in a simple format called [Markdown](http://daringfireball.net/projects/markdown/syntax) to add formatting and section headings. You don't need to learn Markdown, but you might want to.\n",
"\n",
"After you edit a text cell, click the \"run cell\" button at the top that looks like ▶| to confirm any changes. (Try not to delete the instructions of the lab.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 1.1.1.** This paragraph is in its own text cell. Try editing it so that this sentence is the last sentence in the paragraph, and then click the \"run cell\" ▶| button . This sentence, for example, should be deleted. So should this one."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.2. Code cells\n",
"Other cells contain code in the Python 3 language. Running a code cell will execute all of the code it contains.\n",
"\n",
"To run the code in a code cell, first click on that cell to activate it. It'll be highlighted with a little green or blue rectangle. Next, either press ▶| or hold down the `shift` key and press `return` or `enter`.\n",
"\n",
"Try running this cell:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Hello, World!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And this one:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"\\N{WAVING HAND SIGN}, \\N{EARTH GLOBE ASIA-AUSTRALIA}!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fundamental building block of Python code is an expression. Cells can contain multiple lines with multiple expressions. When you run a cell, the lines of code are executed in the order in which they appear. Every `print` expression prints a line. Run the next cell and notice the order of the output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"First this line is printed,\")\n",
"print(\"and then this one.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 1.2.1.** Change the cell above so that it prints out:\n",
"\n",
" First this line,\n",
" then the whole 🌏,\n",
" and then this one.\n",
"\n",
"*Hint:* If you're stuck on the Earth symbol for more than a few minutes, try talking to a neighbor or a TA. That's a good idea for any lab problem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.3. Writing Jupyter notebooks\n",
"You can use Jupyter notebooks for your own projects or documents. When you make your own notebook, you'll need to create your own cells for text and code.\n",
"\n",
"To add a cell, click the + button in the menu bar. It'll start out as a text cell. You can change it to a code cell by clicking inside it so it's highlighted, clicking the drop-down box next to the restart (⟳) button in the menu bar, and choosing \"Code\".\n",
"\n",
"**Question 1.3.1.** Add a code cell below this one. Write code in it that prints out:\n",
" \n",
" A whole new cell! ♪🌏♪\n",
"\n",
"(That musical note symbol is like the Earth symbol. Its long-form name is `\\N{EIGHTH NOTE}`.)\n",
"\n",
"Run your cell to verify that it works."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.4. Errors\n",
"Python is a language, and like natural human languages, it has rules. It differs from natural language in two important ways:\n",
"1. The rules are *simple*. You can learn most of them in a few weeks and gain reasonable proficiency with the language in a semester.\n",
"2. The rules are *rigid*. If you're proficient in a natural language, you can understand a non-proficient speaker, glossing over small mistakes. A computer running Python code is not smart enough to do that.\n",
"\n",
"Whenever you write code, you'll make mistakes. When you run a code cell that has errors, Python will sometimes produce error messages to tell you what you did wrong.\n",
"\n",
"Errors are okay; even experienced programmers make many errors. When you make an error, you just have to find the source of the problem, fix it, and move on.\n",
"\n",
"We have made an error in the next cell. Run it and see what happens."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"This line is missing something.\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You should see something like this (minus our annotations):\n",
"\n",
"<img src=\"error.jpg\"/>\n",
"\n",
"The last line of the error output attempts to tell you what went wrong. The *syntax* of a language is its structure, and this `SyntaxError` tells you that you have created an illegal structure. \"`EOF`\" means \"end of file,\" so the message is saying Python expected you to write something more (in this case, a right parenthesis) before finishing the cell.\n",
"\n",
"There's a lot of terminology in programming languages, but you don't need to know it all in order to program effectively. If you see a cryptic message like this, you can often get by without deciphering it. (Of course, if you're frustrated, ask a neighbor or a TA for help.)\n",
"\n",
"Try to fix the code above so that you can run the cell and see the intended message instead of an error."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Numbers\n",
"\n",
"Quantitative information arises everywhere in data science. In addition to representing commands to print out lines, expressions can represent numbers and methods of combining numbers. The expression `3.2500` evaluates to the number 3.25. (Run the cell and see.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"3.2500"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that we didn't have to `print`. When you run a notebook cell, if the last line has a value, then Jupyter helpfully prints out that value for you. However, it won't print out prior lines automatically."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print(2)\n",
"3\n",
"4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above, you should see that 4 is the value of the last expression, 2 is printed, but 3 is lost forever because it was neither printed nor last.\n",
"\n",
"You don't want to print everything all the time anyway. But if you feel sorry for 3, change the cell above to print it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.1. Arithmetic\n",
"The line in the next cell subtracts. Its value is what you'd expect. Run it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"3.25 - 1.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many basic arithmetic operations are built in to Python. The textbook section on [Expressions](http://www.inferentialthinking.com/chapters/03/1/expressions.html) describes all the arithmetic operators used in the course. The common operator that differs from typical math notation is `**`, which raises one number to the power of the other. So, `2**3` stands for $2^3$ and evaluates to 8. \n",
"\n",
"The order of operations is what you learned in elementary school, and Python also has parentheses. For example, compare the outputs of the cells below. Use parentheses for a happy new year!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"6*5-6*3**2*2**3/4*7"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(6*5-(6*3))**2*((2**3)/4*7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In standard math notation, the first expression is\n",
"\n",
"$$6 \\times 5 - 6 \\times 3^2 \\times \\frac{2^3}{4} \\times 7,$$\n",
"\n",
"while the second expression is\n",
"\n",
"$$(6 \\times 5 - (6 \\times 3))^2 \\times (\\frac{(2^3)}{4} \\times 7).$$\n",
"\n",
"**Question 2.1.1.** Write a Python expression in this next cell that's equal to $5 \\times (3 \\frac{10}{11}) - 51 \\frac{1}{3} + 2^{.5 \\times 22}$. That's five times three and ten elevenths, minus fifty-one and a third, plus two to the power of half 22. By \"$3 \\frac{10}{11}$\" we mean $3+\\frac{10}{11}$, not $3 \\times \\frac{10}{11}$.\n",
"\n",
"Replace the ellipses (`...`) with your expression. Try to use parentheses only when necessary.\n",
"\n",
"*Hint:* The correct output should start with a familiar number."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Names\n",
"In natural language, we have terminology that lets us quickly reference very complicated concepts. We don't say, \"That's a large mammal with brown fur and sharp teeth!\" Instead, we just say, \"Bear!\"\n",
"\n",
"Similarly, an effective strategy for writing code is to define names for data as we compute it, like a lawyer would define terms for complex ideas at the start of a legal document.\n",
"\n",
"In Python, we do this with *assignment statements*. An assignment statement has a name on the left side of an `=` sign and an expression to be evaluated on the right."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ten = 3 * 2 + 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When you run that cell, Python first evaluates the first line. It computes the value of the expression `3 * 2 + 4`, which is the number 10. Then it gives that value the name `ten`. At that point, the code in the cell is done running.\n",
"\n",
"After you run that cell, the value 10 is bound to the name `ten`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ten"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The statement `ten = 3 * 2 + 4` is not asserting that `ten` is already equal to `3 * 2 + 4`, as we might expect by analogy with math notation. Rather, that line of code changes what `ten` means; it now refers to the value 10, whereas before it meant nothing at all.\n",
"\n",
"If the designers of Python had been ruthlessly pedantic, they might have made us write\n",
"\n",
" define the name ten to hereafter have the value of 3 * 2 + 4 \n",
"\n",
"instead. You will probably appreciate the brevity of \"`=`\"! But keep in mind that this is the real meaning.\n",
"\n",
"**Question 3.1.** Try writing code that uses a name (like `foo` or `eleven`) that hasn't been assigned to anything. You'll see an error!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A common pattern in Jupyter notebooks is to assign a value to a name and then immediately evaluate the name in the last line in the cell so that the value is displayed as output. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"close_to_pi = 355/113\n",
"close_to_pi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another common pattern is that a series of lines in a single cell will build up a complex computation in stages, naming the intermediate results."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bimonthly_salary = 840\n",
"monthly_salary = 2 * bimonthly_salary\n",
"number_of_months_in_a_year = 12\n",
"yearly_salary = number_of_months_in_a_year * monthly_salary\n",
"yearly_salary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Names in Python can have letters (upper- and lower-case letters are both okay and count as different letters), underscores, and numbers. The first character can't be a number (otherwise a name might look like a number).\n",
"\n",
"Other than those rules, what you name something doesn't matter *to Python*. For example, this cell does the same thing as the above cell, except everything has a different name:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a = 840\n",
"b = 2 * a\n",
"c = 12\n",
"d = c * b\n",
"d"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**However**, names are very important for making your code *readable* to yourself and others. The cell above is shorter, but it's totally useless without an explanation of what it does.\n",
"\n",
"According to a famous joke among computer scientists, naming things is one of the two hardest problems in computer science. (The other two are cache invalidation and \"off-by-one\" errors. And people say computer scientists have an odd sense of humor...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 3.2.** Assign the name `seconds_in_a_decade` to the number of seconds between midnight January 1, 2010 and midnight January 1, 2020.\n",
"\n",
"*Hint:* If you're stuck, the next section shows you how to get hints."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Change the next line so that it computes the number of\n",
"# seconds in a decade and assigns that number the name\n",
"# seconds_in_a_decade.\n",
"seconds_in_a_decade = ...\n",
"\n",
"# We've put this line in this cell so that it will print\n",
"# the value you've given to seconds_in_a_decade when you\n",
"# run it. You don't need to change this.\n",
"seconds_in_a_decade"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.1. Checking your code\n",
"Now that you know how to name things, you can start using the built-in *tests* to check whether your work is correct. Try not to change the contents of the test cells.\n",
"\n",
"Running the following cell will test whether you have assigned `seconds_in_a_decade` correctly in Question 3.2. If you haven't, this test will tell you the correct answer. Resist the urge to just copy it, and instead try to adjust your expression. (Sometimes the tests will give hints about what went wrong...)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test cell; please do not change!\n",
"from client.api.assignment import load_assignment \n",
"tests = load_assignment('lab01.ok')\n",
"_ = tests.grade('q32')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All labs will have tests like this one. To get credit for a lab, complete the notebook so that all tests pass, then run the final cell to submit your work. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2. Comments\n",
"You may have noticed this line in the cell above:\n",
"\n",
" # Test cell; please do not change!\n",
"\n",
"That is called a *comment*. It doesn't make anything happen in Python; Python ignores anything on a line after a #. Instead, it's there to communicate something about the code to you, the human reader. Comments are extremely useful.\n",
"\n",
"<img src=\"http://imgs.xkcd.com/comics/future_self.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.3. Application: A physics experiment\n",
"\n",
"On the Apollo 15 mission to the Moon, astronaut David Scott famously replicated Galileo's physics experiment in which he showed that gravity accelerates objects of different mass at the same rate. Because there is no air resistance for a falling object on the surface of the Moon, even two objects with very different masses and densities should fall at the same rate. David Scott compared a feather and a hammer.\n",
"\n",
"You can run the following cell to watch a video of the experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import YouTubeVideo\n",
"# The original URL is:\n",
"# https://www.youtube.com/watch?v=U7db6ZeLR5s\n",
"YouTubeVideo(\"U7db6ZeLR5s\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's the transcript of the video:\n",
"\n",
"**167:22:06 Scott**: Well, in my left hand, I have a feather; in my right hand, a hammer. And I guess one of the reasons we got here today was because of a gentleman named Galileo, a long time ago, who made a rather significant discovery about falling objects in gravity fields. And we thought where would be a better place to confirm his findings than on the Moon. And so we thought we'd try it here for you. The feather happens to be, appropriately, a falcon feather for our Falcon. And I'll drop the two of them here and, hopefully, they'll hit the ground at the same time. \n",
"\n",
"**167:22:43 Scott**: How about that!\n",
"\n",
"**167:22:45 Allen**: How about that! (Applause in Houston)\n",
"\n",
"**167:22:46 Scott**: Which proves that Mr. Galileo was correct in his findings."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Newton's Law.** Using this footage, we can also attempt to confirm another famous bit of physics: Newton's law of universal gravitation. Newton's laws predict that any object dropped near the surface of the Moon should fall\n",
"\n",
"$$\\frac{1}{2} G \\frac{M}{R^2} t^2 \\text{ meters}$$\n",
"\n",
"after $t$ seconds, where $G$ is a universal constant, $M$ is the moon's mass in kilograms, and $R$ is the moon's radius in meters. So if we know $G$, $M$, and $R$, then Newton's laws let us predict how far an object will fall over any amount of time.\n",
"\n",
"To verify the accuracy of this law, we will calculate the difference between the predicted distance the hammer drops and the actual distance. (If they are different, it might be because Newton's laws are wrong, or because our measurements are imprecise, or because there are other factors affecting the hammer for which we haven't accounted.)\n",
"\n",
"Someone studied the video and estimated that the hammer was dropped 113 cm from the surface. Counting frames in the video, the hammer falls for 1.2 seconds (36 frames)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 3.3.1.** Complete the code in the next cell to fill in the *data* from the experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# t, the duration of the fall in the experiment, in seconds.\n",
"# Fill this in.\n",
"time = ...\n",
"\n",
"# The estimated distance the hammer actually fell, in meters.\n",
"# Fill this in.\n",
"estimated_distance_m = ..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = tests.grade('q331')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 3.3.2.** Now, complete the code in the next cell to compute the difference between the predicted and estimated distances (in meters) that the hammer fell in this experiment.\n",
"\n",
"This just means translating the formula above ($\\frac{1}{2}G\\frac{M}{R^2}t^2$) into Python code. You'll have to replace each variable in the math formula with the name we gave that number in Python code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# First, we've written down the values of the 3 universal\n",
"# constants that show up in Newton's formula.\n",
"\n",
"# G, the universal constant measuring the strength of gravity.\n",
"gravity_constant = 6.674 * 10**-11\n",
"\n",
"# M, the moon's mass, in kilograms.\n",
"moon_mass_kg = 7.34767309 * 10**22\n",
"\n",
"# R, the radius of the moon, in meters.\n",
"moon_radius_m = 1.737 * 10**6\n",
"\n",
"# The distance the hammer should have fallen over the\n",
"# duration of the fall, in meters, according to Newton's\n",
"# law of gravity. The text above describes the formula\n",
"# for this distance given by Newton's law.\n",
"# **YOU FILL THIS PART IN.**\n",
"predicted_distance_m = ...\n",
"\n",
"# Here we've computed the difference between the predicted\n",
"# fall distance and the distance we actually measured.\n",
"# If you've filled in the above code, this should just work.\n",
"difference = predicted_distance_m - estimated_distance_m\n",
"difference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = tests.grade('q332')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Calling functions\n",
"\n",
"The most common way to combine or manipulate values in Python is by calling functions. Python comes with many built-in functions that perform common operations.\n",
"\n",
"For example, the `abs` function takes a single number as its argument and returns the absolute value of that number. The absolute value of a number is its distance from 0 on the number line, so `abs(5)` is 5 and `abs(-5)` is also 5."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"abs(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"abs(-5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4.1. Application: Computing walking distances\n",
"Chunhua is on the corner of 7th Avenue and 42nd Street in Midtown Manhattan, and she wants to know far she'd have to walk to get to Gramercy School on the corner of 10th Avenue and 34th Street.\n",
"\n",
"She can't cut across blocks diagonally, since there are buildings in the way. She has to walk along the sidewalks. Using the map below, she sees she'd have to walk 3 avenues (long blocks) and 8 streets (short blocks). In terms of the given numbers, she computed 3 as the difference between 7 and 10, *in absolute value*, and 8 similarly. \n",
"\n",
"Chunhua also knows that blocks in Manhattan are all about 80m by 274m (avenues are farther apart than streets). So in total, she'd have to walk $(80 \\times |42 - 34| + 274 \\times |7 - 10|)$ meters to get to the park.\n",
"\n",
"<img src=\"map.jpg\"/>\n",
"\n",
"**Question 4.1.1.** Finish the line `num_avenues_away = ...` in the next cell so that the cell calculates the distance Chunhua must walk and gives it the name `manhattan_distance`. Everything else has been filled in for you. **Use the `abs` function.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Here's the number of streets away:\n",
"num_streets_away = abs(42-34)\n",
"\n",
"# Compute the number of avenues away in a similar way:\n",
"num_avenues_away = ...\n",
"\n",
"street_length_m = 80\n",
"avenue_length_m = 274\n",
"\n",
"# Now we compute the total distance Chunhua must walk.\n",
"manhattan_distance = street_length_m*num_streets_away + avenue_length_m*num_avenues_away\n",
"\n",
"# We've included this line so that you see the distance\n",
"# you've computed when you run this cell. You don't need\n",
"# to change it, but you can if you want.\n",
"manhattan_distance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Be sure to run the next cell to test your code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = tests.grade('q411')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Multiple arguments\n",
"Some functions take multiple arguments, separated by commas. For example, the built-in `max` function returns the maximum argument passed to it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"max(2, -3, 4, -5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5. Understanding nested expressions\n",
"Function calls and arithmetic expressions can themselves contain expressions. You saw an example in the last question:\n",
"\n",
" abs(42-34)\n",
"\n",
"has 2 number expressions in a subtraction expression in a function call expression. And you probably wrote something like `abs(7-10)` to compute `num_avenues_away`.\n",
"\n",
"Nested expressions can turn into complicated-looking code. However, the way in which complicated expressions break down is very regular.\n",
"\n",
"Suppose we are interested in heights that are very unusual. We'll say that a height is unusual to the extent that it's far away on the number line from the average human height. [An estimate](http://press.endocrine.org/doi/full/10.1210/jcem.86.9.7875?ck=nck&) of the average adult human height (averaging, we hope, over all humans on Earth today) is 1.688 meters.\n",
"\n",
"So if Aditya is 1.21 meters tall, then his height is $|1.21 - 1.688|$, or $.478$, meters away from the average. Here's a picture of that:\n",
"\n",
"<img src=\"numberline_0.png\">\n",
"\n",
"And here's how we'd write that in one line of Python code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"abs(1.21 - 1.688)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's going on here? `abs` takes just one argument, so the stuff inside the parentheses is all part of that *single argument*. Specifically, the argument is the value of the expression `1.21 - 1.688`. The value of that expression is `-.478`. That value is the argument to `abs`. The absolute value of that is `.478`, so `.478` is the value of the full expression `abs(1.21 - 1.688)`.\n",
"\n",
"Picture simplifying the expression in several steps:\n",
"\n",
"1. `abs(1.21 - 1.688)`\n",
"2. `abs(-.478)`\n",
"3. `.478`\n",
"\n",
"In fact, that's basically what Python does to compute the value of the expression."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 5.1.** Say that Botan's height is 1.85 meters. In the next cell, use `abs` to compute the absolute value of the difference between Botan's height and the average human height. Give that value the name `botan_distance_from_average_m`.\n",
"\n",
"<img src=\"numberline_1.png\">"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Replace the ... with an expression to compute the absolute\n",
"# value of the difference between Botan's height (1.85m) and\n",
"# the average human height.\n",
"botan_distance_from_average_m = ...\n",
"\n",
"# Again, we've written this here so that the distance you\n",
"# compute will get printed when you run this cell.\n",
"botan_distance_from_average_m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = tests.grade('q51')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5.1. More nesting\n",
"Now say that we want to compute the most unusual height among Aditya's and Botan's heights. We'll use the function `max`, which (again) takes two numbers as arguments and returns the larger of the two arguments. Combining that with the `abs` function, we can compute the biggest distance from the average among the two heights:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Just read and run this cell.\n",
"\n",
"aditya_height_m = 1.21\n",
"botan_height_m = 1.85\n",
"average_adult_human_height_m = 1.688\n",
"\n",
"# The biggest distance from the average human height, among the two heights:\n",
"biggest_distance_m = max(abs(aditya_height_m - average_adult_human_height_m), abs(botan_height_m - average_adult_human_height_m))\n",
"\n",
"# Print out our results in a nice readable format:\n",
"print(\"The biggest distance from the average height among these two people is\", biggest_distance_m, \"meters.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The line where `biggest_distance_m` is computed looks complicated, but we can break it down into simpler components just like we did before.\n",
"\n",
"The basic recipe is repeated simplification of small parts of the expression:\n",
"* We start with the simplest components whose values we know, like plain names or numbers. (Examples: `aditya_height_m` or `5`.)\n",
"* **Find a simple-enough group of expressions:** We look for a group of simple expressions that are directly connected to each other in the code, for example by arithmetic or as arguments to a function call.\n",
"* **Evaluate that group:** We evaluate the arithmetic expressions or function calls they're part of, and replace the whole group with whatever we compute. (Example: `aditya_height_m - average_adult_human_height_m` becomes `-.478`.)\n",
"* **Repeat:** We continue this process, using the values of the glommed-together stuff as our new basic components. (Example: `abs(-.478)` becomes `.478`, and `max(.478, .162)` later becomes `.478`.)\n",
"* We keep doing that until we've evaluated the whole expression.\n",
"\n",
"You can run the next cell to see a slideshow of that process."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import IFrame\n",
"IFrame('https://docs.google.com/presentation/d/1urkX-nRsD8VJvcOnJsjmCy0Jpv752Ssn5Pphg2sMC-0/embed?start=false&loop=false&delayms=3000', 800, 600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, your turn. \n",
"\n",
"**Question 5.1.1.** Given the heights of the last three U.S. Presidents, write an expression that computes the smallest difference between any of the three heights. Your expression shouldn't have any numbers in it, only function calls and the names `obama`, `clinton`, and `bush`. Give the value of your expression the name `min_height_difference`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The three Presidents' heights, in meters:\n",
"obama = 1.85\n",
"clinton = 1.88\n",
"bush = 1.82\n",
"\n",
"# We'd like to look at all 3 pairs of heights, compute the absolute\n",
"# difference between each pair, and then find the smallest of those\n",
"# 3 absolute differences. This is left to you! If you're stuck,\n",
"# try computing the value for each step of the process (like the\n",
"# difference between Obama's and Clinton's height) on a separate\n",
"# line and giving it a name (like obama_clinton_height_diff).\n",
"min_height_difference = ..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = tests.grade('q511')"
]
}
],
"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": 1
}
| bsd-3-clause |
jacobstein123/Flappy-Bird-Genetic-Algorithm | Misc data/LearningCurve.ipynb | 1 | 2354 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(\"output-10.31.15.txt\",'r') as output:\n",
" a = output.read()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"b = a.split('\\n\\n')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import re\n",
"averages = [re.search('(\\d+)$',b[i]).group() for i in xrange(len(b))]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from numpy import *\n",
"from matplotlib.pyplot import *\n",
"\n",
"y = np.array([float(i) for i in averages])\n",
"\n",
"x = np.array([float(i) for i in range(len(y))])\n",
"\n",
"coefficients = polyfit(x, y,1)\n",
"polynomial = poly1d(coefficients)\n",
"xs = arange(-2.2, 2.6, 0.1)\n",
"ys = polynomial(xs)\n",
"\n",
"plot(x, y, '.')\n",
"plot(xs, ys)\n",
"ylabel('y')\n",
"xlabel('x')\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Python27\\lib\\site-packages\\numpy\\lib\\polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
" warnings.warn(msg, RankWarning)\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"z = np.polyfit(x, y, 2)\n",
"p = np.poly1d(z)\n",
"p30 = np.poly1d(np.polyfit(x, y, 30))\n",
"xp = np.linspace(1, 600, len(averages))\n",
"_ = plt.plot(x, y, '.', xp, p(xp), '-')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
fonnesbeck/scipy2015_tutorial | notebooks/1. Data Preparation.ipynb | 6 | 297198 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
" }\n",
" div.cell{\n",
" width: 90%;\n",
"/* margin-left:auto;*/\n",
"/* margin-right:auto;*/\n",
" }\n",
" ul {\n",
" line-height: 145%;\n",
" font-size: 90%;\n",
" }\n",
" li {\n",
" margin-bottom: 1em;\n",
" }\n",
" h1 {\n",
" font-family: Helvetica, serif;\n",
" }\n",
" h4{\n",
" margin-top: 12px;\n",
" margin-bottom: 3px;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" line-height: 145%;\n",
" font-size: 130%;\n",
" width: 90%;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
" }\n",
" .CodeMirror{\n",
" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
" }\n",
"/* .prompt{\n",
" display: None;\n",
" }*/\n",
" .text_cell_render h5 {\n",
" font-weight: 300;\n",
" font-size: 16pt;\n",
" color: #4057A1;\n",
" font-style: italic;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
"\n",
" .warning{\n",
" color: rgb( 240, 20, 20 )\n",
" }\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Preparation using pandas\n",
"\n",
"An initial step in statistical data analysis is the preparation of the data to be used in the analysis. In practice, ~~a little~~ ~~some~~ ~~much~~ the majority of the actual time spent on a statistical modeling project is typically devoted to importing, cleaning, validating and transforming the dataset.\n",
"\n",
"This section will introduce [pandas](http://pandas.pydata.org/), an important third-party Python package for data analysis, as a tool for data preparation, and provide some general advice for what should or should not be done to data before it is analyzed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to pandas\n",
"\n",
"**pandas** is a Python package providing fast, flexible, and expressive data structures designed to work with *relational* or *labeled* data both. It is a fundamental high-level building block for doing practical, real world data analysis in Python. \n",
"\n",
"pandas is well suited for:\n",
"\n",
"- **Tabular** data with heterogeneously-typed columns, as you might find in an SQL table or Excel spreadsheet\n",
"- Ordered and unordered (not necessarily fixed-frequency) **time series** data.\n",
"- Arbitrary **matrix** data with row and column labels\n",
"\n",
"Virtually any statistical dataset, labeled or unlabeled, can be converted to a pandas data structure for cleaning, transformation, and analysis.\n",
"\n",
"\n",
"### Key features\n",
" \n",
"- Easy handling of **missing data**\n",
"- **Size mutability**: columns can be inserted and deleted from DataFrame and higher dimensional objects\n",
"- Automatic and explicit **data alignment**: objects can be explicitly aligned to a set of labels, or the data can be aligned automatically\n",
"- Powerful, flexible **group by functionality** to perform split-apply-combine operations on data sets\n",
"- Intelligent label-based **slicing, fancy indexing, and subsetting** of large data sets\n",
"- Intuitive **merging and joining** data sets\n",
"- Flexible **reshaping and pivoting** of data sets\n",
"- **Hierarchical labeling** of axes\n",
"- Robust **IO tools** for loading data from flat files, Excel files, databases, and HDF5\n",
"- **Time series functionality**: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Series\n",
"\n",
"A **Series** is a single vector of data (like a NumPy array) with an *index* that labels each element in the vector."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 632\n",
"1 1638\n",
"2 569\n",
"3 115\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts = pd.Series([632, 1638, 569, 115])\n",
"counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If an index is not specified, a default sequence of integers is assigned as the index. A NumPy array comprises the values of the `Series`, while the index is a pandas `Index` object."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 632, 1638, 569, 115])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts.values"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3], dtype='int64')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can assign meaningful labels to the index, if they are available. These counts are of bacteria taxa constituting the microbiome of hospital patients, so using the taxon of each bacterium is a useful index."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Firmicutes 632\n",
"Proteobacteria 1638\n",
"Actinobacteria 569\n",
"Bacteroidetes 115\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria = pd.Series([632, 1638, 569, 115], \n",
" index=['Firmicutes', 'Proteobacteria', 'Actinobacteria', 'Bacteroidetes'])\n",
"\n",
"bacteria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These labels can be used to refer to the values in the `Series`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"569"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria['Actinobacteria']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Proteobacteria 1638\n",
"Actinobacteria 569\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria[bacteria.index.str.endswith('bacteria')]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Bacteroidetes' in bacteria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the indexing operation preserved the association between the values and the corresponding indices.\n",
"\n",
"We can still use positional indexing if we wish."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"632"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can give both the array of values and the index meaningful labels themselves:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"phylum\n",
"Firmicutes 632\n",
"Proteobacteria 1638\n",
"Actinobacteria 569\n",
"Bacteroidetes 115\n",
"Name: counts, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria.name = 'counts'\n",
"bacteria.index.name = 'phylum'\n",
"bacteria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NumPy's math functions and other operations can be applied to Series without losing the data structure."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"phylum\n",
"Firmicutes 6.448889\n",
"Proteobacteria 7.401231\n",
"Actinobacteria 6.343880\n",
"Bacteroidetes 4.744932\n",
"Name: counts, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(bacteria)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also filter according to the values in the `Series`:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"phylum\n",
"Proteobacteria 1638\n",
"Name: counts, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria[bacteria>1000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `Series` can be thought of as an ordered key-value store. In fact, we can create one from a `dict`:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bacteria_dict = {'Firmicutes': 632, 'Proteobacteria': 1638, 'Actinobacteria': 569, 'Bacteroidetes': 115}\n",
"bact = pd.Series(bacteria_dict)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Actinobacteria 569\n",
"Bacteroidetes 115\n",
"Firmicutes 632\n",
"Proteobacteria 1638\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bact"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the `Series` is created in key-sorted order.\n",
"\n",
"If we pass a custom index to `Series`, it will select the corresponding values from the dict, and treat indices without corrsponding values as missing. pandas uses the `NaN` (not a number) type for missing values."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Cyanobacteria NaN\n",
"Firmicutes 632\n",
"Proteobacteria 1638\n",
"Actinobacteria 569\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria2 = pd.Series(bacteria_dict, \n",
" index=['Cyanobacteria','Firmicutes','Proteobacteria','Actinobacteria'])\n",
"bacteria2"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Cyanobacteria True\n",
"Firmicutes False\n",
"Proteobacteria False\n",
"Actinobacteria False\n",
"dtype: bool"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria2.isnull()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Critically, the labels are used to **align data** when used in operations with other Series objects:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Actinobacteria 1138\n",
"Bacteroidetes NaN\n",
"Cyanobacteria NaN\n",
"Firmicutes 1264\n",
"Proteobacteria 3276\n",
"dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria + bacteria2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Contrast this with NumPy arrays, where arrays of the same length will combine values element-wise; adding Series combined values with the same label in the resulting series. Notice also that the missing values were propogated by addition."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DataFrame\n",
"\n",
"Inevitably, we want to be able to store, view and manipulate data that is *multivariate*, where for every index there are multiple fields or columns of data, often of varying data type.\n",
"\n",
"A `DataFrame` is a tabular data structure, encapsulating multiple series like columns in a spreadsheet. Data are stored internally as a 2-dimensional object, but the `DataFrame` allows us to represent and manipulate higher-dimensional data."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value\n",
"0 1 Firmicutes 632\n",
"1 1 Proteobacteria 1638\n",
"2 1 Actinobacteria 569\n",
"3 1 Bacteroidetes 115\n",
"4 2 Firmicutes 433\n",
"5 2 Proteobacteria 1130\n",
"6 2 Actinobacteria 754\n",
"7 2 Bacteroidetes 555"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data = pd.DataFrame({'value':[632, 1638, 569, 115, 433, 1130, 754, 555],\n",
" 'patient':[1, 1, 1, 1, 2, 2, 2, 2],\n",
" 'phylum':['Firmicutes', 'Proteobacteria', 'Actinobacteria', \n",
" 'Bacteroidetes', 'Firmicutes', 'Proteobacteria', 'Actinobacteria', 'Bacteroidetes']})\n",
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice the `DataFrame` is sorted by column name. We can change the order by indexing them in the order we desire:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" <th>patient</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" phylum value patient\n",
"0 Firmicutes 632 1\n",
"1 Proteobacteria 1638 1\n",
"2 Actinobacteria 569 1\n",
"3 Bacteroidetes 115 1\n",
"4 Firmicutes 433 2\n",
"5 Proteobacteria 1130 2\n",
"6 Actinobacteria 754 2\n",
"7 Bacteroidetes 555 2"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data[['phylum','value','patient']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `DataFrame` has a second index, representing the columns:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['patient', 'phylum', 'value'], dtype='object')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we wish to access columns, we can do so either by dict-like indexing or by attribute:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 632\n",
"1 1638\n",
"2 569\n",
"3 115\n",
"4 433\n",
"5 1130\n",
"6 754\n",
"7 555\n",
"Name: value, dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data['value']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 632\n",
"1 1638\n",
"2 569\n",
"3 115\n",
"4 433\n",
"5 1130\n",
"6 754\n",
"7 555\n",
"Name: value, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the standard indexing syntax for a single column of data from a `DataFrame` returns the column as a `Series`."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(bacteria_data['value'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Passing the column name as a list returns the column as a `DataFrame` instead."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value\n",
"0 632\n",
"1 1638\n",
"2 569\n",
"3 115\n",
"4 433\n",
"5 1130\n",
"6 754\n",
"7 555"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data[['value']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that indexing works differently with a `DataFrame` than with a `Series`, where in the latter, dict-like indexing retrieved a particular element (row). If we want access to a row in a `DataFrame`, we index its `ix` attribute."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"patient 1\n",
"phylum Bacteroidetes\n",
"value 115\n",
"Name: 3, dtype: object"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.ix[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since a row potentially contains different data types, the returned `Series` of values is of the generic `object` type."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to create a `DataFrame` row-wise rather than column-wise, we can do so with a dict of dicts:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bacteria_data = pd.DataFrame({0: {'patient': 1, 'phylum': 'Firmicutes', 'value': 632},\n",
" 1: {'patient': 1, 'phylum': 'Proteobacteria', 'value': 1638},\n",
" 2: {'patient': 1, 'phylum': 'Actinobacteria', 'value': 569},\n",
" 3: {'patient': 1, 'phylum': 'Bacteroidetes', 'value': 115},\n",
" 4: {'patient': 2, 'phylum': 'Firmicutes', 'value': 433},\n",
" 5: {'patient': 2, 'phylum': 'Proteobacteria', 'value': 1130},\n",
" 6: {'patient': 2, 'phylum': 'Actinobacteria', 'value': 754},\n",
" 7: {'patient': 2, 'phylum': 'Bacteroidetes', 'value': 555}})"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>patient</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phylum</th>\n",
" <td>Firmicutes</td>\n",
" <td>Proteobacteria</td>\n",
" <td>Actinobacteria</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>Firmicutes</td>\n",
" <td>Proteobacteria</td>\n",
" <td>Actinobacteria</td>\n",
" <td>Bacteroidetes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>value</th>\n",
" <td>632</td>\n",
" <td>1638</td>\n",
" <td>569</td>\n",
" <td>115</td>\n",
" <td>433</td>\n",
" <td>1130</td>\n",
" <td>754</td>\n",
" <td>555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 \\\n",
"patient 1 1 1 1 \n",
"phylum Firmicutes Proteobacteria Actinobacteria Bacteroidetes \n",
"value 632 1638 569 115 \n",
"\n",
" 4 5 6 7 \n",
"patient 2 2 2 2 \n",
"phylum Firmicutes Proteobacteria Actinobacteria Bacteroidetes \n",
"value 433 1130 754 555 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, we probably want this transposed:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value\n",
"0 1 Firmicutes 632\n",
"1 1 Proteobacteria 1638\n",
"2 1 Actinobacteria 569\n",
"3 1 Bacteroidetes 115\n",
"4 2 Firmicutes 433\n",
"5 2 Proteobacteria 1130\n",
"6 2 Actinobacteria 754\n",
"7 2 Bacteroidetes 555"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data = bacteria_data.T\n",
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Views\n",
"\n",
"Its important to note that the `Series` returned when a `DataFrame` is indexed is merely a **view** on the DataFrame, and not a copy of the data itself. So you must be *cautious* when manipulating this data.\n",
"\n",
"For example, let's isolate a column of our dataset by assigning it as a `Series` to a variable."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 632\n",
"1 1638\n",
"2 569\n",
"3 115\n",
"4 433\n",
"5 1130\n",
"6 754\n",
"7 555\n",
"Name: value, dtype: object"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = bacteria_data.value\n",
"vals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's assign a new value to one of the elements of the `Series`."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 632\n",
"1 1638\n",
"2 569\n",
"3 115\n",
"4 433\n",
"5 0\n",
"6 754\n",
"7 555\n",
"Name: value, dtype: object"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals[5] = 0\n",
"vals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, we may not anticipate that the value in the original `DataFrame` has also been changed!"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value\n",
"0 1 Firmicutes 632\n",
"1 1 Proteobacteria 1638\n",
"2 1 Actinobacteria 569\n",
"3 1 Bacteroidetes 115\n",
"4 2 Firmicutes 433\n",
"5 2 Proteobacteria 0\n",
"6 2 Actinobacteria 754\n",
"7 2 Bacteroidetes 555"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can avoid this by working with a copy when modifying subsets of the original data."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value\n",
"0 1 Firmicutes 632\n",
"1 1 Proteobacteria 1638\n",
"2 1 Actinobacteria 569\n",
"3 1 Bacteroidetes 115\n",
"4 2 Firmicutes 433\n",
"5 2 Proteobacteria 0\n",
"6 2 Actinobacteria 754\n",
"7 2 Bacteroidetes 555"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = bacteria_data.value.copy()\n",
"vals[5] = 1000\n",
"\n",
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, as we have seen, we can create or modify columns by assignment; let's put back the value we accidentally changed."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bacteria_data.value[5] = 1130"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, we may wish to add a column representing the year the data were collected."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value year\n",
"0 1 Firmicutes 632 2013\n",
"1 1 Proteobacteria 1638 2013\n",
"2 1 Actinobacteria 569 2013\n",
"3 1 Bacteroidetes 115 2013\n",
"4 2 Firmicutes 433 2013\n",
"5 2 Proteobacteria 1130 2013\n",
"6 2 Actinobacteria 754 2013\n",
"7 2 Bacteroidetes 555 2013"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data['year'] = 2013\n",
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But note, we cannot use the attribute indexing method to add a new column:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value year\n",
"0 1 Firmicutes 632 2013\n",
"1 1 Proteobacteria 1638 2013\n",
"2 1 Actinobacteria 569 2013\n",
"3 1 Bacteroidetes 115 2013\n",
"4 2 Firmicutes 433 2013\n",
"5 2 Proteobacteria 1130 2013\n",
"6 2 Actinobacteria 754 2013\n",
"7 2 Bacteroidetes 555 2013"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.treatment = 1\n",
"bacteria_data"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.treatment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Auto-alignment\n",
"\n",
"When adding a column that is not a simple constant, we need to be a bit more careful. Due to pandas' auto-alignment behavior, specifying a `Series` as a new column causes its values to be added according to the `DataFrame`'s index:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 1\n",
"5 1\n",
"dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"treatment = pd.Series([0]*4 + [1]*2)\n",
"\n",
"treatment"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" <th>year</th>\n",
" <th>treatment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value year treatment\n",
"0 1 Firmicutes 632 2013 0\n",
"1 1 Proteobacteria 1638 2013 0\n",
"2 1 Actinobacteria 569 2013 0\n",
"3 1 Bacteroidetes 115 2013 0\n",
"4 2 Firmicutes 433 2013 1\n",
"5 2 Proteobacteria 1130 2013 1\n",
"6 2 Actinobacteria 754 2013 NaN\n",
"7 2 Bacteroidetes 555 2013 NaN"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data['treatment'] = treatment\n",
"\n",
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other Python data structures (ones without an index) need to be the same length as the `DataFrame`:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Length of values does not match length of index",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-40-56b358a0dde9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmonth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Jan'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Feb'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Apr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbacteria_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmonth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.4/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 2119\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2120\u001b[0m \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2121\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2123\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.4/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 2196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2197\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2198\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2199\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.4/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 2354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2355\u001b[0m \u001b[0;31m# turn me into an ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2356\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sanitize_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2357\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2358\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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/usr/local/lib/python3.4/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_sanitize_index\u001b[0;34m(data, index, copy)\u001b[0m\n\u001b[1;32m 2570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2571\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2572\u001b[0;31m raise ValueError('Length of values does not match length of '\n\u001b[0m\u001b[1;32m 2573\u001b[0m 'index')\n\u001b[1;32m 2574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
]
}
],
"source": [
"month = ['Jan', 'Feb', 'Mar', 'Apr']\n",
"bacteria_data['month'] = month"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" <th>year</th>\n",
" <th>treatment</th>\n",
" <th>month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" <td>Jan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value year treatment month\n",
"0 1 Firmicutes 632 2013 0 Jan\n",
"1 1 Proteobacteria 1638 2013 0 Jan\n",
"2 1 Actinobacteria 569 2013 0 Jan\n",
"3 1 Bacteroidetes 115 2013 0 Jan\n",
"4 2 Firmicutes 433 2013 1 Jan\n",
"5 2 Proteobacteria 1130 2013 1 Jan\n",
"6 2 Actinobacteria 754 2013 NaN Jan\n",
"7 2 Bacteroidetes 555 2013 NaN Jan"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data['month'] = ['Jan']*len(bacteria_data)\n",
"\n",
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use `del` to remove columns, in the same way `dict` entries can be removed:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" <th>year</th>\n",
" <th>treatment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" <td>2013</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value year treatment\n",
"0 1 Firmicutes 632 2013 0\n",
"1 1 Proteobacteria 1638 2013 0\n",
"2 1 Actinobacteria 569 2013 0\n",
"3 1 Bacteroidetes 115 2013 0\n",
"4 2 Firmicutes 433 2013 1\n",
"5 2 Proteobacteria 1130 2013 1\n",
"6 2 Actinobacteria 754 2013 NaN\n",
"7 2 Bacteroidetes 555 2013 NaN"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del bacteria_data['month']\n",
"\n",
"bacteria_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or employ the `drop` method."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient</th>\n",
" <th>phylum</th>\n",
" <th>value</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Firmicutes</td>\n",
" <td>632</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1638</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Actinobacteria</td>\n",
" <td>569</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>115</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Firmicutes</td>\n",
" <td>433</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>Proteobacteria</td>\n",
" <td>1130</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>Actinobacteria</td>\n",
" <td>754</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>Bacteroidetes</td>\n",
" <td>555</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient phylum value year\n",
"0 1 Firmicutes 632 2013\n",
"1 1 Proteobacteria 1638 2013\n",
"2 1 Actinobacteria 569 2013\n",
"3 1 Bacteroidetes 115 2013\n",
"4 2 Firmicutes 433 2013\n",
"5 2 Proteobacteria 1130 2013\n",
"6 2 Actinobacteria 754 2013\n",
"7 2 Bacteroidetes 555 2013"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.drop('treatment', axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can extract the underlying data as a simple `ndarray` by accessing the `values` attribute:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 'Firmicutes', 632, 2013, 0.0],\n",
" [1, 'Proteobacteria', 1638, 2013, 0.0],\n",
" [1, 'Actinobacteria', 569, 2013, 0.0],\n",
" [1, 'Bacteroidetes', 115, 2013, 0.0],\n",
" [2, 'Firmicutes', 433, 2013, 1.0],\n",
" [2, 'Proteobacteria', 1130, 2013, 1.0],\n",
" [2, 'Actinobacteria', 754, 2013, nan],\n",
" [2, 'Bacteroidetes', 555, 2013, nan]], dtype=object)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that because of the mix of string, integer and float (and `NaN`) values, the dtype of the array is `object`. The dtype will automatically be chosen to be as general as needed to accomodate all the columns."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([[ 0.4, 1. ],\n",
" [-1. , 2. ],\n",
" [ 4.5, 3. ]]), dtype('float64'))"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({'foo': [1,2,3], 'bar':[0.4, -1.0, 4.5]})\n",
"\n",
"df.values, df.values.dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pandas uses a custom data structure to represent the **indices** of Series and DataFrames."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64')"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria_data.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Index objects are immutable:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "Indexes does not support mutable operations",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-47-d897e4dc5161>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbacteria_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 1046\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1047\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1048\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Indexes does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1049\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1050\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Indexes does not support mutable operations"
]
}
],
"source": [
"bacteria_data.index[0] = 15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is so that Index objects can be shared between data structures without fear that they will be changed."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"phylum\n",
"Firmicutes NaN\n",
"Proteobacteria 632\n",
"Actinobacteria 1638\n",
"Bacteroidetes 569\n",
"dtype: float64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bacteria2.index = bacteria.index\n",
"\n",
"bacteria2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Excercise: Indexing\n",
"\n",
"From the `bacteria_data` table above, create an index to return all rows for which the phylum name ends in \"bacteria\" and the value is greater than 1000."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using pandas\n",
"\n",
"This section, we will import and clean up some of the datasets that we will be using later on in the tutorial. And in doing so, we will introduce the key functionality of pandas that is required to use the software effectively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing data\n",
"\n",
"A key, but often under-appreciated, step in data analysis is importing the data that we wish to analyze. Though it is easy to load basic data structures into Python using built-in tools or those provided by packages like NumPy, it is non-trivial to import structured data well, and to easily convert this input into a robust data structure:\n",
"\n",
" genes = np.loadtxt(\"genes.csv\", delimiter=\",\", dtype=[('gene', '|S10'), ('value', '<f4')])\n",
"\n",
"pandas provides a convenient set of functions for importing tabular data in a number of formats directly into a `DataFrame` object. These functions include a slew of options to perform type inference, indexing, parsing, iterating and cleaning automatically as data are imported.\n",
"\n",
"### Delimited data\n",
"\n",
"The file `olympics.1996.txt` in the `data` directory contains counts of medals awarded at the 1996 Summer Olympic Games by country, along with the countries' respective population sizes. This data is stored in a tab-separated format.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tonga\t1\t96165\r\n",
"Bahamas\t1\t281584\r\n",
"Jamaica\t6\t2589043\r\n",
"Cuba\t25\t10952046\r\n",
"Australia\t41\t18348078\r\n",
"Hungary\t21\t10273590\r\n",
"Bulgaria\t15\t8181047\r\n",
"Trinidad & Tobago\t2\t1196910\r\n",
"New Zealand\t6\t3621200\r\n",
"Norway\t7\t4381275\r\n"
]
}
],
"source": [
"!head ../data/olympics.1996.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This table can be read into a DataFrame using `read_table`. "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>medals</th>\n",
" <th>population</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Tonga</th>\n",
" <td>1</td>\n",
" <td>96165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahamas</th>\n",
" <td>1</td>\n",
" <td>281584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jamaica</th>\n",
" <td>6</td>\n",
" <td>2589043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cuba</th>\n",
" <td>25</td>\n",
" <td>10952046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Australia</th>\n",
" <td>41</td>\n",
" <td>18348078</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" medals population\n",
"country \n",
"Tonga 1 96165\n",
"Bahamas 1 281584\n",
"Jamaica 6 2589043\n",
"Cuba 25 10952046\n",
"Australia 41 18348078"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"medals = pd.read_table('../data/olympics.1996.txt', sep='\\t',\n",
" index_col=0,\n",
" header=None, names=['country', 'medals', 'population'])\n",
"medals.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is no header row in this dataset, so we specified this, and provided our own **header names**. If we did not specify `header=None` the function would have assumed the first row contained column names.\n",
"\n",
"The tab **separator** was passed to the `sep` argument as `\\t`.\n",
"\n",
"The `sep` argument can be customized as needed to accomodate arbitrary separators. For example, we can use a regular expression to define a variable amount of whitespace, which is unfortunately common in some datasets: \n",
" \n",
" sep='\\s+'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scraping Data from the Web\n",
"\n",
"We would like to add another variable to this dataset. Along with population, a country's economic development may be a useful predictor of Olympic success. A very simple indicator of this might be OECD membership status. \n",
"\n",
"The [OECD website](http://www.oecd.org/about/membersandpartners/list-oecd-member-countries.htm) contains a table listing OECD member nations, along with its year of membership. We would like to import this table and extract the contries that were members as of the 1996 games."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `read_html` function accepts a URL argument, and will attempt to extract all the tables from that address, returning whatever it finds in a **list of `DataFrame`s**."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[ 0\n",
" 0 On 14 December 1960, 20 countries originally s...\n",
" 1 Here is a list of the current Member countries...,\n",
" 0 1 2 3\n",
" 0 NaN Country Date NaN\n",
" 1 NaN AUSTRALIA 7 June 1971 NaN\n",
" 2 NaN AUSTRIA 29 September 1961 NaN\n",
" 3 NaN BELGIUM 13 September 1961 NaN\n",
" 4 NaN CANADA 10 April 1961 NaN\n",
" 5 NaN CHILE 7 May 2010 NaN\n",
" 6 NaN CZECH REPUBLIC 21 December 1995 NaN\n",
" 7 NaN DENMARK 30 May 1961 NaN\n",
" 8 NaN ESTONIA 9 December 2010 NaN\n",
" 9 NaN FINLAND 28 January 1969 NaN\n",
" 10 NaN FRANCE 7 August 1961 NaN\n",
" 11 NaN GERMANY 27 September 1961 NaN\n",
" 12 NaN GREECE 27 September 1961 NaN\n",
" 13 NaN HUNGARY 7 May 1996 NaN\n",
" 14 NaN ICELAND 5 June 1961 NaN\n",
" 15 NaN IRELAND 17 August 1961 NaN\n",
" 16 NaN ISRAEL 7 September 2010 NaN\n",
" 17 NaN ITALY 29 March 1962 NaN\n",
" 18 NaN JAPAN 28 April 1964 NaN\n",
" 19 NaN KOREA 12 December 1996 NaN\n",
" 20 NaN LUXEMBOURG 7 December 1961 NaN\n",
" 21 NaN MEXICO 18 May 1994 NaN\n",
" 22 NaN NETHERLANDS 13 November 1961 NaN\n",
" 23 NaN NEW ZEALAND 29 May 1973 NaN\n",
" 24 NaN NORWAY 4 July 1961 NaN\n",
" 25 NaN POLAND 22 November 1996 NaN\n",
" 26 NaN PORTUGAL 4 August 1961 NaN\n",
" 27 NaN SLOVAK REPUBLIC 14 December 2000 NaN\n",
" 28 NaN SLOVENIA 21 July 2010 NaN\n",
" 29 NaN SPAIN 3 August 1961 NaN\n",
" 30 NaN SWEDEN 28 September 1961 NaN\n",
" 31 NaN SWITZERLAND 28 September 1961 NaN\n",
" 32 NaN TURKEY 2 August 1961 NaN\n",
" 33 NaN UNITED KINGDOM 2 May 1961 NaN\n",
" 34 NaN UNITED STATES 12 April 1961 NaN\n",
" 35 More on membership and enlargement NaN NaN NaN]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oecd_site = 'http://www.oecd.org/about/membersandpartners/list-oecd-member-countries.htm'\n",
"pd.read_html(oecd_site)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is typically some cleanup that is required of the returned data, such as the assignment of column names or conversion of types. \n",
"\n",
"The table of interest is at index 1, and we will extract two columns from the table. Otherwise, this table is pretty clean."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AUSTRALIA</td>\n",
" <td>7 June 1971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AUSTRIA</td>\n",
" <td>29 September 1961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BELGIUM</td>\n",
" <td>13 September 1961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>CANADA</td>\n",
" <td>10 April 1961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CHILE</td>\n",
" <td>7 May 2010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Date\n",
"0 AUSTRALIA 7 June 1971\n",
"1 AUSTRIA 29 September 1961\n",
"2 BELGIUM 13 September 1961\n",
"3 CANADA 10 April 1961\n",
"4 CHILE 7 May 2010"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oecd = pd.read_html(oecd_site, header=0)[1][[1,2]]\n",
"oecd.head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Country\n",
"Australia 1971\n",
"Austria 1961\n",
"Belgium 1961\n",
"Canada 1961\n",
"Chile 2010\n",
"Czech Republic 1995\n",
"Denmark 1961\n",
"Estonia 2010\n",
"Finland 1969\n",
"France 1961\n",
"Germany 1961\n",
"Greece 1961\n",
"Hungary 1996\n",
"Iceland 1961\n",
"Ireland 1961\n",
"Israel 2010\n",
"Italy 1962\n",
"Japan 1964\n",
"Korea 1996\n",
"Luxembourg 1961\n",
"Mexico 1994\n",
"Netherlands 1961\n",
"New Zealand 1973\n",
"Norway 1961\n",
"Poland 1996\n",
"Portugal 1961\n",
"Slovak Republic 2000\n",
"Slovenia 2010\n",
"Spain 1961\n",
"Sweden 1961\n",
"Switzerland 1961\n",
"Turkey 1961\n",
"United Kingdom 1961\n",
"United States 1961\n",
"Name: year, dtype: float64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oecd['year'] = pd.to_datetime(oecd.Date).apply(lambda x: x.year)\n",
"oecd_year = oecd.set_index(oecd.Country.str.title())['year'].dropna()\n",
"oecd_year"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create an indicator (binary) variable for OECD status by checking if each country is in the index of countries with membership year less than 1997. \n",
"\n",
"The new `DataFrame` method `assign` is a convenient means for creating the new column from this operation."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"medals_data = medals.assign(oecd=medals.index.isin((oecd_year[oecd_year<1997]).index).astype(int))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the distribution of populations spans several orders of magnitude, we may wish to use the logarithm of the population size, which may be created similarly."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"medals_data = medals_data.assign(log_population=np.log(medals.population))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The NumPy `log` function will return a pandas `Series` (or `DataFrame` when applied to one) instead of a `ndarray`; all of NumPy's functions are compatible with pandas in this way."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>medals</th>\n",
" <th>population</th>\n",
" <th>oecd</th>\n",
" <th>log_population</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Tonga</th>\n",
" <td>1</td>\n",
" <td>96165</td>\n",
" <td>0</td>\n",
" <td>11.473821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahamas</th>\n",
" <td>1</td>\n",
" <td>281584</td>\n",
" <td>0</td>\n",
" <td>12.548186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jamaica</th>\n",
" <td>6</td>\n",
" <td>2589043</td>\n",
" <td>0</td>\n",
" <td>14.766799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cuba</th>\n",
" <td>25</td>\n",
" <td>10952046</td>\n",
" <td>0</td>\n",
" <td>16.209037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Australia</th>\n",
" <td>41</td>\n",
" <td>18348078</td>\n",
" <td>1</td>\n",
" <td>16.725035</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" medals population oecd log_population\n",
"country \n",
"Tonga 1 96165 0 11.473821\n",
"Bahamas 1 281584 0 12.548186\n",
"Jamaica 6 2589043 0 14.766799\n",
"Cuba 25 10952046 0 16.209037\n",
"Australia 41 18348078 1 16.725035"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"medals_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comma-separated Values (CSV)\n",
"\n",
"The most common form of delimited data is comma-separated values (CSV). Since CSV is so ubiquitous, the `read_csv` is available as a convenience function for `read_table`.\n",
"\n",
"Consider some more microbiome data."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Taxon,Patient,Tissue,Stool\r\n",
"Firmicutes,1,632,305\r\n",
"Firmicutes,2,136,4182\r\n",
"Firmicutes,3,1174,703\r\n",
"Firmicutes,4,408,3946\r\n",
"Firmicutes,5,831,8605\r\n",
"Firmicutes,6,693,50\r\n",
"Firmicutes,7,718,717\r\n",
"Firmicutes,8,173,33\r\n",
"Firmicutes,9,228,80\r\n",
"Firmicutes,10,162,3196\r\n",
"Firmicutes,11,372,32\r\n",
"Firmicutes,12,4255,4361\r\n",
"Firmicutes,13,107,1667\r\n",
"Firmicutes,14,96,223\r\n",
"Firmicutes,15,281,2377\r\n",
"Proteobacteria,1,1638,3886\r\n",
"Proteobacteria,2,2469,1821\r\n",
"Proteobacteria,3,839,661\r\n",
"Proteobacteria,4,4414,18\r\n",
"Proteobacteria,5,12044,83\r\n",
"Proteobacteria,6,2310,12\r\n",
"Proteobacteria,7,3053,547\r\n",
"Proteobacteria,8,395,2174\r\n",
"Proteobacteria,9,2651,767\r\n",
"Proteobacteria,10,1195,76\r\n",
"Proteobacteria,11,6857,795\r\n",
"Proteobacteria,12,483,666\r\n",
"Proteobacteria,13,2950,3994\r\n",
"Proteobacteria,14,1541,816\r\n",
"Proteobacteria,15,1307,53\r\n",
"Actinobacteria,1,569,648\r\n",
"Actinobacteria,2,1590,4\r\n",
"Actinobacteria,3,25,2\r\n",
"Actinobacteria,4,259,300\r\n",
"Actinobacteria,5,568,7\r\n",
"Actinobacteria,6,1102,9\r\n",
"Actinobacteria,7,678,377\r\n",
"Actinobacteria,8,260,58\r\n",
"Actinobacteria,9,424,233\r\n",
"Actinobacteria,10,548,21\r\n",
"Actinobacteria,11,201,83\r\n",
"Actinobacteria,12,42,75\r\n",
"Actinobacteria,13,109,59\r\n",
"Actinobacteria,14,51,183\r\n",
"Actinobacteria,15,310,204\r\n",
"Bacteroidetes,1,115,380\r\n",
"Bacteroidetes,2,67,0\r\n",
"Bacteroidetes,3,0,0\r\n",
"Bacteroidetes,4,85,5\r\n",
"Bacteroidetes,5,143,7\r\n",
"Bacteroidetes,6,678,2\r\n",
"Bacteroidetes,7,4829,209\r\n",
"Bacteroidetes,8,74,651\r\n",
"Bacteroidetes,9,169,254\r\n",
"Bacteroidetes,10,106,10\r\n",
"Bacteroidetes,11,73,381\r\n",
"Bacteroidetes,12,30,359\r\n",
"Bacteroidetes,13,51,51\r\n",
"Bacteroidetes,14,2473,2314\r\n",
"Bacteroidetes,15,102,33\r\n",
"Other,1,114,277\r\n",
"Other,2,195,18\r\n",
"Other,3,42,2\r\n",
"Other,4,316,43\r\n",
"Other,5,202,40\r\n",
"Other,6,116,0\r\n",
"Other,7,527,12\r\n",
"Other,8,357,11\r\n",
"Other,9,106,11\r\n",
"Other,10,67,14\r\n",
"Other,11,203,6\r\n",
"Other,12,392,6\r\n",
"Other,13,28,25\r\n",
"Other,14,12,22\r\n",
"Other,15,305,32"
]
}
],
"source": [
"!cat ../data/microbiome/microbiome.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This table can be read into a DataFrame using `read_csv`:"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
" <td>1</td>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Firmicutes</td>\n",
" <td>2</td>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Firmicutes</td>\n",
" <td>3</td>\n",
" <td>1174</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>4</td>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Firmicutes</td>\n",
" <td>5</td>\n",
" <td>831</td>\n",
" <td>8605</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 1174 703\n",
"3 Firmicutes 4 408 3946\n",
"4 Firmicutes 5 831 8605"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb = pd.read_csv(\"../data/microbiome/microbiome.csv\")\n",
"mb.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we have sections of data that we do not wish to import (for example, known bad data), we can populate the `skiprows` argument:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
" <td>1</td>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Firmicutes</td>\n",
" <td>2</td>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Firmicutes</td>\n",
" <td>5</td>\n",
" <td>831</td>\n",
" <td>8605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>7</td>\n",
" <td>718</td>\n",
" <td>717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Firmicutes</td>\n",
" <td>8</td>\n",
" <td>173</td>\n",
" <td>33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 5 831 8605\n",
"3 Firmicutes 7 718 717\n",
"4 Firmicutes 8 173 33"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv(\"../data/microbiome/microbiome.csv\", skiprows=[3,4,6]).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conversely, if we only want to import a small number of rows from, say, a very large data file we can use `nrows`:"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
" <td>1</td>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Firmicutes</td>\n",
" <td>2</td>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Firmicutes</td>\n",
" <td>3</td>\n",
" <td>1174</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>4</td>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 1174 703\n",
"3 Firmicutes 4 408 3946"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"few_recs = pd.read_csv(\"../data/microbiome/microbiome.csv\", nrows=4)\n",
"\n",
"few_recs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternately, if we want to process our data in reasonable chunks, the `chunksize` argument will return an iterable object that can be employed in a data processing loop. For example, our microbiome data are organized by bacterial phylum, with 15 patients represented in each:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<pandas.io.parsers.TextFileReader at 0x10ff36898>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_chunks = pd.read_csv(\"../data/microbiome/microbiome.csv\", chunksize=15)\n",
"data_chunks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Calculating summary statistics\n",
"\n",
"Import the microbiome data, calculating the mean counts across all patients for each taxon, returning these values in a dictionary.\n",
"\n",
"*Hint: using `chunksize` makes this more efficent!*"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hierarchical Indices\n",
"\n",
"For a more useful index, we can specify the first two columns, which together provide a unique index to the data."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">Firmicutes</th>\n",
" <th>1</th>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1174</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>831</td>\n",
" <td>8605</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tissue Stool\n",
"Taxon Patient \n",
"Firmicutes 1 632 305\n",
" 2 136 4182\n",
" 3 1174 703\n",
" 4 408 3946\n",
" 5 831 8605"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb = pd.read_csv(\"../data/microbiome/microbiome.csv\", index_col=['Taxon','Patient'])\n",
"mb.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is called a **hierarchical index**, which allows multiple dimensions of data to be represented in tabular form."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"MultiIndex(levels=[['Actinobacteria', 'Bacteroidetes', 'Firmicutes', 'Other', 'Proteobacteria'], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]],\n",
" labels=[[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]],\n",
" names=['Taxon', 'Patient'])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The corresponding index is a `MultiIndex` object that consists of a sequence of tuples, the elements of which is some combination of the three columns used to create the index. Where there are multiple repeated values, pandas does not print the repeats, making it easy to identify groups of values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rows can be indexed by passing the appropriate tuple."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Tissue 136\n",
"Stool 4182\n",
"Name: (Firmicutes, 2), dtype: int64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb.ix[('Firmicutes', 2)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With a hierachical index, we can select subsets of the data based on a *partial* index:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Patient</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1638</td>\n",
" <td>3886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2469</td>\n",
" <td>1821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>839</td>\n",
" <td>661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4414</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>12044</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2310</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3053</td>\n",
" <td>547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>395</td>\n",
" <td>2174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2651</td>\n",
" <td>767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1195</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>6857</td>\n",
" <td>795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>483</td>\n",
" <td>666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2950</td>\n",
" <td>3994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1541</td>\n",
" <td>816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1307</td>\n",
" <td>53</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tissue Stool\n",
"Patient \n",
"1 1638 3886\n",
"2 2469 1821\n",
"3 839 661\n",
"4 4414 18\n",
"5 12044 83\n",
"6 2310 12\n",
"7 3053 547\n",
"8 395 2174\n",
"9 2651 767\n",
"10 1195 76\n",
"11 6857 795\n",
"12 483 666\n",
"13 2950 3994\n",
"14 1541 816\n",
"15 1307 53"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb.ix['Proteobacteria']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To extract arbitrary levels from a hierarchical row index, the **cross-section** method `xs` can be used."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Taxon</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Firmicutes</th>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Proteobacteria</th>\n",
" <td>1638</td>\n",
" <td>3886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Actinobacteria</th>\n",
" <td>569</td>\n",
" <td>648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bacteroidetes</th>\n",
" <td>115</td>\n",
" <td>380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other</th>\n",
" <td>114</td>\n",
" <td>277</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tissue Stool\n",
"Taxon \n",
"Firmicutes 632 305\n",
"Proteobacteria 1638 3886\n",
"Actinobacteria 569 648\n",
"Bacteroidetes 115 380\n",
"Other 114 277"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb.xs(1, level='Patient')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We may also reorder levels as we like."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Patient</th>\n",
" <th>Taxon</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>Firmicutes</th>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>Firmicutes</th>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>Firmicutes</th>\n",
" <td>1174</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>Firmicutes</th>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>Firmicutes</th>\n",
" <td>831</td>\n",
" <td>8605</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tissue Stool\n",
"Patient Taxon \n",
"1 Firmicutes 632 305\n",
"2 Firmicutes 136 4182\n",
"3 Firmicutes 1174 703\n",
"4 Firmicutes 408 3946\n",
"5 Firmicutes 831 8605"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb.swaplevel('Patient', 'Taxon').head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Operations\n",
"\n",
"`DataFrame` and `Series` objects allow for several operations to take place either on a single object, or between two or more objects.\n",
"\n",
"For example, we can perform arithmetic on the elements of two objects, such as calculating the ratio of bacteria counts between locations:"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Taxon Patient\n",
"Firmicutes 1 0.482595\n",
" 2 30.750000\n",
" 3 0.598807\n",
" 4 9.671569\n",
" 5 10.354994\n",
" 6 0.072150\n",
" 7 0.998607\n",
" 8 0.190751\n",
" 9 0.350877\n",
" 10 19.728395\n",
" 11 0.086022\n",
" 12 1.024912\n",
" 13 15.579439\n",
" 14 2.322917\n",
" 15 8.459075\n",
"Proteobacteria 1 2.372405\n",
" 2 0.737546\n",
" 3 0.787843\n",
" 4 0.004078\n",
" 5 0.006891\n",
" 6 0.005195\n",
" 7 0.179168\n",
" 8 5.503797\n",
" 9 0.289325\n",
" 10 0.063598\n",
" 11 0.115940\n",
" 12 1.378882\n",
" 13 1.353898\n",
" 14 0.529526\n",
" 15 0.040551\n",
" ... \n",
"Bacteroidetes 1 3.304348\n",
" 2 0.000000\n",
" 3 NaN\n",
" 4 0.058824\n",
" 5 0.048951\n",
" 6 0.002950\n",
" 7 0.043280\n",
" 8 8.797297\n",
" 9 1.502959\n",
" 10 0.094340\n",
" 11 5.219178\n",
" 12 11.966667\n",
" 13 1.000000\n",
" 14 0.935706\n",
" 15 0.323529\n",
"Other 1 2.429825\n",
" 2 0.092308\n",
" 3 0.047619\n",
" 4 0.136076\n",
" 5 0.198020\n",
" 6 0.000000\n",
" 7 0.022770\n",
" 8 0.030812\n",
" 9 0.103774\n",
" 10 0.208955\n",
" 11 0.029557\n",
" 12 0.015306\n",
" 13 0.892857\n",
" 14 1.833333\n",
" 15 0.104918\n",
"dtype: float64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb.Stool / mb.Tissue"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Microsoft Excel\n",
"\n",
"Since so much financial and scientific data ends up in Excel spreadsheets (regrettably), pandas' ability to directly import Excel spreadsheets is valuable. This support is contingent on having one or two dependencies (depending on what version of Excel file is being imported) installed: `xlrd` and `openpyxl` (these may be installed with either `pip` or `easy_install`).\n",
"\n",
"Importing Excel data to pandas is a two-step process. First, we create an `ExcelFile` object using the path of the file: "
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<pandas.io.excel.ExcelFile at 0x10fede358>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb_file = pd.ExcelFile('../data/microbiome/MID1.xls')\n",
"mb_file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, since modern spreadsheets consist of one or more \"sheets\", we parse the sheet with the data of interest:"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Sulfoloba...</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Thermopro...</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Archaea \"Euryarchaeota\" \"Methanomicrobia\" Meth...</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Count\n",
"0 Archaea \"Crenarchaeota\" Thermoprotei Desulfuro... 7\n",
"1 Archaea \"Crenarchaeota\" Thermoprotei Desulfuro... 2\n",
"2 Archaea \"Crenarchaeota\" Thermoprotei Sulfoloba... 3\n",
"3 Archaea \"Crenarchaeota\" Thermoprotei Thermopro... 3\n",
"4 Archaea \"Euryarchaeota\" \"Methanomicrobia\" Meth... 7"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb1 = mb_file.parse(\"Sheet 1\", header=None)\n",
"mb1.columns = [\"Taxon\", \"Count\"]\n",
"mb1.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is now a `read_excel` conveneince function in pandas that combines these steps into a single call:"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Acidiloba...</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Acidiloba...</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"0 Archaea \"Crenarchaeota\" Thermoprotei Acidiloba... 2\n",
"1 Archaea \"Crenarchaeota\" Thermoprotei Acidiloba... 14\n",
"2 Archaea \"Crenarchaeota\" Thermoprotei Desulfuro... 23\n",
"3 Archaea \"Crenarchaeota\" Thermoprotei Desulfuro... 1\n",
"4 Archaea \"Crenarchaeota\" Thermoprotei Desulfuro... 2"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mb2 = pd.read_excel('../data/microbiome/MID2.xls', sheetname='Sheet 1', header=None)\n",
"mb2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Relational Databases\n",
"\n",
"If you are fortunate, your data will be stored in a database (relational or non-relational) rather than in arbitrary text files or spreadsheet. Relational databases are particularly useful for storing large quantities of *structured* data, where fields are grouped together in tables according to their relationships with one another.\n",
"\n",
"pandas' `DataFrame` interacts with relational (*i.e.* SQL) databases, and even provides facilties for using SQL syntax on the `DataFrame` itself, which we will get to later. For now, let's work with a ubiquitous embedded database called **SQLite**, which comes bundled with Python. A SQLite database can be queried with the standard library's `sqlite3` module."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sqlite3\n",
"\n",
"query = '''\n",
"CREATE TABLE samples\n",
"(taxon VARCHAR(15), patient INTEGER, tissue INTEGER, stool INTEGER);\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This query string will create a table to hold some of our microbiome data, which we can execute after connecting to a database (which will be created, if it does not exist)."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"con = sqlite3.connect('microbiome.sqlite3')\n",
"con.execute(query)\n",
"con.commit()"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Taxon Firmicutes\n",
"Patient 1\n",
"Tissue 632\n",
"Stool 305\n",
"Name: 0, dtype: object"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"few_recs.ix[0]"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x10f5c16c0>"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"con.execute('INSERT INTO samples VALUES(\\'{}\\',{},{},{})'.format(*few_recs.ix[0]))"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x10f5c1730>"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = 'INSERT INTO samples VALUES(?, ?, ?, ?)'\n",
"con.executemany(query, few_recs.values[1:])"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"con.commit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using `SELECT` queries, we can read from the database."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('Firmicutes', 1, 632, 305),\n",
" ('Firmicutes', 2, 136, 4182),\n",
" ('Firmicutes', 3, 1174, 703),\n",
" ('Firmicutes', 4, 408, 3946)]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cursor = con.execute('SELECT * FROM samples')\n",
"rows = cursor.fetchall()\n",
"\n",
"rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results can be passed directly to a `DataFrame`"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
" <td>1</td>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Firmicutes</td>\n",
" <td>2</td>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Firmicutes</td>\n",
" <td>3</td>\n",
" <td>1174</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>4</td>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 1174 703\n",
"3 Firmicutes 4 408 3946"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(rows)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To obtain the column names, we can obtain the table information from the database, via the special `PRAGMA` statement."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0, 'taxon', 'VARCHAR(15)', 0, None, 0),\n",
" (1, 'patient', 'INTEGER', 0, None, 0),\n",
" (2, 'tissue', 'INTEGER', 0, None, 0),\n",
" (3, 'stool', 'INTEGER', 0, None, 0)]"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table_info = con.execute('PRAGMA table_info(samples);').fetchall()\n",
"\n",
"table_info"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>taxon</th>\n",
" <th>patient</th>\n",
" <th>tissue</th>\n",
" <th>stool</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
" <td>1</td>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Firmicutes</td>\n",
" <td>2</td>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Firmicutes</td>\n",
" <td>3</td>\n",
" <td>1174</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>4</td>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" taxon patient tissue stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 1174 703\n",
"3 Firmicutes 4 408 3946"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(rows, columns=np.transpose(table_info)[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A more direct approach is to pass the query to the `read_sql_query` functon, which returns a populated `DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>taxon</th>\n",
" <th>patient</th>\n",
" <th>tissue</th>\n",
" <th>stool</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
" <td>1</td>\n",
" <td>632</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Firmicutes</td>\n",
" <td>2</td>\n",
" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Firmicutes</td>\n",
" <td>3</td>\n",
" <td>1174</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>4</td>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" taxon patient tissue stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 1174 703\n",
"3 Firmicutes 4 408 3946"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_sql_query('SELECT * FROM samples', con)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Correspondingly, we can append records into the database with `to_sql`."
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"more_recs = pd.read_csv(\"../data/microbiome/microbiome_missing.csv\").head(20)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"more_recs.to_sql('samples', con, if_exists='append', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('Firmicutes', 1, 632, 305),\n",
" ('Firmicutes', 2, 136, 4182),\n",
" ('Firmicutes', 3, 1174, 703),\n",
" ('Firmicutes', 4, 408, 3946),\n",
" ('Firmicutes', 1, 632, 305),\n",
" ('Firmicutes', 2, 136, 4182),\n",
" ('Firmicutes', 3, None, 703),\n",
" ('Firmicutes', 4, 408, 3946),\n",
" ('Firmicutes', 5, 831, 8605),\n",
" ('Firmicutes', 6, 693, 50),\n",
" ('Firmicutes', 7, 718, 717),\n",
" ('Firmicutes', 8, 173, 33),\n",
" ('Firmicutes', 9, 228, None),\n",
" ('Firmicutes', 10, 162, 3196),\n",
" ('Firmicutes', 11, 372, -99999),\n",
" ('Firmicutes', 12, 4255, 4361),\n",
" ('Firmicutes', 13, 107, 1667),\n",
" ('Firmicutes', 14, '?', 223),\n",
" ('Firmicutes', 15, 281, 2377),\n",
" ('Proteobacteria', 1, 1638, 3886),\n",
" ('Proteobacteria', 2, 2469, 1821),\n",
" ('Proteobacteria', 3, 839, 661),\n",
" ('Proteobacteria', 4, 4414, 18),\n",
" ('Proteobacteria', 5, 12044, 83)]"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cursor = con.execute('SELECT * FROM samples')\n",
"cursor.fetchall()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several other data formats that can be imported into Python and converted into DataFrames, with the help of buitl-in or third-party libraries. These include JSON, XML, HDF5, non-relational databases, and various web APIs."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Get rid of the database we created\n",
"!rm microbiome.sqlite3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2014 Ebola Outbreak Data\n",
"\n",
"The `../data/ebola` folder contains summarized reports of Ebola cases from three countries during the recent outbreak of the disease in West Africa. For each country, there are daily reports that contain various information about the outbreak in several cities in each country.\n",
"\n",
"\n",
"\n",
"From these data files, use pandas to import them and create a single data frame that includes the **daily totals of new cases** for each country. \n",
"\n",
"We may use this compiled data for more advaned applications later in the course."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data are taken from [Caitlin Rivers' `ebola` GitHub repository](https://github.com/cmrivers/ebola), and are licenced for both commercial and non-commercial use. The tutorial repository contains a subset of this data from three countries (Sierra Leone, Liberia and Guinea) that we will use as an example. They reside in a nested subdirectory in the `data` directory."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['LICENSE', 'guinea_data', 'liberia_data', 'sl_data']"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebola_dirs = !ls ../data/ebola/\n",
"ebola_dirs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Within each country directory, there are CSV files containing daily information regarding the state of the outbreak for that country. The first step is to efficiently import all the relevant files. \n",
"\n",
"Our approach will be to construct a dictionary containing a list of filenames to import. We can use the `glob` package to identify all the CSV files in each directory. This can all be placed within a **dictionary comprehension**."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob\n",
"\n",
"filenames = {data_dir[:data_dir.find('_')]: glob.glob('../data/ebola/{0}/*.csv'.format(data_dir)) for data_dir in ebola_dirs[1:]}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now in a position to iterate over the dictionary and import the corresponding files. However, the data layout of the files across the dataset is partially inconsistent."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>variable</th>\n",
" <th>Kailahun</th>\n",
" <th>Kenema</th>\n",
" <th>Kono</th>\n",
" <th>Kambia</th>\n",
" <th>Koinadugu</th>\n",
" <th>Bombali</th>\n",
" <th>Tonkolili</th>\n",
" <th>Port Loko</th>\n",
" <th>Pujehun</th>\n",
" <th>Bo</th>\n",
" <th>Moyamba</th>\n",
" <th>Bonthe</th>\n",
" <th>Western area urban</th>\n",
" <th>Western area rural</th>\n",
" <th>National</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-08-12</td>\n",
" <td>population</td>\n",
" <td>465048</td>\n",
" <td>653013</td>\n",
" <td>325003</td>\n",
" <td>341690</td>\n",
" <td>335471</td>\n",
" <td>494139</td>\n",
" <td>434937</td>\n",
" <td>557978</td>\n",
" <td>335574</td>\n",
" <td>654142</td>\n",
" <td>278119</td>\n",
" <td>168729</td>\n",
" <td>1040888</td>\n",
" <td>263619</td>\n",
" <td>6348350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_noncase</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_suspected</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_probable</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_confirmed</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date variable Kailahun Kenema Kono Kambia Koinadugu \\\n",
"0 2014-08-12 population 465048 653013 325003 341690 335471 \n",
"1 2014-08-12 new_noncase 0 3 0 0 0 \n",
"2 2014-08-12 new_suspected 0 9 0 0 0 \n",
"3 2014-08-12 new_probable 0 0 0 0 0 \n",
"4 2014-08-12 new_confirmed 0 9 0 0 0 \n",
"\n",
" Bombali Tonkolili Port Loko Pujehun Bo Moyamba Bonthe \\\n",
"0 494139 434937 557978 335574 654142 278119 168729 \n",
"1 0 0 1 0 0 0 0 \n",
"2 0 0 0 0 1 0 0 \n",
"3 0 0 0 0 1 0 0 \n",
"4 0 0 2 0 0 0 0 \n",
"\n",
" Western area urban Western area rural National \n",
"0 1040888 263619 6348350 \n",
"1 0 0 4 \n",
"2 0 0 10 \n",
"3 0 0 1 \n",
"4 0 0 11 "
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv('../data/ebola/sl_data/2014-08-12-v77.csv').head()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Description</th>\n",
" <th>Totals</th>\n",
" <th>Conakry</th>\n",
" <th>Gueckedou</th>\n",
" <th>Macenta</th>\n",
" <th>Dabola</th>\n",
" <th>Kissidougou</th>\n",
" <th>Dinguiraye</th>\n",
" <th>Telimele</th>\n",
" <th>...</th>\n",
" <th>Mzerekore</th>\n",
" <th>Yomou</th>\n",
" <th>Dubreka</th>\n",
" <th>Forecariah</th>\n",
" <th>Kerouane</th>\n",
" <th>Coyah</th>\n",
" <th>Dalaba</th>\n",
" <th>Beyla</th>\n",
" <th>Kindia</th>\n",
" <th>Lola</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-09-02</td>\n",
" <td>New cases of suspects</td>\n",
" <td>11</td>\n",
" <td>NaN</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-09-02</td>\n",
" <td>New cases of probables</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-09-02</td>\n",
" <td>New cases of confirmed</td>\n",
" <td>14</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-09-02</td>\n",
" <td>Total new cases registered so far</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-09-02</td>\n",
" <td>Total cases of suspects</td>\n",
" <td>49</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Description Totals Conakry Gueckedou \\\n",
"0 2014-09-02 New cases of suspects 11 NaN 5 \n",
"1 2014-09-02 New cases of probables 0 NaN NaN \n",
"2 2014-09-02 New cases of confirmed 14 NaN 4 \n",
"3 2014-09-02 Total new cases registered so far 25 0 9 \n",
"4 2014-09-02 Total cases of suspects 49 15 5 \n",
"\n",
" Macenta Dabola Kissidougou Dinguiraye Telimele ... Mzerekore Yomou Dubreka \\\n",
"0 6 NaN NaN NaN NaN ... NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
"2 6 NaN NaN NaN NaN ... NaN NaN NaN \n",
"3 12 0 0 0 0 ... 0 0 1 \n",
"4 17 0 0 0 0 ... 0 1 1 \n",
"\n",
" Forecariah Kerouane Coyah Dalaba Beyla Kindia Lola \n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN NaN \n",
"3 0 0 3 NaN NaN NaN NaN \n",
"4 2 5 0 NaN NaN NaN NaN \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv('../data/ebola/guinea_data/2014-09-02.csv').head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly, we will need to develop row **masks** to extract the data we need across all files, without having to manually extract data from each file.\n",
"\n",
"Let's hack at one file to develop the mask."
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sample = pd.read_csv('../data/ebola/sl_data/2014-08-12-v77.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To prevent issues with capitalization, we will simply revert all labels to lower case."
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lower_vars = sample.variable.str.lower()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we are interested in extracting new cases only, we can use the **string accessor** attribute to look for key words that we would like to include or exclude."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"case_mask = (lower_vars.str.contains('new') \n",
" & (lower_vars.str.contains('case') | lower_vars.str.contains('suspect')) \n",
" & ~lower_vars.str.contains('non')\n",
" & ~lower_vars.str.contains('total'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could have instead used regular expressions to do the same thing.\n",
"\n",
"Finally, we are only interested in three columns."
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>variable</th>\n",
" <th>National</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_suspected</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date variable National\n",
"2 2014-08-12 new_suspected 10"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample.loc[case_mask, ['date', 'variable', 'National']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now embed this operation in a loop over all the filenames in the database."
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"datasets = []\n",
"for country in filenames:\n",
" \n",
" country_files = filenames[country]\n",
" for f in country_files:\n",
" \n",
" data = pd.read_csv(f)\n",
" \n",
" \n",
" # Convert to lower case to avoid capitalization issues\n",
" data.columns = data.columns.str.lower()\n",
" # Column naming is inconsistent. These procedures deal with that.\n",
" keep_columns = ['date']\n",
" if 'description' in data.columns:\n",
" keep_columns.append('description')\n",
" else:\n",
" keep_columns.append('variable')\n",
" \n",
" if 'totals' in data.columns:\n",
" keep_columns.append('totals')\n",
" else:\n",
" keep_columns.append('national')\n",
" \n",
" # Index out the columns we need, and rename them\n",
" keep_data = data[keep_columns]\n",
" keep_data.columns = 'date', 'variable', 'totals'\n",
" \n",
" # Extract the rows we might want\n",
" lower_vars = keep_data.variable.str.lower()\n",
" # Of course we can also use regex to do this\n",
" case_mask = (lower_vars.str.contains('new') \n",
" & (lower_vars.str.contains('case') | lower_vars.str.contains('suspect') \n",
" | lower_vars.str.contains('confirm')) \n",
" & ~lower_vars.str.contains('non')\n",
" & ~lower_vars.str.contains('total'))\n",
" \n",
" keep_data = keep_data[case_mask].dropna()\n",
" \n",
" # Convert data types\n",
" keep_data['date'] = pd.to_datetime(keep_data.date)\n",
" keep_data['totals'] = keep_data.totals.astype(int)\n",
" \n",
" # Assign country label and append to datasets list\n",
" datasets.append(keep_data.assign(country=country))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have a list populated with `DataFrame` objects for each day and country, we can call `concat` to concatenate them into a single `DataFrame`."
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>variable</th>\n",
" <th>totals</th>\n",
" <th>country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_suspected</td>\n",
" <td>10</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_confirmed</td>\n",
" <td>11</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-13</td>\n",
" <td>new_suspected</td>\n",
" <td>3</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-08-13</td>\n",
" <td>new_confirmed</td>\n",
" <td>15</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-14</td>\n",
" <td>new_suspected</td>\n",
" <td>0</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date variable totals country\n",
"2 2014-08-12 new_suspected 10 sl\n",
"4 2014-08-12 new_confirmed 11 sl\n",
"2 2014-08-13 new_suspected 3 sl\n",
"4 2014-08-13 new_confirmed 15 sl\n",
"2 2014-08-14 new_suspected 0 sl"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_data = pd.concat(datasets)\n",
"all_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This works because the structure of each table was identical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Manipulating indices\n",
"\n",
"Notice from above, however, that the index contains redundant integer index values. We can confirm this:"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_data.index.is_unique"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create a new unique index by calling the `reset_index` method on the new data frame after we import it, which will generate a new ordered, unique index."
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>variable</th>\n",
" <th>totals</th>\n",
" <th>country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_suspected</td>\n",
" <td>10</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_confirmed</td>\n",
" <td>11</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-13</td>\n",
" <td>new_suspected</td>\n",
" <td>3</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-08-13</td>\n",
" <td>new_confirmed</td>\n",
" <td>15</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-08-14</td>\n",
" <td>new_suspected</td>\n",
" <td>0</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date variable totals country\n",
"0 2014-08-12 new_suspected 10 sl\n",
"1 2014-08-12 new_confirmed 11 sl\n",
"2 2014-08-13 new_suspected 3 sl\n",
"3 2014-08-13 new_confirmed 15 sl\n",
"4 2014-08-14 new_suspected 0 sl"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_data = pd.concat(datasets).reset_index(drop=True)\n",
"all_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Reindexing** allows users to manipulate the data labels in a DataFrame. It forces a DataFrame to conform to the new index, and optionally, fill in missing data if requested.\n",
"\n",
"A simple use of `reindex` is to alter the order of the rows. For example, records are currently ordered first by country then by day, since this is the order in which they were iterated over and imported. We might arbitrarily want to reverse the order, which is performed by passing the appropriate index values to `reindex`."
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>variable</th>\n",
" <th>totals</th>\n",
" <th>country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>636</th>\n",
" <td>2014-12-09</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>2946</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>635</th>\n",
" <td>2014-12-09</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>1801</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>634</th>\n",
" <td>2014-12-09</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>3050</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>633</th>\n",
" <td>2014-12-08</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>2927</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>632</th>\n",
" <td>2014-12-08</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>1805</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>631</th>\n",
" <td>2014-12-08</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>3054</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>630</th>\n",
" <td>2014-12-07</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>2869</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>629</th>\n",
" <td>2014-12-07</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>1829</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>628</th>\n",
" <td>2014-12-07</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>3067</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>627</th>\n",
" <td>2014-12-06</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>2869</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>626</th>\n",
" <td>2014-12-06</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>1810</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>625</th>\n",
" <td>2014-12-06</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>3056</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>624</th>\n",
" <td>2014-12-05</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>2867</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623</th>\n",
" <td>2014-12-05</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>1808</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>622</th>\n",
" <td>2014-12-05</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>3056</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>621</th>\n",
" <td>2014-12-04</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>2867</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>620</th>\n",
" <td>2014-12-04</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>1800</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>619</th>\n",
" <td>2014-12-04</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>3054</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>618</th>\n",
" <td>2014-12-02</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>9</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>617</th>\n",
" <td>2014-12-02</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>10</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>616</th>\n",
" <td>2014-12-02</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>18</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>615</th>\n",
" <td>2014-12-02</td>\n",
" <td>Newly Reported Cases in HCW</td>\n",
" <td>1</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614</th>\n",
" <td>2014-12-01</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>1</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613</th>\n",
" <td>2014-12-01</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>9</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>612</th>\n",
" <td>2014-12-01</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>25</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>611</th>\n",
" <td>2014-11-30</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>10</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>610</th>\n",
" <td>2014-11-30</td>\n",
" <td>Newly Reported Cases in HCW</td>\n",
" <td>1</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609</th>\n",
" <td>2014-11-29</td>\n",
" <td>New case/s (confirmed)</td>\n",
" <td>10</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>608</th>\n",
" <td>2014-11-29</td>\n",
" <td>New Case/s (Probable)</td>\n",
" <td>4</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>607</th>\n",
" <td>2014-11-29</td>\n",
" <td>New Case/s (Suspected)</td>\n",
" <td>7</td>\n",
" <td>liberia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2014-08-27</td>\n",
" <td>new_confirmed</td>\n",
" <td>27</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2014-08-27</td>\n",
" <td>new_suspected</td>\n",
" <td>3</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2014-08-25</td>\n",
" <td>new_confirmed</td>\n",
" <td>20</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2014-08-25</td>\n",
" <td>new_suspected</td>\n",
" <td>5</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2014-08-24</td>\n",
" <td>new_confirmed</td>\n",
" <td>31</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2014-08-24</td>\n",
" <td>new_suspected</td>\n",
" <td>0</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2014-08-23</td>\n",
" <td>new_confirmed</td>\n",
" <td>23</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2014-08-23</td>\n",
" <td>new_suspected</td>\n",
" <td>4</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2014-08-22</td>\n",
" <td>new_confirmed</td>\n",
" <td>56</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2014-08-22</td>\n",
" <td>new_suspected</td>\n",
" <td>1</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2014-08-21</td>\n",
" <td>new_confirmed</td>\n",
" <td>9</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2014-08-21</td>\n",
" <td>new_suspected</td>\n",
" <td>0</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2014-08-20</td>\n",
" <td>new_confirmed</td>\n",
" <td>4</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2014-08-20</td>\n",
" <td>new_suspected</td>\n",
" <td>1</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2014-08-19</td>\n",
" <td>new_confirmed</td>\n",
" <td>9</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2014-08-19</td>\n",
" <td>new_suspected</td>\n",
" <td>16</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2014-08-18</td>\n",
" <td>new_confirmed</td>\n",
" <td>5</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2014-08-18</td>\n",
" <td>new_suspected</td>\n",
" <td>40</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2014-08-17</td>\n",
" <td>new_confirmed</td>\n",
" <td>2</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2014-08-17</td>\n",
" <td>new_suspected</td>\n",
" <td>1</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2014-08-16</td>\n",
" <td>new_confirmed</td>\n",
" <td>18</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2014-08-16</td>\n",
" <td>new_suspected</td>\n",
" <td>3</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2014-08-15</td>\n",
" <td>new_confirmed</td>\n",
" <td>10</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2014-08-15</td>\n",
" <td>new_suspected</td>\n",
" <td>6</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2014-08-14</td>\n",
" <td>new_confirmed</td>\n",
" <td>13</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-08-14</td>\n",
" <td>new_suspected</td>\n",
" <td>0</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-08-13</td>\n",
" <td>new_confirmed</td>\n",
" <td>15</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-13</td>\n",
" <td>new_suspected</td>\n",
" <td>3</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_confirmed</td>\n",
" <td>11</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-08-12</td>\n",
" <td>new_suspected</td>\n",
" <td>10</td>\n",
" <td>sl</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>637 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" date variable totals country\n",
"636 2014-12-09 New case/s (confirmed) 2946 liberia\n",
"635 2014-12-09 New Case/s (Probable) 1801 liberia\n",
"634 2014-12-09 New Case/s (Suspected) 3050 liberia\n",
"633 2014-12-08 New case/s (confirmed) 2927 liberia\n",
"632 2014-12-08 New Case/s (Probable) 1805 liberia\n",
"631 2014-12-08 New Case/s (Suspected) 3054 liberia\n",
"630 2014-12-07 New case/s (confirmed) 2869 liberia\n",
"629 2014-12-07 New Case/s (Probable) 1829 liberia\n",
"628 2014-12-07 New Case/s (Suspected) 3067 liberia\n",
"627 2014-12-06 New case/s (confirmed) 2869 liberia\n",
"626 2014-12-06 New Case/s (Probable) 1810 liberia\n",
"625 2014-12-06 New Case/s (Suspected) 3056 liberia\n",
"624 2014-12-05 New case/s (confirmed) 2867 liberia\n",
"623 2014-12-05 New Case/s (Probable) 1808 liberia\n",
"622 2014-12-05 New Case/s (Suspected) 3056 liberia\n",
"621 2014-12-04 New case/s (confirmed) 2867 liberia\n",
"620 2014-12-04 New Case/s (Probable) 1800 liberia\n",
"619 2014-12-04 New Case/s (Suspected) 3054 liberia\n",
"618 2014-12-02 New case/s (confirmed) 9 liberia\n",
"617 2014-12-02 New Case/s (Probable) 10 liberia\n",
"616 2014-12-02 New Case/s (Suspected) 18 liberia\n",
"615 2014-12-02 Newly Reported Cases in HCW 1 liberia\n",
"614 2014-12-01 New case/s (confirmed) 1 liberia\n",
"613 2014-12-01 New Case/s (Probable) 9 liberia\n",
"612 2014-12-01 New Case/s (Suspected) 25 liberia\n",
"611 2014-11-30 New case/s (confirmed) 10 liberia\n",
"610 2014-11-30 Newly Reported Cases in HCW 1 liberia\n",
"609 2014-11-29 New case/s (confirmed) 10 liberia\n",
"608 2014-11-29 New Case/s (Probable) 4 liberia\n",
"607 2014-11-29 New Case/s (Suspected) 7 liberia\n",
".. ... ... ... ...\n",
"29 2014-08-27 new_confirmed 27 sl\n",
"28 2014-08-27 new_suspected 3 sl\n",
"27 2014-08-25 new_confirmed 20 sl\n",
"26 2014-08-25 new_suspected 5 sl\n",
"25 2014-08-24 new_confirmed 31 sl\n",
"24 2014-08-24 new_suspected 0 sl\n",
"23 2014-08-23 new_confirmed 23 sl\n",
"22 2014-08-23 new_suspected 4 sl\n",
"21 2014-08-22 new_confirmed 56 sl\n",
"20 2014-08-22 new_suspected 1 sl\n",
"19 2014-08-21 new_confirmed 9 sl\n",
"18 2014-08-21 new_suspected 0 sl\n",
"17 2014-08-20 new_confirmed 4 sl\n",
"16 2014-08-20 new_suspected 1 sl\n",
"15 2014-08-19 new_confirmed 9 sl\n",
"14 2014-08-19 new_suspected 16 sl\n",
"13 2014-08-18 new_confirmed 5 sl\n",
"12 2014-08-18 new_suspected 40 sl\n",
"11 2014-08-17 new_confirmed 2 sl\n",
"10 2014-08-17 new_suspected 1 sl\n",
"9 2014-08-16 new_confirmed 18 sl\n",
"8 2014-08-16 new_suspected 3 sl\n",
"7 2014-08-15 new_confirmed 10 sl\n",
"6 2014-08-15 new_suspected 6 sl\n",
"5 2014-08-14 new_confirmed 13 sl\n",
"4 2014-08-14 new_suspected 0 sl\n",
"3 2014-08-13 new_confirmed 15 sl\n",
"2 2014-08-13 new_suspected 3 sl\n",
"1 2014-08-12 new_confirmed 11 sl\n",
"0 2014-08-12 new_suspected 10 sl\n",
"\n",
"[637 rows x 4 columns]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_data.reindex(all_data.index[::-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the reindexing operation is not performed \"in-place\"; the original `DataFrame` remains as it was, and the method returns a copy of the `DataFrame` with the new index. This is a common trait for pandas, and is a Good Thing.\n",
"\n",
"We may also wish to reorder the columns this way."
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>country</th>\n",
" <th>variable</th>\n",
" <th>totals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-08-12</td>\n",
" <td>sl</td>\n",
" <td>new_suspected</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-08-12</td>\n",
" <td>sl</td>\n",
" <td>new_confirmed</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-08-13</td>\n",
" <td>sl</td>\n",
" <td>new_suspected</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-08-13</td>\n",
" <td>sl</td>\n",
" <td>new_confirmed</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-08-14</td>\n",
" <td>sl</td>\n",
" <td>new_suspected</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date country variable totals\n",
"0 2014-08-12 sl new_suspected 10\n",
"1 2014-08-12 sl new_confirmed 11\n",
"2 2014-08-13 sl new_suspected 3\n",
"3 2014-08-13 sl new_confirmed 15\n",
"4 2014-08-14 sl new_suspected 0"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_data.reindex(columns=['date', 'country', 'variable', 'totals']).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Group by operations\n",
"\n",
"One of pandas' most powerful features is the ability to perform operations on subgroups of a `DataFrame`. These so-called **group by** operations defines subunits of the dataset according to the values of one or more variabes in the `DataFrame`.\n",
"\n",
"For this data, we want to sum the new case counts by day and country; so we pass these two column names to the `groupby` method, then sum the `totals` column accross them."
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"country date \n",
"guinea 2014-08-04 11\n",
" 2014-08-26 28\n",
" 2014-08-27 22\n",
" 2014-08-30 24\n",
" 2014-08-31 46\n",
" 2014-09-02 25\n",
" 2014-09-04 30\n",
" 2014-09-07 18\n",
" 2014-09-08 18\n",
" 2014-09-09 16\n",
"Name: totals, dtype: int64"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_data_grouped = all_data.groupby(['country', 'date'])\n",
"daily_cases = all_data_grouped['totals'].sum()\n",
"daily_cases.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resulting series retains a hierarchical index from the group by operation. Hence, we can index out the counts for a given country on a particular day by indexing with the appropriate tuple."
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"liberia 78\n",
"2014-09-02 00:00:00 78\n",
"Name: totals, dtype: int64"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_cases[('liberia', '2014-09-02')]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One issue with the data we have extracted is that there appear to be serious **outliers** in the Liberian counts. The values are much too large to be a daily count, even during a serious outbreak."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"country date \n",
"liberia 2014-12-09 7797\n",
" 2014-12-08 7786\n",
" 2014-12-07 7765\n",
" 2014-12-06 7735\n",
" 2014-12-05 7731\n",
" 2014-12-04 7721\n",
" 2014-10-17 167\n",
"sl 2014-11-08 131\n",
" 2014-11-20 130\n",
" 2014-11-10 126\n",
"Name: totals, dtype: int64"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_cases.sort(ascending=False)\n",
"daily_cases.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can filter these outliers using an appropriate threshold."
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"daily_cases = daily_cases[daily_cases<200]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting\n",
"\n",
"pandas data structures have high-level methods for creating a variety of plots, which tends to be easier than generating the corresponding plot using matplotlib. \n",
"\n",
"For example, we may want to create a plot of the cumulative cases for each of the three countries. The easiest way to do this is to remove the hierarchical index, and create a `DataFrame` of three columns, which will result in three lines when plotted.\n",
"\n",
"First, call `unstack` to remove the hierarichical index:"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>date</th>\n",
" <th>2014-06-16 00:00:00</th>\n",
" <th>2014-06-17 00:00:00</th>\n",
" <th>2014-06-22 00:00:00</th>\n",
" <th>2014-06-24 00:00:00</th>\n",
" <th>2014-06-25 00:00:00</th>\n",
" <th>2014-06-28 00:00:00</th>\n",
" <th>2014-06-29 00:00:00</th>\n",
" <th>2014-07-01 00:00:00</th>\n",
" <th>2014-07-02 00:00:00</th>\n",
" <th>2014-07-03 00:00:00</th>\n",
" <th>...</th>\n",
" <th>2014-11-24 00:00:00</th>\n",
" <th>2014-11-26 00:00:00</th>\n",
" <th>2014-11-27 00:00:00</th>\n",
" <th>2014-11-28 00:00:00</th>\n",
" <th>2014-11-29 00:00:00</th>\n",
" <th>2014-11-30 00:00:00</th>\n",
" <th>2014-12-01 00:00:00</th>\n",
" <th>2014-12-02 00:00:00</th>\n",
" <th>2014-12-04 00:00:00</th>\n",
" <th>2014-12-05 00:00:00</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>guinea</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>liberia</th>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>25</td>\n",
" <td>31</td>\n",
" <td>46</td>\n",
" <td>7</td>\n",
" <td>21</td>\n",
" <td>11</td>\n",
" <td>35</td>\n",
" <td>38</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sl</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>115</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>110</td>\n",
" <td>88</td>\n",
" <td>NaN</td>\n",
" <td>86</td>\n",
" <td>NaN</td>\n",
" <td>41</td>\n",
" <td>78</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 128 columns</p>\n",
"</div>"
],
"text/plain": [
"date 2014-06-16 2014-06-17 2014-06-22 2014-06-24 2014-06-25 \\\n",
"country \n",
"guinea NaN NaN NaN NaN NaN \n",
"liberia 4 2 14 6 9 \n",
"sl NaN NaN NaN NaN NaN \n",
"\n",
"date 2014-06-28 2014-06-29 2014-07-01 2014-07-02 2014-07-03 \\\n",
"country \n",
"guinea NaN NaN NaN NaN NaN \n",
"liberia 10 3 4 4 4 \n",
"sl NaN NaN NaN NaN NaN \n",
"\n",
"date ... 2014-11-24 2014-11-26 2014-11-27 2014-11-28 \\\n",
"country ... \n",
"guinea ... NaN NaN NaN NaN \n",
"liberia ... 25 31 46 7 \n",
"sl ... 115 NaN NaN 110 \n",
"\n",
"date 2014-11-29 2014-11-30 2014-12-01 2014-12-02 2014-12-04 \\\n",
"country \n",
"guinea NaN NaN NaN NaN NaN \n",
"liberia 21 11 35 38 NaN \n",
"sl 88 NaN 86 NaN 41 \n",
"\n",
"date 2014-12-05 \n",
"country \n",
"guinea NaN \n",
"liberia NaN \n",
"sl 78 \n",
"\n",
"[3 rows x 128 columns]"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_cases.unstack().head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, transpose the resulting `DataFrame` to swap the rows and columns."
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>country</th>\n",
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" <tr>\n",
" <th>2014-06-24</th>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-06-25</th>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"country guinea liberia sl\n",
"date \n",
"2014-06-16 NaN 4 NaN\n",
"2014-06-17 NaN 2 NaN\n",
"2014-06-22 NaN 14 NaN\n",
"2014-06-24 NaN 6 NaN\n",
"2014-06-25 NaN 9 NaN"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_cases.unstack().T.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we have missing values for some dates, we will assume that the counts for those days were zero (the actual counts for that day may have bee included in the next reporting day's data)."
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>country</th>\n",
" <th>guinea</th>\n",
" <th>liberia</th>\n",
" <th>sl</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-06-16</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-06-17</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-06-22</th>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-06-24</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-06-25</th>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"country guinea liberia sl\n",
"date \n",
"2014-06-16 0 4 0\n",
"2014-06-17 0 2 0\n",
"2014-06-22 0 14 0\n",
"2014-06-24 0 6 0\n",
"2014-06-25 0 9 0"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_cases.unstack().T.fillna(0).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, calculate the cumulative sum for all the columns, and generate a line plot, which we get by default."
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110235dd8>"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1102783c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"daily_cases.unstack().T.fillna(0).cumsum().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resampling\n",
"\n",
"An alternative to filling days without case reports with zeros is to aggregate the data at a coarser time scale. New cases are often reported by week; we can use the `resample` method to summarize the data into weekly values."
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>country</th>\n",
" <th>guinea</th>\n",
" <th>liberia</th>\n",
" <th>sl</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-06-22</th>\n",
" <td>NaN</td>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-06-29</th>\n",
" <td>NaN</td>\n",
" <td>28</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-06</th>\n",
" <td>NaN</td>\n",
" <td>12</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-13</th>\n",
" <td>NaN</td>\n",
" <td>18</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-20</th>\n",
" <td>NaN</td>\n",
" <td>15</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-27</th>\n",
" <td>NaN</td>\n",
" <td>56</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-08-03</th>\n",
" <td>NaN</td>\n",
" <td>11</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-08-10</th>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-08-17</th>\n",
" <td>NaN</td>\n",
" <td>106</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-08-24</th>\n",
" <td>NaN</td>\n",
" <td>122</td>\n",
" <td>199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-08-31</th>\n",
" <td>120</td>\n",
" <td>94</td>\n",
" <td>189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-07</th>\n",
" <td>73</td>\n",
" <td>402</td>\n",
" <td>141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-14</th>\n",
" <td>83</td>\n",
" <td>436</td>\n",
" <td>216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-21</th>\n",
" <td>55</td>\n",
" <td>404</td>\n",
" <td>301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-28</th>\n",
" <td>95</td>\n",
" <td>275</td>\n",
" <td>408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-05</th>\n",
" <td>51</td>\n",
" <td>207</td>\n",
" <td>368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-12</th>\n",
" <td>NaN</td>\n",
" <td>248</td>\n",
" <td>409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-19</th>\n",
" <td>NaN</td>\n",
" <td>330</td>\n",
" <td>496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-26</th>\n",
" <td>NaN</td>\n",
" <td>220</td>\n",
" <td>452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-02</th>\n",
" <td>NaN</td>\n",
" <td>228</td>\n",
" <td>499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-09</th>\n",
" <td>NaN</td>\n",
" <td>54</td>\n",
" <td>236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-16</th>\n",
" <td>NaN</td>\n",
" <td>60</td>\n",
" <td>449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-23</th>\n",
" <td>NaN</td>\n",
" <td>120</td>\n",
" <td>434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-30</th>\n",
" <td>NaN</td>\n",
" <td>141</td>\n",
" <td>313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-07</th>\n",
" <td>NaN</td>\n",
" <td>73</td>\n",
" <td>205</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"country guinea liberia sl\n",
"date \n",
"2014-06-22 NaN 20 NaN\n",
"2014-06-29 NaN 28 NaN\n",
"2014-07-06 NaN 12 NaN\n",
"2014-07-13 NaN 18 NaN\n",
"2014-07-20 NaN 15 NaN\n",
"2014-07-27 NaN 56 NaN\n",
"2014-08-03 NaN 11 NaN\n",
"2014-08-10 11 10 NaN\n",
"2014-08-17 NaN 106 92\n",
"2014-08-24 NaN 122 199\n",
"2014-08-31 120 94 189\n",
"2014-09-07 73 402 141\n",
"2014-09-14 83 436 216\n",
"2014-09-21 55 404 301\n",
"2014-09-28 95 275 408\n",
"2014-10-05 51 207 368\n",
"2014-10-12 NaN 248 409\n",
"2014-10-19 NaN 330 496\n",
"2014-10-26 NaN 220 452\n",
"2014-11-02 NaN 228 499\n",
"2014-11-09 NaN 54 236\n",
"2014-11-16 NaN 60 449\n",
"2014-11-23 NaN 120 434\n",
"2014-11-30 NaN 141 313\n",
"2014-12-07 NaN 73 205"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weekly_cases = daily_cases.unstack().T.resample('W', how='sum')\n",
"weekly_cases"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1103cf7f0>"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x110272048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"weekly_cases.cumsum().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Writing Data to Files\n",
"\n",
"As well as being able to read several data input formats, pandas can also export data to a variety of storage formats. We will bring your attention to just one of these, but the usage is similar across formats."
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"medals_data.to_csv(\"../data/medals.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `to_csv` method writes a `DataFrame` to a comma-separated values (csv) file. You can specify custom delimiters (via `sep` argument), how missing values are written (via `na_rep` argument), whether the index is writen (via `index` argument), whether the header is included (via `header` argument), among other options."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing data\n",
"\n",
"The occurence of missing data is so prevalent that it pays to use tools like pandas, which seamlessly integrates missing data handling so that it can be dealt with easily, and in the manner required by the analysis at hand.\n",
"\n",
"Missing data are represented in `Series` and `DataFrame` objects by the `NaN` floating point value. However, `None` is also treated as missing, since it is commonly used as such in other contexts (*e.g.* NumPy)."
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Taxon,Patient,Tissue,Stool\r\n",
"Firmicutes,1,632,305\r\n",
"Firmicutes,2,136,4182\r\n",
"Firmicutes,3,,703\r\n",
"Firmicutes,4,408,3946\r\n",
"Firmicutes,5,831,8605\r\n",
"Firmicutes,6,693,50\r\n",
"Firmicutes,7,718,717\r\n",
"Firmicutes,8,173,33\r\n",
"Firmicutes,9,228,NA\r\n",
"Firmicutes,10,162,3196\r\n",
"Firmicutes,11,372,-99999\r\n",
"Firmicutes,12,4255,4361\r\n",
"Firmicutes,13,107,1667\r\n",
"Firmicutes,14,?,223\r\n",
"Firmicutes,15,281,2377\r\n",
"Proteobacteria,1,1638,3886\r\n",
"Proteobacteria,2,2469,1821\r\n",
"Proteobacteria,3,839,661\r\n",
"Proteobacteria,4,4414,18\r\n"
]
}
],
"source": [
"!head -n 20 ../data/microbiome/microbiome_missing.csv"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
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" <td>3196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Firmicutes</td>\n",
" <td>11</td>\n",
" <td>372</td>\n",
" <td>-99999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
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" <td>12</td>\n",
" <td>4255</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>107</td>\n",
" <td>1667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
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" <td>223</td>\n",
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" <tr>\n",
" <th>14</th>\n",
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" <td>15</td>\n",
" <td>281</td>\n",
" <td>2377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Proteobacteria</td>\n",
" <td>1</td>\n",
" <td>1638</td>\n",
" <td>3886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Proteobacteria</td>\n",
" <td>2</td>\n",
" <td>2469</td>\n",
" <td>1821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Proteobacteria</td>\n",
" <td>3</td>\n",
" <td>839</td>\n",
" <td>661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Proteobacteria</td>\n",
" <td>4</td>\n",
" <td>4414</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Proteobacteria</td>\n",
" <td>5</td>\n",
" <td>12044</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 NaN 703\n",
"3 Firmicutes 4 408 3946\n",
"4 Firmicutes 5 831 8605\n",
"5 Firmicutes 6 693 50\n",
"6 Firmicutes 7 718 717\n",
"7 Firmicutes 8 173 33\n",
"8 Firmicutes 9 228 NaN\n",
"9 Firmicutes 10 162 3196\n",
"10 Firmicutes 11 372 -99999\n",
"11 Firmicutes 12 4255 4361\n",
"12 Firmicutes 13 107 1667\n",
"13 Firmicutes 14 ? 223\n",
"14 Firmicutes 15 281 2377\n",
"15 Proteobacteria 1 1638 3886\n",
"16 Proteobacteria 2 2469 1821\n",
"17 Proteobacteria 3 839 661\n",
"18 Proteobacteria 4 4414 18\n",
"19 Proteobacteria 5 12044 83"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv(\"../data/microbiome/microbiome_missing.csv\").head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above, pandas recognized `NA` and an empty field as missing data."
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 False False False False\n",
"1 False False False False\n",
"2 False False True False\n",
"3 False False False False\n",
"4 False False False False\n",
"5 False False False False\n",
"6 False False False False\n",
"7 False False False False\n",
"8 False False False True\n",
"9 False False False False\n",
"10 False False False False\n",
"11 False False False False\n",
"12 False False False False\n",
"13 False False False False\n",
"14 False False False False\n",
"15 False False False False\n",
"16 False False False False\n",
"17 False False False False\n",
"18 False False False False\n",
"19 False False False False"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(pd.read_csv(\"../data/microbiome/microbiome_missing.csv\")).head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately, there will sometimes be inconsistency with the conventions for missing data. In this example, there is a question mark \"?\" and a large negative number where there should have been a positive integer. We can specify additional symbols with the `na_values` argument:\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Firmicutes</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Firmicutes</td>\n",
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" <th>2</th>\n",
" <td>Firmicutes</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>4</td>\n",
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" <td>3946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Firmicutes</td>\n",
" <td>5</td>\n",
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" </tr>\n",
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" <th>5</th>\n",
" <td>Firmicutes</td>\n",
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" </tr>\n",
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" <th>6</th>\n",
" <td>Firmicutes</td>\n",
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" <td>718</td>\n",
" <td>717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Firmicutes</td>\n",
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" <td>173</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Firmicutes</td>\n",
" <td>9</td>\n",
" <td>228</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Firmicutes</td>\n",
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" <td>162</td>\n",
" <td>3196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Firmicutes</td>\n",
" <td>11</td>\n",
" <td>372</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Firmicutes</td>\n",
" <td>12</td>\n",
" <td>4255</td>\n",
" <td>4361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Firmicutes</td>\n",
" <td>13</td>\n",
" <td>107</td>\n",
" <td>1667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Firmicutes</td>\n",
" <td>14</td>\n",
" <td>NaN</td>\n",
" <td>223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Firmicutes</td>\n",
" <td>15</td>\n",
" <td>281</td>\n",
" <td>2377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Proteobacteria</td>\n",
" <td>1</td>\n",
" <td>1638</td>\n",
" <td>3886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Proteobacteria</td>\n",
" <td>2</td>\n",
" <td>2469</td>\n",
" <td>1821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Proteobacteria</td>\n",
" <td>3</td>\n",
" <td>839</td>\n",
" <td>661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Proteobacteria</td>\n",
" <td>4</td>\n",
" <td>4414</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Proteobacteria</td>\n",
" <td>5</td>\n",
" <td>12044</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 NaN 703\n",
"3 Firmicutes 4 408 3946\n",
"4 Firmicutes 5 831 8605\n",
"5 Firmicutes 6 693 50\n",
"6 Firmicutes 7 718 717\n",
"7 Firmicutes 8 173 33\n",
"8 Firmicutes 9 228 NaN\n",
"9 Firmicutes 10 162 3196\n",
"10 Firmicutes 11 372 NaN\n",
"11 Firmicutes 12 4255 4361\n",
"12 Firmicutes 13 107 1667\n",
"13 Firmicutes 14 NaN 223\n",
"14 Firmicutes 15 281 2377\n",
"15 Proteobacteria 1 1638 3886\n",
"16 Proteobacteria 2 2469 1821\n",
"17 Proteobacteria 3 839 661\n",
"18 Proteobacteria 4 4414 18\n",
"19 Proteobacteria 5 12044 83"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_sample = pd.read_csv(\"../data/microbiome/microbiome_missing.csv\", \n",
" na_values=['?', -99999], nrows=20)\n",
"\n",
"missing_sample"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These can be specified on a column-wise basis using an appropriate dict as the argument for `na_values`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, `dropna` drops entire rows in which one or more values are missing."
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" <td>4</td>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
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" <th>4</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Firmicutes</td>\n",
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" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Firmicutes</td>\n",
" <td>7</td>\n",
" <td>718</td>\n",
" <td>717</td>\n",
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" <tr>\n",
" <th>7</th>\n",
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" <td>33</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>Firmicutes</td>\n",
" <td>10</td>\n",
" <td>162</td>\n",
" <td>3196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Firmicutes</td>\n",
" <td>12</td>\n",
" <td>4255</td>\n",
" <td>4361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Firmicutes</td>\n",
" <td>13</td>\n",
" <td>107</td>\n",
" <td>1667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Firmicutes</td>\n",
" <td>15</td>\n",
" <td>281</td>\n",
" <td>2377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Proteobacteria</td>\n",
" <td>1</td>\n",
" <td>1638</td>\n",
" <td>3886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Proteobacteria</td>\n",
" <td>2</td>\n",
" <td>2469</td>\n",
" <td>1821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Proteobacteria</td>\n",
" <td>3</td>\n",
" <td>839</td>\n",
" <td>661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Proteobacteria</td>\n",
" <td>4</td>\n",
" <td>4414</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Proteobacteria</td>\n",
" <td>5</td>\n",
" <td>12044</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"3 Firmicutes 4 408 3946\n",
"4 Firmicutes 5 831 8605\n",
"5 Firmicutes 6 693 50\n",
"6 Firmicutes 7 718 717\n",
"7 Firmicutes 8 173 33\n",
"9 Firmicutes 10 162 3196\n",
"11 Firmicutes 12 4255 4361\n",
"12 Firmicutes 13 107 1667\n",
"14 Firmicutes 15 281 2377\n",
"15 Proteobacteria 1 1638 3886\n",
"16 Proteobacteria 2 2469 1821\n",
"17 Proteobacteria 3 839 661\n",
"18 Proteobacteria 4 4414 18\n",
"19 Proteobacteria 5 12044 83"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_sample.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to drop missing values column-wise instead of row-wise, we use `axis=1`."
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
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" <th>11</th>\n",
" <td>Firmicutes</td>\n",
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" <th>12</th>\n",
" <td>Firmicutes</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Firmicutes</td>\n",
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" <tr>\n",
" <th>14</th>\n",
" <td>Firmicutes</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Proteobacteria</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Proteobacteria</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Proteobacteria</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Proteobacteria</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Proteobacteria</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient\n",
"0 Firmicutes 1\n",
"1 Firmicutes 2\n",
"2 Firmicutes 3\n",
"3 Firmicutes 4\n",
"4 Firmicutes 5\n",
"5 Firmicutes 6\n",
"6 Firmicutes 7\n",
"7 Firmicutes 8\n",
"8 Firmicutes 9\n",
"9 Firmicutes 10\n",
"10 Firmicutes 11\n",
"11 Firmicutes 12\n",
"12 Firmicutes 13\n",
"13 Firmicutes 14\n",
"14 Firmicutes 15\n",
"15 Proteobacteria 1\n",
"16 Proteobacteria 2\n",
"17 Proteobacteria 3\n",
"18 Proteobacteria 4\n",
"19 Proteobacteria 5"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_sample.dropna(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rather than omitting missing data from an analysis, in some cases it may be suitable to fill the missing value in, either with a default value (such as zero), a sentinel value, or a value that is either imputed or carried forward/backward from similar data points. We can do this programmatically in pandas with the `fillna` argument."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Taxon</th>\n",
" <th>Patient</th>\n",
" <th>Tissue</th>\n",
" <th>Stool</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>136</td>\n",
" <td>4182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Firmicutes</td>\n",
" <td>3</td>\n",
" <td>-999</td>\n",
" <td>703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Firmicutes</td>\n",
" <td>4</td>\n",
" <td>408</td>\n",
" <td>3946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Firmicutes</td>\n",
" <td>5</td>\n",
" <td>831</td>\n",
" <td>8605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Firmicutes</td>\n",
" <td>6</td>\n",
" <td>693</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Firmicutes</td>\n",
" <td>7</td>\n",
" <td>718</td>\n",
" <td>717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Firmicutes</td>\n",
" <td>8</td>\n",
" <td>173</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Firmicutes</td>\n",
" <td>9</td>\n",
" <td>228</td>\n",
" <td>-999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Firmicutes</td>\n",
" <td>10</td>\n",
" <td>162</td>\n",
" <td>3196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Firmicutes</td>\n",
" <td>11</td>\n",
" <td>372</td>\n",
" <td>-999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Firmicutes</td>\n",
" <td>12</td>\n",
" <td>4255</td>\n",
" <td>4361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Firmicutes</td>\n",
" <td>13</td>\n",
" <td>107</td>\n",
" <td>1667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Firmicutes</td>\n",
" <td>14</td>\n",
" <td>-999</td>\n",
" <td>223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Firmicutes</td>\n",
" <td>15</td>\n",
" <td>281</td>\n",
" <td>2377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Proteobacteria</td>\n",
" <td>1</td>\n",
" <td>1638</td>\n",
" <td>3886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Proteobacteria</td>\n",
" <td>2</td>\n",
" <td>2469</td>\n",
" <td>1821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Proteobacteria</td>\n",
" <td>3</td>\n",
" <td>839</td>\n",
" <td>661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Proteobacteria</td>\n",
" <td>4</td>\n",
" <td>4414</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Proteobacteria</td>\n",
" <td>5</td>\n",
" <td>12044</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Taxon Patient Tissue Stool\n",
"0 Firmicutes 1 632 305\n",
"1 Firmicutes 2 136 4182\n",
"2 Firmicutes 3 -999 703\n",
"3 Firmicutes 4 408 3946\n",
"4 Firmicutes 5 831 8605\n",
"5 Firmicutes 6 693 50\n",
"6 Firmicutes 7 718 717\n",
"7 Firmicutes 8 173 33\n",
"8 Firmicutes 9 228 -999\n",
"9 Firmicutes 10 162 3196\n",
"10 Firmicutes 11 372 -999\n",
"11 Firmicutes 12 4255 4361\n",
"12 Firmicutes 13 107 1667\n",
"13 Firmicutes 14 -999 223\n",
"14 Firmicutes 15 281 2377\n",
"15 Proteobacteria 1 1638 3886\n",
"16 Proteobacteria 2 2469 1821\n",
"17 Proteobacteria 3 839 661\n",
"18 Proteobacteria 4 4414 18\n",
"19 Proteobacteria 5 12044 83"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_sample.fillna(-999)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sentinel values are useful in pandas because missing values are treated as floats, so it is impossible to use explicit missing values with integer columns. Using some large (positive or negative) integer as a sentinel value will allow the column to be integer typed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Mean imputation\n",
"\n",
"Fill the missing values in `missing_sample` with the mean count from the corresponding species across patients."
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Write your answer here "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
dikien/break-captcha | Step4_Generate_Clssifier_sknn_increase_units.ipynb | 1 | 509346 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.externals import joblib\n",
"from sklearn.metrics import accuracy_score\n",
"from sknn.mlp import Classifier, Layer\n",
"from sknn import ae, mlp\n",
"import numpy as np\n",
"import pandas as pd\n",
"from time import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pd.set_option('display.precision', 3) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"escape time : 0.086 s\n",
"the shape of training set 5400 rows, 784 columns\n",
"the shape of test set 600 rows, 784 columns\n",
"the range of training set : 0.0 ~ 0.999996\n",
"the range of test set : 0.0 ~ 0.999996\n"
]
}
],
"source": [
"features = joblib.load(\"./mldata/features_1200.mat\")\n",
"labels = joblib.load(\"./mldata/lables_1200.mat\")\n",
"\n",
"features = np.array(features, 'int16')\n",
"labels = np.array(labels, 'int')\n",
"\n",
"t0 = time()\n",
"def scale(X, eps = 0.001):\n",
" # scale the data points s.t the columns of the feature space\n",
" # (i.e the predictors) are within the range [0, 1]\n",
" return (X - np.min(X, axis = 0)) / (np.max(X, axis = 0) + eps)\n",
"\n",
"features = features.astype(\"float32\")\n",
"features = scale(features)\n",
"\n",
"print \"escape time : \", round(time()-t0, 3), \"s\"\n",
"\n",
"# scale the data to the range [0, 1] and then construct the training\n",
"# and testing splits\n",
"(trainX, testX, trainY, testY) = train_test_split(features, labels, test_size = 0.1)\n",
"\n",
"print \"the shape of training set %s rows, %s columns\" %(trainX.shape[0], trainX.shape[1])\n",
"print \"the shape of test set %s rows, %s columns\" %(testX.shape[0], testX.shape[1])\n",
"print \"the range of training set : %s ~ %s\" %(trainX.min(),trainX.max())\n",
"print \"the range of test set : %s ~ %s\" %(testX.min(),testX.max())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of unit : 1\n",
"accuracy score : 0.445\n",
"escape time : 4.582 s\n",
"the number of unit : 2\n",
"accuracy score : 0.735\n",
"escape time : 3.281 s\n",
"the number of unit : 3\n",
"accuracy score : 0.805\n",
"escape time : 3.174 s\n",
"the number of unit : 4\n",
"accuracy score : 0.848333333333\n",
"escape time : 3.781 s\n",
"the number of unit : 5\n",
"accuracy score : 0.878333333333\n",
"escape time : 3.504 s\n",
"the number of unit : 6\n",
"accuracy score : 0.866666666667\n",
"escape time : 3.462 s\n",
"the number of unit : 7\n",
"accuracy score : 0.885\n",
"escape time : 3.5 s\n",
"the number of unit : 8\n",
"accuracy score : 0.885\n",
"escape time : 3.37 s\n",
"the number of unit : 9\n",
"accuracy score : 0.893333333333\n",
"escape time : 3.534 s\n",
"the number of unit : 10\n",
"accuracy score : 0.898333333333\n",
"escape time : 3.081 s\n",
"the number of unit : 11\n",
"accuracy score : 0.89\n",
"escape time : 3.434 s\n",
"the number of unit : 12\n",
"accuracy score : 0.885\n",
"escape time : 3.725 s\n",
"the number of unit : 13\n",
"accuracy score : 0.905\n",
"escape time : 3.9 s\n",
"the number of unit : 14\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.666 s\n",
"the number of unit : 15\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.359 s\n",
"the number of unit : 16\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.631 s\n",
"the number of unit : 17\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.329 s\n",
"the number of unit : 18\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.519 s\n",
"the number of unit : 19\n",
"accuracy score : 0.898333333333\n",
"escape time : 3.735 s\n",
"the number of unit : 20\n",
"accuracy score : 0.908333333333\n",
"escape time : 4.238 s\n",
"the number of unit : 21\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.542 s\n",
"the number of unit : 22\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.391 s\n",
"the number of unit : 23\n",
"accuracy score : 0.901666666667\n",
"escape time : 4.013 s\n",
"the number of unit : 24\n",
"accuracy score : 0.915\n",
"escape time : 3.652 s\n",
"the number of unit : 25\n",
"accuracy score : 0.9\n",
"escape time : 3.506 s\n",
"the number of unit : 26\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.553 s\n",
"the number of unit : 27\n",
"accuracy score : 0.91\n",
"escape time : 3.997 s\n",
"the number of unit : 28\n",
"accuracy score : 0.905\n",
"escape time : 4.024 s\n",
"the number of unit : 29\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.387 s\n",
"the number of unit : 30\n",
"accuracy score : 0.896666666667\n",
"escape time : 3.538 s\n",
"the number of unit : 31\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.362 s\n",
"the number of unit : 32\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.366 s\n",
"the number of unit : 33\n",
"accuracy score : 0.906666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.898333333333\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.908333333333\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.585 s\n",
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"accuracy score : 0.905\n",
"escape time : 3.444 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.596 s\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
"escape time : 3.653 s\n",
"the number of unit : 49\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.513 s\n",
"the number of unit : 50\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.793 s\n",
"the number of unit : 51\n",
"accuracy score : 0.895\n",
"escape time : 3.861 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.554 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.582 s\n",
"the number of unit : 54\n",
"accuracy score : 0.908333333333\n",
"escape time : 3.703 s\n",
"the number of unit : 55\n",
"accuracy score : 0.911666666667\n",
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"accuracy score : 0.905\n",
"escape time : 3.63 s\n",
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"accuracy score : 0.903333333333\n",
"escape time : 3.798 s\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.91\n",
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"accuracy score : 0.908333333333\n",
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"escape time : 3.773 s\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.748 s\n",
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"accuracy score : 0.896666666667\n",
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"escape time : 3.993 s\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.901666666667\n",
"escape time : 3.948 s\n",
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"accuracy score : 0.908333333333\n",
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"accuracy score : 0.908333333333\n",
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"accuracy score : 0.908333333333\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.908333333333\n",
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"the number of unit : 120\n",
"accuracy score : 0.898333333333\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.906666666667\n",
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"escape time : 10.125 s\n",
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"accuracy score : 0.908333333333\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.91\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
"escape time : 9.859 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 10.125 s\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.906666666667\n",
"escape time : 10.472 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 10.781 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 10.412 s\n",
"the number of unit : 452\n",
"accuracy score : 0.9\n",
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"the number of unit : 453\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.299 s\n",
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"accuracy score : 0.903333333333\n",
"escape time : 12.064 s\n",
"the number of unit : 455\n",
"accuracy score : 0.906666666667\n",
"escape time : 12.39 s\n",
"the number of unit : 456\n",
"accuracy score : 0.905\n",
"escape time : 11.425 s\n",
"the number of unit : 457\n",
"accuracy score : 0.903333333333\n",
"escape time : 11.464 s\n",
"the number of unit : 458\n",
"accuracy score : 0.905\n",
"escape time : 13.146 s\n",
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"accuracy score : 0.908333333333\n",
"escape time : 12.296 s\n",
"the number of unit : 460\n",
"accuracy score : 0.905\n",
"escape time : 13.627 s\n",
"the number of unit : 461\n",
"accuracy score : 0.91\n",
"escape time : 16.598 s\n",
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"accuracy score : 0.903333333333\n",
"escape time : 14.749 s\n",
"the number of unit : 463\n",
"accuracy score : 0.903333333333\n",
"escape time : 14.725 s\n",
"the number of unit : 464\n",
"accuracy score : 0.905\n",
"escape time : 21.925 s\n",
"the number of unit : 465\n",
"accuracy score : 0.901666666667\n",
"escape time : 19.215 s\n",
"the number of unit : 466\n",
"accuracy score : 0.901666666667\n",
"escape time : 14.791 s\n",
"the number of unit : 467\n",
"accuracy score : 0.901666666667\n",
"escape time : 14.321 s\n",
"the number of unit : 468\n",
"accuracy score : 0.903333333333\n",
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"accuracy score : 0.908333333333\n",
"escape time : 14.697 s\n",
"the number of unit : 470\n",
"accuracy score : 0.903333333333\n",
"escape time : 15.668 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 15.879 s\n",
"the number of unit : 472\n",
"accuracy score : 0.901666666667\n",
"escape time : 15.225 s\n",
"the number of unit : 473\n",
"accuracy score : 0.9\n",
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"accuracy score : 0.903333333333\n",
"escape time : 15.597 s\n",
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"accuracy score : 0.903333333333\n",
"escape time : 15.854 s\n",
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"accuracy score : 0.903333333333\n",
"escape time : 15.658 s\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.903333333333\n",
"escape time : 14.745 s\n",
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"accuracy score : 0.901666666667\n",
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"the number of unit : 485\n",
"accuracy score : 0.908333333333\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.901666666667\n",
"escape time : 13.45 s\n",
"the number of unit : 491\n",
"accuracy score : 0.903333333333\n",
"escape time : 13.543 s\n",
"the number of unit : 492\n",
"accuracy score : 0.906666666667\n",
"escape time : 13.913 s\n",
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"accuracy score : 0.908333333333\n",
"escape time : 13.679 s\n",
"the number of unit : 494\n",
"accuracy score : 0.903333333333\n",
"escape time : 13.745 s\n",
"the number of unit : 495\n",
"accuracy score : 0.906666666667\n",
"escape time : 14.137 s\n",
"the number of unit : 496\n",
"accuracy score : 0.906666666667\n",
"escape time : 13.729 s\n",
"the number of unit : 497\n",
"accuracy score : 0.905\n",
"escape time : 13.788 s\n",
"the number of unit : 498\n",
"accuracy score : 0.905\n",
"escape time : 14.382 s\n",
"the number of unit : 499\n",
"accuracy score : 0.906666666667\n",
"escape time : 14.885 s\n",
"the number of unit : 500\n",
"accuracy score : 0.908333333333\n",
"escape time : 15.041 s\n"
]
}
],
"source": [
"score1 = []\n",
"for n in range(1, 501):\n",
" nn = Classifier(\n",
" layers=[\n",
" Layer(\"Rectifier\", units=n),\n",
" Layer(\"Softmax\")],\n",
" verbose=1,\n",
" learning_rate=0.009,\n",
" n_iter=2,\n",
" )\n",
"\n",
" t0 = time()\n",
" nn.fit(trainX, trainY)\n",
" preds = nn.predict(testX)\n",
" print \"the number of unit : %s\" %n\n",
" print \"accuracy score : %s\" %(accuracy_score(testY, preds))\n",
" print \"escape time : \", round(time()-t0, 3), \"s\"\n",
" score1.append(accuracy_score(testY, preds))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of unit : 1\n",
"accuracy score : 0.226666666667\n",
"escape time : 4.472 s\n",
"the number of unit : 2\n",
"accuracy score : 0.473333333333\n",
"escape time : 3.968 s\n",
"the number of unit : 3\n",
"accuracy score : 0.49\n",
"escape time : 3.966 s\n",
"the number of unit : 4\n",
"accuracy score : 0.733333333333\n",
"escape time : 4.07 s\n",
"the number of unit : 5\n",
"accuracy score : 0.768333333333\n",
"escape time : 4.23 s\n",
"the number of unit : 6\n",
"accuracy score : 0.788333333333\n",
"escape time : 4.16 s\n",
"the number of unit : 7\n",
"accuracy score : 0.896666666667\n",
"escape time : 4.746 s\n",
"the number of unit : 8\n",
"accuracy score : 0.89\n",
"escape time : 4.693 s\n",
"the number of unit : 9\n",
"accuracy score : 0.88\n",
"escape time : 4.092 s\n",
"the number of unit : 10\n",
"accuracy score : 0.891666666667\n",
"escape time : 4.14 s\n",
"the number of unit : 11\n",
"accuracy score : 0.886666666667\n",
"escape time : 4.277 s\n",
"the number of unit : 12\n",
"accuracy score : 0.896666666667\n",
"escape time : 4.411 s\n",
"the number of unit : 13\n",
"accuracy score : 0.888333333333\n",
"escape time : 4.293 s\n",
"the number of unit : 14\n",
"accuracy score : 0.891666666667\n",
"escape time : 4.701 s\n",
"the number of unit : 15\n",
"accuracy score : 0.898333333333\n",
"escape time : 4.56 s\n",
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"the number of unit : 423\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.523 s\n",
"the number of unit : 424\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.233 s\n",
"the number of unit : 425\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.447 s\n",
"the number of unit : 426\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.592 s\n",
"the number of unit : 427\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.511 s\n",
"the number of unit : 428\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.56 s\n",
"the number of unit : 429\n",
"accuracy score : 0.896666666667\n",
"escape time : 10.615 s\n",
"the number of unit : 430\n",
"accuracy score : 0.9\n",
"escape time : 10.437 s\n",
"the number of unit : 431\n",
"accuracy score : 0.9\n",
"escape time : 10.689 s\n",
"the number of unit : 432\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.336 s\n",
"the number of unit : 433\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.585 s\n",
"the number of unit : 434\n",
"accuracy score : 0.9\n",
"escape time : 10.76 s\n",
"the number of unit : 435\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.658 s\n",
"the number of unit : 436\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.468 s\n",
"the number of unit : 437\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.645 s\n",
"the number of unit : 438\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.755 s\n",
"the number of unit : 439\n",
"accuracy score : 0.9\n",
"escape time : 10.634 s\n",
"the number of unit : 440\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.479 s\n",
"the number of unit : 441\n",
"accuracy score : 0.9\n",
"escape time : 10.881 s\n",
"the number of unit : 442\n",
"accuracy score : 0.9\n",
"escape time : 10.694 s\n",
"the number of unit : 443\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.59 s\n",
"the number of unit : 444\n",
"accuracy score : 0.9\n",
"escape time : 10.564 s\n",
"the number of unit : 445\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.909 s\n",
"the number of unit : 446\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.671 s\n",
"the number of unit : 447\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.739 s\n",
"the number of unit : 448\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.788 s\n",
"the number of unit : 449\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.744 s\n",
"the number of unit : 450\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.634 s\n",
"the number of unit : 451\n",
"accuracy score : 0.898333333333\n",
"escape time : 11.031 s\n",
"the number of unit : 452\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.685 s\n",
"the number of unit : 453\n",
"accuracy score : 0.9\n",
"escape time : 10.834 s\n",
"the number of unit : 454\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.65 s\n",
"the number of unit : 455\n",
"accuracy score : 0.898333333333\n",
"escape time : 11.116 s\n",
"the number of unit : 456\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.634 s\n",
"the number of unit : 457\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.768 s\n",
"the number of unit : 458\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.038 s\n",
"the number of unit : 459\n",
"accuracy score : 0.898333333333\n",
"escape time : 10.912 s\n",
"the number of unit : 460\n",
"accuracy score : 0.9\n",
"escape time : 10.784 s\n",
"the number of unit : 461\n",
"accuracy score : 0.898333333333\n",
"escape time : 11.287 s\n",
"the number of unit : 462\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.041 s\n",
"the number of unit : 463\n",
"accuracy score : 0.9\n",
"escape time : 10.914 s\n",
"the number of unit : 464\n",
"accuracy score : 0.9\n",
"escape time : 10.674 s\n",
"the number of unit : 465\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.213 s\n",
"the number of unit : 466\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.826 s\n",
"the number of unit : 467\n",
"accuracy score : 0.9\n",
"escape time : 10.878 s\n",
"the number of unit : 468\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.061 s\n",
"the number of unit : 469\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.086 s\n",
"the number of unit : 470\n",
"accuracy score : 0.9\n",
"escape time : 10.989 s\n",
"the number of unit : 471\n",
"accuracy score : 0.903333333333\n",
"escape time : 11.096 s\n",
"the number of unit : 472\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.003 s\n",
"the number of unit : 473\n",
"accuracy score : 0.9\n",
"escape time : 11.039 s\n",
"the number of unit : 474\n",
"accuracy score : 0.903333333333\n",
"escape time : 11.005 s\n",
"the number of unit : 475\n",
"accuracy score : 0.9\n",
"escape time : 11.263 s\n",
"the number of unit : 476\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.922 s\n",
"the number of unit : 477\n",
"accuracy score : 0.898333333333\n",
"escape time : 11.085 s\n",
"the number of unit : 478\n",
"accuracy score : 0.9\n",
"escape time : 11.24 s\n",
"the number of unit : 479\n",
"accuracy score : 0.896666666667\n",
"escape time : 11.175 s\n",
"the number of unit : 480\n",
"accuracy score : 0.9\n",
"escape time : 10.852 s\n",
"the number of unit : 481\n",
"accuracy score : 0.903333333333\n",
"escape time : 11.181 s\n",
"the number of unit : 482\n",
"accuracy score : 0.9\n",
"escape time : 11.424 s\n",
"the number of unit : 483\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.288 s\n",
"the number of unit : 484\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.102 s\n",
"the number of unit : 485\n",
"accuracy score : 0.903333333333\n",
"escape time : 11.532 s\n",
"the number of unit : 486\n",
"accuracy score : 0.905\n",
"escape time : 11.394 s\n",
"the number of unit : 487\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.241 s\n",
"the number of unit : 488\n",
"accuracy score : 0.9\n",
"escape time : 11.258 s\n",
"the number of unit : 489\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.324 s\n",
"the number of unit : 490\n",
"accuracy score : 0.896666666667\n",
"escape time : 11.225 s\n",
"the number of unit : 491\n",
"accuracy score : 0.9\n",
"escape time : 11.231 s\n",
"the number of unit : 492\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.481 s\n",
"the number of unit : 493\n",
"accuracy score : 0.898333333333\n",
"escape time : 11.415 s\n",
"the number of unit : 494\n",
"accuracy score : 0.896666666667\n",
"escape time : 11.184 s\n",
"the number of unit : 495\n",
"accuracy score : 0.905\n",
"escape time : 11.604 s\n",
"the number of unit : 496\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.163 s\n",
"the number of unit : 497\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.523 s\n",
"the number of unit : 498\n",
"accuracy score : 0.896666666667\n",
"escape time : 11.433 s\n",
"the number of unit : 499\n",
"accuracy score : 0.9\n",
"escape time : 11.687 s\n",
"the number of unit : 500\n",
"accuracy score : 0.901666666667\n",
"escape time : 11.456 s\n"
]
}
],
"source": [
"score2 = []\n",
"for n in range(1, 501):\n",
" nn = Classifier(\n",
" layers=[\n",
" Layer(\"Sigmoid\", units=n),\n",
" Layer(\"Softmax\")],\n",
" verbose=1,\n",
" learning_rate=0.009,\n",
" n_iter=2,\n",
" )\n",
"\n",
" t0 = time()\n",
" nn.fit(trainX, trainY)\n",
" preds = nn.predict(testX)\n",
" print \"the number of unit : %s\" %n\n",
" print \"accuracy score : %s\" %(accuracy_score(testY, preds))\n",
" print \"escape time : \", round(time()-t0, 3), \"s\"\n",
" score2.append(accuracy_score(testY, preds))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of unit : 1\n",
"accuracy score : 0.231666666667\n",
"escape time : 3.155 s\n",
"the number of unit : 2\n",
"accuracy score : 0.49\n",
"escape time : 3.209 s\n",
"the number of unit : 3\n",
"accuracy score : 0.796666666667\n",
"escape time : 3.064 s\n",
"the number of unit : 4\n",
"accuracy score : 0.896666666667\n",
"escape time : 3.079 s\n",
"the number of unit : 5\n",
"accuracy score : 0.898333333333\n",
"escape time : 3.149 s\n",
"the number of unit : 6\n",
"accuracy score : 0.895\n",
"escape time : 3.004 s\n",
"the number of unit : 7\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.073 s\n",
"the number of unit : 8\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.149 s\n",
"the number of unit : 9\n",
"accuracy score : 0.895\n",
"escape time : 3.247 s\n",
"the number of unit : 10\n",
"accuracy score : 0.896666666667\n",
"escape time : 3.108 s\n",
"the number of unit : 11\n",
"accuracy score : 0.891666666667\n",
"escape time : 3.133 s\n",
"the number of unit : 12\n",
"accuracy score : 0.898333333333\n",
"escape time : 3.307 s\n",
"the number of unit : 13\n",
"accuracy score : 0.895\n",
"escape time : 3.089 s\n",
"the number of unit : 14\n",
"accuracy score : 0.893333333333\n",
"escape time : 3.426 s\n",
"the number of unit : 15\n",
"accuracy score : 0.896666666667\n",
"escape time : 3.602 s\n",
"the number of unit : 16\n",
"accuracy score : 0.905\n",
"escape time : 3.414 s\n",
"the number of unit : 17\n",
"accuracy score : 0.9\n",
"escape time : 3.402 s\n",
"the number of unit : 18\n",
"accuracy score : 0.898333333333\n",
"escape time : 3.436 s\n",
"the number of unit : 19\n",
"accuracy score : 0.893333333333\n",
"escape time : 3.579 s\n",
"the number of unit : 20\n",
"accuracy score : 0.898333333333\n",
"escape time : 3.458 s\n",
"the number of unit : 21\n",
"accuracy score : 0.9\n",
"escape time : 3.6 s\n",
"the number of unit : 22\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.639 s\n",
"the number of unit : 23\n",
"accuracy score : 0.895\n",
"escape time : 3.561 s\n",
"the number of unit : 24\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.512 s\n",
"the number of unit : 25\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.592 s\n",
"the number of unit : 26\n",
"accuracy score : 0.896666666667\n",
"escape time : 3.797 s\n",
"the number of unit : 27\n",
"accuracy score : 0.9\n",
"escape time : 4.034 s\n",
"the number of unit : 28\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.632 s\n",
"the number of unit : 29\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.86 s\n",
"the number of unit : 30\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.73 s\n",
"the number of unit : 31\n",
"accuracy score : 0.9\n",
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"the number of unit : 32\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.908 s\n",
"the number of unit : 33\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.69 s\n",
"the number of unit : 34\n",
"accuracy score : 0.9\n",
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"the number of unit : 35\n",
"accuracy score : 0.9\n",
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"the number of unit : 36\n",
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"accuracy score : 0.898333333333\n",
"escape time : 3.854 s\n",
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"escape time : 4.008 s\n",
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"escape time : 3.937 s\n",
"the number of unit : 46\n",
"accuracy score : 0.903333333333\n",
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"escape time : 4.008 s\n",
"the number of unit : 48\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.97 s\n",
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"the number of unit : 50\n",
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"accuracy score : 0.9\n",
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"accuracy score : 0.898333333333\n",
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"escape time : 4.365 s\n",
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"accuracy score : 0.896666666667\n",
"escape time : 4.2 s\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.898333333333\n",
"escape time : 4.288 s\n",
"the number of unit : 61\n",
"accuracy score : 0.9\n",
"escape time : 4.27 s\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
"escape time : 4.3 s\n",
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"accuracy score : 0.895\n",
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"accuracy score : 0.9\n",
"escape time : 4.593 s\n",
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"accuracy score : 0.903333333333\n",
"escape time : 4.452 s\n",
"the number of unit : 68\n",
"accuracy score : 0.9\n",
"escape time : 4.464 s\n",
"the number of unit : 69\n",
"accuracy score : 0.903333333333\n",
"escape time : 4.629 s\n",
"the number of unit : 70\n",
"accuracy score : 0.901666666667\n",
"escape time : 4.44 s\n",
"the number of unit : 71\n",
"accuracy score : 0.901666666667\n",
"escape time : 4.55 s\n",
"the number of unit : 72\n",
"accuracy score : 0.901666666667\n",
"escape time : 4.644 s\n",
"the number of unit : 73\n",
"accuracy score : 0.898333333333\n",
"escape time : 4.513 s\n",
"the number of unit : 74\n",
"accuracy score : 0.901666666667\n",
"escape time : 4.527 s\n",
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"accuracy score : 0.896666666667\n",
"escape time : 4.602 s\n",
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"accuracy score : 0.901666666667\n",
"escape time : 4.807 s\n",
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"accuracy score : 0.9\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
"escape time : 4.907 s\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
"escape time : 4.794 s\n",
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"accuracy score : 0.901666666667\n",
"escape time : 5.004 s\n",
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"accuracy score : 0.901666666667\n",
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"escape time : 5.025 s\n",
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"accuracy score : 0.901666666667\n",
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"escape time : 5.205 s\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
"escape time : 5.303 s\n",
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"accuracy score : 0.898333333333\n",
"escape time : 5.262 s\n",
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"escape time : 5.443 s\n",
"the number of unit : 113\n",
"accuracy score : 0.898333333333\n",
"escape time : 5.327 s\n",
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"escape time : 5.275 s\n",
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"the number of unit : 116\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
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"escape time : 13.602 s\n",
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"accuracy score : 0.901666666667\n",
"escape time : 13.403 s\n",
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"accuracy score : 0.903333333333\n",
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"the number of unit : 464\n",
"accuracy score : 0.903333333333\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
"escape time : 13.944 s\n",
"the number of unit : 491\n",
"accuracy score : 0.901666666667\n",
"escape time : 13.745 s\n",
"the number of unit : 492\n",
"accuracy score : 0.901666666667\n",
"escape time : 13.813 s\n",
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"accuracy score : 0.903333333333\n",
"escape time : 14.194 s\n",
"the number of unit : 494\n",
"accuracy score : 0.903333333333\n",
"escape time : 13.812 s\n",
"the number of unit : 495\n",
"accuracy score : 0.903333333333\n",
"escape time : 13.83 s\n",
"the number of unit : 496\n",
"accuracy score : 0.903333333333\n",
"escape time : 13.829 s\n",
"the number of unit : 497\n",
"accuracy score : 0.903333333333\n",
"escape time : 14.049 s\n",
"the number of unit : 498\n",
"accuracy score : 0.903333333333\n",
"escape time : 13.99 s\n",
"the number of unit : 499\n",
"accuracy score : 0.903333333333\n",
"escape time : 13.861 s\n",
"the number of unit : 500\n",
"accuracy score : 0.901666666667\n",
"escape time : 14.308 s\n"
]
}
],
"source": [
"score3 = []\n",
"for n in range(1, 501):\n",
" nn = Classifier(\n",
" layers=[\n",
" Layer(\"Tanh\", units=n),\n",
" Layer(\"Softmax\")],\n",
" verbose=1,\n",
" learning_rate=0.009,\n",
" n_iter=2,\n",
" )\n",
"\n",
" t0 = time()\n",
" nn.fit(trainX, trainY)\n",
" preds = nn.predict(testX)\n",
" print \"the number of unit : %s\" %n\n",
" print \"accuracy score : %s\" %(accuracy_score(testY, preds))\n",
" print \"escape time : \", round(time()-t0, 3), \"s\"\n",
" score3.append(accuracy_score(testY, preds))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of unit : 1\n",
"accuracy score : 0.601666666667\n",
"escape time : 6.096 s\n",
"the number of unit : 2\n",
"accuracy score : 0.888333333333\n",
"escape time : 4.043 s\n",
"the number of unit : 3\n",
"accuracy score : 0.891666666667\n",
"escape time : 4.329 s\n",
"the number of unit : 4\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.976 s\n",
"the number of unit : 5\n",
"accuracy score : 0.903333333333\n",
"escape time : 4.209 s\n",
"the number of unit : 6\n",
"accuracy score : 0.898333333333\n",
"escape time : 4.147 s\n",
"the number of unit : 7\n",
"accuracy score : 0.896666666667\n",
"escape time : 4.532 s\n",
"the number of unit : 8\n",
"accuracy score : 0.903333333333\n",
"escape time : 4.092 s\n",
"the number of unit : 9\n",
"accuracy score : 0.9\n",
"escape time : 4.222 s\n",
"the number of unit : 10\n",
"accuracy score : 0.906666666667\n",
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"escape time : 20.63 s\n",
"the number of unit : 417\n",
"accuracy score : 0.9\n",
"escape time : 20.336 s\n",
"the number of unit : 418\n",
"accuracy score : 0.905\n",
"escape time : 20.721 s\n",
"the number of unit : 419\n",
"accuracy score : 0.896666666667\n",
"escape time : 20.109 s\n",
"the number of unit : 420\n",
"accuracy score : 0.903333333333\n",
"escape time : 20.784 s\n",
"the number of unit : 421\n",
"accuracy score : 0.901666666667\n",
"escape time : 20.755 s\n",
"the number of unit : 422\n",
"accuracy score : 0.9\n",
"escape time : 20.948 s\n",
"the number of unit : 423\n",
"accuracy score : 0.903333333333\n",
"escape time : 20.474 s\n",
"the number of unit : 424\n",
"accuracy score : 0.9\n",
"escape time : 20.954 s\n",
"the number of unit : 425\n",
"accuracy score : 0.898333333333\n",
"escape time : 20.709 s\n",
"the number of unit : 426\n",
"accuracy score : 0.901666666667\n",
"escape time : 20.692 s\n",
"the number of unit : 427\n",
"accuracy score : 0.901666666667\n",
"escape time : 20.447 s\n",
"the number of unit : 428\n",
"accuracy score : 0.9\n",
"escape time : 20.894 s\n",
"the number of unit : 429\n",
"accuracy score : 0.901666666667\n",
"escape time : 20.628 s\n",
"the number of unit : 430\n",
"accuracy score : 0.9\n",
"escape time : 21.106 s\n",
"the number of unit : 431\n",
"accuracy score : 0.901666666667\n",
"escape time : 20.718 s\n",
"the number of unit : 432\n",
"accuracy score : 0.9\n",
"escape time : 21.234 s\n",
"the number of unit : 433\n",
"accuracy score : 0.9\n",
"escape time : 20.806 s\n",
"the number of unit : 434\n",
"accuracy score : 0.9\n",
"escape time : 21.121 s\n",
"the number of unit : 435\n",
"accuracy score : 0.901666666667\n",
"escape time : 20.898 s\n",
"the number of unit : 436\n",
"accuracy score : 0.901666666667\n",
"escape time : 21.308 s\n",
"the number of unit : 437\n",
"accuracy score : 0.896666666667\n",
"escape time : 21.084 s\n",
"the number of unit : 438\n",
"accuracy score : 0.906666666667\n",
"escape time : 21.253 s\n",
"the number of unit : 439\n",
"accuracy score : 0.898333333333\n",
"escape time : 20.869 s\n",
"the number of unit : 440\n",
"accuracy score : 0.9\n",
"escape time : 21.732 s\n",
"the number of unit : 441\n",
"accuracy score : 0.901666666667\n",
"escape time : 21.053 s\n",
"the number of unit : 442\n",
"accuracy score : 0.903333333333\n",
"escape time : 21.255 s\n",
"the number of unit : 443\n",
"accuracy score : 0.903333333333\n",
"escape time : 20.885 s\n",
"the number of unit : 444\n",
"accuracy score : 0.898333333333\n",
"escape time : 21.672 s\n",
"the number of unit : 445\n",
"accuracy score : 0.9\n",
"escape time : 21.07 s\n",
"the number of unit : 446\n",
"accuracy score : 0.9\n",
"escape time : 21.439 s\n",
"the number of unit : 447\n",
"accuracy score : 0.898333333333\n",
"escape time : 21.273 s\n",
"the number of unit : 448\n",
"accuracy score : 0.903333333333\n",
"escape time : 21.713 s\n",
"the number of unit : 449\n",
"accuracy score : 0.901666666667\n",
"escape time : 21.37 s\n",
"the number of unit : 450\n",
"accuracy score : 0.901666666667\n",
"escape time : 21.742 s\n",
"the number of unit : 451\n",
"accuracy score : 0.901666666667\n",
"escape time : 21.261 s\n",
"the number of unit : 452\n",
"accuracy score : 0.9\n",
"escape time : 21.79 s\n",
"the number of unit : 453\n",
"accuracy score : 0.9\n",
"escape time : 21.353 s\n",
"the number of unit : 454\n",
"accuracy score : 0.9\n",
"escape time : 22.411 s\n",
"the number of unit : 455\n",
"accuracy score : 0.903333333333\n",
"escape time : 21.38 s\n",
"the number of unit : 456\n",
"accuracy score : 0.905\n",
"escape time : 22.187 s\n",
"the number of unit : 457\n",
"accuracy score : 0.9\n",
"escape time : 21.499 s\n",
"the number of unit : 458\n",
"accuracy score : 0.901666666667\n",
"escape time : 22.141 s\n",
"the number of unit : 459\n",
"accuracy score : 0.901666666667\n",
"escape time : 21.522 s\n",
"the number of unit : 460\n",
"accuracy score : 0.9\n",
"escape time : 22.267 s\n",
"the number of unit : 461\n",
"accuracy score : 0.901666666667\n",
"escape time : 21.63 s\n",
"the number of unit : 462\n",
"accuracy score : 0.898333333333\n",
"escape time : 22.255 s\n",
"the number of unit : 463\n",
"accuracy score : 0.898333333333\n",
"escape time : 21.552 s\n",
"the number of unit : 464\n",
"accuracy score : 0.9\n",
"escape time : 22.357 s\n",
"the number of unit : 465\n",
"accuracy score : 0.903333333333\n",
"escape time : 22.027 s\n",
"the number of unit : 466\n",
"accuracy score : 0.9\n",
"escape time : 22.326 s\n",
"the number of unit : 467\n",
"accuracy score : 0.9\n",
"escape time : 21.706 s\n",
"the number of unit : 468\n",
"accuracy score : 0.898333333333\n",
"escape time : 22.433 s\n",
"the number of unit : 469\n",
"accuracy score : 0.903333333333\n",
"escape time : 21.859 s\n",
"the number of unit : 470\n",
"accuracy score : 0.898333333333\n",
"escape time : 22.427 s\n",
"the number of unit : 471\n",
"accuracy score : 0.9\n",
"escape time : 21.827 s\n",
"the number of unit : 472\n",
"accuracy score : 0.9\n",
"escape time : 22.469 s\n",
"the number of unit : 473\n",
"accuracy score : 0.896666666667\n",
"escape time : 22.486 s\n",
"the number of unit : 474\n",
"accuracy score : 0.903333333333\n",
"escape time : 22.48 s\n",
"the number of unit : 475\n",
"accuracy score : 0.9\n",
"escape time : 22.091 s\n",
"the number of unit : 476\n",
"accuracy score : 0.896666666667\n",
"escape time : 22.618 s\n",
"the number of unit : 477\n",
"accuracy score : 0.896666666667\n",
"escape time : 22.709 s\n",
"the number of unit : 478\n",
"accuracy score : 0.903333333333\n",
"escape time : 22.803 s\n",
"the number of unit : 479\n",
"accuracy score : 0.901666666667\n",
"escape time : 22.158 s\n",
"the number of unit : 480\n",
"accuracy score : 0.898333333333\n",
"escape time : 22.781 s\n",
"the number of unit : 481\n",
"accuracy score : 0.9\n",
"escape time : 22.292 s\n",
"the number of unit : 482\n",
"accuracy score : 0.896666666667\n",
"escape time : 22.904 s\n",
"the number of unit : 483\n",
"accuracy score : 0.9\n",
"escape time : 22.142 s\n",
"the number of unit : 484\n",
"accuracy score : 0.9\n",
"escape time : 22.893 s\n",
"the number of unit : 485\n",
"accuracy score : 0.898333333333\n",
"escape time : 22.397 s\n",
"the number of unit : 486\n",
"accuracy score : 0.9\n",
"escape time : 23.019 s\n",
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"accuracy score : 0.898333333333\n",
"escape time : 22.446 s\n",
"the number of unit : 488\n",
"accuracy score : 0.903333333333\n",
"escape time : 23.797 s\n",
"the number of unit : 489\n",
"accuracy score : 0.9\n",
"escape time : 22.542 s\n",
"the number of unit : 490\n",
"accuracy score : 0.901666666667\n",
"escape time : 23.118 s\n",
"the number of unit : 491\n",
"accuracy score : 0.9\n",
"escape time : 22.367 s\n",
"the number of unit : 492\n",
"accuracy score : 0.896666666667\n",
"escape time : 23.115 s\n",
"the number of unit : 493\n",
"accuracy score : 0.9\n",
"escape time : 22.695 s\n",
"the number of unit : 494\n",
"accuracy score : 0.896666666667\n",
"escape time : 23.193 s\n",
"the number of unit : 495\n",
"accuracy score : 0.903333333333\n",
"escape time : 22.72 s\n",
"the number of unit : 496\n",
"accuracy score : 0.9\n",
"escape time : 23.095 s\n",
"the number of unit : 497\n",
"accuracy score : 0.898333333333\n",
"escape time : 22.751 s\n",
"the number of unit : 498\n",
"accuracy score : 0.901666666667\n",
"escape time : 23.447 s\n",
"the number of unit : 499\n",
"accuracy score : 0.906666666667\n",
"escape time : 22.733 s\n",
"the number of unit : 500\n",
"accuracy score : 0.898333333333\n",
"escape time : 23.426 s\n"
]
}
],
"source": [
"score4 = []\n",
"for n in range(1, 501):\n",
" nn = Classifier(\n",
" layers=[\n",
" Layer(\"Maxout\", units=n, pieces=2),\n",
" Layer(\"Softmax\")],\n",
" verbose=1,\n",
" learning_rate=0.009,\n",
" n_iter=2,\n",
" )\n",
"\n",
" t0 = time()\n",
" nn.fit(trainX, trainY)\n",
" preds = nn.predict(testX)\n",
" print \"the number of unit : %s\" %n\n",
" print \"accuracy score : %s\" %(accuracy_score(testY, preds))\n",
" print \"escape time : \", round(time()-t0, 3), \"s\"\n",
" score4.append(accuracy_score(testY, preds))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of unit : 1\n",
"accuracy score : 0.62\n",
"escape time : 2.788 s\n",
"the number of unit : 2\n",
"accuracy score : 0.878333333333\n",
"escape time : 2.544 s\n",
"the number of unit : 3\n",
"accuracy score : 0.898333333333\n",
"escape time : 2.546 s\n",
"the number of unit : 4\n",
"accuracy score : 0.896666666667\n",
"escape time : 2.731 s\n",
"the number of unit : 5\n",
"accuracy score : 0.905\n",
"escape time : 2.581 s\n",
"the number of unit : 6\n",
"accuracy score : 0.905\n",
"escape time : 2.564 s\n",
"the number of unit : 7\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.571 s\n",
"the number of unit : 8\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.657 s\n",
"the number of unit : 9\n",
"accuracy score : 0.901666666667\n",
"escape time : 2.786 s\n",
"the number of unit : 10\n",
"accuracy score : 0.903333333333\n",
"escape time : 2.644 s\n",
"the number of unit : 11\n",
"accuracy score : 0.905\n",
"escape time : 2.711 s\n",
"the number of unit : 12\n",
"accuracy score : 0.905\n",
"escape time : 2.724 s\n",
"the number of unit : 13\n",
"accuracy score : 0.903333333333\n",
"escape time : 2.926 s\n",
"the number of unit : 14\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.767 s\n",
"the number of unit : 15\n",
"accuracy score : 0.905\n",
"escape time : 2.785 s\n",
"the number of unit : 16\n",
"accuracy score : 0.905\n",
"escape time : 2.768 s\n",
"the number of unit : 17\n",
"accuracy score : 0.901666666667\n",
"escape time : 2.767 s\n",
"the number of unit : 18\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.011 s\n",
"the number of unit : 19\n",
"accuracy score : 0.905\n",
"escape time : 2.832 s\n",
"the number of unit : 20\n",
"accuracy score : 0.905\n",
"escape time : 2.983 s\n",
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"the number of unit : 22\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.878 s\n",
"the number of unit : 23\n",
"accuracy score : 0.905\n",
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"accuracy score : 0.906666666667\n",
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"the number of unit : 26\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.886 s\n",
"the number of unit : 27\n",
"accuracy score : 0.903333333333\n",
"escape time : 2.957 s\n",
"the number of unit : 28\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.947 s\n",
"the number of unit : 29\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.222 s\n",
"the number of unit : 30\n",
"accuracy score : 0.905\n",
"escape time : 2.952 s\n",
"the number of unit : 31\n",
"accuracy score : 0.906666666667\n",
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"the number of unit : 32\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.012 s\n",
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"accuracy score : 0.901666666667\n",
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"the number of unit : 34\n",
"accuracy score : 0.906666666667\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.905\n",
"escape time : 3.552 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.567 s\n",
"the number of unit : 69\n",
"accuracy score : 0.906666666667\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.711 s\n",
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"accuracy score : 0.901666666667\n",
"escape time : 3.634 s\n",
"the number of unit : 72\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.686 s\n",
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"accuracy score : 0.905\n",
"escape time : 3.754 s\n",
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"accuracy score : 0.905\n",
"escape time : 3.731 s\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.906666666667\n",
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"the number of unit : 110\n",
"accuracy score : 0.906666666667\n",
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"accuracy score : 0.903333333333\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.901666666667\n",
"escape time : 10.896 s\n",
"the number of unit : 491\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.91 s\n",
"the number of unit : 492\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.722 s\n",
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"escape time : 10.928 s\n",
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"accuracy score : 0.901666666667\n",
"escape time : 11.036 s\n",
"the number of unit : 495\n",
"accuracy score : 0.903333333333\n",
"escape time : 11.248 s\n",
"the number of unit : 496\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.699 s\n",
"the number of unit : 497\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.954 s\n",
"the number of unit : 498\n",
"accuracy score : 0.901666666667\n",
"escape time : 10.992 s\n",
"the number of unit : 499\n",
"accuracy score : 0.903333333333\n",
"escape time : 11.434 s\n",
"the number of unit : 500\n",
"accuracy score : 0.903333333333\n",
"escape time : 10.668 s\n"
]
}
],
"source": [
"score5 = []\n",
"for n in range(1, 501):\n",
" nn = Classifier(\n",
" layers=[\n",
" Layer(\"Linear\", units=n),\n",
" Layer(\"Softmax\")],\n",
" verbose=1,\n",
" learning_rate=0.009,\n",
" n_iter=2,\n",
" )\n",
"\n",
" t0 = time()\n",
" nn.fit(trainX, trainY)\n",
" preds = nn.predict(testX)\n",
" print \"the number of unit : %s\" %n\n",
" print \"accuracy score : %s\" %(accuracy_score(testY, preds))\n",
" print \"escape time : \", round(time()-t0, 3), \"s\"\n",
" score5.append(accuracy_score(testY, preds))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of unit : 1\n",
"accuracy score : 0.135\n",
"escape time : 2.826 s\n",
"the number of unit : 2\n",
"accuracy score : 0.26\n",
"escape time : 2.617 s\n",
"the number of unit : 3\n",
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"escape time : 9.665 s\n",
"the number of unit : 409\n",
"accuracy score : 0.375\n",
"escape time : 10.017 s\n",
"the number of unit : 410\n",
"accuracy score : 0.335\n",
"escape time : 10.183 s\n",
"the number of unit : 411\n",
"accuracy score : 0.33\n",
"escape time : 10.0 s\n",
"the number of unit : 412\n",
"accuracy score : 0.303333333333\n",
"escape time : 9.907 s\n",
"the number of unit : 413\n",
"accuracy score : 0.373333333333\n",
"escape time : 10.038 s\n",
"the number of unit : 414\n",
"accuracy score : 0.51\n",
"escape time : 10.233 s\n",
"the number of unit : 415\n",
"accuracy score : 0.325\n",
"escape time : 10.182 s\n",
"the number of unit : 416\n",
"accuracy score : 0.345\n",
"escape time : 9.862 s\n",
"the number of unit : 417\n",
"accuracy score : 0.445\n",
"escape time : 10.169 s\n",
"the number of unit : 418\n",
"accuracy score : 0.375\n",
"escape time : 10.067 s\n",
"the number of unit : 419\n",
"accuracy score : 0.461666666667\n",
"escape time : 10.439 s\n",
"the number of unit : 420\n",
"accuracy score : 0.465\n",
"escape time : 9.945 s\n",
"the number of unit : 421\n",
"accuracy score : 0.331666666667\n",
"escape time : 10.287 s\n",
"the number of unit : 422\n",
"accuracy score : 0.32\n",
"escape time : 10.082 s\n",
"the number of unit : 423\n",
"accuracy score : 0.338333333333\n",
"escape time : 10.344 s\n",
"the number of unit : 424\n",
"accuracy score : 0.396666666667\n",
"escape time : 10.234 s\n",
"the number of unit : 425\n",
"accuracy score : 0.345\n",
"escape time : 10.2 s\n",
"the number of unit : 426\n",
"accuracy score : 0.338333333333\n",
"escape time : 10.224 s\n",
"the number of unit : 427\n",
"accuracy score : 0.333333333333\n",
"escape time : 10.286 s\n",
"the number of unit : 428\n",
"accuracy score : 0.378333333333\n",
"escape time : 10.012 s\n",
"the number of unit : 429\n",
"accuracy score : 0.401666666667\n",
"escape time : 10.629 s\n",
"the number of unit : 430\n",
"accuracy score : 0.446666666667\n",
"escape time : 10.186 s\n",
"the number of unit : 431\n",
"accuracy score : 0.346666666667\n",
"escape time : 10.444 s\n",
"the number of unit : 432\n",
"accuracy score : 0.346666666667\n",
"escape time : 10.057 s\n",
"the number of unit : 433\n",
"accuracy score : 0.445\n",
"escape time : 10.539 s\n",
"the number of unit : 434\n",
"accuracy score : 0.476666666667\n",
"escape time : 10.368 s\n",
"the number of unit : 435\n",
"accuracy score : 0.545\n",
"escape time : 10.252 s\n",
"the number of unit : 436\n",
"accuracy score : 0.445\n",
"escape time : 10.146 s\n",
"the number of unit : 437\n",
"accuracy score : 0.446666666667\n",
"escape time : 10.365 s\n",
"the number of unit : 438\n",
"accuracy score : 0.23\n",
"escape time : 10.583 s\n",
"the number of unit : 439\n",
"accuracy score : 0.378333333333\n",
"escape time : 10.535 s\n",
"the number of unit : 440\n",
"accuracy score : 0.4\n",
"escape time : 10.125 s\n",
"the number of unit : 441\n",
"accuracy score : 0.415\n",
"escape time : 10.356 s\n",
"the number of unit : 442\n",
"accuracy score : 0.305\n",
"escape time : 10.405 s\n",
"the number of unit : 443\n",
"accuracy score : 0.298333333333\n",
"escape time : 10.769 s\n",
"the number of unit : 444\n",
"accuracy score : 0.375\n",
"escape time : 10.183 s\n",
"the number of unit : 445\n",
"accuracy score : 0.328333333333\n",
"escape time : 10.631 s\n",
"the number of unit : 446\n",
"accuracy score : 0.233333333333\n",
"escape time : 10.385 s\n",
"the number of unit : 447\n",
"accuracy score : 0.305\n",
"escape time : 10.81 s\n",
"the number of unit : 448\n",
"accuracy score : 0.373333333333\n",
"escape time : 10.417 s\n",
"the number of unit : 449\n",
"accuracy score : 0.345\n",
"escape time : 10.513 s\n",
"the number of unit : 450\n",
"accuracy score : 0.23\n",
"escape time : 10.525 s\n",
"the number of unit : 451\n",
"accuracy score : 0.303333333333\n",
"escape time : 10.513 s\n",
"the number of unit : 452\n",
"accuracy score : 0.541666666667\n",
"escape time : 10.594 s\n",
"the number of unit : 453\n",
"accuracy score : 0.373333333333\n",
"escape time : 10.607 s\n",
"the number of unit : 454\n",
"accuracy score : 0.493333333333\n",
"escape time : 10.628 s\n",
"the number of unit : 455\n",
"accuracy score : 0.448333333333\n",
"escape time : 10.478 s\n",
"the number of unit : 456\n",
"accuracy score : 0.301666666667\n",
"escape time : 10.43 s\n",
"the number of unit : 457\n",
"accuracy score : 0.375\n",
"escape time : 10.877 s\n",
"the number of unit : 458\n",
"accuracy score : 0.23\n",
"escape time : 10.655 s\n",
"the number of unit : 459\n",
"accuracy score : 0.373333333333\n",
"escape time : 10.663 s\n",
"the number of unit : 460\n",
"accuracy score : 0.231666666667\n",
"escape time : 10.377 s\n",
"the number of unit : 461\n",
"accuracy score : 0.348333333333\n",
"escape time : 10.533 s\n",
"the number of unit : 462\n",
"accuracy score : 0.235\n",
"escape time : 10.859 s\n",
"the number of unit : 463\n",
"accuracy score : 0.348333333333\n",
"escape time : 10.771 s\n",
"the number of unit : 464\n",
"accuracy score : 0.446666666667\n",
"escape time : 10.506 s\n",
"the number of unit : 465\n",
"accuracy score : 0.23\n",
"escape time : 10.434 s\n",
"the number of unit : 466\n",
"accuracy score : 0.448333333333\n",
"escape time : 10.999 s\n",
"the number of unit : 467\n",
"accuracy score : 0.378333333333\n",
"escape time : 10.713 s\n",
"the number of unit : 468\n",
"accuracy score : 0.331666666667\n",
"escape time : 10.711 s\n",
"the number of unit : 469\n",
"accuracy score : 0.203333333333\n",
"escape time : 10.862 s\n",
"the number of unit : 470\n",
"accuracy score : 0.445\n",
"escape time : 10.736 s\n",
"the number of unit : 471\n",
"accuracy score : 0.44\n",
"escape time : 11.019 s\n",
"the number of unit : 472\n",
"accuracy score : 0.441666666667\n",
"escape time : 10.623 s\n",
"the number of unit : 473\n",
"accuracy score : 0.348333333333\n",
"escape time : 10.636 s\n",
"the number of unit : 474\n",
"accuracy score : 0.375\n",
"escape time : 10.682 s\n",
"the number of unit : 475\n",
"accuracy score : 0.345\n",
"escape time : 10.831 s\n",
"the number of unit : 476\n",
"accuracy score : 0.443333333333\n",
"escape time : 11.079 s\n",
"the number of unit : 477\n",
"accuracy score : 0.348333333333\n",
"escape time : 10.944 s\n",
"the number of unit : 478\n",
"accuracy score : 0.373333333333\n",
"escape time : 10.669 s\n",
"the number of unit : 479\n",
"accuracy score : 0.303333333333\n",
"escape time : 10.823 s\n",
"the number of unit : 480\n",
"accuracy score : 0.375\n",
"escape time : 11.044 s\n",
"the number of unit : 481\n",
"accuracy score : 0.373333333333\n",
"escape time : 10.802 s\n",
"the number of unit : 482\n",
"accuracy score : 0.475\n",
"escape time : 11.094 s\n",
"the number of unit : 483\n",
"accuracy score : 0.475\n",
"escape time : 11.167 s\n",
"the number of unit : 484\n",
"accuracy score : 0.376666666667\n",
"escape time : 11.064 s\n",
"the number of unit : 485\n",
"accuracy score : 0.33\n",
"escape time : 11.424 s\n",
"the number of unit : 486\n",
"accuracy score : 0.488333333333\n",
"escape time : 11.159 s\n",
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"accuracy score : 0.376666666667\n",
"escape time : 11.272 s\n",
"the number of unit : 488\n",
"accuracy score : 0.226666666667\n",
"escape time : 10.858 s\n",
"the number of unit : 489\n",
"accuracy score : 0.45\n",
"escape time : 11.089 s\n",
"the number of unit : 490\n",
"accuracy score : 0.376666666667\n",
"escape time : 11.351 s\n",
"the number of unit : 491\n",
"accuracy score : 0.336666666667\n",
"escape time : 10.968 s\n",
"the number of unit : 492\n",
"accuracy score : 0.448333333333\n",
"escape time : 11.127 s\n",
"the number of unit : 493\n",
"accuracy score : 0.398333333333\n",
"escape time : 11.129 s\n",
"the number of unit : 494\n",
"accuracy score : 0.373333333333\n",
"escape time : 11.174 s\n",
"the number of unit : 495\n",
"accuracy score : 0.228333333333\n",
"escape time : 11.542 s\n",
"the number of unit : 496\n",
"accuracy score : 0.446666666667\n",
"escape time : 10.893 s\n",
"the number of unit : 497\n",
"accuracy score : 0.476666666667\n",
"escape time : 11.389 s\n",
"the number of unit : 498\n",
"accuracy score : 0.38\n",
"escape time : 11.362 s\n",
"the number of unit : 499\n",
"accuracy score : 0.375\n",
"escape time : 11.58 s\n",
"the number of unit : 500\n",
"accuracy score : 0.298333333333\n",
"escape time : 11.374 s\n"
]
}
],
"source": [
"score6 = []\n",
"for n in range(1, 501):\n",
" nn = Classifier(\n",
" layers=[\n",
" Layer(\"Softmax\", units=n),\n",
" Layer(\"Softmax\")],\n",
" verbose=1,\n",
" learning_rate=0.009,\n",
" n_iter=2,\n",
" )\n",
"\n",
" t0 = time()\n",
" nn.fit(trainX, trainY)\n",
" preds = nn.predict(testX)\n",
" print \"the number of unit : %s\" %n\n",
" print \"accuracy score : %s\" %(accuracy_score(testY, preds))\n",
" print \"escape time : \", round(time()-t0, 3), \"s\"\n",
" score6.append(accuracy_score(testY, preds))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of unit : 1\n",
"accuracy score : 0.62\n",
"escape time : 4.082 s\n",
"the number of unit : 2\n",
"accuracy score : 0.878333333333\n",
"escape time : 2.631 s\n",
"the number of unit : 3\n",
"accuracy score : 0.898333333333\n",
"escape time : 2.591 s\n",
"the number of unit : 4\n",
"accuracy score : 0.896666666667\n",
"escape time : 2.837 s\n",
"the number of unit : 5\n",
"accuracy score : 0.905\n",
"escape time : 2.777 s\n",
"the number of unit : 6\n",
"accuracy score : 0.905\n",
"escape time : 2.631 s\n",
"the number of unit : 7\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.638 s\n",
"the number of unit : 8\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.869 s\n",
"the number of unit : 9\n",
"accuracy score : 0.901666666667\n",
"escape time : 2.698 s\n",
"the number of unit : 10\n",
"accuracy score : 0.903333333333\n",
"escape time : 2.665 s\n",
"the number of unit : 11\n",
"accuracy score : 0.905\n",
"escape time : 2.706 s\n",
"the number of unit : 12\n",
"accuracy score : 0.905\n",
"escape time : 2.766 s\n",
"the number of unit : 13\n",
"accuracy score : 0.903333333333\n",
"escape time : 2.998 s\n",
"the number of unit : 14\n",
"accuracy score : 0.906666666667\n",
"escape time : 2.824 s\n",
"the number of unit : 15\n",
"accuracy score : 0.905\n",
"escape time : 2.847 s\n",
"the number of unit : 16\n",
"accuracy score : 0.905\n",
"escape time : 2.787 s\n",
"the number of unit : 17\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.022 s\n",
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"accuracy score : 0.905\n",
"escape time : 2.862 s\n",
"the number of unit : 20\n",
"accuracy score : 0.905\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.906666666667\n",
"escape time : 2.937 s\n",
"the number of unit : 27\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.207 s\n",
"the number of unit : 28\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.009 s\n",
"the number of unit : 29\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.089 s\n",
"the number of unit : 30\n",
"accuracy score : 0.905\n",
"escape time : 3.059 s\n",
"the number of unit : 31\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.057 s\n",
"the number of unit : 32\n",
"accuracy score : 0.903333333333\n",
"escape time : 3.308 s\n",
"the number of unit : 33\n",
"accuracy score : 0.901666666667\n",
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"accuracy score : 0.906666666667\n",
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"escape time : 3.347 s\n",
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"escape time : 3.473 s\n",
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"accuracy score : 0.905\n",
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"the number of unit : 60\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.495 s\n",
"the number of unit : 61\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.523 s\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
"escape time : 3.821 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.7 s\n",
"the number of unit : 69\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.689 s\n",
"the number of unit : 70\n",
"accuracy score : 0.906666666667\n",
"escape time : 3.665 s\n",
"the number of unit : 71\n",
"accuracy score : 0.901666666667\n",
"escape time : 3.665 s\n",
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"accuracy score : 0.906666666667\n",
"escape time : 3.943 s\n",
"the number of unit : 73\n",
"accuracy score : 0.905\n",
"escape time : 3.77 s\n",
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"accuracy score : 0.905\n",
"escape time : 3.743 s\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
"escape time : 3.859 s\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.901666666667\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
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"accuracy score : 0.908333333333\n",
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"accuracy score : 0.906666666667\n",
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"escape time : 4.392 s\n",
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"the number of unit : 95\n",
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"accuracy score : 0.906666666667\n",
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"accuracy score : 0.905\n",
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"the number of unit : 100\n",
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"escape time : 4.603 s\n",
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]
}
],
"source": [
"score7 = []\n",
"for n in range(1, 501):\n",
" nn = Classifier(\n",
" layers=[\n",
" Layer(\"Gaussian\", units=n),\n",
" Layer(\"Softmax\")],\n",
" verbose=1,\n",
" learning_rate=0.009,\n",
" n_iter=2,\n",
" )\n",
"\n",
" t0 = time()\n",
" nn.fit(trainX, trainY)\n",
" preds = nn.predict(testX)\n",
" print \"the number of unit : %s\" %n\n",
" print \"accuracy score : %s\" %(accuracy_score(testY, preds))\n",
" print \"escape time : \", round(time()-t0, 3), \"s\"\n",
" score7.append(accuracy_score(testY, preds))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame({'Rectifier' : score1,\n",
" 'Sigmoid' : score2,\n",
" 'Tanh' : score3,\n",
" 'Maxout' : score4,\n",
" 'Linear' : score5,\n",
" 'Softmax' : score6,\n",
" 'Gaussian' : score7}, index=range(1, 501))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gaussian</th>\n",
" <th>Linear</th>\n",
" <th>Maxout</th>\n",
" <th>Rectifier</th>\n",
" <th>Sigmoid</th>\n",
" <th>Softmax</th>\n",
" <th>Tanh</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 0.62</td>\n",
" <td> 0.62</td>\n",
" <td> 0.60</td>\n",
" <td> 0.45</td>\n",
" <td> 0.23</td>\n",
" <td> 0.14</td>\n",
" <td> 0.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.88</td>\n",
" <td> 0.88</td>\n",
" <td> 0.89</td>\n",
" <td> 0.73</td>\n",
" <td> 0.47</td>\n",
" <td> 0.26</td>\n",
" <td> 0.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.89</td>\n",
" <td> 0.81</td>\n",
" <td> 0.49</td>\n",
" <td> 0.34</td>\n",
" <td> 0.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.91</td>\n",
" <td> 0.85</td>\n",
" <td> 0.73</td>\n",
" <td> 0.47</td>\n",
" <td> 0.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 0.91</td>\n",
" <td> 0.91</td>\n",
" <td> 0.90</td>\n",
" <td> 0.88</td>\n",
" <td> 0.77</td>\n",
" <td> 0.45</td>\n",
" <td> 0.90</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Gaussian Linear Maxout Rectifier Sigmoid Softmax Tanh\n",
"1 0.62 0.62 0.60 0.45 0.23 0.14 0.23\n",
"2 0.88 0.88 0.89 0.73 0.47 0.26 0.49\n",
"3 0.90 0.90 0.89 0.81 0.49 0.34 0.80\n",
"4 0.90 0.90 0.91 0.85 0.73 0.47 0.90\n",
"5 0.91 0.91 0.90 0.88 0.77 0.45 0.90"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gaussian</th>\n",
" <th>Linear</th>\n",
" <th>Maxout</th>\n",
" <th>Rectifier</th>\n",
" <th>Sigmoid</th>\n",
" <th>Softmax</th>\n",
" <th>Tanh</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 500.00</td>\n",
" <td> 500.00</td>\n",
" <td> 500.00</td>\n",
" <td> 500.00</td>\n",
" <td> 500.00</td>\n",
" <td> 500.00</td>\n",
" <td> 500.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.39</td>\n",
" <td> 0.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.01</td>\n",
" <td> 0.01</td>\n",
" <td> 0.01</td>\n",
" <td> 0.02</td>\n",
" <td> 0.04</td>\n",
" <td> 0.09</td>\n",
" <td> 0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 0.62</td>\n",
" <td> 0.62</td>\n",
" <td> 0.60</td>\n",
" <td> 0.45</td>\n",
" <td> 0.23</td>\n",
" <td> 0.14</td>\n",
" <td> 0.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.34</td>\n",
" <td> 0.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.90</td>\n",
" <td> 0.91</td>\n",
" <td> 0.90</td>\n",
" <td> 0.38</td>\n",
" <td> 0.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 0.91</td>\n",
" <td> 0.91</td>\n",
" <td> 0.90</td>\n",
" <td> 0.91</td>\n",
" <td> 0.90</td>\n",
" <td> 0.45</td>\n",
" <td> 0.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 0.91</td>\n",
" <td> 0.91</td>\n",
" <td> 0.91</td>\n",
" <td> 0.92</td>\n",
" <td> 0.91</td>\n",
" <td> 0.66</td>\n",
" <td> 0.91</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Gaussian Linear Maxout Rectifier Sigmoid Softmax Tanh\n",
"count 500.00 500.00 500.00 500.00 500.00 500.00 500.00\n",
"mean 0.90 0.90 0.90 0.90 0.90 0.39 0.90\n",
"std 0.01 0.01 0.01 0.02 0.04 0.09 0.04\n",
"min 0.62 0.62 0.60 0.45 0.23 0.14 0.23\n",
"25% 0.90 0.90 0.90 0.90 0.90 0.34 0.90\n",
"50% 0.90 0.90 0.90 0.91 0.90 0.38 0.90\n",
"75% 0.91 0.91 0.90 0.91 0.90 0.45 0.90\n",
"max 0.91 0.91 0.91 0.92 0.91 0.66 0.91"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10c726f90>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10c650f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[['Rectifier', 'Sigmoid', 'Tanh', 'Maxout']].plot(title='linear activation function', figsize=(8,6))\n",
"plt.xlabel('unit')\n",
"plt.ylabel('accuracy')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10c556f10>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11226fad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[['Linear', 'Softmax', 'Gaussian']].plot(title='nonlinear activation function', figsize=(8,6))\n",
"plt.xlabel('unit')\n",
"plt.ylabel('accuracy')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
rnder/data-science-from-scratch | notebook/ch16_logistic_regression.ipynb | 1 | 89285 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Ch.16 - Logistic Regression"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from collections import Counter\n",
"from functools import partial, reduce\n",
"from linear_algebra import dot, vector_add\n",
"from gradient_descent import maximize_stochastic, maximize_batch\n",
"from working_with_data import rescale\n",
"from machine_learning import train_test_split\n",
"from multiple_regression import estimate_beta, predict\n",
"\n",
"import math, random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def logistic(x):\n",
" return 1.0 / (1 + math.exp(-x))\n",
"\n",
"def logistic_prime(x):\n",
" return logistic(x) * (1 - logistic(x))\n",
"\n",
"def logistic_log_likelihood_i(x_i, y_i, beta):\n",
" if y_i == 1:\n",
" return math.log(logistic(dot(x_i, beta)))\n",
" else:\n",
" return math.log(1 - logistic(dot(x_i, beta)))\n",
"\n",
"def logistic_log_likelihood(x, y, beta):\n",
" return sum(logistic_log_likelihood_i(x_i, y_i, beta)\n",
" for x_i, y_i in zip(x, y))\n",
"\n",
"def logistic_log_partial_ij(x_i, y_i, beta, j):\n",
" \"\"\"here i is the index of the data point,\n",
" j the index of the derivative\"\"\"\n",
"\n",
" return (y_i - logistic(dot(x_i, beta))) * x_i[j]\n",
"\n",
"def logistic_log_gradient_i(x_i, y_i, beta):\n",
" \"\"\"the gradient of the log likelihood\n",
" corresponding to the i-th data point\"\"\"\n",
"\n",
" return [logistic_log_partial_ij(x_i, y_i, beta, j)\n",
" for j, _ in enumerate(beta)]\n",
"\n",
"def logistic_log_gradient(x, y, beta):\n",
" return reduce(vector_add,\n",
" [logistic_log_gradient_i(x_i, y_i, beta)\n",
" for x_i, y_i in zip(x,y)])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# [experience, salary, paid_account]\n",
"data = [(0.7,48000,1),(1.9,48000,0),(2.5,60000,1),(4.2,63000,0),(6,76000,0),(6.5,69000,0),(7.5,76000,0),(8.1,88000,0),(8.7,83000,1),(10,83000,1),(0.8,43000,0),(1.8,60000,0),(10,79000,1),(6.1,76000,0),(1.4,50000,0),(9.1,92000,0),(5.8,75000,0),(5.2,69000,0),(1,56000,0),(6,67000,0),(4.9,74000,0),(6.4,63000,1),(6.2,82000,0),(3.3,58000,0),(9.3,90000,1),(5.5,57000,1),(9.1,102000,0),(2.4,54000,0),(8.2,65000,1),(5.3,82000,0),(9.8,107000,0),(1.8,64000,0),(0.6,46000,1),(0.8,48000,0),(8.6,84000,1),(0.6,45000,0),(0.5,30000,1),(7.3,89000,0),(2.5,48000,1),(5.6,76000,0),(7.4,77000,0),(2.7,56000,0),(0.7,48000,0),(1.2,42000,0),(0.2,32000,1),(4.7,56000,1),(2.8,44000,1),(7.6,78000,0),(1.1,63000,0),(8,79000,1),(2.7,56000,0),(6,52000,1),(4.6,56000,0),(2.5,51000,0),(5.7,71000,0),(2.9,65000,0),(1.1,33000,1),(3,62000,0),(4,71000,0),(2.4,61000,0),(7.5,75000,0),(9.7,81000,1),(3.2,62000,0),(7.9,88000,0),(4.7,44000,1),(2.5,55000,0),(1.6,41000,0),(6.7,64000,1),(6.9,66000,1),(7.9,78000,1),(8.1,102000,0),(5.3,48000,1),(8.5,66000,1),(0.2,56000,0),(6,69000,0),(7.5,77000,0),(8,86000,0),(4.4,68000,0),(4.9,75000,0),(1.5,60000,0),(2.2,50000,0),(3.4,49000,1),(4.2,70000,0),(7.7,98000,0),(8.2,85000,0),(5.4,88000,0),(0.1,46000,0),(1.5,37000,0),(6.3,86000,0),(3.7,57000,0),(8.4,85000,0),(2,42000,0),(5.8,69000,1),(2.7,64000,0),(3.1,63000,0),(1.9,48000,0),(10,72000,1),(0.2,45000,0),(8.6,95000,0),(1.5,64000,0),(9.8,95000,0),(5.3,65000,0),(7.5,80000,0),(9.9,91000,0),(9.7,50000,1),(2.8,68000,0),(3.6,58000,0),(3.9,74000,0),(4.4,76000,0),(2.5,49000,0),(7.2,81000,0),(5.2,60000,1),(2.4,62000,0),(8.9,94000,0),(2.4,63000,0),(6.8,69000,1),(6.5,77000,0),(7,86000,0),(9.4,94000,0),(7.8,72000,1),(0.2,53000,0),(10,97000,0),(5.5,65000,0),(7.7,71000,1),(8.1,66000,1),(9.8,91000,0),(8,84000,0),(2.7,55000,0),(2.8,62000,0),(9.4,79000,0),(2.5,57000,0),(7.4,70000,1),(2.1,47000,0),(5.3,62000,1),(6.3,79000,0),(6.8,58000,1),(5.7,80000,0),(2.2,61000,0),(4.8,62000,0),(3.7,64000,0),(4.1,85000,0),(2.3,51000,0),(3.5,58000,0),(0.9,43000,0),(0.9,54000,0),(4.5,74000,0),(6.5,55000,1),(4.1,41000,1),(7.1,73000,0),(1.1,66000,0),(9.1,81000,1),(8,69000,1),(7.3,72000,1),(3.3,50000,0),(3.9,58000,0),(2.6,49000,0),(1.6,78000,0),(0.7,56000,0),(2.1,36000,1),(7.5,90000,0),(4.8,59000,1),(8.9,95000,0),(6.2,72000,0),(6.3,63000,0),(9.1,100000,0),(7.3,61000,1),(5.6,74000,0),(0.5,66000,0),(1.1,59000,0),(5.1,61000,0),(6.2,70000,0),(6.6,56000,1),(6.3,76000,0),(6.5,78000,0),(5.1,59000,0),(9.5,74000,1),(4.5,64000,0),(2,54000,0),(1,52000,0),(4,69000,0),(6.5,76000,0),(3,60000,0),(4.5,63000,0),(7.8,70000,0),(3.9,60000,1),(0.8,51000,0),(4.2,78000,0),(1.1,54000,0),(6.2,60000,0),(2.9,59000,0),(2.1,52000,0),(8.2,87000,0),(4.8,73000,0),(2.2,42000,1),(9.1,98000,0),(6.5,84000,0),(6.9,73000,0),(5.1,72000,0),(9.1,69000,1),(9.8,79000,1),]\n",
"data = list(map(list, data)) # change tuples to lists\n",
"\n",
"x = [[1] + row[:2] for row in data] # each element is [1, experience, salary]\n",
"y = [row[2] for row in data] # each element is paid_account"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"$paid acount = \\beta_0+\\beta_1 experience+\\beta_2 salary+\\epsilon$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"unpaid_salaries = [row[0] for row in data if row[2] == 0]\n",
"unpaid_users = [row[1] for row in data if row[2] == 0]\n",
"paid_salaries = [row[0] for row in data if row[2] == 1]\n",
"paid_users = [row[1] for row in data if row[2] == 1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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d9Pe2RMNUpFIQyWUdVWr+ZMegyWW1zcsu28QNN1wbaYJlviomHTbNjJFL2PBN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\npUwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1d2bc24fb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(paid_salaries, paid_users, marker='o', label='paid')\n",
"plt.scatter(unpaid_salaries, unpaid_users, marker='+', label='unpaid')\n",
"plt.title(\"Paid and Unpaid Users\")\n",
"plt.xlabel(\"years experience\")\n",
"plt.ylabel(\"annual salary\")\n",
"plt.legend(loc=8)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"linear regression:\n",
"[0.25990975738309924, 0.4387570870053767, -0.4274411364799935]\n"
]
}
],
"source": [
"print(\"linear regression:\")\n",
"\n",
"rescaled_x = rescale(x)\n",
"beta = estimate_beta(rescaled_x, y) # [0.26, 0.43, -0.43]\n",
"print(beta)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1d2bc24f208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"predictions = [predict(x_i, beta) for x_i in rescaled_x]\n",
"\n",
"plt.scatter(predictions, y)\n",
"plt.xlabel(\"predicted\")\n",
"plt.ylabel(\"actual\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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FRGJMIS4iEmP/A/dmR79haDlEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1d2bc23e7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sig_x = np.arange(-10., 10., 0.2)\n",
"sig_y = [logistic(_) for _ in sig_x]\n",
"plt.plot(sig_x, sig_y)\n",
"plt.margins(0, 0.1)\n",
"plt.title(\"logistic function\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"$\n",
"logistic(x) = \\frac{1.0} {1+e^-x}\\\\\n",
"p(y_i|x_i\\beta) = f(x_i\\beta)^{y_i}(1-f(x_i\\beta))^{1-y_i} \\\\\n",
"log L(\\beta|x_iy_i) = y_i log f(x_i\\beta) + (1 - y_i) log(1 - f(x_i\\beta))\n",
"$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"logistic regression:\n",
"beta_batch [-1.906182482651773, 4.053083869373743, -3.8788953691426906]\n",
"beta stochastic [-1.9033596650613738, 4.048485018705759, -3.8747571420402442]\n"
]
}
],
"source": [
"print(\"logistic regression:\")\n",
"\n",
"random.seed(0)\n",
"x_train, x_test, y_train, y_test = train_test_split(rescaled_x, y, 0.33)\n",
"\n",
"# want to maximize log likelihood on the training data\n",
"fn = partial(logistic_log_likelihood, x_train, y_train)\n",
"gradient_fn = partial(logistic_log_gradient, x_train, y_train)\n",
"\n",
"# pick a random starting point\n",
"beta_0 = [1, 1, 1]\n",
"# beta_0 = [random.random() for _ in range(3)]\n",
"\n",
"# and maximize using gradient descent\n",
"beta_hat = maximize_batch(fn, gradient_fn, beta_0)\n",
"\n",
"print(\"beta_batch\", beta_hat)\n",
"\n",
"# beta_0 = [1, 1, 1]\n",
"beta_hat = maximize_stochastic(logistic_log_likelihood_i,\n",
" logistic_log_gradient_i,\n",
" x_train, y_train, beta_0)\n",
"\n",
"print(\"beta stochastic\", beta_hat)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Confusion Matrix\n",
"$Accuracy(정확도) = \\frac{TP+TN} {TP+TN+FN+FP} \\\\\n",
"Precision(정밀도) = \\frac{TP} {TP+FP} \\\\\n",
"Recall(재현율) = \\frac{TP} {TP+FN} \\\\\n",
"F1 Score = \\frac{2 * Recall * Precision} {Recall + Precision}$"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precision 0.9333333333333333\n",
"recall 0.8235294117647058\n"
]
}
],
"source": [
"true_positives = true_negatives = false_positives = false_negatives = 0\n",
"\n",
"for x_i, y_i in zip(x_test, y_test):\n",
" predict = logistic(dot(beta_hat, x_i))\n",
"\n",
" if y_i == 1 and predict >= 0.5: # TP: paid and we predict paid\n",
" true_positives += 1\n",
" elif y_i == 1: # FN: paid and we predict unpaid\n",
" false_negatives += 1\n",
" elif y_i == 0 and predict >= 0.5: # FP: unpaid and we predict paid\n",
" false_positives += 1\n",
" else: # TN: unpaid and we predict unpaid\n",
" true_negatives += 1\n",
" \n",
"precision = true_positives / (true_positives + false_positives)\n",
"recall = true_positives / (true_positives + false_negatives)\n",
"\n",
"print(\"precision\", precision)\n",
"print(\"recall\", recall)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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6XZplYGCA/v5+pk2b1nG7yyOtclt02rZpJN5aZQcGBrj++usBmDFjxrDWu554hmqvFe/B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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1d2bc2b4780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"predictions = [logistic(dot(beta_hat, x_i)) for x_i in x_test]\n",
"plt.scatter(predictions, y_test)\n",
"plt.xlabel(\"predicted probability\")\n",
"plt.ylabel(\"actual outcome\")\n",
"plt.title(\"Logistic Regression Predicted vs. Actual\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Decision Boundary\n",
"https://www.coursera.org/learn/machine-learning/lecture/WuL1H/decision-boundary"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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Te6tW9efYY8/scpkcd28uH3ktbSNpM2BzM1uenEilxS2cZMuVg2qStTuk97Oc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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1d2bc3b9390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(paid_salaries, paid_users, marker='o', label='paid')\n",
"plt.scatter(unpaid_salaries, unpaid_users, marker='+', label='unpaid')\n",
"plt.title(\"Logistic Regression Decision Boundary\")\n",
"plt.xlabel(\"years experience\")\n",
"plt.ylabel(\"annual salary\")\n",
"plt.legend(loc=8)\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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
wehlutyk/brainscopypaste | data/notebooks/Model(time=Time.discrete, source=Source.majority, past=Past.last_bin, durl=Durl.exclude_past, max_distance=1) - variation.ipynb | 1 | 3551235 | null | gpl-3.0 |
dhpollack/programming_notebooks | pymc-test.ipynb | 1 | 226659 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pymc\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"disasters_array = np.array([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n",
" 3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n",
" 2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,\n",
" 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n",
" 0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
" 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n",
" 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])\n",
"\n",
"switchpoint = pymc.DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')\n",
"early_mean = pymc.Exponential('early_mean', beta=1.)\n",
"late_mean = pymc.Exponential('late_mean', beta=1.)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"@pymc.deterministic(plot=False)\n",
"def rate(s=switchpoint, e=early_mean, l=late_mean):\n",
" ''' Concatenate Poisson Means '''\n",
" out = np.empty(len(disasters_array))\n",
" out[:s] = e\n",
" out[s:] = l\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"disasters = pymc.Poisson('disasters', mu=rate, value=disasters_array, observed=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'lower': 0, 'upper': 110}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"switchpoint.parents"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'mu': <pymc.PyMCObjects.Deterministic 'rate' at 0x7fbd66579c88>}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"disasters.parents"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{<pymc.distributions.new_dist_class.<locals>.new_class 'disasters' at 0x7fbd6659d198>}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rate.children"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6, 3, 3, 5, 4, 5, 3, 1,\n",
" 4, 4, 1, 5, 5, 3, 4, 2, 5, 2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3,\n",
" 0, 0, 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1, 0, 1, 0, 1, 0,\n",
" 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2, 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2,\n",
" 0, 0, 1, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"disasters.value"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(63)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"switchpoint.value"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(0.10579268109853043)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"early_mean.value"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(1.1865847805942225)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"late_mean.value"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 0.10579268, 0.10579268,\n",
" 0.10579268, 0.10579268, 0.10579268, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478, 1.18658478, 1.18658478, 1.18658478, 1.18658478,\n",
" 1.18658478])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rate.value"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mymodel = [\n",
" disasters_array,\n",
" switchpoint,\n",
" early_mean,\n",
" late_mean,\n",
" rate,\n",
" disasters]\n",
"\n",
"\n",
"M = pymc.MCMC(mymodel)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 10000 of 10000 complete in 0.6 sec"
]
}
],
"source": [
"M.sample(iter=10000, burn=1000, thin=10)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M.trace('switchpoint')[:]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/projects/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbd65f84470>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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2wUe5rJQvItJM6n73sNYO4735+x2LLvn+h8Ar1laarMeFaySsf4XEufQ0k7M5qi+Zv3t8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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd65f799b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"sns.distplot(M.trace('late_mean')[:])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting switchpoint\n",
"Plotting early_mean\n",
"Plotting late_mean\n"
]
},
{
"data": {
"image/png": 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XU1VVza5du9myZQuDBtVQW9ufpWrMmL171qsap/kfPbp/v7Xy8nK6u63Pd/25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\nwOZIkiRJkgD4/zwMoB4ZjeJgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd65f4ce10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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QNIixI1iLhKEW8gm5X/MOVVUbgIaO94qilACaqqrb2t9fBDwJPAWsB65RVXVZ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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd62050dd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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4XLF/gJqafN2vg0GNhgZvvsJz9LGT2DKTaWzBYM+yzU0+irTc71chHDenUhRF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AYklbGJiIj4MKHifRJOlJVNY4IORfJ4+uknaW5uoqysnEMOOTLocEQkIL6SHWPMWOAGYH9gK3Cf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd61f5ce48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pymc.Matplot.plot(M)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Anaconda (Python 3)",
"language": "python",
"name": "anaconda3"
},
"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 |
c-martinez/w2vExample | index.ipynb | 1 | 107442 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Download data from [google](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing) or it's [mirror a](https://s3.amazonaws.com/mordecai-geo/GoogleNews-vectors-negative300.bin.gz)\n",
"\n",
"\n",
"\n",
"Make sure you have widget extensions installed:\n",
"\n",
"```jupyter nbextension enable --py --sys-prefix widgetsnbextension```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import gensim\n",
"from wordcloud import WordCloud\n",
"from IPython.display import display, clear_output\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"modelName = 'GoogleNews-vectors-negative300.bin'\n",
"model = gensim.models.KeyedVectors.load_word2vec_format(modelName, binary=True)\n",
"cloud = WordCloud()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# plot func\n",
"fig, ax = plt.subplots()\n",
"plt.close(fig)\n",
"\n",
"def drawWordCloud(seedWord):\n",
" try:\n",
" closestWords = model.most_similar(seedWord, topn=40)\n",
" except:\n",
" closestWords = [ ('?', 1) ]\n",
" closestWords = { k:v for k,v in closestWords }\n",
" cloud_im = cloud.generate_from_frequencies(closestWords)\n",
" \n",
" ax.clear()\n",
" ax.imshow(cloud_im)\n",
" ax.axis('off')\n",
" display(fig)\n",
"\n",
"def on_input_change(change):\n",
" clear_output(wait=True)\n",
" drawWordCloud(change['new'])\n",
"\n",
"inputField = widgets.Text('Utrecht', description='Seed word')\n",
"inputField.observe(on_input_change, names='value')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# plot func\n",
"fig2, ax2 = plt.subplots()\n",
"plt.close(fig2)\n",
"\n",
"mya = ''\n",
"mylike = ''\n",
"myisto = ''\n",
"\n",
"def drawAnalogy(a=None,like=None,isTo=None):\n",
" global mya\n",
" global mylike\n",
" global myisto\n",
" \n",
" a = mya if a is None else a\n",
" like = mylike if like is None else like\n",
" isTo = myisto if isTo is None else isTo\n",
"\n",
" mya = a\n",
" mylike = like\n",
" myisto = isTo\n",
" \n",
" print '%s : ? :: %s : %s'%(a, like, isTo)\n",
" try:\n",
" closestWords = model.most_similar(positive=[a,isTo], negative=[like], topn=40)\n",
" except:\n",
" closestWords = [ ('?',1) ]\n",
" closestWords = { k:v for k,v in closestWords }\n",
" cloud_im = cloud.generate_from_frequencies(closestWords)\n",
" \n",
" ax2.clear()\n",
" ax2.imshow(cloud_im)\n",
" ax2.axis('off')\n",
" display(fig2) \n",
"\n",
"def on_input_change_a(change):\n",
" clear_output(wait=True)\n",
" drawAnalogy(a=change['new'])\n",
"\n",
"def on_input_change_like(change):\n",
" clear_output(wait=True)\n",
" drawAnalogy(like=change['new'])\n",
"\n",
"def on_input_change_isTo(change):\n",
" clear_output(wait=True)\n",
" drawAnalogy(isTo=change['new'])\n",
"\n",
"inputA = widgets.Text('King', description='a')\n",
"inputA.observe(on_input_change_a, names='value')\n",
"\n",
"inputLike = widgets.Text('Man', description='like')\n",
"inputLike.observe(on_input_change_like, names='value')\n",
"\n",
"inputIsTo = widgets.Text('Woman', description='is to')\n",
"inputIsTo.observe(on_input_change_isTo, names='value')\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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j1KeRkTqX1o79EfOKRvPrJyi6fgGmktSIOkOOGWerfBa58aLUqSm5ZbFsXd2/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\nEQAAw4gAABhGBADAMCIAAIYRAQAw7Be9hzYKGkGEBgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff42b692d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"drawWordCloud(seedWord='Utrecht')\n",
"display(inputField)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"London : ? :: Paris : France\n"
]
},
{
"data": {
"image/png": 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iYnxWK4JCEZp/lN+fjjwBAU1REe7aWvTTpuGqrKRt9Wr006bhdzqDAehjJeXM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\nmAQAxiQAMBYApLw1txVoNwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3fa8d2450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"drawAnalogy(a='King',like='Man',isTo='Woman')\n",
"display(inputA)\n",
"display(inputLike)\n",
"display(inputIsTo)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"widgets": {
"state": {
"fe0593596316430a823391d613f32dc5": {
"views": [
{
"cell_index": 8
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
yigong/AY250 | hw3/hw3-2.ipynb | 1 | 4229 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib\n",
"tp = [('date','i8'),('value','f8')]\n",
"yahoo_data = np.loadtxt('yahoo_data.txt',dtype=tp, skiprows=1)\n",
"google_data = np.loadtxt('google_data.txt',dtype=tp, skiprows=1)\n",
"ny_data = np.loadtxt('ny_temps.txt',dtype=tp, skiprows=1)\n",
"f0, ax0 = plt.subplots()\n",
"ax2 = ax0.twinx()\n",
"yahoo_line = ax0.plot(yahoo_data['date'], yahoo_data['value'], color = 'm', label = 'Yahoo! Stock Value',linewidth = 1.5)\n",
"google_line = ax0.plot(google_data['date'], google_data['value'], color = 'b', label = 'Google Stock Value', linewidth = 1.5)\n",
"ny_line = ax2.plot(ny_data['date'], ny_data['value'], color = 'r', label = 'NY Mon. High Temp', linestyle = '--', linewidth = 1.5)\n",
"lines = yahoo_line+google_line+ny_line\n",
"labels = [l.get_label() for l in lines]\n",
"ax0.legend(lines,labels, 'center left',fontsize = 'medium', frameon = False)\n",
"ax0.set_ylim(-20,780)\n",
"ax0.set_xlim(48800,55600)\n",
"ax0.set_ylabel('Value (Dollars)', fontsize = 'large')\n",
"ax0.set_xlabel('Date (MJD)', fontsize = 'large')\n",
"ax0.set_title('New York Temperature, Google, and Yahoo!', fontsize ='x-large')\n",
"ax2.set_ylim(-150,100)\n",
"ax2.set_ylabel('Temperature ($^\\circ$F)', fontsize = 'large')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Using matplotlib backend: MacOSX\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax0.plot?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.close('all')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax0.set_xlabel?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"[yahoo_line,google_line]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"[[<matplotlib.lines.Line2D at 0x109fbe4d0>],\n",
" [<matplotlib.lines.Line2D at 0x109fde990>]]"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yahoo_line"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"[<matplotlib.lines.Line2D at 0x109fbe4d0>]"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a, = yahoo_line\n",
"print a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Line2D(Yahoo! Stock Value)\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"<matplotlib.lines.Line2D at 0x109fbe4d0>"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
richardotis/pycalphad-fitting | Al-Ni/Al-Ni-L12-Results.ipynb | 1 | 1788910 | null | mit |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/assignment/week3/Analyzing product sentiment.ipynb | 1 | 557233 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Predicting sentiment from product reviews\n",
"\n",
"#Fire up GraphLab Create"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import graphlab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Read some product review data\n",
"\n",
"Loading reviews for a set of baby products. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] This non-commercial license of GraphLab Create is assigned to [email protected] will expire on September 21, 2016. For commercial licensing options, visit https://dato.com/buy/.\n",
"\n",
"[INFO] Start server at: ipc:///tmp/graphlab_server-1152 - Server binary: C:\\Anaconda\\envs\\dato-env\\lib\\site-packages\\graphlab\\unity_server.exe - Server log: C:\\Users\\tvu\\AppData\\Local\\Temp\\graphlab_server_1443305107.log.0\n",
"[INFO] GraphLab Server Version: 1.6.1\n"
]
}
],
"source": [
"products = graphlab.SFrame('amazon_baby.gl/')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Let's explore this data together\n",
"\n",
"Data includes the product name, the review text and the rating of the review. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Flannel Wipes</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">These flannel wipes are<br>OK, but in my opinion ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 3 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Planetwise Flannel Wipes | These flannel wipes are OK... | 3.0 |\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"[10 rows x 3 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Build the word count vector for each review"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"products['word_count'] = graphlab.text_analytics.count_words(products['review'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Flannel Wipes</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">These flannel wipes are<br>OK, but in my opinion ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'quilt': 1L,<br>'it': 1L, 'comfortable': ...</td>\n",
" </tr>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'ingenious': 1L, 'and':<br>3L, 'love': 2L, ...</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'parents!!':<br>1L, 'all': 2L, 'puppe ...</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'cute': 1L,<br>'help': 2L, 'doll': 1L, ...</td>\n",
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" <tr>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'shop': 1L, 'be': 1L,<br>'is': 1L, 'it': 1L, ' ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'feeding,': 1L, 'and':<br>2L, 'all': 1L, 'right': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1L, 'help': 1L,<br>'give': 1L, 'is': 1L, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'journal.': 1L, 'all':<br>1L, 'standarad': 1L, ...</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Planetwise Flannel Wipes | These flannel wipes are OK... | 3.0 |\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+\n",
"| word_count |\n",
"+-------------------------------+\n",
"| {'and': 5L, 'stink': 1L, '... |\n",
"| {'and': 3L, 'love': 1L, 'i... |\n",
"| {'and': 2L, 'quilt': 1L, '... |\n",
"| {'ingenious': 1L, 'and': 3... |\n",
"| {'and': 2L, 'parents!!': 1... |\n",
"| {'and': 2L, 'cute': 1L, 'h... |\n",
"| {'shop': 1L, 'be': 1L, 'is... |\n",
"| {'feeding,': 1L, 'and': 2L... |\n",
"| {'and': 1L, 'help': 1L, 'g... |\n",
"| {'journal.': 1L, 'all': 1L... |\n",
"+-------------------------------+\n",
"[10 rows x 4 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"graphlab.canvas.set_target('ipynb')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
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Standard Size (24mm)\": {\"frequency\": 22, \"value\": \"Medela Contact ...\"}, \"EZ-Freeze Cereal on the Go (Colors May Vary)\": {\"frequency\": 27, \"value\": \"EZ-Freeze Cereal ...\"}, \"Friendly Toys, Little Playzone with Electronic Sound and Lights\": {\"frequency\": 79, \"value\": \"Friendly Toys, ...\"}, \"Safety 1st Easy Saver Diaper Pail\": {\"frequency\": 24, \"value\": \"Safety 1st Easy ...\"}, \"Combi Flare Lightweight Stroller in Mandarin\": {\"frequency\": 29, \"value\": \"Combi Flare ...\"}, \"Fisher-Price Healthy Care Booster Seat, Green/Blue\": {\"frequency\": 67, \"value\": \"Fisher-Price ...\"}, \"green sprouts Wooden Brush and Comb Set, Natural\": {\"frequency\": 24, \"value\": \"green sprouts ...\"}, \"OXO Tot On-the-Go Feeding Spoon, Green\": {\"frequency\": 20, \"value\": \"OXO Tot On-the-Go ...\"}, \"Evenflo Top of Stair Gate\": {\"frequency\": 50, \"value\": \"Evenflo Top of ...\"}, \"Fisher-Price Cradle 'N Swing, My Little Snugabunny\": {\"frequency\": 278, \"value\": \"Fisher-Price ...\"}, \"Summer Infant Complete Coverage Color Video Monitor Set with 7" LCD Screen and 1.8" Handheld Unit\": {\"frequency\": 35, \"value\": \"Summer Infant ...\"}, \"Munchkin Caterpillar Spillers Stacking Cups\": {\"frequency\": 27, \"value\": \"Munchkin ...\"}, \"HALO Early Walker SleepSack Lightweight Knit Wearable Blanket, Blue, Large\": {\"frequency\": 29, \"value\": \"HALO Early Walker ...\"}, \"KidCo S353 Door Lever Lock White\": {\"frequency\": 18, \"value\": \"KidCo S353 Door ...\"}, \"Lily's Home Starry Night Projector and Sound Shooter. With 6 Lullabies and 4 Nature Sounds. Large LCD Alarm Clock\": {\"frequency\": 31, \"value\": \"Lily's Home Starry ...\"}, \"Sassy Baby Food Nurser, Colors May Vary\": {\"frequency\": 36, \"value\": \"Sassy Baby Food ...\"}, \"Boppy Cottony Cute 2-Sided Slipcover, Polka Stripe Green\": {\"frequency\": 23, \"value\": \"Boppy Cottony Cute ...\"}, \"Infant Bucket Seat Liner Color: Pink\": {\"frequency\": 21, \"value\": \"Infant Bucket Seat ...\"}, \"Safety 1st Whale and Baby Spout Guard\": {\"frequency\": 27, \"value\": \"Safety 1st Whale ...\"}, \"NUK Ultra Thin Breast Pads, Pack of 2, White, 120-Count\": {\"frequency\": 39, \"value\": \"NUK Ultra Thin ...\"}, \"Carters Super Soft Dot Changing Pad Cover, Chocolate\": {\"frequency\": 47, \"value\": \"Carters Super Soft ...\"}, \"Babe Ease Original Clean Shopper, Blue Zoo\": {\"frequency\": 20, \"value\": \"Babe Ease Original ...\"}, \"FunBites Hearts - Cuts kids' food into fun-shaped bite-sized pieces . . . Great for picky eaters and bento!\": {\"frequency\": 20, \"value\": \"FunBites Hearts - ...\"}, \"Britax Car Seat Travel Cart, Black\": {\"frequency\": 28, \"value\": \"Britax Car Seat ...\"}, \"Dr. Brown's Microwave Steam Sterilizer\": {\"frequency\": 24, \"value\": \"Dr. Brown's ...\"}, \"Baby Brezza Temperature Control Kettle, White/Grey\": {\"frequency\": 30, \"value\": \"Baby Brezza ...\"}, \"BRICA Day & Night Light Musical Auto Mirror for in Car Safety, Grey\": {\"frequency\": 20, \"value\": \"BRICA Day & ...\"}, \"Steribottle Ready to Use Disposable Baby Bottles, 10-Count\": {\"frequency\": 23, \"value\": \"Steribottle Ready ...\"}, \"Bumbo Floor Seat, Aqua\": {\"frequency\": 51, \"value\": \"Bumbo Floor Seat, ...\"}, \"Joovy Nook Highchair, White Leatherette\": {\"frequency\": 22, \"value\": \"Joovy Nook ...\"}, \"Diaper Dekor Plus 2-Pack Refill Biodegradable\": {\"frequency\": 20, \"value\": \"Diaper Dekor Plus ...\"}, \"Safety 1st Wide Doorways Fabric Gate, Natural\": {\"frequency\": 20, \"value\": \"Safety 1st Wide ...\"}, \"Lansinoh mOmma Feeding Bottle, 5 Ounce\": {\"frequency\": 66, \"value\": \"Lansinoh mOmma ...\"}, \"Woombie Air Ventilated Baby Swaddle ~ Choose Size/Color (Big Baby 14-19 lbs, Love Print)\": {\"frequency\": 21, \"value\": \"Woombie Air ...\"}, \"Regalo Top of Stair Gate, White\": {\"frequency\": 22, \"value\": \"Regalo Top of ...\"}, \"Prince Lionheart Soft Booster Seat in Green\": {\"frequency\": 81, \"value\": \"Prince Lionheart ...\"}, \"Playtex Embrace Breast Pump System\": {\"frequency\": 22, \"value\": \"Playtex Embrace ...\"}, \"Spasilk 100% Cotton Hooded Terry Bath Towel with 4 Washcloths, Beige\": {\"frequency\": 26, \"value\": \"Spasilk 100% ...\"}, \"WubbaNub Pink Bear\": {\"frequency\": 18, \"value\": \"WubbaNub Pink Bear\"}, \"The First Years Clean Air Diaper Disposal System\": {\"frequency\": 29, \"value\": \"The First Years ...\"}, \"Aden and Anais UpAwaySwddleBlnkts\": {\"frequency\": 188, \"value\": \"Aden and Anais ...\"}, \"Leachco Back 'N Belly Contoured Body Pillow, Ivory\": {\"frequency\": 283, \"value\": \"Leachco Back 'N ...\"}, \"The First Years 2 Pack GumDrop Newborn Pacifier, Blue/Green\": {\"frequency\": 20, \"value\": \"The First Years 2 ...\"}, \"Safety 1st Alpha Omega Elite Convertible Car Seat, Lamont\": {\"frequency\": 46, \"value\": \"Safety 1st Alpha ...\"}, \"Fisher-Price Deluxe Jumperoo\": {\"frequency\": 77, \"value\": \"Fisher-Price ...\"}, \"BabyPlus Prenatal Education System\": {\"frequency\": 41, \"value\": \"BabyPlus Prenatal ...\"}, \"Lorex BB2411 2.4" Sweet Peek Video Baby Monitor with IR Night Vision and Zoom, White\": {\"frequency\": 29, \"value\": \"Lorex BB2411 ...\"}, \"Mommy's Helper Kid Keeper\": {\"frequency\": 21, \"value\": \"Mommy's Helper Kid ...\"}, \"Summer Infant SwaddleMe Adjustable Infant Wrap, 3-Pack, Mom & Baby\": {\"frequency\": 77, \"value\": \"Summer Infant ...\"}, \"KidCo Angle-Mount Safeway Gate\": {\"frequency\": 24, \"value\": \"KidCo Angle-Mount ...\"}, \"BABYBJORN Soft Bib 2 Pack - Red/Blue\": {\"frequency\": 129, \"value\": \"BABYBJORN Soft Bib ...\"}, \"Foscam FBM3501 Digital Video Baby Monitor - 2.4 Ghz with Pan/Tilt, Nightvision and Two-Way Audio/Video Camera with 3.5-Inch LCD (White/Gray)\": {\"frequency\": 66, \"value\": \"Foscam FBM3501 ...\"}, \"Metal Wall Decor Butterfly Sculpture 29x15\": {\"frequency\": 20, \"value\": \"Metal Wall Decor ...\"}, \"Jeep Liberty Renegade Walker, Storm\": {\"frequency\": 22, \"value\": \"Jeep Liberty ...\"}, \"JJ Cole Satchel Diaper Bag, Green Arbor\": {\"frequency\": 51, \"value\": \"JJ Cole Satchel ...\"}, \"Diaper Dekor Plus Diaper Disposal System\": {\"frequency\": 126, \"value\": \"Diaper Dekor Plus ...\"}, \"Prince Lionheart Fireplace Guard with Two Corners\": {\"frequency\": 20, \"value\": \"Prince Lionheart ...\"}, \"Sassy Developmental Sensory Ball Set - Inspires Touch\": {\"frequency\": 28, \"value\": \"Sassy ...\"}, \"Ameda Purely Yours Breast Pump\": {\"frequency\": 68, \"value\": \"Ameda Purely Yours ...\"}, \"Thirsties Duo Wrap Diaper Cover with Hook and Loop, Aqua, Size 1\": {\"frequency\": 18, \"value\": \"Thirsties Duo Wrap ...\"}, \"Plug 'N Outlet Cover\": {\"frequency\": 19, \"value\": \"Plug 'N Outlet ...\"}, \"Safety 1st 2 Count Side By Side Cabinet Lock\": {\"frequency\": 30, \"value\": \"Safety 1st 2 Count ...\"}, \"RECARO ProSPORT Combination Harness To Booster Car Seat, Blue Opal\": {\"frequency\": 77, \"value\": \"RECARO ProSPORT ...\"}, \"Munchkin Feeding Set, 15 Pack\": {\"frequency\": 36, \"value\": \"Munchkin Feeding ...\"}, \"The Shrunks Indoor Toddler Inflatable Travel Bed\": {\"frequency\": 93, \"value\": \"The Shrunks Indoor ...\"}, \"Prince Lionheart Corner Guards, Chocolate Brown\": {\"frequency\": 56, \"value\": \"Prince Lionheart ...\"}, \"Regalo Easy Step Extra Wide Walk Thru Gate, White\": {\"frequency\": 18, \"value\": \"Regalo Easy Step ...\"}, \"Davinci Jenny Lind 3-in-1 Convertible Crib, Cherry\": {\"frequency\": 43, \"value\": \"Davinci Jenny Lind ...\"}, \"9V Auto Adapter Car Vehicle Lighter adapter for Medela Pump-in-Style Replaces Part # 67174 Retail Packaging\": {\"frequency\": 22, \"value\": \"9V Auto Adapter ...\"}, \"Evenflo Exersaucer Triple Fun Active Learning Center, Life in The Amazon\": {\"frequency\": 30, \"value\": \"Evenflo Exersaucer ...\"}, \"Tiny Love Symphony in Motion Farm Animal Mobile (Styles May Vary)\": {\"frequency\": 24, \"value\": \"Tiny Love Symphony ...\"}, \"Fisher-Price My Little Snugabunny Newborn Rock n' Play Sleeper\": {\"frequency\": 68, \"value\": \"Fisher-Price My ...\"}, \"The Art of CureTM *SAFETY KNOTTED* Raw Butterscotch - 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White\": {\"frequency\": 35, \"value\": \"KidCo BabySteps ...\"}, \"Natursutten BPA-Free Natural Rubber Pacifier, Orthodontic, 0-6 Months\": {\"frequency\": 34, \"value\": \"Natursutten BPA- ...\"}, \"Jeep Cherokee Sport Stroller, Brick Red\": {\"frequency\": 92, \"value\": \"Jeep Cherokee ...\"}, \"Mother's Touch Deluxe Baby Bather\": {\"frequency\": 20, \"value\": \"Mother's Touch ...\"}, \"Evenflo Snugli Comfort Vent Carrier\": {\"frequency\": 22, \"value\": \"Evenflo Snugli ...\"}, \"Graco DuoDiner LX Highchair, Metropolis\": {\"frequency\": 22, \"value\": \"Graco DuoDiner LX ...\"}, \"Regalo Easy Step Extra Tall Walk Thru Gate - White\": {\"frequency\": 101, \"value\": \"Regalo Easy Step ...\"}, \"Dream On Me 4 in 1 Aden Convertible Mini Crib, Natural\": {\"frequency\": 25, \"value\": \"Dream On Me 4 in 1 ...\"}, \"Luvable Friends 6 Pack Washcloths, Blue\": {\"frequency\": 27, \"value\": \"Luvable Friends 6 ...\"}, \"South Shore Savannah Collection 4-Drawer Chest, White\": {\"frequency\": 22, \"value\": \"South Shore ...\"}, \"Skip Hop Zoo Safety Harness, Monkey\": {\"frequency\": 32, \"value\": \"Skip Hop Zoo ...\"}, \"Snap 'N Go Infant Car Seat Carrier\": {\"frequency\": 46, \"value\": \"Snap 'N Go Infant ...\"}, \"DEX Products Sound Sleeper SS-01\": {\"frequency\": 103, \"value\": \"DEX Products Sound ...\"}, \"Prince Lionheart Faucet Extender, Gumball Green\": {\"frequency\": 31, \"value\": \"Prince Lionheart ...\"}, \"OXO Tot Bottle Brush with Nipple Cleaner, Orange\": {\"frequency\": 25, \"value\": \"OXO Tot Bottle ...\"}, \"Philips AVENT Range BPA-Free Front Teeth Teether, Classic\": {\"frequency\": 24, \"value\": \"Philips AVENT ...\"}, \"Combi Activity Walker Black\": {\"frequency\": 18, \"value\": \"Combi Activity ...\"}, \"Status Veneto Glider and Nursing Ottoman, White/Beige\": {\"frequency\": 24, \"value\": \"Status Veneto ...\"}, \"Odorless Diaper Pail by Safety 1st\": {\"frequency\": 28, \"value\": \"Odorless Diaper ...\"}, \"Prince Lionheart Ultimate Wipes Warmer\": {\"frequency\": 152, \"value\": \"Prince Lionheart ...\"}, \"Munchkin Arm and Hammer Bag Refill, 36 Bags\": {\"frequency\": 25, \"value\": \"Munchkin Arm and ...\"}, \"BABYBJORN Safe Step - Blue\": {\"frequency\": 85, \"value\": \"BABYBJORN Safe ...\"}, \"Fisher-Price Space Saver Swing and Seat, Discover'N Grow\": {\"frequency\": 57, \"value\": \"Fisher-Price Space ...\"}, \"Baby Einstein Octoplush\": {\"frequency\": 57, \"value\": \"Baby Einstein ...\"}, \"FISHER PRICE SINGING STAR GYM\": {\"frequency\": 37, \"value\": \"FISHER PRICE ...\"}, \"Munchkin 3 Piece Silly Sandwich Cutter Set\": {\"frequency\": 22, \"value\": \"Munchkin 3 Piece ...\"}, \"Britax Frontier Booster Car Seat\": {\"frequency\": 22, \"value\": \"Britax Frontier ...\"}, \"Summer Infant Best View Handheld Color Video Monitor with 2.5" Screen\": {\"frequency\": 57, \"value\": \"Summer Infant Best ...\"}, \"Medela 9 Volt Vehicle Lighter Adaptor\": {\"frequency\": 22, \"value\": \"Medela 9 Volt ...\"}, \"Safety 1st Magnetic Locking System Complete\": {\"frequency\": 81, \"value\": \"Safety 1st ...\"}, \"BABYBJORN Baby Carrier Miracle, Black/Silver, Cotton Mix\": {\"frequency\": 50, \"value\": \"BABYBJORN Baby ...\"}, \"Safety 1st Crystal Clear Audio Monitor, White\": {\"frequency\": 21, \"value\": \"Safety 1st Crystal ...\"}, \"Graco SnugRide 32 Infant Car Seat, Zurich\": {\"frequency\": 25, \"value\": \"Graco SnugRide 32 ...\"}, \"Baby K'tan Breeze Baby Carrier, White, Large\": {\"frequency\": 29, \"value\": \"Baby K'tan Breeze ...\"}, \"Cloud B Tranquil Turtle - 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"source": [
"#Examining the reviews for most-sold product: 'Vulli Sophie the Giraffe Teether'"
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"giraffe_reviews = products[products['name'] == 'Vulli Sophie the Giraffe Teether']"
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"data": {
"text/plain": [
"785"
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"source": [
"len(giraffe_reviews)"
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},
{
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"execution_count": 10,
"metadata": {
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" app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 1.226576304850189, \"complete\": true, \"min\": 1.0, \"max\": 5.0, \"quantile\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], \"median\": 5.0, \"numeric\": true, \"num_unique\": 5, \"num_undefined\": 0, \"var\": 1.504489431619944, \"progress\": 1.0, \"size\": 785, \"frequent_items\": {\"1.0\": {\"frequency\": 56, \"value\": 1.0}, \"2.0\": {\"frequency\": 37, \"value\": 2.0}, \"3.0\": {\"frequency\": 62, \"value\": 3.0}, \"4.0\": {\"frequency\": 95, \"value\": 4.0}, \"5.0\": {\"frequency\": 535, \"value\": 5.0}}, \"mean\": 4.294267515923567}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"float\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 785}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 5.0439843750025, \"bins\": [56, 0, 0, 37, 0, 0, 62, 0, 95, 0, 0, 535], \"min\": 0.9534375000025004}, \"min\": 1.0, \"complete\": 1, \"max\": 5.0}}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"giraffe_reviews['rating'].show(view='Categorical')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Build a sentiment classifier"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"products['rating'].show(view='Categorical')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Define what's a positive and a negative sentiment\n",
"\n",
"We will ignore all reviews with rating = 3, since they tend to have a neutral sentiment. Reviews with a rating of 4 or higher will be considered positive, while the ones with rating of 2 or lower will have a negative sentiment. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#ignore all 3* reviews\n",
"products = products[products['rating'] != 3]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#positive sentiment = 4* or 5* reviews\n",
"products['sentiment'] = products['rating'] >=4"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sentiment</th>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3L, 'love': 1L,<br>'it': 2L, 'highly': 1L, ...</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'quilt': 1L,<br>'it': 1L, 'comfortable': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'ingenious': 1L, 'and':<br>3L, 'love': 2L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'parents!!':<br>1L, 'all': 2L, 'puppe ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'cute': 1L,<br>'help': 2L, 'doll': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'shop': 1L, 'be': 1L,<br>'is': 1L, 'it': 1L, ' ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'feeding,': 1L, 'and':<br>2L, 'all': 1L, 'right': ...</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1L, 'help': 1L,<br>'give': 1L, 'is': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
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" <tr>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
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"[10 rows x 5 columns]<br/>\n",
"</div>"
],
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"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\tsentiment\tint\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"| Baby Tracker® - Daily ... | I love this journal and ou... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+-----------+\n",
"| word_count | sentiment |\n",
"+-------------------------------+-----------+\n",
"| {'and': 3L, 'love': 1L, 'i... | 1 |\n",
"| {'and': 2L, 'quilt': 1L, '... | 1 |\n",
"| {'ingenious': 1L, 'and': 3... | 1 |\n",
"| {'and': 2L, 'parents!!': 1... | 1 |\n",
"| {'and': 2L, 'cute': 1L, 'h... | 1 |\n",
"| {'shop': 1L, 'be': 1L, 'is... | 1 |\n",
"| {'feeding,': 1L, 'and': 2L... | 1 |\n",
"| {'and': 1L, 'help': 1L, 'g... | 1 |\n",
"| {'journal.': 1L, 'all': 1L... | 1 |\n",
"| {'all': 1L, 'forget': 1L, ... | 1 |\n",
"+-------------------------------+-----------+\n",
"[10 rows x 5 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Let's train the sentiment classifier"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data,test_data = products.random_split(.8, seed=0)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Logistic regression:\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: Number of examples : 133448\n",
"PROGRESS: Number of classes : 2\n",
"PROGRESS: Number of feature columns : 1\n",
"PROGRESS: Number of unpacked features : 219217\n",
"PROGRESS: Number of coefficients : 219218\n",
"PROGRESS: Starting L-BFGS\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+\n",
"PROGRESS: | Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |\n",
"PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+\n",
"PROGRESS: | 1 | 5 | 0.000002 | 2.856285 | 0.841481 | 0.839989 |\n",
"PROGRESS: | 2 | 9 | 3.000000 | 4.544454 | 0.947425 | 0.894877 |\n",
"PROGRESS: | 3 | 10 | 3.000000 | 5.191519 | 0.923768 | 0.866232 |\n",
"PROGRESS: | 4 | 11 | 3.000000 | 5.837583 | 0.971779 | 0.912743 |\n",
"PROGRESS: | 5 | 12 | 3.000000 | 6.466646 | 0.975511 | 0.908900 |\n",
"PROGRESS: | 6 | 13 | 3.000000 | 7.091709 | 0.899991 | 0.825967 |\n",
"PROGRESS: | 10 | 18 | 1.000000 | 9.989999 | 0.988715 | 0.916256 |\n",
"PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+\n"
]
}
],
"source": [
"sentiment_model = graphlab.logistic_classifier.create(train_data,\n",
" target='sentiment',\n",
" features=['word_count'],\n",
" validation_set=test_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Evaluate the sentiment model"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'roc_curve': Columns:\n",
" \tthreshold\tfloat\n",
" \tfpr\tfloat\n",
" \ttpr\tfloat\n",
" \tp\tint\n",
" \tn\tint\n",
" \n",
" Rows: 1001\n",
" \n",
" Data:\n",
" +------------------+----------------+------------------+-------+------+\n",
" | threshold | fpr | tpr | p | n |\n",
" +------------------+----------------+------------------+-------+------+\n",
" | 0.0 | 0.222284749578 | 0.00424817935171 | 28012 | 5331 |\n",
" | 0.0010000000475 | 0.777715250422 | 0.995751820648 | 28012 | 5331 |\n",
" | 0.00200000009499 | 0.737760270118 | 0.994609453092 | 28012 | 5331 |\n",
" | 0.00300000002608 | 0.715062839992 | 0.993966871341 | 28012 | 5331 |\n",
" | 0.00400000018999 | 0.699493528419 | 0.993467085535 | 28012 | 5331 |\n",
" | 0.00499999988824 | 0.688238604389 | 0.993074396687 | 28012 | 5331 |\n",
" | 0.00600000005215 | 0.678484336897 | 0.992574610881 | 28012 | 5331 |\n",
" | 0.00700000021607 | 0.667979741137 | 0.99221762102 | 28012 | 5331 |\n",
" | 0.00800000037998 | 0.657662727443 | 0.991967728117 | 28012 | 5331 |\n",
" | 0.00899999961257 | 0.650347026824 | 0.991646437241 | 28012 | 5331 |\n",
" +------------------+----------------+------------------+-------+------+\n",
" [1001 rows x 5 columns]\n",
" Note: Only the head of the SFrame is printed.\n",
" You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiment_model.evaluate(test_data, metric='roc_curve')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
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0.014818983305196024, \"tpr\": 0.4284949307439669, \"n\": 5331}, {\"threshold\": 0.9990000128746033, \"p\": 28012, \"fpr\": 0.010879759894954042, \"tpr\": 0.3593816935599029, \"n\": 5331}, {\"threshold\": 1.0, \"p\": 28012, \"fpr\": 0.0, \"tpr\": 0.0, \"n\": 5331}]}]]}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sentiment_model.show(view='Evaluation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Applying the learned model to understand sentiment for Giraffe"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"giraffe_reviews['predicted_sentiment'] = sentiment_model.predict(giraffe_reviews, output_type='probability')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">He likes chewing on all<br>the parts especially the ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1L, 'all': 1L,<br>'because': 1L, 'it': 1L, ...</td>\n",
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" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My son loves this toy and<br>fits great in the diaper ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1L, 'right': 1L,<br>'help': 1L, 'just': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999320678306</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">There really should be a<br>large warning on the ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'all': 1L,<br>'latex.': 1L, 'being': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.013558811687</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All the moms in my moms'<br>group got Sophie for ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'one!': 1L,<br>'all': 1L, 'love': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.995769474148</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I was a little skeptical<br>on whether Sophie was ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3L, 'all': 1L,<br>'old': 1L, 'her.': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.662374415673</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I have been reading about<br>Sophie and was going ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 6L, 'seven': 1L,<br>'already': 1L, 'love': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999997148186</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My neice loves her sophie<br>and has spent hours ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 4L, 'drooling,':<br>1L, 'love': 1L, 'her.': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.989190989536</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">What a friendly face!<br>And those mesmerizing ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3L, 'chew': 1L,<br>\"don't\": 1L, 'is': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999563518413</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We got this just for my<br>son to chew on instea ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'chew': 2L, 'because':<br>1L, 'just': 2L, 'what': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.970160542725</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My baby seems to like<br>this toy, but I could ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'already':<br>1L, 'in': 1L, 'some': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.195367644588</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 5 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\tpredicted_sentiment\tfloat\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Vulli Sophie the Giraffe T... | He likes chewing on all th... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My son loves this toy and ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | There really should be a l... | 1.0 |\n",
"| Vulli Sophie the Giraffe T... | All the moms in my moms' g... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | I was a little skeptical o... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | I have been reading about ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My neice loves her sophie ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | What a friendly face! And... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | We got this just for my so... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My baby seems to like this... | 3.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+---------------------+\n",
"| word_count | predicted_sentiment |\n",
"+-------------------------------+---------------------+\n",
"| {'and': 1L, 'all': 1L, 'be... | 0.999513023521 |\n",
"| {'and': 1L, 'right': 1L, '... | 0.999320678306 |\n",
"| {'and': 2L, 'all': 1L, 'la... | 0.013558811687 |\n",
"| {'and': 2L, 'one!': 1L, 'a... | 0.995769474148 |\n",
"| {'and': 3L, 'all': 1L, 'ol... | 0.662374415673 |\n",
"| {'and': 6L, 'seven': 1L, '... | 0.999997148186 |\n",
"| {'and': 4L, 'drooling,': 1... | 0.989190989536 |\n",
"| {'and': 3L, 'chew': 1L, \"d... | 0.999563518413 |\n",
"| {'chew': 2L, 'because': 1L... | 0.970160542725 |\n",
"| {'and': 2L, 'already': 1L,... | 0.195367644588 |\n",
"+-------------------------------+---------------------+\n",
"[10 rows x 5 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Sort the reviews based on the predicted sentiment and explore"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"giraffe_reviews = giraffe_reviews.sort('predicted_sentiment', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie, oh Sophie, your<br>time has come. My ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'giggles': 1L, 'all':<br>1L, \"violet's\": 2L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I'm not sure why Sophie<br>is such a hit with the ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'peace': 1L, 'month':<br>1L, 'bright': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999999703</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I'll be honest...I bought<br>this toy because all the ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2L, 'pops': 1L,<br>'existence.': 1L, ...</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We got this little<br>giraffe as a gift from a ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2L, \"don't\": 1L,<br>'(literally).so': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.99999999919</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">As a mother of 16month<br>old twins; I bought ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'cute': 1L, 'all': 1L,<br>'reviews.': 2L, 'just': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999998657</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie the Giraffe is the<br>perfect teething toy. ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just': 2L, 'both': 1L,<br>'month': 1L, 'ears,': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999997108</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie la giraffe is<br>absolutely the best toy ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 5L, 'the': 1L,<br>'all': 1L, 'old': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999995589</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My 5-mos old son took to<br>this immediately. The ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just': 1L, 'shape': 2L,<br>'mutt': 1L, '\"dog': 1L, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999995573</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My nephews and my four<br>kids all had Sophie in ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 4L, 'chew': 1L,<br>'all': 1L, 'perfect;': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999989527</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Never thought I'd see my<br>son French kissing a ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'giggles': 1L, 'all':<br>1L, 'out,': 1L, 'over': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999985069</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 5 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\tpredicted_sentiment\tfloat\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Vulli Sophie the Giraffe T... | Sophie, oh Sophie, your ti... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | I'm not sure why Sophie is... | 4.0 |\n",
"| Vulli Sophie the Giraffe T... | I'll be honest...I bought ... | 4.0 |\n",
"| Vulli Sophie the Giraffe T... | We got this little giraffe... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | As a mother of 16month old... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | Sophie the Giraffe is the ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | Sophie la giraffe is absol... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My 5-mos old son took to t... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My nephews and my four kid... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | Never thought I'd see my s... | 5.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+---------------------+\n",
"| word_count | predicted_sentiment |\n",
"+-------------------------------+---------------------+\n",
"| {'giggles': 1L, 'all': 1L,... | 1.0 |\n",
"| {'peace': 1L, 'month': 1L,... | 0.999999999703 |\n",
"| {'all': 2L, 'pops': 1L, 'e... | 0.999999999392 |\n",
"| {'all': 2L, \"don't\": 1L, '... | 0.99999999919 |\n",
"| {'cute': 1L, 'all': 1L, 'r... | 0.999999998657 |\n",
"| {'just': 2L, 'both': 1L, '... | 0.999999997108 |\n",
"| {'and': 5L, 'the': 1L, 'al... | 0.999999995589 |\n",
"| {'just': 1L, 'shape': 2L, ... | 0.999999995573 |\n",
"| {'and': 4L, 'chew': 1L, 'a... | 0.999999989527 |\n",
"| {'giggles': 1L, 'all': 1L,... | 0.999999985069 |\n",
"+-------------------------------+---------------------+\n",
"[10 rows x 5 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Most positive reviews for the giraffe"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"Sophie, oh Sophie, your time has come. My granddaughter, Violet is 5 months old and starting to teeth. What joy little Sophie brings to Violet. Sophie is made of a very pliable rubber that is sturdy but not tough. It is quite easy for Violet to twist Sophie into unheard of positions to get Sophie into her mouth. The little nose and hooves fit perfectly into small mouths, and the drooling has purpose. The paint on Sophie is food quality.Sophie was born in 1961 in France. The maker had wondered why there was nothing available for babies and made Sophie from the finest rubber, phthalate-free on St Sophie's Day, thus the name was born. Since that time millions of Sophie's populate the world. She is soft and for babies little hands easy to grasp. Violet especially loves the bumpy head and horns of Sophie. Sophie has a long neck that easy to grasp and twist. She has lovely, sizable spots that attract Violet's attention. Sophie has happy little squeaks that bring squeals of delight from Violet. She is able to make Sophie squeak and that brings much joy. Sophie's smooth skin is soothing to Violet's little gums. Sophie is 7 inches tall and is the exact correct size for babies to hold and love.As you well know the first thing babies grasp, goes into their mouths- how wonderful to have a toy that stimulates all of the senses and helps with the issue of teething. Sophie is small enough to fit into any size pocket or bag. Sophie is the perfect find for babies from a few months to a year old. How wonderful to hear the giggles and laughs that emanate from babies who find Sophie irresistible. Viva La Sophie!Highly Recommended. prisrob 12-11-09\""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[0]['review']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"I'm not sure why Sophie is such a hit with the little ones, but my 7 month old baby girl is one of her adoring fans. The rubber is softer and more pleasant to handle, and my daughter has enjoyed chewing on her legs and the nubs on her head even before she started teething. She also loves the squeak that Sophie makes when you squeeze her. Not sure what it is but if Sophie is amongst a pile of her other toys, my daughter will more often than not reach for Sophie. And I have the peace of mind of knowing that only edible and safe paints and materials have been used to make Sophie, as opposed to Bright Starts and other baby toys made in China. Now that the research is out on phthalates and other toxic substances in baby toys, I think it's more important than ever to find good quality toys that are also safe for our babies to handle and put in their mouths. Sophie is a must-have for every new mom in my opinion. Even if your kid is one of the few that can take or leave her, it's worth a try. Vulli, the makers of Sophie, also make natural rubber teething rings that my daughter loves as well.\""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[1]['review']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Show most negative reviews for giraffe"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"My son (now 2.5) LOVED his Sophie, and I bought one for every baby shower I've gone to. Now, my daughter (6 months) just today nearly choked on it and I will never give it to her again. Had I not been within hearing range it could have been fatal. The strange sound she was making caught my attention and when I went to her and found the front curved leg shoved well down her throat and her face a purply/blue I panicked. I pulled it out and she vomited all over the carpet before screaming her head off. I can't believe how my opinion of this toy has changed from a must-have to a must-not-use. Please don't disregard any of the choking hazard comments, they are not over exaggerated!\""
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[-1]['review']"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"This children's toy is nostalgic and very cute. However, there is a distinct rubber smell and a very odd taste, yes I tried it, that my baby did not enjoy. Also, if it is soiled it is extremely difficult to clean as the rubber is a kind of porus material and does not clean well. The final thing is the squeaking device inside which stopped working after the first couple of days. I returned this item feeling I had overpaid for a toy that was defective and did not meet my expectations. Please do not be swayed by the cute packaging and hype surounding it as I was. One more thing, I was given a full refund from Amazon without any problem.\""
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[-2]['review']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
NSLS-II-HXN/PyXRF | examples/Spectrum_plot_with_given_area.ipynb | 3 | 662072 | {
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import h5py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fpath = '/Users/lili/Research/Experiment/SRX_Aakriti/2015_11_13_22_26_pyxrf.h5'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with h5py.File(fpath, 'r') as f:\n",
" d = f['xrfmap/detsum/counts'][:]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(10, 10, 4096)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 3D fluorescence data\n",
"# shape is [number of row, number of col, number of points in spectrum]\n",
"d.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# x axis as energy in kev\n",
"x = np.arange(4096)\n",
"x = 0.01*x # approximately transfer channel number to kev\n",
"\n",
"# choose two areas \n",
"d1 = d[0:5,0:5,:] # define y position from 0 to 5, x from 0 to 5, top left area\n",
"d2 = d[5:10,5:10,:] # define y position from 5 to 10, choose x from 5 to 10, bottom right area \n",
"\n",
"# sum spectrum in those two areas\n",
"y1 = np.sum(d1, axis=(0,1)) # sum along axis 0 and 1 to get summed 1D spectrum curve\n",
"y2 = np.sum(d2, axis=(0,1))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" alert('Your browser does not have WebSocket support.' +\n",
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" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1199167d0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# compare spectrum in those two areas\n",
"fig, ax = plt.subplots()\n",
"ax.plot(x, y1, label='area1')\n",
"ax.plot(x, y2, label='area2')\n",
"ax.legend()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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cJdh1tW8eXP16tUr5M2xc57XrFmjhg0bZs1l/mZNyBPTwfMCBICe30LPFkAA6NmtY+EIRKfA979N1dk/nZGr+LTEJ9T+nNty/WzKq7eq+8aPrJ/NqHKGut4zKjrRqBoBBBBAAAEXCNgJTFywbM8uYc6cOYGv6G7fvj3fGswde+YdgPXq1Qv8vrgBoBnDvG/QvHvQ7HVR15AhQzRoUO7XswwdOjSwjoIuExq2a9cu8FVjcxUUAJq7EIO9rr32WuUNDIPpa+c8EwAGI+v/NsGfTv9bUGHJChAAlqw3syGAQDEElq7foYzXeqtVzGprlAzFKOOexSpdpW6ukae81V/d01+3fja7Qi916v9dMWanKwIIIIAAAggUR8BOYFKc+egrrV69WsOGDdOYMWO0bt06VapUSeaOt759++qOO+4I3F2XFZY5EQAa88OHD+vDDz/Ul19+Gbhzb/PmzcrIyFCNGjUCdwOau+3MuwfNxz7yu77//ns9//zzMncE7t27V7Vr1w7cGXjrrbeqT58+gbCOAJDT7WUBAkAv7563104A6O39Y/UIRJXAN8/drvN2HP3CXNa1pUIL1ew/4ziHqR8MUfKyZ62fzyvXSQkPZL8TMKrgKBYBBBBAAAEXCBAAumATWIJjAnbOM3cAOsbv6YEIAD29fZ5ePAGgp7ePxSMQPQKrVv6uuv9NVrmYQ1bRGZkxOnLrJJWJa3scROrnzypx3hDr50tKt1TLh6ZHDxiVIoAAAggg4DIBO4GJy0pgOQhYAnbOMwEgB8gIEAByDiIlQAAYKXnmRQCBkAR+evlu/XXzO7n67Dh5iKqe9q98x5n13ZvqnNrP+t3K2IZqMujoi7C5EEAAAQQQQKDkBewEJiW/SmZEIDgBO+eZADA4W7+3IgD0+w67tz4CQPfuDStDAIEcAkuHdFR85grrJ2srJqj+vRMLNJo3fpQSJtxk/X6DaqpuynJMEUAAAQQQQCBCAnYCkwgtlWkRKFLAznkmACySNWax+gwAACAASURBVCoaEABGxTa7skgCQFduC4tCAIGcAhvXrlSdNzrkQllzwadq0On0AqGWTv9B8d9fZv1+Z2YFVRmyDlgEEEAAAQQQiJCAncAkQktlWgSKFLBzngkAi2SNigYEgFGxza4ockGeVZSWFG9+lp6ergYNTB7IhQACCLhLYOaYkeoy7Z/WonbrBFV4KF2xpcsUuNAV86fopM/OtH5/ODNWsYO2KrZUrLuKYzUIIIAAAghEiYCdwCRKaCjTgwJ2zjMBoAc3OgxLJgAMAypD5itAAMjBQAABzwlMevV29dyY/fXf+eU7q92A8YXWse6Phar3bvdcbfb1X6MTK1TyXP0sGAEEEEAAAT8I2AlM/FA3NfhTwM55JgD051kItSoCwFDFaO+UAI8AOyXJOAggEDaBmY+dpi6HZlrjpza8Xon/eK7Q+TZvTFetV9vlarPttgWqXoc7ncO2UQyMAAIIIIBAIQJ2AhNAEXCrgJ3zTADo1t0s2XURAJasN7NlCxAAchoQQMDVApmZmVqf0lz1YrZY61zYY7janH5Doeves2e3Kj6dO+xbd80U1WvaxtX1sjgEEEAAAQT8KmAnMPGrBXV5X8DOeSYA9P6+O1EBAaATioxhR4AA0I4afRBAoMQENm/dqlovNs0138Yrx6lOfGKhazhyJEOZQ2uodEyG1W7V/41V47bJJbZ2JkIAAQQQQACBbAE7gQl+CLhVwM55JgB0626W7LoIAEvWm9myBQgAOQ0IIOBqgfkzJqjdt+dba8zIjJEGrlNsuROLXPeewXVVMeZPq92yc79Qi65/LbIfDRBAAAEEEEDAeQE7gYnzq2BEBJwRsHOeCQCdsff6KASAXt9B766fANC7e8fKEYgKgalfvqzkuQOtWtfG1lX9QUuCqn1LSiPV1E6r7cI+76lNz+wwMahBaIQAAggggAACjgjYCUwcmZhBEAiDgJ3zTAAYho3w4JAEgB7cNJ8smQDQJxtJGQj4VWDS6/9Sz3X/tcpLOzFZ7e8bG1S561JaqJ42ZfftPULt/3p5UH1phAACCCCAAALOCtgJTJxdAaMh4JyAnfNMAOicv5dHIgD08u55e+0EgN7eP1aPgO8Fpjz9f+q+539Wnal1+irxtjeCqnvl0HZqkpFutZ3Vbbg6n1X4x0OCGphGCCCAAAIIIBCygJ3AJORJ6IBACQnYOc8EgCW0OS6fhgDQ5Rvk4+URAPp4cykNAT8IzH20tzocTssO8Vr2U+crBgVV2rJHuqjFkd+zw8OOjyrxwruC6ksjBBBAAAEEEHBWwE5g4uwKGA0B5wTsnGcCQOf8vTwSAaCXd8/baycA9Pb+sXoEfC+wKqWVGmu9VeeC7sPV9ozg7uJb+FhPtTk03+o7rc2D6tb3Pt+bUSACCCCAAAJuFLATmLixDtaEgBGwc54JADk7RoAAkHMQKQECwEjJMy8CCBQpcOjwER15pI7Kxxyy2q684As16RTcl3zTnjhN7ffPtPpOaX6Pul+dUuS8NEAAAQQQQAAB5wXsBCbOr4IREXBGwM55JgB0xt7roxAAen0Hvbt+AkDv7h0rR8D3AuvXr1HciLa56txxy2xVjWsaVO2znzpbnfZOstpObnSretzwZFB9aYQAAggggAACzgrYCUycXQGjIeCcgJ3zTADonL+XRyIA9PLueXvtBIDe3j9Wj4CvBRbOmqg2X59j1ZiRGaOYhzcppnTZoOqe+ezF6rLrp+wAsN616nHzC0H1pRECCCCAAAIIOCtgJzBxdgWMhoBzAnbOMwGgc/5eHokA0Mu75+21EwB6e/9YPQK+Fkgd+4ESp9xu1bg5prpqDV4RdM2pz1+lxO3fWu2n1Po/db/jzaD70xABBBBAAAEEnBOwE5g4NzsjIeCsgJ3zTADo7B54dTQCQK/unPfXTQDo/T2kAgR8KzDpoyfUc8kwq75lZVqpxYPTgq532ss3qtvmT632U6udp+R/vh90fxoigAACCCCAgHMCdgIT52ZnJAScFbBzngkAnd0Dr45GAOjVnfP+ugkAvb+HVICAbwUmjbhLPde/a9U3p9LJ6tjvm6DrnTbiTnVb/57VPrVyHyX++7Og+9MQAQQQQAABBJwTsBOYODc7I0WLwP79+zV27FiNGzdO06dPD3ytd/fu3apUqZJatmypM844Q7fccovi4uKKRWLnPBMAFovcN50JAH2zlZ4rhADQc1vGghGIHoHpz/ZV0q6xVsHT61ympNteDxpg6tv9lbw6u/2sCierc//gA8SgJ6IhAggggAACCBQpYCcwKXJQGiCQQyAtLU29evUKBH6FXSYMfPPNN9W3b1/bfnbOMwGgbW5fdSQA9NV2eqoYAkBPbReLRSC6BOY+8Td12J9qFT2t6Z3qds1jQSNMffdhJf+R/dGPtPKJaj9gXND9aYgAAggggAACzgnYCUycm52RokFg4sSJ6t27d6DUnj176txzz1XXrl1Vo0YNbd68WV988UUg+Dty5IhKlSqlb775RmeddZYtGjvnmQDQFrXvOhEA+m5LPVMQAaBntoqFIhB9Aksf6ar4I8uswme0T1HXi+8JGmLKR8PUfckTVvsFZdur7cDfgu5PQwQQQAABBBBwTsBOYOLc7IwUDQKTJ0/W888/r8GDB6tNmzb5lvzVV1/poosuUmZmppo1axZ4RDgmJvRIxs55JgCMhlNYdI2hn7aix6QFAsEIEAAGo0QbBBCIiMD6lOaK02Zr7rReL6v9364Oei1TPxuu5PkpVvtlpVuqxUPTg+5PQwQQQAABBBBwTsBOYOLc7IyEQLbApZdeqs8//zzwg1mzZqlTp04h89g5zwSAITP7sgMBoC+31RNFEQB6YptYJALRJ2D+VHZfSh1ViDlgFb/0nM8Un9gnaIxpX49Qt1n3We1XlmqsJg+nBd2fhggggAACCCDgnICdwMS52aNzpPnz52v06NH67bfftGDBgsBjsGXKlAl8AKNHjx667bbblJycnC9OSkqKhgwZEvid+d9lO3fu1AsvvBB4jHbFihWBf/7vf/+r6667Llf/ffv26fXXX5e5027hwoXavn27qlatqo4dO+qKK67QNddcE3j8Nr/r4MGDgQ94mL+mTZum33//XXv27FHlypXVvHnzwOO6d955p2rWrFmsDX355ZcD45jr008/lQkEQ73snGcCwFCV/dmeANCf++qFqggAvbBLrBGBKBTYs2e3Kj5t/hWVfa27eqLqNU8IWmP6mHeVNO0uq/3a2LqqP2hJ0P1piAACCCCAAALOCdgJTJybPfpG+uWXX3TqqacWWfiAAQM0bNiw49rlDACXLl2q008/XStXrszVLm8AmJqaGni8du3atQXOm5SUpK+//lp16tQ5ro0JE995551C12ze52fCRfOOP7vXs88+q379+gW6mzsBL7744pCHsnOeCQBDZvZlBwJAX26rJ4oiAPTENrFIBKJPYO3Kpao/MjFX4Xv+tVwVqwb/J74zx32iLhNvtsbYFFNDtQf/EX2YVIwAAggggIALBOwEJi5YtmeXMG7cOF144YU655xzdNppp6lVq1aBO+k2bdoUuBvQ3M23atWqQH1vv/22rr/++ly15gwA27dvr0WLFunWW2/V+eefr2rVqgXende4cWN179490G/evHmBv9+7d69q164duLvQfJDDBHZmThP6jRgxQocPH1a3bt0CdyWauxFzXldffbWmTJkSCBFNUNioUSOVLl06sE5Tj1mnuUuwVq1aMnc3mnnsXBdccEFgPeYyFgW9L7Cwse2cZwJAO7vlvz4EgP7bU7sVmT9SaVxA5xGSbrU7cAH9CAAdBmU4BBBwRmDJ7N/U8qtzrcEOZ8aq1OCtiomNDXqCORO+Usfx11jtt6myqqekB92fhggggAACCCDgnEBQgUlGhvTnNucm9cJIJ1SXQvjfN8GWtGXLlkB4Zh6/ze8yQZr5Su6PP/4YCPKWL1+e69HcnAFgbGysfvjhB/Xpk/+rWMwjwuYR37S0NHXo0CEQ1uX3mK4ZwwSSGRkZga/x/uMf/8i1NLOGpk2bFvhRDhMymkeXzWPBDz30kB555JFgOax2c+fOVZcuXQJfAm7btm0gSLRzBXWe8wxMAGhH2n99CAD9t6d2KzIBoPk39HP5DDBD0rd2By6gHwGgw6AMhwACzgjMGf+pOk640Rpsq6qqRsrRP6UO9po35QcljL3Mar5bJ6pSyvpgu9MOAQQQQAABBBwUCCow2btFeqqZg7N6YKj+y6UKwT/h4GRFJgwzwZ25ZsyYEQjGsq6cAeANN9ygt956q8Cpv/32W5133nmB35sxzR2DBV2XXXaZRo0aFXiEd+LEiSGXc8899+i5555Tu3btAncdhnIdOHBAvXr1CtRqLvMosbmj0c4V1HnOMzABoB1p//UhAPTfntqtKOulCk3sDhBiPwLAEMFojgACJSMwbfTL6jZnoDXZytjGajIotA94LJo5Qa2/yf4fdQcyy6jckC0lUwCzIIAAAggggEAugaACEwLAsJ0aE35t3LgxcPecuQPPXOZx3Kwv4JqAzwR9WVfOAPCnn34KPEZc0HXTTTcF7uhr2bKlFi9eXGgNWR/gKFu2rMwHQwr6IIgZxHxAZNu2bdq/f3/gQyTmMu/sM2szdyWan+d9jLiwybPWadpce+21GjlypG3voM5zntEJAG1z+6ojAaCvtrNYxRAAFouPzggg4BeBye8NUY/lz1rlLCzXQW0e+DWk8pbNm64Wn+d5VGXwDimG/7MbEiSNEUAAAQQQcEAgqMCEANAB6ewhzPv4zLv+Pv7448C77sxjrwVdTz75pO677z7r1zkDQPP14MK+vGtCxDlz5oS8dvNuQPM+v5yXuatv+PDhGjNmjDZs2FDomCbQDPY9gOZDJwMHHv3DZXOn44QJE1ShQoWQ15zVIajznGd0AkDb3L7qyH+JRH47zdtDk479Zd46b/6qcWxZ5jNEub9tXvh6G0m6W9I5kszfH5D0u6RRkl6RtK+Q7iYALCdpgKT65g89JE02d1KHiYg7AMMEy7AIIFA8gcmv360e67K/Ajer0qnq3G90SIOuXDJXTT46OVefjIEbFVu2fEjj0BgBBBBAAAEEii8QVGBCAFh86GMjmC/2mrv2VqxYEdSYJvAbPHiw1TZnAGjuFCzsTr369etr3bp1Qc2Ts5FZo3n/YNZl7kI0Hxox8wVz5e1fUB/z8REzrrnMXYrmAyR5g8dg5svZJqjznGdQAsBQlf3ZngAw8vt69H7i/K9QAkAT+n0gqUoBYy2RdLakgj5DWdBHQH6Q9HdJTj+7RgAY+bPHChBAIB+Bac9dpW47sl97Or3WJUq64+2QrPL7kvD+e1eqfMVqIY1DYwQQQAABBBAovkBQgQkfASk+9LERzBd4zTv2YmJiAl/4vfzyy9W6detA8FWunLnnRIFHgbOCPRP+mdAv68oZAGY9flvQ4urWrRt4vNi81++1114LugYTxmU9wmseHU5ISAiEf+auvv79+wcCzCZNmqhSpUpWO/Ml4KyPh5hw0/y+sOujjz6S+bqwqdWEjcakQQPzn8HFu4I6z3mmIAAsnrlfehMARn4ncwaA5hORiySdfmxZwQaAHY7drXeipD2ShkkaL+kESZdLuunYeOalCOYOQ9Mm7zVI0gTzNfJjdw62kWT+GOYsSVMk9ZRUWFgZqiQBYKhitEcAgRIRmP3kWer0p7kB+ug1rdHN6nbDUyHNvXn9atUakZCrz647F6tyzbiQxqExAggggAACCBRfwE5gUvxZo3MEE6aZsM9c5rHXxx57LF+IrVu3Wo/2FicANF/TXbhwoeLj47VkibnnJfRrwIABMo8hm0DSPAactf68Iz3zzDO69957Az8uKgD8+uuvdckllwRCxbi4uMCdf82aOfORGTvnmQAw9HPhxx4EgJHf1SGSUo/9tVGS+WOErHulgw0ATdj3F/MuVUnmmTMT2OW8+kv6z7EfmFBvaJBlxx4LBXtJOlfSd0H2C6YZAWAwSrRBAIESF1j0WLJaHzJ/FnP0Sm0zUIl97w9pHTu2blbVF5vn6rPlptmqWb9pSOPQGAEEEEAAAQSKL2AnMCn+rNE5gvlQxqWXXhooPi0tLXBnXX7X2LFjdeaZZwZ+VZwA0Nxh98EH5kE4KdjHcvOux3xF2HxNuHPnzpo5c2aBG3fFFVcE3mlorsICQPPhknPOOUfm4yc1atQIvPPPBJVOXXbOMwGgU/reHocA0H37F2oAaO7om36sjBGSjr5gIPdlgrz5kswfxZh3+9WRdCjI0s3nmMx3181dhdmfxQyycyHNCACLb8gICCAQBoH0Ia3VMDP7XTKzuw1Xp7Oyv0wXzJT79u3Rif8xr1PNvtZdM0X1mpqbq7kQQAABBBBAoCQF7AQmJbk+P831ySefBB75NdfUqVPVrVu3fMu76KKLNHr00XcsFycAHDVqlC677LLAOLfffrvMl35Dvc466yz98MMPatWqlRYtyv5D4JzjmI+CNG3aVH/++WfgxwUFgJMnT9bpp58u8xGUypUr6+effw58+MPJy855JgB0cge8OxYBoPv2LtQA0NxTnRXMJZun1QooyXzcw4R45jKPGP8YZOnnS/pK0vOS/hVkn2CaEQAGo0QbBBAocYFdg+upcsxea95Fp3+g1j3MTdDBX+Zxj9KPZn3P6Wi/1Zf/rEatnP0fgMGviJYIIIAAAghEr4CdwCR6tYpX+axZs6zA67bbbtMrr5hvUea+Xn311UBYl3UVJwA0Xxc2dxma4M68c/CNN96w3tOXXyXz588PhHfmrr+s6+6779aLL76o2NhYTZo0ScnJ5j+rs699+/bp3HPP1fjx5sG7o1d+AaD5GvGpp56qHTt2BL7ya+5yNO8mdPqyc54JAJ3eBW+ORwDovn0LNQD8VVJvSea/Vqseeww4v6q6H3tPoPmdeQQ4+zNLhRtkBYz3SHrOQS4CQAcxGQoBBJwROHBgv8oNMzdJZ1+rLxunRq3NzdahXQcHV1fZmCNWp+UXf6dm7c0bFbgQQAABBBBAoCQF7AQmJbk+P81lPtrRvn17maDNXOax2auuuirwHrz09HS9//77+uyzzwLBmAnbzFWcAND0N+/t69Gjh/bsOfqq+zPOOENXXnll4Ku75kMfmzZt0uzZswOP+Zo79Pr166enn37aYk9NTVVSUlLgn6tVq6b77rsvMF758uUDjwQPHz5c5gzlXHPeAHD58uWBPmYuc5k+f/vb3wrdWvPBEfNXqJed80wAGKqyP9sTALpvX0MNADdLqilprqSOhZRjPj257djvP5XUN0db80yaed5tR57+5r9UzZ2C5pzEmxtYHOQiAHQQk6EQQMAZgfw+3rH99gWqVjv0L7btHVxHFWL2Wwtbcu7natm18P8h6EwVjIIAAggggAACOQXsBCYI2hcwd8KZr+hu327ePnX8Ze7YM3fH1atXL/DL4gaAZgzzvkHz7kGz10VdQ4YM0aBB5huY2dfQoUMD6yjoMqFhu3btAl81NlfeAHDkyJHW74qaP+v3eesOtp+d80wAGKyuv9sRALpvf0MJAMtLOvoSgqMf6CjqGTXzRyIVzOsYJJk7ArMu8831+yT9ZN6deuwrwO2OPSqccey9gm+GSFXUfy3XPfbhk8CfBDnxOfQQ10dzBBBA4DiB5fOnqdlnWR9iP/rrIw9uVqkyZUPW2p7SQNW02+q38PQP1abHOSGPQwcEEEAAAQQQKJ6AncCkeDPSe/Xq1Ro2bJjGjBmjdevWqVKlSmrevLn69u2rO+64I3B3nXlk11xOBIBmHPMKlg8//FBffvll4M69zZs3KyMjI/AhDnM3YK9evWTePWg+9pHf9f333+v555+XuSPQvMPP3J1n7gy89dZb1adPH+UM+QgAOeNeFCAAdN+uhRIA1pJ09B5j6RNJR9+2WvBlvjJs7jE292Pn/BzTKeadqZLMvwnNs28mWDRtJ5q7l3N8ZCQUrcxgGxMABitFOwQQCLfAvN++VsJPf7em2amKqpKy1ta0m1JOUm3rxmsp7S9vq/1fLrE1Fp0QQAABBBBAwL4AAaB9O3q6T8DOeeYOQPftYyRWRAAYCfXC5wwlAGyY47Hc9yRdU0Q55hFe02e5pOZhLp0AMMzADI8AAs4LpH77phJn9LMGTo+tr4aDFtqaaO2QeNXPNH+WcvSa0/MVdexzla2x6IQAAggggAAC9gXsBCb2Z6MnAuEVsHOeCQDDuydeGZ0A0H07FUoA6NQdgOFQ4BHgcKgyJgIIhFVg6kfDlLzkCWuOxWXaqNWDU2zNuWpoOzXOSLf6zkx6Vl3O/oetseiEAAIIIIAAAvYF7AQm9mejJwLhFbBzngkAw7snXhmdANB9OxVKAOjUOwAjocBHQCKhzpwIIFCowJQ3+6n7muxXns6p0FMd+39vS235I53U7MgfVt/UjsOUeKF52wIXAggggAACCJSkgJ3ApCTXx1wIhCJg5zwTAIYi7N+2BIDu29tQAkCzeie+AhwJBQLASKgzJwIIFCow9cXrlLz1y+zQrvp5Srz7fVtqSx9NUvzhJVbfaQkp6nbJPbbGohMCCCCAAAII2BewE5jYn42eCIRXwM55JgAM7554ZXQCQPftVKgB4K+SekvaK6mq+fhRASWZr/5OPva7oeZjSxEunQAwwhvA9AggcLzAzKfPV5c9E7JDu/rXqdtNz9uiWvh4b7U5mGb1ndpqgJIvf8DWWHRCAAEEEEAAAfsCdgIT+7PRE4HwCtg5zwSA4d0Tr4xOAOi+nQo1AHxcUtZ/USZLmlZASQMkDTv2uzMk/a+ES1+QZ77SkuLNz/gKcAnvBNMhgECBAgse7622OUK7afH91O3KQbbE5j1xmhL2z7T6Tm72b/X4e6T/7MVWKXRCAAEEEEDA0wJ2AhNPF8zifS1g5zwTAPr6SARdHAFg0FQl1jDUADApR+g3QtKt+aw0VtJ8Sa0l7ZBUW9KhEqvo6EQEgCUMznQIIBC6wIqhCTopw3ww/eg1s8sT6nLebaEPJGnuf85Uh33ZHxCZ0vh2db8+689hbA1JJwQQQAABBBCwIWAnMLExDV0QKBEBO+eZALBEtsb1kxAAum+LQg0ATQVZjwGbx39PlpT3k5X9Jf3nWKlDJKW4oGweAXbBJrAEBBDILbA1pZFqaKf1w3mnvq2EUy6xxTTr6fPVOcfjxJMb3KgeNz5jayw6IYAAAggggIB9ATuBif3Z6IlAeAXsnGcCwPDuiVdGJwCM/E71ktQ8xzJqSnrq2D9PkpT9OcqjPxyZz5I7STJtT5C0R5J5LHj8sX++XNLNx/osldRV0u7Ily0CQBdsAktAAIFsgYwjR5QxtKZKx2RYP1x+8Xdq1t78azr0a+bwS9Vl549Wxylxf1f3W14KfSB6IIAAAggggECxBOwEJsWakM4IhFHAznkmAAzjhnhoaALAyG+WCfSuDWEZBe3ZeZLMpyorFzCWCf/OkfR7CHOFsykBYDh1GRsBBEIW2LFlo6q+FHg1qXVt+sdM1W6Y889ogh829fkrlbj9O6vD1NqXKfn214MfgJYIIIAAAggg4IiAncDEkYkZBIEwCNg5zwSAYdgIDw5JABj5TXMqADSVNJb0z2NBnwnYDh4L/D6VZG472Rf5cq0VEAC6aDNYCgIISKuWzFHjj07JRXHg/rUqd0JFWzzTXrpe3bZ8YfWdVuNCdbvrHVtj0QkBBBBAAAEE7AvYCUzsz0ZPBMIrYOc8EwCGd0+8MjoBoFd2yvvr5CMg3t9DKkDA1wILp45Vmx/6WjXuyyynE4dssl3z1FdvUfLGj63+qVXPVuK/PrI9Hh0RQAABBBBAwJ6AncDE3kz0QiD8AnbOMwFg+PfFCzMQAHphl/yxRgJAf+wjVSDgW4FZY99T5yl3WvWti6mteoOX2a53yoi71H39u1b/mZX/pi7//tz2eHREAAEEEEAAAXsCf/zxhw4cOKCYmBjFx8crNjbW3kD0QiDCAhkZGVq6dKkyMzNVrlw5NW3aNKgVEQAGxeT7RgSAvt9i1xbII8Cu3RoWhkB0Ckz/7BklzR9qFb+0dLziH0q1jTHlzX7qvib7O06zK56sTvd+Y3s8OiKAAAIIIICAPYF169Zp586dgc7169dX5coFvTbd3vj0QqCkBHbt2qW1a9cGpqtatari4uKCmpoAMCgm3zciAPT9Fru2QAJA124NC0MgOgWmjhyo5JUvW8XPPSFJHe7P/opvqCqTRz6oHiuzv/o798RkdbhvbKjD0B4BBBBAAAEEiimwZ88epaenB0YpVaqU6tatq4oVK3InYDFd6V5yAubOP3OON2zYoCNHjgQmbtiwYeAcB3MRAAaj5P82BID+32O3VkgA6NadYV0IRKnA1FduVvKmT6zqU6ucocR7RtnWmPz+EPX4/Vmr//zyndVuwHjb49ERAQQQQAABBOwJmMclV61apT///NMawDwObMJALgS8IGBCP3OOs64TTjhBjRs3DjzWHsxFABiMkv/bBHda/O9AhSUvQABY8ubMiAAChQjMfPZSddmVfcff1DpXKPm212ybTflomLovecLqv6hsgloPnGh7PDoigAACCCCAgH0BcwfV6tWrc4WA9kejJwKREzDhX6NGjUK6g5UAMHL75aaZCQDdtBvRtRYCwOjab6pFwPUC8544TQn7Z1rrnNL0LnW/5lHb657y6bPqvmCI1X9pmVaKf3Ca7fHoiAACCCCAAALFEzB3UO3du1e7d+8OBIFZj1IWb1R6IxB+AXO3qgn+KlWqpAoVKgR951/WyggAw79HXpiBANALu+SPNfIVYH/sI1Ug4FuB5Y90VrMjy636piUMVbdL/mm73mlfvqRucx+0+v9eurmaP5QdMNoemI4IIIAAAggggAACCIQgQAAYApaPmxIA+nhzXVYaAaDLNoTlIIBAboGNQ5qpTuYW64ezer6qzn2uarQi5gAAIABJREFUtM00/ds3lDTjXqv/ythGajJonu3x6IgAAggggAACCCCAgB0BAkA7av7rQwDovz31SkU8AuyVnWKdCESDQGam9qfUUvmYQ1a1C8/+Qm2S/mq7+hlj3lHXaXdb/dfE1lODQYtsj0dHBBBAAAEEEEAAAQTsCBAA2lHzXx8CQP/tqVcqIgD0yk6xTgSiQGD/3p0q/1SjXJWuumqiGrdIsF39zHGfqMvEm63+G2Jqqe7g322PR0cEEEAAAQQQQAABBOwIEADaUfNfHwJA/+2pVyoiAPTKTrFOBKJAYPPqJar1dlKuSrffvVzVqte0Xf2cCV+q4/jrrP5bVFU1U1bZHo+OCCCAAAIIIIAAAgjYESAAtKPmvz4EgP7bU69URADolZ1inQhEgcAfcyao6ejzrUoPZpZSqUFbVKpUrO3q5036Xgk/XmH136mKqpKy1vZ4dEQAAQQQQAABBBBAwI4AAaAdNf/1IQD03556pSICQK/sFOtEIAoEFvzyqdr+cqNV6SZVV+2UFcWqfMH0n9T2+4utMfapnE5M2VSsMemMAAIIIIAAAggggECoAgSAoYr5sz0BoD/31QtVEQB6YZdYIwJRIjD761fUadYDVrW/x56k5oPmFKv6JbMnquVX51hjHMospTJDthVrTDojgAACCCCAAAIIIBCqAAFgqGL+bE8A6M99dWNVC/IsqrSkePOz9PR0NWhg8kAuBBBAIDICMz4coq5Ln7UmTyvbSe0H/lKsxfy+IFXNP/1b7jEGbZdi7T9WXKwF0RkBBBBAAAEEEEAgKgUIAKNy248rmgCQc1BSAgSAJSXNPAggELJA6ht3KXHtu1a/6RVPVdK9o0MeJ2eHlUvT1OTD3rnGyBi4UbFlyxdrXDojgAACCCCAAAIIIBCKAAFgKFr+bUsA6N+9dXtlPALs9h1ifQhEkcDMF65Ul23fWRVPqnGJet71drEE0lcsVcN3EnONsf/elSpfsVqxxqUzAggggAACCCCAAAKhCBAAhqLl37YEgP7dW7dXRgDo9h1ifQhEkcC8p85Uwt4pVsW/NbhZvW98qlgCG9auVt03EnKNsefuJapYvW6xxqUzAggggAACCCCAAAKhCBAAhqLl37YEgP7dW7dXRgDo9h1ifQhEkcCyx7qpxaHFVsUTWw5UryvuL5bAls2bVPPlFrnG2H7LXFWLa1KscemMAAIIIIAAAggggEAoAgSAoWj5ty0BoH/31u2VEQC6fYdYHwJRJLBuaCvVy1hvVTwtcbi6nXNDsQR27typKsMb5Rpj8w3TVatRy2KNS2cEEEAAAQQQQAABBEIRIAAMRcu/bQkA/bu3bq+MANDtO8T6EIgigT0pcaqofVbFc/72oTr2OqdYAvv279eJT9TJNcb6q39VXPMOxRqXzggggAACCCCAAAIIhCJAABiKln/bEgD6d2/dXhkBoNt3iPUhECUCmYf2K+ax3EHd0kvGKT4h9wc8QuU4fCRDMUOrq1RMptU1/bIf1bB1UqhD0R4BBBBAAAEEEEAAAdsCBIC26XzVkQDQV9vpqWIIAD21XdmL3ZM+T7s+u0vl9m9RRu/+qtXrWo9WwrIROCqwa+NqVX4198c61t88X3H1GhaLKDMzUwdSaql8zCFrnBUXf6uT2vcu1rh0RgABBBBAAAEEEEAgFAECwFC0/NuWANC/e+v2yggA3b5D+azPBBpLHu+hVocWBn57UKW1/fpJqtO4lQerYckIHBVYs3CqGow6w+LIyIzRwYGbVL5c2WIT7Rocp8ox2Y8W/37uZ2retU+xx2UABBBAAAEEEEAAAQSCFSAADFbK3+0IAP29v26qbkGexZSWFG9+lp6ergYNTB7I5XaBtWtWq/6bue+USm1+txKvfsTtS2d9CBQosHTyV4r/3zXW77dlVlL1IWscEds6uKFqxOyyxlpy5odqmVy8dws6sjAGQQABBBBAAAEEEIgaAQLAqNnqQgslAOQclJQAAWBJSYdxnoUzflGbby/INcP0On2VdNsbYZyVoREIr0DamDfVflo/a5IVMQ100uC8/8qyt4YNKU1VV1utzgtPe1ttTr7E3mD0QgABBBBAAAEEEEDAhgABoA00H3YhAPThpnqkJB4B9shG5VzmzHEfq8vEW3KtfFbl09T53196sBqWjMBRgZmjhqnLwicsjvml26ndQ5Mc4VmTEq8G2pg99smvqd1pVzgyNoMggAACCCCAAAIIIBCMAAFgMEr+b0MA6P89dmuFBIBu3ZlC1jXty5fUbe6DuVrMOyFRCfeP82A1LBmBowIz/vtvdV31lsWRemJvJd73rSM8K4e0UZPMtdZYad2fV/szrnNkbAZBAAEEEEAAAQQQQCAYAQLAYJT834YA0P977NYKCQDdujOFrGvqR48recmTuVosLtNGrR6c4sFqWDICRwVmvnytumwebXFMrna+evzzPUd4fh/aUc0zVlhjzUl8Sh3PudmRsRkEAQQQQAABBBBAAIFgBAgAg1HyfxsCQP/vsVsrJAB0684UFgCOHKjklS/narGi1Ek66eE5HqyGJSNwVCDtmfPUfvevFsdvcdep9y3PO8Kz5JGuanlkmTXW7E6PqNMFdzsyNoMggAACCCCAAAIIIBCMAAFgMEr+b0MA6P89dmuFBIBu3ZnCAsDX71LyundztVgXU0f1Bi/1YDUsGYGjAouH9VKrA/OyA8Dm96r31Q87wrPg0R5qezj7gyIzEx5Wl0vudWRsBkEAAQQQQAABBBBAIBgBAsBglPzfhgDQ/3vs1goJAN26M4Wsa9pL16vbli9ytdimyqqeku7BalgyAkcFVg9tp0YZ2Wd4Wuf/qNv5uT92Y9cq7fGT1f7gXKv7jNb3q+tlA+0ORz8EEEAAAQQQQAABBEIWIAAMmcyXHQgAfbmtniiKANAT25R7kTOG91XXnWNz/XC/yqh8yhYPVsOSETgqsD2loappl8Ux97R31OHkCx3hmT3sb+p0IDU7AGxxj7peleLI2AyCAAIIIIAAAggggEAwAgSAwSj5vw0BoP/32K0VEgC6dWcKWdecp85Rx70Tj2uR+fAWxZQq48GKWHK0Cxw5fFgxj9RUbEymRbH8krFqlpDsCM3MJ89Ulz+zP5Izo+kd6nrN446MzSAIIIAAAggggAACCAQjQAAYjJL/2xAA+n+P3VohAaBbd6aQdc1/4lS12z/ruBb7//2Hyleu4cGKWHK0C2zeuEa1Xm2bi2HrzXNVo14TR2im/+d8Je2bYI2V2vhGJV7/jCNjMwgCCCCAAAIIIIAAAsEIEAAGo+T/NgSA/t9jt1ZIAOjWnSlkXUsfS1L8oSXHtdhxyxxVjTvJgxWx5GgX+H3+dDX/rE8uhiMPblKpMuUcoZn69CVK3jPOGmtGg2vV9cYXHBmbQRBAAAEEEEAAAQQQCEaAADAYJf+3IQD0/x67pcLsz2AeXVFpSfHmb9LT09WggckDudwusOKRDjrpyMrjlrn+mkmKa9rO7ctnfQgcJzBnwmh1HH+t9fMdqqiqKWsdk5r87OXqsWuMNd6MuMvV9ZYRjo3PQAgggAACCCCAAAIIFCVAAFiUUHT8ngAwOvbZDVUSALphF4q5hvQhrdUwc91xo6RfPk4NWyUWc3S6I1DyAlNHv6rkOQOsiVfGNlKTQfMcW8ik565Rzx1fWePNrH2Jutz+tmPjMxACCCCAAAIIIIAAAkUJEAAWJRQdvycAjI59dmOVPALsxl0pYk3rhzRXXObm41r9ceE3atrxZA9WxJKjXWDSu4PU84/nLYYF5Tqp7QO/OMYy8cUb1Wvrp9Z4s2qcp853ve/Y+AyEAAIIIIAAAggggEBRAgSARQlFx+8JAKNjn91YJQGgG3eliDVtSWmsmtpxXKulZ3+q+KTTPVgRS452gcmv3Koemz6yGGZW6aMu93zmGMuvr9ymkzd9aI03u9oZ6vTPUY6Nz0AIIIAAAggggAACCBQlQABYlFB0/J4AMDr22Y1VEgC6cVeKWNPOlHqqor3HtVr0t3fVutcFHqyIJUe7wPRnLlHS7uyPdEyPu1JJt7zqGMuvr/1TJ28YaY03t8pp6nDPl46Nz0AIIIAAAggggAACCBQlQABYlFB0/J4AMDr22Y1VEgC6cVeKWNO+wbV1YsyB41rNP+V1tTv1Mg9WxJKjXWDe46co4eAci2F6i3uUdFWKYywT3uivU9a+bo03r2IvJdz7nWPjMxACCCCAAAIIIIAAAkUJEAAWJRQdvycAjI59dmOVBIBu3JVC1pSZmakjKdVVOibjuFZpPV5U+9Ov8VhFLBcBaeXQdmqSkW5RzOrypDqfd6tjNBPeflCnrH7JGm9+hW5q1/9/jo3PQAgggAACCCCAAAIIFCVAAFiUUHT8ngAwOvbZjVUSALpxVwpZ0/4DB1V+WK18W8zu+pQ6nXuzxypiuQhIu1LqqXKOx9oX9PlAbXue6xjNhHeG6pQVz1jjLTqhs1rfP96x8RkIAQQQQAABBBBAAIGiBAgAixKKjt8TAEbHPruxSgJAN+5KIWvauWunqjzbKN8WszoOVecL/+mxilhutAvs37db5f9j/lWUfa26YoIat+zoGM2ED4bplGVPWOMtKZeglg9MdGx8BkIAAQQQQAABBBBAoCgBAsCihKLj9wSA0bHPbqySANCNu1LImjZvWq9ar7TKt8WMtg+q6//d57GKWG60C6xfsVBx73TPxbDzXytUpWp1x2h+/eQZnbxoqDXe8rIt1WzgdMfGZyAEEEAAAQQQQAABBIoSIAAsSig6fk8AGB377MYqCQDduCuFrGlt+grVfyv/O6NS4/sp8cpBHquI5Ua7wNLpYxX/fV+LYW9mOZ04eINiYmMdo/nts5fUe/6D1ngrSjfTSQ/Ncmx8BkIAAQQQQAABBBBAoCgBAsCihKLj9wSA0bHPbqySANCNu1LImlb+vkhN3k/Ot0Vq0zuVeM1jHquI5Ua7wJwxb6vjtHsshvSYODUcvNhRlomjX1evOf2tMVeXaqRGD89zdA4GQwABBBBAAAEEEECgMAECQM6HESAA5BxESoAAMFLyNuddtnCWWow6Nd/e0xvdpKQbnrY5Mt0QiIxA6kePKHFJ9rldUKad2j44ydHFTPr2HfWccbc15rrYONUb5GzI6OiCGQwBBBBAAAEEEEDAdwIEgL7bUlsFEQDaYqOTAwIEgA4gluQQi+ZMVuvRZ+U7ZWq9vyvx5pdKcjnMhUCxBaaPuF1J6z+wxkmteJoS7/2y2OPmHGDK2I/Ufcqt1o82xdRU7cHLHZ2DwRBAAAEEEEAAAQQQKEyAAJDzYQQIADkHkRIgAIyUvM1506b9rPZjLso/AKzTV4m3vWFzZP9327hrv3b+eUjxdSr5v1gPVTjr2UvUedc4a8WT61yhHre95mgF0376Ut1+u84ac1tMFVUfvNrRORgMAQQQQAABBBBAAAECQM5AUQIEgEUJ8ftwCRAAhks2TOPOnTRGHX68PP8AsOZFSrxzZJhm9vawo2ak6/7P05SZKV3SuYGe6dvB2wX5aPULh52sNgfmZgeAzf6tHn8f7GiFqRO+U+L4K60xd6uCKqWsc3QOBkMAAQQQQAABBBBAgACQM1CUAAFgUUL83imBBXkGKi0p3vwsPT1dDRqYPJDLzQJzfhmtjr9cm38AWP08Jd79vpuXH5G17d5/SImPjdP+QxnW/N/e1Uvt6leJyHqYNLdA+tA2apix1vrh9C5PKem8mx1lmjn5R3X536XWmPtVVuVTNjs6B4MhgAACCCCAAAIIIFCYAI8Acz6MAAEg56CkBAgAS0o6TPPMGveJOk/MPxyZUfUMdf3XqDDN7N1hxy/ZpOv/m5qrgIFnt9LNJzfzblE+WvnelDqqoP1WRfNO/1gJPfJ/z6Xdsuek/qaO351rdT+sWJVO2W53OPohgAACCCCAAAIIIBCyAAFgyGS+7EAA6Mtt9URRPALsiW3KXuTMH95Vl6l35bvqmZX/qi7//sJjFYV/uc/+uFQv/LQs10RnJ9TVK1d1Cf/kzFCowJ+7t+uEZ5rkarPq6klq3Lydo3Lz5kxXwug+uccctE2KLeXoPAyGAAIIIIAAAggggEBBAgSAnA0jQADIOYiUAAFgpORtzjvj2zfUdca9+faeXfFkdbr3G5sj+7fbvz+Zoy9mZz9iaiptE1dZ3/+zt3+L9khla5fNUf0PTsm12j33pqtixcqOVrBwQZrafJpnvx/cIJU5wdF5GAwBBBBAAAEEEEAAAQJAzkBhAgSAnI9ICRAARkre5rypo19S4pwH8+0958Qe6njfGJsj+7fbje+katyiTbkKrFmxrGY8lOeOMP8SuLayRZO+Uesfr7bWtzOzgqoMcf7jHEuWLlbLD7vldhiwWirPeyBdezhYGAIIIIAAAggg4DMB7gD02YbaLIcA0CYc3YotQABYbMKSHSD182eUOG9ovpOmnZCk9vf/WLIL8sBsfUdM0fQV23KttHRsjJY9dpZiYvjXbyS3cOY3r6rLzAHWElbGNlSTQfMdX9IfK1eq6cjcX37O6LdMsZVqOz4XAyKAAAIIIIAAAgggkJ8AASDnwgjwX6Ccg0gJEABGSt7mvKmfDFPioify7T2/XCe1e+AXmyP7t9uZz/2qxRt2H1dgWsrpqly+jH8L90Bl094bpG7Ln7dWOq9sJyUMdP4Mr1q7Xo3faJVL5MDd81SueiMPKLFEBBBAAAEEEEAAAT8IEAD6YReLXwMBYPENGcGeAAGgPbeI9Ur9IEWJy4bnO//CsglqM3BixNbm1ol7PvGz1u7487jl/dr/VDWqcaJblx0V65r2yk3qtin7y9WpVc5Q4j3Of8l67Zbtqv9S7o+N7L11hirUbREVzhSJAAIIIIAAAgggEHkBAsDI74EbVkAA6IZdiM41EAB6bN9T33tQictfynfVS0q3UsuHpnmsovAvNyFlrHbvP3zcRF/d0VMdGlYN/wKYoUCBWU+fr857Jli/nxJ3jbrf8qLjYpt27FXt5+rlGnfnDZNUpZGzXxt2fOEMiAACCCCAAAIIIOAbAQJA32xlsQohACwWH52LIUAAWAy8SHRNHXmfEleOyHfqZaWaq8XDMyOxLNfOmZGRqWYPfq/MzOOXOPL6RP2lJe+Ai+TmLX6su1odWmgtYWrLAUq+4gHHl7R970FV+k8dlY7JsMbedvVPqt68q+NzMSACCCCAAAIIIIAAAvkJEAByLowAASDnIFICBICRkrc5b+pb9ygx/e18e6+IbaKTBs21ObI/u+3af0jtU/6Xb3HPX95RF3Ss78/CPVLVuiHxqpe50VrtrOQX1PnMax1f/Z4DhxX7eD2dGHPAGnvjZd+rTuuejs/FgAgggAACCCCAAAIIEAByBgoSIADkbERKgAAwUvI250194y4lrn03396rYhuo8aAFNkf2Z7c12/ep15Pj8y3ukQvb6e/Jjf1ZuAeqyszI0MEhtVUu5pC12sXnfKFWiX91fPUHDh/R/kcaqErMPmvsdRd/qXrtT3N8LgZEAAEEEEAAAQQQQIAAkDNAAMgZcJsAAaDbdqSI9aS+dqsSN3yUb6u1MXVUf/BSj1UU3uUuWr9LZz3/W76T9D+jpe44tXl4F8DoBQrs2rpJlV/M/RGO9denKq5xvONq5lHwrUMaq1bMTmvs9PM+VsMuZzk+FwMigAACCCCAAAIIIEAAyBkgAOQMuE2AANBtO1LEema88g913fRZvq02qKbqpiz3WEXhXe60P7bqsten5jvJLac01QNntQ7vAhi9QIHVi6ar0Sd9cv3+wAMbVK7cCWFRWze4qerFbLXGXnnmO2qSfGFY5mJQBBBAAAEEEEAAAQTyCvAOQM6EEeARYM5BpAQIACMlb3PeGS9dq65bRufbe4uqqmbKKpsj+7Pbz4s36oaRM/It7oqkRhp2cYI/C/dAVYsmfKrW42+0VrpVVVQjZXXYVr5qcEs1jtlgjf/HX0eoae/LwzYfAyOAAAIIIIAAAgggkFOAAJDzQADIGYikAAFgJPVtzD3zhavUZdu3+fbcoYqqmrLWxqj+7fJd2nrd8eGsfAs8p32cXr6ys3+Ld3llaV88pfZpj1qrXFQqXq0fTg3bqn9PaavmWmON//spL6r5qdeEbT4GRgABBBBAAAEEEECAAJAzkFeAOwA5E5ESIACMlLzNeWc911edd4zNt/eezBNUcUj2HU42p/BVt09npKv/Z2n51tS7RU29949uvqrXS8WkjrhDievft5Y85YRT1P3+r8NWwpKUDmqpldb4S3s8rfjTbwrbfAyMAAIIIIAAAggggAABIGeAAJAz4BYBAkC37ESQ65j97MXqtOunfFsfyCyjckO2BDlSdDR7b8pKPfxV/l9G7tCwqr66o2d0QLiwytSnLlDi3l+yA8C4q9X9lpfDttL5Q7qqXeYya/zFSY+r1dl3hG0+BkYAAQQQQAABBBBAgACQM0AAyBlwiwABoFt2Ish1zHnmPHXc/Wu+rY9kxihm8HbFxnJTcRbQiAnLNWzM4ny9mtasoJ/v/UuQ8jRzWmD+Yz3V7tB8a9jUVvcp8fIHnZ7GGm/u0O7qkLHQ+udFnQap9QX9wjYfAyOAAAIIIIAAAgggQADIGSAA5Ay4RYAA0C07EeQ65j51tjrsnVRg6wMDN6tc2bJBjub/Zs+NW6rnxmXf9ZWz4poVy2rGQ7m/Qut/EfdUuDKltZponbWgecnDlXDmDWFb4MxHTlaXI3Ot8Re0f0BtLx4QtvkYGAEEEEAAAQQQQAABAkDOAAEgZ8AtAgSAbtmJINcx7z+nK2HftAJb7+m/VhUrVAxyNP83G/b9Io349Y98Cy1bOlZLHz3L/wgurPDQkQz9ObS+Ksfss1a34txPdFLXM8O22umP/lVJh7O/CD2/TT+16zsobPMxMAIIIIAAAggggAACBICcAQJAzkCkBPK+DK20pHizmPT0dDVoYPJALjcLLHjiNLXdP7PAJe745x+qWq2Gm0so0bU9PHq+3pu6qsA5lz9+tkrxyHSJ7omZbM3mbWrw8km55t15w2RVadQ2bGuZ8viZ6n5wijX+/Pg71e7Kx8I2HwMjgAACCCCAAAIIIEAAyBkgAOQMRErAFwHg/kNHNGpGeiC4+b8uDWXu5IqWa+Gwk9XmQPZjjHnr3nz7ItWqXS9aOIqs895P5+qzmWsKbLdw6Bk6sazJwblKUmDu/DR1+Kx3rikz71+pmBOqhW0ZE4edp14Hst+fOa/ZzUr4+1Nhm4+BEUAAAQQQQAABBBAgAOQMEAByBtwi4MlHgK96c6om/b41YHhm27p67e9d3OIZ9nUsebyHWh7M/6u2ZvINN6Wpbv3GYV+HVya444NZ+m7e+gKXO+vhPqpegXcmlvR+/jp+jE6ecLk17UGVUdnBm6WY8H3AZsKTl+iUP8dZc85rfK0Srn+hpEtnPgQQQAABBBBAAIEoFVizZo0aNmyYVb35m4LvVIhSo2goO3z/xRMNetRYHAHPBYArtuzVnc+M1P2lP9IRxWrY4Sv1RcqNqlguOu7iWvZYklocWlLgnq+5broaNGlZnDPhq743jEzVz4s3FVjTlAdOU1yVE3xVsxeK+eGzt3Tm/H9bS91cqrZqPZz/x1qcqmf8U5fr1L1jsgPABlcq4cZXnRqecRBAAAEEEEAAAQQQKFSAAJADYgQIADkHkRLwXAD4S9pydf68pyrH/BkwW5TRUNX7papOlIQ4yx/trGaHlxd4XlZeNUlNWrSL1Hly3bw57xbNb3Hj7/2LTqpZwXXr9vuCvnnrMZ2X/h+rzFXlW6vxgKlhLfvnZ/6u03Z/bc0x///ZOwuwKrbujb+Hxu5GUbAAaSXE7sDubq/dhSKl2J/dfc1rXLtbQRFEQlFUTGwUbCUO53tGYIaBwxU4OYc1z3Of/3fmzF7rXb89cP+8d++9ynaGxfAtCs1JwYkAESACRIAIEAEiQASIQBoBMgDpXSADkN4BVRIQnAEYfGwt7IKn85g9HxSOShXzxrbXZz6WMBb/R1OLbhdhYpZ3tkT/6Yen67rrCHoWl+Vjp8bVQ82yhf4Uhr6XM4Gjy8eiXdx2NmpU0fowHXdMzln44c4vHYymnw9wBmDpdrAYsUOhOSk4ESACRIAIEAEiQASIABEgA5DegfQEaAUgvQ+qIiA4A/DG3nlwipzP4/WoywVUtbBXFUOl5o32NoNR8qsscz7qeAZVrRyVqkmdk7Vf5Yewl5+zlHhopDNsKiqu8YQ6s1Gltou+HdE44SIr4WHF7qg2aINCJZ1b8Reaxe5hc9wr0RJmo/9RaE4KTgSIABEgAkSACBABIkAEyACkd4AMQHoH1IGA4AzAS1tmodGLlTx291v9g5oOLdWBp8I1vPaqjnKSt1nmue96FDXtGihch1AStFx2FZFvv2Ypd89QRziZFBdKORqjM8LTDuaI4gxA21mo1m6KQus7s2osWnzgVh3eL9YENcf+q9CcFJwIEAEiQASIABEgAkSACJABSO8AGYD0DqgDAcEZgFfWjkWDd9wf8QzE8PobYNm4uzrwVLiGd14mKC35kGWeiFYHYO7QTOE6hJKg8ZLLeBLzPUu52wbWRsPqpYRSjkbojE8SI96nPHuOJ1PUizY7UbG2q0LrO7N2Mlq828jmiCxcDzUmHFdoTgpOBIgAESACRIAIEAEiQATIAKR3gAxAegfUgYDgDEC/lUPg8nE/j12Iw1LYtBqkDjwVruGDpzFKIOsz7cKb7oGlS2uF6xBKApcFF/EyLqVhjLRrXR87tLQoI5RyNELnizdvUXE9v1P112GBKFhOsd2rT29wQ8vXq1mGDws6oNqksxrBlIogAkSACBABIkAEiAARUH8C1ARE/edIGQrpDEBlUKYc0ggIzgD0X9YXdT9xnTyZooKs5qB2xzF5YoY/eVZAEWS9pTW00TZYN+iYJ1hkp8g6c8/j/df4LB9d3sMa7a3LZyd6t2eNAAAgAElEQVQUPSMnAqG3b8L6aHNeNInbG4j08skpg/Qwpzd7omX0UvbLqPy2MJ1ySaE5KTgRIAJEgAgQASJABIgAEUgjQAYgvQsMATIA6T1QFQHBGYA3lnSD09czPF6BNaajTo8ZqmKo1LxfPcuiIH5kmfO2ywbYNs0b26GzA97a+yw+/UjM8tEFnWuhe+2K2QlFz8iJgN+ZfXC5MZSN9kVUEIU8XsopetZhTm+bi5bPFrIPPDGshSrT/BSelxIQASJABIgAESACRIAIEAGGABmA9B6QAUjvgCoJCM4AvLmoAxy+81ftBFQZC8d+PqrkqLTcPz1LwRBZr2i75bQG9i16K02Puicym30aPxLErEx9HRHikyTsZ6925ujvbKzuZWiUvhN/L0abJ9zP6wvdKqg4M0ThNZ7esRgtH3N5n+nXgPGMmwrPSwmIABEgAkSACBABIkAEiAAZgPQOpBGgFYD0LqiKgOAMwKD5rVH7lz+P1w2joXAavFhVDJWaN9GjGHRFnKGVMXlg7WWo02agUjWpczJTt5NISpbAXPQMvrqbUErrCxYndMHB5Pq/Zbu1roFh9U3UuQSN03Z05US0+7iZrSuqsBNMJ5xWeJ1n9qxAiwfubJ5ovSowclO88ajwwigBESACRIAIEAEiQASIgCAI0ApAQUyTwkWSAahwxJQgCwKCMwBv+zaBbcItXjnXy/WH87AVmj/JEgngVYRXZ7JEBC0Rt6Ltpu1COLQbrvksslFhcrIEVdxO/n7yuJ4bLLSe/f7f8RJdNIpfgtcogYnNqmFsk6rZiEaPyIvA2YW90PzHCTbcg3IdUX3YNnmFzzLO6X3r0PLeNPb71zpGKDfrrsLzUgIiQASIABEgAkSACBABIsAQIAOQ3gOGABmA9B6oioDgDMDwufVgmRjONwBL94TziHWqYqi8vOIkwKc4L993iT7yi7gtwTcs58Kp02jlaVLjTL8SxajhfhoVRO/hpz+ep3RuYi9sFLfFyIYmmNqyhhpXoXnSrvs0hbM4iC0sssYo1Ojhq/BCzx3aimZh3HvwTrssSrtHKjwvJSACRIAIEAEiQASIABEgAmQA0juQRoAMQHoXVEVAcAZghI8jzMX3+QZgiS5wHs1tKVQVTEXnTY7/Aa15ZXlpYlEIxfCFvXfD3ANOXScqWoog4n/+mQgrr7Nor+WH5XpreJqPix0xOnEsBtWtjNmuZoKoRxNEJoqT8cDLFhZaT9lyntedj0rNRii8vAvHdqNJMJfno1ZxFJ/9ROF5KQERIAJEgAgQASJABIgAESADkN4BMgDpHVA1AcEZgA+8bVA9mf9H+/Wi7eA8boeqWSo8f/z3OOgv4jeseC0qhXKS92xu/xozUbfHVIVrEUKCmK/xqD33PEZrH8Jk3f08yfeTjdAqYQF6O1TE3I61hFCORmh8EvMNBVfVREkRZ1p/77IX+S1aKby+y6cPomHAIDbPJ1FhFPF4ofC8lIAIEAEiQASIABEgAkSACJABSO8AGYD0DqiagOAMwCdeFqgiieZxu1GoJZwm/qNqlgrP/z3uPfIv559X91zLCJWSOR7+Vaegbu9ZCtcihASvPv1E3fkX4auzCb10LvIkx0oKwDZ+AzrbVsCSblZCKEcwGp9//I6t/s9QooAeBrlURj49HVb7hTvRaHLQgl/LiOtAaXOF13ft4gnUu9qLzfMd+ZDf843C81ICIkAEiAARIAJEgAgQASJABiC9A2QA0jugagKCMwCjPavDCG953ALyN4HjlH9VzVLh+b+8f4lCa/hGySMdU1RNimJz+1cZj7r9vBSuRQgJnn74jkaLL2Or7gI00g7jSRZLRKgavwOtLMtjdS9bIZQjCI3MFt+qM0/xtD6Y0xL6Otq/7+0+cw29brTl1zL1KZCvmMLru3HtHJwudGHzxEMP+p4xCs9LCYgAESACRIAIEAEiQASIABmA9A6QAUjvgKoJCM4AfOtRGWVEsTxuN/PVh8PUY6pmqfD8sa+foNgGG16ee3oWMEvgOpn6VRoFl4GKb6ig8GLlkODB269osewqTulNR02tzFs9rX+th31NE2zqX1sO2SgEQ2D5+UdYev4hD0b6VZZr/96JEU9Gsd8nivSgO/s9IFL8UbhBAVdR+7QrmzsZImh5xCklN70dRIAIEAEiQASIABEgAkSAugDTO8AQUPxfPsSZCEgnIDgDMNajPIqJvvGquWXgCPvpZzR+jmOeR6LkVgdeneEGtWH5i+uo6mc0DC6DF2k8i+wUeOflZ7iu8oOf/lhUEH3INKRR/BKUN6mFnUP4TLMTm56RTsB4+gmpXzyd1xoikQhLly7AhM+cQf3Z0AiFp3EGtiK5hoYEwfpIU34K94+ANrdFWZH5KTYRIAJEgAgQASJABIhA3iZABmDenv+06skApPdAVQQEZQAmiZPxy7ssCoh+8XiF6NnDxu2CqhgqLe+bx+Eou6Mev/b89WDz/Rp7z6/cQLgMW6Y0TeqcKPh5LDqvvYEw/SEoLPqRSWqneE9oV3LA/r+c1bkMQWnLygD0n94Y5YsYYqn3WExI3s7WFFuyDoqNOqeUGiMiwmG+n//zA7fXgF5+peSnJESACBABIkAEiAARIAJ5mwAZgHl7/skApPlXNQFBGYA/E8TQmVsSuiIxj1u4rhUsZ15VNUuF53/5IBgV9jTm5Qku3Ax2nzkDxa90H7iMWK1wLUJIcP3xB/TaGIDH+n2gLZJkkjw4YRLel22MY2NchFCO2muUSCSoPOOkVJ0+HSzQzqocTs3tgh46l9lnvlbvjII9tyiltoePHqLargzbvac9AwyLKiU/JSECRIAIEAEiQASIABHI2wTIAMzb808GIM3/nwhMBbAg9SEnAAF/GpDD7wVlACJZDHhnbhYQoWMO81nXc1i68B5/HhGASvtbsMITJdoIL9YCdnGc6eJXsjtcRm0QXnEKUHz5wXuM3HoN9wwGSY0+OXE4woq3wbmJDRSQPe+F/PgtHnZzzmdZuKerGWqd6Qo7rUfsM+Jmc6Fdd7RSYD198QKVt9Ti5Uqc8AC6hcsoJT8lIQJEgAgQASJABIgAEcjbBMgAzNvzTwYgzf9/EagJIARAEgBmjxoZgAk/AN+ymZhFaldDDXfuHDxNfa2ehF5FlcNcE4OfEj1ElGwF+w9H2JL9i3dG3THKWVGl7pzPRrzFrB3nEWjANZ1Ir3luYi+cLtwV16byV1Wqe13qqu9xzDc0WXLlP+UF6I9CGVEc90zPvUD1Vkop6dW79yi/tiov1/eRYchfylgp+SkJESACRIAIEAEiQASIQN4mQAZg3p5/MgBp/rMioA3gBgBm3yLTUrMPGYAMDQnwMw73j69EzXv/Y9lFaVWG6exQjX+booLPw/RYZ7bOrxJDPCjdBvbvD3AGYNF2qDtuh8azyE6Bx8NfY+me47igP0Xq42uS2mGrYX8EzczQGCI7wemZTARCXsSh45qsV+LqIREPDfrzx424DpQ2VwrNmLgvKLnciJcrbnAAihox/62FLiJABIgAESACRIAIEAEioFgCZAAqlq9QolMTEKHMlPJ0ugHwAGALgHEvmL+aaQVgKv/QU5thfXMiOxvPtIxgPFs5nUSV9wpkzvTg5ilUP9WD/SJOUhCPy7aB/du97L0bhVvDacIeVcpUm9z/3n6J7fsP4oj+bKmatic1wxKdoQj35LZVq414AQphtlwP2Jr1Slxj0Rtc1p/Er2x6NGBQSCnVfvkZj0ILSvFyve97GaVMbJSSn5IQASJABIgAESACRIAI5G0CZADm7flPq54MQNW/B8xfhXVS/2FOiWf+KZ4qi2lZOSAHEisCGAugDQDmf8cDiAKwD8AaAJnbkfKDWwAIBjAHgA+AbWQA8gGFntsJa39uW2e0qCyMPCJzMEXCfPS+3xHUPN+PFR+DInhe3hX2r7gVfwEFm8FxErciUJiVykf13sAXOHZ4N3bpzZMacH9SfczESDyco5wtqPKpSn2j7Ax4jlmHszbi62rd4c2FxKAIRNOfK62g+CQxRD4loZeuidCr7mdQvqaj0jRQIiJABIgAESACRIAIEIG8S4AMwLw79+krJwNQ9e9B5hahnKacGICM6bcLQOEsSnoAoDWAJ1l8r5Pa6IP5v4wJmUgGYGZSYZf2werKUPaLtyiBMp6PVf8WKVhBxJUDML80mFf3SyNX2EdvZe/dzN8IDlMOK1iJMML/feMZrh/binV6y6QKPiGug1GJ4/HEtzW0tOjXsKyz6nvyPjZc5X61NTcrDW0tEU7dffs7dHftS1igu5FLU6YW8JefrGmzPZ7pUvzDszTyi5j/JpNyPet4BMZWDbMdgx4kAkSACBABIkAEiAARIAK5JUAGYG7JadY4+stT9fOZ3gCMBnAfQPNUWdk1AK0AMAdg5QPwDQCz7OgSAEMAzL7NNMeKWarGmHvMMxkvZq+iOwAHALdTv6QVgBko3bl6BLUucivhPqAISngqbyWRql7XOxf2oNa1v9j0L1Eab407wP7ZevZekGE91J52XFUS1SrvpmtP8OD0WizSld4V+bLYCgMSp+G+d0sY6jHHbtIlC4HpB8OxN4j59ZlyDXA2hmc7cyQnS1DF7SQm6uzDWJ105nSNtkAP5r+XKO/65FEeRUTcr96o1vtgWoe2gCtvBigTESACRIAIEAEiQATyLgEyAPPu3KevnAxA1b8HXgCYw6uYf94BYNpCPk2VlV0DkDH7mKUkTNfe+qlNPNJXxpzltzD1BnO+n3eGshkDkcm/BMCMdN+RAZgB1N3rp2BxljsL7zPyo7Dna9W/RQpWEH72b1heH8NmeS4qj5gqHWH/eBV7L9jAEXbTzyhYiTDCr74UhQ/nl8FDV3pTlMDk6uiW4IHQ2c1QJJ+eMIpSY5Ujdgazq/0YmWObVMXEZtV+KxYnS/B6RQsYfbrJVeA4EmgpfXu2osqM8ayEkvjEho9s9jdq1G2vqHQUlwgQASJABIgAESACRIAIsATIAKSXgSFABqD6vQc5NQCZFX2BqWUwy7G4ZVpcbVoAmAOymJaTcQBKp27xTXuCaWOrD8A69dzAtPtkAGZ4P+4HnkfNk1w33G8wRAHPlG2GmnyFntoE65tcE4WnWhURa9oZdg+XsmWH6NnDxu2CJmPIdm1Lzz1E8uUFmKQr/UzEiORKaJMwDwEzmqBMYYNsx6UHpRPovSkA/lEf2S9ntamJIfWqcA97ZjgZocU8wGmkUnG+9jRFOcSwOe823AiLht2UqoGSEQEiQASIABEgAkSACORNAmQA5s15z1g1GYDq9x7k1ACcC4Dp3MtczIny6Za58Iqbnro1mLnJbDE+l+7b/zqHMH2QjgDkdchbBQC/9+xFR0ejQgXmo/pfD29fRrWj3KqdnxI9GHpxf9SrfwW5UxhybC1sgplXKOWK0qqML9W7wvZ+2sJSIEzXGlYzr+QugYaNWnA6Evn9fDFa54jUyp4ml0ajhKW4PLkhjEvk17DqlV+O60o/3Hn1mU28qIslutobpXz++QlYUIkvqtsOwKydUoVGe9WAkeQNmzPMeRWsmvdVqgZKRgSIABEgAkSACBABIpA3CZABmDfnPWPVZACq33uQUwPwKoB6AL4DKJK6DVhaVU6p5wQy3zFbgJmtwGnXpiwwMNuJqwI4CvxeusLs92RWC8rjEqQBGBXmB9NDTL+VlCtBog09r1h58FDrGLcPr4BtKHNEZMr1QLsqvpt1h+0dpmF0ynVHxwK1ZvmrdR3KEudz/B7KBPhgqM5JqSnfS4qgTvwanB5fDzXKFFKWLI3N02DRJTz/yDU5X9/XDi3My6TUG/ccWG7Jr33qUyBfMaXyeOpticrJ3Hmht2svgW2bIUrVQMmIABEgAkSACBABIkAE8iYBMgDz5rxnrJoMQPV7D3JqADLGXAkAYalbeLOqqCiANKdqP4Ds7D2jLcAZaD6NuInK+9N6tABiiQhannEQiTT7R+nWwSWwv8MdHXlfpybiLXrAOpTzke9rV0dN97Td6Or3g6VMRe6H76LqLU/000m/0JZT8E1iAIv4LTg8qi6sjRjfni5ZCNh4n0XcD6Zxecq1Z6gjnEyKp3yIOg/s5Lbt/77n8QlQ8s9slI8tTMVcx/BbNr6wbz9KlrJpLBEgAkSACBABIkAEiAARyBYBMgCzhUnjH9Js10KY05cTA5A5POxnapknALT9Q8lMC0pmv2EAAGZF4J8uWQzAP+3pZZbnMI1HBLUF+HlkMCrtbczjJnaPhba2ZndyvbVvPuzvcU0T7urWgtiyJ6yC03afAw+0TFB9dloD6T+9Wpr9/bQD4bALnYVuOtK3RCdLRDCJ34G9w5zhUCXVqNJsJAqrTiKRwHTmqd/NPtKuE2NdYF4u9dy/Pb2AB8yvx9RLpAV4MEehKvd6MMcB1ZOYRuwpV6CFB+p0mahcEZSNCBABIkAEiAARIAJEIE8SIAMwT057pqLJAFS/9yAnBmBJAO9TS/gHANeeVnpdTJfhUqkNQWplo3RZDMDsnisoKAPw5aNwVNjF7Ljmrl/T38HAQLMbOQTtmYPaDxaxRYfr2QDWvWAZyDSYTrmiRMYw9WAWotI14Z9QNLw7A+21r2cJo+avLVg3qD4aVGN+jOnKLYFv8Umw8OB3n742tRGMiuVLCZnRAGTueXLnBeY2b07H3fOtB7OEcHZYQI3pcOyRvul6TiPS80SACBABIkAEiAARIAJEIHsEyADMHidNf4oMQPWb4ZwYgMwp9y9SS9gBoN8fymGeZcYw+9BMFVy6RhqAb57eQ9nt/MWT3ya/RIECBRWMU7XhA3d6oE7UMlZEqEFt6Nj1gYX/OPbeU1EFVPaIUK1QNck+atdtuEZORUvt34tcpV52v9ZiXt/GaJ52Vp2aaBeajNeffsJ5/kWe7DCP5ihsqJtyb0ND4HUI971ZB6DbdqWXeXd+I1j84lbIXjeZCOe+6Y9iVbokSkgEiAARIAJEgAgQASKQRwiQAZhHJvoPZZIBqH7vQU4MQEWvAJSFjkZuAX4f/QilNtvzuHwe/wyFizBHLGruFbjdDXWermYLvG3oBIPa/WB2dQR7LxplYOT5QHMh5KCyIdtvoXfURDTSznpFZL34pZjWsyXaWpbLQWR6NCOByLdf0HLZNfY2c7Tf47mtoaUlApKTAe8MP5t9DgKmTZUOMnxhC1j+YE5fSLn8K41G3YFME3e6iAARIAJEgAgQASJABIiAYgmQAahYvkKJTgag+s1UTgxARZ8BqEg6guwCHPvmGYqtt+Jx+Tj6EYqXYHZWa+4VuHUq6jxfzxZ4K189FHTsj+oXuS6mr1EC5Ty5JgeaS+PPlfXbEoi/no6Ds/a9LB9uGT8fw7q6opPtn7zyP+fLy08EPo1Ft/U3WASFDHQQ7tki5fOdA8DBwXw848KBopWUjixssSusvjFN21MuvwpD4TJksdJ1UEIiQASIABEgAkSACBCBvEeADMC8N+fSKiYDUP3eg5wYgIx6RXYBViQdQRqAX2JeodBqMx6Xd3/dQ+ky5RXJSuWxb26eAIfoLayOoAKNUNh5IKqd5Xadv5cURSmvZyrXqg4Cemy4gSkvx8BO61GWcjrFe6Jbx87oUaeiOkgWrIbz995hyN+3WP0VihrCb1pqo55zswH/5fzaZscCWspv2hOytDNsPp9ntfiV6Q+Xv1YIljsJJwJEgAgQASJABIgAERAOATIAhTNXilRKBqAi6eYudk4NQGZJCdOV4juAIgCSskjLHFyX1pHAG4CqD58SpAH4Le4dCiyvxkP8ekgYylVgpk1zr8ANo1HnNXPMZMp1s2AzlKw3GFVOcn1nYiUFUczrpeZCyEFlndb4w/vtSFhoZW2I9k2YjmauPdHPSbPfnRxgy9WjB4NfYtJ+bqu1WdlCODkutVHP9nbA03SdmAuWBSZxnXhzlTCXg24v7wHbuFPs6Gsle6LeqHW5jEbDiAARIAJEgAgQASJABIhA9gmQAZh9Vpr8JBmA6je7OTUAfQGktZJ0ZLyZLEqaDmBe6nfM/rizSi49Y3cIHQC/nbTo6GhUqCCMbZC/vsbCYEllHrroAcEwMlZ0TxUlz1aGdIHr/kKdt3vYuwGFW6NMg8EwPtqZvfdFYoiCnm8gYg5hy+NX25XXsDRmOKpqvcqSxPCE8ajdqj+G1KuSx2nJVv5W/6fwOsZttXasUgx7h6U26vEszA/eZStg0Um2hLkcfXtVP9h+OMKO9ivWGS5juVW1uQxLw4gAESACRIAIEAEiQASIwB8JkAH4R0R54gH6S139pjmnBmCddKYfc0jbX1JK0gJwF0BNAJ8AMAfWJSq5dI0wAMW/vkJ7Pt+sfNY3AMYmDFrNvYLWDEbt9wfYAm8UbYcKjYfB6GBb9t5PiR50Z7+DjjbzuuXtq8XSq9gYNwgVtZgd+tKviQl/waTZUIxqpNnmsaLfhOXnH2Hp+YdsmuZmpbGhnz3w5TXwvww/lwNPAZWcFS1JavzgdUNh93Yf+51/4baoO2GXSrRQUiJABIgAESACRIAIEIG8RYAMwLw131lVSwag+r0HOTUAmQrStgEz23/rA+BOxE+pbwqAhamlegHwVIOyBbkFWJL4E6K5ZXj4nvT0Q5XqtdQAqeIk3FrVH/YfDrMJrhfvDONmw1Bub2qzBcZRlmhDPCsGBrrKP19NcZXnLnLjxZex52t/lBYxfrv0a1biQBRvOBITmvG3lOcuY94d5XP8Hjb7PWUBdLGrgMVdrYAbq4EzbnwwY24DxU1UAuvWprGwf7mdzR1QoCkcJx9UiRZKSgSIABEgAkSACBABIpC3CJABmLfmO6tqyQBU/XvgAiD9EqASABalyvIHsCmDxG1SJNsAYJ41BPANALMt+FLqZ+aQtmGpY5hlMvYAvqq+bAjSAIQ4CfApzsP3qNslVDWzVQOkipNwa0Uv2MeeYBNcL9kdpi3+QqmdjXhJv0yLQSFDPcUJEUhklwUXceJHbxQW/eAU6+YHEpmjOlOueYk9oVVvPKa1rCGQqtRT5pT9YdgfzJ09OahuZcx2NQOOjQOCM/y69PgEqGiL+q3t02D/lDvz71Y+F9hP5X6m1JMuqSICRIAIEAEiQASIABHQBAJkAGrCLMpeAxmAsjOUNQLzF2r/HATJas5cAewEUCiLWIz51wZAVA5yKfJRYRqAEgngxfRa4a4Hnc+hei1mJ7bqrpiv8fA8GoEnH76jl0NF9HWsJFcxwcu6we7TGTamf+k+qNF6BIpvrcvLEzvxNYoVyi/X3EIM5uB7Htfiu0FPJObkF60MxHEr1ZYndcQXx6lwb8vvKi3EelWpefiOWzgT8Y6VML5pVYxvWg3IeP6f02igxVyVSb212wv2D//H5g/Vt4f1jAsq00OJiQARIAJEgAgQASJABPIOATIA885c/1elZACq/j2QlwHIVMK4PuNSjT7GYEtINfz2A1gFIN1yJJUXLkwDEIDYowi0RRIW4P0Op1DTWjXniqWJcDt0B7tvvmA1nZ1QH9VKF5TbJN/+X0fYfrnIGYDlBqBW29EotIFZUMpdA8seQRen6mhjWVZuuYUYqI73SQQm9+RLN3IAorkePZuSWuG5/Sz4dLAQYolqo7nHhhsIeBLL6vFwNcNA26LAggwmuAobgDDibu1fCPsIzoCM0LWA+Uxm4TZdRIAIEAEiQASIABEgAkRAsQTIAFQsX6FEJwNQKDMlfJ0a0QSEmYYEz+LQA3PcYsp1z/UYzOyYoxdVdxlP528lHFjXGB6u5nITFLrYFdbfmKMmUy7/CkNh3X4M8q+25OWw/LURX5Afu4c6wNmE2c2eN6+6HgfhLxrEL76mK3D/GHtvd1JjhFl7YUEXPsO8SSz3Vbdefg333nxhAyzpaoXOpV4Dm5vxg44LA4oyR6yq5go+shJ2IbPY5A+1q6Ka+y3ViKGsRIAIEAEiQASIABEgAnmKABmAeWq6syyWDEB6D5RFQGMMwJ+epWCIeJbbndaHUKtOY2VxzJQnISkZ1Wad4t13tSqHlT2ZoyHlc4UvagnL71xvGb+KI1G701joL+OfX3dKXBt7xY1R1q4t5nfOu8ZWw1k7cFlnNB9+7SFAEHek52GxM65YzMPS7tbymaQ8GqXewouIjv3JVr+hrx2aBw0Bnl3jE/H8rFJCoae2wPrmBFbDMy0jGM9mmrPTRQSIABEgAkSACBABIkAEFEuADEDF8hVKdDIAhTJTmqdTsFuAv3mWRYF0u6nDW+yHpVNzlc3Q45hvaLLkCi//AGdjeLaT3wrAOwuaotbPIDaHf+WxcOoyAVqLKkutu328N47MY3aj582rmdsGnNNjmm+nuxpMA64sYG+cFtfGsRoLsbq3ZjeQUfQbYON9FnE/Etk0e4Y6wulkC+DjIy41s/KPWQGowivi0j8wv5LWjwl4jVIo55lOowq1UWoiQASIABEgAkSACBABzSZABqBmz292qyMDMLuk6Dl5ExCsAfjZszwK/262nHKFNt0Da5fW8uaT7Xj+UR+wYPNueOtuhSESMD+pJ4ydOsp1C3DE/IYw/xXCavI3nYS63SYBvuWk6jyg64ouM5meNHnvEidL0HbmWpzSn8EWLxFpQ9TUAzg3m713WWyFnab/w6b+/HMU8x6x3FcskUh+r35NFHNncp4YZg3zvzM0VrHpA7RfnftEchj58MZxVDvTm430UVIYxb24czvlkIJCEAEiQASIABEgAkSACBABqQTIAKQXgyFABiC9B6oiIFgDMM7TCEXBnTkW0mgHbBq0UxVHnAx/DZMDzVBd6+VvDZ8l+TDf7DDmdXeQm6bIeS6oEX+HjedfbTrqdp8C+BSXmsNfZIu6Hpfkll9IgX4litF99ioc0efMvmQdA2g18wZOTWVLCUiuiTXGK/D3INV2kBYS24xaGdY13E/zbj8wmg/9mHD+o1OfAvmKqbTUp6GXUflwe1bDN4kB8nu+hUhE/xpW6cRQcutL+uMAACAASURBVCJABIgAESACRIAI5AECZADmgUnORon0l0c2INEjCiEgWAPwg5cxSkjiWCjB9bfCrnEnhUDKTtCD/hHofI7fhXhF2fkYO3xEdoZn65mHcx1RLfE+++x1M3c4d5ucqSNy2gN3kivDdFYwDPW0sxVfkx768isRg71WYL++N1tWsn5haDX3AY6NZe+FJJtiQYVV2DvMSZPKV2otH7/Fw27OeV7OZwa9MmtQ8fl/jKA3D26h7J4mrDaxRITEmR9goKejVGaUjAgQASJABIgAESACRCDvESADMO/NubSKyQCk90BVBARrAL7zMkFpyQeW2y2XjbBv2k1VHLH31AX0uMk3IFcWm4ExY6fLTVPUHHuYJnHnld2o5QOnzmPx07Pk723HGa83kmKIHR4K83KF5aZBKIEYU2qM7zLs1vPlDMB8JaHV0hf4dyh7735yRcwssw7/jqwrlNLUTufzj9/RYNFlVlc9rXDs0JvP11nRGRjEb5KjikJio++j2GZHXuqP41+geJG89zOiCv6UkwgQASJABIgAESACeZkAGYB5efa52skApPdAVQQEawC+8aqKspL3LLcgp7Wo3ULKqiMlkd2zbzd63uOv9vs7X3/0m7pCbgqe+NigivgJGy/Aej4cO4zAF89yKITvmfIkSLQR1CcSdauWkpsGoQR6+/kXpi9YjG16izgDsGB5aLWaD+zry957klwGY0tuwvEx9YRSmtrpvPvqM9qu9GN1SV39NzkKKFBS5dp/fnwJw5X8xjyvht1D+XLlVa6NBBABIkAEiAARIAJEgAhoNgEyADV7frNbHRmA2SVFz8lKICJDAGbfWzXmXnR0NCpUYPxAYVyvvGugfPIbVuzNOivg0Lq/ysTv3bIMPV548PKf0G2KNjMPyk3Tc59aqCTmGhbctFsCB9ch+OhZCcXxSWqes21uoHntDM0Y5KZIfQNFx/7AnMULsV5vKSsyuWgVaLVeCOzqwt57LSmGAUW24eyEBupbjJoru/nkI7pvCPitMj9+IsJgcGbFHp8ANThnT/IzDqIFxjx9UX0CYWpaXc0pkzwiQASIABEgAkSACBABoRMgA1DoMygf/WQAyocjRfkzAY0xAF94m6NickrDDeYKsP8fHNtKMR7+zEQuTxxY7YYuMfwOp+dETmjmwW+OIEuyjKZnoMMK1GnVH2+9TFFGEiM19DGXQ3Bt2liWtIIc+zjmG5YunY9VeitZ/ZISNSBqswjY7srei5UUQKcCO3F5SiNB1qkOoi/cf4fB22/9llJHdB/79H34shrPAupPUQepQFICMIe/EvFup4uwsLRTD32kgggQASJABIgAESACREBjCZABqLFTm6PCyADMES56WI4EBLsF+JmPJYzFz1kUN2wXwandMDmiyVmow8vGocOnbbxB1yRWqOd1NWeB/uPpt15VUSbdtudbzmth37wXXnnXRPnk11JHHrLbjo6uHeSmQSiBHrz9ivUr5uB/eutYyZIylhC1+R+wuSl777tEH80M9uD6DK4xhFBqVBedR0JfYdze0N9yumhfwWLd9Xxp7h8BbTVpsiGRIMmrGHSQzGq83eoobB1oBai6vE+kgwgQASJABIgAESACmkqADEBNndmc1UUGYM540dPyIyBYA/DJHBtUSeLOw7thNQ9OHUfKj0wOIx1fMhRtv+7jjQpOrgorj0DoaGvlMJr0x2O8KqOkJJYzLupvhm3jLnjmaw/jBK45SPrR+83XomtX1Z2NKJfCcxGEOZdu5xpvzNfdxI2uUBtouxRY58LeY7rAOujswy335rnIQkMYArtuPsfMQ3d/w5iosw9jdQ5zYKq1BHr9o1agvnuW+b1VOe262WgPHBq0ViuNJIYIEAEiQASIABEgAkRA8wiQAah5c5qbisgAzA01GiMPAoI1AB/PsYdJuo6412v5wLnzWHkwyVWMMwv7oMWPY7yxD5PLo9zMcBTQl8/qpzhPIxTFFzZHSKO/YdOgPaIWNYTp9xCpuveYLkbPPlzX21wVJ8BBt1/E4dB6T/jopluVWaku4LoCWMXf7mmD3QjxbCPAKtVD8vorjzHvVORvMVt1F6CRdhgnzGEEwDReUaMr1qsSikm4MzP9nDbBpUVXNVJIUogAESACRIAIEAEiQAQ0kQAZgJo4qzmviQzAnDOjEfIhIFgD8NHcOqia+ICl4G/uibpdJ8iHSi6iXJzfBY1/neONZBpM6E+JRPEC+rmImHlIxm6/4c32wrJuK0QtaQrTr0FSc+yq6I3eg8bJJb+QgjCNKc5udoe77i5OdpVGQPtVwFJ+F1i7pC0IntNZSOWpldYlZx9g5cUoGOIXwvWHQlck5vR13ABYdVcrve98qqO0+C2r6bLtcjRsN0CtNJIYIkAEiAARIAJEgAgQAc0jQAag5s1pbioiAzA31GiMPAgI1gB86OuIagn3WQZ+Nd3h0n2yPJjkKoafbxu4JPjxxn6RGOLrhKcoX8QwVzEzDvrhWQr5EM/evtPyIGo5NsWTpc1R5fNNqTl2lJ6GviPc5JJfSEH8Hn3A9W1umKqbbvspsx21/WpgkQmvlNrxaxHo2xMiNehSKyTGaVo9j0Zg2/VnqCp6iXP6U/klTH8BGBRWq7JezrVChcRnrKYL5vPQpKvqjg9QKzgkhggQASJABIgAESACREBhBMgAVBhaQQUmA1BQ06VRYgVrAEbOc0GN+DucAVhtBlx6TVfZ5ATOaYw6ScG8/MkSEZ6NeoEqpQrJRVeCR3HoiZLYWPfaHoWZfQN8OjgORe7wG5CkPfR3sTHoN3aOXPILKcilyPcI3TEdE3QPcrJrugId1gHzyvNKcYlfjktz+kFXTmc1ComTPLRO2R+G/cEv4a6zA4N1TvFDen6WRwq5xng2rw6M47nVw2dN3dG8j+r+44Fci6NgRIAIEAEiQASIABEgAmpLgAxAtZ0apQojA1CpuPN0sogM1TOH01Vj7kVHR6NCBcYPFMZ1f3591PzFnTV2zXQq6vWZqTLxYT7OsBJnxAtEDnqAGhXLyEVXskcRaIkkbKzIjqdRw8oJeHET2CK9icWOgkPQd9ISueQXUpAzEW8RtWcKRukc5WRbdAE6bQC8i/FKaRK/CEc9ByG/nM5qFBIneWgdsTMYp+6+xWrdZWijHcgPqYYGYNTCBjD9kdK1mLnOVJyIFoM85IGCYhABIkAEiAARIAJEgAgQgSwJkAFILwdDgAxAeg+URUBjDMB7CxrB7OdtlttVk0mo33e2sjhmyhPpbYsayY8z3b/TJwy1TI1l15UszmRcPex6EdXM7QCJBAhYC0nwNog+cCubmKQ7DHqj7/Q1sucXWITj4a/xZt8kDNU5ySm37g10WAOJdwmIkhPZ+23i52LnrGEoml9PYFWqh9y+m2/i2qMPmRuAMPLU0AB8uKQFqn0NYOGdLjsCLYerV6MS9ZhZUkEEiAARIAJEgAgQASIgTwJkAMqTpnBjkQEo3LkTunLBbgGOWNAE5j9vsfyvVB6PBv29VDYfTz3NUBmvMuW/3eUGbC3MZNYlSfgBkW9ZXpwnPf1QpXot3r3XW/ui3HNu1dsO7Y7o6y59e7DMotQ4wKGQl/h8cAIG6JzlVNoNAFyXQ+JbAaKEr+z9zvEeWDNjJEoXMlDjitRXWvtVfgh7+RlbdBeisTa3sg6NZgENpqid8MjlHVAj7hJnAJYYgJajl6udThJEBIgAESACRIAIEAEioFkEyADUrPnMbTVkAOaWHI2TlYBwDcCFzWH+g2t8cbXSGNQfqLqz7t56VkEZfMw0H4HtLqGOra2s84TEH5+gu7ASL87zfoGoVKU6717M7r9Q8uEe9t7O5Bbo471P5vxCC7AvKBpJR8aglw5n9KDOcKD1QkgWmkL0I4YtqVeCGxZMHgOjYvmEVqZa6G2w6BKef/yBf/Vmw1YritPUdhlgP1AtNKYXcX9NT9R8z60MPVO4G1pM2Kh2OkkQESACRIAIEAEiQASIgGYRIANQs+Yzt9WQAZhbcjROVgKCNQDvLmoJi+832PqvGI1Eg8HzZOWR6/EfPYxQXPQl0/jrLU/C2bFuruOmDfzx6T3yLavKi/NqcCjKG1Xm3ft8aDIKh3Fmxr6kBnCdfQiGetoyaxBSgJ0Bz2F4YhQ6a1/jZDuPAZrPgWSpBUSfo9n7gxImw238BJiWKiCkEtVGq7X3WXz6kYgLepNgovWG09V1O2DeQW10pgm5v3EQar7imsOczd8OzafsUDudJIgIEAEiQASIABEgAkRAswiQAahZ85nbasgAzC05GicrAcEagHeWtEGtr35s/VcqDEeDIQtl5ZHr8V88yqKQ6Eem8Vcb/4v69ZvkOm7awC/vX6LQGnNenPcj7qFUaX5H25+nZsPwJred8YjYGY5TDuW57a1b/Z+i+OkRaKfNmcSoNwloMhtYVRv48JBlOTJhLEaPngKzcvLp1izzZAsoQHKyBKYzTyJZAgTrD0dxEbe1Gv2OAFUaql01kdtGo8YzzvC7YNAMTaYfUDudJIgIEAEiQASIABEgAkRAswiQAahZ85nbasgAzC05GicrAQEbgK6o9fUqW//lckPQcJhqut1KJBL89CyNfKL4TPNxyWU3GjVtI+s84eOrJyi+0YYXJ27sYxQtVoJ3L+nSAuhc8WXvnRHbw3TsEZiUzFur2zZcfYyK54ajpXYQx6ehG9BwGrCuHvA2nL0/MeEv9BsxA9ZGRTLNU6I4GftvvcTz2O/oZm8kN45HQl9h3N6U8/KmtKiOUY1MZX5HVBHgy69EWHqehTbEeKjfD9rpulRj+FWgrJUqZP1nzge7p6L6w/Xc7w7demg487ja6SRBRIAIEAEiQASIABEgAppFgAxAzZrP3FZDBmBuydE4WQkI1gAMX9oBlp+5890ulxmEhn8tlZVHrsYzJhG8S0BXJM40/nydLWjaunOu4qYf9O55JEpvdeDF+TrpBQoWLMyP7b8cOMd1Q74stkLRYUdhJcXcklmUGgdYfSkKNS8O5jelaOoJuEwANjcHornzI90SB6PDkFmoU7lYpooWn3mAVZe4c+3uerVAAX0dmSp/+/kXHOdd4MU4P7E+TEsVlCmuKgZHx/5AvYWXUEEUAz/9cXwJU54A+YurQtZ/5nx00AtV7/yPfea6lh2cZ19UO50kiAgQASJABIgAESACRECzCJABqFnzmdtqyADMLTkaJysB4RqAyzrB8hNnolwu3R8NR6yQlUeuxv+IT0C+eSWljj1jswYt2vfOVdz0g15FhaH8zvq8OL+mv4OBQYbOtTc3AKe4zqsByTWR3O84nE35KwVlFqTmAZaeewj7qwNRT/sup7SFL+A0CtjuCjzlVo96JfZFkwGecKnKZyROlsDEjWsWwQQqX8QQ/tMby1Q9sz3Z69g9Xoy2lmWxqtd/N4tZceERll94hCKGutgyoLZamLp3X31G25V+aKQVgq16i7ia9AoAM14CIvX719vjkytgEujOag1FNVh7plspKtPs0mAiQASIABEgAkSACBABIiCdABmA9GYwBNTvLySal7xCQLAGYNjybrCKO8PO0+WSvdFw1BqVzNunL19R5H8MyszXKYulaNVlkMy6XtwPQsV/mvLiiN3joK2txY99+2/g6BjO3Eg2wfvuJ9HcvIzMGoQUYOHpSDS43h8OWpGc7NaLgTpDgV3dgEfcu7MgsQdq9/VG4xqleSVKW6nHPBAwownKFM5gvOYAjseRu9h+43mmEc/mZ71V/OmH72i0+DJvzNN5rSFSscHmH/UBvTfdxGDtk3DX3cnpM3IABp/NARXlPfrafxfKnRvJJnycXBYVZkdAXydvNcpRHnHKRASIABEgAkSACBABIsAQIAOQ3gOGABmA9B4oi0BEhkTMXsZqzL3o6GhUqCDdxFKWuJzkCV3ZE9YfudVZV0r0QIPR3LleOYkl67MxHz+g5EoTqWFOVPdFm56jZE2Bx+H+MPm3NRsnUaINXa/YzHHD9wH/DmXv30+uiPsdTqGTrXDmVmZYAOaeuIc2N/vAWusxF851BWDXH9jXD7h3hL2/PKkTqvfwRUuLsrzU9998Qavl6boIp367a4gD6sqwonLivlD8e/tVpjIjvFogfxbbi+edvI/1V5/wxvwzzBEOVVS7xfbknTcYues2ZujswnCdE5y+Wl2BzpvkMZVyj/Ht3lkU2NeVjRsjKYSECQ9/r+6kiwgQASJABIgAESACRIAIKIoAGYCKIiusuGQACmu+hKxWcwzAVb1h/YE7uP9K8W5oMGajSubm9euXKLeB36E3TchxEw+07TtRZl0Pgy+h2rEObJyfEj0YesVkjnvvKLCvL3v/SXIZ+Lc6g75OxjJrEFIAz6MR6HqrJ8y10q2067gesOoB/DscCN/LlrMuyRVluyxAe2t+R+Xrjz+g10burMC0AQOcjeHZTvp8Z4fRwK2BuPQg89xdmNQgyyYjU/aHYX/wS154F9MS2DmEfy5kdvLL85ndN1/A7dAdLNNdhQ7a17nQzmOA5nPkmUpusZJfhkBrE9edmDHTn454hmplqAu03CBTICJABIgAESACRIAIEIFMBMgApJeCIUAGIL0HqiIg2C3AIav7wSaGW8V1tVhn1B+7RSUcXzx/gopb+R1604QcqzgdroNmyKzr/s0zqHmqGxvnC/KhkOebzHEfnQN2dWHvv5YUw5FG5zGiofQVijILU9MAjCk1MKQ7qmqlW2nXZStg0Qk4Oha4vZ1VvjWpBQp0WIKu9ka8atJWt2UssaNNeSztbp3rytuv9kdY9KdM4/9rZeHUA2HYd4tvAJYsqI+gmfxt4bkWlcuBay8/xoLTkdijOwdO2unONUw7bzGXcRU6LO4ZsJzfnTi8bwQsTfLWKlmFMqbgRIAIEAEiQASIABEgAmQA0jsglQAZgPRiqIqAYA3A22sGwvb9vyy3a0U6oN54ztRRJtDHjyJhskv6Sqxj5SfAdainzHLu+h2Dxfk+bJw4FEJRz+jMcZnmFkyTi9QrVlIAm50vYEqLGjJrEFIAxjAbFd4FlbTec7K77wJqtgVOTgECN7D3dyc1hsR1GXo7VOKVuOvmc8w8lK6JSOq3zcxKY2M/+1zjqLfwIqJjf2Yav7irFbrYSTehxu0NwZHQ17wx2lqi3wZgsfx6udYi68D5pyKx7spjXNCbBBOtdIZ0581ALc6IljWPXMf//AQs4M/1rY7XYG9lKdc0FIwIEAEiQASIABEgAkSACKQnQCsA6X1gCJABSO+BqggI1wBcOxi27w6w3K4Vbod6E3aohOPDe2Goto/foTdNyLHSI+A6Yr7MusIuH4TVZa6ZSAyKoqTns8xxo4OAzdyqsO8SfSyyuyTTllWZxasgwIR/QjHlXkeUE6U7J7H3AaBqM+DsLOD6SlbVAXF9fGu5AgPqVuYpXX0pCovOPMikXpattxKJBGazz+BnojhT3CktqmNUI1OptHpvCoB/1MdM3+0e6gBnE9V1eJ7x7x3sCXyBu/qDUED0i9M34CRgXFcFM5+NlMnJEHsXhzaS2YcDmh2GY91G2RhMjxABIkAEiAARIAJEgAgQgdwRIAMwd9w0bRQZgJo2o8KpR7gG4PphsH3zD0var1AbuEzcrRLy98ICYXaomdTcx0oMhuvo/8msK+T8Xtj4DWfjvBWVRBmPqMxx34QD6+ux98USEaaaXcYSGbasyixeBQFG7b4NrwftUUL0hcve7yhQpQFwcQ5wdRF7/5jYEW+brcXQ+lV4SqWdu8c8YFuxCP4dmTtzK+ZrPGrPPS+VSB/HipjToZbU71ouu4rIt18zffdXAxNMb6W61Z0jdwXjyp2niDAYzNc25jZQXH23nX/2qoDCEo7ndZetcG7aSQVvKqUkAkSACBABIkAEiAARyCsEyADMKzP933WSAUjvgaoICNcA3DACtq85w8+/YCvUncQ1dlAm0PBb12B5vK3UlMeL9EHb8atllhN8egfsAkazcV6JyqC8R+bVafjwCFjF3546qsoZrO7nKLMGIQUY9vctLH7cFoVE6bbaDjwNVHJKMf8YEzD1Oiu2w6PGGzKtvuu+/gZuPs3cablGmYI4PV76is8/McqqszAzrmnNUtjUv3amEMyqQVufc4j7kZjpO2eT4tg9VHVzy6xMfP34Li7pT+Jrc3sN6OX/Ew6Vff/KpybKi7kt1X42/4NL+wwmpsrUUWIiQASIABEgAkSACBABTSRABqAmzmrOayIDMOfMaIR8CAjWAAzeOBp2r7gtv9cLNIfz5P3yoZLDKCE3zsPmTGepo04U7IY2k2TvThx0YjNqB3HdhF9oVUDF2RmbOgP4FA0ss+BpGVr+EDYObZzDqoT9eP8tgVj/vDUMROlMs6EXgfJ2Kdt/mW3AqdcVsSWC62/GxGbVeEU7+J7Huy/xmUCULWyAGzOa5ApQ0LNYdF13Q+pY83KFcGIst3oz7aHo2B+ot/CS1DF1TYtj1xDVGYBtV15DwTc3sEdvLqdPvxAwQ8r5lLkipphBj+Y6oGpiJBvcr6Y7XLpPVkwyikoEiIDKCHz+kYiAe49gUlQHpib83/EqE0WJiQARIAJEIM8SIAMwz049r3AyAOk9UBUB4RqAm8fCLppr+hGQvwkcp3BNQZQJ9NbVE7C/2EtqytP526PllL9llhNwZB0cQ6axcZ5pG8PYPSxz3G8xwGL+OXL9i+/C9jHSVyjKLExNA/RYfx27X7eGlkjCKfzLHyhjAQRuBE5yZk9Ack1ccd6GaS25rbTMqrvKM05KrU4kAh74tIKejlaOq78U+R4DtwVJHZdPTxt3PFuAae6R/gqN/oQOq/2ljjEtVQDnJzbIsQ55DWAamjT/fADuuru4kKXMgZHX5ZVCIXHuLGiKWj+5ebheeQyc+3OrQhWSlIISASKgVAJf7p5BoQPd2Jwvqg5Axd7LlaqBkhEBIkAEiAARSE+ADEB6HxgCZADSe6AqAsI1ALdMhN2LzSy3gHwN4Tj1iEo4Bl48hDpXB0jNfdawFZpPk31r8vWDK+B8x53N8VjHFCazgjPnjP8KzON3ku1TYCN2Tub+CFIJJCUn7bL6Cg7EtONnHRUElKwG3P4bODqG/S4k2RTH6+yAe1sz9l7U+29o+r8rWaoOdGuCUoUMclzVsbDXGLMnJMtxN92aoHSGuFcfxqDflkCpYwoa6Pw2DVV11fI8g3FJWzFE5xQnwbIH0Gm9qiRlK2/Qks6o/ZU7i/Fmub5wGLYqW2PpISJABNSfwPfQw8h/uH8mofGjQqBfkn/eq/pXQwqJABEgAkRAUwiQAagpMylbHWQAysaPRueegHANwG2TYfeM21obmK8+6kw9lnsSMoy8fuYfON8YJjXCRf0maDxD9pWJ/v8sRt37PmyOR7o1UHXmzcw5xUmAT3He/Z66K7BnZuY/hGQoWe2Hdl5+FgfjuvJ1jgsDihoDYf8Ah7j5updcCXvtdsO7Pbd1Ovh5LDqvlb5Vlwl6cVIDVClZIMccdt98AbdDd7Icx6zmY1b1pb9OhL8B09Qkq+u+d0sY6mnnWIusA8TJEpi4ncRCnfXoppPOLHUcCbScJ2t4hY6/vnIgnD9yP5dBxduh9hjVdBFXaKEUnAjkRQI/YoGF/K7uaRgOJjdAZ++jeZEK1UwEiAARIAJqQIAMQDWYBDWQQAagGkxCHpUgXANw+zTYPV3HTluQoQtqTzuhkmn0P7EDdYO4Bh3pRVzRdUGDmbLr8ts9Hy4POVPlgZ4FqrtJ3xYq8SoGkUTMyuiKBdjv+ZdK2KgqaafFx/Dvtz789JMeAgVLAxGHgf2cIRqVXA6brP7B/M6W7PPXHsWg72bpq+6Yh46NdkGtCoVzXN6Gq4/he5I7ey5jgCOj6sLKqAjv9p7AF5jxb9am4ZUpDVGpuPIbbsR+T/jdnGSt7lK00k63rbnBdKDRjByzUeaAgM2T4Bi9iU0ZaOiCOir6/aHMuikXEdB4AknxwJxS/1nmp6kxKJJPT+NRUIFEgAgQASKgfgTIAFS/OVGFIjIAVUE9b+bM2DVCB8DvU7Gjo6NRoQJ/66g6Iwre4Qa7x1x33VsGTrCfflolkq8d2Yx6IVyDjvQi/HQc4DLrrMy6ru3wRr3HS9g49w2sUXO69C2q4rnloJ34nX22S4In9s8dDxFzeF0euTrN34d/fw3lVzvtOWBYBHhwGtjTnf0uOrkkllocwP+6WbP3zkS8xfAdUrZYpz6xe6gDnE1K5JjmkrMPsPJiVJbjdg9xgLMpP+6fTMN9w51Qp3KxHGuRdUBaR+M9unPgpH2PC9fCF3AaJWt4hY4P2+8Lq4gFbI5QHUtYz7qm0JwUnAgQAcUTkPivgOgcd1yGtIzzE3tg2px1iE8UI+rdJ1QtWxT6OspfRa14GpSBCBABIkAE1I0AGYDqNiOq0ZN3/ipXDV/KyhHQHANwlzvsHq1gKwvWd4DdDNmNtty8LFcPrkX9O9OlDr2pZQOH2ZdzE5Y35spWdzR4ztV7L589zKZekBpXvKAKtH9+ZL/rmTATmz0nIp8e4/fmjavjnJ04lJTBhJr5DtA1AB5fAnZ0YEG8lxSBd/VDWNXLlr13OOQVxv8TmiWsOR0s0MexUo5heh6NwLbrz7IcN69TLfSsU5H3/eIzD7DqUtam4apeNmhrWS7HWmQdcPnBewzYGoRreuNgpBXDheu6DTDvKGt4hY6POLkO5oFcU52Hosqo5pH1fCtUDAUnAkRAbgR+zK2MfImxf4znmdgPnropDbo2JLVB47HrYVqq4B/H0QNEgAgQASJABGQhQAagLPQ0ZywZgJozl0KrRLhbgPd4wu7BUpb3bb3asHXjDvVX5kRc2bcMDe55SE15S2QOew/ZO6Je2TQVDV5yjRXuFnCGxeR0jRfSZRcvMYP211fsnYEJU7BgxmSUKpjzphXK5CjPXJ08N+NfZFiV6fEJYFZBPr8BbG3JpvssyYeppsewvq89vO5w7QAAIABJREFUe+9PZ/UNb1AFM1rVzLHkyfvDcCD4ZZbjhrhUxqx0zUiYB2cfuYu/bzzPcgzTvGSwi/TzrnIsMAcD9gVFw+3gbUTqD4COKJkbOewyUM4mB5GU/2jk1X2ocZFbIfoaJVDO87HyhVBGIkAE5EfgP87++1OSAQlTsM131p8eo++JABEgAkSACMhEgAxAmfBpzGAyADVmKgVXiHANwL0+sItczAIP1bOFtdsllUzApd0L0ejhXKm5w1EVlp63ZNZ1Zd14NHi7lY0TXqghLCdK73qcvMIWWrGcmTE8YTymT5yKyiWUf06czIXnMkBn99U4qO3Gjk7W1oeW+/uUz69uAxsbsd/9kuhihPFJbB1Yh7236doTzDlxP8vs/Zwq8ZqGZFfm8B23cCbiXZaPd7c3woIu3FmEzIMT/gnFoRDO0M04mFkxyKwcVPa14sIj7D9/Ddf0J/BTT30K5FP+luSc1P/49gWYHO3EDvkmMUB+z7d5apt8TnjRs0RACAQk11dBdHYmTyrz778pzoVgesv7P0t4llwaZdzvw0CXtgILYa5JIxEgAkRAqATIABTqzMlXNxmA8uVJ0bJPQLAG4O1/fGF7nzvDK0zXGlYzpZ+Jl30cuXvy4o65aPx4odTB9yTGMPMKy13gdKOurB6BBjG72TuhRVvAetw+6XHXOAHvuTPZxiaMwtCR03LVtEJm4SoIIJFI0HXmUhzQ82Kzi/UKQdstOuXzu3vAWieesj7lz2DnUEf23soLj7Dk3MMs1Xe2rYAl3axyXF3vTQHwj+K2ZxfQ18G3+CQ2TutaZbCmtx0v7uBtQbgQmWpeAsinp40fCVyTF+bhp/NaK928mnnoDp4FncAuvXQdf/ULA9Ofp6y0VOPr1aNQlN/VgKfw57R3MDTMO6tk1Xh6SBoRyBWByNU9UCOGvzL+sGsYOtgZ4+V8e1T49eg/4x52DUEHuyq5yk2DiAARIAJEgAhkhwAZgNmhpPnPqPdfSprPPy9XKFwDcP8C2Eb4snMXrlsLljP9VDKXF7Z6oMnzZVJzR0nKw9QrXYOEXCq8umIw6sceYEeHlHCFzeid0qNtaAi8DmG/m5I4DJ0GTYeTSfFcZhfWsISkZAyYvQi79bj3Q2xYAtrTUldFfnwMrOTO+2Oq61X6KHaP4AyhBacjsfZy1ltCpRl12aHUfpUfwl5+Zh+tWqoAHr3/xn6uV7UEdgx24IXqtu4GAp9xZ1q1NC+D0xFvec9cmtxQ6Ss8XRZcRL0vxzFPdzOnpUwt4C/V/Bxmh3/aM7HvolFsrQVvSMyIuyhZ2ignYehZIkAE1ITA28+/8GqJC+y0+Cbfy3FvUKFoPiQeGQfdkG3/qXaO7hjMmjlHTSoiGUSACBABIqCJBMgA1MRZzXlNZADmnBmNkA8BwRqAIQcXw+aOD0shQscM5rNuyIdKDqNc2OSGJi+5jsTphz9PLoWKXg9lXp11bWlf1Pt8lA0dUrozbEZska50cwsgOoD9blbiQDTsPQNNzUrnsDJhPv71VyLGei/AVr1FbAFJBctDZ1KqEfv5JbDUnFdcj+IHsHdMM/ben5p1SDPqskOr8eLLePKB6dAswSDt02hfMBLnvhpjo7gN4qEHK6MiODKqLi9U86VX8PDdN9iIHmG27g5UKqqHkTGdEJBsxj4nrXtwdvT86Zn3X35hR8BzFDbURV+nSmynTOZ+Hd8LmKqzFyN1uPcSNV2B7lkY039KpsTv4+N/Qn9eGV7G6F5XYFSN6wStRDmUiggQARkJbL78AIMvc8c4MOHGJ4zEQu+50NPRAi7NA67M/88sp5ProKX3ORmV0HAiQASIABEgAlkTIAOQ3g6GABmA9B6oioBwDcBDS2ET5slyu6ddA2buN1XC8eL6yWj8ZqPU3G8lRVHU/TFrnORWoP/i7qj77TQ7/Ha5XrAdtlZ6uO3tgKfcdmjvxL6w7DIDHWzK5za9oMZ9+BaPWb6+WKfHrcoUFzWB9rjbKXV8/wAsMuHV1LPwTuyZ4Mrem3ogDPtucc06iubTRdyPRPZ74+L5cHkKd45gdgHVnnseMV9/IUh/JEqKuJWA/0vsghXiTqhSMj8uTmrIC2fjfRZxPxJwWW8ijLW48wNNf/2NJKR0dpZ3J2BmG/WBG5G4cXwbXklK4qakJlytymFlz5TmHvtuRWPqgXCs0l2Bttqc2QznMUBzYayg+eZRGgVEv1jWD9scRLXaTbM7lfQcESACakLg053TKHKweyY1XeJn48C8SSn3paz8zjggUqcGasxSzf8foSYoSQYRIAJEgAgomAAZgAoGLJDwZAAKZKI0UKZwDcAjK2AT4s5OSaR2NdRwD1LJFF1YMw5N3kvfWhQrKQDdGc9Q0EBXJm03FnaE04+LbIzbFQfCdpD0bcfY1RV4dJZ9dkFiD5R3dUMfx0oyaRDK4FeffmLhQm8s11vDShaXMof2yNRuzPFfgXnMq89dvQpsxu7JXdgbo3ffxvHwN78/O2rdw19FgnDpcxnsEDdDMrR+ryh5OKdVjpHUcD+F2uJQ7NDjr0S5k2wM1wRflCyoj6CZnAmVKE5G1ZmnUBJxCDIYxcu3PqkN5iX1/n3Pp705+joZ51hPVgNm7gvC3HucjnNiOwxNnITr0xujXBFDeB+7h63+jxH428j8woVpvRiow3XXlZsgBQR662WKMpIYNvIlu1Vo5NpXAZkoJBEgAooikPTsOnS2Sf9d/HFIEIpXqMalDt6OX36rYBCX9fmu8OT+w4yiNFNcIkAEiAARyLsEyADMu3OfvnIyAOk9UBUBwRqAocfWwDp4BsvtgZYpqs8OVgnHi6tGoPEHrkFHehHfJfqIn/oSxfLryaTt5vw2cPjFna0WUnk4bPpLbzyCf/oA94+x+ZYmdoZh85n4qwF/1ZtMgtR48JOYb1i3zBMLdblVmZJydhANSzVQxYmATwleBb0NV2PXtD7svUHbgnAx8j0qid7irN406ItSVv/5JvbEBnHKSsFHc1tBV1sr2yTSzLxpOnswQoebn7QAxr9S3qFIn5ZsJ8rPPxJh5X0WVUSvcVF/cqZcaWMmNquGsU2qZlvLfz3InKX1cUkdmGs95z02M3EQtGoPhk8HCzDdjM0frMRYncP8UANOAsb8LcxyEaWAING+tjBK4M55PF3NCy17jVdAJgpJBIiAIggkhP8LvX8HZh06KzMv9glwdTF+RofC8GMEb3zC+EjoFSmrCLkUkwgQASJABIgAyACkl4AhQAYgvQeqIiBYAzDsxHpYBU1luT3Sqoyqs0NVwvHS8iFoFLdfau5EiTY+TnyNMoVl6y56y7cZ7BMC2Ryh1cbCuhd3BiIv+YHBwF2uYcjqpHb4VX8WJjWvrhI+yk56/80X7F7lDh/ddKsyK9UFBp5MkSKRQOJdDCJJMiutj+7/sHPmYPZzjw03EPAkFhN09mOcziH2/i+JLmrEM3FFCJ3dDEXyZd/Y/fQjAdbe5+CjswV9dc5nwmLyawfE0Max0S5sx2bGjHOcdwG1RZHYr++daUzaNuABzsbwbMc/1zC33Ptvvont0c2lDjf7tQVnprZCvYWX8MygV+ZnZscCWtq5Ta3UcVGLGsL0O9cs50yFcWgxJDNjpYqiZESACGSLQHBIMOyONM7yWXG3ndA24451kPbg5zePUXg9vyFUjIs3SjYdly0N9BARIAJEgAgQgZwSIAMwp8Q083kyADVzXoVQlXANwJObYBWYerYPgMeiSjDxCFcJ80tL+6PR5wwrodIpeTHqJSqWLCiTtttzG8I2kTMrQs2mwLrbLOkxD48CQrlGDJuSWuGVgzs8XOVjEMlUiBIGh0Z/wvF1bpilu4vLZtIY6MsZeWKfMtAW/2S/7689D9vdR7Kf07r1husPRiER9xzzQKP4JXgqKQu/aY1+d5fM7hUd+yNr4wxA+3hvhElMkb6hx7MP39Fw8WUM1D4FD90dmVLZ/FqHOBRCB+tyWNYj5Xw+Wa8OM5bjsP5sqWGSJFpYZ7ELy4ITEWXQL/MzAto+F7G8M8zjOCP2YoleaDw6i3M1ZYVK44kAEZAfgU8vgGW1/jteNn4XSZLiIZpTihcnqsZfMO2xQH5aKRIRIAJEgAgQgXQEyACk14EhQAYgvQeqIiBYAzD8zFZY3uC26z0RGaGKx12VcLy8uBcafjuRZe7HQx/BpDz/j4ycCg33cYalmNuqFG41C5Ydp0gPc3wCcIvrEPx3UjOEW7ljcVernKYV5PM3n3zE1c3TMUV3H6e/emug5x72c9K8StCJ/8R+HgBPbPOcwH5u9r8r+PQ+OtO5e8wD3eLdESipiTPj66N6mewbu/def0H3FWdwx2CIVK7LkzpiaVJXbOhrh+bmKR1qmdWMrZZfw0Kd9eimwzV2SQtQP34pXkhKw1BXG/d9WsplvkJn28Ba68l/xmoT74sT+m78Z+wGAK7L5aJBGUFCNo6AzStu635AgaZwnHxQGakpBxEgArIQ2NoGeM4diZExVMKoEOiVrJK9DJ6Fec/dqTYatXrNzd5YeooIEAEiQASIQA4JkAGYQ2Aa+jgZgBo6sQIoS7AG4J1z21HLfyyL+JmoPIw97qkE+dWFXVH/B9d0I6OIyH7hqFFFtgYc97xrwyyZO7j8rt0cWLiOkV7v6RlAANcA45+khrhUfTbW9bVTCR9lJ736MAahf0/mn09n3hHoym0JTlxYFbo/3rPSBondsMVnGvu57vyLMPtyDRv1/pdJ/uCESbiQbIeDI5xhV6lotssLfBoLjw17cUqfO7sy/eBlSZ2wLKkLlna3QkeblCYlIS/i0HHNdQTp/8VvtpE6sFeCG64nW/z+9MS3NbS0ZPvXyd1Xn2GxseIfaxJLRNAWSfjPTXoAFEwxLoVwBe90h13UClZqiH5t2Mz4P3tnARVHsoXhb/C4EHd3dyHu7i4b37i7ASHuyUY3unG3F3d3dzeSjetGkJF3GkL3NMzAQLBJqs/Z8950V9269VUD4edK0NRsa9iL8FEQ+G0IfP8IE8z/PNV2PIpdSsv/2HV0QgNKf1e+7q+nbk6u9iIS+Ld5n8RGBQFBQBCIZAJCAIxk4NF0uZ/7jS2abkq4ZRUErFYAvL5/BbmOK11Rn2qSk8b1dpRAPz6hLi7fD5ld+3qzs+TK+nP19+665yOL4ZG8xs1ik8hRtZPpNfe5wgmlQ/BmXUk2pnVlRYeiUcInshfdd/MVD1f14U87o6jMvM2g3jzZFZ8puXH476n8uYNvPxaOUdJeC3jso4XXGvrZK7UUAwb38enCZn0plrUrQuksiS3e3pG7b5ixZAWbHN1MzlmkrYaHthVutXLQpmR6vzEnH7yl14LdJiMRpedHdblp7esvKJ4dVoEkcX6u1mSHWdtZ+Na/s3CorliJYcD9UE2J6sGXNk0l/1V35WvKNhs5RpyJarfE+oKAIBAMgT3/W0uVC6Z/9m2ocJyGpUJIDQ5ke+f0LlT/qEQCP3AuQ8Ye28QZCAKCgCAgCAgCEUJACIARgtXqjAoB0OqOzGodVre7Azsgi7QbT09PUqXyjzqyhuv6wdXkOtpZdvUZSUnlpkTIReYeTo6rQQlv8+lIl+sfJV8eyyMSTPn+yC0H6XkuP7pTaiZZK/xhepuHxsGR8fKznboizE/qytbuLpGJJcrW2n71X96u60UbO6OozEDpqT4zCuHw4Z7sY1efnvw1ehS2PyLoso3YxSDDEtra7Qmyjwm+TZmrq82cFgWontvybpGSMLlsxWKWOyhnY2x8g640/X07Y9zQ49Dt10z6Zz07A6fbGk0MaB5yZEBZ0jrH+inunYe6Ms9BEY8tNla0M1SzrrpZ1/YsJvcpJe37kSYV6V0Df4u0mIAYKAgIApFAYNbYPnT3UUpcBCz5qvtDkiZyDrUHa+eNpsnLSfK8f2NmJcVApeFWqA2KCYKAICAICAKCQDAEhAAoXg+JgBAAxXsQWQR+GQHw5uF15DjcUeb2nCSkdFMEncgCKq1zekxlivmajxy6WGsvBQr+XPSdp2tmUmuUlNX7Ff4mU6kmprd5bAocULqZ7tflZ2w8Nw72LxuZWKJsrY0XnuGzuTvN7IyiMgMJVD6zS+LwRqkZKUX1jXUbQwwHW/R6AxmG7mSy/Twa2h4Nso8AoW5iwzw0LpTa4n3uuPqCbWvmMd+MwHZXn5LKPpOonjsZc1r4p2vvvPaChavWmI0alMYE1AHc3LUE+dNYnpIc2HGtTs/Ikf0Za79IfnRLn5rstfvBdqXepskN15kN+VtazCI6DHx85n+k26X4/NoQn1hDHxDLUfq7iLgEAUEguhEw6LRoPIKKfBOSTGBQV+UPgqHxe/PqBdS701+e8to2CUlGRM2/JULjtxgrCAgCgoAgYJ0EhABonecW3l4LATC8iQp7lhKw2hTgW0c3kv1gO3mfL3AmuVvwjQsshRLacedGl6Ow9qLZaeeqbKVw8Z8T3165piOp5oO8xuOqy0lXrLbpNU/Nhj1Kg4Zjulz0dXLn3LCKqvEP33xBb4BMSWKHdsvRevyqM09x2t6F+rZGUZkle0ElRRT1mV8Bhxfn5X0M9u3AkGHjiRfTnq/eWnK67uFv+ylUtr0QZK+X9Rmp6+OBa60ctP2RqmsJkM2XnnF8/V9McVBSkQPPy+v1NzkzpmVVx2J+jzZdfMb6DatY7WC+KH0t79FcM2SgRdE0jKkXuvQ34/XfffFmybhu9LdfL98+71iUQkP2guc5WKR+f1S+t9kJ6UpagiHajPF+fBbHpZVkf7wM9nh2fUTmpJY3dok2mxGOCAK/AYFL2+aQ/2LQGqq6kR/l6O3QYjh1aAfFjzSXp33DiZhur0JrRowXBAQBQUAQEAQsIiAEQIsw/fKDhAD4yx9xtN2g1QqAt49vIdt+JQX2NQlI4vY4SkBf8ChFQd1Vs2ufKreO4mWqhNk3KSLti3sK4mq+yTae1t1EmnwVTNs8uwB2KhENZ/TZ+MPgxm2PavL46fvvMn2/f5RD17IZGVg1W5j9i24Tl5x4RJLdnahha5TGVWYQlFNEUd9F1bD3PCm7PsK3DT0GT/CroffmP28Kj9nPLofBZLdR6gQGDP7PEIPc3gvpXzkr3ctntnj7a84+5c7WibjaLzc7p6z3FGImy8rOXqX8xqw884T9W5ezxEFJUQs8OaApSdakcdjTp7TF/gQeKL0TBY+0pZStEhn5LnMjnFss9BvqtagWTp5BIyL9Hva/B7F/rtN1mB0P68S392GWujHO8aY3cMlmPaUQwrp1MU8QsEYCt0bmDvI9eW3MZjQZaP6PKiHt8/qVc+TarP7jhmHYKzT2P1dPNaR1xXNBQBAQBASB35OAEAB/z3MPvGshAIr3IKoIWK0AeOfk/8i6V0nfe2OIT2L3J1HC8fKo4uTTm+9AfLLUP5SoUDfMvnn56rAdnRh7jU628W/TPaTI5h8lFuS6uBy2dZdvX9ZnoK7PaO6NqYa9rQ3ffLTkH7UPb61eHnN0QDnSOMcMs4/RaeK8Iw/ItL89FW0vKW5VGAml+smfdUvrYPv4sPzZw7clbftPIlWCmDx9940ykw5wx/EPHIyYG++xgNc8GpfJz+Bqlguny0495v0Od3rbbTKLS4rmex8vJycGl/cbs+j4I87vXMJchxlm58zR1maitqnf88fja4TpKAwGA+mH7OSxkxIJ42codyNo4C8A8uUNTM4U1H6sJDDAClPmvr6FSRlV+9lZ5TDVi+cPE0MxSRAQBCKOgE5vQOfuHOR7smejXaTOWSLMCz/1fEqaRerI6e89bhDDWfwhIMxQxURBQBAQBAQBswSEACheDomAEADFexBVBKxWALx7ZgdZdilixTtDXJzdPaOE4zX3QuQ2mBdAjhX9m1LVzNTrs8DjT1++Em9yCtXI121OkCRdLtOzr66DTUp9xFv6NFTzGc+lEZVIEMuB2y8/U3X6MdXcTqUzMLR6dgu8if5DZuy/R4EjbVSRbFQZC8WVrtH6lY2wuac0CRnv25RGvaeQMXFsPz7Np+/gopP5mlIVvSdSvGhJPOqaOQMTmBYee4jtnsEmG4sEDP/Tpzcn7Etw3d0/YnT2ofvc37eQaQ5zzYLfqytIJ19/cTOsAuCHrz4U9djJXadAjWWKd4cqRunHJ2bAPqVbMvaxoN8tcIoX/V+MwB7qtBContiWYuupW7Wy9e1FeCwI/OIELt99RL5V+VS7fJ+yPAk7bv6pnb/7/I0EU1JgozHIdt61OohzRnV08E8tIiYLAoKAICAICAI/CAgBULwKEgEhAIr3IKoIWK0AeP/sHjLtbCxz+2iITXx3pUtuZAK95ZaP7Dwyu+SRAjMpU9tMx14LHH37+iWJ5mRVjfzQ+SoJkqU1PfvmVljXWn72QJ+cCj5TCIjyO3DrFe3/UerfSQMLpU3Ahi5hj6KwYBuRNmTi7tuUPdmaIjZ3lDVrTIHCHeTPhjUt0NzeLn+e4tuQ6t2nkT15XC48+cDgeevY5zjQrM9NvEeQPG8Fpje1PFps1sF7JD/UmwbGtQkDrTBDW49p2kZytOaUvXf4dmQmI+xXmPUl4HylAQ/HVsfmRyfj0AD3fP+NlpNWc8Sxr3pahwOQqpD63rsHYNBD/LRg5xCaZaLd2C+jUhNb/1n2a0PO2TRsZF3NTKIdVOGQIBABBM7O70KRF6tUlrXdLmCX2ERUcijW99Hq8fJIqSqx8azuJlKZK7ERCttiqCAgCAgCgoAgEJiAEADFOyEREAKgeA+iioDVCoAPL+wnw/8ayNw+G2IS1/1FlHC855qTzJpnZtc+lGci5er/GWbf/n1yjxRL1CLM1z4PiRUvaDdEv0Xu7IbVSsThM0MiXLxnsqOnCzlTxENKRR25Vd0QOmlcR84MDabJQ5i9j/yJHttvUvtsC/LaGDWFCdyldn1buKGk4s7U1qXMnzPImzo+x++9ZfLilWxxNIp0C7SNTj590GWpwaI2hS3e4Lhdtyh8sos6NTnQ7C26EvT27c6F4RVxju3I6O03SX56FO3tdikjE2aA98refA22ZPH+BwM2XHOrTBwne4t9Chg4cMMVXl7cwTKHCeq5bp9CbcuaJrwcl49k3op4vzNVH6p3cLOmLQhfBYHfg4CbiSjjEe/A9ue7dr9wTU9yzXuZ44NKS8hYsv7vwVXsUhAQBAQBQSBSCQgBMFJxR9vFhAAYbY/ml3fMegXASwfJsLWefEBSY4Y47i+j5MAeuWYlvcb82gezj6J8k15h9u3BzYtkXFdONV8/7A029mairx4cguVKzcE3hngU9p7Lmk7FKJbBGSlCbs7hByp7Gg08GBP66LG3X7xx3XaDh2++0rxoGloWTYNGMhaF1/At12h5sRnZbIxSwhssgtwNFa82dYKra+XP87S1KNhhJoXTJWT/zVcsWvFPsJ13PXxbcDV1S9Z3tjxqcujma9S71I7CNnfNinnn9Vlo6OPGgX5l/NKRpb2UuNCX6sYNTfI2gyurVYSlmoTvicvJweVJET9GqOhL6b/5PfbR226Duj5h0tzQxaiTcqisWsfg6381Ide73bKzx+LXpVTvf6zDeeGlIPA7ETAlAIbTHygeueUgPUoGwc2SM8hRqc3vRFfsVRAQBAQBQSCSCAgBMJJAR/Nlova35WgOR7gXoQSsVgB8fOUI6TbXluF8MzgS0/11hMIyZVxqnvDMNTOpbd6YXXt/5hFUbKF05Q2tkzfPHyHHdmWvPgY7HNzfmTfz5CQsUTr+fjLEJK/3Qpa2LUzZrEn8RKUVp4N2t73iWpl4MUIXPRbYVsAaod1jeI4fsP4KXa81Ir3NK8VskxWQvZbyeWs3uKSk1S7UViPbH7NwyZyI7Vf/ZfOahSxymGLWrS8GJxrEXx+qrrvdV12k5+2WZLExSlXP1QCub5TXeWlIQDHv2WzqWoICaRLQb90VWl1vSz4bI8G22kTYNQhQalbV93bjoiEL23u4kCul5fX4pPfXY/stFp94xF/2M6lle1rZc74WUHdOeB5NtLN1aVFP8nsqgt/JWBUoMcB8k5ZotwHhkCDwGxD45u1DzHGJVTv9bhuHGCPMR96HBsvtUYXIplfq+F4pMIa8tZVGWqGxJcYKAoKAICAICALBERACoHg/JAJCABTvQVQRsFoB8On1E6TZUF3m5m2wx8HtTaRHn0n1g957ZCCZ5oPZM9yXfiCV/hgW5jO+dGw7+Q+0kOd/Ijbx3IKpd/j8Aizw7yIrXV4Ge7J5/8OC1oWolCMpPVdfYtuVf4P4c2xgOVInDF0n4DKTDvHk3TeVrbA2oggzoEATpf0Nvl2fFEYpXbTYAJkrKSO394Hzi+XP/2grkaTJX1TLnZwNF55xeOM8Zjn8FaxLJRw3cXJIBYvdbrPkLBMeNyKp5qMyR+pOfGCU/Fln0JDRewVL2hShXLYkdFt1kb63m5PRxii9vdE/cGgsvFVqHI7wbcNyXWXmtChA9dzJLfZJapgybb9/RGKQDsCVR0OJHhbbssaBl1ePJN8dpcPyeYfCFBq63xq3InwWBH5ZArvXzaPqTemPHsq1P6sbFZv1CZc9Xx1Tijy+V2VbF7MPokCToeFiWxgRBAQBQUAQEASMCQgBULwPEgEhAIr3IKoIWK0A6HnzNKnX+XdKlS4fgy02I99iZ2sTqSy/emvxGpsOZ81/8ro6bLBFL3/em7o3ldu7h9mvs3vXUOSkUkPwtcaZJK5G9e0CW355HeaVlO/qDRoyeK9gTouCfuLQH4vPcuRu0IjFsESPpR+yM8i+zg6tQJK4TmHe789O/HP5ecber6s6E/74H6QvrZiWIujOzJM/r9KWQ1tjOq2Lp2PF6Sdc+d8sJtn/HawruQzr5G69lvjcZP4p/vm3Fk4aX2V4vb9hcyfV9Mxey5jUpBB186ek7ZKzeDxuRirNW2VM83VwcRkYNTGZoa3PNG1DRtbMQTuX9Ja44zcm3eAdfv9byuYqyx0knsZdAAAgAElEQVTGq+fVnQf5mllsyxoHXt06nTyXXGXX79lkIPPIS9a4FeGzIPBLEtDrDawa2YCWdgdU+3vZ4zHJnBOEy54vjK9KQa9Tsq1zGbpRuPXYcLEtjAgCgoAgIAgIAsYEhAAo3geJgBAAxXsQVQSsVgB8dvscqdYoTSukyCntiHc42tlGKkupfprtxDTE1XyX1/1mE4uY+q/y5z0pulKl07gw+3Vy20JKXOwnz39mm4pUI9RNPFTG396HWQVVtzJ5LWNK00LUyZeSenNOcOmpURTaj5ErOxSlZKZEFvt5499P1JgZtEbcqDo5/YS0qLqkSLtZj2sRW+OluNB+H6QuonzeOwJOzpQ/b9K58Lj0NPpWysLCYw95uns6o+yDrwWXzmsV98dU8xOdpahBqbZipiSxWfRHYWI4BH0PG/x1kI3vlLqVfou33gbLlPRu6VYerwX0rVWYNiXTU/OvYyx925xEGqVTrd+cG5vhwhLZ/5XaCgzTtqdr2YwMrJrNIvQnH7yl+YIzfmODRP9JN011ALbIsvUMunp0G3kOtpId9sYeR9c3EMV1LK2HoPBUEIhYAk9evSPt3AxBFwmn+n+S4bOT61PkiyIwnk35B0U6Kj8fInaHwrogIAgIAoLA70RACIC/02mb36sQAMV7EFUErFYAfHH3EslXlVVx+z7kHTEcf74jYGgO4/VnL+JOSaWK6vpol5j4WiXCbk/SjlTpMjk0ZlVjj66bTumbSpTSQ/tMZBh2wby9j09hem7V85xei3BrWJRGhVJTfsphv6Ydga/Qpo+O3HqdZaeeBLEzqGo2upTNGOb9/uzEZn+fZtnz6thrdIqpP49C8rzK5yOT4NBo+fM+XQHOl5jDkGrZmX3oPv/tn8Rg+zVmXXlniENB7/lcHlmJ6fvvsfTkY9VYU2nQDSdvY8MXRWzym9D1DMwpqppbxGs2LSoWpVfFzBQavZ/Dvi3UYma7vfDoqMr/s/qsNPbxf0csTcEOiP6rYHPBdL3Dke/BJnIF9Z89+9DOv37zGrnWuainDX4KTpbXUQztmmK8ICAIWE7g4dHVZDjYWTVhW+xG1O6/0HIjIYw8NaMVxT9sk0edSdyQot0WhZt9YUgQEAQEAUFAEAggIARA8S5IBIQAKN6DqCJgtQLgyweXSba8jIrb18FviOVkpjNuBBH2fP+NFDNSYKtRGjK8dUxNIm+lA+2eRK2p0j34enLBuXd4+RjKPpgoD7njlIesg4+Zn/LlNUzOrHqe32seA+qV9OvUm2X4LqTahYGvcfVz06xIGotJ9V5ziS2Xg9YSHFItG3+WiToBsOHso2x4Y9TwQ9pRt7OQOKuytzPzYddA5Rc+fTZ2FVqMW+2cTNl7B7uj4+llZ9QMIkYC+K7UeQwQAA/2K0P5KUeCMJPuZ0gcW3W/0dhVrPfpoh478BFMVKfslvaeRoUSRRlaPbvfWd1zaImdxui8JDFTOuOVSlfj7wYHcnkvQoctt0ZVNRmBaLzw84/fKTn+ICl5wwknMx2qwzHCxuKXKpIHer58S+p56nf1Q4czJEhlWRRlJLsrlhMEfjsCN9eMIMdtdTTezXILyVGmUbixODW3M8VfKZ3Vz8WrSuE+Spf4cFtIGBIEBAFBQBD47QkIAfC3fwX8AAgBULwHUUXAagXA14+vk2SpUudOAvh5wAvixgpdE4ufBf/w1ScyzFWLZi9jZSXZV6VBw54ETanSa36Ylzq4aCjlPWfL82/GKkqOAXvN2/v+ESakVT0v5vUXXWqXpny2JJSaeMjk3MHVstE5FMKd27YbQSLfJMOSDclWVF21p+5l2+dAvxz2ugIJjNKSr6yBzUpdxZv6tCzPtxJJBB2z4yaJT42mk51/fTy/K1EWeOvfLEO6pKYzWb2XsqB1YTouOx9kq8vbF6FUZnXXyqajFrBGr3SDNmhs0Ix4B6PUdayqeo8nR77i9K+SldLj93LfqbXafrdzIAmSkzOp7pfxnsoTQzJODC5PyvgxgsUv1TkcvuW66dRfaWa2mtB0ZVQdYaStq9Pp8R2VRBXBe7LcWkqUqRppPoiFBAFBwDyBLa61qasJ9EeWcI5OPrWoP8U9F8hOXIxZigIDt4tjEQQEAUFAEBAEwp2AEADDHalVGhQCoFUe2y/htNUKgG+f3ibRYnXq5Me+nsSPGzdSD+bOs9dkXaiOtnseryApPykpunvjNqByX6XjbGgdPDi3N+VfKfXersUrS+4+W82b8fWCMUlVz6WostbVy1IoXULqzj5hcq6Utiul71p69Vt3hY0XnwUZ3rhQKiY2NEq3tdRgOI2rMnYLe3z+UFvrdwfiJFPu3doOa5XOyo/1SZmZax1TG+djxJbrZDnvSis7o26wGcvDg4Mqmzm8FtO7ej7G7rwdxHNTImiLEdNZaaukcuvs42A77Bm4qdNN12jLsi/TcAZVy0bDaTu56qRuEkLv6xAvFYxJDlql9uRsbW0maZuyq1cpsicP/uug07LzPLh1kQOOA0xTH/QEYsQPpxOJ3mbejspIIr3SZGVX7hlUa9AmejstvBMEfgMCOr2BB245yWKjdL2f49iBrkOmhOvuz6xwo+j9abLNK44FyTtE/f0+XBcUxgQBQUAQEAR+WwJCAPxtj161cSEAivcgqghYrQD47tk9nBcWUnF71/MRzgkTRirLGw+fkXNZTtWaT51LkeadkqK7L3ZtKvVfHma/Ds3sSLn36+T5VxNVJ093JV0piGG9PkhUWWXvCdSpXJH8aeLLjR8Cz5PSg8fWU9cODM5pqdvunhuvggwplzUxS9oaNdwI887DNrHsyFUctgmUajvosX/UXMB1fz+saCB/fGlIgEeWTcxuUYAB669Q9mp/atieVcYXag/n1TWhSnlPo2ShQqw5p6R7G3tsXIvPYDDQdthYljooqdy+sZJhP+BOEAFQstEg6S6/jr7d52zimGMfNYghz8AxTpB5C7XVGK1txZpOxSiWwTlYeEXG7Cfb17Msc5hgetxvkP4bsHHPcQVJ7X1f5rAnsytVWvQN28snZgkCgkC4Ebj36j+SzclMHKMmW2tyzKNp4/DtTn5+wxQKXR8l+33TLjs5hp8Ot30IQ4KAICAICAKCQAABIQCKd0EiIARA8R5EFQGrFQA/vnhI/Pn5Vdze9nhIIufghY/wBn35zgPyrS6gMvs4WRXSvdwj39sfoxoVB5lvKBGST0emtKDMf0o60tXkjcjzZwgF0EcnBa3SBbe2twcly1SmUNoEtP8naMqq5EPNPMmZ1Vy9l+B8a77gNCcfvAsyJJ1zTA4PKBfStiLkuVanp/zwpRwNLJoNewn2Rmmxj0/A0uqyDx8NseifYSsL/yhMj9WX+PNWG3LZGDX2qDkNdg9RMa3n7Y59uqKcffSeGHiRWvOGR4bk+OLfiMZYAPzuo6OfmxtzHJRaVr7xM2Lf+6JJAbBi3G2MqZuLUQtWs8NxmMJKYwsj3/l3qZ1dDN7ckp9JnYz7+nZlQJWsdCunTg82hi1F1WQetpO+tmvpbmcikjRDWWgdTIRphJxc1Bm9O6kCWb4qXxP7UvWkUgePqHNIrCwICAJ+BGpN3sn/vqjFPq8uF3BKav77W1jQXd7xN/nOKdHQDzRpyeh6NSymxBxBQBAQBAQBQSBYAkIAFC+IREAIgOI9iCoCVisAfn71lLhz1dFqr7veJUkSdeprRIM9f+0mhTYWVy3zMHV9MngqDSQOOlag/BCjhhKhdOrYhPqU+n5AnnU1bRvytJ0RvJUJ6VRNK5p4jyBlvoqUy5bET+AydYU2cq/2rONcffYpiKk4TnZcc6sSyl2Gz/Cv3lrqui1in6PS4MPP8sgPYGOjLPL8AiwoL3/2MtjTMc0Olrcv6lfTb+T9pqS2UTo502wN7OgPn5WU5/Y+/bgSswROX5+x2n603/in+sTU9fHgPXFVAuD7rz6MGzuMSfZ/y2v6Js2HfZcjsLU7XFIiRP81JKS2/QImN8rD3/8sYZXDWMXvmM4w8KH/5+PTYL+b/OyELictfP3FwuA6AT9595WKk/ZzL3BtwQBLHQ5AKnV0bficTvS0cmlqffJ/Vr6+pFTqbqPDHrEbPXcpvBIErIuA9Mecu+75yGGj7jRvGPYSjfEfc8JhWzcPrSHHEaUm7HOSkNLtXjhYFiYEAUFAEBAEBAE1ASEAijdCIiAEQPEeRBUBqxUA/3v7jDiz1Km3L/68SfLkKSOV5ZmLlyi6raxqzQcZWpHxoSIgHLYvTdlh/wuzX6fGVqW4zyl5/rUsXcndfFzw9qbmgM9K3aQ2PgPRZ6pEzdzJGbjRdGRD4XQJWN+5hMV+lp98mIdvvwYZb6OBB2Oro5Gi1CL5koS21qPns91xuLyywdYBzQgjMU968uomzFULt42T7mJdlxI0mX+Kuf82JKHmi+L9H9th73B4cVm+N9C3I+t05Rhot4audtvk+/O0NRmvbc4N9yrEcvSPBnz24RuLJg/E1V55L7RpXbBruwMeHYN/asrzPxtiUlC7iL+a5WfrqrnMdTASexNmhJ4X/cfe3ArrlAYh0ry83n9jwIZH48zz33DhGYc3zmOWg4nO1LaOMPyVf4Thb3Idn9EGlw+b5d2u0paj+egtv8nuxTYFgehJYNelJ1TbmkflnBSpHd89aOf5n93B/TM7ybRLiTR8b4hDQveg9W1/dh0xXxAQBAQBQUAQEAKgeAckAr/Pb1rivKMbAasVAL++f0GsmeqGFc87XCNlKnVH3ogGfvLMaUrsUke73c/WmUy358lLH7UtTukRu8PsyjmPMhTWKcLTzdwDydHAKC3UlOW/CsI7pa5ZZ5/evE9blSo5k+Gx/aZJX7Ili8Pu3qUt9rOAxz4kwc3UddujKk72thbbCq+BLz59p/v4uWx0dJdNGhzjoJHq5hlf7x/BzHyqW42cN7G+RwVcJhzkwLfGOGp8lecdD8G+kfBYqe3o7tuKJbpq3Hb8Q9VFVpqUzmsVY+rlokVR/27M91//x5YZfehvv17xK0s1NM3XwJu7MLuwypesXktxrVeQq9tmMN7eKN07ZUHo+KM4/ed/YWp21bxy3lP80pBvjqpCTAd/8THwlXPkbrrrV9DFzoQoPfw12DmG13FYhZ2D8/tT/oXSAfSoLjelPY5bhe/CSUHgVyUwZvE6hj3tqNqeFB2dwv1RuG/5+fXjpNxQQ7YrdXk3DH8VJT/Dwn1zwqAgIAgIAoJAtCIgBMBodRxR5owQAKMM/W+38I1AO5YUgizSPU9PT1KlkvRA67i8Pr3BaZq6DtCzdldIlSZdpG7g2PEjlNpfW7Xmgzx9yXh1qnzvuE0hXEYqKYahdfCeex4yG5Q0qDuFR5G1Rq/gzcxzgZfX5DF9fLrwIEVNymdLwvT9plObUieMwbGBSlpscAv46vRkGb4Lg8H0qPPDK5IoduQLSY/efmXY1FmqtFlDrMRoBihiqJ/H/72EKVlVzjeKu5L1fWuSc/j/uGHXUr2x7uf9IwDvKkLuRN8mzNHV4bFT8yAQJAGwSs6kzG/ln0p79dlHTs7rTmdj0S13Y2iwAL69h4npVTaKef1F4wrF8D48lSH2Rg1fMlaAVkbp5OPTgJeSht3MZxin9Dk5Mbg8KeMb1Tz8Yd1bqyPr8N0mfabRUshZL7Svp9WPf3BoGRmP9JD3IaVxpxkV6H2x+l2KDQgC1kVg/oqV/Hm/q8rpZQU30rpWxXDfyPvH10i41EVl903v5ySOHzvc1xIGBQFBQBAQBMKHwN1X/1F39gnZmCSo3BhVNXyMR6AVIQBGIFwrMi0EQCs6LCt39ZcRAH2+fMBhslrse9rmAmnShW9x8JDO+/DhfZQ93FAeJjWA8CwwkAwXlbptpzV5KeZ6NCRTpp/r9ejcE2KrUZS2O1VWk7W40sDC5MRFVcBT6WI41Lc95xPVoXTmxCw8bjqCIkFMey6NrGyRn68/e1FkrHlR8+iAcqRxjmmRrfAcdPvlZybMnMkSh0myWUO8VGj6BHr1v3+ECf7ReQFXo5iLWN6nHkVGbOCqUye1W31vwZ5hcEMR3/7S1mWKtrFZAVASWxe38Y/sO/PwHXcXd6KV3X7FbsG2UGs66HUwSt29uor3ePIWLEG6y5NV6cXkagANFys2ZheFN7flz718urJV78L2Hi7kShkvCNrrzz/R6K/93HJqp35WZhCUGxqeR2E1tp5d3EOqbY1lf98bYhPP9Rm2Ui67uAQBQSDSCUhd0194ZCOF/qVqbYPrxwgpLeH17ilOf6lrCnt2vEnqlJFbUiTSQYsFBQFBQBCwYgLSv/mrTlcyc6TqNY/GKdHc0XVrQgCMricTuX6J3zIil7dYTSFgtSnA2m8fsZuoFnAetzpLuozqqK6IPuwD+3ZQ4YQSAfYdJ14XHUzaM0pzhnPkoLCbUsMvND7pvn/CdoI6rfle81NkzpIjeDPL6sLDQ/IYD9+W7I3XgBIZErH2vKfJuQ62NtwdU80i96RIu3KTD5sdu6tXKbInj2uRrfAcdMXzI3PnTmOew3TFrHMm6HFBvYzWG0YnUd1rbD+Lmd0bUX/cWk469VSPH+zp3wX48gr5/iJtNWZr63DRqXOQLXTy6cP3jNX8mopI16E7r3m/oi0NbI1SS0v0gMqj/eeOSwPeSiRfI++ROGQsSfXHE2lhZyS0FmoPNZXoUv6pDY+OyOsv0FZnjLYlS9sWpmxW9f6kQUfvvmHakpVsdnRV+1zRHVx6h+dRWI2tV7dPknSN8t57G+zwGvSSeDHtrWYPwlFB4FcicOvfj2T/W/3z3W9/bkGbToXHvg3fP6CRGmcZXXebnyZLFnWJhfBYS9gQBAQBQUAQCB8CQgAMH47CStQQEAJg1HAXq4LVCoAG7y9oxqn/Ov+wxSkyZA5BGAvnU9+3azOVzrSRrf6nic2nksNIdXyIfO+iITMF3M+HaeW3zx+SaEF+1dy33e6SKHEI3Y5XN4c7O+R5k3wbsy5mE6RGHzuvqaMqjI3fGV0VR7uQa/fdevGZajOUv7oF3tyGzsUplE4d1RYmAKGcdOL+W9YvnsJ0hznKzKS5oIuSIuD3wGDA4J4ADUpkZRObybh3akL3GavZH6SL8HvYPRjOKl1812nLsENfjH8cJgTx8rUhPn8kXIEkhErXrmsvsFnXkiq2Ru9B2aFQdpD/3Bn54IMSmTnEtz2n4tei/+fx1LRVIjkp1Q8qjFTW2/QnXF2jWl9KP66QLQmLfkQfGj/suOQ0C56Y6NDc9QwkUdfUDCV6qx3+37ObxFmobghzoMF1KuRObbV7Eo4LAtZMYMfpK9TYbaIebQQJgOi04OGsQna9zh5y5S9mzRiF74KAICAI/NIEhAD4Sx/vL785IQD+8kccbTdotQIgvl4wRi2CPWh2jIxZ1V0DI5r8nm1rqHLxT3mZjzYJ+F56OMkP95PvXdOnI5f75TClLt26cpbsmyuptqEf/hYbuxCikza0h+sb5HmztHWYb9uCfGnic+zeW7NYLgyviLMFtfsuPf1AvTknzdr5p10RymRJHNH4g9hfffYpN7ZOZbT9EuVZ6mLQfk+QsbrRybDVfpfvN9Z50K9tC1YunMJMh9nq8dIvnsenwX4lslM61yeGZGqBzmhWEfuNnB3mX69q1ZmnZNzRiKI2SrouVSdAsR/Rg2tbwS3jTsK1mK5pwT8aN/WcKmOheDdllc2d4YpRjUCghvcYbhjSB2kEInUinjN5OGPtFwU9l4j6xTrS34AwLGiimcqhMhsoV079dRcGy2KKICAIhIHAsi07aX1Z6crrZyJ/S6gT6PtyGGybm+LllhgnlKZWFyuvp0AJy0pihKMbwpQgIAgIAoKAhQQe3L3Bo+VG/yZGQ0UPJfvJQjORPkykAEc68mi5oBAAo+Wx/BZOWa8AqPWB0WqB6V7jw2TOoY6Wi+hT3LV5GdWuKA0E3tomRl9xFEn2dJGXvqtPSbqR13Gwswm1O3t3b6Py6VbyPCnFOIbbq5DtbO0Ol5bL46R01fGG1uRMEY/Lnh/Nzre0dt+pB+9otsAoMi2QxTktClA9d/KQ/QznEeN33cbm+FQG2q9VLGeuDC2U7rsBD3Tj02Hr9UEe19RnOM2btMCwoT11bAOJm5JAdncPrFJqxfl1pNS8N7uDTD6ruTu6GjY2Gibuvk3tkw3JZmOUfl1/AeT5Ye9/veDCUtnWUm1lRmlbc8GxMwk0X5Q1mqyE7DWVz9c2wMb2Kh98DLZk8V5OsyJpGFc/N1LDlszDdvmNMdWwBKf4MFhpMhPORxL9zZn4XnI83yRc6gaqAxn9dyI8FAR+CQJ/TRpBj68z1Xtx/QhSgacIut67pyGhQUkxPuuyiCIVlfq+EbSsMCsICAKCgCAQRgKPbpwh/XrlDzV6gwYbd/O/44RxmXCfJgTAcEdqlQYj7l80VolDOB2JBKxXADTROOFuw4NkyVUwEvHBzvULqX5DifZ7bZcCu2pjSfg/JS1Y6iqaYOgt4jiFvqbYto3LqH1NERg/2joTf8TDkPe4cyCcnS+PW6Utz1BtB9I5x+Txu29m5+/o6eInEoZ0STXt2i45R3GbG6x2GOM3/IE+Oa18hvAvifyEJ0mAiuyr68oL5L41jS7G3XZzNYSGQaPedFNyYPvfc9nFNj4DKFSxCTEOjqC9nb9gJl+SAPj0DCxW/qHx1eBILI232S1m8lrG+ZHViB/TgSGbrtHrSi2SaRTBkRYbIfOPjpa7h8JpJbpFSi+eravDEce+avs9L0HCDMq917dhjn+dQeMrnddKQMPDsdVpvfgsx++/xREf7jgp76U8vt1eSBPURmSfXZSu56Z+59cnH0CjP4dHqUticUHgdyQgRSo/nVaBEjY35e2/iZebxH2M6qdGAJh/R0lNR17Ilk8Wmk6Jmm0jYCVhUhAQBAQBQSA8CAgBMDwoChtRRUAIgFFFXqxrvQKgwQDu8VUneLv+PrLlKRKpp7p99Vxq3hksr/nCPg0x60wi3oYm8r1XhvjYDrhLIgtSawM7v2XlbOreU7qzvnJITdKh10Pe4z5XOKE0wtikc6Gvb1diOdjy1Udndv7aTsUomkFdC8nU4N3XX9B3xUluBuomu11XlO6+vehXKQs9KmQO2c9wHlHzr2M0fTWNlqrGGe2g5rQgKxlmFkTz/r58v7NPbwzZa1H6zlh1442MFaDVJnhzB2Zb/n7V9B7N9D5tyJQkDn3WXsb9ZlXiapSUY9rtgTQ/akwd8IBjk2Vf/qcrxjxtLXY4DpPv6TV22Li+U+/DRO0qaUALnyGc0CtdLWPgFbTzb4ClkR/AJvTRqeF8dFFq7ugIF0rbXpN9GOPbnGFj5kapT2JxQeB3JLD18nMKbi5NKo1SqsI3RwPsGxt1P48AMA9HFyCD9oFs+XguD1waBmoGFQHrCpOCgCAgCAgCYSMgBMCwcROzogcBIQBGj3P4Hb2wXgEQ0LnGx1ajNHG4VXcX2fOViNRz3LZiBrXvK00ZnjtmJEGDacRcVVv246MhFt/6PCBF/Bih9m3zonHU8xwvz/OMkZ3Ug8yn3soDD0+Aw2Plj7t1hens2yfE9Re3KUT5bCE0GAHWnffk9KZZTHWYF8Rmeq8V/FEiA261c4a4XngOMBgM5HHfy2jddHUKr0sfqKjU7pPXnOsCrxTRp7dPV47HrMAw76nUszVqGhIw/7+XMMXyLtP7dAWh2Woq5UjKn8vPM+t+Zew1RuLrn0cheV5/d45OhoMesmv7dAVYoK3BOkflntYhHnZDnwZFNs8FXir7kAZIaeeVfSb5jXXCm0n286ll3EzE2MrvXP/vB4ftIypT0/aMTGWOtjZdRysp9OH5ngpbgoAgYJ7A9t07qXk6UP2/NjshXckIxXZ7nAvZvJXvo8cyDaRUS+UPMBG6uDAuCAgCgoAgEGoCQgAMNTIxIRoREAJgNDqM38wVqxYAfd0SYo8iqNyotYOcBV0i9Qi3LZ1E7cej5TU9nbKRrNks7Jf8SO0EvAz2vOjxhPSJYoXat/HDOzPYTmny8ChuYdL33R+ynRMzYd8IedwRXR7+8FUiFQMe2GhAr2iozGiajzr51N2VTS228NhDnu+eiqt9UJGkgNc8yhXIwZTGP8StkL0NlxEfv/mQb9Q+lthPoJztFcWmJP5JIl7ga2FFeHZOvjvYtwNrdOVZYD+FSrYXlNHlh0PpAZhqPBOc4wd0+blZdoFfJGSbRadY6llVPbzbOUicxf/eqTmwR+kcfVafFUmEWurgL+JJl2/sFNj3vxV0yc8vYGrQDr7lvKewyH4SGWzMd332MyYEQLZNbEvtb5tktkd1uSntEbEph+Hy0gsjgsAvRmDHtM7U+KT8zPtiE4fYw59GeJTytUlVyP1V+ePasTRdKdVu3C9GV2xHEBAEBIFfh4AQAH+ds/wddyIEwN/x1KPHnq1aAPR2S4QjvjLJazW2krtw2Uglu23RGGp7TpTXfBwzN2lbz0MzTx2tcPvPp2RLHnJtPWPnpeYNK12b0MZur3z7RpKa5Owq1XcL4Tq7AHb2lwed0WejiY8SqRjwIGX8GDz/qKSljq2Xm+ZFQ67dN2XvHd4emc84Ex1la3iPJVWOosxvVSgkL8P1+a0Xn6k24xgbHVwpaHNPsS2l/xZqF3StpTXh8TH5vptva5bqqrLSfgwlbW8o46uOh2I/mrqMTgpaL4v8lpivzjGP6U3z02rOfpa/bqCe1/saxP/B+vYOWNNcfv7JEJNhvu2Z5fCXfE+bMAt2PRXBUmUsUA07ixwMGCQEQK7uWUKeU71lbFLdzmQj74apcU+o2IvBgoAgIBOQoriPjixNGdurys8uQw6Kup+KcEqXp9Un36cD8jrHk7XGpbPy/TfCHRALCAKCgCAgCISKgBAAQ4VLDI5mBIQAGM0O5Ddyx6oFwO9uSYiB0oTharVN5ClaIVKPb9vfrtT+V6m19yh2AdK3Wwwz86n8uN76Btz5nicAACAASURBVLkySLgtv6Ri6A+nVlbVJjuaoj2lO00N2cillbC1qzzuqj49tX38m3UYXwXTJuDCE6UxxdDq2ehUOmOI9kduvU7+8wPVqbI/Zg3y7ciz9A1Z2eFHfbsQrYXPgBFbrrP89BMOOPQjo41SzJ2GiyFXIPFNWnJlI7iniKvjfZsyT1ebLQ4jyGej1IKi9iwo8KMT8+Qs8MWCLsyAlPrdI/UmlncoRvPp/2PVx5bqjfa/D7F/dLL+8ARm5FE9n+bbgD72G+V7hhQF0HQ6ZBpWWAXAqhOgWOfwOQArtnL/8jEybVG6K2sNNtxsf5c8adSdxq14i8J1QSDaE5C6y8f/pwzZjbqlz43RkS6DlPqoEbWJC3+1pOC7/8nmTzg3pGSPoM2jImp9YVcQEAQEAUEgdASEABg6XmJ09CIgBMDodR6/kzdWLQB+dUtKLJRorKtV1pOnuNKlNTIOctvcIdR+NUde6kHcomTssg4mpFUtf7nBcfLlVpoyWOLbwzdfcJiVV1UM/VHZWaQv+0OMCs7Ijc2wXun4KnXoreAzRTVDSv+tkjMZu64rKaI9y2eib+WQ69z1WnOJGbdNR1vO1NblQPJObO0WtppNUhTI/ddfSBjLAecQGqdcePKeUdtvgcHAlWefAAOPnVqoyRh32zV+sq413Nwq35mlrcNkbRP2OQwgs43SHZiGSyBXff9xEzPAt0CNOH5YMDjFR+P1UbV2o/hrWd+7Kk0mrGHt9z/Vfg15Bo5x/O8ZDOhHJ8VGpwjaW3QlqGt7UpmTrhS02W761MemAp//LHmt1GNGvgcb29DP+8VmaL+8x25yetWurtQ7TN68+X+xnYrtCALRl4DUxGn2m3aktXktO3k4/wzK1jHRvTyct3FhXkcKvlwnWz0RryYl+1gQbR/OfghzgoAgIAgIApYREAKgZZzEqOhJQAiA0fNcfgevrFoA/M8tOXH4Jp/T5UpryVcyUJ21CD7FHX/1psa7JfIqdxOWJUuPLWjdE2KHXr5/sdpWChQNXXryzeefyPB3Rpw0Spqzof0+NKkt6ET78AgsUxqRfDDEJr/33yoacZ3sqJorGevOP5PvtyuZnpG1coRIrc2Ssyx9UsnkuKXayixP0I0D/UK3X8mYJP41/fs0Zx69x8nehjktCphtSqLXGyg18ZAqhTmj5jkHHAeo/Wq3F9IUDerrrkFwRmliIgluvX27c8KxByk1RiJf8/WQ5YewHEyknSFVETTPzqrWqewzma1u7Wg9finr9X3VPgQS33STsmD7VYkulFKIi9rcVuZkqQbN15g+m0BiZogHKA0oNwzKDLRo6O8w6ItrUmJrlD8oXKm0lryR/P3kd+As9igImCOQbvAOHjsppRCkcb7NN2OfpXyEQ7uwqBcFPZfK65yKVYHiA5S6oBHugFhAEBAEBAFBIFQEhAAYKlxicDQjIATAaHYgv5E7Vi0AfnJLSTy+yMd1scJKCpRS0vgi4xx3T/+Tqh8VUeZW4qpk77aWwL6dL7uCQmVrhcqlK7fvkndNYfWcnpcgYYaQ7by8DkZ1CPUGDZm9l6FDifaS6v9VzpmUJScey/YaF0rFxIYhN++oN+cE8141J6lGHfEmGdqoc8HDvheXR4Y+GrP/+itsuKAIkgXSxGdTV9ORhJ7vv/kJgMZXRZsLLHRQRzrS4yI4m0hrPj0Pdg9S3h99Jur7jOKqY3viapS6iLTdBWl/dJcOLtU2SzUMT06g8f4s22zmMwyP3l0YOnMR6+xc5fs6+1jYDvtX5bt+VhFs3t6R7/1niEEcYz9yNYSGZlLSbu+ENYE6Z5p7S+KmhAqukKcxaMSPnwBML9wzkdzwRqZ2xWUeeStayDTkr0gxQhAQBEIg0GTyZtZ+CRTt134fWPJHr5+ke2nZYPI/nCtbOetUgiKDd/2kVTFdEBAEBAFBIKIICAEwosgKu5FBQPwGFhmUxRqmCFi1APjBLTUJUMSWi2WXUaBsnUg96f1TWlPxPyWN9Eby+uT8cwnv3NPjbHgv+3Kq5EKKV2oUKt8WLZ5H+6eKQKUzaLAd/gLsY4Rs5/O/MDW7alw+r/l85EfKKVAx8Ufaxb/Msgcx2a2XhEYN1XMnY06LgiHarzj1CGs/tcBZYzrtNJ3XKh6Nq44mFALTvx+/U2L8wSBrPx5fw6Q/E3bfZu5ho1p9QAWbCywKLAC6fjQtdN3dA6say7bfGOJS3nsq15w6qNczFhDnlIDXRg1CjEdKdQZfXoO3d+W7PX26UbtlL5avWMQ/DhPk+9pYybEbYBTdJ0U/Ts+N5uNT8+wLtIbaZorSa31gtAX16hosgtwNQzzf33HA3VEFyKJX3qcrBceSt1a33xGF2LMgECUEBgwbwCR7daQ6HQ5AqohvKHVl7Sjy3lL+eHTRvgAFhpmpuRoldMSigoAgIAgIAsYEhAAo3gdrJiAEQGs+Pev23aoFwHduaXFGiUC7UGYpBcvVi9QTOTKpMWW+7pHXvJa6Bbnbz+HVqCwk1SvpnMcLz8KlhgW1+4y87zV0CDMclPqCj/VJSTdKEZeC3aj3fzBO3XTExXsGzwz+IlFiPnDGqQc2P9KUh/q2Z5WuAqWzJGZZu5BTjIuN3sNJ3ybYaAwm3SjoNZdlPWuQM4XlnY+7r7rI9qtGzTt+WD4yoCxpnWOp1nn12YuiY5WOjQEPa9qcUnXOJXZS6G+G2b+X4e8ysl1vgx01fcayzzFQWuywV2Dv5D8uULdelVP5W8GHx6rOwh6+LXGu1Idbe5fwl8MsebjJjr4hNfIoPRDKDzN/7Cdmwr4Rwb//I96BrV2kfo1Yy2IXR5ehgPay7O6l7P3J3yQEntayOeGnIBDNCUgR3dum/kk3u21qT42bJUXgHq5vnkKuK6PkFa7a5iDPiIjvPhyBWxKmBQFBQBD4pQkIAfCXPt5ffnNCAPzljzjabtCqBcA37ulJbBRld85lEYUrRm5004nxtSnpdUT5pSF9R/L8MZnnHjlJqVNSWVemHU2Ltj1C9SIMHtaP8fYL5TkX9JkpOOq8ZTb0ehiV0K8pRsBV1Xs8tw1p/D6uth9NcdubKltS1F5wKbfGg0uMWMdJ245mfWnoPZLe7f7AJXMiy/wFpPpP5q7AUYAe22+y6PijIMMb2R5WR5CkyA+dDps2+/YezFJHlrTyGcxyh/HKeKd4MNgoKu/dA/irgGl7hTvCJ0+4u1t+LqXxuubYTfKrsxlgrxSY16UthW3bQA09QhIA6y/wT9s1d/l889/PZ6MGJsZjUxaEjkEjLC0+oF984JExNSjje1ze5doYjWkyaMEvvmuxPUEgehBYf96TRttzBXXGTWruFPHXrV1zyX5msLzQTU1GcrhejPiFxQqCgCAgCAgCYSIgBMAwYROTogkBIQBGk4P4Dd2wagHwtXsGkhiUZg3nSv5N4UpNIvUYz46tTBGfM/KaV7L0JG9zD56MzktarVJbz82+D27D3ELl2/9GVKGW7Wl5zv3YBcnUPxQCzrjUYFSPThLlzhuy+dkLXGhduicJgFmTxmFPn9LB+umr09N0+HQ2OrqbHdfDpzv1Wvc028DD1ER/AdBABZuLVLc9w3pdWU7r/RuSBE4nrjP7BFc8/aM/7dFSyeY8n4lFes0LPOyVQu6kLQltd5r200Sa9GDfDirRlYQZoafRL4Fen2F8atP2/tgOq5sF6cZb03k7zV5No4WdUcRiwTZQa4baTkgCYLO1kDWEJjd6HTw4CCsDCeFO8aHRUshYLlTv4O80eI1rQ5pq9slb3u5QjZpDzTRd+Z3AiL0KApFAYM32XTQ931S1kq7sMGzLRk6jovuHlpHpiPJHugekIqObmXIPkcBDLCEICAKCgCAQPAEhAIo3xJoJCAHQmk/Pun23agHwpXtmkhleyydwptgcilZtEakncnlMafL5XpHXvJxzEPkaDeWaa35yax7K9xc7D6Bdj+EmfZNSn2I62OIc21F+/ujtV9LPSqEar7WLhd1wdeOIYDc7NYcqGqyNzwAO6/MTAy9uObULMlUSAKXGICcGB99x8f1XH9zGuDHTYbbZ5Qf5dqRcs35UzZXcr7Pv4btvuPnvZxoVSkWSOD/SaY1mSx19MwzdSeAIvkm+jZmtq8vZYRXkeZK9/B77+PjNl0R84rxTF/MYMlWElhtNP//+ESakVT2bpa1DdzulpiOpikAHRRTCYICxKcH3a1CbAx/B+jbwSIkIlQbl9V7IaLuFKjGXUv2gwki1jStrYXMn83tpswPSuVj2fuu0cGOzf+1DaY5DLHBU6j9aZuT3GrV2QieafF8rb/qIvQtlhpmPSv296IjdCgIRS+Di/D8p8CKQ4B6a73k/6d6TkxtJu1f5uehpSExq9/s/aVVMFwQEAUFAEIgoAkIAjCiywm5kEBACYGRQFmuYImDVAuC/o7KSQv9S3teZorMoWi10dfZ+9rW46VGEHDqlc+vlfO7kq9ub51NKk/I/RRhcHL8n7Xp7BFkuoOutg50N0xrno0ae5H5jjtz0pMy6n0yHml0U3iiNJqSovP/pS9DE9hAT7IOmNmbyWkZMJyeuulUJFsvjt19ZMG04Y+wXmx3n5tua/I0GUydfSsbuvMXfRxUxtGQmZ5a3K4qNjfKt7+UnL4qN289jp6ACbhavf2hRMjOutXL6rffi03eKj5MiIQ0mx6ucKtwBagTqChwwQOcLHuoU5a26EtSxPamYyFodmq1W7zMQV/nh8Ddwdxesa60a39/3Tybbz1fbqOgOLr3V9yRx0T2+efZSKrOU0iyuCCFwd8s4slxW0r9P6HNRcMRRnOyVztkRsrAwKggIAqwdXocmdoHKNfS5CfFSRgqdF5f3kHyLuilUvBFPkH42i0sQEAQEAUEg+hEQAmD0OxPhkeUEhABoOSsxMnwJWLUA+HxUdlLqlYi404VnUKxGm/AlFIK1B6PyklGvpPpeKTKJvNU78WJmJZK/PyvPXhynM+36KV1gpQeH77ymzZJz8pjEcRw5N6yi3+c9x09TZb9aiDMU64qm6jjL97ewIjxT7A/xbc9qXQWm2c+mnu2JIHYKe83mvU1C7o+pFmz33mvPPrF97iCG2AcSxowsbtcV41udhTQskMovsi/wtaRtYcplTSLfvv78Ex6zFrDWMahIWt/bjYuGLATUAdxz4yUzV2xkh+PQkFmU6AGVR5sf554ADHrzz/O3hDqBIh1NperaOsCIN+D1Ccb711kMuL4ZHImp8VavUW0SFDUR7be4Gjw1EiCNZ/W6AgnShbxnMSJMBD6fWkrcPb3kubf1qYnV+yypE8YMkz0xSRAQBCwj4K3VcX1UUQra3FNPMNfB3TKzoRr1/vZxEq5ROs5/MTihHeRJ/JgOobIjBgsCgoAgIAhEDgEhAEYOZ7FKxBAQAmDEcBVWQyZg1QKg56icpNYrjTZOFZxK8VrtQ951OI7wdM9GaoPSufaqy2zyVGzJv3NqkeL1UXml9U6NaDRYaeghPTDV9CJA5FqzZDpNn7iqPe1/D2IrolmI21hez78e3I9rtG8LFupqcNKxOyk074NMr+Y9jluGtNwaVZUYDuajnk7cf8vFpf3oYbclWBeap9zDX83yU3D0/iDjcqWMy/YepeT7R+6+4f3yP0wKkxN9GzNHV1cWAF3Xn8H9RuUQt+83wKUPVAym9mJIdfdc+kLFQOdgbk5AsfqQbEp+SfX4cproWL2yMdxTukqrNhmJvwxbBvfXGmW4tw+NUe1ErcGGO+1ukTNtsl9ro2I3gkA0I3DrxWeyzzdRWzWSGoBIOL49vUzMxUpXeJ1Bw8ve/5IygfgDQDR7XYQ7goAgIAj4ERACoHgRrJmAEACt+fSs23erFgCfeuQmjU7p0Hoy/2RK1DHfmTa8j0qqRffaLT1JNR9k0/eqLCNz8TrcX9mHTPeUFNnjdsVwGa4WdkwJgEcGlCWtcyy83BLjhI/a5dD+MiSlot5U6tnN0NZnmrYhBx36ksFGSZ0OWKSFzxBO6HP71du7//oLUh3C6rmSkyCWOgJi9/UXvF7Tg9Z2RrXxTMCVagpu6VaSurODRhtKw407+7ZZdIqlnuYbXEi2pPEfvvoweeygYNOPVa50PQNJ/BufmLxCEutMReqFhwDY/z7EThzUpTu7YbWJRjZFOkH1SeH9Cgt7xgS+voVJGVVM1pTYTtPKilAtgAkCgkD4E+i79jJTbynim7xCaH/m/YRrujf3sJ2t7gp/r9NDMqdw/gmrYqogIAgIAoJARBEQAmBEkRV2I4OAEAAjg3L0X0Mq/jUKKAykBxIAbwGpwJyUg7jJr+hZ+F5WLQA+9shLOp2Sfnsy3wRK1O0cvoSCsfbpmy+aCWmIq/kmj3rdcAtJcpXjzkYPsl6bLN8/qclPCVd1fSNTAmDDgqmY3CgvBBKZvjjnJnaP46Hb29ZucGmFPGeRthoe2laccexKUo1/B13jq6dPN7bpS1Ivf0o2X3ouP7rhXoVYjnby53XnPIm5rT01bZXux6Yck0S7AVWyMmmPUiMxYFy8GPZccVWi+BoPmcw6E+m/AeMzei3nkls18rjtZZDdarrY/c8yFiH9AhmSANhkBWSvpV7L1Jz4aaD3Nf9xM/LBh0fB+zfkOTjGDjrGXB3Apqsgm5KeZtnmxahQETAY8HVzxl6jk6c10I1jo0fXUJkRgwUBQSB0BKSfhYE7039NVYpYHbaHztDPjP70DKb515kNuK61ukbujOqSDj+zhJgrCAgCgoAgEH4EhAAYfiyFpcgnIATAyGceHVfMBFwGTgNS6zkpR1PK95TUB+l/pa4NwbQIDdOWrFoAfDQ6P+m1SnOJE3nGUrJ+tzCBCMukZx++kXR6KpVg8K3tIWKmLcD9bZPIdFGpPXdCl5NCrsdwtFNSa00JgFIX3kP9y+IwWtJ/letztibEbfp36NzcPQROz5HnrNWWZZC2I3cdW+NgJHIEDJijrc1EbdMga6RzjsnhAeXk+/OOPCDv/pYUt72pjI2XBj4p0ZjSA0kAbFo4NWvOeQaxmTlJbPb19Y/4OP3wHTcXd6Wd3W6z+5M6GN+JU5wXn7zoZbuRPvZmOvsaW+hzA+JJr3gwV0gC4NAX4BAoBWx9W7gh6fFGl3FK7+XVsCUEIXrke7Axk2Y9Pi14BRJoh70E+xihO38xOtQE3rimJrHmszxPiopdOXZwqO2ICYKAIGA5gXSDtwdt6CR1b5e6uEfW9fUdTMqgWu1Kk7PkzZ41sjwQ6wgCgoAgIAiEgoAQAEMBSwyNdgSEABjtjiRKHJLUAOld0AZaPc4PUTAHILWFvRGO3lm1APhwdEEyaCWt1P86kWs0JRv2CEc8wZu6/+I9meZLwZrKpe1yFrukWflyfD6x9w+UH5zTZyFxz8OkSxRLvmdKAMyTKh4p49ox96G6AcjnPw4TN30oO8AeHANHJ8rr7dAVoa9vV+44mW6UIjXu6O7b0+Sm742phr2tfzfEIZuu0fRya/LaKOIrRf6Es0qnW6l+Wibv5T9eadMcA1KAJQ5dbbcw0H5dsMAlQVG6Otn+j6HBNCDxGyQ17pAaeIR0zSkBr818ScVNBX1NPLu9E9Y0U1s2rs/44THMyBv8ysFFJp5fDNv7KPOlLsZSN2NxRTiB+yOzk8lGaSw0yLcjE8YokbwR7oBYQBD4DQmUH7KAg4791TvvcRGc1Sn5EYrG5yuMTaFa4lK9I+TPmy9ClxXGBQFBQBAQBMJGQAiAYeMmZkUPAkIAjB7nEJ29mApIikBdQCnq9vMeW7UAeH9MYTL53pUpHM/hhktjI+Hk5/kEa+HGQ09yLpM0WaOrz02IlxLDxeVotnWXH1zRZ8Cu82Fypojnd++zl69fOmvgK0kcRyp92xGkxp2+51VsEqYN3Y5OzIR9I+Q5R3W56e3bjYtOpqPTHuiTU8Fnisk1jg0sJ3dDlQS7XQ6DyG5jFNlXbhgcGqOam9VrKd741w9Mzjs6223DgIbZ2rq8IT6H+5f1s5lx6E562G6in/2GYPfX26crn4lJQ9ujVLdVOiybnBRS6m/ApMBim7Gxwh2hhgnxx5TAZxwpaCKSJIiPwfknpQHf3gEPD0P60pCjdujOXYwOO4FAEaGfDTGI6x60XmbYFxAzBQFBwJiAj1bPHvdq1LKVkh+MroGPIGbCyIOl18ModeT9hZq7KVioeOT5IFYSBAQBQUAQsJiAEAAtRiUGRkMCQgCM+kORUmyL/PhPqsEn/RdQ+fkfwHTIlGm/pYIxUhiVVLBL+v/eP1J6pfAmKR9TKRhn2b6dfkQA5gGkXJR7lk2zaJR1C4Bji5LJ57a80ePZR+LSpJ9FGw+PQeeu3aDwxhKmf2m5uh42KVFbt/Sp+a/tUYqk9/+F5ua/n6k+85hJN2ba/0Vt21PqZ8PfgJ26GUeIezi/BLb3lodd0meip283jjmaF0nTea00GbW3umMximd05sWn7xQfd5ADDv3IaKN0P0aKUtuhZt/MZxin9DlxxEcVdehtsCOr9zI/vyrnSMrem6+449gaR41R8KudE2i9QtyiyQFNVkL2mpbN/fcS/F3W9NhC7aDmNNPPVjSE+z+aoORrAXWVVGu03jA6hG7NlgqUlu1CjAovAiZSwr2Hv1el7ofXUsKOICAIgFRKI9WM5EFRjHgHtkrt2chg5e2WCEd85aXOV95IoRKRmIYcGZsUawgCgoAg8IsQEALgL3KQv+k2hAAY9QcfXHON0AiAkugnKSj+YV5BL6kbQnXAKHcyyCCpGYik2kj5lpKKII1PDbgDbuGMyqoFwLtji5PFR6lDdzzrMFyaKWm34cwqiLmxK3Yw9H5z9f1hr8Deyb/7rtSF98clRdc9bXGUcln9haFDd17Tdsk5Ey4agtZCkkaFRTC6tgE2tlet0cGnHwsdTEf5SQMLe83xi84LfEmNSaQGJevOezJww9UgBduRauCtV+vkx3S5aOU7lKF2K+lkt0Nlsq9PZzbpS/vdi8sXrjoFKm+Ztzlc8U/5DfHS2EDLTXB3N6QtGbqIOXNNN6RFS/aGStKXnYlL6+NfB9DWHnLUDVrPL7jagkW7QLXxIW5LDIh8Ap9mlCDeB3Xa94kW9yiZOQRBN/JdFSsKAr8EgfOP31NoqbqUht/GwvIz7yeJvHNLhzMfZCsXSy+iQPmGP2lVTBcEBAFBQBCICAJCAIwIqsJmZBEQAmBkkTa/jrEAKOU13pKCk34Mt1QAlIp+nQSkjgFfgHGSzgNIlfulzgodf9iTQtakCENpjKkrHWDcQlT6c/RQQFJtRBdgI2J3xrmQ1ftH51XgWJYhlGoeeQX7m49eyCqtEvWmM2iwdfsAGg3c2Q2rm8jevjQk4EKDU9TI4x/psObsUwZvUnwPGJhfc4/Njq6q92Jv4rZU7jY99F8lt/4Ha9V18A7o8lPB9pJZW+W8p/DIEDQao1zWxCxpW4Tlpx4zfut5bjiphUVabYbl9YLYler2Be7uGDAooKZfHZvjzHAwiqCTBpQeAEcnWbbnfC2hrtQoO4zXiysw31+MVF3dz0OizGEz6pEYdD6m55boAZWVBjFhW0DMiggC149uJtdBtZB9sPYpyheQSrCKSxAQBMKbwN6L96i8rZDabLLc0DmUXe/DwbGHo/KQQf9EtnSl8CTy1gjv3mvh4KgwIQgIAoKAIIAQAMVLYM0EhAAY9acnhflI4VjSf6+kBqZGIpylAqAk9km5hFIeo6QmBMrhZAAQ0JFBUnhGhbBtqSmIFPkniYeSf1IIVWMTTUJ+hp5VRwDeHleKbN5X5f0fzTSQ0i2H/QwPi+def/6JkbMWs8lRCcr8YnAitrv0+khvzzH4R52Guq3iYWq7+DfyGLHlOstPK79oBCxc3eY0cxxmqvx4UXQEyasFKpBuiafPL8ICpXuvJVNqeY/mmkHdCTFg3oOx1dly6TnHNs5memDBrs0OWCoFwKqvSb6NGWCmuUdAjUB3uyX8YfcjnTZguomagmb973UVEoSyPmJgY7sGw5m5yt3MlaHFekuQmR5zdgHsNHNmrbdBBv8OyOKKXgR8vL1xGKeO9ttfbgsVy4Tu6yh67Up4IwhEXwJbD52izpGqagd7X4P4UgWVyL2uepQgj06JAL6WZxi560deVkHk7lasJggIAoKAdRMQAqB1n9/v7r0QAKPfGxBaAVCK6AvoSiC1QjXVZUFK6b0OZAe/HJOkYFRsJngGAeJhV8BIpfhpcFYtAN4aX5bsXko027GM/SjVauRPQzE28PjtV1acfoJzbEfauaSTa4F1+Oc83+4cYJXDWHn4f3YJiDP8sf/nzy9gajaVL7dSNyV7e/9OuaY6AEv3q9qcZZ5DoGi/quOhWJfQ78tEUfOQjHj4tmSRTso6D3od7FeGc4/fc3/LOIbZB0rPbbERtvXg/+yddVxUWxeGXxq7sEXCDgxAEOzu7rh267UTlRADW6+tV/2u3d2dYIAdGJTYCiKikjPf74Ce42EGmII5Z2adv66w99rveta5Cu/svRe+cR1UM1rLLWEgdiQ1lH/kuZEHcD6N47d/Bs5TEhgnu5Myo7Xlfv/bh5R7FpkjxeZpneJXMHJsNODD+PdyHo+olF2i9AiSwE/PgsgGbvfm4Rpb0a4VNWIRZLFIlOgJrD9wAkMepOqq7h4pe61CFmQaMLs+HBK5nykeVpwIu65cI60skEBLEAEiQASIgIIEyABUEBQNEyQB+k1QeGVR1gBk2p8yx3SZpyaAm2mkxJxPZY4GMw9zxDjVtqc0QTDHi+8BYBqJcOdK1ecmagPwyfyGqPgzgKVwxXYc6vbR3DWJP+MSsGf+IDRKuoYHElvcruQOj+51ktdjDLymhrex3pRrEhFlVgx5pzGnx5nD2lLAS/YuPeZeo4QkCcpMP5k8rBg+Y4LJHpgjHisSO2KT6QIUM4jkV1ad3RDehYAkpg+NYs/KxHZYlCj/Fds1pCZefIxB/RONYGn4iR+wSbRLXAAAIABJREFU5x4gewHg30aKLZTRqCbegCQxYxNQCxfFZySd/f61ZcA5/nFujA8Ecsu58F7hoDQwswnEzLJETkk0u8zuimvQrWuquz4zWwTFJwJ6QEAqlaK12yocN0u1c18L9/8xuG/ObQrneO7Ht4dl/4ZdT7quQQ9eRUqRCBABERIgA1CERSPJLAEyAIX3MihrAF4BwDhD34HkDgp/tDPlJefy655A5ovMEeBU7kCaIFoAOAGA2XbVS4O4RG0APl7QBJV+/N54CVyxHo26/bw1huf8ka1odGcUG29VYluMnL01+c+MAbjeZDGaGnEG5LvcVVB0/B+dfVM1ggiUWKL8rEdsJ10mzj5TTzgaPk9fszq/DC0sDXxPZdals9qWxCZwT+wvd0SForlRNI85NoXK6Yro9haI+wYsZhpVa+DpcxgoUQOYWyztYJU6ILn5iJCfmI8p6iRJQM7CgCGzEZgeIRP4Oqc08iRw/89stl6I/v3oHjAh14y0iZPA47dfEbimJzoZpbrvT51/89RA4TuvNVzjuH/DH5YaCru/ft/cokZgmkoEiAARIAIaJ0AGoMaRUsAsJEAGYBbCVnApZQ1A5rdFCwD3AVRLZ418AH5v72IuGGPu9Pv9MPOY5h9fU83PD+D8r7h/AdimYA6KDBO1AfhoYVNU/s59Wn+15EjUGcAdyVUEQHpjQuc5wzqO6dnCPVKPKBgYGKD2tP/hmtkY3vcelugBu0Fr2a89XNoOdl8vsX8OkRRGxIAb6Lw25XrIeob38Z/p/PRlFncEBjPlV/FJrxstE7L6X8DdFFOTeQ4luWJuQi/MNfkXjX81C5mcMBh7kpjrLQ3Q0+g85ppslBXD/MLGmFyzmNdVzYcxyiY8Szkmu7EpEJ7GhtqRt4GCZdVcjKYTAT6BSJ/KyB/L9IJKedYX9cKQoUxjdnqIABHQJIEzj9+j6V45HxppyQC8Or8j6vzk/r19YNUXVfrz7+TVZP4UiwgQASJABFQnQAag6uxopvYJkAGo/RqkVqCMAWgO4OevAEyjDn7nB9ncmO6/OQDcAMDsCPz9MBe/DfrVOZjpDsHsJmQ6GzCdFXIC2P/LMJQogYsx+NJ7ivxqfILw8HCUKJHRcCVWzoKhDxe2gN13pvFyynPFcjjqDvTR2MoJXhYwkTJNmLkndnokgj7FIGJNK9Q14t8996C6F6q044yCsN1TYPWUMwSZKNax25ONNFMk4Ll534y1DjgNlGROlav4ZGQANnIHznP9aJguwR+ledHDmOlpwz3TEwZge1JjBJv1gqFBqmbUrqOBpr92Xma0niJpTA0HzHOnjEyIBeYw12XKeaa9AcyY/zXoIQKaIxCxyBEFYl6wAXflGYju45ZobgGKRASIQDKBdZeDMPSivSwNbRmAi3ugzjfmsEXKc7doN1Qfup6qRQSIABEgAgIkQAagAItCkhQmQAagwqiybKAyBmBBAL/O+WH3r6696Qll2sQybSaZhiB2fwysDWDgrzsEmXOP2X/tFrwDYAuAXczNckoSUHi8GA3AB4taoUoMd3ToSomhqDtIc8d1vs6yRh4J06+Fe4KHvMQVPz/0e8hsxuQ/cR3/B7MqHdgvxm/pDNNg/jWPv7vs1jF8gK2mCpiVk0OA7GrsqltcHvj2Tv5rY5YHaOwOHJ+g0Gv1Vppf9n5CZiZz/NeU8bQZ+3oecFmBvNJaMZ8NMIa57vKPJy4GeHoEOPRHIxRLZ2DgGYV00yAioAyBz8vqwCKK6y5+ycgV9Wem3NlJDxEgApoj4DTnHG4ldJINqCUD8Po//VAr8iCrx9+iPRxH/ae5hCkSESACRIAIaIwAGYAaQ0mBtECADEAtQM9gSWUMQKbV56tf8ZizlH0yiM2MZeYEASidyanrtgG4uA2qfGOuX0x5rhQfhLqDF2sM6ZvZdiie+Lu0KWHPNTgMixvzUO0ns4Ez1dNtG1ChDfvFhOOTYXI7pevv7+daUiX0TpiO0UYHMN5kX8Za3b+od29c8CVgSzv56xiZAu1WAweYjadqPH/+sian+7FSkRvOAOoyTa/lPB+eAJfmAsbmALNzMW9JpULTYCKgCIFP6zui4FvuGOAjiTVyjfGFVYFfJrciQWgMESACGRIoM/UwXpjzf2SSOg6CQWvN/TueoYg/BtxYMww1P+xkv3IrT3M4jWM+16WHCBABIkAEhEaADEChVYT0KEOADEBlaGXNWGUMQE3tAMyMzDI60yvqI8D3l7RH1WjuqOrVov1RZyhzklozzzvvCiia9JYX7Hy15Wh0j3/3Hzug33HAmtnImfJIPgTCcI2zjBjr2B3wMV6P7sbc/YBpKtbEToi0juXmKgq0+QfY0UU9YKk1hvkBm5vLxqzvBliUAfbJbzKSPGH6B8CEOVVPDxHQDoGf/tuR7dgIdvEkqQFOtL6NNjXKaEcQrUoEdJTAUDcPrDNN9W92vxOAdS2tZHzz3zFwfs01lrqdsyFqTOR2BGpFFC1KBIgAESACcgmQAUgvhpgJkAEovOopYwBq6g5AbVAQdROQe0s7otpXbqfOlSJ9UXeY5i7s/uJVEvmk/J4su3P1RbdvaRwJmhkBGBnz66jOnXia6nLrUxKITd1bBkDu4kDnTcCmZuq9e/JMyo9PgdV/3F34+8huUgLgzfTLSePRhOGpXjY0W98JfHsv0816o9NJDGzpqu9kKH8ioFECa2b0xnDjo/yYQy4BxaprdB1Fg93632Q4hXK79gOy14bDZOZqZ3qIABEgAkRAaATIABRaRUiPMgTIAFSGVtaMVcYAZBRpogtw1mTGX0XcBuCyzqgWxd2xd7Vwb9QZvkojHKUSCRJnWcAESbx4CVIjmBjwv5Y8wGko0FLO/YPqGICTgoAc6ZhlimZ6yg24IYdLYTug0wa+UadozD/HpWXaSSRA2HUgMRYo1RAwNEqZ9WCv/GPHLRcBToNVUUBziIDmCMhpPLPabg9GdFLTKNecQopEBERPIEkixb/uvTDUOJXBNuMjYGymlfxub3NHjZfL2bXvmjuh+lT+Pb5aEUaLEgEiQASIgAwBMgDppRAzATIAhVc9ZQ1A5iK6Or869+YFkJhGSkzX399ta5nWqx5aTl3UBuDd5V1R/ctpFuHVQj1RZ8QajSCNjPqK/MsUu2NOCgMYTHwB5GROg6d61DEAZ3wCjE3VzyciCFghp9PiX4eAguWAJRXUW0OVXXuvbgDBl4F81ilNSpijweVaAgb016F6xaDZahOQSiHxyg9DcA3Xl5fZjDG9OqodmgIQASKQQuDFh2+4taIvehlzu/g/m1vBYirXgCerWfnvmg3HwIXssg9Mq6GK2+WslkHrEQEiQASIgAIEyABUABINESwB+o1XeKVR1gCcC2DarzSYc48300hpKoB5v77HbCfJ6jamj1PpYs6rlmW+JsYuwHf/6YHqkSfYlK4W7I46I/lNN1R9tUJDg2H9P8WOIUnHPoJBXqavi5xHVQOwVCPgrwOqyped9/QYsLsX93WnIUBzHyDhJzCvuHrrqGIAqrcizSYCmUogflZRmEp+sGusLTYHw4aMytQ1KTgR0CcCj958xYe17dDI6C6b9vVi/VFriObu8VWW5539i2H/kPlsNuV5bFwRlWb4KRuGxhMBIkAEiEAWECADMAsg0xKZRoAMwExDq3JgZQ1Apz9MP8aBGiZnZUMAjwAw262iABQCkKCyQtUm6pYBuKIXqkccY0lcK9AFtf/+VzUyqWa9fHQLpfc1USyWeyR3vDX1jJNTgJtrFYvze1TlTgBzHDZ7fuXmqTJaKgW8mE2rKj4tFgDOQ1WcTNOIgDAJxM62hHliNF8cGd3CLBapEiWBm8ERKPg/V9gavmf1H7B2R8d+E7SWz70jK1HtznR2/WeGpVHOPUBremhhIkAEiAARSJsAGYD0doiZABmAwquesgYgk8HvY8DM8d+6AFJ/bDwJwO9L4rwAeAogbVEfAb6zsg/sPx9mMV7L3wm1R2/SCNbnfkdR9nRvxWKlZwx8fQMsrZhxnHw2wI8IoP5UwGVkxuM1OUIVk5JZv8ZgoMkswDS7JtVQLCKgfQLydu6SAaj9upACnSFw9skHVNtdAwUNuAZV28ouR++e/bSW48OT/8LuJmdAvjSwQmkP7R1J1hoIWpgIEAEiIAICZACKoEgkMU0CZABq/+WoDaD0HzKYzgu/L4K5DiD1trL/yZHMnBdlxmYDEAOAORZ88defuwMY8mvOcwCOAL5pP22I2wBc3Q/2Hw9yBmC+9qg9Jo0OvUrCDjy7GeWvj81wVnidBbBslMEOuO1dgBfpnPauOwloOCPDtTJtALML8OnRlJ2KTNOO30+hisDHJ/xl7bqmNA6hhwjoMIG45TVg9oX5q5p7bvUNgZNNFuzK1WGulBoR+E1gzK67WB5YnwfkbeejKFaZ+fxUO0/Q5e0odXEEu3iItChsvAK1I4ZWJQJEgAgQgXQJkAFIL4iYCZABqP3qMYZeXyVkpFWzNgC2AcidRizmN8pWAF4qsVZmDhW1ARiwZiAcPuxj+VzP2xa1xm7VCK/Hx1aikj93FCitoOEtt8LSqW36a744B2zvJDum9VIgjyVQurFwml/EfAKS4oE8v+4F/BEJfAoEClUAsuXTCFsKQgSETiD+2WmY7uzKk9mtyHHsHsZ8VkQPESAC6hJoN205Dpu588MM9wUKV1I3tMrz3/sfRpFjfdj5r6UWKDD9ObKZ/upgr3JkmkgEiAARIAKaJkAGoKaJUrysJEAGYFbSlr+WpgxAJroVgDG/jD7GYIv/ZfjtBbASAHezvPbzFrcBuHYwHN7vYSlez9MatcZt1wjVhwcWwu7B7Axjve90CEXsGmQ4DkEXgSuLgLBrQAknoIkXYOWa8TwaQQSIQNYT+BIGLK/CW7di7CY88ZFj5Ge9OlqRCIiawK2QSFT6X3nkMIjj5zH6LpDfVmu5RT8+i9x7O7Prf5LmASa+QMFcZlrTRAsTASJABIiAfAJkANKbIWYCZACKuXri0q5TTUAC1g2Fw7tdbAV8c7eA63juz+qU5v7uWaj6dHGGIaL7XURua/sMx9EAIkAEREQg5iOwqAxPcCPDjTjvzpkDIsqGpBIBQRHotOIi9ke0l9U0OSRrml+lQePHy6vIvq01+92v0uyIGReM4nmZm13oIQJEgAgQASERIANQSNUgLcoSIANQWWI0XlUCOmUA+q8fAce33I4/31zN4DqB2xGoKiRm3t1t01D95eqMQwz3Awor0OQj40g0gggQAaEQiP0K+JTkqakbtxRX5g0QikLSQQRES6DVtJU4bibnig0tN9qJD7sF081NWK4/pab48HcorC1yiJY1CScCRIAI6CoBMgB1tbL6kRcZgPpRZyFmKe4jwP+OgsNr7s4/v5xN4DKRuxNQHeB3N41F9VebMw4x4yNgTMeDMgZFI4iAiAgkxgOzC/IEL0vsiAEz1iO3uYmIEiGpREB4BDpOW4oDZp58YVPCgGx5tSpW+v4hDNZy93wmSQ0QNCIcZQvn0qouWpwIEAEiQARkCZABSG+FmAmQASjm6olbu7gNwI1j4BDONWS+kaMRak46oJGK3F0/DNXf7sw4lpZ3LGQskEYQASKgCoGv3jbIkxTJTt2VWB+1xu+EZf7sqoSjOUSACPwi0MNtAXaazuHzEMK/pZ9fACsdeboeDQxGZcsCVDsiQASIABEQGAEyAAVWEJKjFAEyAJXCRYM1SEDcBuCm8XB4tZHFcTN7fThPPqwRPHdX90P1jwfTjXUqexs0n8w0faaHCBABXSPwclkrlI66xqZ1IskJn1tuQB8Xa11LlfIhAllG4FtsAsbNmod/Tbk7dhNyFIXJpMAs05DmQnKa/9z76zGqlWJ+VKKHCBABIkAEhESADEAhVYO0KEuADEBlidF4TREQtwG4eSIcwjawLG5lrwunyUc1wubeP91RLfJkurGelR+Bct3naWQ9CkIEiICwCHy9sBR5rnDHFK8mVcb5Guvh2baSsISSGiIgIgJBn2IQ9k8rNDS6x1cthB2A3z4Ai8vydPl39YdjRX5DIBHhJqlEgAgQAZ0lQAagzpZWLxIjA1AvyizIJMVtAP43BQ4ha1mwt7PVRo0pxzUC+sGS9qgSfTHdWE+ruqFChykaWY+CEAEiIDACAf8BR0ezou5JbHHadSemNC8vMKEkhwiIh8CN4AjU3GIrK1gIBmD8D2BuUZ62W63PwcmxhngAk1IiQASIgJ4QIANQTwqto2mSAaijhRVgWjrVBThgixscglexmP3NXeA49ZRGsD9a2ByVv/ulG8u/+lw4thupkfUoCBEgAgIj8PQYsLsXK+qLNCeW25+GZ7vKAhNKcoiAeAi0WXENRyNaCdMABBDrWRDmiGf13W60BzXqNBMPYFJKBIgAEdATAmQA6kmhdTRNMgB1tLACTEunDMA722bA/uUKFnOAmTMcpp3RCPan8xugws876RuAlWfAsfMkjaxHQYgAERAYATn3gS0ouwuTe7YQmFCSQwTEQ8B66nGEmvcUrAH40csWhaQRrD5/13VwbNpdPIBJKREgAkRATwiQAagnhdbRNMkA1NHCiiAtcR8B3uEJh+dLWcx3TGvA3u2cRrA/n+uCsvFP0o0V2ecS8ttW18h6FIQIEAGBEZBK8WOOFbInfmWFrS8+F0MG065fgVWK5IiIgFwD0LoO0O+YILIIn1URlpI3rJZbNZbBqVV/QWgjEUSACBABIsARIAOQ3gYxEyADUMzVE7d2cRuAu2bDIXAhW4H7ptVR1e2SRioSPNsetolB6cfyiAIM6H9fjQCnIERAgATeL66FIt8escr25x+MTqMXCVApSSICwieQmCRBuenHEGT+F19sxw1Ala6CSCDEuxpskkJYLTer+cC5/XBBaCMRRIAIEAEiQAYgvQO6QYAcBN2ooxizELcBuMcHDk+4LrwPTarAbvpVjdQh3LsSLJNepxnrtbEVSsx4oJG1KAgRIALCJPByXS+UfsftTPI3c4ajhq4ZEGbGpIoIZB6BmLhENPLYiZvmo/iLdN8JlG+ZeQsrEfnFbCeUSXzGzvCr7AWXzmOViEBDiQARIAJEICsI0A7ArKBMa2QWATIAM4ssxc2IgLgNwP2L4PDQm83xiXFFVJyRfuOOjID8/v57rzIoIv2Y5vAwk1Kwmp7+HYGKrkXjiAARECaBB0dWoMqdGay455LiKDsr/asBhJkJqSIC2ifwNuonJi9Yjm2m3Ad3yapmfAKMTbUvEMDTubVQIZ7b9etb3g2u3acIQhuJIAJEgAgQAY4AGYD0NoiZABmAYq6euLWL2gD0P7gcjvfd2QoEGpdD+Rm3NFKRCE8rFEBU+rE8ubvBNLIoBSECREBQBAL9TqD86R6spmhpdhxucQN/1bQSlE4SQwTEQGDz9RCUODUQTYwC+HIF9G/pI58GqBzLfbjnV3o8XHp7iAEvaSQCRIAI6BUBMgD1qtw6lywZgDpXUtEkJGoDMODwSjjcnc7CfmFUCmVmamZXXoxnUeTEDzIARfMqk1AioHkCb5/5o9jORrzAtrHbEOzTRvOLqRMx/jtwaR4Q9QpwGgpY11InGs0lAplCgGkAstZkKZob3RasAXh/fjNU/XmD1edrPQqu/eZkCg8KSgSIABEgAqoTIANQdXY0U/sEyADUfg30RcHjVIkaAyjLfC08PBwlSjB+oHge/6Pr4BgwmRUcbGgNW/f7GkkgwSM/TAyS0o8loF0LGkmaghABIsAj8PPzK2Rbacf7WpXY9Xjg001YpI5PBG5vSNFkaAxMeAbksBCWRlKj1wQiv8fD3vssbpmNQCEDbnd9fB4bmI67Jxg2dxa1hX3MZVaPX4lBcBm0WDD6SAgRIAJEgAikECADkN4EMRMgA1DM1ROXdp0yAANObITDrfFsBcIMLWHlzt3do2pppJIkGMzKz5seVboD8r48yH2tuQ9QkzoDqsqY5hEBURBgdtbNLcaTWiduKa7OGyAs+Z55+Hpqjwca07FFYRVJv9WsuxyEeScDEWrekw+iYnug63+CgROwpDMcos+yenyL9oHr0BWC0UdCiAARIAJEgAxAegfET4AMQPHXUKwZiPsI8KktcLjxN8s+3KAYLD2eql2Lnz++I9sC/i/97zofRVE/L+CNP1DSFei+HcjONwnVXpgCEAEiICwCUikSPAvwdgO3jpuNY/O4v3e0KliSBFxbAlyYzZdRoS3QbatWpdHiROA3gYQkCcpMPwlDSBBs3psPxqE/0GaZYGD5/9MTjpHHOQOwYFe4jvy1u1YwKkkIESACRIAI0A5AegfETIAMQDFXT9zaxW0AntkBB19uF947g0Io6vFC7Yp8+vQJBVeV5sX5MuQu8hWxBhJjAWNzwNBQ7XUoABEgAsIn8NnDEhYG0azQnvFu2DFXAF1BpVLgf62BsGsyECWGJjB0/yx8uKRQLwgcuPMa4/fcRz5E4675MH7Ofx0ESjUUDIfbq/qjxqcDnAGYvz1cRwtnh6JgQJEQIkAEiICWCZABqOUC0PJqESADUC18NFkNAqI2AO+c3wv7q4PY9D8aFEAhj2A1cKRMDQ57BdvN/Hu/4sc+g2neImrHpgBEgAiIi0CQe3mUMnzHih4XPxxe7t7IbW6i3UTC/IDNzdPWQHeUarc+tDpLoJTbCSRJpChj8Bpnzbh7e5MHzPgIGJsJhpb/umFwfLeT1eOXuwVcxu8SjD4SQgSIABEgAikEyACkN0HMBMgAFHP1xK1d1Abg3UsHUP1Sf7YCEciLAp5halfkUeAzVN7lxI8zJRTIlk/t2BSACBABcRG4526PaoZBPNFXe71EnTIFtZvIOa+U479pPe6R+BonwdN30ahULDdyaduw1C4tWl2LBJjuvznxAw/MBsPQQMoqSTLJCaPpb7SoTHZp/41j4Ri+mf3GjRyNUHMStyNQUGJJDBEgAkRAjwmQAajHxdeB1MkA1IEiijQFcRuAV46i+gXuPqEo5EJez9dql+Lewweotr8OP47bW8A0h9qxKQARIALiIhDtUQS5DX7yRJ/u+AjNqlhqNxHm3r8rC9PUMM1sOnZ+rcR+f8cgZ7iWps7A2i2afq7OGICdDK9gselaPoC8VsDYB4KCEvDfFDiEcDpvZasNpyncnYCCEktiiAARIAJ6TIAMQD0uvg6kTgagDhRRpCmI2gC8d+0Eqp3rwaKPRg7k9nyrdinu3L0N+8ON+XFmfgaMtHzkT+3MKAARIALKEvjiURz5DGJ40w43v4l2NcsrG0qz4y/OAy77pBuzS5w7rAw/4K6kNIKkxRE8tyUMDelHDs0WgqJlRIAxAO+bDUIegx/8ocUdgcHnM5qepd8P2O4OhxfL2TUDzJzgMI3rCpylYmgxIkAEiAARSJMAGYD0coiZAP00LubqiVu7qA3AB36nUeV0V7YCMciGnJ7v1a6I/63rcDzRkh/HIwowoP9V1YZLAYiAyAgcntkC7Yx8eap317uAbg0ctJvJ5QXAxTkKa2gXNwuWdnWwsqe9wnNoIBHQBAHGALxlNgKFDKL44Uo4AYOEZa7d2T0H9k8XsDrvmVRDtemXNYGBYhABIkAEiIAGCZABqEGYFCrLCZCrkOXIacFfBERtAD68cR52pzqyxfwJM2Tz/Kh2cW/7XUSN0+3ZOAkwholnhNpxKQARIALiI/CX2zxsNeXvtLtsNRr1+ntrNZnYiwthfnm2whoCJGXQKd4LlyfVh1UBus5AYXA0UC0CsQlJKD/zFM6bTuA100kO2nkzUJn7N1ythTQ0OWDfQjg84v6/emxcCZVm8D8A0NBSFIYIEAEiQATUIEAGoBrwaKrWCZABqPUS6I2Ax6kyNQZQlvlaeHg4SpRg/EDxPI9uXUDlEx1YwXEwgZnnZ7UTuHn1DJzPd2Hj/IA5snt+UDsuBSACREB8BCpNP4zHJn1khWu5y+79XV6oGphOExA5qK1jd2BVT3u0qlJUfIUgxaIk8CE6Fs5zz+OI6XRUMQzh59DnMGBbX1B53Tm0Avb3ZrCanhmVQbmZ/oLSSGKIABEgAkSAugDTOyBuAmQAirt+YlKvUwbgk4ArqHi0Dcs/AUYw8YxUux43Lx6F82WuuUg0ciK3p7A6FaqdJAUgAkRAIQLH779Fq4MVBGcAnlg/Ay3frlAoh9+DGANwSdeq6Ggvrg97lEqSBguKgG/QZ/TccBM7TWbDxegJX9uA00DJmoLSe+f4etjfnsRqCja0gq27sBqVCAoYiSECRIAIaIkA7QDUEnhaViMEyADUCEYKogIBUR8Bfnr3Oioc5u7qS5IawMgr1R1DKkDxO7cfLtcGsDMjDfIiv0eYCpFoChEgAmInIJFIYTgrr0waX6d8Rp5s2mkMFBbxHQ+XdUBro5tK4S0X+z94dXRAd6eSSs2jwURAFQJSqRQ2004AkCLUvJdsiKFXgKJVVQmdaXPunt6C6n5/s/FfGRRDSY+nmbYeBSYCRIAIEAHVCJABqBo3miUMAmQACqMO+qhC1Abgs/s3Ue5gU37dNNCsw+/0Trj4DWPjfjSwQCGPIH18PyhnIkAEGAKeeWQ4HGz3GB2qZ/1Outuhkbj17xiMND6iUm3+1/gu+tW2VWkuTSICyhB4+fEbGi+5gmaGt7DOdJnsVPcvgKGhMiEzfey987tQ7epQdp33KIgini8zfV1agAgQASJABJQjQAagcrxotLAIkAEorHrokxpRG4DPH/qj7P5G/Hq5RwKGRmrV0Pf4Frje5nYAvDUsimLugWrFpMlEgAiImIAcA3Brs/v4y8U6y5Pqs2QftkQPVHnd/VU2oFNHrnu6yoFoIhHIgMDZJx8weIs//M2GwcIgmj+6bAug5y7BMbx/5TCqXuDu/IxAXhTwpBMAgisUCSICREDvCZABqPevgKgBkAEo6vKJWryoDcAXT+6izJ5UF4jP+AQYm6pVFN8j/8L1zgQ2RriRJSxnPlJFazu4AAAgAElEQVQrJk0mAkRAvAT8d3rD8dkiNoEv0pyoa7AJDz2bZXlSy2b0x1jjAyqve7pgfzQbKWc3lsoRaSIRkE/Aeurx5G+EmveUHdBgBlCPu2tPKAwf+p2C3elurJxvyI5cnu+EIo90EAEiQASIwC8CZADSqyBmAmQAirl64tYuagMw6NkDlNpZh1+B6e8Bk2xqVeX6wTWodX8qGyPU2AbWM+6pFZMmEwEiIF4C30JuI9d/jXkJ2MZuQ9C81jAwyLp/wpk71V54VEJZQ9WbEu1KrI/usw+LtxikXBQE3kb9hKvPhbQNwHpTgAZugsvlif9FVDzWntUVCxOYe34WnE4SRASIABHQdwJkAOr7GyDu/LPutwdxcyL1micgagMw5MUT2Gx34VGRTnsNA7NcapG6vm85aj1yZ2O8NCmD0tP91YpJk4kAERAxgc8vgZUOvATGxI/AfK/ZMDdR78oBZais2n8WIx92TntKY0/gnGe6IY8kuaDRjGPIYWaszNI0lggoRWDjtRB4H3uCQviCW+YjZefWGgs08VIqZlYMfnrPFxUOtWCXkkgNYOj5BchCoz8r8qQ1iAARIAJiJ0AGoNgrqN/6yQDU7/prM3tRG4ChQc9gvdWJxy9pchiMsst27FQG8vXdC1Hr6Wx2yjPTiijn5qdMCBpLBIiALhGIfgcsKc/LKE5qgoA+gXAtZZElmcbGxePnXBvkM4hJe73xT4ElFdLVcyXJDlWmXUDe7OpdlZAlSdMioiWw4vwLLD77HD2MzmOeyUbZPIZdA4rYCS6/4MC7sN3Fv1pEMv0TDE3o/xfBFYsEEQEioNcEyADU6/KLPnkyAEVfQtEmIGoD8FVoEEr+z54HP35CMExzFVCrIBe2zEbD4IVsjKdmVVFh2hW1YtJkIkAEREwgNhrwsZRJoHneozg1tm6WJLby3w0Y9Xpi2ms5DgSaegNzi6Wr56HEGoUn3kSh3OZZopsW0U8Cqy6+xMLTz9Db6Cxmm2yWheD5VZBg3oU9Q9HN/A8Wv40PRa7c+QSpl0QRASJABPSVABmA+lp53cibDEDdqKMYsxC1Afg6PAwlNlbhcY8d9wLmeQqpVYsDK6ei4+c1bIygXDVQasI5tWLSZCJABERMQCoFvGR3FlvH7kCoT6ssSezczPpobHQ37bUmPANyFpar889JzyXFkW2sPyzzZ88S3bSIfhJwO/gQO26+wmHTGahqGMyHUGMw0IprqiMkQtGfwpF7VWWepPfDnqBIkeJCkklaiAARIAJ6T4AMQL1/BUQNgAxAUZdP1OJFbQC+ffMKxTbwjxB9H/0UOfKnvwMmo4odWTYKbaO2cgagRUOUGnUwo2n0fSJABHSZgGcemezsYv/FQ58umZ51TFwiXs2xR0XDMPlrOQ8HWvikfO/0dMBvZfJ/BkjLY31CC6wzXcrOC5YUgWSUP0oXUu+u1ExPmhYQLQGJRApbtxPJ+u+ZDUZeg+/8XAadB0o4CjK/pO+RMFpow9MW3McftrZlBKmXRBEBIkAE9JUAGYD6WnndyJsMQN2ooxiyeJxKJHMLfFnma+Hh4ShRgvEDxfO8e/8WRdfy77uKGfkQOQuWVCuJ04v7o9m3A2yMZ0XaoNywbWrFpMlEgAiInMD1f4CzM3lJLE3ohHFzNmV6YmER32G1Ip0PNv48TsnsVnx2EoiLBiq2x7HDO9H60VhW42upBb4OvYNKxWQNzUxPRAALHH/wDvNPBSY3QZnX0Q7VLNW7M1YAKQlKQlxiEvpuuoUbwZHJukLNe8rq84gSblON+O8yx+gfdb6CypWrCooziSECRIAI6DsBMgD1/Q0Qd/5kAIq7fmJSr1MG4MdPH1FoFf9T+ehh95G7iLVaNbm4oCsa/DjNxnhs2ROVBnJHgtUKTpOJABEQJ4EXZ4Htsh14X454g9KFcmZqTk+DwlBhK/+6g98LJpZwgfGgU2mu//3JGeTYw+1S/CDNizcD78G+pP7dafY9LhGOs8/hZ0JSMq/KxXPj2N91MrV2+hQ8SSJFh9XX8eB1yv1+BRGF2+Yj+AjarQKq9xYulqREwJt/j/DtVqdQo4aLcDWTMiJABIiAHhIgA1APi65DKZMBqEPFFFkqoj4C/CkiAgVX2PKQRw0JQN5ipdUqg9+8VnCJu8bGeGA7BFX6cE1B1ApOk4kAERAngVc3gU1NZbSPKnsRK3vymxFpOsF7926j2qHGcsPG/XUMZqXSMbFCrgL/tWbnRkpz4kZnf7S0K6ppmYKPd+LhO4zYfoen8+WcFjA2MhS8djEIPPXoPYZtC2ClTjPejqHGx/nSe+4ByjYTdDqJnvlhjBSTmHmuNdyH2nWbCFoziSMCRIAI6BsBMgD1reK6lS8ZgLpVTzFlI2oDMDLqK/Iv4x/3jRh4EwUsy6tVg9tzm6BG/C02xv2yY1C15yy1YtJkIkAERE7gYyCw2lkmiaxoBHLT9yKcz7SXWftW7qZwGrcn/eOUcozL5bX9Maax/t1pdvjeG4zZdY/H8b57U+TJbiLyl1MY8sfvuYcDd96wYjabzEcDo/t8cQNOAyVrCkNwGiqivKyQVxrFfveS0zrUb9ld0JpJHBEgAkRA3wiQAahvFdetfMkA1K16iikbURuAUd9ikHcxvzNfRH9fFLCqpFYN7s6ph+oJ3C+J9ytORtWu09WKSZOJABEQOYHod8AS2Q8XMtsAZO5U6zVzGfaZ8T+EeGHdEza9V8LY2Ch9sG/uABsa8MbMq3oW0zo4ibwgystn7v8buYO/A/Dq5AbUEVl5lHJnTNx7H/sCXrPf22IyD3WNHvLHDvcDClfU0IqZE+a1d2WUSApng1+pugB1OwzNnMUoKhEgAkSACKhEgAxAlbDRJIEQIANQIIXQQxmiNgCjf8Qi94LCvLJ96nMFBW3Vu6z7kbcLKic9YePer+qOqh0m6OHrQSkTASLAEpDTHID5nmdCH3jOWZFpoM49+YDwHX+jvzF3L+l3g5zI4cHttEp38fePgLW1eEN2FZmI7sP4DU0yLQEBBT758B2GpzoCzMhb0rUqOtqLqwmWgLCyUibtvY+9fxiAT836IZtBPF/quMdAHmGzfj6nJsomPGV1+1aYAdduk4SInDQRASJABPSWABmAelt6nUicDECdKKMokxC1Afg9NgE5fCx44N/3uogiZdS7jytwliPKS16wcR84zEGVNqNEWWASTQSIgIYIMN11vdLoGPtnF14NLfc7zJAt/lgf3Eg2qqJrvgkANjTkzT+auwfajF+rYaXCD3fo7huM3c0/AsyoNjcxRMCMJsmdgX8/Lz/GwNTIECULZBd+YgJROGXfA+z2/71zTopQ816yyqaEAdmE3Xn5yby6qBjHHV32LTcVrj2mCYQyySACRIAIEAGGABmA9B6ImQAZgGKunri1i9oAjE1IgsnsAjAykLJVeNf9HIqWr6FWVYK8qqCUNIwzAJ0Xo0qLQWrFpMlEgAjoAIFF5YCY9zKJ/HSLRDbTDI7iqpi+9dTjCDXvqboBGBkM/FOdN39/zp7oNFH/Ops3X3YFge+/ya3E6l72yY1RbodGostaP3aMe+uKGFDbRsXq6de0aQceYOetFAOwu9EF+Jj8q/p7q0V0j3waoHIsd1Tct8wEuPZy16IiWpoIEAEiQARSEyADkN4JMRMgA1DM1RO3dlEbgIlJEkhmWcDUgOvWF971NCwrqnfB+Cuv8igpfcdW9mGtlbBr8pe4K03qiQARUJ/A02PAbtldTYfaPUH76vz7SNVfLCVCi2mrcdKMv/tICgMYeHJNCjJcyzMPb8ie7N3QdfL6DKfp2gDGTE3rGVDLBh2qF0eblVwHeGZsnmwmuOfeBAYG9KNaRu/DtAMPsfPWq+Rhq02WoaUR10wr+YutFgM1hP9h2sP5jWH38zabrq/tWLj28cooffo+ESACRIAIZCEBMgCzEDYtpXEC9FOlxpFSQAUJiNoAZHKM9bCAuUECm25Ix2OwqVJHwfTlD3vrYYtiBhHsN1803oQytTupFZMmEwEioCMEUplpTFb1chzC5Un8RhuayJb5kOOqZ32ZTqpv6/igWKPhCi/x+t+eKPGaM7+OmLZCW7cdCs/XlYHpGYDp5fh8dguYGhvqCoZMy6PTGl8EhH1Jjr/JZAEaGqU6bu0RlX7H6kxTplzg+/OboerPG+wkP5tRcOk7R7kgNJoIEAEiQAQylQAZgJmKl4JnMgEyADMZMIVPk4DoDcAfHoWQ3SCOTfB5m0Mo66D6L+I/4hPxfY4NChpEszHft9+DItWa0WtEBIgAEUDC0qow+RrKI+FgtA8BM5tonM6FwA9ouKusbNzpHwATc4XXC94xAbbPueOYZ4zqoenMIwrP15WBqhqA16c2RPG82XQFQ6bk8TkmDo6zz7Gxt5nMQW2jx9xajT2B2uMyZW1NB727sBWqf+d2gvqVHAaXAfM1vQzFIwJEgAgQATUIkAGoBjyaqnUCZABqvQR6K0D0BmCMZxHkxE+2gE9a7ENFZ9V/EX8b9RM5l9ogtwEXM6bXCeQsw++iqbdvDCVOBPScQKL/fzA+NppHoW/8FPw3103jZMbsuguPp62R3yCGH1vRBiC/ZoUc9ILN/SVsjKsGDqjjcUHjeoUc8ODd1xi3m2vsoIzWgbVtMLN1RWWm6N1Yn5OBWHs5KDlvUyTguXlfPoPWSwHHAaLgcmdRW9jHXGa1+lkOgsvAxaLQTiKJABEgAvpCgAxAfam0buZJBqBu1lUMWYneAIz2LIbc+M6yvt9kJ6rWaqky+7CI77BaUYw3P27ARZiVVK+zsMqCaCIRIALCIiCnq26yQCVNOUWScp59BjcTu8gOVXKtkBPLYHPLg40TgApw8OSOOCqiRcxjmJ3dzO60H/HcfbHK5hPq00rZKXo1vuHiSwj+lPJvcTvDa1huupqff/s1QDU5zWwESMl/cUc4fjvPKvMr3g8ug5cLUClJIgJEgAjoLwEyAPW39rqQORmAulBFceYgegMwyrME8oLr6hjQYCsc6rVVuRov331G6XWlePOThvnBqAjt/lAZKk0kArpEIPodsKS8TEbfB15DDks7jWba280H20zn8WIeLTAQbf7mdvMpsuCrS5tR8tJYdmigtCTKez1UZKrox0ilUsw+/hQbr4WolQsZgGnji0+UoOyMk+yAJ2b9eVdzJH+j8yagsjju0r29tCtqfD3N5nOj6F+oOXSlWu8PTSYCRIAIEAHNEiADULM8KVrWEiADMGt502ocAdEbgJFeJZFf+pXN6FadzXBq1FHlGr++ug0lzo/kzx99F8hvq3JMmkgEiICOEZDTCOSr8yTkaTFDY4kyu9ZOerdDJyN+V9oP9ReicP0hSq3z5tYhFD/BHcl8Iy2AIh5BMDLU/R8/mK60THdadR8yANMmePjeG4zZxTX8CDWXs9Ov+w6gvDh2Ud5c1gPOUSfYhG8W7g7n4evUfYVoPhEgAkSACGiQABmAGoRJobKcgO7/BJ7lSGnBNAj8cSN38ghjAMk3zIeHh6NECcYPFNcT4WWDAtJIVvR1lw2o1ayryklE/dcDeUO4H/yTA41/CuTmHwtWeQGaSASIgOgJSPf0gcGTw7w8ZiT0R9/R3ihTOJdG8mNMFcn+wehgdJ0fr8t/QKX2Sq3x4ck1FN7DmS9xUmMkub1DdjNTpeKIcbCqjT9S50oGoPzqJyRJUGY6t/uPGSXXAPzrIFCqoSheoZvL/4LzF65Jzs2CXeA8kmuiI4okSCQRIAJEQMcJkAGo4wXW8fTIANTxAgsoPZ0zAD/NKoWCks8s4qtOa1Cnper3DH3aNggFX+7ll2ziCyBnIQGVkaQQASKgVQKRIcA/1XgSJiUMwd6k+ngyqxmymzKfraj37At4jUZHnJEvdQOQ3geA0o2UCh7x7hUKrOMfT/4y6DbylZDTYVipyMIfTAZg5tbo7JMPGLzF/49FpAg17yW76JQwIFvezBWjoeg3VvRDzYiDbLRbFh3gNOp/GopOYYgAESACREATBMgA1ARFiqEtAmQAaos8rSv6I8AfZ5VBIclHtpKXHVaiXpu/VK7syY1eaBGe6n4t90jA0EjlmDSRCBAB3SMQ7l4aloaf2MQuJFXDgITJ8GpbCX1drdVO+LD/S7Q75iAbZ+A5wLKGUvG//YxDoo8tz0z83GI9LJy7KRVHjIPJAMzcqrVZcQ0P33DXcPxjsgJtjfxkF1WycU3mqk4/uu/KQXD9zH0Q6J+/DRxHb9OmJFqbCBABIkAEUhEgA5BeCTETIANQzNUTt3bRG4DvvcujSNI7tgoXqy9Hg3b9VK7K9OnjMMdkE3++iH5xUTlxmkgEiIBSBK7OrIU6Ro94c8rHbkaXmmXh3b6yUrHkDfY9vRuufnLu+ht6FShaRan4EokUj73sYWcQzM4Lcl2AUk2HKhVHbINvBkeg23rNdDsO9G4OcxP6ICj1O9B82RUEvucacck9/tvmH8CBu4NS6O/RxX+GokHkLlbm/qQ66OR9TOiySR8RIAJEQK8IkAGoV+XWuWTJANS5koomIdEbgO+8K6Jo0hsW+PkqS9Co40CVC+AxfTS8TP5j5/slVYSLt5zdDCqvQBOJABHQBQLnZ9ZHI6O7vFQYo+CBow+82qlvAN44tx81rw2QRTXyNlBQ+aO7T7ydUTEpkI0XUMUDDh3H60Ip5ObwPS4RlTy4Tq7qJnppYn1YW+RQN4zOza+/8CJCI36weck1ADusB6qKZ7fp0WV/o03UFjank0k10ML7nM7VjhIiAkSACIiZABmAYq4eaScDkN4BbREQvQH4ZnZlFE8MZ/mdrbwATTqrvqvFe/pIzDThjvpcSbJDXW9+F05tFYvWJQJEQDgEfL0bwzXptowgT3tfeLatpLbQmxcPw/lyH9k4Kl5J8GRuHVSMf8DGu152Kmr1nKa2TqEGcJ13Hm+/xqYpz9jQADnNjRH1I0GhFA6PrIWqluK4w06hhDQ0yHnuOXyIjkuOZgAJQsx7y0bucxiwra+hFTM/zK7lk9D9y3p2Ifo5IPOZ0wpEgAgQAWUJkAGoLDEaLyQCZAAKqRr6pUX8BuCcKiieEMZW7UzFeWjadYTKVfzXZwwGxXKXfTP3ejX0vqxyPJpIBIiAbhIIWtEOpSIuySQ3y8EP7m0qqp30maO70DSA/2HG13yVkWdMqq7ACq70dEEjVPjBNWu4aDUWDfp7KThbXMO+xSbAzvNMuqK3D3JGpWK58frLT7RekfGHPFsHOqFOmYLiApHJauMTJSg7g+sAPNLoECaZ7JFdVUXTOpPlpxn+7sFlqH7fg/3+PWkZVPP6s9GJtpTRukSACBABIvCbABmA9C6ImQAZgGKunri1i94AfD23GkrEh7BVOFVuNpr3+FvlqhxfOR6tPm9k5/ubu8Bx6imV49FEIkAEdJNA/J6BMH2yTya5arHr0MyxIiY0K4tCucxVTn6s93wsS5rLm58w5hFM8lmqFDNwcQuU/+bL/V1ZdBiaD52vUiyhT3r2/huaLbuSpsyWdkWwuhfXYCX083fUXyRr5v4ZYGHnKujiqBp7ofNSVd+BO68xfs/95OnGSMRLczk7VovZA0MuqrqEVuY9PrMJlXzHsWsHG1jC1oN/36dWhNGiRIAIEAEiwBIgA5BeBjETIANQzNUTt3bRG4Dh8xxgGfeSrcLJ0p5o0Zv7wV3Z8hz/ZzRaRXJ3AD4r0BDl/j6obBgaTwSIgK4T8F0BnJkhk+W8hB5Yl9Q6+UDkY69myGFmrBKJidMnY5HJOv5cNRoSPf+nHcpGcibX0QL90ebvZSppE/qkUm4nkCSRpimzbdVi+KdHdd73M+oWPKSuLdxaVhB66lmqz2bacUh/YWYazBw1k/3/AcOuAUXsslSXuos9ubADFa8MZ8O8QhGU9HymbliaTwSIABEgAhokQAagBmFSqCwnQAZgliOnBX8REL0B+MqnBkrGPmcLetx2Jlr1mahygW8s6oSaMdxl388KNkO5kXKONKm8Ak0kAkRAJwjcXAecnCw3lR2JDeGWOAhuLcsn7wIslMsMrqUtFE6bOVr5ZJYDqhlyXXuTJ6thAAav7gLbj9yx2IO5eqLDhDUKaxLLwPDIH6izIP0dZ6l3ADK5ZWQAdqxeHEu6VRMLhkzVGZeYhE3XQjH/FNdUZrzxHow2PiS77t93gAKlMlWPpoM/vbwXFS4OYsO+gwWKegZpehmKRwSIABEgAmoQIANQDXg0VesEyADUegn0VoDoDcAwn5qwin3KFvCY9TS07jdV9YJ65uHNfVa4FcoN36F6PJpJBIiAbhJ4fgbY0SXN3CrEbsJPcEeAp7Uoj6H1FDNCPsfEwWJRIdnYahiAYRt6werNMTbmXvPO6DKVu+5AV4q05lIQz5iSl9f+4S5wsMrP+1bt+ReS7wP8/RTPmw1vorg/1y1bEFsGOOkKJrXymLLvAXb7c823mGByu/8y3xj7CMgrrqPTgdcPo/xZ7jjzR+RDIc9QtZjRZCJABIgAEdAsATIANcuTomUtATIAs5Y3rcYREL8BOL8WrH5yd/McLTkZbQZMV7nGEs98MISEnf+oRHdUHpTqGJ7K0WkiESACOkNAkgTM4ptIf+ZWI3YVPiEfL91Qn1YKpX/rxmU4nWorO1YNA/DNfwNRPIS7s3CXURt0n8l1PFdImAgGrbschHknuZ1pqSWXzJ8dlyfVh4EB/0evEw/f4e+dd5OPDttY5EA/V2t4HHnMTq9mmReHRtYSAYHMl5h6t2QO/MRj84HyFx7/FMhdLPNFaXCFZzdOoNypHmzEL9JcyOf1WoMrUCgiQASIABFQlwAZgOoSpPnaJEAGoDbp6/faojcAQxfWgfX3B2wVj5SYgLaD3FWrKnOZkVde3tzzVZegUYc0frFRbRWaRQSIgK4QSLVj+M+0asctx2spv2usQgZgUiLivYvAFAkaNQA/7hyJQs84w2+XtAm6e8k2MRF7adquvIYHr7+mmUbIvJYy5t/vwYHvo/Eq4gdql7HAmccfMHb3PTZO+SK5cGpsXbHj0Yj+1AbgMKMjmGqyS35st7eAaQ6NrJtVQZ77n0PZY53Y5b5JsyGX1/usWp7WIQJEgAgQAQUIkAGoACQaIlgCZAAKtjQ6L0z8BuCi+rCOucsW6nCxcWg3xFO1wiUlAt4FeHMP1z6Mdo3rqxaPZhEBIqDbBG6uB05Okptjo7iFCJIW531PIQMw+BKwpZ18bmrsAPxyaAry3VvLxt2fVAdtPI7A1NhQp2qU3l1++4a5wNE67V2bf4I49egdhm27w36J2RV4cSL9W8AASc04zeO/zGA13lltvZhB966g1KE27PKxUhOYe33WlhxalwgQASJABOQQIAOQXgsxEyADUMzVE7d20RuAIYsbwuZbAFuFQ0VGo/0wb9WqkhgHzObfu3W8/nG0ql9btXg0iwgQAd0mIJXi61kf5PH1kclzcPx4nJU48r5+060R9gW8RrG85mhfrbj8nWhPjwG7e8lyK+kCDDilMs/vZ+Ygh+8Cdv7JpBpwmHwsuUmJLj3pGYAKGbC/YFx89hH9N9/moVFmvi4xTZ2LwgbgwHOAZQ3RoQh+dAO2+5qxuhOlhjD2+iK6PEgwESACRECXCZABqMvV1f3cyADU/RoLNUPxG4BLmsAm+hbL92ChkegwYq5qvOO/A3P5dxWdbHQaLerUVC0ezSICREDnCcR/i4DpYluZPO9JbNE+fnaa+Y9pVAbjmpSV+f7pQ1vR7N4o2Xkd1gNVu6nMM+n6ChidncHOv5JkhyKjTqJs4VwqxxTixHYrr+G+nCPAfVysMKtdZYUl+wVFoMeGG7zxL+a0gImRbu2YVBjIr4Gbr4fA6+gTdpoBJAgx7y0bZuQtoGA5ZcMLYnzY0wBY7W7I0yKZGQlDIyNB6CMRRIAIEAEiAJABSG+BmAmQASjm6olbu/gNwGXNYBPF/ZJ2wGIYOo6ar1pVYqMBH363wjNNz6Gpq/h2MKgGgGYRASKgNIHEeGA2/66/3zE2JzZDsLQotic1hgSyxtGg2jZwa1kBhobcjwGD3bywwXSJrIwu/wGV2istj53gvxk4Npb9Y4CkDBL7nYazLf/aA9UXEMZMeTsAKxTNjQ19HFAiX3Yg/gfgtwqIjQJqjgDy8I9p/87iZnAEuq3nG4A7BjvDtZSFMBLVkorUfFsa3sBq039k1Yjw6O/vJMJePITVdv7O/4Rp72Filk1L1GlZIkAEiAARSE2ADEB6J8RMgAxAMVdP3NrFbwAubwmbL9fZKhzIPwgdRy9WqSoxUZ+Rc1kp3tzzLS+jkVM1leLRJCJABPSDgGRRORjGpN0kYFFCF6xM6pAMg+kybmPwDp+keRGNHNg7zAU1/riXbrSbG/4xXSULruceoCx3LFFpsg/2AAcGs9OeSKywstxmrO7loHQooU6ITUhC+Zn8Y9Kz2lVCb2crzmTdNwB4tD8lhTyWwJgHgKGsOfvgdRTaruT+bWGGr+ppj1ZVigo1/UzXJZVKYTPtBG+du2ZDkM8gRnZtERuAr0OeocR/TrycPv0dhIIF9Nv8zfQXjBYgAkSACChBgAxAJWDRUMERIANQcCXRG0HiNwBXtIFNxBW2YPvzDkCnsUtVKuCh6w/Q/mwd3txLba6hvoOdSvFoEhEgAnpCIPQa8L9W6SZ7JMkFB5Lq4G/jg3AwfIEIaS4MiJ+EHLbO2DGYu2ZgyvSJmG+yQTZWn8OArRpNKJ4cAfb8xcYNkhRFo/jF0KV77fxDI9F5rR+P3dQW5TGs3h8f7KTu3GxRDijbFHD5G8hVmJ2bJJGilBvf7NJ3A9D35Wf0/Pcmj2+aDUDEbACGh6HExiq8PA83uYR2tarryV9olCYRIAJEQPgEyAAUfo1IYdoEyACkt0NbBMRvAK5sD5vPFzkDME9fdBon5ziSAoS3n7uNXtca80Ze7XADdapWUGA2DSECRECvCaQ2lhSAcSjJFbrPO2wAACAASURBVNuKzcS+4a7Jo8Mjf2Dj4inwNNkiO3vAaaCkGveRvjgHbO/Ei2sdux2hPq0VUCqOIddefEbvjXyDalKzchjZoHRKAlIp4JVXfjKWzgBjsvquAGI+As7DUHVVML7+TGDHp3VvozjoqK9yzK67OHzvLYrjE0YaH0YcTNDf+LRs4CGXgGLiNcu+fvuGPIuZH4+450jdI2jbsJ76ECkCESACRIAIaIQAGYAawUhBtESADEAtgadlIX4DcHUn2Hw8x5Zyf67e6DRBzvE5BYq95cwN9PHlH7G71P426leTvahfgXA0hAgQAX0ioIIByODpVeI0tg+qiUl772NvwGuMNDqESSZ75Jgql4FialxHEHod+F9LXtwuce7YO2+CzlTpzOP3GLKV6wrPJDa6URmM/91sRU6nd17yljWB8F/3/mW3wIhCW3EikOv+2squKFb1stcZXsokwh3/leKi6XjYGH5Ie7rbO8A0uzLhBTc21sMC5gac+evbYA9c66lxBF9wGZIgIkAEiIC4CZABKO766bt6MgD1/Q3QXv6iNwBD13aB9fsznAGYswc6TVyrEtH/Tl5D35v8Y3zn2gegcbVfu0dUikqTiAAR0AsCKhqAA6zPYWLTcmj5z9VkTGkeqRxxEyhUXnWUbwKADfzOpk9gg4qe91SPKbCZe26HY/L+BzxVYxuXwdjGvz7Eif0K+JRUWHW0eXE4Rc1GLMyS55QtnBNnxunnLrBHb76i9YprqGbwEofM3NNnODMCMDJWmLMQB0Z6WSG/NIqV5lfrX7g06SJEqaSJCBABIqCXBMgA1Muy60zSZADqTCkFn8jjVAqZn9CTfzMKDw9HiRL8Iy+Cz4b5ZXldd1i/O8lK3Ze9GzpPXq+S9PWHL2LIXX6XzdPt7qBZdX5jEJWC0yQiQAR0m4CKBqC90T50cSyBdZeDMdF4N0YZH5bPaZQ/YFFGdYYfnwKr+UeII6U5kd/rjeoxBTZz6dnnWH7+BU/V9akNUTxvtpTuv3OVb+BxMqkGhieMY2Pq0p2JypTv36vBmH38KRoa3sEm00XpTxXx/X+/E/swqwwKSz6yefo6rYJry97KIKOxRIAIEAEikIkEyADMRLgUOtMJkAGY6YhpgV8EdM8A3NAL1m+OcQZgts7oPGWjSgWf9d9RuIfwf8A/3vYeWtnbqBSPJhEBIqBHBJZVAaLClE64R/x0+EkqJc9Lc/cf882xD4G8iu9ekxESGQL8wz9C/ENqhoQpb5Anu4nSuoU24VtsAuw8ud3gv/UlG3Yqmn+/Y9SOW4bX0kLJf7w2pQFK5BP38VZVamc99XjytMaGAfjXdHHaIbLlB6aEqLKEoOa89a6AYklvWU3XHZahVpv+gtJIYogAESAC+kyADEB9rr74cycDUPw1FGsGoj8CHLaxD6zCuR0z+8w6ovO0zSrVw3PzQXiG9ePNvdL9KeqWL6ZSPJpEBIiAHhG4uhg4P0vphB9JrNE6fm7yvLQMQGkROxgMu6Z0bN6Ebx+AxbL3mS6v7Y8xjdXYWaieKo3Ndp57Dh+i43jxnKzzY88wF+DeDuDQcJXX2ptYF5MShyXP7+dqDc+2KYatvjyJSRKUnp6y076p4W2sN12aduq9DwClG4keTbh3ZVgmhbN5XKm2CHXbDxZ9XpQAESACREBXCJABqCuV1M88yADUz7oLIWvxG4Cb+8Mq7ADLcp9pO3R2k9NBUwHanhv3wTN8IG9k4owIGBuL+y4jBVKnIUSACKhLwH8TcIw7KqpMuH7xkzHdeDvKGKZxHHfCMyBXEWVCyo6NjQZ8LGW+PtD6HDb2q6FebC3PDv4Ug4aLL8uoKJjLDLenNwY2twTCrqul0jp2R/L8Lg4lsLBLVbViiW1ybEISys88lSy7meFtrEvPANSB479MnmGzq8IqMZQt1eXKc1Gv80ixlY70EgEiQAR0lgAZgDpbWr1IjAxAvSizIJMUvQH46n+DUDJ0Lwt3v0lrdJq+XSXYHhv2wOtNqk/4deSXGZWA0CQiQAQUJxD3DZin+XtUXzTeiDK1OyuuI62RSQmAt4XMd/tZncX/+jupH1+LESbvu489/q9lFDQqXyjF3NzaEQg6r5ZC61jm3xUDdK9hCZ9OVdSKJbbJMXGJqOxxOll2C8ObWGO6XH4KVboBHVW7g1doTELmOMAm4SVnAFb0Qr2uY4Umk/QQASJABPSWABmAelt6nUicDECdKKMokxC9ARi+dRgsg3ay8A8ZN0P7GXtUKsa25W7o/WUVfy4ZgCqxpElEQC8JXJgDXFmg2dTdvwCGhpqJKadRyV+WZ7B1oLNm4mspyoD/3caFQK5hAyPDGIm4XO8lipt8Ax4fBL5wu7lUkdk3fgouS6qih1NJzOtop0oI0c75+iMBVWel3K+43GQl2hn5ys+l+w6gfCvR5vmn8KC5zigVH8h+6WI5dzToMUEncqMkiAARIAK6QIAMQF2oov7mQAag/tZe25mL3gAM3TMV1k/WsBzPG7qikTvXFVgZwK/mVEfJhGAyAJWBRmOJABHgCEilgFdezRLR5IcQcgzAHsVOY+cQfndgzSaQ+dG6r/fDjeBI3kKzjDejj/FZjS3++67GHk6WmNdRv3YAfo6Jg+Psc8ks021U02sfUKaJxphrM9CLebVQJu4RZwCWnooGvadpUxKtTQSIABEgAn8QIAOQXgcxEyADUMzVE7d20RuAwUcXwjZgNluFAFSCg2cauxMyqpWcX46hyV++M1qfvk8EiID4CXwJA1bYA5JEzeSiyb+DUv0d91pqgb8Lb8HBEbU0o1VLUTqt8UVA2Bfe6ukaVSrqZO4B7OpYAgs669cdgB+iY+E8N+UIdbpc+xwBbOupSFdY057Pr4eyP++xoi7YTETDvjOFJZLUEAEiQAT0mAAZgHpcfB1InQxAHSiiSFMQvQH48uwGlL4+kcUfCGuU97yvUjlCZtvDJjGInfvD1ALZ3bg/qxSUJhEBIqB/BD6/BF6cBopWxb5zV9H5tY/qDDRpAD4+BOzty2qJlOaEfdx6BHo3h7mJkeoatTyzy1pf3A7NGgOwk30JLO6qXwbg6y8/UHv+ReTATzw25zfK4pW+/0nAylXLb4Nmlg9c0Ajlf/hzBqDVWDTs76WZ4BSFCBABIkAE1CZABqDaCCmAFgmQAahF+Hq+tOgNwOCLW2B7+W+2jEHS4ijl9USlsn6cVQaFJNw9Upftl6Ne234qxaJJRIAIEAGGgNe+m/B41FR1GJo0AN/cATY04GlhdrUt714N7aoVV12jlmdaTz0uoyAzdgBWj12L+tUrYGm3alrOOGuXD4v4jnoLL2GxyWp0MrqW9uIjbgCFKmStuExaLWhBXZT6wX2Y+CxfPZQbcySTVqOwRIAIEAEioCwBMgCVJUbjhUSADEAhVUO/tIjeAAy5uhs254ewVQuTFILVrBcqVfGLlzXySbldJJecN6B+i64qxaJJRIAIEAGGwIXADwjZNgYDjVW7m1Sj1xB8fgGsdOQVpk7cUvRsVg/D65cSZcEuP/+EvptuZYkBuDChK4zrT8K4JmVFyUoV0ffDo9Bu1fXkqRmaqpNDgOz5VVlGeHPoShDh1YQUEQEiQAT+IEAGIL0OYiZABqCYqydu7aI3AENvHIL1Ke5I21tpfljMDIKpsfJdM2O8iiGn9Dtb0UuuW1C/aTtxV5jUEwEioFUCEokUK5fOwuhvS5TXYWwOzPig/Ly0ZkS/BZbwd2jtTGyAyEaLMLJBac2tk4WRpu5/gF23wzVvABarDry9KxN3ZV1/jGpYJgsz1N5S119+Rq9/b7IC0jUAzfIAU8MAAx35kZYMQO29eLQyESACREABAmQAKgCJhgiWgI78tCRYviQsbQKiNwBDbp2AzYkebIafpbnxos89uJQqoHTd4zwLwgzx7LzL9XajXoPmSsehCUSACBCBPwkkBmyH8dERykMxZ0yVV8rPS2tGbDTgYynz3aW1bot2V5ud52l8i03dcEWKUPNe6nFrNhc47SYTgzkyLfY7ExUF03zZFQS+/5Y8vJJBKI6byfJgY7VdCdj/pWhowY/7PM8OFnGp/t/T5HF8wRMggUSACBABYRMgA1DY9SF16RMgA5DeEG0REL0B+OnJZRTc05bH71TnQDSvXFQ5plIp4JWXN+dyo8OoV6e+cnFoNBEgAkQgNYF7O4BDw5Xn4jIKaDZH+XlpzZAkAbNkj2h6VL8Or3aVNbdOFkaSd/+fq00e7HjXKmMVjMEa+1X+uG7bgN29Zb7HGIDe7SrhLxfrjOOLfITD1B1YZfoPaho+zTgTHTPHjv3PB61D57F5vzezRpFpqjUYyxgejSACRIAIEAFlCZABqCwxGi8kAmQACqka+qVF9AYg3gQAGxryqubb9QFcK1opV8lvH4DF/HudrjQ7ibouutHRUDkYNJoIEAGNEvgeASy0VT7kxJdAzoLKz0tvhpyjjSPLXsSqnvaaXSeLoskzANsXi8KySAV2XNYaC1xfJl/pkEvAetkPgBgDkHlCfRQwGLOIQWYts2T6QIw32adYeB0zAPfvWI9OzyexuX82KQaL6QoYoYrRolFEgAgQASKgJgEyANUESNO1SoAMQK3i1+vFddIAfFR3LSo35I4FK1Thc17ANf4dXVdbXUKdGtUVmk6DiAARIALpEriyCLg4BzDJAcSnHKtM70notgsmFVpkNEz578sxAHsUO42dQ2oqH0sAM+QZgJezT4GVRPZeQBm5I28Bq5zkZ/H3HWCFrCmqLwagVCqFQapd8emWW8cMwH17tqDzk7/ZlL8a5UeemSECeONJAhEgAkSACDAEyACk90DMBMgAFHP1xK1d/Abgl1BgeVVeFR7WXg27xkre/yTnl2LfDjfgWpV/Yb64y03qiQAR0CqB+O+AgSEwp0jGMgaeAyxrZDxO2RFy/q6rn+MQLk1qoGwkQYxPbQAaIxEvzfukr63daqBaT+BHZNo7M8cHAkvKy8T5bQAGz20JQ0Pd/fHtxYdvKLOG+RFBwUfHDMC9B/aiy4NBbPI/DXIgm8dbBWHQMCJABIgAEchsAmQAZjZhip+ZBHT3J8jMpEaxNUFA/AYgQyHVL7QBdTfDoWFH5fjI+aX4Ud8nqGxTXLk4NJoIEAEikBGBB3uBA5y5IHd4/1OAlUtGkZT/vpy/6xhT6+rkBrDMn135eFqe0W7lNdx/zd3jZ2vwFhfMJqavauZnwMgk5f4/n5KyY3OXAIZfA+bL3vP32wDcOtAJdcpo+Hi2HNU/4hOx8PQzbL4eilzmxtjQxxE1bRVrchURE4dzTz/AtmBO1LCWvftRLqSEWHw67oWHAb5oaHRP8erqmAG45+gxdA3gPkhMhDGMPSMU50EjiQARIAJEIFMJkAGYqXgpeCYTIAMwkwFT+DQJ6IQBGO1ZHLkRwybpX3sDHBt3Va7scn4pfjXqLUpa5FAuDo0mAkSACGREgGk69GAP4sNuwPTOJvmj+x0HrGtnFEn575+cAtxcy5tnG7sNubOb4Z57U+XjaXHGy4/f0HjJlT8UKNj91yMKMDAA4n8Ac+U0jGJ2/2XPD8wuJJPdbwOwWB5z+E5rlOnZr7r4MtkA/PPZO8wlQ0MvOjYBjRZfxqdvcclTl3arig7VM97RJ728EAYXZyufl44ZgHtPnUeXG6k+SPxtHCtPh2YQASJABIiAhgmQAahhoBQuSwmQAZiluGmxPwjohAH4zqsMiko/smn5u66BY9OeyhVajgEYPfUzcpubKBeHRhMBIkAElCEg5++e5OmMCZVbyW7miqz76Tmwin+0uGLsJvyAuegaWwzfFoCTj94nZ20ACbaazENto8cZU/htViUlAN4W/PE5CgKTXgJyOsMzA38bgMx/Z0UjEHl3HHayL4HFXflXX6ROevWll1hwim8cZqQ38H00yq+1zJhf6hG1xwONPZSfJ+AZ+y/4odOV5nyFU8MB89wCVk3SiAARIAL6Q4AMQP2ptS5mSgagLlZVHDnphAH4ZlZ5FJe8Y4nfcl4BpxYZ3AGVuj5yfgmXekTBgNklQg8RIAJEILMIyDMAq/YEOqzJnBXldDy3j12LSOTOEkNLU0mFRXxHvYWX2HCTjHdhpPERxcL/NgDlmXwTngG5ft3RmKo2P6WmqBD3P3aNjAw1xcSkP0qeAcjMyGjtzmt84R/2hRc8vTnMUeNKHqcRYqbgh2dFqgCQAsUdgeY+gIm5JtIVTIxDvg/Q/kwdvp6JL4CcsrtCBSOahBABIkAE9IgAGYB6VGwdTJUcBh0sqkhS0gkDMHxWRVhK3nAGYI1lcGrVX6kSBLuXg61hyk4S5vkgzYvCXmFKxaDBRIAIEAGlCaQ2AJkmIe6RKUdUM+ORc++dS+wKvEMB+M9oDIucZpmxqsZjuh18iB03X7FxQ80VNK6YGX8eV72zFTg2DpAkALXGAk28OK3/VAcig9k/+0vKonO8J/vnl3NawNjIUCa3mLhEJCZJkDe7abp53w6NxJiddxH1MwGTm5VDd6eScDvwEAfuvkHe7Ca4PKkBqnqdkRvjgWdTrLrwEtGxiRhWzxZWBfjXVXRd64dboZG8uakNQMb0W3T6OUI+xyR/2HUh8CMU4bi07FaM69lW4zUVUsDjAUFodTRVF+gx94F8svdCCkk3aSECRIAI6AsBMgD1pdK6mWcm/ZSvm7AoK40S0AkDMMzbDlZJ3C+CN+0XwbntYKVAPXW3QwVDLsa4+OFYOtdHqRg0mAgQASKgNIHUBqBNPaCvgjvZlF4MQGI8MJvfvKJB3GKESItiTofK6OVspUrULJ+TemecIsZVssiWiwCnVP8+xHwEmOPAeVI1fQr4Dzg6ms3trqQ0OsTPYv/83wAn1CvLZ3nq0TuM33MfP+KTMKpBaUxsVi5NNu1WXcf98Kj/s3cW0E2dbxh/IjXcZbg7DHf3MWCwwcYGTIDBmKHDtbj+kbEhM2ADBgMGY8wYDBkMd/fi3mKpJPmf29KU2yTNTXKT3pv73HN2zmje75Xf+xWSJ5/Ev27U6zC4ZUmM23BCEstGJXPEC3aJT7+mxVG9UBZUf3ZBSMf5O7H7QsoC4PgNx7Fw2wXkxD0MDPoRwYhFG8NOl/EnVf8vPtdAfv44eg3NVpUSl9h7F5Aj2c8CGQJrIwESIAEFE6AAqODmMDWXBCgAukREAx8RCAgB8EL4iyhkvmBDtKviZNRo20syMqvVijOjyqC4PmkV4Ycxn+DzCeGSfdCQBEiABDwikFwALNEK6PSDR64kDbJaYR2bBTqrxWbeMnoiTlgLIPyVsuhSI4AFwB5/A3kqS8IUb3RgKfDzhzb7Q5bCaBuTdEHGnE4V0brCCyJ/NSduwvVIk+1nu4c2Ro4MjrfHOtveKz1Be8sBzYrjlYp5MGDlIew6n7IAmBh/dfBIVNKflRz2Zr+byOmkJslOFG647cxt1FhaAkE6c1KmPbcBuYWtz3xIgARIgARSmwAFwNTuAON7Q4ACoDf0AmussPygg7BGAYDw9bpwEJHwDn4HgCkA/pO53IAQAM+Nq4wicUkfXnaWH4+a7T+SjComzgJhG3ERfdI5gv11/TF91EjJPmhIAiRAAh4RWNsbOPi9X0UGy/jc0Mc+scVsFz0GB6zFMKZNGbxdSx1bHD1aAejuTbUHlwFrk75MOmIpiNYxE2zcJrUvF79td92ha/j18HWUz5fR7uKNca+URWcnoqovBMCU5mDyLcBC/LR4imOh3SRP3as56iNPbx+uUJWciW8Nhe3ZZb4piTS6hFuUhcfcbRMM+ar4NjC9kwAJkAAJSCJAAVASJhoplAAFQIU2JhXSEvacDgJwDsA/AIT9PcUAvBJ/ySHQCcCPMuYVEALgmXFVUSzutA3Lv2XHotZrn0rG9PDJU6Sf8uzQ92ej+uk/w4yRwyT7oCEJkAAJeETg/iVgTU/g7lmganeg/iDfnf/3LEHr2KzQWeJs6Q6K7YEV5oYY1bo03q1dyKMy/DXIYrHirxM38f6SfaKQkrYAuysAHv4RWJ20Xfi4pQBeipkoips+1IiHpiSWyTm8U6sgRrcp4xCPvwXAZqVzon2lvGhRNuHfOyF+BjzC4dD3pbdPI6vgjlyJRP6FJZFRlySUH2u+AmVqJrsZWDo5WpIACZAACchIgAKgjDDpyu8EKAD6HbliA7YHcBvAtmQZClfRbQLwEICw3yjpK2nvSgkIAfDU+BooEZt0btLO0iNRs2N/yWQij/2BjCuFhZdJTz/jUMwYLmixfEiABEggwAgk23Z8xZoNdaJnI0+mMGz9rCEMeuW+LRmy+jCW7Y6wa4hPBMAjq4CfklbHnbTkQ4uYyW5Phs0DGmDFngjkz5IGb1TNB/0zvv4WABMT/7NvPRTLmd4zAXDYDSAozG0Gahtw5uZDZJpXGtl1UbbUw7NOwoiPP1BbKcyXBEiABAKSAAXAgGyrZopS7jttzbRAFYX+DqAZgKoA9sqUcUAIgCcn1ELJmGM2JFuKDUGDtwZLRhQ7pzqC7p4U2e+qtQA1mr0u2QcNSYAESEA1BJKfOwigsukL3EVGDH2pJN6vV0SRpZhizSg54jeHuflEADy6GliVdKP8GUseNI2Z6hWbPk2KoU+T4hBWMhYe+qtbvoTtuiV0EbhkzRnfK2+egc1LYOrvp5AJD3EwtKc0V/ogYOQdabYqt4q49wTGWWWQW5d0juL4zOEY9mnSpTAqL5HpkwAJkICqCVAAVHX7NJ88BUBlTIEcAKo9+08Q2YT/sj5L7TsA77iRZn4AwrvEVgCE/xdW7AmH1Anbd+cBSNpTIt3pL8/8VQRwUPqwFC0DQgDcOaImahqO2wqdEdQD/YZNk4woZnY1BN87JbKP7rQaISUaS/ZBQxIgARJQDQEHAuCU2I6YZxZOmwCSnxWnlLruPIpGlXF/OUzHpQDYagZQVfpZd/FBjv8M/NjVFu+cJTcax0z3GsdL5XKhRdnc+GTZAZe+suM+RgQtRXHdFZTUJ6x8vGdNh84xQ3Hc6v15jVkRiX2hEle1fXYBSJPFZc6BYHD7YTSeTi2D/HphU0bCMzHTKAzp0y8QymMNJEACJKB6AhQAVd9CTRdAAVAZ7bemkIY7AqAg+gknujv7el5QmoRLPs67UbYgIgqH3N0HIIh2z11L54YXe9OAEAC3j6iFOoakFYDjzV0xLHyOZDCmWVURej/pDMH4gW+vBwrVk+yDhiRAAiSgGgIOBEAh94KmhNuHlSoACqJM1fEeCoAj7wN6vXstOvELsOIt25iLlpxoEDPTPR9eWi8Imo5mBvF5h4LLbeay6BI71EvvQHXdCawIkXDjvc4AjBLfKux1cAU7eGiKxa0J5USXg03NOAwD+36m4KyZGgmQAAlohwAFQO30OhArpQCojK4+LwAKX7MLh8oJW26FR6oAWAHAvwDSAHgkfGEMYDMA4cCcNwAkniYu7DcVVhgKNq6eIADCJx5BjRKWIixxNcCN1wNCANw5pj5qWpMWRU6xdMZnYz+XjOHp/6oi7EEyAfDdjUCBWpJ90JAESIAEVENApQLgzSgTqk8QjsO1f1yuAHT3AhAhxKmNwDLhn+6E57Y1A6pGf+nXNqdUV6Jg6yyhMJhghAUP49+S2D+5cBe7Qj+WVs8by4CSwneX2nhizRacHVMepZ6tuhSqnpNpMD7uM0QbAFglCZAACSicAAVAhTeI6aVIgAKgMibIGAB7nv13U1gMAeDCs9SkCoCC2NcAgHAloCDY7UxW2kAAU579bBSAsS5KF5YrCLE7A1gIwI2r+iRBDQgB8OLsVih4b7ut4OUZu+GNvjMkARCMnvyvKtIkFwDf+wPIX12yDxqSAAmQgGoIrO4JHF5ul67SVwBeuf8EdSYL/8zaPz4RAK/uAxY2EgWrYFqASKTzW6s9FQA7GLZgnPFr6GHFpLhO+MosFu/q6Q9hcbAbF5qMuAMYhO8jtfMcHvkiyusT3wYCfxYfjaZv9tUOAFZKAlogcPM4sPJt4P5FoMYHQJMxgI4fzdXQegqAaugSc3RGgH/LKHNuuCsACiv6dj8rZT6AXg7KEgS9owBKPdvOmxNArJPyhXmxCMB7AJYKm1IBWGRGFRAC4OXP2yL/7S02NAvjXkKPccsko3o0syrSRSZbAdj9byBvZck+aEgCJEACqiHw5B4wpZDqBMCLdx6jwbSkv+uTCrDiYmjSVl2HffBkBWD0Q2Ci8M9k0tMpZhh2Wsr4rdUpCYDlTQsRhbTQwYJ2+u3IqXuAleb6uIf0+C+kt+0GW4tVhwrRC20rAd80bMKEoK/cq8ETfu5FUJz1kTHVUM6adD7w5uLD0PBNbgFWXKOYEAl4Q2BFF+DEuiQPvXYAucp645Fj/USAAqCfQDOMTwhQAPQJVq+duisAjgeQeCBPDQD/OclAuJ5W2BosPMIW4z8d2AlCoSD+CdcPCkpWFxnP/Xs+XEAIgFdnN0Oee2Lct/vfQvb0IZImwcMZVZE+KpkA+P4W4AXhvhU+JEACJBCABBxsA05cAbj+ozool9e7W2Z9QezMzYdoOnOrnWs9LDgfKiyUT+HxRMAyxwHhiXeBJfiW6yIQqXxSEgBnxr6KWeZX8ZlxOXobEz7AXrdmQavoCdgfKv4O8qzlBbweMwLl9OfxbbCbNxlX7Ay0lX6shtTalG53elJdFDcdtqW5pchnaNBlmNLTZn4kQALuEEj+b2FIBmBIwoVLfJRNgAKgsvvD7FImQAFQmTPEXQFQ+FRSF8BjAJmebQN2VFnNZ+cECq8JW4CFrcDPP8+LfysACMsa5Lr0I3k+ASEAwsEH2b3vXECVgtJuK7wzoQyyxVwRs/l4P5C1iDJnJrMiARIgAW8JJPt7U7hZtlL0gnivs954EW1fzONtBNnHH78WhZdmb7PzG4ponAwVvi9z8rz8P6BKCq+nlGkKQqlcBebEPXQzbsQjaxgWmFvBhMQvr1Je2fiLuTq+jWuOVSHie0mMrgAAIABJREFU00TWmGujnWGHLOkNje2GCWOnAAajLP7U5OTE5EYo9TTpApYtBfuiwTuj1VQCcyUBEnBFwNGZuEOuACHpXY3k66lMgAJgKjeA4b0iQAHQK3w+G+yuAHgbQDYAhwC8mEJWmQEkXqW3EkDH52wF8U/Yl/MOAOG1N1MQEuUoPGAFwAPvXUTF/AJq18/9UXmQWZfsPpbPLgBppAmIriPQggRIgAQURuCH14HTv9mS2mkujU6xw+P//FmLEujdoKhsCUfHmfHdvxfxKNqMt2sWQNZ00lZnJ09g86lbePcb4ahe8fNj8BhU0ydt1Yx/tVB9oGx7IHMhoHB9z2vxsQAobN/dGtwX+fTCWwhgg7kaPoztE///QYjDmVDh7i/nj7C9V697/g4zz0t1NHJclZ0Y/nJpeZ2qxNvRqc1Q9nHS7oKt+T9EvfcmqCR7pkkCJJBIwGq1Is5ihUGng16f8LHbFGvGjD9PY+huYdNWsqfVdKBqdwJUOAEKgApvENNLkQAFQGVOEHcEwFAAT5+VsQHAyy5KEtSmtAB2ARBWBCY+wlfLwopA4fVZTsS/tQCSrrxNOZD48CJ721zPLj1BREQE8uZ1Za7MRjlaAXik+2XJW9iiRuVCBl1i+57VOPIeoDcotGCmRQIkQAJeEji4DFibtE30giUnGsbMtDn95eM6KJojHf4+eQvZ0oWgWiHPvxD5dPkB/HzwWrzvwtnSYlP/+tC5ecj6nov30OFL8b1aZXQXkVt3F4uCp9vD6LkVyF3BS0iAo39fXN2+607Q2voj+D448VSQhJFfxbXEanNd1NUfxuAg+8ta3PHvrW3ciPswGoTvJrX3HJ7WCuUfJV0wtjVvT9TrnniPm/Z4sGISUCOBh6ZY9P5+P7aduYOK+TNhQZcq8UcEjV53DGd3rcfSZH//xtdYfzDQkDd+K73fFACV3iHmlxIBCoDKnB/uCIDZAdx6VoawbfcNFyUJtwzneHYhSLnnbL99dtlHSsOFfUyCnZRH8rKAQBMAj71/GWVekHaG1d8jG6CR/oCYpyfnRUnpCG1IgARIQAkEruwFFjW2ZRJrNaBY9BJRZmXzZMDRq1G2n3l6NmDBwcL3YknPD92ro1ZRYcG8+BFWCl57YEKO9CFIG5K05fSv4zfRffFekXF7/VZMC5rvfPVb3+NARhm2MTtYAbg0rjEmxL2FJxC++/Pu6Wz4E+OCvvHOia9G5yoP9LLfcu2rcErze2DGK6gYlXTr9PqwNmg9SPw7orScmQ8JkICYwLLdlzFk9RHbD/s2KY5PmxTD4GEDMClooUNc1rKvQfeamxclEbzfCVAA9DtyBpSRAAVAGWHK6ModATAfgMvPYgvvDlPes5NgK4w5B0C+fVb2xWtWAJS8AtBiAcaKtwpfzdkIeT5YI+NUoisSIAESUBiBWyeAeeKtTwVN3wNw/pakdO4MWNmrJoQVfX+dSPjO69/BjfBCpjCnxQlbrwoN+VX0+pRXy6NjVeGfwKTn/uMYvLXoPxy/HoU8mcKwpFu1eL/T/ziFhdsu2PlP6XKMeOP+p4D0wiJ3Lx9H50MBEM7f+yj2U4+dZ0UkMuse4q8QBd8q23gUULefxzWqfeC5KfVQ5IlwqkvSYx31wO3Vq2rnwPxJQM0Ekn8BJdSSOU0QDlg6pFwWFwIovu0UABXfIiaYAgEKgMqcHu4IgHKtAJSbhKs9vQG7BVjyGYCn/wB+EL8JOFeuL4q8yoO+5Z6M9EcCJKAgAnfOAHOriBIqalqMOKR82cO4V8pi+NqjonHnJrwEw7NzlZJXaLZYUWSoWACc1L4c3qiWX2Q6b8tZTPkt6Ry/dhXzoFyejBj7y3GH0PwmAG4aC2xzsMUYgCCYdjD8g9r6o9huKYdV5npOBdRG+v1oY/gXRy2FcNOaOX71YoguVkETwkEqw24CQd6vclR2kSlk50D8vd3/Vvz2QT4kQALqIOBIAGym34MFwUlHXjishAKg4htMAVDxLWKCFABVNwfcEQDlOgPQ35AC9hIQybcA/9IP2Cte5n/+xUEo/MpQf/eC8UiABEjAfwTuXQBmi++rKmn65rkbaKWnIqzWq1tM+B7M/rly/wnqTE7aRilYTGhXDm9WFwuAjj6kOctADwvOh3ZOOcFPDwOZC0gvwpllxB7gqyYOX+0R0w8Lg2fYXuse0x9/WSqjqu4kGhkOYJ+lePyfS+ou47eQwd7n4m8PI+5q8vZfG2YHAuCNvjeRK6OGRVF/z0HGIwEvCST/tyUYsTgU0gNhupiUPVMA9JK874dTAPQ9Y0bwHQGuAPQdW288uyMACnHkuAXYm3w9GRuwAuDOzmdRs6jjD6QiUBv6A3sWiX50ocpwFHp5oCc8OYYESIAE1EEg8gows4wo1/KmBYhCOrfzf79eYQx9qZTduFX7rmDASvEWSsFIWEXYuUaSOPc0xoxSI5NuJHaVQEP9AXwTPDVlM7nEq5vHgC9qOYwVYcluu71XMIi0psFScxN8aFxnsz9kKYwK+vOuSlLm66MeAG5e1qLMQjzMyoEAGPHJdeTLksZDhxxGAiTgbwIlhm9EdJwFRsShgf4QcunuSTp31TLyge3GYH/nzHjSCFAAlMaJVsokQAFQmX1xVwDcCqAugMcAMjm5wVeoVLj1999nJY99dutvahEIWAFwxxsnULvkCy65xq3vD+M+sQB4scZYFGzh+dlOLoPSgARIgARSm8DDm8D04qIsjlkKoFXMhBTPAXSU9sDmJfBhQ/vjbIsO/RVxFvujaMe2LYOuNYV/YoHTNx/ina9341qkSTIRl9t/280HKri6i0tiuEe3gGnFJBoHmJnGV8D8PrkTmj8Vb18/88EVFMuZPsAazXJIIHAJVBjzByKfxuCHoPGoZXB8pISj6rd0OoMGJYT7GvkolQAFQKV2hnlJIUABUAol/9u4KwAKn5oS74wXTlb/z0nKwj6gic9eaw7gDz+WdixZLOGwp/hPgIF2C/DWDodRr4zr7V+nvumFEpeWibBcrj0J+Zt+4Me2MBQJkAAJ+JnAk3vAlEJ2QTvHDIk/z86dp1f9IhjQrDg+33wOO87dQd2i2fBBgyIoOmyjUzcnw1tAOB+wzKjf3QkVb+tSAJRTuLKYgbFZ3M4xIAbIyVGFQCbMnouh94bZMr9tzYib7x9B2TwZVVgNUyYBDRJ4cg/bp76KOtb9bhc/qsiPGNNF+JjGR6kEKAAqtTPMSwoBCoBSKPnfxl0BsNpzot98AL0cpKwHIJyeLuyVegBA+GrJn6eAa0YA3NJ+PxqUL+Jy1uz6vBtq3F4lsrtSfzryNuzuciwNSIAESEC1BExRwCTxTbxCLSviGmBQ3Ptul/W/119EnxUHbeOCDXrEmC1O/VQtmBltKryAET8n/2fJdWi/CoBCOk5uAnadqcotNC4Ajp/7JYbdGWRr4h1rBlx89xCqFNSoIKzy6cz0NUhg2wxg0xiPC/8upBMqvDkeLxbI6rEPDvQdAQqAvmNLz74nQAHQ94w9ieCuACjESNwGHAdAuA5wZ7LAwsFyU579TPgXKbWvmg3YLcB/tdmNJpVKuOz7rs+7o8btlSK7a43n4IW6XV2OpQEJkAAJqJZA7FNgvHARvPgRbrIdEOvo+6vUrzQbIjEjaB7qGY6knIzcwtV/84GNn6U+AH9nIDdHf+fvZbzweV9hxK1+Ni/CGY9HOh9BnWLZvPTM4SRAAn4hIMOXN+MzjMSwfv39ki6DuEeAAqB7vGitLAIUAJXRjzoAnj/ESHiHl3jK+A4A4oPigG8dpF0RgGAbBuCRcNkhAOH6Q+HPwoFEicsqTgOoAuBhKpcesALgH612oFnVsi7x7vy8O2omEwBvNpuHnLXecjmWBiRAAiSgWgLmWCDcXshYY66NvrEfylKWAWYYYUY0gmXxN9C4XHTBhlOncgtX1w8D84UjfjX2yM1RZfjGfvEtRt5MOg/4kTUUu14/jCalc6qsEqZLAholIIMAeN6SC4XHntIoQGWXTQFQ2f1hdikToACojBkiCHpvu5GKs761BrAUQAYnvgTxrxWAs27E8pVpYAiAe78BfukjYrSh6Ra0qi3osSk/Oz7vgdq3fxQZXW/+JXLX7ORqKF8nARIgAfUSsFqBMcJ9VfZPOdMiPIR3N52W153D/OCZyK27h+/immJU3DtuXy6SPDOXW38TB8gtXN08Dnwh3N+lsUdujirDN3b+Uoy8niSGR1uD8Oerh/FyedcXjKmsVKZLAoFJQAYBMM6qh3HM/cDko/KqKACqvIEaT58CoDImgFwCoFCNcPuE8LWxIPQJIlvMM8FP2Gs6F8ATZZQcn1uEkIuqLwF5dBuYJr6B8vuaG/BWc2FRZ8rP9rk9UOeOWACMaLUE+aq2cTWUr5MACZCAugmk8OFoSVwTTI57A4/cFAJ1sKCJfj8WBs8QsWkRPQknrfld8hLGt9bvRAbdE6w218UThNrGpJoAeOskMK+6y9wDzkDjAuCkb1Zi8KWk84AFIWBtmyN4rbLw1okPCZCA4gnIIAA+sKZFxtFXodPx47rS+k0BUGkdYT7uEODfKO7Qoq03BALzEpC4GGBcdhGXxtFTsWmi64PsD3zxHire/Ek09uFbvyJ9sdrecOZYEiABElA+ARcfjhbHNcXIuHfdqmOicSE6GYWTL8RP4tZiQeALQwxMCIYFwr1Y4meM8Ru8bfwz/ofCiqtq0Z8jEuni/5xqAqCaVwC2nAr89yVw75xbfUTOssAHwokm2n0undiLAisaiwB83+IQ3qohHBHNhwRIQPEEZBAAhRpjh99FkNGo+HK1liAFQK11PLDqpQAYWP1UcjWBKQACMI3KhlBd0oXKbaPH4ueJSWf3OGvKf18PQPXLC8UvD70OBHu3/U3Jk4C5kQAJkEA8AQkfjkqavoEJIZKApcMTHA11fIP6T+a6GBvbBQuDp6Oa/hQOWgqje8xA3EFGkW9HIl+b6HActhZJPQHwxlHgSxV+KdT/FJA+FxDzBIjYBVgtgFBLZASwJ9mxxoLgl6cSsH8xEJYZ6PAtULiBpL4HrNGds8DcyqLyvm60B+/VKx6wJbMwEggEAlfuP8Hpmw/RaLk8v6vWDHmg++BfIMzxsRmBwEyNNVAAVGPXmHMiAQqAnAupRSAwtgADuD0qH7Lromwc34oZgu8nDHbJ9d9F/VDryldiO41ve3IJjQYkQAKBQUCCACgUKoh34bGd8QDpU6y7of4AvglOvDtLbPpjXH0ctRbE2KDvbC9Mie2IeeZXXAqAgsGiuJbobtwojbvcf4ffOAJ86fpICWnJ+dHKGYfTfwA/dBAn0v80kD4nYIoEDMFAkHB3mcafexeA2S+KIMyvsx09m5TTOBiWTwLKJbB450WE/3wIHQz/YEJQsvf33qTdejZQ2Z2j4r0JxrFSCFAAlEKJNkolQAFQqZ0J/LwCRgC8Nb40csRetXWsZ0wfzJ8wxmUHt335MereWGyzO5ixMV7su9rlOBqQAAmQgOoJSBQAE+s8YcmP8LjO+Nfi6IZ1Ky6GOr89fVlcQ4dbgwuafhBhlLzNNyX4cguATm5MVnz/nXE48xfw/avi9AeeA9La3wqt+Bp9meDT+8Bk8XbfbyuvwTutG/kyKn2TAAl4QaDg4A34MmgmWhj2eOHFwdBizYC3hKPc+SiFAAVApXSCeXhCgAKgJ9Q4Rg4CASMAPppdG+nuHbUxGWLphYljJ7tktHluLzS8s8xmdyhLC1T4ZIXLcTQgARIgAdUTcFMAFOq9ac2EmtFz48/vy6+7iT7Gn+L//09z5fhbf509/5jLo77hsN3LggCYDZEYHfQd8uluoYL+vPdY5RYAhYyELbMb+nufm7881PgQaDHBcbRzfwNL2olfG3geSJvVX9mpJs6jsfmQzpK0u2B16Vlo31G40ZoPCZCAEgmUH/wjDof2kD+1XOWAXtvl90uPHhOgAOgxOg5UAAEKgApogkZTCBgB8PGCFkh7baetjaNju2LUuNkub+36a2Y3NIlcZRt3NEdrlO29VKPTgWWTAAloioAHAqDAZ6O5KvrG9saG4KEoor/uFbJCpqWYZFyE141bvPIjGuwLAVBNqwArvQ20mOT8LNvz/wCLk910zxWADuffxUnVUdB00vba34X6o9HbI+Wbq/REAiQgG4GLdx6j8/SV2B7i+gxwd4NGZSyB9H3+c/m5wl2/tPecAAVAz9lxZOoToACY+j3QagYBIwA++rYD0l38w9bHabEd0C98IfR6579eMXEWfD/6Dbxr/N027lju9ijT8xutzgfWTQIkoCUCHgqAciI6bCmE8voLcroEfCEAWizA2Mzy5ukLb11/dn15x8XtwLetxNETzwD0RU4q9nlo2suo8GibrYIdeXugdvdpKq6IqZNAYBKwWKwoPPRX5NXdwvaQPrIXedKSD9ubrkP3uoVl902HnhGgAOgZN45SBgEKgMrogxayCNhbgB/+8C7Sn046u+/LuJfRbcwSBBn0Tvv688GreLjqY3Q2brLZnMjbEaW6J7sVWAszgzWSAAloj4ACBECfQPeFACgkmhq8ircETku8/GTIVSAknWukwq3Ak/IBlrgE2zRZgQFnAL3B9ViNWeyf0Q6Vov62Vb0x81to+ek8jVFguSSgYALClzNxT7Hj4iO89fVeVNCdxc8h8q/SPWXJi+YxU3BxUrIvTxSMJtBTowAY6B0O7PooAAZ2f5VUXcAKgFE/fYoMR761sf4+rjFeG70SIUbnH2hemrUNXW5PFx1Mf6LAWyj1Lt/cK2nSMhcSIAEfEUgNQctHpYjc+koAXP4WcPIX31dQvAVw+jcgS2Gg7efANy3FMQvWBdLlBI4mHV8Rb+BO3Xu+An4bDOgMQJvZQPmOvq9LhRH2znwNVSL/tGX+ZVxr9Axfwm2AKuwlUw5AAk/uAcvfBC7vxHF9cXzwtBf+Cennk0LPWPKgacxUCoA+oeuZUwqAnnHjKGUQoACojD5oMYuA2QIc9ctwZNg7x9bDn8210GzELwgLdi4ANpnxDzZEtkeI7tkqCAAnC72Nkm/P1uJcYM0kQAJaI0AB0L2OxzwG1vYGjq91b5y71qMeANFRQFCahJHhyW7n/ewC8PtQ4FDSBVZuC4DCgFgToNMBxhB3M9SM/c6ZnVAz8ldbvYviWqJijy9QuYAKtoNrpkssVLME/p0L/DHML+Wfs+RG45jpFAD9QltaEAqA0jjRSpkEKAAqsy9ayCpgBMDIP6cg447xtp5tMldE9eF/IV2I0Wkfu0z6DktMn4heP1m0O0p2nq6F3rNGEiABrRNQuwDYZg5w4wiwe4G4k+6shHN3Dtw+DXxe1d1R7tknz3/nvATBD1agbn+g8UggeR7CqsE3eYO9e6BdW2+f2Rl1ItfbDL+La4r8XeahYYkcrgfTggRIwLcEZPg3bIe1PGpnfQTcS/kG+vOWXGgUM4MCoG876pZ3CoBu4aKxwghQAFRYQzSUTuAIgFvnIePfQ2yt220pgZJDdyBDaJDTds4P74me5uWi108V74USb07W0BRgqSRAApolIMOHp1RlJwhlUdeBOZWA2CcJqQiiYKWuvkvr7rmEeCk97eYDa3q6zqHjYuBHB7k6EjAfXAYsZiBLoSS/f40BdswCMuYFXl8C5K7gOiYt3CKwZebbaBCZtOLzh7hGyN1lPgVAtyjSmAR8QODqfmBhQ68db7FWQoOh67H9rzWos+cjp/4uWnKiQcxMCoBeE5fPAQVA+VjSk/8JUAD0P3NGTCAQMAJg1M5vkeH3T219FW6WzD94NzKlCXba6z9Gt0Az7BS9frrkhyj+xgTODxIgARIIfAK7FwK/DlBnnXmrAd2fnc125wxwbC2QoxRQ6mXf1nP/IjArBaGt1w4ge0lg/SfAwe9TzkUQ+pKLsIJ4KYiYfBRB4M+Z3dA0MumsxS3mCrB2XkUBUBHdYRKaJbB1GvB3uCzlb7eWR50x27Bq3xW8tr6MU58maxBKRn9HAVAW6vI4oQAoD0d6SR0CFABThzujBpIAuHsZMvzay9bTE5Z8yDloP7KkdS4A/jW6GZrgP9E8OFfqAxR5fRLnBgmQAAkEPgGrFTi8Arh1PGElmZqeAnWAdzf4P+MHEcD/yjqP+/xNvALfP0cA/zoR9AQB8Me3xWcKfnIg4fIPPoogsHFmT7SMFO8U+O+ljaherZYi8mMSJKBJAjKuXv83pC5qDfnFpQAocG4ZPREbJ/bWJHIlFk0BUIldYU5SCVAAlEqKdnITCJgVgA/3/4T0696z8REO680w8BCyp3d+uPnW0Y1QD/tETC+V6oECr0+TmzP9kQAJkICyCcj4gcovhRaqD7y9zi+hREGirgEzSjmOW7gB0PVn8WuPbgHTijm2FwRAYUvxml5AZARQ8yOglvMtaP4vlhHXz+yN1pHilZzXiryOF7okO3eSqEiABPxDQPhiZUwm2WJtbvwzGtZtgJV7I9DhlxS+3AGwNK4x3hz7E/R6fnSXrQFeOKIA6AU8Dk11AvxbJNVboNkEAkYAfHR4PdKt7mxr5GVLdoQOOIocGUKdNve/MXVR3XpY9HpEyW7I98YMzU4IFk4CJKBRAmoTAAs3BLr6+DZeR1MhJUFPuJ03TRb7URe2Ad852Jrsy8tKNDqN5S577YwP8UrUUnu37J3cqOmPBKQRMMcB4Vml2bqw2lNyIKp0HAqdXi9JAIy2GoERtxBiNMgSn068I0AB0Dt+HJ26BCgApi5/LUU/lqxY4Yrc4sLPIiIikDevoAeq83l8/Hek/bGjLfkb1syw9juB3BnDnBZ0YHQNVMQJ0etXyvRE3g5T1AmBWZMACZCApwQoAEoj9/guMNXBFt0em4E8KVwO4ogvRSRpzFPRat30nmjzULwFOD4d9i4Vu8LQmiYQawLG55QHwXO/x8IKwDNrxmNo0LIUfT8cfAfpU7hgUJ7E6EUKAQqAUijRRqkEKAAqtTOBl1fACoBPT29G2A+v2Dp215oeT/ucRt7MaZx28fiYSihtPScWACv2Rd62owOv86yIBEiABFIiQAFQ2vx4eh+YXFBsW+FNoN0XKY+nACiNr8Ks1k7riVceUQBUWFuYjpYJRD8CJubxiIDZqoNBZ00YW7UH0CrpyB/hEpDPVh7Az8HDUU5/0an/Bz32IFOe+LUTfFKZAAXAVG4Aw3tFgAKgV/g42AsCAbMFOPr8vwhZ3NKGIsoahshPzyNfFucC4O3RBZEd90X4LtedivyN3/cCKYeSAAmQgAoJqE0ALNII6LLG/6CjHwITk62Wf/1717cPUwD0f69kiPjTtF549ZF4RdC9bFWQ5aNNMninCxIgAbcJPH0ATC7g9jBhwFsxQ/CO4Q9EWLPjvRFfASHpbH4EAXDAykPoYNiCqUHOz/i8mqkK8vTh779HDZB5EAVAmYHSnV8JUAD0K24Ge45A4AiAl/Yi5JvGttJM1iD0L/EHZnSs4PisDosZGGt/VtP1D88hd/ZsnCQkQAIkoC0CFACl9TvmCTAht9iWAqA0diq0+nHqB+j4+AdR5hF5WyNfdwfnAqqwPqZMAmogsO/SPVy5/xSNSuZAenOU42MYJBRS0JT0u3xxUivRiEQBsJNhEyYGfZWyt/YLgfJJxw5JCE0THxCgAOgDqHTpNwIUAP2GmoGSEQgYATD22mEELahrK89i1aFw9FJ80rg4+jV1sFQ/Yg/wVRMRjvDYtzBk7FwYDXpOFBIgARLQFgG1CYCpdQlIXDQwLkcyAXApUKp1yvOFKwBV+fv0w5TeePOJ+Bbg+EJ4BqAq+8mk1UdgxZ7LGPTTEVviH1ROi0HH2npUSEoCoHAG4MBVh9FUvxcLgyVcBsi/AzzqgZyDKADKSZO+/E2AAqC/iTNeIoGAEQDNt07BMK+aqLNFTYsRByOSf8sXb7RzHvD7EJF9/egZ+GdiN84OEiABEtAeAbUJgO0XAeU7+L9PjlaPd/sLyFeVAqD/u+HziHMmD8bHTx2c7zjiLmAQ7lHjQwIk4EsCBQdvsLnPjgdYGTwGBfU3PQopRQA0wIxdIR8huy7Sxd/pLl73KEMOcocABUB3aNFWaQQoACqtI9rJJ2AEQOu9C9DNflHUueGx72KpualjAfDURmDZGyL7hQ33oUf9otrpPislARIggUQCahIAX6gEvPc7YAxOnf4tfws4+UtC7Owlgd67AJ2Lt3IT8wHRUUn5GoKBEbdTJ39GlUzgpam/4tfHnezth1wBQtJL9kNDEiABzwgkCoAldJexPHgcMuseeeYIgJQtwILzQrrrGGVcjAaGQ85jcQWgx32QayAFQLlI0k9qEKAAmBrUGVMgEDACIKKuATNK2XVV+Mfe4QrAkxuA5W+K7L9tehDv1C7EmUECJEAC2iOweyHw6wDl1127D9BoOGAISr1chXMAd30OxD4FavQG0ko4N/bkr8Dy54SkjouB0p5tY0u9wrUXufakv7HD1M6u8LhBETCGZdAeEFZMAn4kMHb9cXy940J8xK+CpqKx4YBX0VMSAH87eh29lu4X+V8cNBH1DEnbj0UvUgD0qhdyDKYAKAdF+kgtAhQAU4s84waOAPj4DjC1iHQB8Pg64McuNvvzllzY8dIf6FLDs5vFOJVIgARIQPUEInYDXzVVdhmdlgMlkm58V3ayz2VntQL7vwPObQYK1wcqv+t61aBqigvcRKuM+xNfxXyGCvrzoiKj+l5Ahoz2F4kFLglWRgL+JXDroQnVxifdtnsxVPylvSfZpCQAxsRZIPy+R5ni4l3XKJwFHSLG4VXDdsehKAB60gJZx1AAlBUnnfmZAAVAPwNnOBuBwBEATZHApPzSBcDDPwKre9jsz1pewO5Wv+PN6vY+OF9IgARIQDMElLwVODgdMPAcEBSqmXaw0NQlsGz3ZZT6pS1eTCYAbm2/H/XK23/pmLrZMjoJBA6Bv47fRPfFe20F+VoAFALtvXgPM/48jbQhRoyL5s5aAAAgAElEQVR8uTT2zOyA9hQAFTupKAAqtjVMTAIBCoASINFEFgLHknkRTrCOvyI3IiICefMKeqBKH2Er1vhc0gXAL+sCNw6L7Fe0OoLXq1IAVOkMYNokQAJyEFCyAPjaN0DZ9nJUSR8kIInAQ1Mszk2ojhf150T27dL/gDX9W0nyQSMSIAH3CWw+eQvvfrsHwYjFB4Z16Bv0k/tOko1IXAE4qX05vFHN9fv9NSNeQjvDDsdxuQLQ635464ACoLcEOT41CVAATE362ooduAKgo5sZnx346/AMQAcfcle+fBQdquTT1oxgtSRAAiTwPIFvWgGXnGx58gepPJWBq/vsI732NVD2VX9kwBgkICJwcFRlvKg7K/rZi6b5ODhJfJEYsZEACchD4FDEAwxYeQhnbj3CRONCdDJu9trxzDSfYNa9GqhbLBu+6FwZ6UJc3+JNAdBr7D51QAHQp3jp3McEKAD6GDDdOyUQOFuAhRIdiHpOLwFxYLu6zTG0r6TiVZCc6CRAAiTgLYHL/wE/dgUe3XDtKVc54IaTA9Jdj3ZsIayqeHgTmB6/OD3poQDoKVGO85LAgVFVUVF3WuSlkulL7J/k4HZgL2NxOAloncD+y/fRft6/8RgMMONcaNJ53d6wMY98gOg4M0KNBuj10j56rx3REq8YEnKxe7gC0Jt2yDKWAqAsGOkklQhI+1solZJj2IAmQAHwufb+/MpxtH0xT0A3nMWRAAmQgEsCwoUVT+4BUws7N81UAOhzGNj3HbD+kwS7NFmBJ3dduk/RQPhQJdyyOyG32OzVr4Byr3nnm6NJwAMCe0dWQxX9KdHIWnFf4t9xFAA9wMkhJJAigdZztuPI1ch4m1b6Xfg8eLY8xDwQ7NaNaIE2hp2O43vgT55C6CWRAAVAzgU1E6AAqObuqTt3CoDP9W99uxNoXeEFdXeU2ZMACZCAHAQe35UmAAqxzm4C7p4FSrUBZpT0LrrwoSouBhiXXeyn/SKgfAfvfHM0CXhAYPfIaqiWTACcWGo1hrze2ANvHEICJJASgYKDN9heHm5cgu7GjfIA80CwWz+iOVobdlEAlKcDsnuhACg7Ujr0IwEKgH6EzVAiAgElAN5bPwJZ9iV9U3jDmhk1oj+H1DMAN756Ei3LJVt1wglDAiRAAlok8PgOMDWFW07LdQReXWhPJvnxCrkrALdOAuZosW2abMCTOw7GRwKOznRtvxAo31GLnWDNqUwgYnp95Ht4UJTFbzl7oMUH01I5M4YngcAiEHHvCepOSTrvb4RxCbqlogB4aGY7VIj8mwKgQqcZBUCFNoZpSSJAAVASJhr5gEBACYA3D/+FnKuTDom/Z02HStELJAuAf3Y8jaalc/oAM12SAAmQgMoIpCQA6o3Ax/uAzAVdC4AlXwYaDAGO/gRY4gCdHhDODhQu9Jgv3Mae7AxBYZWGsAV5TCaxbwqAKptAgZPu0wXNEHbtP7uCTMPuITTIEDiFshISSGUC87acxZTfkrbbzw2ahZcN9r97HqXpwQrAJ993Rpoz6ykAegTc94MoAPqeMSP4jgAFQN+xpeeUCQSUAHj35HZkXd7KVvFjawjKRH8jWQDc0ukMGpTIwTlDAiRAAiRgjgXCs4k5NJ8ARF1LEO/yVHLMaE7lhO3Aic+bK4HizRzbzq8HXD8kfi3xQ9rUosDj20mvDTwPpM3KvpCA/wl8+zJwcZtdXCt00PU7DmTg0SH+bwojBiKBBVvPYcKvJ+NL+zzof2hl2C1fmR4IgPEXYh3/2XEO/U8B6XPJlx89uU2AAqDbyDhAQQQoACqoGRpLJaAEwMjze5FxcdKZPLFWA4pFL5EsAO7ofA61iyb7wKuxCcFySYAESMBGYNcXwO9DAasFqDcQaDTcNZzzW4AVXYDoKKBoE0AQAPV6x+MWNASu7XcsAAofutZ8AMQ+SYhbb4Dr2LQgAV8Q+K4NcOEfx56LNAa6rPZFVPokAc0R+HPNtyh+YDwK6G/JX7snAqDwb9mJdY5zqdMXaDJa/jzpUTIBCoCSUdFQgQQoACqwKRpJKaAEQMvNk9B/UV3UusKmpTg/qbXoZ1arFbrk28sA7OxyHjWLcIWJRuY+yyQBEpBCIPJKwpl8mQtIsU6wES4QeXoPyFLEufgn2C1sDFzd61gAFH4q3AZsNQMh6aXHpiUJyE1gcVtAELadPZ4IC3LnSH8koGICT2PMWL7rPNptboRM1ijfVOLJ7+mKzsAJJ1uAhSw98emb6jTplQKgJtseMEVTAAyYVqqukIASAHHvAjD7RVETGgQvw5ahL4l+9svha3h5dSm7ZlEAVN38ZcIkQAJqJrCoKXAl2RYvfqBSc0cDM/fFrwDnky4msCuSczYw+86q/EbgrUW78PTcTqwO8dGKumwlgI882E68/C3g5C8U//02E9wLRAHQPV60VhYBCoDK6kcgZ3MsWXFGAMWFn0VERCBvXkEPVPETdR2YUVJUQJOg7/DXsFdEPys27FecCepkV+h/Xc+jemGuAFTxDGDqJEACaiLwdQvg8k5xxhRT1NRBbeS6pD1wbhNFAG10m1X6mcC1B09Ra9LfqKw7hZ9CxsgTPX8t4PK/Sb7e3QgUqOW+77UfAgeX8nfffXJ+GUEB0C+YGcRHBCgA+ggs3doRCGwB8Mk9YEohUdEtjQuxcXhH0c8KDt6Ai6Fv2sHZ/fYFVCuUhdOGBEiABEjAHwS+eQm4tIMCoD9YM4bnBCgAes6OI0nABYGjVyPx8pztqKQ7LcsKwD3Wkqg6aCNwYStw6V+gWNOE/zx5HOwsErnhF1aeUJVtDAVA2VDSUSoQoACYCtAZMp5AYG0Bjn4ETMwjau0KXUu8Pmq5JAFwzzsXULUgBUD+bpAACZCAXwic+g1Y9npSqIJ1gXdS2G7ll6QYhASSEaAAyClBAj4jcOxaJFrN3o6KujNYEzLK4zglTN9CuJlbbwzCyXGtPPZjN3B0Rue+KADKx9kDTxQAPYDGIYohQAFQMa3QXCKBJQCa44BwB1t4k/0D7WwF4N53LqAKBUDN/RKwYBIggVQiIFwusuwN4MwfQJqsQKflQL5qqZQMw5KAEwKrugFHV1EE4AQhAR8QOHE9Ci1nbUMF3Vn8HDLSowg1TXNwHQnv/0OD9DgZ3tIjP44GnZvbDkXu/O3YHwVA2Th74ogCoCfUOEYpBCgAKqUT2ssjsARAoX+OvqmTIABuMVdAtl7rUTZPCt/0aW9+sGISIAES8C0BqxUQbhoOy8Tbfn1Lmt49JXDnLDC3MgVAT/lxHAk4IWC1WjFq3TEs3nkJ5XTnsT5kuEesCpp+sI0b0Kw4PmpUzCM/jgatWb0M7Q73ogAoG1H5HFEAlI8lPfmfAAVA/zNnxAQCmhUAt4d8gry6O7Z50DOmD74cPxo6HX8d+ctBAiRAAiRAAiTwHAFuA+R0IAHZCaw5cAV9VxyK91tGdwEbQoZ5FCNRACyaIx1W9qyJzGmDPfLjaNDBy/dxa+GraGbYZ/fyrf43kSN9qGyx6Mg9AhQA3eNFa2URoOKgrH5oKRtNCoAvD5mDX5J9y/iZcTCmDB+ipd6zVhIgARIgARIgASkEKABKoUQbEnCLgHAkT+LjqQB4Pyw/rnfZgVizBcVzpkdYsMGtHFwZC6sUO4+ei+919qsTp9XcjQHNS7hywdd9RIACoI/A0q1fCFAA9AtmBnFAQJMC4JYR9dDAkPCNY+IzIeMoDO3bj5OEBEiABEiABEiABMQEKAByRpCA7ASeFwA93gLcZS1QpKHsuT3v8MSeTSi1ob1dDGHl4cVJMl444tMqAs85BcDA66mWKqIAqKVuK6tWTQqAjs4JHJ1+FEb3pwCorOnJbEiABEiABEhAAQQoACqgCUwh0Ag8LwC+qDuLtW5eAhJtDULImKTjfHzF5+z+f1B0XRsKgL4C7KFfCoAeguMwRRCgAKiINmgyCe0JgOZYIDybXbOHpxuDcQP6aHISsGgSIAESIAESIIEUCFAA5PQgAdkJPC8AVtKdxuqQ0ZJjRFnTYEGuURjwgZMLOiR7cm144eA/KLSWAqBrUv61oADoX96MJi8BCoDy8qQ36QQ0IQDeHXALWdOFJFCJfgRMzGNHaGjaMZgwkAKg9KlDSxIgARIgARLQCAEKgBppNMv0J4HnBcAqupNYFTJWUvhG0dNw25oJc96tjwYlckga441RxOEtyLe6rZ0LbgH2hqr3YykAes+QHlKPAAXA1GOv9ciaEAD3vHMBVQtmSei1KRKYlN+u75+lGYspn32q9fnA+kmABEiABEiABJIRsITngN4c7ZjL6EjyIgES8IDA8wJgNd0J/BgSLslLQdP3AHR+O3/vxtEtyLWKAqCk5vjRiAKgH2EzlOwEKADKjpQOJRLQhAC46fXTaFwqZwKSJ/eAKYXs8PQPC8f0QZ9IxEYzEiABEiABEiABrRC4tX4Mcuyb4bjcUQ8AHd/Ka2UusE75CDwvANbUH8Oy4PEunU+JfR3zzAlinL8u4Lh9bAuyr6QA6LI5fjagAOhn4AwnKwG+a5AVJ525QUATAuDatsfxSsVn234f3wWmFrZD1CdsPP436CM30NGUBEiABEiABEhACwTubRiDLHucCIA9twG5y2sBA2skAVkJPC8AdjX8jrFB37n0L2y7TXz8JQDeP74FmX+kAOiyOX42oADoZ+AMJysBCoCy4qQzNwhoQgBc0vwQutQsmIDl0S1gWjE7RB+FjMfcIRQA3Zg7NCUBEiABEiABTRCI2jgWGf6b7rjW3C8CPf/RBAcWSQJyEnheANwd0hs5dA9SdH/Xmh6Vo+f7XQB8eHIz0i9/xS63IqYlODfJ/nIQORnRl3MCFAA5O9RMgAKgmrun7tw1IQB+3eQg3qvzbNtv1HVgRkm7rvUKnogvh/ZWdzeZPQmQAAmQAAmQgOwETH+EI/Tfac798hxA2ZnTYeATSBQA8+A2doS6Pof7jjUDqkR/6XcB8MmpzUizzF4A7BnTB/MnjAn8Rim0QgqACm0M05JEgAKgJEw0koHAsWQ+jACKCz+LiIhA3ryCHqjyx8FNfYsa7UP3ekXjC7M+iIDuf2XtinwvaAq+HtZT5cUzfRIgARIgARIgAdkJbJ4A/DOZAqDsYOlQywQaDVmItDBhRXA40uicXLLzHKAr1myoEz3b7wJgzJnNCP7eXgDcZK6IxuFbtNzCVK2dAmCq4mdwLwlQAPQSIIdLJqBJAbBN2DdYN6h9PKSHN88j/RcV7YC9ZZyO74d3lwyShiRAAiRAAiRAAhoh8Pd4YOsUCoAaaTfL9AOB3Qth2TAQep1VcrAuMYOxzZJ03qa/zgA0n90Mw1J7AXCLuQIahG+VnD8N5SVAAVBenvTmXwIUAP3Lm9GSCGhiC/CsuHb4dNy3CQLgjTNI/2UVuzkwOv+3GP1eO84NEiABEiABEiABEhAT+HscsHUqBUDOCxKQi4CDHTspuf4urinGxnWFGQabmb8EQJz7G1hi/xlhq7kc6oVvl4sI/bhJgAKgm8BorigCFAAV1Q5NJaMJAfCGNTOyjjyPIIMeUVdOIMOiGnZNvtR5BwoUtd8arKnZwGJJgARIgARIgATsCWwKB7bxDEBODRKQjYAbAuATawhKR39jF9pvAuDlXcDXze3i7zSXRs3wnbIhoSP3CFAAdI8XrZVFgAKgsvqhpWw0IQDetGaCfsBpZE8fgvuXjiLzN7Xte9z3GJAxAM5A1NLsZa0kQAIkQAIk4A8Cm8YC25zcAgxgTt29+LhxMX9kwhgkEBgE3BAAr1uzoGb03NQTAC0WYGxmu/i7LSVQbezuwOiHCqugAKjCpjFlGwEKgJwMqUVAEwLgbWsGmPudQa6Mobh74SCyflffnveAM0C6HKnVB8YlARIgARIgARJQKgEXAmBB0w84NqY50oYId6vxIQEScErg3GbcXDMUOR8dlwypa8wgbLVUSD0BEMBHQ4djbvAcUQ6HrMVQYcxeyXXQUF4CFADl5Ulv/iVAAdC/vBktiYAmBMC71vR42uc08mZOg6h/5iHD5iH2c2DQRSDM/ts9ThYSIAESIAESIAGNE7h7DphTySmENebaOFSyH0Z2agy9nm/rNT5bWL4zAnExsE4rCp0pUjKjzeYK6B47QHT2nzC4Yv5MWNPbwY4eyZ7dM6w35GtsDekrGnTUWhhlxxxwzxGtZSNAAVA2lHSUCgT4TiEVoDNkPAFNCID3rOmwqOYmfNaiJKyjM0EHBzeODbkKhKTjtCABEiABEiABEiABewIutiwKFwLcbLsMHarkIz0SIAFHBE5tBJa9IZlNrNWAYtFLHNqv/6gOyuXNKNmXt4bjl27EsLPi3E9YC6DUmMPeuuZ4DwlQAPQQHIcpggAFQEW0QZNJaEIAvG9Nh4rRCxB/WLCzN/DDbwPGYE1OAhZNAiRAAiRAAiTggoCEM8uErcB+u5iADSMBtRE4sgr4qZvkrB9bQzCg2EZsPHrDboy/f8++WPs3Pjgovgn4jDUvio05JrkeGspLgAKgvDzpzb8EKAD6lzejJRHQhAAYZU2D8tGLcHxsc6SZkNVx/0c9AHT8VeQvBwmQAAmQAAmQgAMCFAA5LUjAKwKWwyuhX91dso8/y05F09feR8HBG1JdAJy3dgt6H2wryuOCNRcKjTkluR4aykuAAqC8POnNvwSoOviXN6NpVADc0+oWsm/q47j/o6WfR8IJRAIkQAIkQAIkoDECEgTAQqaluDCptcbAsFwSkEZg7/r5qLLvM0nGwpb6gh+vR/4cmRUhAH6+9h98eLCNKPd71vTIMuaKpHpoJD8BCoDyM6VH/xGgAOg/1owkJqCJFYDrzTUwPPY9HAp933n/KQDyd4MESIAESIAESMAZAQkCYFHTYpydJF4lRKAkQAIAzLEYOHIopgYtSBHHOnNN9In9EBbocWJsC4QFGxQhAM79eRs+OvCyfe6fXQDSZGGLU4EABcBUgM6QshGgACgbSjpyk4AmBECBSXjsWxgR9L1jPC/PBKq85yY6mpMACZAACZAACWiGgAQBsITpW5yaJD4nTDN8WCgJOCNwbC2sP38IXcwjl4zej+mLPyxV4+0Sz/lTwhbguet24KP9L9nn33gUULefy7poID8BCoDyM6VH/xGgAOg/1owkJqAZAXBy7BsYFLTccf+HXgOC03JukAAJkAAJkAAJkIBjAhIEwDKmr3B4wqsw6PnWntOIBOIJWK3AtGLA49uSgLwT8xm2WF4UCYBDVh/Bst2XbeMr5M2Inz+qI8mfXEZz1u3Ax44EwAx5gH7H5QpDP24QoADoBiyaKo4A3yUoriWaSUgzAuDE2E4YErTMyZt6nv+nmRnPQkmABEiABEjAEwISBMDypgX4sGUV9KxfxJMIHEMCgUfg6QNgcgHJdXWKGYadljIiAfDqg6doO3cH7jyKRppgA77vXh0V82eW7FMOw9nrd+KTfS3sXFkz5oOu71E5QtCHmwQoALoJjOaKIkABUFHt0FQygScAziwLREbYNXF87JsYFvSD3c/vWDMg2xh7e03NAhZLAiRAAiRAAiSQMgEJAmAl05e4hwy2rYtESgKaJ/D0PjC5oGQMr0aPwj5rCZEAKPxBEP8OXH6AEjnTI3/WNJL9yWU4a/1/+HRfM3sBMHNB6D49JFcY+nGDAAVAN2DRVHEEKAAqriWaSSjwBMDzW4DF9gdwn7DkQym9E6GPF4BoZsKzUBIgARIgARLwiIAEAbCq6XPcRmbfCYDCaqqI/4CsRYGsXGXoUR85yL8EntwDphSSHLN19DgcsRa2EwAlO/CR4f9+2Y0+e5vaebdkKQr9J/t8FJVuUyJAAZDzQ80EKACquXvqzj3wBEChHxLepIvaRgFQ3bOY2ZMACZAACZCArwlIeG9R0zQH15HVNwLg47vAgvoJuxyMocCbK4DCDXxdNf2TgHcE3BQAW0RPwklrfvUIgFmLQ//xHu8YcbRHBCgAeoSNgxRCgAKgQhqhwTQCUwD8fRiwc670dlIAlM6KliRAAiRAAiSgRQISBMA60f/DFWsO3wiAf48Htk5JIp+5ICBl66FwCUPM4wTR0GDUYudYcyoSMD+6A8M06atVm0VPxmlrPsUJgLN+2Y1PHawANGcuBMOnB1ORsHZDUwDUbu8DoXIKgIHQRXXWEJgC4PXDwPy60jtCAVA6K1qSAAmQAAmQgBYJSBAAG0RPx0Vrbt8IgNNLAg+vi8m7ev8SawJWvQuc+hXIWixh1SC3Dmtx9qZKzbeiTJj80zZMv/Sa5PhNoqfgrFX4eALf/B5JzkRsOPuX//DJXvszAC3p80Dfn7cAe4jVq2EUAL3Cx8GpTIACYCo3QMPhA1MAfHgTmF5celtdvYGW7omWJEACJEACJEACgUhAggDYOHoqzlnz+Ea48EQAPPoTsOq9pG5U6gq0mROI3WFNCiTQcf5OXLhwHntCe0vOrlH0NJy3vqA4AXDmhv3ou6ehXR2WsGzQDzonuT4aykeAAqB8LOnJ/wQoAPqfOSMmEAhMAdDN80ZAAZC/DyRAAiRAAiRAAikR+GM48G/K4lnz6Ek4Zc3vIwGwFPDwmjhDV+9fphQBntxxbwxnAQnIQCAmzoLiwzciB+5jd+iHkj0mrqIVBlyc1EryOF8bTvntJNr92x7F9FftQ7n6PfR1chr1TwFQo40PkLIpAAZII1VQxrFkOQqHwcQvlYuIiEDevAlL7lX/CLfkTS4gqYxV5np4LXy9JFsakQAJkAAJkAAJaJRAXDSwbTrwz2SnAFpFT8Axa0HfCBczSgNRycSHV79KONuvZCtA5+DjxORCwNN74nwpVmh0Avu3bFOsGSVH/IY6+iNYGjxRcvB60TNx2Zoz3l5pAuDiLUdwNLS7fS1DrgAh6SXXSEN5CFAAlIcjvaQOAQqAqcNdi1G1IQBGPwIm5pHU31eix2LtxE8l2dKIBEiABEiABEhA4wRS2ArcOnocjlgL+0a4cCQAJraiynvAyzPtGzOpAGB6QAFQ41M2Ncp/GmPGJ6PHYWHwDLfC14mehSvW7IoUAOdtOYeLoW/a19M0HKj9iVt10th7AhQAvWdID6lHgAJg6rHXeuTA3AIsHHo9PuHbQ1ePcGPf9onvujLj6yRAAiRAAiRAAiQApCAAdovpj02Wyj4SAMsAUVecd2DEHcAQJH59Yn4gOlL0M+uoB9A5Wi3I3qZI4FF0HP44dgMvZApDjcJZScsFgScxcYgbnxcZdE/dYlXLNBvXkA15MoVhx+BGbo31pfHU30/i881OBMByHYBXF/kyPH07IEABkNNCzQQoAKq5e+rOPTAFQHMsEJ5NUmdqmuZg56SukmxpRAIkQAIkQAIkoHECLi4DaRMdjnUTfbAaaIYLAXDIVSAkXTIBMB8QHSX6Wd9S/2Dm6y9qvInulR9rtqDZzK24cOdx/MCxbcuga82C7jnRmPXj6Diknei+UFrDNAc3kBXfd6+O2kWlvZf3B9pEAXBa0Jd4zbBVHLLCm0C7L/yRBmM8R4ACIKeDmglQAFRz99Sde2AKgBYLMDazpM5UNX2OPZM6S7KlEQmQAAmQAAmQgMYJuBAAN5iroVX4n/JDmlkWiIxw7tfROWQT8gAxj0RjCpp+wC8f10HZPBnlzzFAPf607wr6rzwkqk5J59MpEftDUyzST3JfwFtQdSPKliiOWgoS/wS+vx29gV5L92GScQHeMG4RI6/YBWg7V4ltCOicKAAGdHsDvjgKgAHfYsUWGJgCoIDbxRv0xI5UMn2J/ZM6KbZBTIwESIAESIAESEBBBCS8v4gefg8hRoO8SbsSAAdHAKEZxDHH5wZin9gJgB/XyY3+lQ1A1mJAcBp58wxAb/1/PISf9ou3X1MATLnRUaZYZPBAAMSAM0C6HIqbRWaLFU1n/IPuD/6HN42bxfk5O4NTcVUEVkIUAAOrn1qrhgKg1jqunHopAMYsxP4JHZXTEWZCAiRAAiRAAiSgXAISBMDWWTfg5w9rQ6937y3+yRtRGPTTEdx/HIN+TYvjlYrPXWg2sxwQedk5l8GXgdBkq/rG5QLixGewvREzHMuDxyX56fE3kKcybj00offS/TgQ8QDNy+TE9A4vIixYZhFTuV1NMbN+Px7E6v3iG5gpAKaALGIPIm9dQsb13dzv+MBzQFr3Vw66H8j9EcKqxnXjXsdbxk3iwdXeB16a6r5DjvCKAAVAr/BxcCoTcO/dQSony/ABRUDzAuCUCr/js3Y1AqqpLIYESIAESIAESMBHBCQIgLVNszD27ZZoXErahWSJmXZasAs7z9+N/2OQQYe9w5siY9iziz1cCYCDLgFhmcRFh2cHzDEpgzCGAsNvYsafpzF70xmb7exOFdGmwgs+gqgutxQA3ejXnkXAhv5uDEhm+tkFIE0Wz8f7eOSS4a+ii/EvcZQavYEWE30cme6TE6AAyDmhZgIUANXcPXXnrnkB8PPqf+PDlpXV3UVmTwIkQAIkQAIk4B8CEgTAj2I+RvaanTCqdRm3cio4eIPIfnirUuhet3DCz/5XDniQwgpAR8KJhFzjfY+ORPLYwo+5yi0Bfb8VB7H6AFcASprMUuecM2eOhGxJgf1jtHj4a+hqTHbGZ61PgGbh/kmAUWwEKAByMqiZAAVANXdP3blrXgCcWWsX+jYrpe4uMnsSIAESIAESIAH/EJAocMTqghH0wVYgh/T3GMlFuIHNS+DDhkUlCYDXehyBOSwb8mV57kw/ibk+HHwH5Ub/YcePAmACkr4rDmINBUBpv18S55xTZ47OspQW2S9WS4e3R+fkW4Dr9gcaj/RLfAZJIkABkLNBzQQoAKq5e+rOXfMC4Izae+LP2eFDAiRAAiRAAiRAAi4JuCtwDLsBBIW5dCsYJBcA+zctjo8bF3smAJYHHlxy6qeK6QvcQUZ82rgY+ia+r5GY64JG+zHh15MUAJ3QpQAoafomGEmcc049DrkKhKRzI6B/TUPZmCEAACAASURBVH8f0RjNDXvFQesPBhoO8W8ijAYKgJwEaiZAAVDN3VN37poWALeay2Fvva8pAKp7DjN7EiABEiABEvAfAXcFjpdnAsItoRKe5AJgnybF0KfJsy8p/5eyAFjV9DluI3N8lCOjmyF9aJBkMaag6Qe77HQ64MLEVhKyDnyTPssPYO3Ba6JCuTrSSd/d/f1I7sYNwTw1Zt65kSVRRH9dHLr5RKBm79RIR9MxKQBquv2qL54CoOpbqNoCKABSAFTt5GXiJEACJEACJOB3Au4KHHUHAI1HSEozuQD4SeNiSV9SzqoA3L/o1E9101zcRMLlCSt71UTFfJlgDE8QBF09jgRAg16HcxNecjVUE69TAHSjze7+fiR3PfwWYAxxI6B/Te9OKI2sMeLzINFmDlCpq38TYTSuAOQcUDUBCoCqbp+qk9e8ALiv3tdJW2VU3UomTwIkQAIkQAIk4HMC7goc5ToCry4UpXXg8n189+9F5Mkcho8bFUNokCH+9eQC4MeNiqJ/sxKA1QqMSXbDb7JCa5lm4wlC8LJhF25Ys2CPpQQOhb4vCcd/lpLoHjMAD5F0fqBRr8NZDQqAZ24+xNhfjiMmzoJBLUuiUv7M+HT5AfzMFYCS5pLXW4BH3AEMz26+lhbRr1YxU0sh+LF4NShazwYqv+3XPBgMFAA5CVRNgAKgqtun6uQpAFIAVPUEZvIkQAIkQAIk4FcC7gqAQnKjI20pPngSg2rjNyHGbIn/2Tu1CmJ0m4Tbgp8XAN83rEffsA0Iy5IPMBiB64dSLLNh9HQsCZ6IvLo7HuEYFfs2vjM3t40NMuhwZrz2VgC2nLUNJ65HxXPIkT4Eu4Y0Rp8VB7HuELcApzixIq8CG/oBp3/zaP7ZBo28D+j13vnw4ejYyUUR9PS2OMLL/wOqvOvDqHTtiAC3AHNeqJkABUA1d0/duVMApACo7hnM7EmABEiABEjAnwS8FACn/X4KczefFWWceJ5cogCYB7exI/RTt6qaFdcenxpXuzXmeeODliJ4JSbc9qNggx6nx7f02J8aB0Y+jUWFMeLbkFf1qonFOy9RAHTV0J+6A0dWurJy/fqoB4BwAKVCn7iJBWCMfpBMAJR+zqdCy1JlWhQAVdk2Jv2MgHL/lmOLAp2A5gXA8y2W4J3ahQK9z6yPBEiABEiABEhADgJeCoAfLzuA9U5WkyUKgCONi/Ge0b2VVNvMZVHXcNTjCiMs2dEoZjpiYYz3EWLU49Q4bQmA9x/HoGL4nyKGS7tVx4q9EXY9OzWuBUwxFmQIM0KnYMHK4wnh7kBPfi8cxXhutay7KfjD3jw+Dwyxj8ShWs0AqnbzR3jGeI4ABUBOBzUToACo5u6pO3dNC4DbLeVQcdgWpA1JeLPLhwRIgARIgARIgARSJOCB0HHp42sokDVtvNsPf9iPDYcTbxG1orF+P6pmjcWxrE2w/mSCsDDG+A3eNoqFKFdd+ddcGrUMx12Zpfj6ZUt2dIsdiDPWvAgN0uNkuLYEQGF79otj7QXA5Xsu45dnPQtBDAYaV6C7cWM8ywPp6qF8z69gSJ/DK/ZqGWy1WrH51C2YYi1oVjonjIZn23U9+L1wWLPCBUBLeA7ozdHi1F+aBlTroZYWBkyeFAADppWaLIQCoCbbroiiNS0APshdB5l6blBEI5gECZAACZAACZCACgh4IHQMKLMV0zpUSBAAv9+PDUcSBMCPDGswIChh2+Q5S240jZkKC/QeCYBykVtnrolPYj9GWJABJ8JbyOVWFX4cCYBLulXDij0RNgHwPcNGjAxaIqrnetE3kLvzfFXU6G2So34+iu92Xop306RUDix6uyqElZOZp2b31nXCeIULgHETC8IYfV9cKwVAeXrvphcKgG4Co7miCFAAVFQ7NJWMpgVAFG4IdF2rqYazWBIgARIgARIgAS8IeCAAFjT9gMRz/np/vw+/HrkRn8DF0DdFiXwQ8yk2WqpjtPFbvGMUn0XnRcZuDxXyTRNswPGx2hIAI5/EosJYMXdBAFy+J8K2ajN5z2xwFS5cuT0JHAyIM1tQdFjCysfEZ/OABthx9g46/1ZejhCKFwCv7FyBvL8nu1275VSgurQbt+WBRC8CAQqAnAdqJkABUM3dU3fuGhcAGwBdf1Z3B5k9CZAACZAACZCA/wh4KQD2WrIPvx1zLAB+Gdcak+I6YZTxO7xr/N1/NSWLJAiAaYMNOKYxAdDZCsDluyNsqza1LACaYs0oOUJ8NuXX71TBqn1XMO9MI+/na/VeQMvJ3vvxoYcz1+6i2ILCogjWFpOhq9HLh1Hp2hEBCoCcF2omQAFQzd1Td+4UACkAqnsGM3sSIAESIAES8CcBDwTAnebSSFukBnbk6ITJW2/bsk0uJn0R1xqTFSIApgsx4uiY5v4km6qxhNVti7ZfwKSNJ0V5xK8AlCAAbmh/Eq3K507VGnwd3JEAuLBrFUz57ST+jGrjXXjhIo3K7wL6Z2cKeufNZ6PP3nqEm3OaobbhmC3Gg3xNkKnbTz6LSceOCVAA5MxQMwEKgGrunrpzD1gB8Ni6/6HM/lEpd6cwVwCqe/oyexIgARIgARLwMwEPBMDEDK9Zs6B29GxYkSByJBcA58W1wZS4NxSxAjB9iBFHNCQA9ltxEKsPXLWbTIvfE7YAX3a6bTtxgLBqcttnDZEvSxo/T0j/hXsSE4fSI8UrU9c0uo/gXbNQxnLau0QGXQLCMnnnww+jz99+hKuzm9vfuD3sBhAU5ocMGCKRAAVAzgU1E6AAqObuqTv3gBUAERcDjHNxIDEFQHXPXmZPAiRAAiRAAv4m4IUAKKTaNWYQtloSLgRxJgCONC7Ge0bxVkt/limIWelDjTgyWhsrAGPNFhRLdrZdIm93BMDXKue1Xfbiz375K9bj6DiUGZUkAGbCQ+wP6w291ex9CoMvA6EZvffjYw8X7zzG5VnNUc9wRBTJ2nYedBXf8nF0un+eAAVAzgc1E6AAqObuyZt7ZwB1AVQGUA5AMIB3AXwrbxibt8AVAIUSXb1JpwDoo2lFtyRAAiRAAiQQoARcvbdwUfbw2Hex1NzUoQD4eVwbTI17A0oQADOGBeHQqGYB2kRxWU9jzCg10rHg+p2wAnD3ZWw86vjcxkRPgmhav3h2CPaB+jw0xaLc6KRLUj4xrEa/oFXylDs4AgjNII8vH3qJuPcEF2Y2sxMALS2nQs+LQHxI3t41BUC/4mYwmQlQAJQZqIrdXQRQAMAdAI+f/T8FQE8b6upNOgVAT8lyHAmQAAmQAAlok4Cr9xYuqAyMfR8rzQ0cCoBz49piWtzrGGFcgm5G8W2r/oQtiFmZ0gTh4EhtCICOtrYm8hYEvWX/XXZ6ccvzAmCDEtnx7buBKwBGPo1FhTFJAuBw4xJ0l2ueqkQAvHL/Cc7OaIEGhkOiX8nYFlMRVIM3Afvz7ykKgP6kzVhyE6AAKDdR9fprAuAMgEsABgOYyBWAXjTT1Zv0QvWBt9d5EYBDSYAESIAESIAENEXA1XsLCTBaRU/AMWtBuy3AiQKgrMKKhHySm9QyzcY1ZMPZ8S1hNCj7Ugap5d2MMuHE9SiUeSEj9Drg0JUHKJErA/JkCkPyra3P+3RHAGxUMge+fqeq1JQUZ2e1WnHl/lOkDTEiS1phE5L4iXwSiwpjkwTA+UEz0NywV546hlwBQtLL48uHXq49eIpz05vYnQF4qWY4CjT/xIeR6To5AQqAnBNqJkABUM3d813uFAC9ZevqTToFQG8JczwJkAAJkAAJaIuAq/cWEmg8sKZF1egvcCa0q8h6TtwrmB7XEbOC5qKt4V8Jnnxj8m1cM4yOewfC+Xf1irs4T9k3Kcjq9dSNh+g4fyeEFWzPP8JNx8t61ECh7GlR9rmz7Z63EQTAH/67hN+P3Yz/cfJzGxNthVWTTUrlwKK31SsA9l1xEGsOXEVYkAGzO1VE09I5RbzuP45BxfA/43+mgwUXQoWTi2R6hlwFQtLJ5Mx3bm5EmpBrppiLEG1hho/Qo9943wWmZzsCFAA5KdRMgAJg6ncvBwBhzb7wn/Avt/Bf1mdpfQfgHTdSzA9A+AqoFf7P3nmAR1GtYfjbTdnQew8Seu9FUEAQC17svQKKIKKAio1elCKoiGIXC4LSRKWqiBRFiqCA9C6h95aQZDe79znJJmQ2uzszu7O7M7PfPA/3SnLOf/7z/uck5M2ZGUD8dzqAPQBmAfgAQKrCWBSACkH5bCb3j3QKwGAJsz8JkAAJkAAJRBeBKTcDyWuCnrM4BbjQNkgSJ0cA+pJMQQ+qIoAQWi2TSmB272tU9NJn0x5f/oWlO054Te7qqiXxWbcWkmfb5W345eMt8c3ag/hlm7wAFMLs064t9AlBJqsth8/j1vf+yG1VunA81g/JflZlznX6Ujqav/5r1l9bWnZgtm2UdnMddASIL6RdvBBFOnExDWXfyi8AB9ufwOjRE0M0KsN6I0AByHVhZAIUgJGvnstPCmoEoJB+0wH4eo3VTgD/A7BPwZQpABVA8tuEAjBYguxPAiRAAiRAAiSQl8CGr4D5wd/qd0f6KPxoGyZh+67jTrztuN/nKbNwFkIIwNbVSmJGrzbhHDYkYyW9utBvXPGyk7zPtsvbWI0AvLl+OXz8mDEF4NhF2/HxSumPJwfGiR9rrlwnL6aj5ehsAXiDdQM+i39Lu3oNOgrEF9QuXoginbqUjtJvinMj0osCMETA/YSlAAw/c46oHQEKQO1YBhoprwBMBrAdQM6Tj5UKwMYAxP0a4rvXJffz+5YBKADgQQA93cntcJ8wFG38XRSAgVYzpx8FYLAE2Z8ESIAESIAESCAvgX/nAN/1CJrJXekj8b1tuCTOJMddeNdxN/YmPBZ0/GADCAHYtkZpTHvy6mBDRby/nADcOOxGNBmVfWur5/WF+wTgEgUnADvXL4+PHmse8fkGksDYxdvx8Qr/AlCcfms1emlW+A7Wf/Bl/IRAhvLeZ/AxIE78yKTv60xKBkpOyH9b/CB7D4wZ/ba+kzdZdhSAJitolE2HAjDyBR8J4C/3H3HGPwnAfndaSgWgkH3itW4OAO0BrPaY1ksAxrs/Jv7FJ3dungIw2HWx6l1gyVDfUXgLcLCE2Z8ESIAESIAEoouARgKwW8Yr+Cr+DQk7IQA/c3TBvwlPRpypEIBmeautnAD8e+iNaOZ+tp0n+FKF4nE6JSPrw0WRgs0JOb/Pl7YUvP7XsDw+eMRYAlC8GGXd/jP4bccJrNh1UjKpvCcAHZlOjF28A1P+2I+rLdsx0/aatmt08HEgLkHbmCGIdi41A8XH5xeAsx3tcd/r80MwIkP6IkAByLVhZAIUgPqrnloBKJ4ZuM49jY8B9PYyJfEatS0A6gI4C0A8QEL6NGJpJwrAYNdF6hlgfFU/ArA90I3frIPFzP4kQAIkQAIkEDUENBKA3nhNctyN6Y5OWJfwTMRxmuGlFjkQ5QTg+iE3oIX72Xa+wBfFJSyyDUKi5ZTXJoJXl0YV8P7DzSJeO6UJbD50Dvd+uBoZmU6vXfIKwHeX7sbbS3ahLM6GZn0aRACKF8kUe6O0d8QjzitFz3YaEKAA1AAiQ0SMAAVgxND7HFitABSvfcp5knNrAGt9RM6ReuLT4hZj7/cbZHemANRiXfi7DbgqBaAWiBmDBEiABEiABKKGwObZwNzQnNB7x3E3fsy8FstsAyKOUwgtI9/SmhegnABcN7hT7q2tvsD3i5mLF+Lm+P7BIe0b3NqoAiYbSADe9cEq/HPwnM855RWAOQxHxX6BrrH+fnwJcOkOOQHE2gLsHL5uafZMJIwuSQEYPuQ+R6IA1EERmELABCgAA0YXso5qBeBKAO0ApAAo7r4N2Fty4knK4jmB4hK3AEsf/iLtQQGoRXn9CcDObwCtvR3W1GJgxiABEiABEiABEjAdgRALwCWZLfK9HTgSDI14os0XJzkBuHZQJ1w9JvvZdr6u5fHPI8ma/SZgb5fgdXvjinj3oaaRKFdAY8px8SYAp8aNRfuYfwMaz2+nISeB2Hjt42oc0eVywTJS/Kjn5eIJQI1p+w9HARhW3BxMYwIUgBoD1SCcWgEoHpohzoNvAtDEz/glAJxxf342gPv9tKUA1KCQ8CcADfLGMS0wMAYJkAAJkAAJkIAGBEIoACfa78GfzvqYbZN7TLQG85AJIYTWHU0qYtKDxhFavqYkJ7pWD7webcb+5peIEgF4Z5OKeMdAvOS4hFUADj0FxMSFfmFrMYKvny0oALWgqzgGBaBiVGyoQwIUgPorihoBKJ5Ye9k9hYUAbpWZjnj7byEAawCIE4F5L3FPSVv3BxoCEA8SWQVgj/tjPwAQf5ReiTINy7tffILk5GQkJso1Vzqsjtp9/zSw6Zt8CW0ofz+a9/5UR4kyFRIgARIgARIgAd0TCKEAfNt+Lza7quHL+Jx3xkWORv+MPihZvxOGP3JD5JLQaGQ50bXq1etx7Tj/AnBF/HOoYj3hM6NqadPQuew5fPDULUAhH8+I02g+SsMcPX8Z245cQMNKxVC2aP4XbMhxCa8APA3ExCqdWmTb+RKAQw00h8gS1GR0CkBNMDJIhAhQAEYIvJ9h1QhA8SqonH8RzATwoMx0xP0DZd0vBBGSL+/1JYBufvqLtxWPUIHLpbStaQXg5XPAG1XyYVhd6XG06fmOUjxsRwIkQAIkQAIkQAJACAXgRVcBDLL3wHvxk3VBOiWmKAr1/RMoXlkX+QSahJzo8hb3euvf6BM7DydcxTHC3k32xRcHnWVwlfUkMmKLIP7RmUDStYGmq0m/Hccu4L6PVuNimgMlCsbhu6evQbUyhXNjZzpdqD5okd+x+neqiVnrk3H0fFpuu6/jxqBdjHinYeDXYPsTGB33uTTAsDOANSbwoOHs6UsADjwE2IqEM5OoHosCMKrLb/jJUwDqr4RqBKD4V9FB9xS+BtBVZjqireizF0CNEE+dAlAA9vKN+s9Kj+MaCsAQLz+GJwESIAESIAGTEQihABSkMlwxiLdk6gdas67A7e/pJ58AMlErAIvhEtbbnkacuw5rnXVwtXWH8pHLNQCeFjfwRO7q+vk6rNwlnlCUfXm+ofjPPafw8Ge+3lnoO+9pcaPRNmZrwBPLdFkw0PEkxsd53IUz7CxgtQYcN6wdfQnAF3cDhcUZD17hIEABGA7KHCNUBCgAQ0U28LhqBKCWJwADz9h7T7l7es1/C7DgQgGo9bpiPBIgARIgARKITgKbZwFze0bP3K2xwLDThp6vWgHYN2YuBvh5468iGBF+Hpy3Oee9pbfDhGU4cDpV0VTyNtJCAL7q6IkJcZ9IxzaDAHxhB1C0gmqm7BAYAQrAwLixlz4IUADqow55s1AjALV8BmC4SQhBmCwGNe0twD4E4OqK3dGm16Rw8+Z4JEACJEACJEACRiYQbQLQEgMMz3l/nTELp1YADo6dhp6x/m+PlSWhcwHY7LUlOJOSITsNzwbBCkCHy4qfqw1El/2jpaGHnwMsBvmR2NcJwOe3AsXkzl6oRs4OPghQAHJpGJmAQb7aGRmx6tzVCEARPBRvAVaddAAdKAADgMYuJEACJEACJEACUUog6gSgFRh+1tDFpgDMLl/eE4CBCsDpcaNxbRC3ALssMbDcNgmY96x0TUVYmKpa4L4E4HP/AsWvUhWKjQMnQAEYODv2jDwBCsDI18AzA7UCcCWAdgBSABQH4PAxJfHW3z/dnxsFYHiYp+750A7xuq1aIodoOwH4Z8XuuIYnAMO8/DgcCZAACZAACRicQJQJQBcsuPjqSRRNiDNs4SgA8wvApqN+wdlUu+qaBisAIW4pFwLwx2fMJwD7bQRKVlXNlB0CI0ABGBg39tIHAQpAfdQhbxZqBeAYAAPdAVoD8PVU3VcBjHW3uxnAL2GeOgWgGzgFYJhXHocjARIgARIgATMQiDIBKErWKu47TO3RCnXKF9WkghfT7Lhsz0TZIuIpOsFd6Y5MrNpzKitWg0rFvAZTIgArW47jeutG1LQcwqOxS4NLSvQO5kTbnl+Bi8eQVrMLpv59Bul2J7q2SUKxgr4l7Kbkczh5MR3tapWGBRbUGrI43xxyTwC6XHh+1OtwpadikfNq2BGDW6zrEA8HFjhbwwFxPkB6tbFuxV3WP3B/7Irg2JhZAPb9GyhVPTg+7K2YAAWgYlRsqEMCFID6K4paAdgqj/T7GEBvL1MSr7baAqAugHMAxGui1P/qTVtWUXwLcDe06fWutjQZjQRIgARIgARIwNwEtnwHzHnC3HP0mF1S2je4qV45fNK1RdDzXrbzBPp98w8upjvwUKurMPbuhgHHzHS6cNcHq7D50PncGB892gydG0hfxCAnACvgNH6xvYwilssB55KvY6ACcNUkYMmwrHDH4hJx7cWxyEQM6lcsioX9xM1G+a+v1/yHoT+IHzGAxonF0KdjDTz19YZ8DXMF4KKXgXXixxXgz8x6OOgqiwdjl2f9fVlmYzxhfwmDY6fj/pjl2O1KxDuOe/B1/Dht2Fjj3CcA+0jjBcpLm6zURfF1C/Cz64HSNdXFYuuACVAABoyOHXVAgAJQB0Xw/LcOgP3uj30FoLuCFHNuAxa3/7YHsNqjz0sAxrs/NlL8blBBzFA3oQAMNWHGJwESIAESIAESMA+BjFRgQnXA7n6DavlGSD+6HTZLpH+nGzrEQgCKK+8z5AId7dpxv+HwuSuibXH/dqhbIbCThT9tOYbe06Siq0BcDP4eeiMKxMfkpignAMfFfpIrwAKdV75+gQotD7n0bEZfLHCKJwgBC/q29XrKUW5+Obll1c/lAkaKpxX5vgZk9MZb8R9phkISyAwCcOWbwG+v5efTZy1Qtk5ouDFqPgIUgFwURiZAARj56rUFUCNPGqUBTHD/fRWAzzxS/NJLyk0BiLYFAFwCIG4LXub++4MAern77AIgfoV6MfLTRtQKwD8rdsM1PAGogyXIFEiABEiABEjAYATEKcBfhgHxhYA7JqPPh/MxOe49WC0upLpsKGhJN9iE/KerpQD0lFW92lfDoP+Jm2PUXy/P2YRZ6w/l6zjxgca4q+mVt7HKCbK/bb1Q0iL+6a7hpZEA/MJxM0Y6umUlNqVbC3SqWy5fknLzy+mQJQDtacDo/DE0nLn/UDHxwIu7gfFVAZczu22pmkDf9WFLIeiB0s4D47y87OPp1UC5ekGHZwBlBCgAlXFiK30SoACMfF2E0Mv+7qrs8lWz2wBMA+DrV5lC/nUBsEfZMCFvRQEYcsQcgARIgARIgARIwMwEhIBpZNmLqpaj2OCqhT9sz+l+ukPsj+P1uC8U5alXAfjcjH/ww8Yj+eYw7u6GeLDVFUEjJ8j+sfVCiWgRgJfPAW9UUVT3kDQSAnDoSWDNR8AvgwFbEeDuz4CaN4RkuJAF9XYbcO8/gPKB39IeslxNGpgC0KSFjZJpUQBGvtBaCUAxE/Fdtb9b9AnBluEWfrMBTAbgvmck8pMGovcE4OoKXdHmqfd0UQQmQQIkQAIkQAIkYFwCeQVTYaRiS8KTup/MIxkDMT0+5710/tPVqwB8ZvrfWPjv0XzJi+cKiucL5lxGFoCfOzpjlKNr1lQ+69oCN9QL8gTgxePAW7Uitz5jbMDQE9njO52AxZL9x2iXNwH41EqgQmOjzcSw+VIAGrZ0TByAAb/qsW4GJcC3ALsLRwFo0BXMtEmABEiABEhAZwTyCqaCSMO2BP2/JOShjMH4Nn60IpKhFIBPta+GgQHeAtxz6nos2XY83xzG3NUQD1+tXAButPVEcUuKIhaKG/m4BdglnsEnfvjzJb08xJLmAvDsf8CkRoqnoXnDvAJQ8+BhDOhNAPZcBlRqFsYkonsoCsDorr/RZ08BaPQKGid/CsBcAfgY2jwlDmTyIgESIAESIAESIIHACeQVgAWQhu0UgD5hejuNt2nYTShWME7SR7zhN8PhREKcNUuWXThzAhunDULa5RQcadgHdevUw3u/7cEfe07lG6tS8QJY1L8dihWIg4hTfdAiv8UNhQC8seg8fPxYc1QrlAEsGw1kpGJz9Scx7cdFuCVjCc6gCIbbu6Nj4xp4s3NZ2Pb+DJSuDXz5P0muOQJQrKvP7qyIa5s3AeIKYOn241i7/wyqlS6EV+f+i8qW43DBikOuMvnmWhIX0NG6EW/1uS/7ltv3WwW+2IPtaWYBWOsW4KFvjXmiMdi6RqA/BWAEoHNIzQhQAGqGkoFUEojaZwCurvAo2jz1vkpcbE4CJEACJEACJEACUgJ5pZYNGdiZ0F33iB5IH4qZNi9vMvWSeShPAIrhht1aD0+0rZo78rHzaXhy6l/YcvgC2tUsjQ8eaYaT73dGtYvZL4rY7yyHjhlvZ91ElYB09I+di4qW0/gpsyW6x/6M2pZkfJPZCTOKPoGDZ+SfvLPJ9iSKWeTbqSmqYHZ/i0SMvzwS2LvUZ9db0sfixyLjEZ9x1msbIQCnOG7BtPgxqGo9nvXCjJVtpqDrnCsvPxkQOwt9Y3/I6r/RWQ13ZryeG6soLuFX28soazmXfdNZ2+eAPyaqmYq2bc0sAAWpJ5cCieJdj7xCTYACMNSEGT+UBCgAQ0mXsf0RoADk+iABEiABEiABEiCBIAjkFYCxcGC77XHEWTKDiBj6rvenD8UsnQhAMdusN9S6r3GLd+CjFXtz//7WXXVwz2LprZV3po/CRlcNjIr9Al1jl3gFJm5zXu2sLwtzs+1JFA2BAMyaV8LDsuP7ayDkXwwy0T32l9xmv5d+AI8duiPr70WRgs0JPSUhHswYgjXO7LfR9o/5Ds/HfRdUDpp2NokAPDftcRTfMzc/mspXAz2u1EpTdgwmIUAByAVhZAIUgEaunrFzj1oB+G9SdzTsPsnY1WP2JEACJEACJEACESfgeVvrp3Fv4caYDRHPy18C96YPwxzbKEU5hvoEYJYoyyMAPXl6e65i94yXsdzZxK9gO+IqGtOMTwAAIABJREFUiWvS5R/3stnWA0UtlxWxUNool5kGArBH7OJ8w+bEb2bZhbm2EZLPn3MVQpP0T7M+tjB+IOpb/1OadujbxSYAQ/I/tzH0A2s7wv4d/6DqjA75g5atB/RZre1gjOaVAAUgF4aRCVAAGrl6xs49agVg2sPfI6HW9cauHrMnARIgARIgARKIOAFPYWWE5wCuvu4btFmh7HSaHgVgt4xXsMLZWPaEXU7u/hZJ6ASgCwcSHglqfYoTgGoFoBgwZ94L4gehgfVAUDlo2tkkAnDPiUso837N/LeOUwBqulz8BaMADBtqDhQCAhSAIYDKkIoIRK0AxPBzfEivoiXCRiRAAiRAAiRAAv4IeHuxRbC3foaa+M5bv0PtBfcoGibSArB4TBo2xknfrKylAPzX1gNFQnAC0AIn9ic8qoixr0bmE4AFgCHHgmKih84HTqWg0Ht1UcZyXpoOBWDYykMBGDbUHCgEBCgAQwCVIb0S4FuABRbxlrVn13GJkAAJkAAJkAAJkEDQBCgAlSP0xkr09ncLcOn4DKy3Sl+sEi0C8DPHLXjSzy3ATS278b1teL4C5Ejb+fGD0FBXJwDNIQCTz6QiblI9lLd4vLyFAlD5F4MgW1IABgmQ3SNKgAIwovijanAKQFHusvWBPn9GVeE5WRIgARIgARIggdAQMKIA3H3rd6hpkBOAFRIcWI2ukuIZQQBa4cS+IE8Amk4AxhUEBh8NzUYMY9Qj5y7DNbE+KllOS0ctUxd4Zk0YM4neoSgAo7f2Zpg5BaAZqmjMOUTnLcDlGgBPrzJmxZg1CZAACZAACZCArggYUQDuvfU7VFcoAHtnPIezriL44sHqKFj3JiC+kCL+afZMnE7JQKlC8Th6Pg2lCsej0Qjvb0i9umpJDLipNlpVLYkcnglIRwycKFXIhpWZ0ltp1QhAIeLiYUca4gHk/7Fri+0JFLakKZqT0kbiBJ54e+/ehMeUdvHaTk4ANrHswQ+2Yfn6Nkj7DKUt5zE57j19PQPQJALw+IU02N+qj0TLKQn7zNJ1EPPs2qBqzs7KCFAAKuPEVvokQAGoz7pEQ1ZRKgAbAk//EQ315RxJgARIgARIgARCTMCIAnDfbXNQbf69gZGp0BipscXRZc/t2O+qgEaJxfDBI82QWKJgVrz1B85g36kUTP5tDw6eSc0dQ4hAIQR9XQXiYvD30BtRd9hPuNm6Dm/HfYgCyMBmS200wQ5JtzOuwtjmrIK2MZ43t0ijt0+fiM/i3kQt62EsyWyOZ+19UdVyDJcRj1SXDX8lPBMYA5leMxwdMNzRHTsTpLcuqx0sUAGodpywtY8rBAw+ErbhQjXQqUvpSBnfAFWsJyRDHLNVRfmBG0M1LOPmIUAByOVgZAIUgEaunrFzj04BWL4h0JsC0NhLl9mTAAmQAAmQgD4IGFEA7r99DqrOC1AAurGvddbBAxnZp8/ualoJEx9ogjGLtuOTlfsCLszouxpg8Pdb8LetF0paLgUcJ6fjTEcHPBC7PDfOBVcBFLVchtNlgdXiCjq+vwDfZbbDPTG/BzXGIVfpfKfMRMCcZ/z5OgEY1KCh7FywFPBy4OsjlKmpiX0mJQPn32iAqtbjkm47nYmoPcq/lFYzDtv6JkAByNVhZAIUgEaunrFzj1IB2AjoHdw/yIxddmZPAiRAAiRAAiSgFQEjCsADt81BUqAnAPOAyxFR4kM7XuuMOkN/CgrrSzfXxoSfd0Lvb1EOapIadDasAHxoJlC7swYEIhvibEoGzr7RENWs0jca73BWRp1RWyKbXJSMTgEYJYU26TQpAE1aWANMiwLQAEViiiRAAiRAAiRAAvolYEwBOBtJ8+8LGmpeAbh+yA1o8fqvQcWkAFSGL4d7Y8se/OjlGYDKooSv1TkUQcGm9yC+ywQgVjyL0djXudQMnBlHARjJKlIARpI+xw6WAAVgsATZP1AC0SkAKzQGnloZKDP2IwESIAESIAESIIFcAkYUgP/dPgtV5t0fdBXzCsANQ25A8yAF4Is31cKbv+ziCUCZyhhFAHZKn4C9rkpYN7gTyhZJCHq96SXA+VQ7To1riOpW6RuNeQIwfBWiAAwfa46kPQEKQO2ZMqJ3Ap4PpYgFUEs0TU5ORmKi8IEmvEYUk06qQhPgqRUmnCinRAIkQAIkQAIkEG4CRhSAB2+bhavmaysAxQs8mr22JCj8L9xYC28voQCUg2gUAZiT54FxXeSmZKjPn79sx6mx+QUgnwEYvjJSAIaPNUfSngAFoPZMGZEC8AoBTwFYsSnQ68oDoblYSIAESIAESIAESCBQAkYUgMm3z0TleQ8EOuXcfklp0wFk/yjzz9Ab0TRIAfj8DbUw8VcKQLnC5Ii1Rpa9mGcbKtc8Yp83qwC8kGbHyTEUgBFbWAAoACNJn2MHS4ACMFiC7B8ogei8BbhiM6DXskCZsR8JkAAJkAAJkAAJXJFgry7MR0PvL7E4dNtMJM4PXgBWTZsGF6xZ89847EY0GRXcCcD+nWpi0tLdvAVYZn9RAEb2C9DFNDuOj2mEGtYjkkR4AjB8daEADB9rjqQ9AQpA7ZkyojIC0SkAKzUHev6mjBBbkQAJkAAJkAAJkIAfAkY8AXj49hmoNO/BoOtaLW0anG4BuGnYTWg86pegYvbrVBPvLhUnAB8JKo7ZO1MARrbCl9IdODa6IQVgBMtAARhB+Bw6aAIUgEEjZIAACVAABgiO3UiABEiABEiABEhAEDCkALztW1Sa/1DQBayRNhUOiEdKA5uG34R2I+einOUcDrjKw+7+uPicBU70i/ked8f8ju2uKhho74GzKJpv/EHXFsG8PzdhgW1I0LmZOcDnjs4Y53gI9S0H8L1tuG6natZbgFPSHTgyuhFqWg9L2PMEYPiWIgVg+FhzJO0JUABqz5QRlRGIUgHYAui5VBkhtiIBEiABEiABEiABPwSMKACP3P4tKs4LXgB2TH8L+10VsujcV+k0Xj41GGUsF7L+PsLeFRuctTAw9hu0tO5EnCUzl+IHjtsx3iE9gXiDdQM+TJiMOGc615sCAi/Ze2FU7JcoYMlQ0DoyTcwqAFMzHDj0eiPUogCMzMLiMwAjxp0Da0OAAlAbjoyinkB0CsDElsCTv6qnxR4kQAIkQAIkQAIk4EHAqwAs1B3I1K+YOdBjC5KmNAi6likuG5qlf4x0xGNq3Fi0j/lXccwcOZTT4W9bL5S0XFLcnw31T8CsAvByRiaSX29IARjBJcgTgBGEz6GDJkABGDRCBgiQQHQKwFq3AA/PCBAZu5EACZAACZAACZDAFQJeBeCj6cCcx/WJ6bpXsa9BX3z4zkhMiPsk6ByH2rvj68ybVL+4w1MA6v3FKUGDisIAZhWAafZM/PdaI9S2HpJUdYezMuqM2hKFlQ7/lCkAw8+cI2pHgAJQO5aMpI5AdAjAnwYCaz64QqbPWqBsHXWk2JoESIAESIAESIAEvBDwKgDHdcGjg8ZhWvxY/TEbcR77T6Wg45vL0cm6AbUth7DI2QrLbQMCynW6oxMGO3pQAAZEz9ydok0A8hmA4VvPFIDhY82RtCdAAag9U0ZURiA6BGDqGeCnV4FTu4DmjwPNuymjw1YkQAIkQAIkQAIkIEPAlwAUH99mexwFLcE/026TsxoG2Hvjoqsg1iY8G1xNRpzHgVMp6PDmckmcQE/gUQAGVw4z9zarAEx3ZGL/qMaoY02WlI8CMHyrmQIwfKw5kvYEKAC1Z8qI3gls9fiweG1bLfGx5ORkJCYKH8iLBEiABEiABEiABEhAKQF/AnC7rbsmL2lYntkY3e2vZKUUqKjLnc+I8/jvdAqum0ABqLTGbBcYAbMKwAyHE/tGNaIADGxZaNKLAlATjAwSIQIUgBECH4XDUgBGYdE5ZRIgARIgARIggdARCIcAXJbZGI9rKAAPnk5F+wnLJFACFYs8ARi6tWX0yGYVgPZMJ/aMbIS6PAEYsSVKARgx9BxYAwIUgBpAZIiACETHLcABoWEnEiABEiABEiABEpAn4E8A7rB1Q4LFLh9EpoXWAjD5TCrajacADLowDOCXgFkFoCPTid0jG6Ou9aBk/rwFOHwbggIwfKw5kvYEKAC1Z8qIyghQACrjxFYkQAIkQAIkQAIk4JVAOATg0sym6GF/KWv8QE/q5SY/4jwoALmYw0HArAIw0+nCrhHiBCAFYDjWkbcxKAAjRZ7jakGAAlALiowRCAEKwECosQ8JkAAJkAAJkAAJuAn4E4A7bd1g0+AE4K+ZTfGkhgLw0NlUtH1DmxOA3ziuxyDHk6rFZI4cyllIQYtNrkjdETCrAHQ6XdgxojHqWf+TMOcJwPAtQQrA8LHmSNoToADUnikjKiNAAaiME1uRAAmQAAmQAAmQgFcCRhSAh89dxrXjfpPMJ1ABRwHIjeGLgFkFoMvlwvbh+QXgLmcl1Bq1jQsiDAQoAMMAmUOEjAAFYMjQMrAMAQpALhESIAESIAESIAESCIKAfwHYFTaLI4jo2V2XZDZDT/uLWf8dqKjLTWLEeRw5dxnXaCQAz7sK4nXHo5gQ94mqeT6UMRgNLPvxm7MpalkO4cP4Sar6s7H+CZhVAAry24Y1yncCMKsiI87rvzAmyJAC0ARFjOIpUABGcfEjPHUKwAgXgMOTAAmQAAmQAAkYm4A/AbjL9hjiLZlBT3BJZnP0tA/QTAAePX8ZbcZqcwIw6MkxgGkJmFkAYkQx73V7fitQTPyIxSuUBCgAQ0mXsUNNgAIw1IQZ3xcBCkCuDRIgARIgARIgARIIgkA4BOAvmc3RSwsBmNQO6L4Ax86nofXYpZJZB32yMAiG7GpOAlEpAFv3ATqPNWdBdTQrCkAdFYOpqCZAAagaGTtoRIACUCOQDEMCJEACJEACJBCdBPwJwN22xxCnwQnAnzNb4Cn7C1mAf45/GbWth/zDLlMHOLlD2sZWDHh0DlC5FY5fSMPVYygAo3PFhm/WQgC+dkd9PNYmKXyDhmskXycAW/YEurwZriyidhwKwKgtvSkmTgFoijIachIUgIYsG5MmARIgARIgARLQC4FwCMCfMluit/35rCk3tuzB1/FjUdRy2TeCCk2Aoxulnx90BIgvlPWxExfS0IoCUC9LyJR5XEBhjKy3CKPuqI9CtljzzdGXAGz1FPC/8eabr85mRAGos4IwHVUEKABV4WJjDQlQAGoIk6FIgARIgARIgASij4A/AbjH9ihiLc6goSzObImn3QJQBKtiOYYVtuwTgXmvTJcFwx3d8XqVjcCRf6SfzPNyghMX09BqNE8ABl0YBpAQWJVZH9fGbAVibMC9U4C6t5mXkC8BePXTwC3jzDtvncyMAlAnhWAaARGgAAwIGzsFQGCrRx/x67ha4mPJyclITOQDawNgyi4kQAIkQAIkQAJRTMCfANxrewQxFlfQdBZltkIf+3OSON6e2dc2fRIOucrgQLU3gSN/+xSAJy+mo+XoX2XjBZ04A0QVgWpp07Cvb0WgUBmg+FXmnrsvAdjmWeDm0eaeuw5mRwGogyIwhYAJUAAGjI4dVRKgAFQJjM1JgARIgARIgARIwB8BfwJwn+0RWMMoAHNfulD9LeDwBp8C8NSldLR4nQKQK1tbAmL9HRjXRdugeo1GARjRylAARhQ/Bw+SAAVgkADZPWACvAU4YHTsSAIkQAIkQAIkQAJAOATggsyr8ay9vwS3txOAVwTg28Dh9T4F4OlL6WhOAcjlqzEBCkAA1/QDbnpNY7IM50mAApBrwsgEKACNXD1j504BaOz6MXsSIAESIAESIIEIE/AnAL1JukDSXZDZGs/a+ykXgDUmAof+8ikAz6RkoNlrS2TjBZIr+0QvAQpAAG2fB24YEb2LIEwzpwAME2gOExICFIAhwcqgCghQACqAxCYkQAIkQAIkQAIk4IuAPgXgO8ChdT4F4NmUDDSlAOSi1pgABSCAdgOATsM0JstwngQoALkmjEyAAtDI1TN27hSAxq4fsycBEiABEiABEogwgXAIwPmZrdFXzQnAmpOA5LU+BeC51Aw0GcUTgBFeOqYbngIQQPuXgOuHmK62epsQBaDeKsJ81BCgAFRDi221JEABqCVNxiIBEiABEiABEog6AuEQgPMy26Cfva+Erd9nANZ8F0he41MAnk+1o/GoX2TjRV0xOeGgCFAAArjuFaDjoKA4srM8AQpAeUZsoV8CFID6rY3ZM6MANHuFOT8SIAESIAESIIGQEgiHAPwx8xr0tz8rK+xyXwJCARjSmjO4dwIUgAA6DAQ6vMolEmICFIAhBszwISVAARhSvAzuhwAFIJcHCZAACZAACZAACQRBIBwC8IfMa/CcGgFY6z3g4GrprEacz/37+ct2NB7JE4BBlJ1dvRCgAATQcTBw3ctcHyEmQAEYYsAMH1ICFIAhxcvgFIBcAyRAAiRAAiRAAiQQGgLhEIDfZ16L5+3PSCbg9xbgWpOBg3/6FIAX0uxoNIICMDQrInqjUgACuH4o0P7F6F0EYZo5BWCYQHOYkBCgAAwJVgZVQIAnABVAYhMSIAESIAESIAES8EUgHAJwbmZbvGDvo1wA1n4f+G+VTwF4Mc2OhhSAXNQaE6AABNBpONDuBY3JMpwnAQpArgkjE6AANHL1jJ07BaCx68fsSYAESIAESIAEIkyg+xfrsHznydwsGlYqhvl920KIwWlxo9E2ZmvQGX6X2RYDVAnAD4D//vApAC+lO9Bg+M+yQjHoxBkgqghQAAK4YQTQ9vmoqnskJksBGAnqHFMrAhSAWpFkHLUEKADVEmN7EiABEiABEiABEshDYNfxi7jz/VVIzchEfKwVM3u1RtOrSuDacb+hyPmd+MkW/AsBJjnuwkTHfRLuO2zdkGCx537shKs4WqV/gKqlC2FZ6TeBA7/7FIAp6Q7UpwDkOtaYAAWgEIAjgbbPaUyW4TwJUAByTRiZAAWgkatn7NwpAI1dP2ZPAiRAAiRAAiSgAwIHTqVg3YEzaFK5OGqVK5KV0ao9p/DolLVogt3oEbsYHcunoVDHAcCaD/M/n09mDhOa/Iz315xGpeIF8HLn2pi0dDdqnVmOj+Im5vbsmvEKVjob49uerdHm925+BWBqhgP1hvEEoA6WjmlSWO+shXXXf4s+HWqYZk5+JzKimPdP8wRgWOpPARgWzBwkRAQoAEMElmFlCVAAyiJiAxIgARIgARIgARIIjMCfe09h3f4zaFujNFoklcwOYk8DPr8ZOLpRPmjd24Fr+wOJLeB0umCxABbxP+JyuYBNM7JE3/rYpphrb40Otcrgpvrlga9uA/avlMbP8xbgyxmZqDvsJ8nnvb1URD5B87aYl9kGm5zVMTRumnknqeHM5rWchi633IoYa5T8aEsBqOHqUR+KAlA9M/bQD4Eo+SqpH+BRnInnQ2hiAdQSPJKTk5GYKHwgLxIgARIgARIgARIggbAQ8CURcgbPI+1U5fPV7cD+FT4FYJo9E3WGUgD6YypuZxUXxaiClVe2HtBntYKGJmria+/yJSBhKTIFYFgwc5AQEaAADBFYhs1HgAKQi4IESIAESIAESIAE9EIgVAJw6h3AvuUUgEHUmQJQBbwaNwKPzlHRwQRNKQAjWkQKwIji5+BBEqAADBIguwdMgLcAB4yOHUmABEiABEiABEggSAIREoDpjkzUHsITgP6qRwGoYm1TAF6B1WkY0G6ACnhsGggBCsBAqLGPXghQAOqlEtGXBwVg9NWcMyYBEiABEiABEtALgZAJwDuBfcuks8xzO3GGw4laQxZLPs9bXaW4KABVbBIKQApAFctFi6YUgFpQZIxIEaAAjBR5jksByDVAAiRAAiRAAiRAApEiECoB+PVdwN7ffApAe6YTNQdTAPorOwWgik1BAXgF1vVDgfYvqoDHpoEQoAAMhBr76IUABaBeKhF9eVAARl/NOWMSIAESIAESIAG9EAiZALwb2LvUpwB0ZDpRgwLQ7yqgAFSxSSgAKQBVLBctmlIAakGRMSJFgAIwUuQ5LgUg1wAJkAAJkAAJkAAJRIpAqATgtHuAPb/6FICZTheqD1ok+TxvAZbiogBUsSlq3gQ8MltFBxM09bV3rx8CtH/JBBPU9xQoAPVdH2bnnwAFIFdIpAhQAEaKPMclARIgARIgARIggZAJwHuBPUt8CkCn04VqFIB+1x8FoIrtWfNm4JFZKjqYoKmvvdtxCHAdBWCoK0wBGGrCjB9KAhSAoaTL2P4IUAByfZAACZAACZAACZBApAiESgBOvw/Y/YtPAehyuVB1IE8A+is7BaCKTUEBeAVWx8HAdS+rgMemgRCgAAyEGvvohQAFoF4qEX15UABGX805YxIgARIgARIgAb0QCJkAvB/Y/bNPASg+kfTqQsnneQuwFBcFoIpNQgF4BVaHQUCHV1TAY9NACFAABkKNffRCgAJQL5WIvjwoAKOv5pwxCZAACZAACZCAXgiESgB+8wCw6ycKwCDqTAGoAh4FIAWgiuWiRVMKQC0oMkakCFAARoo8x6UA5BogARIgARIgARIggUgRCJkAfBDYtZgCMIi6UgCqgEcBmEcADgQ6vKoCHpsGQoACMBBq7KMXAhSAeqlE9OVBARh9NeeMSYAESIAESIAE9EIgVALw24eAndJn/GHEecmseQuw/0VAAahik1AAUgCqWC5aNKUA1IIiY0SKAAVgpMhzXApArgESIAESIAESIAESiBSBkAnAh4Gd0mf8UQCqKzIFoApetToDD89U0cEETX3t3eteBToONMEE9T0FCkB914fZ+SdAAcgVEikCFICRIs9xSYAESIAESIAESCBUAnDGI8COBVK+PAGoar1RAKrAVesW4OEZKjqYoCkFYESLSAEYUfwcPEgCFIBBAmT3gAlQAAaMjh1JgARIgARIgARIIEgCFIBBAgxddwpAFWwpAK/Auu4VoOMgFfDYNBACFICBUGMfvRCgANRLJaIvDwrA6Ks5Z0wCJEACJEACJKAXAhSAeqlEvjwoAFWUhgJQCqvv30Cp6pKPnUvNwPB5W+E6tAEDbN/jqooVYblxFFC0gmLQpy6lY+gPW7D35CU83OoqdLsmCRZLtkoQn3tlzmbsOHYR9zSrhOduqAWrVZlmWLj5KCb8vANFC8Rh7N0NUb9iMcU5ad7w4nHg1+FA2gXgupeAik29DkEBqDl5BgwjAWU7M4wJcaioIUABGDWl5kRJgARIgARIgAR0RyBUAnDmo8D2+dLp8hZgVeWnAFSBiwJQCstiBYaflXxs3OIdmLJiJ9bbeqOYJTX7c9U7AY/NVQx6+I9b8NXq/1AG5zA6bgquK30Rtja9gFY9MWbRdnyycl9urDm926BFUkm4XC5sPnQ+S+5VLV0o31ipGQ70HzEaL8bOwgUUxGB7D/w0+inF8lBx8kobTrsH2PNrduuEYsCLe4DY+Hy9KQCVAmU7PRKgANRjVaIjJwrA6KgzZ0kCJEACJEACJKBHAiETgI8B2+epEoD/NpuPItu+1SOlsOd00lUULdM/yhr3QMLDYR/fcANSAOYv2cBDgK1I7sfFW7e7WNfg/fh3/e5Lf7XPeXP36NgpeCR26ZWmz/yFpLd2S7rWrVAUi/u3Q++vN+CnrccgDgOOuashHmx1laTd3DU70XlxOxS0pGd9fK2zDmKeWJwlDyNyeX5NvPdzoME9+VKhAIxIdTioRgQoADUCyTCqCVAAqkbGDiRAAiRAAiRAAiSgEYFQCcB9K4Cpt19JslwD4OlVkqRnr0/GwLn/wuF04ZGrr8LoDkXg+KwzYlOOajQ5Y4ZJc8XhaftzWObMvvXwmZgf8FLcLGNOJlxZP/YDUL1juEbTxzhye/eVA0CBErm5Cnn3YMxvGBf3mTR/j5O5/iaXIwDzSek6tyJpo1RUVylVEJMfaobbJv8hCXlgXBfJ31d/9y7a/DtU8rHbSy/EvGfbRoazJ9cubwMte+TLhQIwMuXhqNoQoADUhiOjqCdAAaieGXuQAAmQAAmQAAmQgDYE5CSCCjkgScjpBGZ3yz4FmFAceGAaULVdvpyPnU/DZXtm7q2Bh48cxouTp+Pb+NHazM8gUT6v9BrG7K2CRpZ9OO4qgcMok5t5CVzAP1U/BI5uMshswpvmugJt0eql+YDVGt6BIz2a3N59eT9Q8MopOiHv7o9ZhvFxn2ovAKteh6TtT0niCgH4QMvKGP/TTr8CcP3sCWix9XVJm1tLLcCCvvm/XoQFOQVgWDBzkMgSoACMLP9oHp0CMJqrz7mTAAmQAAmQAAlEloCcRAhUAIpZuVzA+UNAQtHsZ2kpuA6dTUXbN5aJziiLc3gz7iO0j/lXQU9jNxldfTo+3er7R7IDr98IXDoOxBUEJtYDHGlSqeIshyTrcV1AuDZtElYl9A86l/XOWmhh3SWN0z9bgs5cfwiTVp1C5UKZGPpQJzRILB70eIYLILd3X9oHFCqVOy0hAO+LWY4JcZ9Ip6pij/s8AehDAN7fojIm/OxfAG6YPR7Nt0qF/22lFmJ+X54ADMWaPHToECpXrpwTWvzHoVCMw5j6JkABqO/6mCm7rR6TiQVQS3wsOTkZiYnCB/IiARIgARIgARIgARIICwE5iaBCDmiR7xUBmB1tZOwX6Ba7RIvQuo7xWrVvMGWb7xQlt03uWw5MvUPS+KCzDK6yntTFHBulfYrNCT2DzmWM/SEMivN4JuTAw4CtcNCxTRFAbu++tBcoVDp3qkLe3RuzAm/GfSydvoo9rlYA3tc8EW/+IpW4nrcA/z37DTTbOkaS0x2lF+JH3gIckmVKARgSrIYLSgFouJIZNmEKQMOWjomTAAmQAAmQAAmYjoCcRFAhB7Rgc/jcZVw77rfcUNEiAEdW+xZfbHP5ROgpTeBRNz0JwAZpn2FLwpNBL4fR9ocxOO4baZxBR4D4/G+SDXowIwaQ27sv7gYKl5UIwHusK/FWfPbLZXIvFXtcrQC8p1ki3l7iXwDjZb3LAAAgAElEQVT+M3ssmm4dJxWAZRbhx2eujUxVeAtwZLhz1LASoAAMK24OlocAbwHmciABEiABEiABEiCBSBGQkwgq5IAWUzhy7jKuoQDMh1JOACY7y6CyTk4A1kv7HNsSngh6OVAAyiCU27sDdgFFykkE4F3W3zEx/kNp4OHnAIsyHaBWAN7dNBETf/UvADfNHovGHgLwzjKL8AMFYNB7yFsAngAMCVbDBVW24w03LSZsAAIUgAYoElMkARIgARIgARIwKQE5iRBmAXj0/GW0GXvlBOCI2C/RPfYXk8K/Mq3h1Wbiq22ZPudpJAFYN+1zbA+ZADwKxBc0/XpQNEG5vfvCDqBoBYkAvNP6B96J/0AaftgZwBqjaEi1AvDOJpUwaeluSWzPtbx59mg02jpe0oYCUFE5AmpEARgQNtN1ogA0XUkNMyEKQMOUiomSAAmQAAmQAAmYjoCcRKAADEvJh1WbhanbHAELwEOu0ki0nApLrnKD1E77EjsTuss1k/386/ZHMCRuurTd4GNAXAHZvlHRQG7vvrAdKFpRIgBvt67Cu/HvS/EMPQ3EiMeyy19qBeAdTSrhXRkB+O/s0WhIASgPX6MWFIAagTR4GApAgxfQwOlTABq4eEydBEiABEiABEjA4ATkJEKYBeCx82loPXZpLtRoOQE4tNosfG0SAVgr7SvsSugW9MagAJRBKLd3n98KFLvygkUh7263/ol34ydLAw85CcTGK6qXWgF4e+OKeO+3PZLYnicAt8x+DQ22vilpwxOAisoRUCMKwICwma4TBaDpSmqYCVEAGqZUTJQESIAESIAESMB0BOQkQpgF4PELabh6TPQJwMHV52D61gyfy0vuFuDDrlKoZDmti+VZI20q9iR0DToX7wLwOBCXEHRsUwSQ27vPbQGKV86dqpB3t1pXY3L8ex4C8AQQa1OERK0AvLVRBby/bK9fAbh19muoTwGoiL8WjSgAtaBo/BgUgMavoVFnQAFo1MoxbxIgARIgARIgAeMTkJMIYRaAJy6koVUeATg89is8Hvuz8TnLzGBQtTn4Zps5BGD1tK+xN+GxoGtGASiDUG7v9t8MlKgiEYBdrGvwfvy70sAqbqtWKwC7NKyAD5b7F4DbZo9Eva1vS3LiCcCgt4/PABSAoWNrpMgUgEaqlrlypQA0Vz05GxIgARIgARIgASMRkJMIFIBhqebAat/h223pPseSOwF4xFUSFS1nwpKr3CBV06Zhf8Kjcs1kP+9VAA5RflpNdgCjN5Dbu/03ASWSJALwFutafBg/STrzQcpfrKJWAN7SoAI+WuFfAO6YPQJ1tk6kAAzTeqQADBNonQ9DAajzApk4PQpAExeXUyMBEiABEiABEtA5ATmJEG4BeDENrUZfuQU4Wk4AvlJtLmZuSwtYAB51lUQFnQjApLTpOJDwSNAL/zX7Ixjq+RIQFc+rCzoBvQeQ27v9/gFKVpMIwM7Wdfgo/h3pzAYeBmyFFc1WrQDs3KA8Pl6xTxLbU2bvnD0ctbdKc+IJQEXlCKgRBWBA2EzXiQLQdCU1zIQoAA1TKiZKAiRAAiRAAiRgOgJyEiHMAvDkxXS0HP1rLuaoEYBVv8fM7ZcDFoDHXCVQ3nJWF8szKe0bHEh4OOhcKABlEMrt3b5/A6WqSwTgzda/8HG89LQdBh4CbEUU1UutALy5fnl8stK/ANw1exhqbZWeSqQAVFSOgBpRAAaEzXSdKABNV1LDTIgC0DClYqIkQAIkQAIkQAKmIyAnEcIsAE9dSkeL16NPAL5c7QfM2pZKAZiHgFcBOPQUEBNnum0Y0ITk9u6zG4DSNSQC8CbrX/jEUwC+ehBIKKYoBbUC8KZ65fDp7/slsT1PAO6eNRQ1t0mfS3hX2UX4vs+1inLSvJEn1y5vAy175Btm/9a1qDr7ptyPO10WWEee0zwdrQNSAGpN1JjxKACNWTczZE0BaIYqcg4kQAIkQAIkQALGJCAnESgAw1LXF6v9gDlBCMDjruIoZ9GHfNDuBOCjGBo3Tcp/6GkgJjYsNdH9IHJ799n1QOmaEgF4g3UDPot/Szq1V/4DChRXNF21AvCGuuUw5Q//AnDPrCGosU36ZmIKQEXlCKgRBWBA2EzXiQLQdCU1zIQoAA1TKiZKAiRAAiRAAiRgOgJyEiHMAvD0pXQ0j8ITgAOq/ojvtqf4XF5yLwE54SqOshSAptuefickt3efWQeUqS0RgJ2sGzDFUwC+vB8oWFIRO7UC8Po6ZfHFqgOS2J5ree+swai+bbKkDQWgonIE1IgCMCBsputEAWi6khpmQhSAhikVEyUBEiABEiABEjAdATmJEGYBeCYlA81eW5KLOVqeAfhC1XmYu/1SwALwpKsYyljO62J5hvQE4LAzgDVGF/OMeBJye7fPWqBsHYkA7Gj9B1/ET5CmHkIB2LF2WXz5p38BuH/WIFTd9j4FYJgWFAVgmEDrfBgKQJ0XyMTpUQCauLicGgmQAAmQAAmQgM4JyEmEMAvAsykZaBqFAvC5pHn4YQcFYN7dMsr+GIbFfS3dQMPOAlarzjdVmNKT27tPrwbK1ZMIwA7Wf/ClpwB8aS9QqLSipNWeAOxQqwy+Wv2fJLbnCcADs15F0rYPKQAVVSD4RhSAwTM0QwQKQDNU0ZhzoAA0Zt2YNQmQAAmQAAmQgBkIyEmECAvAYbFT8UTsT2Yg7XcO/asuwI/bL/hsI3cL8ElXUZSx+O4fToBanQCkAJSpmtzeffpPoFx9iQC8zroJX8W/IQ384h6gcBlFS0StALyuVhlMDUAA8i3AisoRUCMKwICwma4TBaDpSmqYCVEAGqZUTJQESIAESIAESMB0BOQkQpgF4LnUDDQZdeUW4GgRgP2qLsC8IATgKVdRlI4GATj8HGDhj65ZX4fk9m7vP4DyDSUCsL11E6Z6CsABu4Ai5RR9aVMrANvVLI1paw5KYnvK7P9mvowq2z+WtKEAVFSOgBpRAAaEzXSd+FXUdCU1zIQoAA1TKiZKAiRAAiRAAiRgOgJyEiHMAvB8qh2NR/2SizlaBOCzSQuwYEfgJwBPu4qglOWiLpanVicAR9ofw3DPW4DDvB51AdRXEnJ796nfgQqNJAKwrfVfTIsfK404YCdQpLyiqaoVgG1rlMb0tf4F4MFZL+GqbZ9QACqqQPCNKACDZ2iGCBSAZqiiMedAAWjMujFrEiABEiABEiABMxCQkwhhFi7nL9vReGT0CcBnkhZh4Y5zPleU3C3AZ1yFUdLi+xmC4VyqWglAr7cAh3k9hpOb6rHk9m6vFUDFJhIBeK31X0z3FIAvbAeKVlQ0vFoBeE310vh2nX8BmDzzRVTe/ikFoKIKBN+IAjB4hmaIQAFohioacw4UgMasG7MmARIgARIgARIwAwE5iRBm4RKtAvDppMVYvOMsBWAeAhSAMl9g5PZur+VAxaYSAdjGuhXfxo+WBn5+G1CskqKvZmoFYJtqpTDjr2RJbE+ZfXjmAFTa/hkFoKIKBN+IAjB4hmaIQAFohioacw4UgMasG7MmARIgARIgARIwAwE5iRBmAXghzY5GI6LvBODTVRdj8fbABeBZV2GUMNkJQN4CHKQA7PkbUKm5vAB8bgtQvLKir2ZqBWDrqqUwc71/AXhk5guouH0KBaCiCgTfiAIweIZmiEABaIYqGnMOFIDGrBuzJgESIAESIAESMAMBnQnAi2l2NIxCAfhU0s/4ecdpnytK7hbgc65CKG5J0cWK1OoWYArAIAXgk78BiVIBeLVlO2baXpMGfu5foPhVitaOWgHYKqkkZm84JIntuZaPzHgOFXd8IWlzV9lF+L7PtYpy0ryR59fELm8DLXvkG2b/1rWoOvum3I87XRZYR/q+jV/zPAMMSAEYIDiTdaMANFlBDTQdCkADFYupkgAJkAAJkAAJmIyAzgTgpXQHGgz/ORdytLwEpFfSL/hlx6mABeB5V0EUs6TqYnFSAIapDHJ7t8evQOWWuckIedfKsh2zPAVg/01AiSRFSasVgC2TSmKOjAA8OrM/Kmz/kgJQUQWCb0QBGDxDM0SgADRDFbWbg/hOMRJAGwDxALYCeAfAN9oNkRuJAjAEUBmSBEiABEiABEiABBQRkJMIYb4F2FMADo39Gj1iFyuaipEb9Uz6GUuCOAFoRgE4wt4VI+KmSssa5vWo6zUlt3d7LAEqt5IIwBaWHZhjGyWdVr+NQMmqiqaqVgA2r1ICc/8+LInteQLw2Ix+KL/jKwpARRUIvhEFYPAMzRCBAtAMVdRmDh0AiF+7ZgCYAeA8gLsBiO8KgwGM0WYYCkCNOTIcCZAACZAACZAACagnICcRwixcUtIdqJ/nBGC0CMAeVZZg6c6TPusndwvwBVdBFDXZCUAKQJntLLd3n/gZuKq1RAA2t+zEdzZxziPP1fdvoFR1RV87VAvAq0pg7j/+BeDxGX1RbodU9PIWYEXlCKgRBWBA2EzXiQLQdCUNaEKxAHYAEKfyxOm/f9xRigBYDaA2gHoAdgcU3XsnngDUECZDkQAJkAAJkAAJkIAqAnISIcwCMDXDgXrDrtwCHDUCMOlXLN1xIggBWABFLZdVlT5UjbW6BZgCMEgB+PhPQBXxI132JeRdM8suzLWNkAYOoQBsWrk4fth4RDKep8w+MeMZlN0xTdKGAjBUuxOgAAwdWyNFpgA0UrVCl6t4iqn4F5d4CusTHsM84D4ROBbAIA1ToADUECZDkQAJkAAJkAAJkIAqAhSAqnCFqvETSb/ityAE4EVXARQxmQAcbu+GkXHSW0MRZiEdqnprEldu73ZfBCRdeZGGEIBNLbvxvW24dPhn1wOlaypKSe0JwMaJxTFvEwWgIrhhakQBGCbQOh+GAjDyBSoLQDykQfwRz+ATf0q50xLf+bqrSFG8xqkfgC4AxH+nA9gDYBaADwD4ekKwuL13IICH3LIv75AlAJwB8CcALV/JRAGoorBsSgIkQAIkQAIkQAKaEpCTCGEWLp4nAIfEfo0no+AZgBSA+Vc1BaDMTpfbu90XAkltc4MIedfYsgc/2oZJAz/zF1CmlqIvK2oFYKPE4pgvIwBPffs0Su+UPmqeJwAVlSOgRhSAAWEzXScKwMiX1OUnBTUCUEi/6QCK+Yi3E8D/AOzz8vnZAO4F0ALABi+fFw8mEXkKWanVRQGoFUnGIQESIAESIAESIAG1BOQkAgWgWqIBtX+8yhIsC+IZgJdcCShsSQtobK07aXUL8DB7N4ziCUDf5ZHbu93mA1XbSwRgI8tezLMNlcbssxYoW0fRMlArABtUKoaFm49KYnveAnzq294ovfNbSRsKQEXlCKgRBWBA2EzXiQIw8iXNKwCTAWwHIG7JFZdSAdjYfUKvIIBLAMTtussAFADwIICe7njiOX/ihKFok/f6BcCNAMQZcHFi0PPa634+oE1DXBSAGsJkKBIgARIgARIgARJQRUBOIlAAqsIZaOPuVZZgOQWgBB8FoMxqktu7XecB1a7LDSLkXUPLPsy3DZEG7rMGKFtX0dJVLQArFsPCf/0LwNPfPIVSu8S7J69cFICKyhFQIwrAgLCZrhMFYORLKl7H9Jf7z3HxnFYA+91pKRWAQvaJt/g6AIhf94gXd+S9XgIw3v0B8fAHj3fAgwIw8uuAGZAACZAACZAACZBA+AjISQQKwLDUIlgBmOKyoZBFPPUn8hdPAIapBnJ7V7zR0VkDK5yNcF/MClSynPaa2E8x16FfyhNwWOLQqW453N+iMjIO/oXC22ehSKV6OFH3MfSe/g/uj1mOmpbDKG85g9ti1khjlayGp47djhdi5yAWmZjouBcLnG3waI0MJB74DkWRgsqWE2gXs0UVnJSYYigQ44Q14yKmOTrhu7hbEVuuDioVS0D3hOVoIt5NWagUkJGK1J2/oeDF7B+fzxSpgxK3j4al5g2AywVs+hbOef1xPKYcfqg2Co/dfTsKx8dg3/KpSF/3FQq6UpDQ8lGU6/gMMEo8+SrPdcNIoO5twD9f43LhyphysDwaHZ6BagUvI/HoktyGTpcF1pHnVM0vEo0pACNBXX9jUgDqryZqBaA40bfOPY2PAfT2MiUrAPFVV/yK5yyAcgDsedrxFmD9rQNmRAIkQAIkQAIkQAKhIyAnESIsAAfHTkPP2EWhm79OIne7aglW7BJP2/F+ed42CY+6XXbFo4AlQxezoQAMUxnk9q6KNGY72uMlR/aPj+VwBn/Y+iPOkpn19zfsDyIDsRgaJ31Tr1z4V+w9Id7ireWt6adcRXFN+nvoYl2DifEfyqUA9F4FnNoJzJG+33Jsnbl4vsZRJCx4RhLD2XEIrMtezx83vjCQ4XnznLQZBaB8OdhCPwQoAPVTi5xM1ArA0XneztsawFofU3rVfWuw+LS4xfjKry0AvgREf+uAGZEACZAACZAACZBA6AjISYQwC0BHphM1Bi/One//rGvwQfy7oZu/TiJP67wZQ37wfTqKAtBdqDCvR50sD69pnB3fBCVSc24YCz5TIW7FNSb2UzwcK24s0+d1V/rI/G8y9pVqUjvgwO/5PjvD0QEPxi7XdIIUgJriZLAQE6AADDHgAMKrFYArAbQDkAKguPs2YG/DtnE/J1B8TtwCnPc98DcD+AnAFwCkvyYBHnC/GVg8V3BQAPPx1YXPANQQJkORAAmQAAmQAAmQgCoCOhOAIvfXFmzDlD+yxcbNdUtj4P5uSIL0OWKq5qjHxhWaAEc3Zmd29dNIu2E0bpn0O/afSoHFkn3XorisFuCNexrhvhaVpbPwqFuaKw4Jlrw39kRm0uLNvV9l3owDCQ+rSqBXxvP4pNJi4KR4VHn2qbMU2PgSED8UU3evRMHpt6ni7K9xtgB0YaetG2wW8UQpfV4PZgzBjHgvp/S8pZtQHEjLf1vudmdl1LWKx+5rd1EAaseSkUJPgAIw9IzVjqBWAIp7BkoD2ASgiZ/BxEMNzrg/L275vT9P21gA4i3BlQCIU4Tuf5WgiPt5grUB1AewS+1k/LSnANQQJkORAAmQAAmQAAmQgCoCOhSALpcLv+8+hXSHE9fXKYtbxi/Ewstdc29JVDW/cDZuNwAoUgHYPAs4lPNkHi8JXNMX6DAwu11CUaD+3RDWLyXdgd93n0RiiYKoW6Eo1h84g1KFbahRtnD+ILMfB7bOzf34NyWfxfWnp6O8RTzlR9l1svnzKLNhorLGHq3G2h9CKmzoHvMzLqEALHBhsuNOPNL9GcRZLSg3tS2qW5VJ228c1+PjYv2wol9LvDZhLHanFsJKZ2NUtxzGUpt4hHmeiycAJTguH9yItz7+BEPipgdUx7ydhAAcHTsFj8QuDTpWKAMMtj+B0XGfKxuCAjAfJz4DUNnSMXsrCkD9VViNAEwAcNk9hYUAbpWZjniAQSEA4umt4kRg3qsjgJ8BiKcIi/exXwBwN4CqAMQro8StxmouIfj8XeXdLz5BcnIyEhPlmqsZmm1JgARIgARIgARIgAT8EtChAPTM98mv/sJ1u8fhsdhf9V3MJ38DEpsDB1YBX/7Pe66lawNdfwSKVghuLid3AdPuAc4fBBJbYdeNX+Cjr7/B2478/1S/4CqIzNq3ocSumdIx7/wQ+OHpgPLol/Es5jmvydc351blEZM+wIizA2Vjb3RWw+MYiXcebYPrapXB0u3H8dyMjbiY7kC/62vghZSJWS9wgMUKdHkbaPG4bMxoa9B+/DKsTL0z6Gm3TnsPaxL6Bh0n1AHEKdORceIdmQouCsB8kCgAFaybKGhCAai/IqsRgGUAnHBPQXxnf1BmOuItw2XdLwRp6KVtKwDircRCDsYD2ArgHQCB/GrJfQODPGAKQHlGbEECJEACJEACJEACmhLwJwA7jwNaByaItMxx38lLWDWpW0gF4GeOW/C+4w7cHLMe4+I+k6T/oeM2PB07X35KOQLw7AFgUmNp+9Z9gBY9gGKVgLgC8rGUtMhIBVJPAUUqAjGxSM1woOCYUvl6Nkr7FAtvuYzKy/pJP9dtPvCV+ltIU102NE3/GOlZPyZIrxwBeO+Hf2LPfwdRynIBNtgxLX4MSlqkL1E48/BixCc2QWycDQlxMbmB0uyZsGc6USQhLvte6ONbgPhCQMlqSqhEXRutBOCTGQPwWfxbuudHARhciSgAg+Nnlt4UgPqrpBoBKB4KctA9ha8BdJWZjmgr+uwFUCPEU6cADDFghicBEiABEiABEiCBgAl4E4BV2wOVmgMdBgGx+SVPwGMF0fG/qb1RZZ+4OSU01yTHXZjouA83WDfkkyBCjFS0nMr/PDrPVPwJwHYvAp2Ghib5vFG91FPc2vn7Lae8CsD5Mz7CbeniBiJll3jW4KMZA7HeVcdrh7wCcP1/V25H/jX+RdSwHpH24e28yqDLtKIA9AOIJwDzwaEA1GTbGT4IBaD+SqhGAGp9AlBLGnL39PIWYC1pMxYJkAAJkAAJkAAJqCHgTQDqUcwseAFYP0XNzFS19ScAe2QMwFJnc7SxbsW38X6ehmNAATh59mI8m/qBYlbiBQxrnPV8tqcAVIxSs4YUgBSAahYTBaAaWuZtSwGov9qqEYBaPgMw3CT4EpBwE+d4JEACJEACJEACJJBDgAIwiwQFoLItQQGojFM4W1EAUgCqWW8UgGpombctBaD+aqtGAIrstXgLcCQoUABGgjrHJAESIAESIAESIAFBgAKQApAnAA39tYACkAJQzQKmAFRDy7xtKQD1V1u1AnAlgHYAUgAUB+DwMSXxYo8/3Z8bBWB4hKdOARjhAnB4EiABEiABEiCBKCZAAUgBSAFo6C8AFIAUgGoWMAWgGlrmbUsBqL/aqhWAYwAMdE+jNYC1Pqb0KoCx7s/dDOCXME9dvFE47xULoJb4AN8CHOZKcDgSIAESIAESIAESoACkAKQANPTXAQpACkA1C5gCUA0t87alANRfbdUKwFZ5pN/HAHp7mZIVwBYAdQGcA1AWgD3MU6cADDNwDkcCJEACJEACJEACPglQAFIAUgAa+gsEBSAFoJoFTAGohpZ521IA6q+2agWgmEHObcDi9t/2AFZ7TOslAOPdHxspnvqig2nzFmAdFIEpkAAJkAAJkAAJRCkBCkAKQApAQ29+CkAKQDULmAJQDS3ztqUAjHxt2wKokSeN0gAmuP++CsBnHil+6SXlpgBE2wIALgEQtwUvc//9QQC93H12AWgB4GLkpw0KQB0UgSmQAAmQAAmQAAlEKQEKQApACkBDb34KQApANQuYAlANLfO2pQCMfG2F0OumIg1fNbsNwDQARX3EEvKvC4A9KsYKZVMKwFDSZWwSIAESIAESIAES8EeAApACkALQ0F8jKAApANUsYApANbTM25YCMPK11UoAiplUAdDfLfqEYMtwC7/ZACYDSI38dHMzoADUUTGYCgmQAAmQAAmQQJQRoACkAKQANPSmpwCkAFSzgCkA1dAyb1sKQPPWVm8z40tA9FYR5kMCJEACJEACJBC9BCgAKQApAA29/ykAKQDVLGAKQDW0zNuWAtC8tdXbzCgA9VYR5kMCJEACJEACJBC9BCgAKQApAA29/ykAKQDVLGAKQDW0zNuWAtC8tdX7zHgLsN4rxPxIgARIgARIgATMS4ACkAKQAtDQ+5sCkAJQzQKmAFRDy7xtKQDNW1u9z4wCUO8VYn4kQAIkQAIkQALmJUABSAFIAWjo/U0BSAGoZgFTAKqhZd62FIDmra3eZ0YBqPcKMT8SIAESIAESIAHzEqAApACkADT0/qYApABUs4ApANXQMm9bCkDz1lbvM6MA1HuFmB8JkAAJkAAJkIB5CVAAUgBSABp6f1MAUgCqWcAUgGpombctBaB5a6v3mVEA6r1CzI8ESIAESIAESMC8BCgAKQApAA29vykAKQDVLGAKQDW0zNuWAtC8tdXbzPgWYL1VhPmQAAmQAAmQAAlELwEKQApACkBD738KQApANQuYAlANLfO2pQA0b231NjMKQL1VhPmQAAmQAAmQAAlELwEKQApACkBD738KQApANQuYAlANLfO2pQA0b231PjPeAqz3CjE/EiABEiABEiAB8xKgAKQApAA09P6mAKQAVLOAKQDV0DJvWwpA89ZW7zOjANR7hZgfCZAACZAACZCAeQlQAFIAUgAaen9TAFIAqlnAFIBqaJm3LQWgeWur95lRAOq9QsyPBEiABEiABEjAvAQoACkAKQANvb8pACkA1SxgCkA1tMzblgLQvLXV+8woAPVeIeZHAiRAAiRAAiRgXgIUgBSAFICG3t8UgBSAahYwBaAaWuZtSwFo3trqfWYUgHqvEPMjARIgARIgARIwLwEKQApACkBD728KQApANQuYAlANLfO2pQA0b231PjMKQL1XiPmRAAmQAAmQAAmYlwAFIAUgBaCh9zcFIAWgmgVMAaiGlnnbUgCat7Z6nxkFoN4rxPxIgARIgARIgATMS4ACkAKQAtDQ+5sCkAJQzQKmAFRDy7xtKQDNW1u9zWyrR0KxAGqJjyUnJyMxUfhAXiRAAiRAAiRAAiRAAmEhQAFIAUgBGJatFqpBKAApANWsLQpANbTM25YC0Ly11dvMKAD1VhHmQwIkQAIkQAIkEL0EKAApACkADb3/KQApANUsYApANbTM25YC0Ly11fvMeAuw3ivE/EiABEiABEiABMxLgAKQApAC0ND7mwKQAlDNAqYAVEPLvG0pAM1bW73PrAqAAyLJdevWoUKFCnrPl/mRAAmQAAmQAAmQgHkIvF03/1xe2K6/+f06Etg8I2R5TXF0xqeOW9HOuhkT4j+RjPNiRi/84WyE5tadeD/+Pd85PDQTqNAIOJcMfH6TtF2rp4C2z4Us/9zAXurZOm0y5nY8g4qrh0nHv/cLfLFgGR5Pm6o4rz4Z/fC3M+vpPV6vNYM6ZX2819T12HzofG6bb+NfQ1XrcWkfPa4zxST00/CeD/7Ed2k9gk7opYxe+dZ+0EFDEEQeWcQAABeVSURBVOAt+30YEDdbWWRbUSD9Qr62u50VUdN6RFkMha2cLgusA7YpbB25ZkePHkWrVq1yEkgC8F/ksuHIkSJAARgp8hy3BYC/iIEESIAESIAESIAESIAESIAESIAESCBsBFoCWB+20TiQbghQAOqmFFGXCAVg1JWcEyYBEiABEiABEiABEiABEiABEogwAQrACBcgUsNTAEaKPMe1AWjoxnASQKYBkJTPc2pRfNE8ZoCcmSIJGJUA95tRK8e8jUqAe86olWPeRiTA/WbEqjFnoxLgfsuuXAyAMu4i/gsg3agFZd6BE6AADJwde0YfgdwXlwCoDOBQ9CHgjEkgbAS438KGmgORQBYB7jkuBBIIHwHut/Cx5kgkwP3GNUACbgIUgFwKJKCcAL95KGfFliQQLAHut2AJsj8JqCPAPaeOF1uTQDAEuN+Coce+JKCOAPebOl5sbWICFIAmLi6npjkBfvPQHCkDkoBPAtxvXBwkEF4C3HPh5c3RopsA91t015+zDy8B7rfw8uZoOiZAAajj4jA13RHgNw/dlYQJmZgA95uJi8up6ZIA95wuy8KkTEqA+82kheW0dEmA+02XZWFSkSBAARgJ6hzTqAT4zcOolWPeRiTA/WbEqjFnIxPgnjNy9Zi70QhwvxmtYszXyAS434xcPeauKQEKQE1xMpjJCfCbh8kLzOnpigD3m67KwWSigAD3XBQUmVPUDQHuN92UgolEAQHutygoMqeojAAFoDJObEUCggC/eXAdkED4CHC/hY81RyIBfo/jGiCB8BLg97jw8uZo0U2A+y2668/Z5yFAAcjlQALKCfCbh3JWbEkCwRLgfguWIPuTgDoC3HPqeLE1CQRDgPstGHrsSwLqCHC/qePF1iYmQAFo4uJyaiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAcg1QAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAImJkABaOLicmokQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAHINUACJEACJEACJEACJEACJEACJEACJEACJEACJiZAAWji4nJqJEACJEACJEACJEACJEACJEACJEACJEACJEAByDVAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiYmQAFo4uJyaiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAcg1QAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAImJkABaOLicmokQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAHINUACJEACJEACJEACJEACJEACJEACJEACJEACJiZAAWji4nJqJEACJEACJEACJEACJEACJEACJEACJEACJEAByDVAAsoIXAWgH4AuAMR/pwPYA2AWgA8ApCoLw1YkELUEygJo5f7TEoD4U8pN4ysA3VWS6QyglzteGQAnAawD8AmAn1TGYnMSMBuBZgDEHmkHoAEAsf/sAI4A+BPAFAC/q5g095sKWGwaVQSKAvif+3taCwCVAIjvSQUAnAOwDcAi9547rYBMGwB93Hu3PICzADYB+BLADAX92YQEopnAeAAv5QHQEcByGSD8/hbNKyYK504BGIVF55RVExDSbzqAYj567nT/42+f6sjsQALRQ8DlZ6pqBKD4vvWRW/75CikkYG8A/saMHvKcabQRWAGgvYJJfw3gSQAZftpyvykAySZRTeAGAEsUEDgF4FEAP/tpOwzAcABWH23mA7gfQJqC8diEBKKNQGMA6wHEKhSA/P4WbSuE880iQAHIhUAC/gmIbybitERBAJcAjAWwzP2b3QcB9HR33+H+7a9ow4sESCA/gbwyLhnAdgA3uZupEYCjAQxy9/sHgPht714A1QG8DKCp+3Oi3RAWggSikIA4nS72gzjtN9t90u8ggBgA4nTRAPcpJYHmWwAP+2HE/RaFC4hTVkVACMDP3f823ABAfH876pZ4iQDuBXC3e/8J2S5Ov2/2MoKQ8Z+6Py6+p40B8C+AigD6AxAnmcQlfiEtRCIvEiCBKwSENF/j3l8n3KfexWf9nQDk9zeuoKgkQAEYlWXnpFUQELKvAwCH+0TFao++4pi5EBDiEr+1HaUiNpuSQDQRGAngL/ef4wCSAOx3A1AqAGu4xaH47a74La845XQ5D0Qh6sXpJ3EbltizddxyMJo4c64ksADAVADfAcj0gqM0gFUAark/J/aRt9uBud+4lkhAnoAQ6972Wd6edwL43v2BuQDu8Qhb3P39UPy/kPXNAYgTgzmXGEP0v839gesArJRPjS1IIGoIPAdgIgBxIEPslYHumfsSgPz+FjVLgxP1JEAByDVBAr4JiN/SimeKietj9y2Fnq3Fb5y2AKjrfk5LOfdzlsiVBEjAP4FABOD77mcjicjiJJP4ba/n1RpAjqifDKAvC0ECJJCPwK0AxO2E4nrXfcLIsxH3GxcOCWhHQJx6F7+UEmJPPCMw75X3l8kP+XjWnzhNeMB9klBI/hwZqF2GjEQCxiRQ2f2szcLuE3/i4IY4lCEuXwKQ39+MWWtmrQEBCkANIDKEaQnkPRoupMJaHzN91X1rsPi0uKVRybNgTAuNEyMBhQTUCkDx/UrcWiUesC5+wyuku69LfL42gEPul/bwWYAKi8JmUUNA/KB00T3bhQCEEMx7cb9FzVLgRMNEQJxaFyf7xKNiiniMKU7kXgPgglsO+noup3jB1c3uF9GJk7x87EyYisdhdE1A/DJLfA/LuZtkhIwA5Pc3XZeTyYWaAAVgqAkzvpEJiNsrxBsUU4D/t3enMdOdYxzA/0HEEkRJiS9CEVvF2ggtscceFUkRS5DSVCwh9kbspDQE1dCqUFvxxdJaqghCQqKNNXbCB7HGvkXkqnuS480z88w8Szsz1+/+9L7vzDlzX79rTs55r7mX1LSMmlK4U6uRSLVOYLWaAjz71WmTY9d3AoctsGoB8KaT6bzzRuTO+lyv1w7B1eq42VTjw47J+QlsisBRSWY7ktZ/nh52RMddb5uSSf3cBIH6warW86upvFUIrBkms3bV8ZxZS1vUBiG1I+m8VtMaa23Aavce6w5uQvz6SOCwBGpTnA8m+d0YYfvrJLsVAN3fDisbzrsRAgqAG5EmnbyCBOomUr+wXprk9gv6cN1x46m31ILrdTPSCBBYLLBqAbB2465pT9WeneSNC05fr58xXq/jLpAMAgT+T+ARSWotsmqnjw10pm9wvfnCENifQK1JWyPWa6pubVBVS8RUe1yS8yanvs1YSqb+6U1Jai2zeW163Z6a5Mz9ddHRBDZaoAZn1NT6G45NGc8e0exWAHR/2+i06/x+BRQA9yvo+G0VuNpkc4GdpkcdGXdNw7jmWJOsRgRqBAgsFli1APi0JG8bp3xUkg8vOH3tuljF+Gp1XI0I1AgQ+J9ArV1b62QeN0BqNFKNSpo215tvC4HVBZ6Y5NwFh71+FAOny1LUiL8LxzG1FmC9Z16rDa5qM61qr51sdLB6Tx1BYPMF3j4KfzUL6/gks+tqtwKg+9vm514E+xBQANwHnkO3WqAWaK5t5KvV0PKTdom2djU9evyKe+xWywiOwMEIrFoAnC6S/sAktRbSvFavz0b9PTfJGw6my85CYCsEnjMpMtRuiSfuEJXrbStSLYjLWWBeAfCS8WPUTmtJ1w9a549+npLkrAV9rqnE3xmv2+Tqck6uj1srgSr41VJNtQP3HccU+1kHdysAur+tVSp15vIWUAC8vMV93qYI1I5SPx+dfU+Sx+/S8XpvHfOjJLW1vEaAwGKBVQuAp401Nuus90ly8YLT19pInx2v13GvlAwCBC4TuGeSi5LUemP1I9ftktQPWEc215svDIHVBWpKYu3WW+3qSY4Zy8LU1N16PqzpvbOlLGZnrynB7x5/eXKSdy742OnaZeckecrqXXQEgY0XqHUzq6heBfGdlrDYrQDo/rbxXwEB7EdAAXA/eo7dZgEjALc5u2JbB4FVC4B+sV2HrOnDJgvUWmNfTFLr1v5j7Cb6hTkBud42OdP6vm4CVeSrHUprimIV+d416aARgOuWLf1Zd4FZga8GX9x6bKIz7fNuBUD3t3XPsP4dqoAC4KHyOvkGC1gDcIOTp+sbIbBqAdCaLRuRVp1cU4GbJPlSkhuNKVNVdKjpv/Oa621NE6lbGytQy8nUJnF/GTNGfj8isQbgxqZUx68AgVuOzRlrFODDk3x0hz7sVgB0f7sCEucj10dAAXB9cqEn6ydgF+D1y4kebY/AqgXAhyT52AjfLsDb8z0QyeELVNGvRv7V9MEagVTrlM2mHM77dNfb4efFJ/QSeEyS946QH5vkfePPt52sX2YX4F7fCdGuLlCbup2c5MdJXjzn8NoI7pHjtVdM1s2sZ8gqwLu/re7uiC0SUADcomQK5cAFanHZE8bNotZ1+fecT6hdf2sHqmovT/LSA++JExLYPoFVC4DTtY/qAbB+wZ3XZg+I9Xod95Pt4xMRgaUErp+kpvnWNKlqT0/y1iWOdL0tgeQtBFYQuF+ST4/3vyjJa8afayTTX5NcOcmnktSIwHnthUlePV6stW4/t8LneyuBbRCo6fNP2GMgNRL+p+O5sNbkrOZ5co+YDttcAQXAzc2dnh++QD1k1cNWtbsm2Wn3tnrtBZMHuQdMHvAOv4c+gcDmCqxaAKz71S/GFMbvjcWf50X/3SQ1TeSXY6pVjXrSCHQTuM7YLKd2SJzdq163JILrbUkobyOwpMB0h+BnJHnz5Lj6Ebl+TP5jklqD+p9zzvnJsXZnreFZ7/vTkp/tbQS2ReAgCoDub9vybRDHngQUAPfE5qAmAsdNin7zfiG6UpJvjWLEH5IcneRfTXyESWA/AqsWAOuzzkxyyvjQ+s/SV3foQBXrvzL+vd5/6n466VgCGypwjfFj1N1H/1+V5CUrxuJ6WxHM2wksEPhEkgeN1++V5POT9z4vyaw4/+gkH9jhPLW7cI1eqpGCFyR5MG0CBHYU2G0NQM+TvjitBRQAW6df8EsIzKYB1/Tfe0wKC7NDpztJvSxJ3XQ0AgR2F9hLAfAWSb6d5CpJvj6uyb9NPurqSeqavfOYsl/THn+we1e8g8BWCdSUwlrr6P4jqt3WFZsXvOttq74WgjkkgRrZVwW7vy84f61be8Z4vYp4Nz9iWZmjxppmNWr3Z0nulOS3k/NV0a827Xno+DfTfw8pmU67FQLLFADd37Yi1YLYi4AC4F7UHNNJ4A5JvpykCgt/Hmuv1Jor9feTxkK05fH9UXQwHaPTt0Osqwgcn+RmkwNqbbLTx9/rGjv7iJPVNI+dWq2bVNPuq31jjJqotVyOSfL8JHXNVqv31TpLGoFuAh9JcuII+uIkzxqbf8xzqOmGdQ9zvXX7poj3IASqoHetJHXd1U7bdT+q58X6t2OT1IYfs5G4da3VyL2LdvjgpyY5a/x7naNG7X5zLHtR13CNGqz2/iS1oYhGgMDOAssUAGfPiZ4nfYvaCSgAtku5gPcgUL+4npfk2nOOrf841QPdD/dwbocQ6CKw6rot8+5PNe3+HUmetADunFGc/08XXHESmAisuuZljTiqEbk7NdebrxaBxQJVALzxEki1hm3dtz6z4L01k+S0JPPufzX1t3Y3XTTacImueAuBrRZYtgDo/rbVXwPBzRNQAPTdILCcQD3cPXMU+modlvoVtwp+H0rylrGD23Jn8i4CPQUOqgA406u1lE5OcpckNZrwN0m+NnZ0u7AnsagJXCZwkAVA15svFYHFAjX6/L5jhN6tktwgyfVGke5XSS5J8vEk5y/5rHi3sXbtCeNctb70pUnOHaP/5IMAgcUCyxYA3d98k1oKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgooALZMu6AJECBAgAABAgQIECBAgAABAgS6CCgAdsm0OAkQIECAAAECBAgQIECAAAECBFoKKAC2TLugCRAgQIAAAQIECBAgQIAAAQIEuggoAHbJtDgJECBAgAABAgQIECBAgAABAgRaCigAtky7oAkQIECAAAECBAgQIECAAAECBLoIKAB2ybQ4CRAgQIAAAQIECBAgQIAAAQIEWgr8F4YwET479mbiAAAAAElFTkSuQmCC\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11a344210>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# log plot\n",
"fig, ax = plt.subplots()\n",
"ax.semilogy(x, y1, label='area1')\n",
"ax.semilogy(x, y2, label='area2')\n",
"ax.legend()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(1, 13)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# log plot and zoom in\n",
"fig, ax = plt.subplots()\n",
"ax.semilogy(x, y1, label='area1')\n",
"ax.semilogy(x, y2, label='area2')\n",
"ax.legend()\n",
"ax.set_xlim([1,13])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img 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QAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzUkAbE4CYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNSQBsTgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgDYnATA5iQANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzUkAbE4CYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLybwqsDPnpl/d2Z+9cw8/ed3zczbZuZ/nJn/fGZ+9ENAJQAeoAqADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEngSeot9/PzOf+AE4/q+Z+VUz8+0vmUsAPAAVABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCXzezHzdzHz8zPzwzHzFzPz5mfm4mfnCmflNL4i+dWY+/8XMy1ITAA8kBcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsF2KKQL7BATAfTv3YgJPse+Xz8yPz8wvm5mv/2kkXzwzX/niv/vymfn9L5FMADzAFAAblgDYnATA5iQANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ci3cLPH1F3199QfBHZua3vB+Oj56Zb56Zz56Zvzcz/8jMvOclsQmAB5ACYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXvxboE/NDO/6wXBL56Zv/IBOL70xbcGP/30r5iZP/eS2ATAA0gBsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr14t8DXzswvnZkfmZm3vPg24Pcn8kte/HsCn37u6VuAn74V+GX8EAAPFAXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH079+LdAt8/M58yM399Zn7Ba1B80sz8wIuf/xMz8xteEpsAeAApADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kX7xX42Jl554vn/5mZ+TWvQ/H0JwR/wsx8w8w8fUVg+fEU+F7rx6fNzDc+DXz6V33JvPGTP7H8NdfOCIBt9QJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzL94r8DNn5vtePP+Pz8wXvg7F987Mp774A0E+N7K9N84JgAFKAAxIMyMANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH079+K9Am+dme968fyvnpl/83Uonmaffs23zczPjWwCYIQqYwJgURIAm9KMANikBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXvxXoEPx1cA+hbgl3hfAmDD9BWAzUkAbE4CYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvXivwIfj3wH4err+EJDXE3qfnxcAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3It3C/hTgD+C9i8AtmUJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr14t8DXzswvnZkfmZm3zMyPfwCOpz/19+te/Nzvn5kvf0lsvgLwAFIAbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbpZgisE9AANy3cy/eLfDvzczvfEHwi2fmr3wAji+dma948XO/cmb+7EtiEwAPIAXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH079+LdAl/wPtHvj8zMb3k/HB89M988M589M++YmU+dmfe8JDYB8ABSAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJvDqtwE/ffvvL5uZr/9pJF88M1/54r/7fTPze18imQB4gCkANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcT+Kdn5i/PzMfNzA/PzNO3Bf/5F//3F87Mb35B9Ldm5hfNzA+9RDIB8ABTAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJvAk8C/PzH83Mz/jA3A8xb9fPTNve8lcAuABqADYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgVYGfMzO/9UXoewpz734R/P7EzPynM/OjHwIqAfAAVQBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNSQBsTgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgDYnATA5iQANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzUkAbE4CYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNSQBsTgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgDYnATA5iQANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzUkAbE4CYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCdwU+KkA+HP/y98+b/qUn3Hzszz73/sPfu6ffvaf8Tl8wJ/3pu9/Dh/j2X+G7/jxT3r2n/E5fMBvfudbn8PHePaf4dvf+SnP/jM+hw/4jf/Pz34OH+PZf4Z3fOdbnv1nfA4f8OO+5w3P4WM8+8/wMX/vvc/+M978gO/+4XfMt3z1H3j1Izz9j97bb34evzcBAh8+AQHww2ftdyJAYEYAPLgCAbBhCYDNSQBsTgJgcxIAm5MA2JwEwOYkADYnAfC1nQTAdkemCDyigAD4iFv1JgLPV0AAPNiNANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCDMJydcAACAASURBVID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwI/FQB/1lf8rnnjJ73l5md59r/3P/vz3/bsP+Nz+ID/zCd+53P4GM/+M/zoT3zMs/+Mz+ED/uT4f43KHr7lhz6tjK2f+cb/+zPWGxSAj/nuV8rY+pmP/Tv+/lSO4OO/7yfL2NqZd/3oO+ab/vQffPX9b52Zt6/F8HACywT8r8iyhXsugcsCAuDBAgTAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnATA13YSANsdmSLwiAIC4CNu1ZsIPF8BAfBgNwJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgDYsATA5iQANicBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+devFvgF87MvzQzv3Rm/qmZ+dSZec/MfM/MfN3M/NGZ+YsfQiIB8ABXAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3Mv3ivwF2bml4Xnf/XM/Fsz8+4wezoiAB6ICYANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxXsF3jYz//iLr/b7Ey++0u+7ZuYNM/NLZuZ3zMw/+oLnf5iZf+NDQCUAHqAKgA1LAGxOAmBzEgCbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FewX+l5n5YzPzJ2fmJ94Pw6fMzF+emX/ixc89fbXgy/52YAHw4P4EwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAq8l8Gtm5mteDHzVzPzWl8wlAB6ACoANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQReS+DNM/NDLwb+zMw8BcGX+UMAPNAUABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCbyWwD88M3/3xcDTVwL+Ky+ZSwA8ABUAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJvJbAr52Z/+nFwB+emS95yVwC4AGoANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOADCXz0zHz9zHzBi4HPn5m/dsj1FPhe68enzcw3Pg38rK/4XfPGT3rL4V9+17gA2PYtADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAh9I4HfMzH/44if/1Mz8ug+C6r311wiAry8lAL6+0dOEANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCbw/gX9hZv63mXnjzHzfzPz8mfneD4JKAPwg0D7QLxEAG6YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ/HSBf3Jm/uLMfNLMvGtmfuXM/IUPksm3AH+QcO/vlwmADVMAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUE3lfgM2fmLz39K/lm5idm5l+fmadv//1Q/fCHgBzICoANSwBsTgJgcxIAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQReFXiKfk9f+fePzczTt+7+xpn5Yx9iHgHwAFgAbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbpZgisE9AANy3cy8m8CTwKS++zfdzXnD8OzPzn30YaATAA2QBsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwCfOzP8xM7/wBcWXzsx/8GFiEQAPoAXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH079+LdAh8/M392Zv65Fwx/aGZ+z4eRRAA8wBYAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3Iv3CrwyM18zM7/iBcF/MjO/7cPMIQAegAuADUsAbE4CYHMSAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sV7Bf7kzPy6F89/+hbgp/j39Id/fKAf756Zv/WSuQTAA1ABsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr14r8Brxb73p/KdM/MZL5lLADwAFQAblgDYnATA5iQANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ci/cKCIAfYbsXANvCBMDmJAA2JwGwOQmAzUkAbE4CYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgK8APNAXABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNSQBsTgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgDYnATA5iQANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzUkAbE4CYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNSQBsTgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgDYnATA5iQANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzUkAbE4CYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JwEwOYkADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgM1JAGxOAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicBMDmJAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCdwU+KkA+Jm//cvmTZ/4lpuf5dn/3j/2aT/+7D/js/iAr/zks/gYz/1DvOHj3FPZ0SuvcCpOP/Yjr5Sx9TNv+q6PWW9QAD7+e/0jSXF6w4+9t4ytn3nz3/b38dc6gne98x3zV//3r3h15K0z8/b1RwOAwBIB/2u7ZNGeSeCZCAiAB4sQACOWAJigBMDENAJgcxIAm5MA2JwEwOYkADYnAfC1nQTAdkemCDyigAD4iFv1JgLPV0AAPNiNABixBMAEJQAmJgGwMY0A2KAEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMQSABOUAJiYBMDGJABGJwGwQQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgAYsQTABCUAJiYBsDEJgNFJAGxQAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvAEYsATBBCYCJSQBsTAJgdBIAG5QA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuAEUsATFACYGISABuTABidBMAGJQA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDEEgATlACYmATAxiQARicBsEEJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4AGLEEwAQlACYmAbAxCYDRSQBsUAJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwBGLAEwQQmAiUkAbEwCYHQSABuUANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgBFLAExQAmBiEgAbkwAYnQTABiUANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgxBIAE5QAmJgEwMYkAEYnAbBBCYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ABixBMAEJQAmJgGwMQmA0UkAbFACYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ARiwBMEEJgIlJAGxMAmB0EgAblADYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ARSwBMUAJgYhIAG5MAGJ0EwAYlADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMQSABOUAJiYBMDGJABGJwGwQQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgAYsQTABCUAJiYBsDEJgNFJAGxQAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvAEYsATBBCYCJSQBsTAJgx9v2LQAAIABJREFUdBIAG5QA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuAEUsATFACYGISABuTABidBMAGJQA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDEEgATlACYmATAxiQARicBsEEJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4AGLEEwAQlACYmAbAxCYDRSQBsUAJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwBGLAEwQQmAiUkAbEwCYHQSABuUANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgBFLAExQAmBiEgAbkwAYnQTABiUANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgxBIAE5QAmJgEwMYkAEYnAbBBCYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ABixBMAEJQAmJgGwMQmA0UkAbFACYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ARiwBMEEJgIlJAGxMAmB0EgAblADYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ARSwBMUAJgYhIAG5MAGJ0EwAYlADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMQSABOUAJiYBMDGJABGJwGwQQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgAYsQTABCUAJiYBsDEJgNFJAGxQAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvAEYsATBBCYCJSQBsTAJgdBIAG5QA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuAEUsATFACYGISABuTABidBMAGJQA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDEEgATlACYmATAxiQARicBsEEJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4AGLEEwAQlACYmAbAxCYDRSQBsUAJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwBGLAEwQQmAiUkAbEwCYHQSABuUANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgBFLAExQAmBiEgAbkwAYnQTABiUANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgxBIAE5QAmJgEwMYkAEYnAbBBCYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ABixBMAEJQAmJgGwMQmA0UkAbFACYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ARiwBMEEJgIlJAGxMAmB0EgAblADYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ARSwBMUAJgYhIAG5MAGJ0EwAYlADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMQSABOUAJiYBMDGJABGJwGwQQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgAYsQTABCUAJiYBsDEJgNFJAGxQAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvAEYsATBBCYCJSQBsTAJgdBIAG5QA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuAEUsATFACYGISABuTABidBMAGJQA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDEEgATlACYmATAxiQARicBsEEJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4AGLEEwAQlACYmAbAxCYDRSQBsUAJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwBGLAEwQQmAiUkAbEwCYHQSABuUANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgBFLAExQAmBiEgAbkwAYnQTABiUANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgxBIAE5QAmJgEwMYkAEYnAbBBCYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ABixBMAEJQAmJgGwMQmA0UkAbFACYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ARiwBMEEJgIlJAGxMAmB0EgAblADYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ARSwBMUAJgYhIAG5MAGJ0EwAYlADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMQSABOUAJiYBMDGJABGJwGwQQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgAYsQTABCUAJiYBsDEJgNFJAGxQAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvAEYsATBBCYCJSQBsTAJgdBIAG5QA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuAEUsATFACYGISABuTABidBMAGJQA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDEEgATlACYmATAxiQARicBsEEJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4AGLEEwAQlACYmAbAxCYDRSQBsUAJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwBGLAEwQQmAiUkAbEwCYHQSABuUANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgBFLAExQAmBiEgAbkwAYnQTABiUANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgxBIAE5QAmJgEwMYkAEYnAbBBCYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ABixBMAEJQAmJgGwMQmA0UkAbFACYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ARiwBMEEJgIlJAGxMAmB0EgAblADYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ARSwBMUAJgYhIAG5MAGJ0EwAYlADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMQSABOUAJiYBMDGJABGJwGwQQmAzUkAbE4CoADYLsUUgX0CAuC+nXsxgZsCAuCBvgAYsQTABCUAJiYBsDEJgNFJAGxQAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvAEYsATBBCYCJSQBsTAJgdBIAG5QA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuAEUsATFACYGISABuTABidBMAGJQA2JwGwOQmAAmC7FFME9gkIgPt27sUEbgoIgAf6AmDEEgATlACYmATAxiQARicBsEEJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4AGLEEwAQlACYmAbAxCYDRSQBsUAJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwBGLAEwQQmAiUkAbEwCYHQSABuUANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KSAAHugLgBFLAExQAmBiEgAbkwAYnQTABiUANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgxBIAE5QAmJgEwMYkAEYnAbBBCYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ABixBMAEJQAmJgGwMQmA0UkAbFACYHMSAJuTACgAtksxRWCfgAC4b+deTOCmgAB4oC8ARiwBMEEJgIlJAGxMAmB0EgAblADYnATA5iQACoDtUkwR2CcgAO7buRcTuCnwUwHws77oy+aVN7/l5md59r/3uz752X/EZ/EB3+t/yZ7FHh7lQ3zUex/lJR/ad3z0ez60f/1H+au/8g4HVXb5xneWKTNveqd7KlfwsX/X36Bey+nHfuzvzzf8pX//1ZG3zszbi6sZAgQ+8gX8Y9NH/g69gMBHkoAAeLAtAbBhCYDNyVQTEACbkwDYnATA5iQANicBsDkJgK/tJAC2OzJF4BEFBMBH3Ko3EXi+AgLgwW4EwIYlADYnU01AAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTqaagADYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JxMNQEBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5mWoCAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgczLVBATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOZkqgkIgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNyVQTEACbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5OpJiAANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnU01AAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTqaagADYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JxMNQEBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5mWoCAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgczLVBATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOZkqgkIgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNyVQTEACbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5OpJiAANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnU01AAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTqaagADYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YA2JxMNQEBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXkzgpoAAeKAvADYsAbA5mWoCAmBzEgCbkwDYnATA5iQANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgczLVBATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGbAgLggb4A2LAEwOZkqgkIgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDNyVQTEACbkwDYnATA5iQANicBsDkJgAJguxRTBPYJCID7du7FBG4KCIAH+gJgwxIAm5OpJiAANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlADYnU01AAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTuCkgAB7oC4ANSwBsTqaagADYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJvCBBL5yZr74fX7yX5yZ//MlcwmAB6ACYMMSAJuTqSYgADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJvD+Bz5uZvzYzbxQAn8+BCIBtFwJgczLVBATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYGfLvDRM/MNM/P5M/N9M/OpLwZ8BeDlWxEA2wIEwOZkqgkIgM1JAGxOAmBzEgCbkwDYnARAAbBdiikC+wQEwH0792ICP13gt83MfzQz3zozf2pmfqcA+DyORABsexAAm5OpJiAANicBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ciwm8r8BbZ+ZvzsybZ+bpK/5++cx8uQD4PI5EAGx7EACbk6kmIAA2JwGwOQmAzUkAbE4CYHMSAAXAdimmCOwTEAD37dyLCbyvwNfMzK+Zmf92Zn7jzPxeAfD5HIgA2HYhADYnU01AAGxOAmBzEgCbkwDYnATA5iQACoDtUkwR2CcgAO7buRcTeFXgN8zMH5+ZH5iZz5qZ7xcAn9dxCIBtHwJgczLVBATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MYEngbfMzLfMzKfNzG+amf/qBYuvAHxG9yEAtmUIgM3JVBMQAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sUEngT+ixfh7+tm5p+fmfe+pAD46a/D+xQcv/Fp5rO+6MvmlTc/dUg/PpCAANhuQwBsTqaagADYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJvAU/L52Zn5iZn7hzPyN9yH5B/0KwFdD4usqC4CvSzQC4OsbPU0IgM3JVBMQAJuTANicBMDmJAA2JwGwOQmAAmC7FFME9gkIgPt27sW7BV6ZmW+amc+emT88M1/y0zgEwGd0HwJgW4YA2JxMNQEBsDkJgM1JAGxOAmBzEgCbkwAoALZLMUVgn4AAuG/nXrxb4NXA910z8zkz8yMvOQD6FuCXeF8CYMMUAJuTqSYgADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3Iv3Cjz9Sb9/fWaevgrwX52Z//n9UPyDfgXg6+k+BcLvfhryLcCvRzW+Bfj1if6/CQEwQhlLAgJgYhoBsDkJgM1JAGxOAmBzEgAFwHYppgjsExAA9+3ci/cK/JGZ+c0z8+0z87s/AMO/NjO//sXP/YGZ+Zsv/vPXvJ+vFvxgJAXAAzVfAdiwBMDmZKoJCIDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/fivQL/zcx80Qf5/M+cme/4IH/t+/4yAfAAUQBsWAJgczLVBATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp178V4BAfAjbPcCYFuYANicTDUBAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4LUE/DsAn9F9CIBtGQJgczLVBATA5iQANicBsDkJgM1JAGxOAqAA2C7FFIF9AgLgvp17MQEB8CPkBgTAtigBsDmZagICYHMSAJuTANicBMDmJAA2JwFQAGyXYorAPgEBcN/OvZiAAPgRcgMCYFuUANicTDUBAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515MQAD8CLkBAbAtSgBsTqaagADYnATA5iQANicBsDkJgM1JABQA26WYIrBPQADct3MvJiAAfoTcgADYFiUANidTTUAAbE4CYHMSAJuTANicBMDmJAAKgO1STBHYJyAA7tu5FxO4KfDpM/PdTx/gs77oy+aVN7/l5md59r+3ANhWJAA2J1NNQABsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE6mmoAA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicTDUBAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOZlqAgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMy1QQEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmZKoJCIDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzclUExAAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTqSYgADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2J1NNQABsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE6mmoAA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicTDUBAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOZlqAgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMy1QQEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmZKoJCIDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzclUExAAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTqSYgADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2J1NNQABsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gpIAAe6AuADUsAbE6mmoAA2JwEwOYkADYnAbA5CYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWANicTDUBAbA5CYDNSQBsTgJgcxIAm5MAKAC2SzFFYJ+AALhv515M4KaAAHigLwA2LAGwOZlqAgJgcxIAm5MA2JwEwOYkADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHMy1QQEwOYkADYnAbA5CYDNSQBsTgKgANguxRSBfQIC4L6dezGBmwIC4IG+ANiwBMDmZKoJCIDNSQBsTgJgcxIAm5MA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmAzclUExAAm5MA2JwEwOYkADYnAbA5CYACYLsUUwT2CQiA+3buxQRuCgiAB/oCYMMSAJuTqSYgADYnAbA5CYDNSQBsTgJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQA2J1NNQABsTgJgcxIAm5MA2JwEwOYkAAqA7VJMEdgnIADu27kXE7gp8FMB8PN+7e+ZVz7hLTc/y7P/vd/zZn+LLkv6yTdxSk5vKFNm3vCu90IIAh/zg5wC07zxnZyK0yt//8fL2PqZN/7oe9YbFICP/tF3l7G1Mz/27h+cr/2Wr3r1/W+dmbevxfBwAssE/FPTsoV7LoHLAgLgwQIEwIYlAEYnATBBCYCJaQTA5iQANicBsDkJgM1JAHxtJwGw3ZEpAo8oIAA+4la9icDzFRAAD3YjADYsATA6CYAJSgBMTAJgY/IVgNFJAGxQAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlAEYnATBBCYCJSQBsTAJgdBIAG5QA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmA0UkATFACYGISABuTABidBMAGJQA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmB0EgATlACYmATAxiQARicBsEEJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YAGJ0EwAQlACYmAbAxCYDRSQBsUAJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQBGJwEwQQmAiUkAbEwCYHQSABuUANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgNFJAExQAmBiEgAbkwAYnQTABiUANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgdBIAE5QAmJgEwMYkAEYnAbBBCYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWABidBMAEJQAmJgGwMQmA0UkAbFACYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUARicBMEEJgIlJAGxMAmB0EgAblADYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDRSQBMUAJgYhIAG5MAGJ0EwAYlADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHQSABOUAJiYBMDGJABGJwGwQQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgAYnQTABCUAJiYBsDEJgNFJAGxQAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlAEYnATBBCYCJSQBsTAJgdBIAG5QA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmA0UkATFACYGISABuTABidBMAGJQA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmB0EgATlACYmATAxiQARicBsEEJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YAGJ0EwAQlACYmAbAxCYDRSQBsUAJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQBGJwEwQQmAiUkAbEwCYHQSABuUANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgNFJAExQAmBiEgAbkwAYnQTABiUANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgdBIAE5QAmJgEwMYkAEYnAbBBCYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWABidBMAEJQAmJgGwMQmA0UkAbFACYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUARicBMEEJgIlJAGxMAmB0EgAblADYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDRSQBMUAJgYhIAG5MAGJ0EwAYlADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHQSABOUAJiYBMD+BdU1AAAgAElEQVTGJABGJwGwQQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgAYnQTABCUAJiYBsDEJgNFJAGxQAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlAEYnATBBCYCJSQBsTAJgdBIAG5QA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmA0UkATFACYGISABuTABidBMAGJQA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmB0EgATlACYmATAxiQARicBsEEJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YAGJ0EwAQlACYmAbAxCYDRSQBsUAJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQBGJwEwQQmAiUkAbEwCYHQSABuUANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgNFJAExQAmBiEgAbkwAYnQTABiUANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgdBIAE5QAmJgEwMYkAEYnAbBBCYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWABidBMAEJQAmJgGwMQmA0UkAbFACYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUARicBMEEJgIlJAGxMAmB0EgAblADYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDRSQBMUAJgYhIAG5MAGJ0EwAYlADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHQSABOUAJiYBMDGJABGJwGwQQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgAYnQTABCUAJiYBsDEJgNFJAGxQAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlAEYnATBBCYCJSQBsTAJgdBIAG5QA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmA0UkATFACYGISABuTABidBMAGJQA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmB0EgATlACYmATAxiQARicBsEEJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YAGJ0EwAQlACYmAbAxCYDRSQBsUAJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQBGJwEwQQmAiUkAbEwCYHQSABuUANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgNFJAExQAmBiEgAbkwAYnQTABiUANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgdBIAE5QAmJgEwMYkAEYnAbBBCYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWABidBMAEJQAmJgGwMQmA0UkAbFACYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUARicBMEEJgIlJAGxMAmB0EgAblADYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDRSQBMUAJgYhIAG5MAGJ0EwAYlADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHQSABOUAJiYBMDGJABGJwGwQQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgAYnQTABCUAJiYBsDEJgNFJAGxQAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlAEYnATBBCYCJSQBsTAJgdBIAG5QA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmA0UkATFACYGISABuTABidBMAGJQA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmB0EgATlACYmATAxiQARicBsEEJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YAGJ0EwAQlACYmAbAxCYDRSQBsUAJgcxIABcB2KaYI7BMQAPft3IsJ3BQQAA/0BcCGJQBGJwEwQQmAiUkAbEwCYHQSABuUANicBEABsF2KKQL7BATAfTv3YgI3BQTAA30BsGEJgNFJAExQAmBiEgAbkwAYnQTABiUANicBUABsl2KKwD4BAXDfzr2YwE0BAfBAXwBsWAJgdBIAE5QAmJgEwMYkAEYnAbBBCYDNSQAUANulmCKwT0AA3LdzLyZwU0AAPNAXABuWABidBMAEJQAmJgGwMQmA0UkAbFACYHMSAAXAdimmCOwTEAD37dyLCdwUEAAP9AXAhiUARicBMEEJgIlJAGxMAmB0EgAblADYnARAAbBdiikC+wQEwH0792ICNwUEwAN9AbBhCYDRSQBMUAJgYhIAG5MAGJ0EwAYlADYnAVAAbJdiisA+AQFw3869mMBNAQHwQF8AbFgCYHQSABOUAJiYBMDGJABGJwGwQQmAzUkAFADbpZgisE9AANy3cy8mcFNAADzQFwAblgAYnQTABCUAJiYBsDEJgNFJAGxQAmBzEgAFwHYppgjsExAA9+3ciwncFBAAD/QFwIYlAEYnATBBCYCJSQBsTAJgdBIAG5QA2JwEQAGwXYopAvsEBMB9O/diAjcFBMADfQGwYQmA0UkATFACYGISABuTABidBMAGJQA2JwFQAGyXYorAPgEBcN/OvZjATQEB8EBfAGxYAmB0EgATlACYmATAxiQARicBsEEJgM1JABQA26WYIrBPQADct3MvJnBTQAA80BcAG5YAGJ0EwAQlACYmAbAxCYDRSQBsUAJgcxIABcB2KaYI7BMQAPft/P9t786DbV3vgs7/uMlNyOhl7FTTgdiCjFGQQSwZmsKCjoKokK5AFYiIXSBdCKKoTTPELttyQKWlQahGBBtxgHTLVJYocyQSEEXElqJxIEKABELIzXRz7+164zqpU7fOPef77uxz3r3281n/ELKftff7fH5P7j37e9Zarx0TOFJAANyhLwA2LAEwOgmACUoATEwCYGMSAKOTANigBMDmJAAKgO2kWEVgPQEBcL2Z2zGBIwUEwB36AmDDEgCjkwCYoATAxCQANiYBMDoJgA1KAGxOAqAA2E6KVQTWExAA15u5HRM4UkAA3KEvADYsATA6CYAJSgBMTAJgYxIAo5MA2KAEwOYkAAqA7aRYRWA9AQFwvZnbMYEjBQTAHfoCYMMSAKOTAJigBMDEJAA2JgEwOgmADUoAbE4CoADYTopVBNYTEADXm7kdEzhSQADcoS8ANiwBMDoJgAlKAExMAmBjEgCjkwDYoATA5iQACoDtpFhFYD0BAXC9mdsxgSMFBMAd+gJgwxIAo5MAmKAEwMQkADYmATA6CYANSgBsTgKgANhOilUE1hMQANebuR0TOFJAANyhLwA2LAEwOgmACUoATEwCYGMSAKOTANigBMDmJAAKgO2kWEVgPQEBcL2Z2zGBIwXeEgA/8HlfNE9+6gNHXsuV/9kPP8k/osuQHnoKp+J0/2sfLcuWX/PE1z+yvEEBeOKDD5dly6+5/zUPLW9QAN7mIeepON336teVZcuveZvXvHZ5g9sBvP7h18z3/eLfurHk2TPzMmAECKwh4LemNeZslwSuioAAuGMSAmDDEgCbkwDYnATA6CQAJigBMDGNANicBMDmJADe3kkAbOfIKgLXUUAAvI5TtScCV1dAANwxGwGwYQmAzUkAbE4CYHQSABOUAJiYBMDGNAJggxIABcB2UqwisJ6AALjezO2YwJECAuAOfQGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJvEXgHWfmM2bmE2bmN83M283MK2fm52bmB2bmRTPzw5fsJQDuABUAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwJsFnj8zXz0z73Abj384M7/vkr0EwB2gAmDDEgCbkwDYnATA6CQAJigBMDEJgI1JAIxOAqAAGI+KZQSWExAAlxu5DROYT5uZr5+Z+2bml04h8Idm5ldm5lmnVwN+/Mz82ikUXiaZALhDUwBsWAJgcxIAm5MAGJ0EwAQlACYmAbAxCYDRSQAUAONRsYzAcgIC4HIjt+HFBd57Zn58Zp48Mz84MzdC361YnjQzb7xkLwFwB6gA2LAEwOYkADYnATA6CYAJSgBMTAJgYxIAo5MAKADGo2IZgeUEBMDlRm7Diwv8k5n56Jl5xcxsMXD7v/fyIQDu0BYAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG14YYH3mpl/e9r/l83MCw+wEAB3oAuADUsAbE4CYHMSAKOTAJigBMDEJAA2JgEwOgmAAmA8KpYRWE5AAFxu5Da8sMAXz8yfPe3/fWfmp07/ebv773ZH4O0zALe7AN/NhwC4Q1cAbFgCYHMSAJuTABidBMAEJQAmJgGwMQmA0UkAFADjUbGMwHICAuByI7fhhQW+c2Z+9+nmHlv0+5SZ+cKZ+S03mfz7mfmGmfnymXnNXbASAHegCoANSwBsTgJgcxIAo5MAmKAEwMQkADYmATA6CYACYDwqlhFYTkAAXG7kNrywwBb3njMz/2pmtrv+fs5tLH5yZj52Zn5+p9cW+G732O4y/NJtwQc+74vmyU99YOe3X2u5ANjmLQA2JwGwOQmA0UkATFACYGISABuTABidBEABMB4VywgsJyAALjdyG15Y4Ndm5pkz84bTXYBfNTN/emZeNDOvnpnnnt4i/LyT0T+bmQ+fmUd2mD1a1wqAd5YSAO9stK0QAJuTANicBMDoJAAmKAEwMQmAjUkAjE4CoAAYj4plBJYTEACXG7kNLyzwppl5wmn/D8/Mh83MSx7jcd/MfMfM3IiAz5+Zb9lhJgDuwLrTUgHwTkL/5esCYHMSAJuTABidBMAEJQAmJgGwMQmA0UkAFADjUbGMwHICAuByI7fhhQW2z/R72mn/f3dmPvlxLLYbhGxvAd4e26sDP3GHmbcA78C601IB8E5CAmAT+i+rBMCmJQBGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhteGGBX5iZ7TP4tscfnJlvvI3Fy2bmXWbm52bmXS/RzE1AdmAKgA3LKwCbkwDYnATA6CQAJigBMDEJgI1JAIxOAqAAGI+KZQSWExAAlxu5DS8s8CMz88Gn/X/0zHzPbSx+eGY+9PR5gW97iWYC4A5MAbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chhcW+PqZ+fTT/j9mZr77NhY3YuGDM/P0SzQTAHdgCoANSwBsTgJgcxIAo5MAmKAEwMQkADYmATA6CYACYDwqlhFYTkAAXG7kNrywwB+amb952v9nz8zfuI3FK2bmHWbmp2fmPS/RTADcgSkANiwBsDkJgM1JAIxOAmCCEgATkwDYmATA6CQACoDxqFhGYDkBAXC5kdvwwgJb0Ns+B/D+06v/tlcB3urxkTPzfacvfN3MfOYlmgmAOzAFwIYlADYnAbA5CYDRSQBMUAJgYhIAG5MAGJ0EQAEwHhXLCCwnIAAuN3IbXlzgq2Zme/Xf9tjuArzdDfjmxzNm5gdm5v1P/+WHzMxLL9FMANyBKQA2LAGwOQmAzUkAjE4CYIISABOTANiYBMDoJAAKgPGoWEZgOQEBcLmR2/DiAu80Mz96urPvm05vA37RzLx6Zp47M39qZt7rZPTVM/NHL9lLANwBKgA2LAGwOQmAzUkAjE4CYIISABOTANiYBMDoJAAKgPGoWEZgOQEBcLmR2zCBee+Z+baZeffbWGyfFfhZM/PQJXsJgDtABcCGJQA2JwGwOQmA0UkATFACYGISABuTABidBEABMB4VywgsJyAALjdyGybwZoGnnd4K/Ekz8x6nO/3+0sy8eGa+Zma+9y45CYA7YAXAhiUANicBsDkJgNFJAExQAmBiEgAbkwAYnQRAATAeFcsILCcgAC43chsmcKiAALiDXwBsWAJgcxIAm5MAGJ0EwAQlACYmAbAxCYDRSQAUAONRsYzAcgIC4HIjt2EChwoIgDv4BcCGJQA2JwGwOQmA0UkATFACYGISABuTABidBEABMB4VywgsJyAALjdyGyZwqIAAuINfAGxYAmBzEgCbkwAYnQTABCUAJiYBsDEJgNFJABQA41GxjMByAgLgciO3YQKHCgiAO/gFwIYlADYnAbA5CYDRSQBMUAJgYhIAG5MAGJ0EQAEwHhXLCCwnIAAuN3IbJnCogAC4g18AbFgCYHMSAJuTABidBMAEJQAmJgGwMQmA0UkAFADjUbGMwHICAuByI7dhAocKCIA7+AXAhiUANicBsDkJgNFJAExQAmBiEgAbkwAYnQRAATAeFcsILCcgAC43chsmcKiAALiDXwBsWAJgcxIAm5MAGJ0EwAQlACYmAbAxCYDRSQAUAONRsYzAcgIC4HIjt2EChwoIgDv4BcCGJQA2JwGwOQmA0UkATFACYGISABuTABidBEABMB4VywgsJyAALjdyGyZwqIAAuINfAGxYAmBzEgCbkwAYnQTABCUAJiYBsDEJgNFJABQA41GxjMByAgLgciO3YQKHCgiAO/gFwIYlADYnAbA5CYDRSQBMUAJgYhIAG5MAGJ0EQAEwHhXLCCwnIAAuN3IbJnCogAC4g18AbFgCYHMSAJuTABidBMAEJQAmJgGwMQmA0UkAFADjUbGMwHICAuByI7dhAocKCIA7+AXAhiUANicBsDkJgNFJAExQAmBiEgAbkwAYnQRAATAeFcsILCcgAC43chsmcKiAALiDXwBsWAJgcxIAm5MAGJ0EwAQlACYmAbAxCYDRSQAUAONRsYzAcgIC4HIjt2EChwoIgDv4BcCGJQA2JwGwOQmA0UkATFACYGISABuTABidBEABMB4VywgsJyAALjdyGyZwqIAAuINfAGxYAmBzEgCbkwAYnQTABCUAJiYBsDEJgNFJABQA41GxjMByAgLgciO3YQKHCgiAO/gFwIYlADYnAbA5CYDRSQBMUAJgYhIAG5MAGJ0EQAEwHhXLCCwnIAAuN3IbJnCogAC4g18AbFgCYHMSAJuTABidBMAEJQAmJgGwMQmA0UkAFADjUbGMwHICAuByI7dhAocKCIA7+AXAhiUANicBsDkJgNFJAExQAmBiEgAbkwAYnQRAATAeFcsILCcgAC43chsmcKiAALiDXwBsWAJgcxIAm5MAGJ0EwAQlACYmAbAxCYDRSQAUAONRsYzAcgIC4HIjt2EChwoIgDv4BcCGJQA2JwGwOQmA0UkATFACYGISABuTABidBEABMB4VywgsJyAALjdyGyZwqIAAuINfAGxYAmBzEgCbkwAYnQTABCUAJiYBsDEJgNFJABQA41GxjMByAgLgciO3YQKHCgiAO/gFwIYlADYnAbA5CYDRSQBMUAJgYhIAG5MAGJ0EQAEwHhXLCCwnIAAuN3IbJnCogAC4g18AbFgCYHMSAJuTABidBMAEJQAmJgGwMQmA0UkAFADjUbGMwHICAuByI7dhAocKCIA7+AXAhiUANicBsDkJgNFJAExQAmBiEgAbkwAYnQRAATAeFcsILCcgAC43chsmcKiAALiDXwBsWAJgcxIAm5MAGJ0EwAQlACYmAbAxCYDRSQAUAONRsYzAcgIC4HIjt2EChwq8JQB+wMd90Tz5qQ8cejFX/Yc/9LT7rvolXonre8IbH70S13HVL+LJr3r4ql/ilbi+J76OUxnEfW/gVJye+MoHy7Ll17zNG964vEEBePTXX1OWWfOIPxfc7hC8/pHXzPe/6ptvLHn2zLzMoSFAYA0BAXCNOdslgasiIADumIQA2LAEwOYkADYnAbA5CYDNSQBsTgJgcxIAm9MIgLeFEgDjObKMwDUUEACv4VBticAVFhAAdwxHAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JALy9kwAYz5FlBK6hgAB4DYdqSwSusIAAuGM4AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSACC54DUAACAASURBVJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAbE4CYHMSAJuTANicBMDmJAAKgPGkWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA5iQANicBsDkJgM1JAGxOAqAAGE+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQBsTgJgcxIAm5MA2JwEwOYkAAqA8aRYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDmJAA2JwGwOQmAzUkAbE4CoAAYT4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAGxOAmBzEgCbkwDYnATA5iQACoDxpFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOYkADYnAbA5CYDNSQBsTgKgABhPimUElhMQAJcbuQ0TOFTgLQHw/V7wxfOkpz1w6MVc9R/+6BOu+hVejet7yisfuRoXcsWv4kmvetMVv8KrcXlPeMPDV+NCrvhVPOHBN17xK7wal3ffK199NS7kil/Fo69/wxW/wityeQ/5312ZxCMPvq4sW3bN6x997fzgQ//Pjf0/e2ZetiyGjRNYTEAAXGzgtkvgYAEBcMcABMCGJQA2JwGwOQmA0UkATFACYGIaAbA5jQCYoATA2zMJgOkYWUTgWgoIgNdyrDZF4MoKCIA7RiMANiwBsDkJgM1JAIxOAmCCEgATkwDYmEYAbFACoADYTopVBNYTEADXm7kdEzhSQADcoS8ANiwBsDkJgM1JAIxOAmCCEgATkwDYmATA6CQACoDxqFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOgkACYoATAxCYCNSQCMTgKgABiPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAjE4CYIISABOTANiYBMDoJAAKgPGoWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA6CQAJigBMDEJgI1JAIxOAqAAGI+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQCMTgJgghIAE5MA2JgEwOgkAAqA8ahYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDoJAAmKAEwMQmAjUkAjE4CoAAYj4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAIxOAmCCEgATkwDYmATA6CQACoDxqFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOgkACYoATAxCYCNSQCMTgKgABiPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAjE4CYIISABOTANiYBMDoJAAKgPGoWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA6CQAJigBMDEJgI1JAIxOAqAAGI+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQCMTgJgghIAE5MA2JgEwOgkAAqA8ahYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDoJAAmKAEwMQmAjUkAjE4CoAAYj4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAIxOAmCCEgATkwDYmATA6CQACoDxqFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOgkACYoATAxCYCNSQCMTgKgABiPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAjE4CYIISABOTANiYBMDoJAAKgPGoWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA6CQAJigBMDEJgI1JAIxOAqAAGI+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQCMTgJgghIAE5MA2JgEwOgkAAqA8ahYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDoJAAmKAEwMQmAjUkAjE4CoAAYj4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAIxOAmCCEgATkwDYmATA6CQACoDxqFhGYDkBAXC5kdswgUMFBMAd/AJgwxIAm5MA2JwEwOgkACYoATAxCYCNSQCMTgKgABiPimUElhMQAJcbuQ0TOFRAANzBLwA2LAGwOQmAzUkAjE4CYIISABOTANiYBMDoJAAKgPGoWEZgOQEBcLmR2zCBQwUEwB38AmDDEgCbkwDYnATA6CQAJigBMDEJgI1JAIxOAqAAGI+KZQSWExAAlxu5DRM4VEAA3MEvADYsAbA5CYDNSQCMTgJgghIAE5MA2JgEwOgkAAqA8ahYRmA5AQFwuZHbMIFDBQTAHfwCYMMSAJuTANicBMDoJAAmKAEwMQmAjUkAjE4CoAAYj4plBJYTEACXG7kNEzhUQADcwS8ANiwBsDkJgM1JAIxOAmCCEgATkwDYmATA6CQACoDxqFhGYDkBAXC5kdswgTcLPGlmPnVmnj8zv3Vm3n5mHpqZ/zwzL56Zr52Zl9wFKwFwB6oA2LAEwOYkADYnATA6CYAJSgBMTAJgYxIAo5MAKADGo2IZgeUEBMDlRm7DBObZM/OdM/PcO1j81Zn5gpl59BLNBMAdmAJgwxIAm5MA2JwEwOgkACYoATAxCYCNSQCMTgKgABiPimUElhMQAJcbuQ0vLvDEmfkXN8W/n5iZvzIz/25mnjEzH3aKfk87OX3hzPylSzQTAHdgCoANSwBsTgJgcxIAo5MAmKAEwMQkADYmATA6CYACYDwqlhFYTkAAXG7kNry4wCfOzLecDH54Zj58Zh5+jMkHzsz2tftn5ldn5p1n5k2X5CYA7oAUABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhteHGB7dV+n38y+L0z8+2P4/Gimfn9p69tbxX+yUtyEwB3QAqADUsAbE4CYHMSAKOTAJigBMDEJAA2JgEwOgmAAmA8KpYRWE5AAFxu5Da8uMBXzsznnAzeb2b+zeN4bG/7/ROnr33QzPzYJbkJgDsgBcCGJQA2JwGwOQmA0UkATFACYGISABuTABidBEABMB4VywgsJyAALjdyG15c4HNn5itOBuUVgNsNQB6YmVdfkpsAuANSAGxYAmBzEgCbkwAYnQTABCUAJiYBsDEJgNFJABQA41GxjMByAgLgciO34cUF3mlmfmZmnjkzL56Zj7zFZwB+wMy8ZGaeNDPfPDOfcolmAuAOTAGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJvPmz/b5pZp4yMz8+M39tZn56Zp4+M7/zdBfg7Y7A/3JmnjczL99htgW+2z2eNTMv3Ra83wu+eJ70tO3FhR6PJyAAtrMhADYnAbA5CYDRSQBMUAJgYhIAG5MAGJ0EQAEwHhXLCCwnIAAuN3IbJvBmgfeZmT8+M58xM4/958AvzsxfmJmvnZkHd3ptbxlODwHwzkwC4J2NthUCYHMSAJuTABidBMAEJQAmJgGwMQmA0UkAFADjUbGMwHICAuByI7dhAnP/zHzpzPyRmXnnx/HYXqX3wpn5zp1eAuBOsNstFwAbpgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbXhxgafNzHfNzEecPvvvy2fm62fmZ2fmbWfmt8/Ml8zMh83MFvM+/6abhhQ6bwEuSnGNANigBMDmJAA2JwEwOgmACUoATEwCYGMSAKOTACgAxqNiGYHlBATA5UZuw4sL/OXTZ/xtDJ8+M99wC48nzsw/npmPmplHZma7KchPXJKbm4DsgBQAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG14YYHtf++vmJm3P9304z1vY7HdDOSHTl/fbhKyvRLwMh4C4A5FAbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chhcW2O7A+wun/f+9mXnBbSy2twO/7vT1f3S6G/Bl0AmAOxQFwIYlADYnAbA5CYDRSQBMUAJgYhIAG5MAGJ0EQAEwHhXLCCwnIAAuN3IbXljgHWfml0/7/9aZ+aTbWDxjZl59+vp3zMzHX5KbALgDUgBsWAJgcxIAm5MAGJ0EwAQlACYmAbAxCYDRSQAUAONRsYzAcgIC4HIjt+GFBe6bmV+dmWfOzM/PzLvNzJsex+PjZubbT1/76zPzuZfkJgDugBQAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG14cYG/MzOffDL4spl54S083u70+X/vc/rax55uCnIZdALgDkUBsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGFxd4r5n5sZl56slhe5Xfdifgn52Z7XP/PnRmPm9m3vX09X86M7/rEs0EwB2YAmDDEgCbkwDYnATA6CQAJigBMDEJgI1JAIxOAqAAGI+KZQSWExAAlxu5DRN4c9D75pnZPhPwdo/vOX1O4Pa24ct6CIA7JAXAhiUANicBsDkJgNFJAExQAmBiEgAbkwAYnQRAATAeFcsILCcgAC43chsm8GaBd5iZP3y6u+/7zswDp88DfPnMvHRmtrcKf9vMPHrJXgLgDlABsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECAuAOfgGwYQmAzUkAbE4CYHQSABOUAJiYBMDGJABGJwFQAIxHxTICywkIgMuN3IYJHCogAO7gFwAblgDYnATA5iQARicBMEEJgIlJAGxMAmB0EgAFwHhULCOwnIAAuNzIbZjAoQIC4A5+AbBhCYDNSQBsTgJgdBIAE5QAmJgEwMYkAEYnAVAAjEfFMgLLCQiAy43chgkcKiAA7uAXABuWANicBMDmJABGJwEwQQmAiUkAbEwCYHQSAAXAeFQsI7CcgAC43MhtmMChAgLgDn4BsGEJgM1JAGxOAmB0EgATlACYmATAxiQARicBUACMR8UyAssJCIDLjdyGCRwqIADu4BcAG5YA2JwEwOYkAEYnATBBCYCJSQBsTAJgdBIABcB4VCwjsJyAALjcyG2YwKECbwmA7/HZXzL3P+OBQy/mqv/w+x666ld4Na7vKb/8yNW4kCt+FW/7qoev+BVejct74oOcyiTu/5XXlmXLr7nvl1+1vEEBeOQ1D5Zl1jzkDwblEDzy+teXZcuuef2jr50fmu+6sf9nz8zLlsWwcQKLCQiAiw3cdgkcLCAA7hiAANiwBMDmJAA2JwGwOQmAzUkAbE4CYHMaATBBCYC3ZxIA0zGyiMC1FBAAr+VYbYrAlRUQAHeMRgBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuT+lLf/wAAIABJREFUANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkgAC4Q18AbFgCYHMSAJuTANicBMDmJAA2JwGwOQmAzUkAFADbSbGKwHoCAuB6M7djAkcKCIA79AXAhiUANicBsDkJgM1JAGxOAmBzEgCbkwDYnARAAbCdFKsIrCcgAK43czsmcKSAALhDXwBsWAJgcxIAm5MA2JwEwOYkADYnAbA5CYDNSQAUANtJsYrAegIC4Hozt2MCRwoIgDv0BcCGJQA2JwGwOQmAzUkAbE4CYHMSAJuTANicBEABsJ0UqwisJyAArjdzOyZwpIAAuENfAGxYAmBzEgCbkwDYnATA5iQANicBsDkJgM1JABQA20mxisB6AgLgejO3YwJHCgiAO/QFwIYlADYnAbA5CYDNSQBsTgJgcxIAm5MA2JwEQAGwnRSrCKwnIACuN3M7JnCkwLvNzH/YLuA5n/p5c//Tn3nktVz5ny0AthG97SsfaQsXX/XkVz+8uEDb/hNe6zwVqftf9bqybPk19/3Kq5c3KACPPPjassyaNz3EIAg88vo3hFXrLnnDvG5eOt97A+A5M/Mf19WwcwJrCQiAa83bbgkcLfBBM/PSoy/CzydAgAABAgQIECBAYD54Zn6UAwECawgIgGvM2S4JXBUBAfCqTMJ1ECBAgAABAgQIrC4gAK5+Aux/KQEBcKlx2yyBwwWePDPPPV3FL8/MVXhP4rNuelXi9oeglx+u5ALOWcB5OufpXb1rd56u3kzO+Yqcp3Oe3tW7dufp6s2kXtETZuadTov/9cx4z3SVs47AmQsIgGc+QJdPgMBbLfCWG5PMzLNn5mVv9Xf0DVYWcJ5Wnv7l7915unzTlb+j87Ty9C9/787T5Zv6jgQIELirAgLgXeX1zQkQOAMBf4A9gyGd0SU6T2c0rDO4VOfpDIZ0RpfoPJ3RsM7gUp2nMxiSSyRAgMDNAgKg80CAwOoC/gC7+gm43P07T5frufp3c55WPwGXu3/n6XI9V/9uztPqJ8D+CRA4OwEB8OxG5oIJELhkAX+AvWTQxb+d87T4Abjk7TtPlwy6+LdznhY/AJe8fefpkkF9OwIECNxtAQHwbgv7/gQIXHUBf4C96hM6r+tzns5rXlf9ap2nqz6h87o+5+m85nXVr9Z5uuoTcn0ECBB4jIAA6EgQILC6gD/Arn4CLnf/ztPleq7+3Zyn1U/A5e7febpcz9W/m/O0+gmwfwIEzk5AADy7kblgAgQuWcAfYC8ZdPFv5zwtfgAuefvO0yWDLv7tnKfFD8Alb995umRQ344AAQJ3W0AAvNvCvj8BAlddwB9gr/qEzuv6nKfzmtdVv1rn6apP6Lyuz3k6r3ld9at1nq76hFwfAQIEHiMgADoSBAgQIECAAAECBAgQIECAAAECBK6xgAB4jYdrawQIECBAgAABAgQIECBAgAABAgQEQGeAAAECBAgQIECAAAECBAgQIECAwDUWEACv8XBtjQABAgQIECBAgAABAgQIECBAgIAA6AwQIECAAAECBAgQIECAAAECBAgQuMYCAuA1Hq6tESBAgAABAgQIECBAgAABAgQIEBAAnQECBAgQIECAAAECBAgQIECAAAEC11hAALzGw7U1AgQIECBAgAABAgQIECBAgAABAgKgM0CAAAECBAgQIECAAAECBAgQIEDgGgsIgNd4uLZGgAABAgQIECBAgAABAgQIECBAQAB0BggQIECAAAECBAgQIECAAAECBAhcYwEB8BoP19YIELijwLvOzOfOzO+Zme0/v2FmfmZm/v7MfNXMvPaO38GC1QV+28z89zPz4TPzfjPzzjPz0Mz8/Mz8s5n5upn5wdWR7P9SBP7izPzJm77TR83M913Kd/ZNVhB4x5n5jJn5hJn5TTPzdjPzypn5uZn5gZl50cz88AoQ9vhWCzxpZj51Zp4/M791Zt7+9O+9/zwzL56Zr52Zl7zVP8U3IECAAIFLFxAAL53UNyRA4EwEtuj3TTPzGx7nev/dzPzumfnZM9mPy7z3At8/Mx8RfuzfnpnPnJk3hrWWELiVwPZL9o/OzBMFQAfkAgJbqPnqmXmH2zz3H87M77vA9/aUtQSePTPfOTPPvcO2/+rMfMHMPLoWj90SIEDgagsIgFd7Pq6OAIG7I7D9Mr29OuupM/OamfnzM/O9M/OUmXnBzPyR04/9f2fmg09r7s6V+K7nLLC9WnR7Jc32ar9/cHql33+amSfMzO84/fLzLqcNfvPMfMo5b9a1HyZw3+nVNNs/i37p9CrT7WK8AvCwkZzVD/60mfn6mdnO0XZ+thD4QzPzKzPzrNM/wz5+Zn7t9Iqus9qci72nAttfQPyLm+LfT8zMX5mZ7S9MnzEzH3b6997TTlf1hTPzl+7pFfphBAgQIHBbAQHQASFAYEWBLfb9dzPzptMruB77tqftbXbb2+22x5fOzJ9dEcme7yjwHTPzjTPzrTPz8C1Wb2+5294O9ZtPX9teLejtwHdkteAxAp83M9uraba/kPi/Z+bPnL4uADoqdxJ475n58Zl58umfPTdC362et72t06uU7yS69tc/cWa+5USw/blp++iLx/677wNPbyW/f2Z+9fQXFtuftTwIECBA4AoICIBXYAgugQCBeyqwvYrmR04/8Wtm5rNu8dO3V0r85Mxsvzxtf4D9r06fb3NPL9QPuxYCHzcz337ayf8+M3/sWuzKJu6VwPZ2u5+amaefXvG3/cXF9pcS20MAvFdTON+f809m5qNn5hWnf59t/9eDwEUFtlf7ff7pyb/3pn+3Pfb7bZ8n+ftP/+X2VuHtz1MeBAgQIHAFBATAKzAEl0CAwD0V+HMz8z+ffuKHzsw/f5yf/qdPbw3evvwxM/Pd9/Qq/bDrIrCFm18/bWb73KQtCHoQqAJbPN7OzDfMzKfPzJcJgJVu+XXvNTP/9qSwnZsXLi8C4K0V+MqZ+ZzTN9luevVvHucbbm/7/ROnr33QzPzYW/uDPZ8AAQIELkdAALwcR9+FAIHzEdjudri9beXBmXng9DbgW1399hlu2+cEbo/tLcA3XnVzPjt1pVdBYLs74nanze2xxZztVRMeBIrA/zAzf+/0WW1bzPllAbCwWXMS+OKbPr7ifU+vJN2+tN39d/t4gu0zAG/8swkagSLwuTPzFaeF5RWA2w1Atj9nvbp8c2sIECBA4O4LCIB339hPIEDgaglsv0Rvv/z8q5l5/9tc2vZL0vYL0vbYbvCw/TLuQWCvwPY2qO3tUNtje1XE9qHoHgTuJLD90ry9emu7ScN2U6L/8/QErwC8k5yv3xDYXnG83cl+u7nH9u+z7SZE2z9/fstNRP/+9OrSL3ezKwcnCLzTzGw3v3rm6fNtP/IWnwH4AaebFm2fKenmVwHVEgIECNxLAQHwXmr7WQQIHC3wtjPzutNFlLdjbncI3u5m95LTXV2Pvn4//7wEts+S3D4o/UNOl719/uSPntcWXO1BAl97Cn/bq5C3O2tur6TZHgLgQQM5wx+7xb3nnP6ya7vr7423bt5qK9tntH3s6Y7mZ7hVl3wPBba/1PqmmXnK6QYzf21mfvr0OaW/83QX4O2OwP9yZp43My+/h9fmRxEgQIDAHQQEQEeEAIGVBLa/vf6l04a3t9a94A6b/8XTHey2X462D7L2ILBH4Atm5i+fnrDdvfUP7HmytcsKbMFv+6iC7e6av21m/vVNEgLgssdi98a3V/5tr9R6w+kuwK+ame2zbbdXJG9vydz+nbZ9vMUWabbHFpu3j8d4ZPdP8oTVBN5nZv74zHzGzDz2d8ntz01/YWa2v8TYPmrFgwABAgSukIAAeIWG4VIIELjrAtsdNf/T6af87Zn5tDv8xG3t9pz/b2be/a5fnR9wnQS2t0Ztd+B84ik6b2+7234x8iBwO4HtbXPbK2e2O5Df6i3jAqDzUwXeNDNPOC3eYvIWlrdXs9/82F6l/B03RcDnz8y31B9g3ZIC958+E3n7aIJ3fhyBl55uOrO908KDAAECBK6QgAB4hYbhUggQuOsCXgF414n9gJnZPnD/B0+fu7W9+mZ7a933kyEQBG4Evu0vH7ZX2Tz2FTQCYEC05M0CNz7CYvvPf3dmPvlxXLZ/Xm2vct8e26sDP5EfgccR2D4S5btm5iNOr1DePjvy62fmZ2dm+4iV3z4zX3LTxxZ8/k03DYFKgAABAldAQAC8AkNwCQQI3DMBnwF4z6iX/UG/cWa2z9v6r0+/IG2vqNne/utB4E4C251+t5sTba8C/ISZ+bZbPEEAvJOir98Q+IXTTWS2//8Pzsw33obmZTPzLjPzczPzrggJPI7A9pEW20dbbI9PP91A5rFLt1e9/+OZ+ajT28m3m4L8BFECBAgQuBoCAuDVmIOrIEDg3gm4C/C9s17tJ23Rb3vl3397umnD9gvS7X7pXs3Hfm8v8DUz8z+eXk3zRY+z9JNueoXW/zozP3Va9+0+b8vxeozAj8zMduOh7fHRM/M9txHablb0oafPC9z+osyDwGMFtt8ZXzEzb3+66cd73oZouxnI9hdh22O7Scj2SkAPAgQIELgCAgLgFRiCSyBA4J4KbB+uv33Q+fbWugdmZvucpFs9fsfpQ9G3r20flP6l9/Qq/bBzE3jH09t8t7dtbo//aWb+j3PbhOs9VOBvnV6pdZGL2F55+h8u8kTPubYC21szt7+E2B4fMzPffZud3oiF278Xn35tRWzsrRF41sxsryrdHne6idrN77b4Rzd9xuRb8/M9lwABAgQuQUAAvARE34IAgbMS+N9m5s+crnh7xcM/f5yr3+6W+OdPX9s+w217S4sHgVsJ/IbTq2u2O7Zuj+3sbHdB9CCwR0AA3KNl7Z0E/tDM/M3Tos+emb9xmydsr+x6h/DKrjv9TF+/vgLbX3Jt76DYHt86M9urkR/v8YzTnaa3r283mfn468tiZwQIEDgvAQHwvOblagkQeOsFPuSm6Le95e6zbvEttzsjbh+Kvt2J81WnO9099Nb/aN/hGgo89RSHt7c8bY8/NzP/yzXcpy1dDQGfAXg15nAOV7EFve0VW9tdW7dX/22vArzVY7tj+fedvvB1M/OZ57A513jPBbY/F/3qzDxzZn5+Zt7tNu+g+LiZ2T6WYHv89Zn53Ht+tX4gAQIECNxSQAB0MAgQWFHgxtuAt7f/bnez2z7/6ObHn5yZv3j6L144M9sv3R4EHiuw3axh+yXnxi/WXzEzn4eJwF0UEADvIu41/NZfNTPbq/+2x3YX4O1uwDc/tldqbf8+fP/Tf7n9BdlLr6GDLV2OwN+56W7S2z+Ltj8fPfbxdqfP/7vxcRjeQXE59r4LAQIELkVAALwURt+EAIEzE9juSvfimXnKzLxmZra3BX/v6f9/wemD+Lct/fTMfNDM/PqZ7c/l3huB7W1Qf+D0o7YP2N/i36O3+dFvPJ2pe3N1fsp1FBAAr+NU796e3mlmfvR0Z9/tL7y2twG/6PT2zOfOzJ+ame3u09vjq2fmj969S/Gdr4HAdlZ+bGa2V75vj+0vwL7hdOOi7XP/to9V2f49eONO0v90Zn7XNdi3LRAgQODaCAiA12aUNkKAwE6B7TNp/q/T21lu9dQt/v2emfmZnd/X8nUEbhf7bqXwH2fmOevw2OldEBAA7wLqNf+W20dZfNvMvPtt9rl9VuD2cRg+6uKaH4ZL2N4W9L55ZrbPBLzdY/tLse1zAre3DXsQIECAwBUREACvyCBcBgEChwhsn2Hzx06h77+Zme0VWlvw+wcz85Uz89pDrsoPPRcBAfBcJnV9rlMAvD6zvJc7edrprcBbkHmP051+f+n0Svjts3C3V8B7EKgC2+dL/uHT3X3fd2YeOH0e4MtPbyHf3iq8Ree9/46sP986AgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgK34cKiAAAAx0lEQVQC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKCAAHhBOE8jQIAAAQIECBAgQIAAAQIECBAgcA4CAuA5TMk1EiBAgAABAgQIECBAgAABAgQIELiggAB4QThPI0CAAAECBAgQIECAAAECBAgQIHAOAgLgOUzJNRIgQIAAAQIECBAgQIAAAQIECBC4oIAAeEE4TyNAgAABAgQIECBAgAABAgQIECBwDgIC4DlMyTUSIECAAAECBAgQIECAAAECBAgQuKDA/w/7ObrbQ7ntJgAAAABJRU5ErkJggg==\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11236d410>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# choose a roi to plot 2D image, like Fe\n",
"d_fe = d[:, :, 610:660]\n",
"fe = np.sum(d_fe, axis=(2)) # sum along axis 2\n",
"fig, ax = plt.subplots()\n",
"ax.imshow(fe)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:pyxrf]",
"language": "python",
"name": "conda-env-pyxrf-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
leewujung/trex_fish | Compare E_max, total energy, and SI for all runs.ipynb | 1 | 614936 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"run_num_all = [79,87,94,103,115,124,131];\n",
"ping_num{1} = 4:9:877; % run 79\n",
"ping_num{2} = 2:2:1000; % run 87\n",
"ping_num{3} = 3:3:899; % run 94\n",
"ping_num{4} = 3:5:911; % run 103\n",
"ping_num{5} = 2:2:926; % run 115\n",
"ping_num{6} = 2:2:973; % run 124\n",
"ping_num{7} = 2:2:1000; % run 131"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"run_num_all =\n",
"\n",
" 79 87 94 103 115 124 131\n",
"\n"
]
}
],
"source": [
"run_num_all"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"ping_num =\n",
"\n",
" 1×7 cell array\n",
"\n",
" Columns 1 through 4\n",
"\n",
" [1×98 double] [1×500 double] [1×299 double] [1×182 double]\n",
"\n",
" Columns 5 through 7\n",
"\n",
" [1×463 double] [1×486 double] [1×500 double]\n",
"\n"
]
}
],
"source": [
"ping_num"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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fADWPy0RDelmoFfrszbvzX0svB1ONhKQNTmZW/rHQl2Vj0vFdgj2s+Q3Nnybn7wn37L7A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\ngpTlAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iR=1;\n",
"A(iR) = summarize_run_fcn(run_num_all(iR),ping_num{iR},1);"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"A = \n",
"\n",
" struct with fields:\n",
"\n",
" energy_in_bnd: [98×1 double]\n",
" si_W1: [98×1 double]\n",
" si_W2: [98×1 double]\n",
" max_W1: [98×1 double]\n",
" max_W2: [98×1 double]\n",
" ping_time: [98×1 double]\n",
"\n"
]
}
],
"source": [
"A"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NBrUFdBUxj40epXHh3iq1EcmDM1t2PoxWSL8f1HpLkLSuk9UYSfTC+4t5jurB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\nkvr7SjyC2xNmh2InufqxG2bMWyAHE9YGXeapZa6GIDex0P7o5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gfGr586bVJN6iTinfbu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\nUIOEIAiCkAJqkBAEQRBS+H/SAK4OG8rfigAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iR=2;\n",
"A(iR) = summarize_run_fcn(run_num_all(iR),ping_num{iR},1);"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"clear A"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"A(1) = summarize_run_fcn(run_num_all(1),ping_num{1},0);\n",
"for iR=2:length(run_num_all)\n",
" A(iR) = summarize_run_fcn(run_num_all(iR),ping_num{iR},0);\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"A = \n",
"\n",
" 1×7 struct array with fields:\n",
"\n",
" energy_in_bnd\n",
" si_W1\n",
" si_W2\n",
" max_W1\n",
" max_W2\n",
" ping_time\n",
"\n"
]
}
],
"source": [
"A"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mXhdfq1/uw96Yp9o9lF/YZRpmyyAsRCjmVfcVh0YIndjUO4iNcPL4J1sjNLhB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\nAADQAhQkAAAAtPD/AUh1mhF/rHzZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure\n",
"for iR=1:length(run_num_all)\n",
" subplot(311)\n",
" plot(A(iR).ping_time,10*log10(A(iR).max_W1)+5*(iR-1))\n",
" hold on\n",
" ylim([60 120])\n",
" subplot(312)\n",
" plot(A(iR).ping_time,10*log10(A(iR).max_W2)+5*(iR-1))\n",
" hold on\n",
" ylim([60 120])\n",
" subplot(313)\n",
" plot(A(iR).ping_time,10*log10(A(iR).energy_in_bnd)+5*(iR-1))\n",
" hold on\n",
" ylim([160 220])\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mlEQKXVHhMOXF50333HlBC3q889YlWdUEgLSoYWD6OCqlGAWT5SiCJ+SObctP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fkOgAQlDBLV9brpjluG8JqrWjWkh56ENN3mDHff9SlxfWy+eQaflSyEsk6lBdOmZIAqznUKUi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\nhARUkBAEQZCQ8P8BPUzDRHdiPHgAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure\n",
"for iR=2:length(run_num_all)\n",
" subplot(311)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).max_W1),1))\n",
" hold on\n",
" ylim([70 85]); grid on;\n",
" subplot(312)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).max_W2),1))\n",
" hold on\n",
" ylim([60 85]); grid on;\n",
" subplot(313)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).energy_in_bnd),1))\n",
" hold on\n",
" ylim([165 195]); grid on;\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GER/hlpdWOe339KWXS9DrzmCwenyJoixPN2v5AACiE8f3YP0fjOARaD/5+7Y/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\nJAAAAFj4f+HUwN9U+oOmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure\n",
"for iR=2:length(run_num_all)\n",
" subplot(311)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).max_W1),5))\n",
" hold on\n",
" ylim([70 85]); grid on;\n",
" xlim([18 32])\n",
" subplot(312)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).max_W2),5))\n",
" hold on\n",
" ylim([60 85]); grid on;\n",
" xlim([18 32])\n",
" subplot(313)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).energy_in_bnd),5))\n",
" hold on\n",
" ylim([165 195]); grid on;\n",
" xlim([18 32])\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n",
"Warning: Ignoring extra legend entries.\n",
"> In legend>set_children_and_strings (line 658)\n",
" In legend>make_legend (line 334)\n",
" In legend (line 282)\n"
]
},
{
"data": {
"image/png": 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8XtzhMrNogc96UcLUsuY/mCg3KWxgTEim2aHlMW8vFwXJPYWraQctfDQtNMfZsJVowQxt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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure\n",
"for iR=[2,3,5,6,7]\n",
" subplot(311)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).max_W1),5)-max(smooth(10*log10(A(iR).max_W1),5)))\n",
" hold on\n",
" ylim([-10 0]); grid on;\n",
" xlim([18 32])\n",
" legend(num2str(run_num_all([2,3,5,6,7])'),'location','eastoutside')\n",
" subplot(312)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).max_W2),5)-max(smooth(10*log10(A(iR).max_W2),5)))\n",
" hold on\n",
" ylim([-10 0]); grid on;\n",
" xlim([18 32])\n",
" legend(num2str(run_num_all([2,3,5,6,7])'),'location','eastoutside')\n",
" subplot(313)\n",
" plot(A(iR).ping_time,smooth(10*log10(A(iR).energy_in_bnd),5)-max(smooth(10*log10(A(iR).energy_in_bnd),5)))\n",
" hold on\n",
" ylim([-15 0]); grid on;\n",
" xlim([18 32])\n",
" legend(num2str(run_num_all([2,3,5,6,7])'),'location','eastoutside')\n",
"end\n",
"subplot(311)\n",
"title('Max echo level in W1')\n",
"subplot(312)\n",
"title('Max echo level in W2')\n",
"subplot(313)\n",
"title('Total energy')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Matlab",
"language": "matlab",
"name": "matlab"
},
"language_info": {
"codemirror_mode": "octave",
"file_extension": ".m",
"help_links": [
{
"text": "MetaKernel Magics",
"url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
}
],
"mimetype": "text/x-matlab",
"name": "matlab",
"version": "0.13.0"
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| gpl-3.0 |
dennissergeev/classcode | notebooks/satelliteII.ipynb | 1 | 8311 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading MODIS level 1b data in netcdf format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a walkthrough of how to read metadata and data from a level1b file. I've used the the converter I downloaded from\n",
"[this link](hdfeos.org/software/library.php#H4CF Conversion Library) to produce the Aqua granule netcdf file in this download directory: http://clouds.eos.ubc.ca/~phil/Downloads/a301/MYD021KM.A2005188.0405.005.2009232180906.nc (right click to save to your local drive)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm going to use http://unidata.github.io/netcdf4-python/ and two new modules (modismeta.py and netcdflib.py) to read the granule"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from __future__ import division\n",
"import os,site\n",
"currdir=os.getcwd()\n",
"head,tail=os.path.split(currdir)\n",
"libdir=os.path.join(head,'lib')\n",
"site.addsitedir(libdir)\n",
"from modismeta import parseMeta\n",
"from netcdflib import ncdump\n",
"import glob\n",
"from netCDF4 import Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the glob function finds a file using a wildcard to save typing (google: python glob wildcard)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nc_filename=glob.glob('*.nc')\n",
"print(\"found {}\".format(nc_filename))"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nc_file=Dataset(nc_filename[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"netcdf files have attributes"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(nc_file.ncattrs())"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(nc_file.Earth_Sun_Distance)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(nc_file.HDFEOSVersion)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"netcdf files have variables -- stored in a dictionary"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#print(nc_file.variables.keys())"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"the_long=nc_file.variables['longitude']\n",
"the_lat=nc_file.variables['latitude']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the following cell shows how to write code out to a file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"%%file snippet.py\n",
"size=100\n",
"lat_data=the_lat[:10,:10]\n",
"long_data=the_long[:10,:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the following cell shows how to read code into a cell"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"%load snippet.py"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"size=10\n",
"lat_data=the_lat[:size,:size]\n",
"lon_data=the_long[:size,:size]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot some lat/lon pairs"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig=plt.figure(1,figsize=(9,9))\n",
"plt.clf()\n",
"ax1=fig.add_subplot(1,1,1)\n",
"the_points=ax1.plot(lon_data,lat_data,'r+')\n",
"ylim=(22.65,22.9)\n",
"xlim=(149.,149.8)\n",
"ax1.set_xlim(xlim)\n",
"ax1.set_ylim(ylim)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lon_bins=np.linspace(xlim[0],xlim[1],3)\n",
"lat_bins=np.linspace(ylim[0],ylim[1],3)\n",
"lon_indices=np.digitize(lon_data.flat,lon_bins)\n",
"lat_indices=np.digitize(lat_data.flat,lat_bins)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lat_indices"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lon_indices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with a partner, make a copy of this notebook and add comments that explain what this code is doing and why. Upload you notebook with this commented cell to connect "
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lat_array=np.zeros([len(lat_bins),len(lon_bins)],dtype=np.float)\n",
"bin_count=np.zeros([len(lat_bins),len(lon_bins)],dtype=np.int)\n",
"lon_array=np.zeros([len(lat_bins),len(lon_bins)],dtype=np.float)\n",
"\n",
"for point_num in range(len(lat_indices)):\n",
" bin_row=lat_indices[point_num]\n",
" bin_col=lon_indices[point_num] \n",
" lat_array[bin_row,bin_col]=lat_array[bin_row,bin_col] + lat_data.flat[point_num]\n",
" lon_array[bin_row,bin_col]+=lon_data.flat[point_num]\n",
" bin_count[bin_row,bin_col]+=1\n",
" \n",
"for i in range(lon_array.shape[0]):\n",
" for j in range(lon_array.shape[1]):\n",
" if bin_count[i,j] > 0:\n",
" lat_array[i,j]=lat_array[i,j]/bin_count[i,j]\n",
" lon_array[i,j]=lon_array[i,j]/bin_count[i,j]\n",
" else:\n",
" lat_array[i,j]=np.nan\n",
" lon_array[i,j]=np.nan\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Now get data from Channel 31 (see modis "
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"longwave=nc_file.variables['EV_1KM_Emissive']\n",
"longwave.ncattrs()\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"band_names=longwave.band_names"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"band_names=band_names.split(',')\n",
"index31=band_names.index('31')"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"index31"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"chan31=longwave[index31,:10,:10]\n",
"chan31\n",
"#chan31 = scale31 * (chan31 - offset31)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## For Wednesday, use planck_inverse to calculate brightness temperatures a 100 x 100 pixel section of your Channel 31 image and bin the brightness temperatures into a regular 0.05 x 0.05 degree bin"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 0
} | cc0-1.0 |
AstroHuntsman/POCS | examples/notebooks/Mongo Query Example.ipynb | 1 | 242227 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pocs.utils.database import PanMongo\n",
"from astropy.time import Time\n",
"import pandas as pd\n",
"\n",
"from matplotlib import pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting data\n",
"\n",
"If you are not local to the unit and want to explore the data you might want to export from the unit and import to your local machine.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Export data from unit\n",
"\n",
"There are some convenience scripts that can be used to export the data from the NUC, which can then either be imported into a local mongo instance.\n",
"\n",
"> Note: Your output will vary\n",
"\n",
"Export data:\n",
"```bash\n",
"➜ python pocs/utils/database.py --help \n",
"usage: database.py [-h] [--yesterday] [--start-date START_DATE]\n",
" [--end-date END_DATE] [--collections COLLECTIONS]\n",
" [--backup-dir BACKUP_DIR] [--compress]\n",
"\n",
"Exporter for mongo collections\n",
"\n",
"optional arguments:\n",
" -h, --help show this help message and exit\n",
" --yesterday Export yesterday, defaults to True unless start-date\n",
" specified\n",
" --start-date START_DATE\n",
" Export start date, e.g. 2016-01-01\n",
" --end-date END_DATE Export end date, e.g. 2016-01-31\n",
" --collections COLLECTIONS\n",
" Collections to export\n",
" --backup-dir BACKUP_DIR\n",
" Directory to store backup files, defaults to\n",
" $PANDIR/backups\n",
" --compress If exported files should be compressed, defaults to\n",
" True\n",
"➜ python pocs/utils/database.py --start-date 2017-01-30 --end-date 2017-01-31\n",
"Exporting collections: 2017-01-30 to 2017-01-31\n",
" config\n",
" No records found\n",
" current\n",
" No records found\n",
" drift_align\n",
" No records found\n",
" environment\n",
" No records found\n",
" mount\n",
" No records found\n",
" observations\n",
" 293 records exported\n",
" Compressing...\n",
" Writing file: /var/panoptes/backups/20170130_to_20170131_observations.json.gz\n",
" state\n",
" No records found\n",
" weather\n",
" 2094 records exported\n",
" Compressing...\n",
" Writing file: /var/panoptes/backups/20170130_to_20170131_weather.json.gz\n",
"Output file: [\n",
" '/var/panoptes/backups/20170130_to_20170131_observations.json.gz',\n",
" '/var/panoptes/backups/20170130_to_20170131_weather.json.gz']\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Import data\n",
"\n",
"```bash\n",
"➜ mongoimport --db panoptes --collection panoptes.observations --jsonArray < 20170130_to_20170131_observations.json\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to mongo instance"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"db = PanMongo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show available collections"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['config',\n",
" 'current',\n",
" 'drift_align',\n",
" 'environment',\n",
" 'mount',\n",
" 'observations',\n",
" 'state',\n",
" 'weather']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.collections"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find current value for collection"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'_id': ObjectId('58d8b587535f695281fa2676'),\n",
" 'data': {'ambient_temp_C': 25.87,\n",
" 'date': datetime.datetime(2017, 3, 27, 8, 37, 23, 932000),\n",
" 'errors': {'error_1': '0', 'error_2': '0', 'error_3': '0', 'error_4': '0'},\n",
" 'gust_condition': 'Calm',\n",
" 'internal_voltage_V': 3.0,\n",
" 'ldr_resistance_Ohm': 37.3029315960912,\n",
" 'pwm_value': 9.970674486803519,\n",
" 'rain_condition': 'Dry',\n",
" 'rain_frequency': 2560.0,\n",
" 'rain_sensor_temp_C': '33.63',\n",
" 'safe': False,\n",
" 'sky_condition': 'Very Cloudy',\n",
" 'sky_temp_C': 22.53,\n",
" 'weather_sensor_firmware_version': '5.51',\n",
" 'weather_sensor_name': 'CloudWatcher',\n",
" 'weather_sensor_serial_number': '0884',\n",
" 'wind_condition': 'Calm',\n",
" 'wind_speed_KPH': 0.0},\n",
" 'date': datetime.datetime(2017, 3, 27, 8, 37, 28, 692000),\n",
" 'type': 'weather'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.get_current('weather')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'_id': ObjectId('58d8b570535f695281fa266e'),\n",
" 'data': {'camera_board': {'accelerometer': {'o': 0,\n",
" 'x': 0.05,\n",
" 'y': 0.01,\n",
" 'z': 0.99},\n",
" 'count': 343263,\n",
" 'humidity': 61.3,\n",
" 'power': {'camera_00': 1, 'camera_01': 1},\n",
" 'temp_00': 21.7},\n",
" 'telemetry_board': {'count': 333309,\n",
" 'current': {'cameras': 312, 'fan': 43, 'main': 328, 'mount': 0},\n",
" 'humidity': None,\n",
" 'power': {'cameras': 1, 'computer': 1, 'fan': 0, 'mount': 0, 'weather': 0},\n",
" 'temp_00': 0.0,\n",
" 'temperature': [21.56, -127.0, -127.0]}},\n",
" 'date': datetime.datetime(2017, 3, 27, 11, 26, 12, 487000),\n",
" 'type': 'environment'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.get_current('environment')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Work with a date range (and mongo cursor)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cursor = db.environment.find({\n",
" 'date': {\n",
" '$gte': Time('2017-04-10').datetime,\n",
" '$lte': Time('2017-04-12').datetime \n",
" }\n",
"}) #.sort([('date', -1)])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num records: 163467\n"
]
}
],
"source": [
"print(\"Num records: {}\".format(cursor.count()))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"environment = {\n",
" 'telemetry_board': {\n",
" 'temp_0': list(),\n",
" 'temp_1': list(),\n",
" 'temp_2': list(),\n",
" 'temp_3': list(),\n",
"# 'humidity': list(),\n",
" 'date': list(),\n",
" },\n",
" 'camera_board': {\n",
" 'temp_0': list(),\n",
" 'humidity': list(),\n",
" 'date': list(),\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for record in cursor:\n",
" try:\n",
" data = record['data']['telemetry_board']\n",
" board = 'telemetry_board'\n",
" environment[board]['temp_0'].append(data['temp_00'])\n",
" environment[board]['temp_1'].append(data['temperature'][0])\n",
" environment[board]['temp_2'].append(data['temperature'][1])\n",
" environment[board]['temp_3'].append(data['temperature'][2])\n",
"# environment[board]['humidity'].append(data['humidity'])\n",
" environment[board]['date'].append(record['date'])\n",
" except KeyError:\n",
" pass\n",
" \n",
" try:\n",
" data = record['data']['camera_board']\n",
" board = 'camera_board'\n",
" environment[board]['temp_0'].append(data['temp_00'])\n",
" environment[board]['humidity'].append(data['humidity'])\n",
" environment[board]['date'].append(record['date'])\n",
" except KeyError:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# camera = pd.DataFrame(environment['camera_board']).set_index('date')\n",
"computer_box = pd.DataFrame(environment['telemetry_board']).set_index('date')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fcd306434e0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Iycnx1UsD/KRnGKD3H14i+7+XAJC/6lW6/+IXPm6REEIIIYQfaaYHtzNs3LiRjRs3Mm7c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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd3065e898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"computer_box.plot(figsize=(12,9), title=\"Computer Box Temperatures\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Work with a date range (and a list)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cursor = db.environment.find({\n",
" 'date': {\n",
" '$gte': Time('2017-04-10 12:00:00').datetime,\n",
" '$lte': Time('2017-04-10 23:59:59').datetime \n",
" }\n",
"}) #.sort([('date', -1)])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num records: 40822\n"
]
}
],
"source": [
"print(\"Num records: {}\".format(cursor.count()))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Convert to list\n",
"records = list(cursor)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'_id': ObjectId('58eb73c1ba6c190d5c50e6b4'),\n",
" 'data': {'telemetry_board': {'count': 1047161108,\n",
" 'current': {'cameras': 507, 'fan': 45, 'main': 548, 'mount': 148},\n",
" 'date': '2017-04-10T12:00:01 GMT',\n",
" 'humidity': 1.8,\n",
" 'name': 'telemetry_board',\n",
" 'power': {'cameras': 1, 'computer': 1, 'fan': 1, 'mount': 1, 'weather': 1},\n",
" 'temp_00': 9.1,\n",
" 'temperature': [7.31, 7.56, 11.31]}},\n",
" 'date': datetime.datetime(2017, 4, 10, 12, 0, 1, 987000),\n",
" 'type': 'environment'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Examine first record\n",
"records[0]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_power_reading(records, key):\n",
" # Get the timestamps\n",
" ts = pd.Series([rec['date'] for rec in records])\n",
" \n",
" power = pd.Series(\n",
" [\n",
" rec['data']['telemetry_board']['power'][key] \n",
" if 'telemetry_board' in rec['data'] else None\n",
" for rec in records \n",
" ],\n",
" index=ts\n",
" )\n",
"\n",
" if key == 'computer':\n",
" key = 'main'\n",
" current = pd.Series(\n",
" [\n",
" rec['data']['telemetry_board']['current'][key]\n",
" if 'telemetry_board' in rec['data'] else None\n",
" for rec in records \n",
" ],\n",
" index=ts\n",
" )\n",
" \n",
" \n",
" return pd.DataFrame({'current': current, 'power': power}, index=ts).dropna()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df0 = get_power_reading(records, 'computer')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>current</th>\n",
" <th>power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-04-10 12:00:01.987</th>\n",
" <td>548.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:04.004</th>\n",
" <td>542.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:06.017</th>\n",
" <td>789.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:07.029</th>\n",
" <td>557.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:09.043</th>\n",
" <td>615.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:12.062</th>\n",
" <td>560.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:14.083</th>\n",
" <td>514.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:16.092</th>\n",
" <td>687.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:18.101</th>\n",
" <td>736.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:20.118</th>\n",
" <td>705.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:22.136</th>\n",
" <td>671.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:25.164</th>\n",
" <td>699.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:27.186</th>\n",
" <td>741.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:29.208</th>\n",
" <td>676.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-10 12:00:31.232</th>\n",
" <td>783.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" current power\n",
"2017-04-10 12:00:01.987 548.0 1.0\n",
"2017-04-10 12:00:04.004 542.0 1.0\n",
"2017-04-10 12:00:06.017 789.0 1.0\n",
"2017-04-10 12:00:07.029 557.0 1.0\n",
"2017-04-10 12:00:09.043 615.0 1.0\n",
"2017-04-10 12:00:12.062 560.0 1.0\n",
"2017-04-10 12:00:14.083 514.0 1.0\n",
"2017-04-10 12:00:16.092 687.0 1.0\n",
"2017-04-10 12:00:18.101 736.0 1.0\n",
"2017-04-10 12:00:20.118 705.0 1.0\n",
"2017-04-10 12:00:22.136 671.0 1.0\n",
"2017-04-10 12:00:25.164 699.0 1.0\n",
"2017-04-10 12:00:27.186 741.0 1.0\n",
"2017-04-10 12:00:29.208 676.0 1.0\n",
"2017-04-10 12:00:31.232 783.0 1.0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df0.head(15)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fcd1d5f6860>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PT14vtT7fq9SS6BNAjf4pC795Ox3ZKTo77FvzczHgmcSvhe+l3324Ah8t3WG5\n+srny3cC8L9DhQE0xVWin7RlvTBts+7jv5+0Cte+EXvrt7KmPmIkfKr4dNkO/PZdyclWTCKwt+bn\n4tNlO4TLuPnp2bS7OOHKZu0+XIEt+70JGH79VjpO92jyGs4qmXwSPbNu895ADfqissT6jrrlpRnZ\nKEmweQ12FJXh95NWRgzS3nWwHDkenZOSDQNo8l34ifr3H62MX0Nc5OTE998ZWQk3HbldT3y3Ccu3\nH0ReWHWQQ2XVEflpohz48Jj6pRnZeOI7/0aEXzN+Ma54baFv+5MxbPRcXP6quE3frs1H2qhpKKm0\nVhM8Y4+zQXGigOvJqRkAgMPl1Y72kQjY05767L7HBUcqkTZqGmYn2R1TWc/9mIm5WQVYlFMYemz4\nS/Nwmck5qbFgAE2+C+91bsyjnjVvz98a7ya4LnyGyLOemxUxBbgov03LgZYdEOJ2bHOwLPkCvncX\nBFKB8g9WxLklsapS6fsddsFQUV2HeVkFwsWrautsBVaVNXW44e10bPRo1s9obl4gfLw0D09NbTxl\n0LSL0E9M7pTFW0FJJVbayqPn1aMIA2jyXZMEv1WYiCpr6vDi9MyYescFNlIOjlTW+J6qsHG3TjCg\n04Wp/ZhXs5SSZVYHYGrsTh7RmHtmn/huE+6atBJZ+4x78V+cnoU/frwKq3dYC1w27S7Gqh2H8MwP\nGU6baUn4t3H4S3NtVR15amoGPl7qcTDpwefOdvpKknwHrnp9UUQnhiztO87KWfoYQJPvmjCCtuyj\n9Dy8u3Ab3l0Y2Vttpwd/+EvzcO4Ls91qWlzxk9Tw4yYKaGvq6vHd2t26vf9eln9K1SA7ryiQolQq\nSN3aEVzmcLm11Bo3TV65C7d+sEy4jN6F166DFVi7M3bwM0XSjl2ix5cHSgN31qxWt9CWtvryVuUd\nxCWvzEd5tbXURrsdO1rxDb8ryjCAJt8l+LnGV9skJ0fQitu7UeTejx90qd5QwclO9jOSovGZJTLH\navzcXDz45TrM2LTP5hac27S7GG/OyxUuk7WvBK/PyfG0Hf/31hL0eWy6p/sA7PfaufmZ/ufXG7Ak\nt0i8v1Avo4s7Fu5PxWaH+feJJll+075fv8fWelY/Gy9Mz8S2wjJk7k3twYYMoMl/yXK28cEljXBy\nhMoa84sAo+DDcPpofqaECkoqAQCH4tgbes34xXj5Z/H07f/3ZjrGztri6diItTsPW5qwJ5THbTOy\ndbNTLFlJ+Z/oAAAgAElEQVRqNotMXJKHq15fhOXbxIF9MvD77aitq8dny3eg1qQj5ZrxizBh8faY\nxw+FjfGoq1dNt5OIn7fi8hqc8czPWKEzk63fGECT71KldJ0Ru7moqUT0Hr/0cxYAYL3FQVI7i8px\nyhMzMHnlrtgnk/CQb9l/BMUVzgNamd4h0TJe9jzqzTYZbu3OQxHBbEUwncRqRREvab3mVvPytcNa\naXTRZ7JeoiutqsU/pqw3fa/Gz8mJGMC2aU/ge7/rUOIMenUaJ3qRI1xfr+KZ7zMi7lJ+vmInHv92\nEyal5wnX3bS7BM/9qF9aVTPynXSc9PhPMY+v29WQuhNK4fD7QynY37r8wzhSWYvxc2PvVPmdq80A\nmnyX6PlidiiI/4VBUWkV0kZNQ/rWA3FthxmZShd6RzK3MHA78KdNqTFL5eWvLpSvmS1BJghIpIu7\nDfmH8X9vpeO12VtinhsflsaRW1CKGS685zV19bq9cmaKSu1VZjlQGsjnHPX1Rkvrid4hLzoE60Mp\nHNbOX5OWbMeU1fl4d4G4itArs7ZEDmDz+SM4Y9Ne9Hp0mlQ+rtXfJi87aLcdKMWk9Dz86eNVoce0\n9Du7F97h7/Eagxz3orDzcyi9x+S37avV+dhXXGmpLSWVNUgbNQ1zMnUq1STOaUqIATT5LlXHEPoZ\nnExZHTvdsjboZ8Ii60GCn5pI/ErpLSKcQjpJP1NZLsyqJvejb7yQl4dO9I3YXxIIMM3yYS8duwD3\nfLrGcVsmLckz7ZXTY/eCX5vl0WiWUtP92tutZVrOud8lHN14fV9JBPBjZ22BqgI7D5pXmwkPiA+U\nVknnavv1HYpbRoXgBRZX1OCRKetx+4TlljaZsz/Qs/6GybiIaImUVsIAmnwX755ar5m9PjdOAAuy\nxTVoNRXVdXhjbo5prpsdm3YXI23UNN1ZqUQXE3YDkldmBvJn1+5idQA94ce8vLrWduDmF+1C2q+f\nwyMeBLKitntxO1m0v73FFUgbNQ1Lcq3dgdLSZopKE3uGP73z6iNT1uPFn7KE62kX3vWCU6DeW3XF\nqwtx1euLLLXRXfG56A0n893UUrAKbX5+7P4c6n2/WIWDiBwJP4W8MS8HY2ZuweRVsT3WTv2wITCi\ne3amXDCvaSqaiTD4f70fy9yCQI+F1Soim3YX49YPlhkPQDRQXF6DZR4NdLJ7oldVFfd8sjpiZjDt\nWIVv8uznZulO0623W0/zBgUvU7sTYZYnrWfdrsPYX2LtlnFqX7YDq/IOAQjkyXphQ/5h7C1OnLxl\nWduDs6Ja/ZwVSfTIy25xY34xdh+2eexMdvLD+j3411cb7G3bbNfBYya8iJRYxk2J0//MAJriIBVT\nOBLhS621YW7YDGllVYGg0Wqt39mb9yNt1DT7J30BmRQON9d7/LtNWJJbZLl01q0TluGm95Z5Mh31\n1HX2yknV1KmYkbEPd01cKVwuutJJvMYdCNOagm2yUBAj5Po3l+DiMfNttckq4QBM0Xqut0TM63PQ\ntW8swfkvzvV4L/4oLq9BsQsVaULBo8mb/as3FmPYaGvHTtvmtuAFAKD/ffrrF2vx5SqdgdUu4kQq\n+hhAk+/4ZbROpvNErzSX7Ak+2uTgCVlmOmGrud92q0bYDaA1eq0U9QRv2h0IuF+dFTvIzSmrvaca\npxM3JMKFnkZ7P+32xpdXW6xuYfOYJVLKmehY+d0TKCNbJ8ff68/gvKwC04vegc/OxMBnZza0yXGj\n3D/qwi024t/QRHrlDKDJd434uw8gPgNBvDjkosDCbtAhDKZsB0DO6NWtvnPiCkfbtB0A61RMcPp9\nEq1+IFjZ5X8epAWEcqB9+j74HQjH6zxnt4PCi46NXYKBe14cn+LyGtw1aWVE5QorLFfhsLUX+5yX\n2xNs2+Y2G0rdiQ9e2qhp+Pvk9XIb9aCdNXX1eO7HzREXV5t2F+ODRdtsbpEBNMVBIvXouEWBt4FA\n9LlJb1d6569E6nHU2O1JdvqpcfP9mZ9daL6QgP0LDG1994jejh1FgQBI7xax08OpHQM7OdAythaW\nIt3igDqrhIMIHb7HVvfnlF9nZb086pz9R6THG4iOgVare3tY2oPGbsAuM6mP3xdLvuUb2zzfGH2G\nv17TMBbHiwt/0Wfjxw17MGHxdvxnembosWvGL8bz0zIFa4kxgCbfpWoPtFYiy4uAwO4m9Xos4832\nrfREzOPVcaSyBo9+swFlgqoP9lMwjFM4xMGctpC/l1Si3XndAz3ilQW45YOG0lrJ9rmzW5PYbnNt\n90BaXHHZttgZ5C57dSFuem+Z1PriqijOPkx6r6XgiL10q2SjH5A6SxlLJFohqlo7gy4MNHNtS0SS\nUnEQIQCkbw30oPgRouie7ETZD14WWtDZrxdl7GR7rhdsKUTXdi1x2vEdTJdVVfePzXn/mYOy6jqc\n0LE1/nJJX1e3rTexgUzznb5G2xdwgue0i7qlCT6ls91BhE7pfq8S8ZZSQop9Z4QpZw5TqrygW6bN\nu90JiY6d3oWb43OqcDYhh9t2EXugyXcnHdM+3k2IKzvff5kUDv19eXe28SIol+pFNVnhjg9XRNRv\nbWin8dZFM3tZ/ZEsszi4zQq9qXWtNE839cdmGBi+1pqdhyxXehFdSFv91B6prDEd8OpFsJtAv+WO\n2T0+fvVOxmsWTZnvvzdjTATP+XXMJV57Kg1stooBNPmufavUvvEhOo/MzNiHPo9Nt7xNxykcgmV2\nHSxH2qhpWL0j9taqF22yG7CJeqCd9hKKRu17cYK3m1ITr0oLoubuK67Er99Kxz91atGKBoWKjoHV\n1/fHj1bhV28sRo1gwiD7QYfdQXl292e8viiI9Pt2+7sL7A++Ahy0U5zDYYv9nmSfwz+HXd5WD11D\nCqCj3SaE8HPRkcoa0xksZTCAJvLRuwsd/ugET4H65zN7ORzpWwMDrb5c2TBQ7JAHtY8lmmKyovFT\nUj0lPv+4ejkTXfjFhJW9aHXBLe9XN00nQJvxcNNu85KH4dw8PNrslHqlHJON09nU/OpJ1mZ29CKN\nQe8YeFmBQrSMsHJFnAJMvwbihy7KfNmbNVaPuTbjpgIFL0zLNJ3BUgYDaPJdIn4Z3WQ5DcHStq39\nili6vR+28MrgrGYi4l7f1HmX43XrWI+qdbBaPLwHjgQuiP47I/ZHw+mPv2hKbrtHzurFh7a0eBxA\n8n8mhRU6PPyYisrR+S0Rzy1+t8n2OUk0C6zdEqJq7ELxytUX7fepqRkAgD2HK1BS6XwSHYABNJEr\nZH+c3aoEIJ0DLZHC4cXJXziIULSe+78LQokTGlujAFi6tQgj30kPjSoX/QBWWMxPjtmf4PiKpuS2\n3+tvbcVEjI39rsfstOKO6JAfqTSuKGOX1c+GlcWt9iTb5WlZQU+S9i12wFhY3NX2CsdHODvqZdXu\nfZYZQJPvVATqdNYK8hVTlRe1YcXLyOdFWj0t2U8pdT5ozdruAmsmS5qjSEOeq4JHpqzHyrxD2F9S\n5f1+dY6B9n5ob6duAO1dk3R5cbdANxhzfS8N4pXCYXt/Plew8LuiULx4MVGViOgOktX9SaXLSG7r\ng0XbIma0/Gp1oJb0oTLv0gxlMYAm3y3MKcTFY+bjpMd/indTXCP9o+fzr5te2bMYvufv2VxPePvR\neL19xYE6rtqkIH7x4oe+PtTL2PDYgVLzANqLuCA6HzuewYeXbdB7G2V2o7fe8m1FuOCleSh3sRfM\nLYlYys0qcfqc+2Xs/Ob4UFs9h0rkeDtuk8kH6Plpmbg6rKrS9I37AOhPluM3BtDkO6ejt5OFTGAj\ny/nUz/LLei28LbsPx85KZsRuz5y2jy9XGk9HnYh5lXq0Y2B3Nkc9ok1VBVM/SiUmhdH/AdZL63B/\nYFIoB1q0jM4O9xyuwO0TlgtL8LlZB/o/0zOx82B5RI+aW5x/fW3eHbO5N8vrWUknsLppp1WOPDh9\nCD93npyu7B0Ex7OjSrwYvclPEuG6jQE0kcu0AGHv4dgZrOyeZByPQLf5nNeGjZ4b+UC8Bp8Iy4PZ\n40VQXm/zx8puS74IVmbJLSg13nao99e9N89pmT9ZQ0fPxaKcA6Hbwm7uRzRbpMzr0/v8SL08Fz92\nm/eUmC7jRZ67aLY4L89XyTZbZaKSqx/tdCdON+AcA2giF4Tnf2r/atnc/tdr0+5i4VTQspzmhIp+\nGLSg6ru1u61tU2IZ3ZxbiV8pqwGUzPJuBoZO+V3rV2ZaelHvr+3eScuDCO0FpKH92VzPai+h9rK8\nmI3Vi8/pZ8t3uL5Njai1H6Xn6SyfON9Djd9t8vtU5EcZO91t270ok1jRzWPIAJrIBZl7I2/J1tTV\no7Yu9psqM5q9oroO14xfjHs/WxN6TPRDvb8k0NMtuoMuN4jQWhWFrOBt6BxB76QeT8qJBdu5tzi2\n119GsqRw6JWMinnKItH7USGYVTE6nUQm2A6sZ6FxkmRSOIRsNsp6JQktILGbLmF+p8T+Z9nuMXB/\nPdHMoHa5cdqpqK6LSPeRGmPiAb8KdMhUdtG7qPfjAt9+6pDq2vuV2lPCEfkk+ut4x4crkL61KOKx\nnUXl2LzX/JZodbA6ydodDbWYRb9RpVW16GbaPlEvmr2Tid1eNHFJUcGtW50Vj1jopRfW0PUghUPc\nFntb1RtE6JRoU3OzCkzX1z4HcZ3DRMvDFhT28fv2vCgFw8sAw+8ZF2UvnKKJVrOb42+3lLGsfk/N\nQMc2zbHuqcvd37gEx3cULe8vuJ5UVQ1rWxd+bKTG7MT/jgR7oIlcpgAxwTMA7DjofNSw8Da0Xg+C\nlY3rpk0YL97EZgSdbPmCtnM8PQi9RbdU43ZYQ4MIrd3B8OIiwG79cdv7tZjCIXORIVrEab6xeGp1\nu9u2uZ7gOVEALZWuY/G17D4UGGicJTG483C5+73jevweROgwnm2UGEATJRHLMxE23Nc1JOwR9qCX\nKJHI3Pa2n8drc0WJbeod+3j1x2jHTv8Czp/R8yXB1KiCI/Yq34imAPc78NaOo+ULMNEdFg8/HG5+\nP279YBlW5R20fXfL6h0szYq8gxF/T1m1K2YZv0sP6l0oWH0fN+QfdtQGmV7eBdmFAIB9JQ3pczLt\ndPrzEf/+ZwbQRK5QDf5tR0kw/y88PUE0Kl3qZOWwTXqaJlAAnYgDjLyg3S63euhFAYnTWRxFE6nY\nZXdLny0zHvQmep2inmG9QGbNzkBgojfOQaOfU2qeA/3FikC5xZV5hwyXEbF7QWyX0wlRPgl7z5bk\nFuGRKet17255eRFwMGpSjn98tUFq/36fdd6avxWAfNrdtW8sifhb5sIt4jGJ9dbnFxs+Z3V/TslO\nOObW7wUDaKIEo1cbedycHABAgWDWOf3ePvkThd6SomL1buaGhtqQkD1l4jVvn7Dcchk0PZU1dfhh\n/R6TtgT+b7mMnQ8XO3pBqDDv3IsfUMFzos+dKPgXvf81ggBaVIVD9HbsLTaujS5M7/AkZchcVa29\naeK1Y/Hkd5tinhOncBhv0+7gMJnvr/77aa9KRUllDX7O2CdcxoscX6ubXLtT68EWj1xxsr9knsCH\nATSRT2RP7qKlioI9JbLnnPKqwI+bKIDSntKr+SoKLOwGZfYD7/gwO08vyjmAR6asd7yfF6Zl4q9f\nrMXybbH589Gs3t71slcylHIgmQMtVUNZ8JwoYHM6IYYTfR+fjt+8nR76W+81yJQhtDsYUyY4t/sy\nRdvcVmhvbIfVdAuZtnt5J8ruQOysfbHn1Qf/tw5//mQ1dh30d3ZUEbvpL/olR+X26EQi3HVkAE3k\nAr9GBOvnahrve0awl0PmfJa9P3YAjehEWGRzpsUEyvwIcTP9QLPT4tThe4J3HkoEpQ7ttjNeZaXE\nF2D29jN1nXEvvd0gwO6FYvhTNXUqVodVztHboUzZs1KJUpcidnth7V5kefHdEaYcCdabtmEvAGC/\n7p06Z18Cu5/Xcp1SkDuDgXOFYAZML3iRUuH0/bf7rpjtduq63TGpOQ37dOeEyACayAV+3CIHvPmx\nElbaEDzpd9ky0f6c9jwKVxc8ubXQuAZ2ocULjOXbDwbbZH5LVO9tEY/ad//zGd1MNz+awvba/Bzk\nHzJOjbD7WfaiLJjehayVbQuX8eD84fY5SYX9AcpajrBw+16cQ6P+rqtX8eR3m7DrYLnuawlVjYnT\nwE+rvPx1E6Ylic6FgvUKjlTib/9bhz99vEpnm/JtM8MAmshtDr+gwqL1MgGf7jZttkXwXJd2LWxu\n017Fi/o4FRqen61fCzm3oBRZe42DHauHvDQ4aFSc5xrQRFFi3lPRD0NtnaBAskv0AikvpnkWlU8U\n7S6nwPi9st1TbrGntGHyGcE2HfKiPKCXKSe6+9XZoahSiqZUUBde22S1xe+C1SD3y5U70eex6fhk\n2Q48PHmd7nutBdV+pyF4MeGLaAIWEe1tqK51/9ykvc6IO0JBemOM7GIATeQCqbxOyXOXaDmZHxEn\n+7ayXofWzW1ts4nNs06dh10y4QFUZU0d7v+8YRbIMoPZ+C4du0AYCNkftGj8nN0qHPOCpaZ092dt\nU4b0glDdoNrhfvyeBlsYdAheTFO9ShJhWzVy/FGtDJ9bmXcwYia8iG2LAr2Y/VtTJghMj25r70Ja\nRO/orNsVrHwiOAfKfDa8CCLDv49PTs2IeE63Bzr4kN+9zHsEwaNwAK5wJkLResbPfbU6UCowQ2fs\njVPNoj4IMzY1DNh0c5ZLBtBEPpE9WbZoavy11HLmrJ54hQNgbD9nz2X9zOZN1Gf34kEje+t2fnZh\nKJfSjKg3tEMrexO91gh6yOxW4bCq0EY9Zetlvuy9CtHtfbu3xO1O+CJ6fXq9X06vHu6auBJPTY2t\nWuEVrbmir57dcpZGm9xhMmOruGa3+fnKanOtLh99ntH7vGrVjTYKSsB5QdT7an9KdnvPlVU5y/8W\nnSej3fPpakf7MsIAmsgFbnYkdG3fEgDQslns19NpEOkXUZDTXOd1RdO7tSksNSbXLOP1IzYgvzVR\n8NC2pb0AWqakmtc59+e+MBuZZtPOS+RA+52zLwoCenRqY/hcjU8TqTSk4JgvY8Rotrwtwdxp0S1x\nuxcKwrtiHrzHdstCyvRAW22uuGdf7zwVua7eHbeq4Hv01Rrn5S+jiXqZ7X6WFQQC1nlZselset+5\nw+X6g/fCyZwb3MgRT996QG5BGxhAEyWI6tp67CtumM2pU5vYW6MyZbD0GC1feKQKD365zvJ6ZlpI\nBMl6RAGQdqu4Uxt7qSO6+wseT7u9L3q36Ru27b7QIDQYvzdfrtzpyr6slieTDZZlFjsomC5ZdMz/\nt3KX4S3aM3t2NFxPFHS6ea2idwE0+IXZuDtssJPdeHRSeh6AhlSHcD9u2IO0UdMMqxI4Yfu748FF\noOiO0Guzc1zfn94doejvgeh1isYmiA6rKA1hf9iMgNGclB4dNzsHd01aGfOcXjPzoioQ6aUAeXFx\nrQnf8i3vL/dsPwygiVwQfi4wOi2YnbtGfb0B5704J9Q7IdqPW+eeLSYj/v2qLhKuqrYOvxq/OObx\nWwafCAC4eXBPW9u1m6snIhzQ5sHgqvAqHEbL/evrjRb35zA1JnhkdX8QhZs2fnL7AePqJmYVGp7/\ncbPu48IybT4N5gq/ANIUHKnCzM37Q39fd9bxAIDLT9NPdTJ7u/R6hD8KBte5BcbHVUR07OzeFXPz\nQljG9yaTFBkRXbBpws+T0YdfdA7VZrRclGM8RkHPjiLji1u9CyiN3fEDChTsMKhZLXP+KNBJCbs0\nmMrXq0tbqTaIXpdlMQOw7X2GGUATuUw0e5/IrOCPqJbbdaQytpdBNLuZFxf0RudbVVVDZdf0nzfe\nplk7txaUYatO76d28vdi5iq7AyJFTXF7sNuW/Ufw1vzcwH51D4K7H4BpG60FHHp7t9vL1E6Q/mJ2\nWCttjOq3WhFCZj09O4I9c6LPcNd2gRSu7oKUExG7x1zvjpdGtEXBZIy6Tj22PQCgc/B1ukqiLVYv\nloR3mSQ2ZRaA19bV4/YJK2K3LWincOp5wb4UJfD78sK02ItM0WtZmXfQcJZUux0TWi96n65yAfT1\nby4xX0hrk0+jMxlAE7nAjY7a6K+8XvUHmdnGgEDJt/CTiFEQYHaeMdrPNpsXCU5YzX3dFlWj2WZn\nqG22A32Dxlz/5pLQJCJe3BeIPobTN5pMNRzVTtkOaJkA5rijWhs+58UMmKISiUrUcuHHyW7PtReV\nIDT1OtcPolZef2agx/ssQYqLsDKMxR5oLaD0K8iR0bZFU8PnZN4pw44GmF9Iu30URHfFmigKfs7Y\nh/cXbddph3FLnjW4qwPY67ipqK7D89Myg3/pVCmBVubPGukyrxLjN2TYG+VCRBF2SMw6J/uTKfNl\nNgtWTnnyJ5zYWe7KXkQBMHnVLhwV1UNr9uNne1pYK40zcckrC5A3+uqwjcdu/c25ubj41GNwQkfj\ngE1E2Psiqndt44WGly9LxNkc9ahqIM9er/au3dntnNyGNiJ8O8JW6/3Y9FDACYh7bWXYrW0umgVU\nNKW63veyuaDqj7ZeZ0HNd1EKh9776PSzK/rcHN+xta2JaGQuyuwGWaLPnV4JQFVVTdsjvGMiaovi\n/kB08aBn/cfDxxwky7lMDwNooiSinWyiTzqVNXWoC+t6qqlTbec7hmvRrCn++dUGx9txg9MOq9Y6\nvUyvz83F63NzseKxETbbZNyoNoJeLfE2zZdREDuRil1edgSqUHHuC7N1n7M7s6QXeflW0h6+W7cH\nPY9ug50Hy3Ur5Xi133CDDI4pYL8qhijQE71O0ec8T5Cra1fblsb7u6J/N9MA2nIVDsFzS3IDFR5K\nBNOv6120dG7bAkVl1Rhx6jHG6wl2LDoGdmf+0btzIUN0fGZm7DOdcEs/Gc3huAyfbm4whYMoQUhN\nxhI83YSfdFQApz45Aw99ud76Pg1OVB2DA3z6H99BbjuqiuXbisL+ttwUaVZvf18X7DG8duAJrrcl\n/HVW1QZ6iHsHB8VceEpX1/en0fuNbGp3hpogL94z3XSCUM6+oOdKsE3TW+JG2xWmAIm3KfLFip2W\ny641VNOxWUNZ2ANps062zlEXNW9w2tEAgBvP7WG4zGnHHSXYnzVpnQM54cLykFENHj/H/cob4b5Z\nuxsADEs+Gh2+Ef0CgfOgtE6WzmZaj/Vvzu5uuIxoe2f37Gj4mbNdjlCw2rsLt+HSsQvEExVZTM3z\nosfa7tefATRRgjCa8S6clkphPTfM2hrafmROVsNO6ozJq3bht+8ts9iqWKpqvk+9H3o16v/hWjUL\n9NbYrb3bW3KQyzPfb8a42Tmh/HBxyoCzaFWvl615U3/vhcqlGhkvJBpsK07hsP46n/1hs/BOinAq\nb5PUj0e/2YhHpkRevHoxPXHDPq1/duwO9Aqtr7OB5s0CK/75k9V4ddYW29uWJXWxEdXQV6La9e6C\nrZ7U0jfaphrbJGl6q50YvIiw8x0AgC7t9Qdufrd2t+07jWafxxqdUaZOzn9md332l1Ti8+XGpTzt\nlljVwxQOIr9InvNW5hlXt9CrTuDkN8qsN1fm5N+xdYuYup+eVASR+v0037HVKik3CXrYwm3eW4Iv\nVrhTg1mPEla7Tu9lypTbclt1bT0W5xhPVCB6N0Z9Y1xuT/g+2niZHy6JHTQVzu2atJe9usDV7SWi\n8HPHuDk5eOiyk2OXcbHX341Bhy/+lGU5+JTZrReTyYjYH7yq//UZG3ahcUq39tbaIpNyJjjmVu8o\nmu3v95NWCqcHb9ZEcW0UNnugifwiec574rvYqXq126VXDzjOzRYZnojNzi92fi8GC/I2nW7byrrP\n/pAR89jEJXmG28jce8Twosar+sHzsgqQNmqacJnoHx6nlR3svJKXf87Cw5ONU4e8qIXd3GGqip7T\njzdONaiojs1vNXvfZQYVe0XUNr1n/E638rICiYwynfdTpkWi4+pmr7an74fExYP1qctttCPsiFve\nn/A51XSyoEt08s5ZB5oohWk5yXoj5mW++p52kEieAPWK6VthtzpJ6DnBeu8s2Gr43Ldrd2PkO0vN\ndx6zPxVjZ2Yb3t4W+UxwCxII5DImArPZCq1eYGi3qC8+xXhwVesW4p8tK3nFWm+baDIH0QyGVoRX\nzkigCm4heoGtF+20e9EptVacSjrYzee3u00R0eDxONykMmW5KpPDz+Swk7o420AYBtBESUQrByY6\nt9otyabHaDfzs8UzZ9n/kVRNfwNFP/R6nZNe/qa2bCautPH63FyMsziQSaZ6h6IosXWYDY75hny5\nGbys/mDLLG21Y67vMe0AAEcFLxi/XWttcJ5doma2bRGbNmUntrnJ4hiBrH0lSBs1LWJwrgzdtkk0\n2Iu7KeLgyOLnzaXmWa/CoWJRTqHhRCKAIAfa5SsQmc1pU7rrkUnzytpnfMdNt022cvIb1tG9IyE5\n2DW8tKcTcRlEqChKR0VRvlIUJUtRlExFUc5XFOVoRVFmKYqSE/x/p7DlH1UUJVdRlGxFUa5wsm+i\npOMgkKsInij++VXs7fL8Q+a3i41OcnbP7y9MzzRdZmN+MX7x37n2diAgOmG3ah4bfG7aUxxYz4Pe\nNFF9XJFj2reKeUybzKHH0fZmoDNy7RuRM3it3nHI9rasDo6zHkBEfkn0KsuYbdLPCTqs7CrHYlnJ\n9K2BwPnnjP0mS0Zq2VxU19m9qgYy6+nlG1tN4VgVFcwZHfNdB8uxdKtxPn5ofZ3HtLsMPQ2+e7dP\nWIF/fW2csy9K4RDV5faLdqeliSJX/tLKHTeng4mt/i72D0u3uu+zNRHPzczYbz45mLXdCTntgR4H\nYIaqqqcCGAggE8AoAHNUVe0LYE7wbyiKchqAmwD0B/BLAG8pimKvUCpRI6NdaVfWxAYwv5+0ynR9\no8kS9pVUCtc7VC7OJwOg+4ukqsBrs7cg/1CF6epbJCc+sPtDv2m38YASN9VZKKTarUMggA4fsNNG\nVP9JdyIAACAASURBVJ4ripMfgZd/zor428qP+e7D5u+n2bbdCh66SE4FXSFR3cZvF42Zj9fn5EgF\n+7sOyQ3QveP8EwEAN54TO+jVySHfvKcEr+vcRfErCLwhGMyZ9XRe+PI8rMyzd3F42WndAts42Vrp\nSa2s3LFHxV4Q26V1TujeSHBtL+45poP7U7KLXmf4935uVkHEcw9+uQ5FZc5SBa2wHUArinIUgAsA\nTAAAVVWrVVU9DOA6AB8FF/sIwPXBf18H4H+qqlapqrodQC6AwXb3T9QYZO+zPqtWOC2ndN1O/dv4\nZqWL7pq40nQf0zbuxfpdkduvtXDv/oa306WWe3fhNtNl/O7ZCd9fTKAuNTpdf1syonvyZNc3Ws7O\noTNbx9u3o2Hrg3sdbbhUv6dmmG8pDpHJJ8t2SC03a7NcD7R296WZYGZBPWav/fo3l2CKxTrXcju2\nuLjJ8uGnnPN7d7a/IQuL3zqkJwDgmgHHGy+kQ9QZMGGxuGKME158zof2CeQU6w3OMxIxiFDn+YZa\n8ZGPZ+87gtmZ4u+DXtk8M3aPi5Me6F4ACgFMVBRlraIoHyiK0hZAN1VV9waX2QegW/DfJwDYFbZ+\nfvCxGIqi3K0oyipFUcy71ohS2BWvLYx5TFVVbNxdLLW+lr/5gcRJWXbSFD3a7WbN9W8ukf59jLj9\nqRr/uByxONsX4O/t/GjTN+01X0hSUWmVJ/VrnRAd2/atAp87o1vijvYr/aB3EnEQoEb2Ts2mqHOI\nUWqF3jTsbrdFhpVjLkxjcaEtGm3Aak7BEZRU1uguE31ulGU3l11P+PvgdhUUrUmi/Orogb0ROdA6\nHxKj87neHdOdNireuHUEnATQzQCcDeBtVVXPAlCGYLqGRg2cYS2/46qqvqeq6iBVVQc5aB9RQnFy\n4oqc8c6bSRriUUdY1pGwHycrx/H7sIE/XpWcM1IqCPitGjMz27VtGR0FN4PChtkfY3vmZN4Ho7ZU\n1tShKpTG5N7nVYWK79buxq6D1n6MZ2zaJ7VcjYMg1CrZtJlrxi/GgfDKIAjMJrpiu9wAMrkcaKlN\nJb3pG/fhpnf1B4nqlSXV3o9Nu0tw0uM/edk0KXEqXgIAKKsKnCc3h9VutnKO33rA2tgCwL2LKCcT\nqeQDyFdVdXnw768QCKD3K4pynKqqexVFOQ6AlqSyG0B4clb34GNEZCL8C/9zhvhHOyItwMI+3D6H\n2un9PVBajax9sTnLT3/fULtZC8Cit//oNxsxtE/krdu//W9dzLbsTp/shrp6Fcu2FQkncyivrtO5\nTRlV81lv4LoaWSpNlifVF4LtffbHzdbWM3lrTn3SIB3Dhbf0wS/XoXPb2EGhoqPz6mzzEoW/HdQD\nf/hI/maq7c+njdUqqusijrnebKJh8/e4yuom3bqb5OprCdvYZoPpvEW+XLXLfKHw3Vneg/fkLoiN\nl5mbVYCq2joUhp27zGYbdJvdc6DtVqqqug/ALkVRTgk+NALAZgDfA7gj+NgdAKYG//09gJsURWmp\nKEovAH0BrLC7f6LGSi8olLWtsOFq3eiWo1uMUtFicnfD/n3V64t0J+YoLjdv6xcrduKvX6y10kRd\nD/zP+TaMvLNgK279YDkW5sSWAdQCmWkb9FI/Ig9mUVm1brB5zfjFLrTSmFcpMS9Oz8RpErnK0Sqq\n6zB+Tg5qXerhLTKZhMGOg+XVWLhFXPYxnO1jLFMNIervB780P5e4/Zb7cf0q2oUoWPpk2Q4siyob\n6OTlG60rdwwSMVw2Jvu2vq8zlqWqtl6/WknUMdDdRxwPk9OpvP8K4DNFUVoA2AbgLgSC8smKovwB\nwA4ANwKAqqoZiqJMRiDIrgVwv6qqiTc8miiFRN86Lq5oCEQHPDPT031bCRqsKCgJ9FR4NdublUEo\nhywGXNo04vtNqp+Y2VZYhmPax45+31sssV3DQYT+/BLpBWQyA0T1vD43B2/PN54ER5qHL91oEKDd\nONK0TJc4eoywesch9BZMImOFXvqLqDfdej1md5jtVzSpklsmrwoMyjQ6OnuLK7BuV+w4FzcuZry6\ngBE1bX1YLfoXpmfGjLdR1fheLtg9ro4CaFVV1wHQy1MeYbD8CwBecLJPomT0w/o90mW33CSalSra\n+vxiTFm1CyMHxZbBcpPVQK26tj7ipD9ldT5eHjkQF42ZH75R8/0Gl9knE2RK+m6dcRaa8Fa8CxUI\nKqImEbjq9UXWNuqQ1z94eyRK5pVXuZNnPtbibJFab5mb6Q2HyqqDdXrdj3BkvnNGeaeyr/EtnQsZ\nUaqSVQk5cNPh6zNa/fwXI+vn19er6P3YdEf7Ary5SJZ5X7bsj/wdqokaEL0xX25QvFc+WLQNf7mk\nr+X1OBMhkQ/cSC3ww0dL83zfp9lP0D90Jo9xwk6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EREREqSxlA+gWzZqEqm3884pTYwLQ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd1d5eaa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df0.plot(figsize=(12,9))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fcd1d402c18>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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3yKy1OOOK53MxMkPdTOgnMyxYtwP/5/LxuGHCq2RuNg506Elvmm9wYtS+4unF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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd1d14aef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Zoom in a bit\n",
"df0[0:1500].plot(figsize=(12,9))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mobeets/blog | _notebooks/testing.ipynb | 2 | 368823 | {
"metadata": {
"name": "",
"signature": "sha256:06923e8dcf0e8debc4f9f777db0dbd032f2ade4836353ff0404d943b34236edc"
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Adapted from MATLAB code by [George Mather](http://www.georgemather.com/Model.html)_"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 484
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.signal import convolve2d\n",
"from scipy.linalg import toeplitz"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 616
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing hello $$YOU BUTT$$"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"1. Create component spatiotemporal filters"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"a. Define the space axis of the filters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx = 80;\n",
"max_x = 2.0;\n",
"sx = 0.5;\n",
"sf = 1.1;\n",
"dx = (max_x*2)/nx;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 486
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_filt = np.linspace(-max_x, max_x, nx);\n",
"gauss = np.exp(-x_filt**2/sx**2);\n",
"even_x = np.cos(2*np.pi*sf*x_filt)*gauss;\n",
"odd_x = np.sin(2*np.pi*sf*x_filt)*gauss;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 487
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(x_filt, gauss, linestyle='--', label='gauss');\n",
"plt.plot(x_filt, even_x, label='even_x');\n",
"plt.plot(x_filt, odd_x, label='odd_x');\n",
"plt.ylim(None, 1.1)\n",
"plt.legend()\n",
"plt.title('spatial filters')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 488,
"text": [
"<matplotlib.text.Text at 0x112bd8150>"
]
},
{
"metadata": {},
"output_type": "display_data",
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FBTF79mx69+5N06ZNTzmysTqTJ0/mb3/7W8XXDz/8MBdeeKGJ32n17J82IUSA2HpkK12a\ndan24JXadI81Ev+wjsOMF/76y5jRc/759i8IDSXksku5d+sXbNx4B+ec42LgdZx60pwtS/U057aF\nsFgsjB49mvPPP5/U1FS2bdvGlVdeSbdu3Rg4cCALFizg5ptvBmDOnDlcfvnlBAcH8+WXXzJt2jQ+\n/PBD+vbty7Rp05g4cSK//PJLRd0zZ85k3rx5HD9+nOTkZEaPHs3FNfz5+corr9C7d29mzZpFx44d\nmTlzJn/99ZdrD8JZthNmfP1hhCKEd33818f6ys+vdPn6N9a+oSd/PfnkC/fdp/Vjj9V80RdfaH3B\nBS7fsy7w15/ntWvX6ujoaH38+PGK1+655x59ww036P/85z/6Auv/F4vFotu2bat/+uknrbXWI0aM\n0P/6178qrsnOztYhISE6MzNTa621UkovWLCg4v2LL75YP/fcc7XG8+uvv+qYmBjdvn17PW/evBrL\nVvdMra87lW+lq0fUeWlpsG2ba9e62r9vkxRXaS5/WZnRv19dN4/NRRcZU4/84CSmQLN7924KCgqI\nj48nJiaGmJgYZs6cyaFDh5gwYQJr1qzhwIEDrFq1iqCgIM63/uW2e/dunn322YprOnfuTFhYGPv2\n7auou3fv3hWft2rVigIHtmU9++yz6dixIwCXX365yd9t9STxizrvvvvghx9cu9bVqZw2p/Txf/+9\nsTFPt241XxQeDsOHw1dfuXxf4Zq2bdsSGRnJwYMHycnJIScnh2PHjvH1118THR3NRRddxKeffsqc\nOXOYOHFixXXt2rVj6tSpFdfk5ORQWFjIgAED3IrnrbfeoqSkhPj4eF544QV3vz2HSeIXddqff8LG\njcaxiq5Iy3Zt8ZZNq8atKCkv4XDhYfjkk9pb+zbjxxuLvIRXnXPOOSQkJDBlyhR27dpFeXk5qamp\nrFu3DoCrrrqKWbNmsWDBAq666qqK626//Xbeeecdli9fTklJCXl5ecyfP7/a+2gHtqTetm0bU6dO\nZfbs2fz3v//lhRde8FofvyR+Uac9/zzcey80aOD8tcVlxWTmZdK5aWeX76+UOrmQ68cfYVTtJ3gB\nRrkff4T8fLQGS82bfAqTBAUF8fXXX5OVlcWAAQOIi4vj1ltv5dixYwCMGTOGHTt20KpVK3r06FFx\n3dixY3nqqad44IEHiI2NpUePHixbtqzi/arbeSulatziu6ysjEmTJvHII4/Qo0cPOnfuzDPPPMOk\nSZMoLS01+bs+nRzEIuqsnTvhnHMgPR2aNHH++pSDKVzx+RVsvqOGjdYccPPimzkv8gxuGPME5OVB\nkIPtqZEj4brruPX7Kxg6FCr1LNR5chCL+eQgFiGAl1+G2293LemDdWA3zvWBXZuk2CTy/lgN3bs7\nnvShortn7Fh47jljCYAQ3iCJX9RZjz9uDOy6Ki07je6xrvfv23SP6w6bUuHMM527cMwY+PZbLrnY\ngtbwzTduhyL8zO23305kZORpH//3f//n07gk8Ys6Kz4emjZ1/Xp3Z/TYdI/rTtT2TKjUJ+yQ5s0h\nNha1bSuPPGK0+kX9MmPGDPLz80/7ePvtt30alyR+EbDMSvxto9rSOauIgq4ubPvQvz/8/jt//7ux\nt1ulhaBCeIwkfhGQyixl7MzZSddmXd2uKwhFj0OKrS1d2Pahf39Yt46QEHjmGbBOLhHCo2SvHhGQ\ndh7dSevI1oSHOrF/c3UOHCAoOIS/OEBfZ6/t1w8+/xyAK690PxQhHCEtflGnLFtmLNpyl1ndPACk\nppLTqTWbqzuUpSZnnWWsQPPC3G0hbCTxizqjvBzuvtuc7hBTE39KCuXdazh/tyaRkdChg3FQsBBe\nIolf1BmLF0NMDAwa5H5dm7Pd25ztFKmpRJx1jmuJHyr6+YV/+eijjxhUwz+25ORkPvjgAy9GZB5J\n/KJO0NrYnuHBB6GGlfAOM7vF3+zsIRwsPEhhSaHz1/frB7//fspLpaXIQS1+rrZtGfyZJH5RJ/z8\nMxw5AmPHul+XRVvYmr2VxNhEEyqzwObNhPTsTZemXdiSvcX5OqxTOiu7/3544w33wxPCHkn8ok54\n6y144AEIDna/rgMFB2jSoAmRDSLdrywjA2JjoUkTOjftTHpOuvN19OoFW7ee0sS/+WYj8RcXux+i\nOFVaWhrJycnExMRw5pln8pV1e+wjR44wZswYmjVrxrBhwzhw4NSzlL/77jsSExNp3bo1jz32GFD7\nLpy+PF6xJpL4RZ3w/vtw3XXm1JWek07HmI7mVJaSUrFVQ8eYjq4l/oYNITHROLbRqmdPYyHw7Nnm\nhOl3lDLnw0mlpaWMHj2a4cOHc/jwYd58802uuOIKtm3bxh133EGDBg3Yu3cvb7zxBq+//npFV052\ndjbjx4/nqaeeIjMzk5iYGFavXl1rV88rr7xCSkoKs2bN4qeffmLmzJn897//demRmUkSv6gTIiON\n/GgGUxN/amrFVg0uJ36w293z0EPw4ov1dMtmrc35cNLatWvJzMzkH//4ByEhIQwdOpSzzjqLTz75\nhIULFzJ58mTCw8M544wzGD58eMV1S5cupWnTphVn8N5xxx0O9e+Hh4fz8ccfc++99zJp0iT+/e9/\nEx8f73TcZpPELwJOek46CdEubK9gT+rJzdkSohNIzzUv8Q8dCo0ayUFdZsrKyqJr165ERERUvNav\nXz8OHDhAWVnZKccn9unTp6IrJysri549e1a8Fx4eTmKiY2NEvjpesSaS+EXA8WRXT0ZOhmv12JnS\nqRS88AJERbkbpLCJj49n27ZtFBaenH31+++/07JlS0JCQtiwYUPF6+vXr69o1bdq1eqU07FOnDjB\nli2ODeT76njFmkjiFwEnIzfDnMRfXGycAmNt+bWPbs+eY3sos5Q5X1f37pCZCfn5p7w8bBgkJ7sf\nqjAMGDCAtm3b8vrrr1NaWsrKlSvZsGED11xzDePHj2fGjBmcOHGCzZs380Olg5xHjRpFTk4On3/+\nOWVlZbz99tt+f7xiTdxO/EqpwUqp9UqpjUqpu+y8n6yUylNKbbB+PO7uPUVgmDULfvvN/HpNa/Fv\n3WqsurUOPjQMaUjziObsPbbX+bpCQ40R3T/+cD8uUa3Q0FC++uorli9fTlxcHHfeeSdz586la9eu\n/Pvf/6aoqIg2bdpw9913c9ddJ9NZbGwsn3/+OY899hjt2rXj6NGjnHfeeTXey9fHK9bEraMXlVLB\nwFbgQmAf8DswUWudVqlMMnCf1npMLXXJ0YuiwvHjkJAAK1dCkkkLbAFOlJ4g5vkYCqcUEhzk5tzQ\nOXNg0SKodOj2kI+GMG3INC5IuMD5+u65B9q0MVap1XFy9KL5/OnoxbOBHVrrXVrrUmAecJmdcnVz\neZvwmZkzYeBAc5M+wK7cXbSLaud+0odTZvTYJEQnuN7P36+fbN0gvMLdxN8a2FPp673W1yrTwECl\n1Cal1FKllEnr5EV9VVoKL70EDz9sft2eGti1MXtKZ2XHjsHhw65VLTzHX49XrIm7+/E78rfceqAt\nUApcBywGOtsrOH369IrPk5OTSZZRrYD06adGN8+AAebXbdrALpwyldOmY0xHlmxf4lp9Xbsa+1Jk\nZxurgat46SXjLR+f2ieqmDFjBjNmzPDa/VauXMnKlSvdqsPdPv4BwHSt9Qjr148CFq3189WUV0A2\n0EVrfbTKe9LHLwAYNcrYfvnii82v+75l9xEfGc8DAx9wr6LCQiM5FxScso/E6j2ruXfZvfx686+u\n1TtkCEydCnaW9R88aHR9paVBixauBu4d0sdvPn/q418HdFFKdVBKhQFXYLToKwfVQp1c4jYaOFE1\n6QtR2RdfwEUXeaZu07p6du+Gdu1O2zzIra4eMPbtqWa6X4sWMHEivPqq69ULAW529Wity5RSNwKL\nrHW9r7VOU0rdZn3/XeBvwGSlVBmwEfuDv0JUCA31XN2mrdrNyDD6o6poEdGCwpJC8ovzXdsErlcv\n+PHHat9+6CHj0K4HH4RmzZyv3pvq6pbFgcDtM3e11j8Cfaq89m6lz98C3nL3PkK4S2ttXot/1y5j\nDn8VSikSYhLIyM2gZ4uep71fq169atyPuX17GD/eaPU//bTz1XuLdPP4N1m5KwJG9vFsGoQ0IKqh\nCXsgVJP4wc3unjPOgO3boaSk2iJTpxrjIEK4ShK/CBimTuWspqsHoGO0G4k/PNyod3P1xzi2awfn\nnuta9UKAJH7hB7SGK64wtr3xJFN35ayhxZ8Q48YiLqhxgFcIM0jiFz733XewcaPRf+1Jprb4a+vq\ncXV7ZpDELzxOEr/wKa3hySeNfmszjlWsiWmJPz/f2EyoeXO7b3tySqcQZpDEL3zq+++NxapXXOH5\ne5m2atfW2q9mumJCdAK7cndh0S4enWVL/A7MjDl4EL791rXbiMAliV/4jNZGS98brX0wscVfQzcP\nQERYBFENotifv9+1+lu2hKAgyMqqteixYzBpEhyVJZHCCZL4hc8cPgwdOxqrUT2tpLyE/QX7aduk\nrfuV7dpV7YweG9tcfpco5XB3T5cuMGECPPOMa7cSgUkSv/CZ5s1h9myjcetpmXmZxEfGExpswrLg\njIwaW/xgQj9/794O9/NPmwYffmjsIiGEIyTxi4DgrRk9Nm7N5QenBnhbtYI77jC6zIRwhCR+ERAy\ncjLoGO3FxG/GzJ4//3S4+IMPGgPl+/a5fksROCTxi4Bgeou/lj7+jjEdXe/jB+MA98xMY9qoAyIj\nYcsWaF31GCQh7JDEL7wqJwdyc71/3/TcdBJiTFi1m5dn7KNTy9aYCTEJ7rX4Q0OhWzfjsBcHRbqw\nGagITJL4hVc98YRvZqCYPpWzli2HW0e2Jvt4NidKT7h+L1nIJTzE7W2ZhXBUSopxrOKmTd6/t6mJ\nv5ZuHoDgoGDaR7VnV+4ukuJcPDFeEr/wEGnxC6+wWGDyZPjnPyEuzrv3zjmRQ7mlnGbhJpxc4sBU\nThu3+/ndTPzl5a7fWtRvkviFV8yaZXSN33KL9++dkZtBQkyCOSdCOTCjxyYhOoGdR3e6fq9evYzd\n61w41OSTT4xftELYI4lfeNzx4zBlCrzzjne2ZqjK2zN6bNxu8TdrBk2aGPd00ujRsGQJrFnj+u1F\n/SWJX3hco0awejX07eub+6fnpHt1Dr+N23P5wen5/DZRUfDyy0arv6zMvRBE/SOJX3iFg41kj8jI\nMWlXTq2928cP0KcPbNjg0qVXXAGxsfDmm+6FIOofSfyi3kvPNamrx7YAISbGoeK2Fr9bB4/37Qt/\n/OHSpUrBW2/Bv/4Fe/e6HoKofyTxi3ovPcekxVsOzuG3iWoYRWhQKNnHs12/Z79+sG6dSwO8YKwB\n++gjiI52PQRR/0jiFx7h4E4DHlduKSczL5MO0R3cr8yJbh4bt/v5bXswuLEJz6WXQuPGrocg6h9J\n/MJ0x44ZY5KbN/s6EtiXv4+4RnE0DGnofmVOzOixcbufX6mTrX4hTCKJX5ju7rth6FDo3t3XkXh/\nO+aqTJnZ40Y/vxD2SOIXpvr0U2Pu+Kuv+joSg2n9++BS4k+IdnOzNjC9xS/TO4UkfmGazEy46y7j\nVK2ICIw9A/LyfBqTqXP4fdHHDydb/O7MDqpk9Gg5oD3QSeIXprlncgnvXPYt/RY/AcOHQ9Om0LYt\nfP65z2LKyDVxDr+LXT1uz+WPjzfOp9yzx716rB59FK67DrZtM6U6UQdJ4hfmyM3ls9zhjP/rCaOl\n/49/QHo6rFplfO6jvh/T+viPHoWQEKfnRbaLakdWfhal5aWu39s2wGtSP//gwcbc/lGj4MgRU6oU\ndYwkfuG+vXth0CBC+/dBrV17Mqs0a2YcGr56NXzwgfELwMtbRpo+h99JocGhtGrcisy8TPfu37ev\nqf38N98MY8fChAnG5nkisEjiF+7ZvBnOOw+uvdZo1QfZ+SfVrh38/LOx0+Tf/+610cWCkgKOFR+j\nZeOW7le2eze0b+/Spab085vY4rd57jnjD5jPPjO1WlEHSOIXrlu92pi3+fTTxmnfNa1ojY6Gb76B\nrCyv9fnvyt1FQnQCQcqEf+aZmcYvMBeY0s9va/GbNMALxk6pn30GV19tWpWijpDEL1yyduZmCi8a\na+wHMGmdDOePAAAgAElEQVSSYxc1aAAPPQSvv+7R2GxMncO/Z48xUO0CU1r88fEQFmb8AjJRWJjD\nO1CIekQSv3Daik8O0OqWUey9+0W45BLnLh4zBg4cgN9+80xwlaTnpJMQbdIcfjda/KbM5QdZyCVM\nI4lfOGXx3EKa3TCaoBuvp9sz1zlfQXAw3HmnV1r99arFD17buiEnx9QeJeGHJPELh82bXU7oDVfT\n7uLutH3vCdcruummk/39HmTaHH5wO/G73ccPXmvx33IL3H+/cU6yqJ8k8QuHlBRrQh+5n0E9j9Fs\n4fvudQxHR8PEicZZjB5kWou/pAQOH4ZWrVy6PLZRLCXlJeQW5boXhwcGeO15/32jJ27MGKP1L+of\nSfzCIWGvvcCE6O9pvHyhMSLorrvvhvfeg6Ii9+uyQ2tNRk6GOXP4s7KgZUtjAZcLlFIkRCeQkeNm\nq79VK2jY0Jha6kExMfC//0GXLjKsUF9J4he1+/BDmDHD2ODFrBM9unUzssrcuebUV8WBggM0DmtM\n4zATNqJ3Y2DXpq7184eGGssynn8eRoyAHTs8fkvhRZL4xWlyc6GgwPrFV1/BlClG0rcdCmKWe+4x\nBnk90HXhL/37Nqb283txb/7LL4fUVOjc2Wu3FF4giV9UsFhg1ixISrLu3vjzz8ZA7OLFRgvdbBdd\nBMXFxn1M5i8zemxMa/Gfey788ov79TihRQuv3k54gSR+gdbGXmqDBhmHcy9eDH9r9Yuxkcsnn0D/\n/p65sVLGIO/ixaZXbWri96eunvPOgw0bKv1J5jvr1sne/nWVa6NVot7Iyy/l3MvXcrDxctpd9hdR\nzY+x4L3ddJq7h4eubUkz9T3XHmrNGc3P8EwAw4YZ8/pNlp6TzqB2g8ypbM8euPhit6owbRFXo0bG\nL+JVq2DkSPfrq0VWfhZzUuYwN3UuuUW5RIZFEtkgkiZhTUj9qRMnNg3n3nHJ3HlLJJGRHg9HmEQS\nfwA6XHiYhWkLWbJ9CT/u/pGWF3Xmlh4XcU7r6+kx6xvaLt3C/q8XcEe3dny26TNGzB5BXKM4rut1\nHZP7TyYs2IRZPTZnn21s35ydDbGxplWbkZvBdb1cWGBmjwkt/g7RHcjMy6TcUk5wULB78Vx4IaxY\n4dHEv2DzAt5f/z6/7vuV8YnjeWn4S7SNakt+cT7Hio+RX5LPpvab+PzP13ji0FU8cX8fzmk2gvsv\n+RvjBnf1WFzCJFprtz6AwcB6YCNwVzVlnrW+vxZIrKaMFp5hsWj9a+oh/cyyd/WwWcN01LNR+srP\nr9RzU+bqQwWHjEIlJVrfdpvWvXppvXfvKdeXlZfpFTtX6Av/e6G+dM6luqi0yNwAR43S+tNPTa2y\nzStt9K6cXeZUFhOj9eHDblcT/3K8zszNdD+eNWu07tnT/XrssFgsetr/pulub3bTc1Pm6sKSwlqv\nKSwp1B/+9I3uO/UOHTGtpe49o7f+16p/6W3Z2zwSoziVNXc6lbeVdmNGhVIqGNgKXAjsA34HJmqt\n0yqVGQncqbUeqZQ6B3hdaz3ATl3anVjESWVlsGWLZv7KzSze8jWby7+iNCaFc2Iv4cFL/s4lnS8h\nPDT85AUrVhh75XfsaJybWM3f7KXlpVy18CoKSgpY+PeFp9bhjtdeg7Q0ePddU6orKisi+rloCqcU\nut+6LiiA5s2hsNDt3cwGfTiIp4c+zZAOQ9yLqazM+Oto2zYjNpNorXn8h8dZvG0xKyatoEVj50d1\nyy3l/Jz5M/M3z2dB2gJiGsYwuutoujKaxMbnclbvYMJN+mcjDEoptNZO/eN0N/GfC0zTWo+wfv0I\ngNb6uUplZgD/01p/av16CzBEa32wSl2S+J1gscDx49DYOk293FJO6qFU1uxdw8cr1/DbwVU0aFjO\n2dGjuab/aCaem0x4aMNTK9mxw1ibn5oKL78Ml11Wa3Irs5QxadEkso9n8+WVX9IotJH730xKCowb\nZ9pk8a3ZWxk1ZxQ77jahvrQ048SSrVvdruraRdcytMNQbuhzg/txXXaZMTB+5ZXu14WR9B9e8TDL\ndi5jxaQVxEXEuV2nRVtYl7WOr7Z+xaw1izmevYcGuwYQc7wvXRr3pX+7nlx+RRRd+jYxFg4Il7iS\n+N3t428NVD4IdC9wjgNl2gAHqScsFigtNWbH2D4sFuNMkkZ28mJhoTFeWFpq7AZQUmIsYG3SxJim\nXdXGjfDsSyfYn3uEQwVHOHL8CEfL9pE0cAc9knew4+gOtmRvoWXjlgxsO5Drhp7Hu20f5Iy4M1BV\nE3lxMfzwAyxYAF98Yeyj/9lnxpbJDggJCuHjcR9zw5c3cOmcS/lq4ldEhEW48NQqOfNMyM93+ZSr\nqvxtDr+NaXP5wRgU//57UxK/1pr7l9/Pyl0r+eHaH2jWqJl7FRYVwYoVBH37LWenp3P27t08lZmJ\nJUhRFLaG4tIfKbWUUq5LCX83mPJiC+WhwZQ1boSOjETHxLAnvxmHS5tjiYxDR0ajo6JQUVGceV4U\ncR0iICLC+OFq1AjCw9lzMIwTlgaENAojJDyUkAbBBIWFEBMbTIPwoNMaNKWlJ39GlTr5dtDpResl\ndxO/o030qo/S7nVlQXaeuL3/CTXd1VpeufDHQ8WtrNc6Otc1CKicNi2AVkY1JUFgUWAJUpRb/1sa\nBBFKURocRGiwIjhEQajiWIMgVkQojocp8hpochtojoaVcyS0nOAGmrimTWiWEENQs2ZExbehXdsk\nElqOoHP/znSL7UbT8KanBlZaCvv2GUv809Nh2TJjgn6PHkYr9umnja0InBQSFMJHl33EpEWTePC7\nB3l71NtO13EKpU4msptucq8u/Gc75qo6xnRk2c5lptTFhReado7x7JTZfJf+HauuX0VMeIxrlRQX\nG42JRYtg+XLjyM1LLzWW/bZvD+3aERQVRSPA1hY6Xnqcrdlb2XFkO7v2p7F/7xZyDu7CknOEskOb\nCDu2hsgTZTQtCiEqL5ioQ4r8LYrGZdCoxEJ4iaZRiYXQMk1wsaZJmSbMogkr1wRrCLEY/7X9PJer\nkz+XGuPnvdLbFWXsqZpPTvnaxRzlsfIOcDfx7wMqN4faYrToayrTxvraaaY/+ljF54MHDWbIkMEE\n2+mitVjs7xyoFBXlNaCsJy/ZWuD2VK7fggalKlrtACpIGYMhylavtv5j0da6jVcs2oLWGoul3Pgo\nL8NiKae8vBRdXo6lrBRLWSm6vAxLcTGUlqCKiwkrLiKsqIjo40UEnygi6EQRQcePE1ZQTFjBcUIL\njhOSX0jwsQLU/hzYdBRyjkJeBuR9bXzTTZqc/qdFaakxU6Zly4ofPC680Fgpa8KKnOCgYP498t8k\n/juR2/reRq+WvdyrcNgwY6zBhMS/NXsrXZuZNLPExBZ/l6ZdePO3N02pi6QkI9mmpxtjMy4qKCng\nkRWPMP/y+a4lfa2NdRj33w8JCUb309tvQ1ztXUWNQhvRp1Uf+rTqA2faL1NcVkxRWRHF5cUUlxVT\nXF5MuaXc+DnUmlI0ZSiCVFDFh1KKYBVMcFCw8RqKYIz/Bmkjj1aURRlfW8eCFKrir2RlzbhKKSN/\nVPpTwKKN/FLdXwj2jpa2aE1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4avu6Srl0IB+wAG9rrd+vpj5t1oHTon7YeXQnr6x5hc3Z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\n8QshRID5f055X+a3hqLwAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x112bceb10>"
]
}
],
"prompt_number": 488
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"b. Define the time axis of the filters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nt = 100;\n",
"max_t = 0.5;\n",
"dt = max_t/nt;\n",
"k = 100;\n",
"slow_n = 9;\n",
"fast_n = 6;\n",
"beta = 1.0;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 489
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t_filt = np.linspace(0, max_t, nt);\n",
"t_fcn = lambda n: (k*t_filt)**n * np.exp(-k*t_filt) * (1./math.factorial(n) - beta*((k*t_filt)**2)/math.factorial(n+2))\n",
"slow_t = t_fcn(slow_n);\n",
"fast_t = t_fcn(fast_n);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 490
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(t_filt, slow_t, label='slow_t');\n",
"plt.plot(t_filt, fast_t, label='fast_t');\n",
"plt.legend();\n",
"plt.title('temporal filters');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Uj69Ocr1kdhwK7ncvPwsKxphoqZRB4VTBKTbt30Ra47RyXXfsmLPe0dCh3pSj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\nCDsm4SY6pgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112c5f610>"
]
}
],
"prompt_number": 491
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"c. Combine space and time to create spatiotemporal filters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"e_slow = np.outer(slow_t, even_x);\n",
"e_fast = np.outer(fast_t, even_x);\n",
"o_slow = np.outer(slow_t, odd_x);\n",
"o_fast = np.outer(fast_t, odd_x);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 546
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_imgs(imgs):\n",
" for i, (name, img) in enumerate(imgs.iteritems()):\n",
" plt.subplot(2,2,i)\n",
" plt.imshow(img, cmap=plt.cm.binary);\n",
" plt.gca().axes.get_xaxis().set_ticks([])\n",
" plt.gca().axes.get_yaxis().set_ticks([])\n",
" plt.xlim(nx/4., 3.*nx/4)\n",
" plt.ylim(0, 1.4*nt/3)\n",
" plt.title(name)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 558
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_imgs({'e_slow': e_slow, 'e_fast': e_fast, 'o_slow': o_slow, 'o_fast': o_fast})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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k8NnZ2ZKDzBxox8fHC4I4PDxc+H6A5cCn6E/DWC5zKDc/kzaKWjxCEs3dx1gC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\ntoIkm0QisRX8fwAPWHSTmyINAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11353f950>"
]
}
],
"prompt_number": 559
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"2. Create spatiotemporally oriented filters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"left_1 = o_fast + e_slow;\n",
"left_2 = -o_slow + e_fast;\n",
"right_1 = -o_fast + e_slow;\n",
"right_2 = o_slow + e_fast;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 544
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_imgs({'left_1': left_1, 'left_2': left_2, 'right_1': right_1, 'right_2': right_2})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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gfBiI4ZFj/o7PG9ssg0HzbFqUDSqXVQ2rGyUcfWo8G2U6UxhEw3NqQDaYwAfm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\n",
"text": [
"<matplotlib.figure.Figure at 0x11373c250>"
]
}
],
"prompt_number": 545
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"3. Convolve the filters with a stimulus"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"stim_width = 4;\n",
"stim_dur = 1.5;\n",
"x_stim = np.arange(-stim_width, np.round(stim_width-dx), dx);\n",
"t_stim = np.arange(0, np.round(stim_dur-dt), dt);\n",
"\n",
"nl = max(len(x_stim), len(t_stim));\n",
"signal = toeplitz(xrange(nl))[:len(x_stim), :len(t_stim)];\n",
"noise = (nl/7.)*np.random.random([len(x_stim), len(t_stim)]);\n",
"stim = signal + noise;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 822
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(20,2))\n",
"plt.imshow(stim, extent=[x_stim.min(), x_stim.max(), t_stim.min(), t_stim.max()], cmap=plt.cm.binary);\n",
"plt.title('stimulus');\n",
"plt.xlabel('position');\n",
"plt.ylabel('time');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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q2tQoSyaZaqwOjQjX0lxJpmWYBMcRYCYjYwXJz6NRHQJ0J4n6VND0ZzjohD4n\n3dMB17fHeHKYVis4b4KqA7Ne0MNAStKYiMV9WwLWJccdAQQb9coiAANg1TyYsKhSNSeWaZwp2KZK\nv19DffqcdR7BUkUpfx6J9x6tGjmxTDabVgZ9LK6jVJUWESC5I6lI+uLfGDyoK8qFq/1eiGHwZZGA\n82ahJR3EoNamW86U7KdVZp8DcefoKL+uP5FEji/J9b5YPSKS9yU9bCOCTzz1a2lLCVuZXEim3J2R\nyLnPhwUB6irZBROkqjlOVs13YpAEJbuiLdHGkyzcp50ge0tYwJWSROh5L8bYhIFp3i6f7XY70w1t\ngLaXbDTZN5NyLzBpzOne9D3vUzZ56CVMOs91RH3qnJHO2WcqIHqBmn5NP3L71nlM9pnsMEkaFVCW\nuI3PM9ls8vt0D413KdlJK47kUYprbhcpMXd70jgcS5aSVXI4vsU3rcQ57iR78pb0OfJ7yTXFc7ed\nJe7ssqD8PGZ5MSLJmAf9fMSZnMOOYiEXRdx+mCjT3p3zJxyjTXC8jCsuP+ed7ls8J2ER7/n8889X\nf5qT1Q996EN7gSxto3h8fvzkz97860n4og4dJCvqj8mVj2WkIJf7drvdP8ytL55OBJArbaO5Mei4\nkbOl5IbO5omCgq+u5TyXAmsC8iRT6H5GTm5ubiYvRBklvdRJInOJPOm+HjgcAHgN9U2dJxsQSfSV\nCr6cS/OW7FWtIhBTzgQjgixtp7U2+TL1q6ur2u12k+RGlVefZwJ+L0aIJLo8U5BigPHVTbfBVFl0\nMpV0MgLHBNRqLGp4ICcJ8X5dD8QLVaDl4/IjvniIekn35jkJmyhvv4b2QrtZIhDJvtIWPpfhZvPk\n6wNk254IkKzquL29nVynooXrRhjiRSwWrhjkq2rWTyJU7s8us0R8SExGSTT70f+ZnCVSRjJA+6Ys\nRaYScXNd8kgVcZ7D+ScspQwSKeP5qSiZiozEfz6iQ/JEHYjI0YepT87ZZepbahOJdZ9ggTphD5Pi\nm5ubury8rKurq7q8vKy7u+lqtb+s0eeUkme/t2wpJRMuCxJ6P9/5D+3Z/+4+Jb7GogH9NZHspcKa\n7IscoKom2OrvL2HzvlIiw/F4oUaPhDlfcwyWPEY8wM9LfpYSF+JFWk2nHyVsor6SDXqcTVhAO0iN\nWMlreu8z/qOijxc9E1+jrljsSvdKds/fKavEXRKXYfHBfS8Vj3h/6dNx5e7ubsKryP2qaiIrYcOI\nLzJX8cPyJLB/AAAgAElEQVR9SDsf/Gua9HiE+7D7iObvfev/LPZyMaG1Vh/84Aef7mT1Pe95j36+\nN2HwRmBLq0+J0NsYZoBQNX9+zMeon/1zs5lvWSIgLR0aP5PXVGVK82dwSTIcOa73RUfTvEgAmYi6\nnJMzeWLijpr6cYDyMRFQSfbUTwoMLqOq+VbAlPCPgHqUyFIXaiRPDtZcGfQ5s9rGpGNEIJfu7Ym7\nxkpC7/8bgR/nKf8cJb2J9NPenUD4mHwsnggwweT4aAcpCSbBYh9JfrSPFBSZNKWxUsb029R3SjqZ\nZNO2E+lhS7hGMpX04LYxagz06b5Jpklefk4iV5SFF0EV1KlzJ8opiUt2S/tL+qPdSH+cwyj26TzO\nyW1AvuOJnn//qNthsu2UQPJNybTtFLeSLqmzJWLr90nJ6Minq2qW7JBwOfF23SS8dTtwfXM+qTk2\npb/rZ8YrJ3e+CkguQ9mSEKt/JkTEj7QrhUkb2whfie+cb5KRX3N3N3+TaEpUUoxLPMf9lW/tHY3F\n73coptIePIalFbTEs0bN+avznRGH88Q+4XeSTUpEq6Yrl6lIPIr5qZBAu13iu6OfU0v4uIRnIy7I\ngwUezYvF0xEGO+d1W3a70HnkvMmmU+xJnCjFRdc/i3MJx+9rl34/Hsx9eC/N87d+67eqP83J6i/+\n4i9Wa9NXrp+cnOxBjA9hMxlkS0aVQDQRxuQM+n+qshJkGeiTkdF5tSqjSl5KBpmgjRIMNypWXVP1\niuCQwJHVSN2bW6y8MfgtJfuwh6gPJjuHSGJKHvg33jM1ByxtwUkB0OWXtrIlcjsan+xDdqGjtfkq\nqXTs1auUGCcZug+wmNP7/PmVEfA50NNWEglK9pT0QP/jz0xkUp/c7jMKhm6XyU6WAr8SvxR0vYkM\nUF5cKaRfpe1anDttKckiBUnikZoImD+XyMRlJB/KIsmPCaTLgitzjh9MuH11RQkan6d0MsxdO+5r\no2e8dK1/dYb8kX5Nn5EcXQbEVY5PccRxhH4kWbjMEsakFTNvlPFIFike+Xwoz1RoIKFPSRv9gZjA\nsXghxq9NRVBiMleZUwwdFUzcrn3Fw7dTur0lzGPiwHmlc3wFRi9MIXfQuNS4qpVwmsl90jljlAjq\n6FsCUsznHLSilzjXCD8ki9RGxJ9JCXHQdUxbcj+vmq8ojxIOYhVjXUou3W74rDWTEu8nYYrHvWT/\nxGAmno6vSy9to25SfEr8I3EevzbJij6kHTr+rClt2TmA8II+knROu0r+wHlS58l22E/KLTg+Xkd9\nyveYHCauf8hn/G+eR3ghjXah8bhtpILc7/zO71R/mpPVD3zgA9EYUvJIkK3KWb8bUjJEgi8DDgNp\ncii/txqVmhrHS+CigacEyBMBJyZLAKr+vXlFhNV3H+tSgpnmNponx0E9CORT1Xy0RcLtQtem+XJs\nKbCyIODjH/kTZaOEg9sn05i80bYTmLMxMfV+vD9vIlMkRj422QULHZQHD5KOlNAmPzokVx5JlkvJ\nvxMRbk05VPThnKvm32XrSaY+udVaY/SDyQ0DVyJYR0fTZ3y9ik9MWLILykvz8vt7H8RDfVI3Ixz2\ne3EOXrl2LBhVmNW/25Uwj7acSBBJxWhLbfKhEYFmos7zfSzUF4kPbTv9nAoAJAej+Ok4SX3yHqP4\nynHTt5lw05+Ik8n+Fef8WiZ1VfOVcuJSGicLuU6mddCn3Y+YOLn+E26n5MRlvBTPR4WPxDdIJEf+\n6fc6VKAeJYhJzj7v5MOHkvKEeSnBT7rim5EPccPkR+ybfjzy75EsnId6TNhs5t9RzTm6/auNOHCy\nbU+aiOOMafzbSJ+0uUP8RwU4P1JRij6akv2Em4eSOPdfXy32Rr1rHsRl2pDrvWpeTHXdUJ7SQ+L2\nPmbNi/Y44jkeP5nX0N5SjEicLvFJn/9SfPK//dqv/Vr1z3Sy2lr7hqr6B1W1rap/2Ht/M/5/XlX/\na1W9vqo+WVX/U+/9/wj9TN4GnJKi+wRtfY4ObwwGhwLXCBCo1IGcZqtWHD+rYAr0NODRfDQ2fY5A\n1efg57HvRPrZn1f0UwW8qibzXlrVpbwIGpR7Wi0bjSc5dwIfJxRJ1gRir8bzWaOq+TMuBClWqw6R\nMg8wIz0vkdw0F/+ZQaH3+SoR9XSo/xQgSYYpc1ayl44Ezmk81J/bQGvzlxOlqiHlw+d9eI7bu+sr\n+d4ogUykIgWXhH/Ug2MeyQAJA8kJ+01zSuTcGwlqCtAcn+MvMcGxij59CPspi+Sfwm2N02NAwlf2\n6yTbV59GRca0ysyixSjO+SrD6MVDnjAmTEuYPMJfys7JVBofx+lz4fZrEqMRDrFfJqsuU0/CvaWE\nh3PnPN0uNEbi9MivR3HP5ZwIoNs6txnKtvx9HGzJPw9hSu/5O0ETYSVZ91hMvE72QbtNfIwJULLt\nlCDRLqkX9+NRnEntPjKlTp0LctUtYZdjxVI8oL2leaZ7LelGPzMW0o9am26X9d2OGttuN32hl95u\nu1QM13UscNNnqD/N3eVMvih8dVmTo2vMvrPG7ZqYPGqMB4mnsDGuMl4kG0vtEB7TTvQ7E+WED16s\nEN76V+QoxrvMqqp+/dd/vfpnMlltrR1V1W9W1TdW1fNV9UtV9Zd7779h5/zNqnp97/2NrbUvqaqf\nrqo/2jGo1lr/uZ/7uaqaJzcpAUmB4XE/i8TtEMnnvatq5igER/ZdlYPUiPSnBMkDgTeu5DAoJZKu\nQM/KtZ+n/nzbFxMt3n+zyS8DYkWcJIWOK0dYOmgXI4KRVscSIBDkCOAMQu6UAlSfh2zH5aPA4Pfh\n+EYkzsfo13lS7tex6CJ5ccw+36SHdA2JWrLRFPT8OiblslufK4P+KCl3n0l+5QEzFQBIwHxVhsmo\nz1NAPHrtP+9F+9LP1HfVfBXEK6H8TlcPtvQh4qbLSnKmjn1OV1dXsZ9Eag/5+e3t7WSrOrfQqqBD\n22Y/9EfHArclb9SVr1Z4cGaizkQsxQjKlNhRNf96g7u76aMsd3d3kxdx6QUuJCKje/mxtPo0IiJM\nDl3nniD6PZmkiMQ6OUm4w8bY0/v8u5wTMaIcyA9cfl7o8Hkn32eRigSVj3hojr5bQrbDgsdSEjLC\nTs6B8yapdmxYIsPExar5W46TrmhHIw5FnPHrUlGIdpAIPeMB9SBfY+LpLcUwkmzqgcmAJ2fkDn4u\n8VU+4gla8uuRXfgnOcKIJ5E3ej+bzWa208dfZKZVtlT0oe4SBjOep0JH4ugc7xIfo/7dBlhk9yPZ\nCe1slNy7HBn3WEBhvqB+yS/YRj48SogT/gvPnMd7scOvIV7RVijzzWYzedRgu93GRwuIBSlfeu97\n31s9JKvzb9V9+dq/UVX/T+/9d6qqWmv/pKr+QlX9hp3ziap6trV2XFWfU1UPe7K2qnruueeqKn+N\ngxIsEhkZjguXxM3PF4CMjEwKplO5EtzY9XcaGQMpHc9BzYnuYznunYREXERDFUQnkh4QaeAkmkx6\nHVxubm7qxRdfnFyncXny4YDZWtuDvtoIWAiwo8RTTfdxx0qgn5JuJnEjUsagm8bIa1Kl7vr6ui4v\nL/fOn4iibMKfZZYtjipu1AOThUTsfHwKQgl4mdxQBwyMus5B1+fRe5/4kIMl5zoKuCM9+Jj06aCs\nxEBz9ypwIgf+3CFl4b7tduBf5UFMGo2fWMFiCOejZO/u7q6urq5mSUfa4pj0q7G7/ScCr7+fnp5O\nxk/Sz+RVMk66cZLHN5v6dkqfe/IX+kPvT5Kby8vLfTJBH9hsNnV6elrPPPPMXp+J/Eo3jttMQFjY\nUCP2eH/udy4b2RtXlHWd+0sqypLkEwdGNsiiIG3PYyf92QmXky76hJ+v2EV745xSUYC2VDV/Ts3n\n5dv6SMxcVx53T05O6vz8fIKRLAZut4/eF8B7Kwbd3t7WxcXFrNAnWbmeHANc9k585Ru0N7+/60ov\n09L1Gqtjkcsyxb2R3DQ+yUMtJbjeN/2V8cMLTn7IvqXTqtr7YSL4yc6r5m+KdX4iDPKxqEDHeMS3\nT7eWF0ncRmX30o0K0j5fcomEeW4rbi8sSC/pN/ma93F5eTnR0cnJSZ2dne3lr4Lf5eXlJN5LFj53\nt2v/dD0RnxOvS/NyHVPe/rM4j372ZCxxJ8qN8YCcRdjARF42R/vxcbvOdD8WpJnv8Gfxfi/4Mq5R\nfsR0l42fm2wxLa7JpoU5msvZ2Vk988wzE1l5TKXMR+2VTFa/sKo+ZL9/uKq+xk/ovf/j1tqfr6p/\n/ngsXzvq7BOf+MQk4eDKpZMggYEU6UTTV7U8cKovJolK/LyfqmnF3wMISZTfS+OjU/qc9AIEBluS\n5c1mM3ltempyUD9Y8WJC7JXY3W5XFxcXM4Pf7aYviRKhIrB6Vd3fejZadXbQIGBQxymA6nomVg5C\noyJFCsyJOFXNq0EiM779iAloSlbUpwi9b9XiS0tkN3R4natVGMldX3egFQ2ORzr3r0pK1TS3JZ6j\nfh1cZa/6SgmRewU73csDXtIjbd1l4UUf97VEcpOvHR8f1/n5+XAFgfZR9SRh8TdR0iZJ4F2WLgv3\nHV4nEqFrErnjITxSsNjt5i+QaG3+VT+SsRNu+kHahpVWq4mviZDS11PC5HPnudJDkrt/yi7Ozs7q\n/Pw8kmHiFRMX2Sa/h5k+wZ+5quWEKWEZ9UiCJ2xxe3IcOj4+rrOzs9nXmKgP4a9jAeVHLPWVFSdc\nfihR0DwVR+n7HmMkV7d5xQ6PI5QFiXRq1EXCMiYEHrs98XOiyMYdAVqVZzxRLGcxhP7uch9xmeQL\nsnPGohRnfI6OX2538m2Xof/eWpvoXDGW2K0dAdSNn+OY7bHaG33i7u7RYoF2lvQ+f1GN5ucv0knJ\ng7fdbjd7mdPoO4TJKYVrwtKqJ9xA17jcNReNxTFTY3T7ury8rNvb28kOi+Pj4/04dJ77mmyCyUzC\ncnIcynSUQBC3R3HJfdm/9kRfjUd9k6MwJhD7ZbfMB5jEaR7erwrimo/rSHNyfPMVdt6bRRViNXkt\nsZex2uMG+Yf78Ha73fM37XbU/x8+fDgphvj9fV5JbwmbeOh69Utsk7zTotwogXbZpvZKbgP+d6vq\nm3rvf/3x799WVV/Te/9bds5/Uo9WYP9mVf2JqvqhqvqS3vsOffV3v/vd+98JoqxI0VlIaJYE5v0m\nx0sVQp2frvP/eUskgQGNgJCMzPtPBsZzmGS2Nn9BBOeZnClVUKkHBs2UMLoOdI4DgPoZycWDqs+V\n+nVQ5bmuQ5LYZCduD0m3yQ5SJSqRHD8ETq4bt3/6Ae89sr10TSI0vEfyAeLHSH5M+DkGyou6Tb+n\nOSbSmhIZJ1wjHVNHyW5ZofdrSbKTjtmUnIh0KTHgVkTvl76VArYTEceQkR1Ljt6H+zkJjcvdZcmi\nQdK1/ka79qIjn1NjYux9ERsSeRgRrKXD9cIXT6S4M4oZKbCnWJP8gcUQP0h+ZQe0FfoSW8JIzim9\nATTN1a/hmL3gRH/0+TLxpE8yxifccNLpRcZDmOfx0rfN+ZGe2aNtpyIpfY3FraQT4htlrH5c714k\nU5JEzLsPZ+F4iEOUX+I4KVbzmoS5tP2q+Wr6CJO9v1TQ1Djc1zhetuRDI1zTJ/kn41Eqmh/ihT4W\n/3lpHD4H9kt7oy3pfxw3d1T4tn3uODkUBzg+FjoS1/c+0pgZW8iXEg6ReyW+yWII7cX9QwkyZS5e\n4HE52QD1tMRVHUtob64rycsX3FKjrJM/Ur/p/kkP5CDvf//7q3+GtwE/X1VfZL9/UT1aXfX2DVX1\nPb33h1X1i621j1TVl1XV+9nZW97ylv3PX/VVX1Vf/dVfvZ+874euGr9ta8kAmJDJ4Zw80jESQeEq\nQzqHLYGY9yNDTAQ0ORDJp/+svvxcl48qmJKDKvZKnPy19rret36oOUhobAlwudLEoHOIWHJ+DliU\nVdIVK//UOW1HgX+z2Uy2aCQQYgDQvc7OziYAkVYiWCnV6hcbVwccEBkQWRV3mW02m0lVfLfbxWfF\naGPUAe2SeiEQu11qHJqXBxzJgUGR+nGQdpLKAEdyR9KVkhufB4s5HAeJ+e3t7T55oB7cfn0Mkq90\nwVVfnxv1ScLXe37WSESdyQMDuHzc5yUsSKu4xCDaUkqcqKtUaGLBgzisc1yHtEFf+dJqRXo2l76o\nv2mlg8HY/Z3B2A8SCU+iGLM8AVg6ZC/eSHYTRkkebk8sUMjGfYy6ToSNJFrjkR4d1/3gOInlHgd1\n7oigus7TiqPf17fSuS2ykCNMub29rcvLywkWOHZtNk+2u40Ipg5uTVcjoSOx9MJQivnUu+tec5Bt\nE3dSQYAyTPxHuKL7ehzyAp2eY9ts8tttiR8ar3YsEfOEKSx+uP+Jm/h23YT19BnaoMcVYrNflzgc\n8SPhlGKmsMCf+/PipOMBYy+TJNod/c4XJdwGyCX8Hu4XjGGu+yTfZEu6jnL3/yUu4/dmouV2oE/u\nFGGM0M8eBxPOkEPILlRUFu5wMYjz8h0kKSdw/uAJt3MN9uk+7gUCxTmNSfFeq68un/Pz84m9OUYz\nH2ERWecsLTL5fN12PvnJT9YnPvGJOtReyZXVbT16wdKfraqPVNU/q/kLlv5GPVpR/dtV9S9V1Tt7\n738s9NV/+Zd/eQaGBLFUPeXWD5IJ/ezK9+TMj/RGV1ZN2BLJJvgQROSUaW4pCdbhVWKNh8lzIpIM\nVJ5ApmoydLOXYSJQDICpKsbKCvuljDyIOxHx+yZZMXlg1bxq/jKblOTw92Cvs58ZVN3W1FeyCxKq\ntLJE+x69tEH/V6LtOubKr4OYZE3ZyL4TQDlxoKy4ouFydYLKZMx91wmn+vLzl3zEr3HC7PKhjdK2\nuQoyCrZMxjgvjo99+L19nLxPsttkJ96PF3Q8WU026SSNlfTRdjf6uB+02UR0q/IL9fx3khXNwc8l\nLtK2WRF3HZMwcNwJmxiUad8JJ5fIgWJGwpAle0vJIb9f1hMDfaaCGW0y7axJWOCyurubvkhKPuv6\nSvNM/kF/8E/p1+fpxFZ9p2SCW7+TLKjbtGuGMZfkM8V/NvpMOhJWpHuRwKcCih/u575tlAfxnp/q\nx2VHOyXmc16u15EtCXeS/NyXRkmcyynxRxY30uLGKNFzP2VsSXIfnaNxkuNKNy5Tj//iAJxXKs6m\nQgjtnQd17vjFOKKDq5bkbErKkj3R5omDS3ihPokFqRjB4m5aFEgcNsXypYO27MmpPiUT3ybsBZ9k\n/yMZJdn4kWKjvzjy5uZmxgESn0j6JE7zqKp6z3veU/2z8NU1/2ZNv7rmf36coFbv/btba6+tqv+2\nqr6+qj5WVd/Ve39H6Kc/nsCEmHjVd4kMhP5mwtbf1Rz0R+RA55FA0sFJoB0o088pKfF76X40Kl8R\n9Af4OZZUrfXG5LlqupKUCI4n5WmbVaqOpsSPjpQCNIN8kmWSFx2VAaeqZsQjgcvSeEfnuLNrReq+\njfaVknuf76EAmMgAg7bLzD+Z1HngTEQkkQHqfwRafl8GOC/E0DaXDsprt9tNyLOeWXKym0hFGh8P\n6ioR8ZQUJDLndkV759jc11z3DL6+pVXPM7IgN8Ip/xttdGQ3JB0+RsnQSUPqk/pO42Ihgb6Zzktk\ngOfQlhSP3CYpYydzHKcnKcSQlBRQ1smu6UOMI6NEJiVN6ldz8eSPdkAbTGNOBRT6nmO3GvGCc/Xz\nR1ifbIey4M93d3fx3vRPYmsi1IdsMGFe8rdDeFY13058aHzyvaVEVLjNGEIbIhldkoM+R8R1KR74\nvV1XxDzqj9xm5I8Je+j7xGiel/TAIrtjtsdqclqXXYqFft5mM/8mhhQvl2SccCW1NO8U8/16t2Pa\nBf1+hPc6WIiUnDmHJbsS3yYXXCqacXzCM9qS22fSg7h9avSlZKtJxiNdivM6DtNOnbcwNo/mJvtz\n3qQE1sctmbkM0yLJ6G3Ar2iy+nK11lr/2Z/92fi/o6Oj2bI7qzac41Ji5UZVtfz8Su99VhHRthMd\nbgwpINKRH883ks1RlXcUSNVXClL6v5N135ZDIDmU7DBBkZxZ1aRu2LQV9ezsbP/wuFeP+EKLEWml\no7fWJrpTZZEV8dPT08mRiJzLX6s77ria+ygwuG35wcBFUEhg69s8tArOQgJtKd07kSCOIW2tcx/p\nve9fDKFPySvtLFg6GHA4T5e7JyWJFPoYU0WX1xEL3JZZ4PH+D5FPP18/c54MSlU1CaIuPw+mvC5t\nu2JgYIU+JUlc7bwPYaasUoFJMnXbdLuU/SXC4DJN4yBR4zaw5AMpiRslvWqexI227MmGXIYshlTN\nyQCxwMmAdMXKupNm1wfxgqSCMuTPwiaXCWMU9ZuSOJe1n+e2S5nL/n3MtH3KSlV9YhXnla6j/j2W\npzdscszEf8dcx4NEPpfwX3Li8+sJP9LXMnm8SkljSpAYQ+mvu91udi/q08eWdJBieVXNHsdxnudx\njoUFn4cwxH3Pi1KOBd4cI+lXS74iWbmeKTMe5CxeQEwrxrIBt0lPDNSfY7364HVLnNS5jM/VE25f\nVfame/r3j3rs1ktBR4XJUYIpe0s7iNy2OY9UJOOqKpvszQt0CS94L7bEnQ8lg46dOtftIr1Ui7sG\nhNHOHRQ33G7FgT2WcHFN4/O+E7Yn+yGnI5dJ7V3vetfTnaz+xE/8REzgqub73pnUJXJF4adEZwmU\n9Dd+8iBhSZWLEZCxX56XjIGrsYn00HFG91VL1zCoE9CpF/XjKw9eAfRtYYcSovtU3Pwa/UydU66p\nIEAQ1zwSEfcEg/qTHfq8HSAEPj4GEhgfc9KTz5WycHlwrimZJonwAoXbH4l4AlACOxtl7Mmq65dV\nxKS/EVlh/6mwoOtG/ud/S7gxakkH+vmQ70k+fowIT7JTnsdxkJCSbHLM9GEP4p44q42Ih8td55BY\nJpmOMNnlRhnS91O/KQbcx74YmClPyjvJPNkZseqQzigjl6+POSVElF2KhSM9LMWPROjYiOWj+TEJ\nZpxLu3j8cF8YJU3EM9+m7ONLcdcP9eP2zPk4GU7bbBNO9p6/H5JcJyVxJPS0xYTRKeGmrSTb9WtS\nDPN+nEu4H3H1aclGvUjAvum79Ff3BY9haZXNfSbtNGCimXioy5w6oBz1+6EE0nmF5JWKOSwUjezW\nk9y0JToVhhLfdl158qoCwCiJS3FwFKeSHbIv+ms6vCUMlM6TLFw/yT/ILVy+7hOaX2vz58UdS3wX\nj9uq65fx85D8kg+7/Ljlngkt43zyxXT/VJx86ldWf/zHf1w/Tw5XqKpMOk+fo0TUjTqBXyKAdIpk\nwElB/jkiwU4qCXyaqxunG9UoaUpJCefJv41AFjqZyFCNhsjA6gAvebl8DgFJVX5dOZNn6tfHpp8Z\nlFLynmxnlNAcsOGJ3FTRctCi/BLw8mfaKmXHVYbRymAKFN5PAu8RGfT5ug+5L3l/CdBH8/HxcHcB\nA0WSe/IRNhJ8JyLSFf2eRJJBXLZO30vFJBZrKMOUXC/5EclnKlCkxMltwBMbn9Nms5kREWEVV8Fd\nNkn2tDW3N9qpEyyXT2tt9tUTqSK+hHkjkpNsh/ZPnd/d3e2/4kRf3ZB8b0T23baJOfSRkS+6T3DX\nQO/zr7QgMWGy7LbksSclSRw/+yFmsGBXVbOdNSOfJdFye3f7kg45BpKy29v8ngruUKDvpcN9KuGk\nx3fpImFnKoZQ17TbhIu0Kdqcx+9UhEq80XHc/YhxN/nSaMzkW47j90ncR7HDsSXFcJLuVLAi72Nx\nhV/dVzX/+iS2UYHHdUWMkV8v7Zqpmr6Pg89jayxM3Lkyl8Z4n+ZxxxNjT76448Rlqc9kk8knvKkw\n5DE1xT33haV76R4pVo0ScP89FRKSjbotJp2n2Ex/IQ4yVrv+dC0xT/fyuabnxWlPlJ/swGVBHVdV\n/eqv/urTnaz+5E/+5AwsEpET8VgisSNwrqqJITKwa4uqiJAvY3uSOQIoApUOJ3IiBylIJpBdSpZS\nABwltKOEwwk+K5/etxu0zh8ZdCLbdCoSIQIF5+l9CLxJcHxL1+npaSRB3CqjlhIBT7z8YNXc5Z4C\ntICQAKf7pUB/KNlyeVBWqV9P9J2YOIinZJUVNga7RGBYKeZYUkCmzAhyLj8GXw/kXtgSEHPMrJZS\nDikQJP+jbRxKuFkdXUrUSVb5+AETtN1uvlqdtpGygEO/l30tBV/HHZEDJvLsJwX3RGypB+E0CyEk\nIiTn9Af+nmIE8UyJsRdKEwnxz0RoZKsJb1OioHNJJFP1nXNKuuJ2tyW5q59E/t1OEgEc9csx8m/J\n1+kLjB9c2UxYSeyqmhJxX1llfPFPEsvEHfz/3tyHUwGMvs/xJVxiP0xShN2MNRwXSXTiVTzoe5SF\ncwffOk98oM9SV+J+fp+URDGOJH8kDjAepUY5HJqD+7g+PeFWfCJn85VK9ZOwnWN2WXFVNyV3/Jt+\nJ88bJdjkRLSdJAvaSUr8KAcm5cSQlBz63N1HfcwJjylj8njayyjGM+4QQ5Lt0CYTL2cu47v1mIhq\n7rSLxL9p8x5jfXx+nnDH7YXXkL+P5PW+973v6U5Wf+qnfsp/33/yUHPB0JmS0EbVBAe/0Tl+sNGQ\nNW5WF0jePdHxZHUEMOmTpD+tkqY+R2TvELFklSgF+ENEl2QmEdQkA4JqsKHZJ/WrfgjyJCa0JU9+\nWIlaCqQjAPL7UXcMBm5P6ouJ58g2lu5LeVVNVxRUfEg69ePu7m6SEMmPXGZui/pMVTkGegZIyisV\nOhJmjOwrkQB9JkJNHSdSnZIOlzlJYgrGqSVbIjlIyXiyJb+eyasIlicC97EvtoQz/nfZVgqstEuS\nEBJAVtarapaoj4oPh3CR+JoKHeyX8nObSH7Jv6vR35isehK3JD/ek0noiLwnMkU/8mvcp30nges4\nyZkxNNn2UoxPHMfx3+MV48ihQpH7p/pN8dyTM9eF65jPSrpcXd+0UfcDxps0bs5TPuFzuLu7m60M\nJnMe1xIAACAASURBVB1zHokPjQozaomsj+K7v8U6JXo+Ll1PG072S7umHTOZSnyIyTTjcDpG8SkV\nRZh8Lc2BRQ3HAk/2mVQy9qV7E0MoGyaCI35N2+VOM48/vtXVD/Kh3sdf+0iO6T4zwkO3h4RFnOeS\nrlIb3XsJf+nn+mRhJhUWaKspnifZJNvx8Y3m5v1wjpyPfv/5n//56p/h71l9Wdtzzz23F6yDFBXq\nDl81XnEkaPrW2qr8fIh/R1lVfktiGt+hIJ5Aye+jn1MQcAPVPZUgOEjofPapn5eORIRTEPf+Wmt7\nh/HKv29tTlt3mCRWzUkOg5L34aDGoMDgqrHpRVhJN8m5NWcnM5eXl/vvrdpsNpNtiP4SFQXmQw7u\ntupy58FrHeD1XW0ksSmRuk+TrZ+cnNSDBw+qqiZ9Xl1dTebgtuNydtu6urqKBEdzODs7m1SXJYtE\nypbm4YSPySy3xrD4QNB32/IEkCt8WsH3l625fHa73eSlCZIFxy2Zqy/14biXKtpaUSJe6Dt7U/GB\nLyFzO9Hug83m0feunp+fT0ijr2IxyJKsj0ggyZxj/FI/komTW40zVcS32+3kBWqj5OY+ZMP1SZIo\neZHAeJKQkp3R4fd0Wac56D6sdlNXKQFxP5F9EYO9+X10Lzb1q+uZMOmaFFv8k5iovv0c6pH6HJFP\n6U6+KAw4Ozvbb03Utm5hhtukziEm9N73fp4KQzz8BTiK6SLifj/eJ+mTcmbRorW2xx/NzbnDs88+\nu8cCt78lG6XMmdC6zBUfhE30GS/keZOM3B+IZ8Rg6ptJR/Ijtyc/z+2PfrbZbGbJl8dd9e/fAUue\n4NjtzbEy4aJ4qGTgWOGFAPd1FQBGGOixizbHRk7M1f2HDx8uJmN+31ERUZ/kNtSVc1FPkhOujOxC\nPzu2aZXedw160dM5xqiA4n17/Jafyx+4yDSag8ce3yno8VyN8h7pW43JN+/nPF+7jPxxirS7Jfne\nUntqVlbf+c538m+zn90o3NAZaD1x4bmeLJI4kfTw+hR0aIh3d/O3ShJomBQlslK1/MZBBmCvvrjD\nedDmdfpZyZcbPWXDcaYAphUO/5JiVqdImhikaOAEX9rBqArt56Xkz39mEKbumTQlgsDx+FxGQf4Q\nCaA8XM5+jIAp6Z3BnME5gdZonKO+R35K/0v9+s/Ud7JdBp90H24HT35OGxndy8fJBJuyoJxoE0zc\nXZ/u17pXksXSWFNVPRHEJHufE1cVquaEy4OWJyssHLg8UrJCf01zGyVf7nvErtE5LmcW1qivFPhd\nJ5JXejwi+cBSXBn56KEj4WtqyXeTnagfx0Rf0SBWk1gy+Upzon8QO1wPOpZ0rrFQn5JrwmQniD6P\nRNaTzY3Iuc+LhQ75iBdwR/bln9Tf0t/U0vh4PjmTrhvZ1+ichHHeyDf03DkLru6b7Mft0u2LzeXu\n23ddnm5bniByzGmXhXODkb4O4dlSjB7pmfjh148SMx/LCHd83KOfKau0giyZJTxy7uwF4N7nX+k4\nwotRzBrxj2QXtFfuTCIWpXm4blzmjkW0AcqVuk/XLOnJP1NyyGtHWODHyAa8pX6ZN/D8qvHbgJ+a\nldVPfvKT+0o5X4FdNSaBAh+tbKSqYmvT1QH/yo2jo6M6PT2dBGMPQAI5VT9ZYfPmSkuBlcv4rNR5\nH4lAjIizz8XJBAOwJ9MuI4K1G7TOqZq+eXi3202eD1VV02WcAr3rTi0VFfSzEkEfh8bMvjh/6c2f\nL+bzW17k4FvsNAYGDCc/CpJpbCIj+htJrIK2bJ39tDbd+ufJsicjLhv/2W2K/pDIsBKO6+vr/apR\nKrIwCJJYJEBNwdnHnMi8E1Efs9/f5ykCofEzAJ6dndWDBw9mPpTGNkpcRkmEbD3NmwmRVydba7OV\nX1Vc/dl54gALdAxM/rOv8qmPke24nbsshIdMYLRacHx8HAuGDOqpWusFH/qjzyORDpex7qGxSXa+\n+4BJrMvVV3qTXJIfJXn5eEbFBz+/av71La5z709zS/rye6drXmqhKBEzlwMx0mOJ60K7LmQrlD+J\nt2OeViodD/ylWvL1pDOfgyfYHCevcdtm4qJ5sqBJO6EeUjKXkmlf1U1f45YKHkzqNEef24iQpvOI\nJb5ay/vIDrRDJc2ddqa/X1xc1IsvvjjDTteD+hW2c5VZ+KE4KtzQIfzXTgutAvruuNbaZHXd7Yu2\nqjh+fX29n296lEvz5E4H+s0oCfbdab7TQZ9uM8IDX+lVHNHhO4i8L+cWGp/7Yhqf+7DPi+NzPNts\npl/P4i92o++kpMzHw91Ofh2b44JjWeKblEVKgg8VHKtq8r3mbtt+PVdxJZurq6v9GLiA5BxttGuG\nvu7YJ/0l/0wcIPmvx4qURzDBd7zz2JfaU7WyOgqij8+ZXONCo/F4wkBgHim19z5LZDzBUFBkwpkq\nVCn4Uw++hSIZIqtVVdMVGJE7Jgs0TAUYrgzSaV3GdKwlIDiUSHNV2QmrrmHizuR55CgECwJIChT+\nciVuG6Jd0UZY1XPbTMSD8mRLwMfqsleOBfqUOfXZ+/hNin6vpTnRHtQS+aJ9J3JMcPRAzcKCXhKS\nXkJGv+LcE0liS+NPcycZv2/hiOP0azjedN4oUaBtul7crxJ+JHkxmDgR92MpiKtPx02RfCfdfG5U\nY6ccWXCi/KqmbwsXXpBQUqZJfmkeoxgxui7FLNqKCK0XFLkKeMiW3G4dO5O8Rn3pZxIIL5r5NkPf\n4qg5uNwT6eG9XC9eKHUbYCFBMnO9EOuX/CHhlsfKpVXTpL8UU+mfbnfJBmW33o+KI14MP6S71Bj/\nJFM/PIaocEW50/49rukeTIgS/vN9HM6JRnNxWXBVXp/Sn9sUdZHieYpzI192mfohfuZbH0db3JcK\nMQljRn40whbKL3EAt3cvTJKP8eVcOsfnRbxIMYyrzImvcaFGyXxaSEkcXff28aXdGvw9xWqXoe7l\nY5Of068p9/R74no8Lz2/zmQ6rTKnGOM2p/v7GLiYwHmn/pI9OX4prxjpXI1zqqr66Z/+6epP88rq\n5eVlNGgnYR6k3Yi84sPgzWqCK6VqXoVgIPTqVapkMLB5oE3nKECScFVNyUgCVQGmms/Pq3skgjRW\nNzgnhC5jPmvRe06ADlWaRrLye3l1zFsKJIlgShZOuOQYLjuBhOR4dXU1S5qq5s69NBcGOCeEtFvK\nynWk6zyQ+POLCiYpGJNE+kqaZMQ5OZF2W2Uyy9U7yUrPRcq+vNrtlUMFEyeIPhbNSf0+ePCgnnvu\nuX2g0MFVNgIpE2MngCkh8+vc95xkeMItu3D5MZikIMV7y05IdJO9OR75s+pKHlLy57on6SG+ioS5\nnaaAo90TiUQwmDP4np6e1vn5+cQPib/0Z78Pf6968iKphw8f7ldoifvu+6kx6N+XaNBOJPNEstTk\nH77iTztO5IrY5KtMtEkvbnmyIN933yNWc16Xl5ezORIfNpvNZDVAq4BpRwDty+Uv+dF/aFe6Xn4g\nOfjOJNpjSjg0H9nqKIHw2Og2IxmngkkifcRa93vNWVjv86Sd+HUc0xIOjZKx5Bf0B8clFRB13maz\n2ccn+mpaieN9Rvbusdt15xxJMuC9hHu0Iz5bmrCejy+xH9eFmvtOkt+oue2rT1+Z9nMSD9R4yBdd\nD1VPvvnAYxibdnHoHQVMiLhq6rLxg3r2eKbxpXilc9NKYfJpyYAx38coTuSyYJFKfbhvOdZQp0kH\nzltGOKQ5ce4q6NIHiLmSD/MgFY0dx8W1kv24zsjPE18f2XLi6DovcXDPF3zXUmpPTbLqyZMLK1Xc\n1CQ4J0hSoD90r0BFsuzNjVUrqGpuAKkS5MScRuigxCSiqib9aP5eySSpSEmbJ0g0Mr+vtjsnw+SK\nHhMq3tNBVfL0lQMvErguZdCnp6f7uTEJ9uRbldkENnRI/f3m5qYuLy/3c+D9XK6+bVnkRSCvbUEk\nez4n2qlXoXRfAZlXMZUoe5VQzn19fV0XFxf7ggDPkdylZ28iL6PmAE/SIFKiecovpU8VLdxu2a9f\n434sm+G93Z5pZy53bvdxgPbnldxGNQZuuWFyyCDgvqkxeZLOApiOlIyPgoDLhUUn+ZzbudvlKLCm\nYgjvp3GKfLoNaDxe9JAtJaLg8+JKTiqMjMiyB0BiHxMr9zcRUY2Ryb0fXFGomn/tUVrJ9/+7bbA5\nUSQGEndSQYKJqebgq1i0+c3myfsatNVZOKTHMGT/IhDn5+eRGEn23C7Oc+QXss2jo0fb67VLxeWv\n5JwFTh+PEj3ahMcpt2M/z2XuMcztkPbmiREfHSKh5334v6urq7q8vIwJHvXJBM45ie9W8PjhYziU\n/CimeAElJerEC7c94pkw5+zsbLIDbBR7aB9ejOR4ib+KL3ocRratQy/3kyzPzs4m/ShmpsKV42Zr\n428JWMLOqtp/reHZ2VnkO55Q6B5ujyqUpxjARJh2l5JDyYyxNsV2cWfiPfXJhEP8y8en/oiLTJRc\n345DOjjGFJNZCPFrhHGctyeQI27gsUmHn396elpV0zd4393dTbCLhXjHXMYtYr4O4jhfOOjzZ2JK\nufnLI30nnutB+mOxg/yfuyPUT8LkQ8d921OzDfiHf/iH9fNEKU4ivZrAwE5jJfFwxSQi7hUaJ4g0\nOgcfVp3oUN4PHZ4KZTBLiR+Nyq/TeTxH83QCqvFCB/fSFUkmk2p3yHStO5//fcm4HZx1MOE51Lca\nCT1BMgE5r0kH55mCJuXHoMnK4khH6Zylv6V5O+EfETv6g+zNbZIBlWNJMqauPAgy2I70IDteCm6j\nTwbsZH+s2Dux9FUsP5Q8+apuSrpGckoBmbjFJJjXub6Sb1LHLALJVpy8ecLkGEyC57/TH9KceG2a\ne9JhImX8G/15KVl1fbptu03yHslPRn6Q7C21ke8SB/y+lCn7S36U7J22Qt323me2Tf0RU9L97msr\no3HqM/kQG4l4+nQfd53z4DgSgU92mxIMj+deUJFMHTOS3tJ42Ci7UWxg3Pa/eT+6p+OAFgjSdliO\nhf7oupT9cXWYtkQsTf6whAn34QnJ1nguY6rHOb4hNfVDWY94HlfQWOhw7jDyEeqX8mKR0eflvCXZ\nlvsS5cP/V81fDOl9jfRHjlk13+KeFjKIMffh4OQp9FmXuxd00oKD69DxVJ+HcDKN5z4+QltVYsut\n32krP6/1luI89XaIIynu/uiP/mj1p3kb8HPPPVdVcwPyKkkCaRf0CLhTv1VTopuAo/fp69I9mCQS\nSCPRfVNyk64fKV8/Sw6np6f14MGDYQKkLUWSDY2MAbtq+oKmzWYzedZMVU1uN/WxcnuNQINFgyQv\nBmhW492hnXx6vwQSrmppjk7G9GyWQJHBX9dqhV7bhBKJoE6px1QBJOC6vHxbN4GfIEbbITBzbjon\nFQB83E6yNC/pRC8CODo6mrxkSzJ1QJSsVZWWffnXKWw2m0k1L61IyRavr6/3L+ZgNZS2JJ/wQzr3\nghNJq+SrezrJ4ZZr+RYDisDag5knlZI1q9SJNLku1A/xhNeoX+0aYILotir9uF0qkGk+2qWSMDVh\nIP3PCZHG50Hd56mxpUox9am/s4iRVng1r0Rg5WM+xiXSL1mkZDIRDyduJFOOEzp3tMLN+Mi+fUxp\nDD5OnUtb1sro5eXl7HlKJnrqQz7suqd8EpYl+/emfn3lzf2autE90xY0zdEfY5C9aGeNDvcz/ySR\n1LU+H8dl3YNfdZZWd4jnKUmifJKNMo5wp4GINuflh1buXO7Cao3fbZnE3jGYnC3p3AtmHne0I4w7\nITyB1Sex02Wn8yR37tJy2ZLg995nX8vHmOF8QStzLA5oV4v8S/illV8lPJqL/NvtnbatGOY8xb+2\ni5whcSTnG8mOKUv6CWM1uYpzHue0vviT5C47cx1JPp4cui0lXzw9Pd0fo5cWphyBn4qhS8mqmsvb\nV0zdP7VaTz/2lVa3N+78EX+6u3u0u0C2JBvwuKEmf5WPpYR/FMdT0q9zyEudg3AMqT01K6vveMc7\nIoEnKXOCQCAYBUUPrB5gR/dKSnHi687ugVT7/9OKVUpmEsnRp99XRzKGJBv2RcLm9x7NlT8zILE6\nlJIxzZuVMYIYEww5Pp8DcLLkc+R81RJYcn5MwHkvOZwfvE6khskhV00JggQfydQPFlBktxxPKoRQ\ndyxaUBZpntQFSY+A189hxTPZGW0wjS0FV/89BVKXrxM+nysDuJN1rg6QYHlyRZkmv04FCW9uS/5i\nNw/2o8BPgkqMS2SF17GKXzWvgPt1lJfmT7ugXpNdco70z4T1rlsSjTRfHa5b6SrJ0I/dbv68m8/H\nCWoa5wibOFZfpaSd+rmpX9p7OkbXqX/aDotxyQ5T7EmYcogspwQ7JWjsn3NYSu78dx7Ur+K1Nz5G\nUDX3a8on4W7SC22H19A/RZZdFpSv+vPz0uoTV3s4PnId+RELQ9QBY4aItCd6yc/ZWEAR6edOENdz\nWuVlXEtFWuIX9ZD8mvjjeqS9sSBQNeVmfng8EC/gHBKuJ7tlMp3iEXknsZrzHOHF0rx8jMR6chDn\nDrqX81v6GrmD69ztjfqgjpN+fTzu187zeI4//6yFo7TotWT/KZ4RO6vmC1Hkix7jvbDh90n2ykJa\nio9uA+LA7o/uV/L93nv9zM/8TPWneWVVz7wkoiHBiQT58y7Hx8czw+y9T8BSzx1qhcGrci7gtFpH\nBXgwcwXq96raB7REsPznJVJBclj1xDD9oXn2yWRbBuNAkCqgJFR0SiZg7vRyyqq8hcpXRz0gswJL\noBzZQ/q75MM+BdIChERwBNr65D001meeeWa24ueyoW41Jtcdyejt7W1dXFzUpz71qcm9CLxqAgmt\nUnpi5bbjoKbnhnze0pvbfAp+GqPr3uUt+/Kk1Fc69VUxCWjVj8ajxCiRDD6c79d4UCYpkz/6QVyh\nreprCWiL7h+ym1ES4bI4Pj6u8/PzyYvKvMKcAr/jDMfrJMMDiaqtnuQd8ic+00Ny5Yf6HPXD56dG\nOKBKMG1QyYMnENS5kxd9jogRk4Kjo6PZewzc19NqhexKVWqSPfVNufmnV8hH28PdH/R/r9aLeDCZ\nSLYsWTkG+f2YgKTYpx0MvrOAZEgy8C2sfKuwy0Xjcpl7PGIBwLHV56m3ltIf2VKSSa6Q9EmdupxG\nibT0p7+5zbt/brdPvsbH44bLRlyHxQH147xAZN1txTFG7yBwH5BeKHe2hDssTug8YpHup3F6IS7Z\nbeJCkp9j0WazqbOzs4mcWcDUjgC37e12u4+X8sOHDx/OVpk1Via05IIp5gvT3baIDaltNk++oo9x\nx2OPxye3QV89S/YivuC+r2OUfGkO2lXHhNaLtUyCpTOXnz8z7Nc43jsW+MKPx43e+yRmcKFAY6Rf\nc3zUjfuG+4i/5DLZburHMcLPlVzv7u7283Pu5WMTL1Dc2W63k7fr6/l0jpn+SdxWHPE5ce5+js8j\n7YTS73pBLu/vPui2nNorurLaWvuGqvoH9Sgp/oe99zeHc/5kVb2pqp6tqt/rvf/pcE5/29veFifr\njdl9IkYp2UqVn5RcJIMjiI4qIi5nJk1uvDbnyacbgwzBjTBtO1QwGCVzmlMi64mo+98SaDLBJEF0\nouSycF0M7Gj/OSJ9iYD6pwCfW2j9HqMkl+cxaPgYOU793cloWpV0Us0Em/Ol/RMASbK8uuUE3gGW\noFaVV/3cdiRTyfPo6Gi25YbzlF1SZsnf/F4jW0g26OTKiZTPIR28l2MFgzbtXwSS23LoE7SLVBGn\n/aZKv9uJApfLIM2NwZh2QTvVCgKLO9R5etEPbTs9y0YcXNJJmqdfM8K3kU7TvfzviWC4bTl59z6W\nKv9eYHI7TuNN+Ob98pxUbPD+fI4phpHA+nm0N0+63B8Yg5w8iUCnQgLjkf+/tRZJEPGD8qIsFOc8\nUXZ7kC8q+Va1P3EJ8olEABkzl+KlMJGrDJqXX5v8wWO3J/2exLAA4DL1hI++Tl04DlMO6eclos54\n7/aYFhioL++PdpOwlrakghOLFrw3CxE+TjUm7bvdbpY0+bw0BuJHii1JRpQlY/WIq7qfk5+19mT7\nqQomviWZzy+qJVxMfk0/YgxL/IZcJ8Un4s6It3rhg7JM+Ju4jR++eMF56iDnPDo6msiULxhLXHfE\nMUdF2VGRJ+UCVcv5CO89Ggv9IeEB45Bshzj41re+tXpYWX3FktXW2lFV/WZVfWNVPV9Vv1RVf7n3\n/ht2zh+pqp+rqn+r9/7h1trn9t7/eeir/+AP/mBVzVfmZAT+vCDBOBHbx/3uP+9zcPUnKYDEPIFm\ncjivOItsLpGv5BgMNJKXgxINk8HOKzSqnldVfBOmy3BE/hiQEhH3MQnEfLWVBJUyFdi4jIINRdLv\n1ySwHAVbnxf1QrtIhCZdt9QSwC7ZcCL0rifqjHNLsqD9p3Gzb6066RkJkQFPXGT/vvrmhIJbU3g/\nHUvkSuPinJRkOqmmf6S5saXgSkD3SrEqqAmsaWssYlA3aZ4c7263m8l9ZNOHbCwVpbgV0f1R9u/z\n9OSelWyfE/GDuk9JAGXoWObVd7e50RxIYBjU6etpy7aTm7TaygTCdZw+k12MCjGUH+1N/Tixowxl\nO9wq6QRf82QxhGSKZI+ySORTuibpYmGPtpF81e2KCVnvfU8khQnJz3h/2qrbun5O/IO64pZjkV3v\ni/7KGMZ4n+zC50+78v5TLEs+7XyHdkKeMvLjRGS9kffp/pwHYyxlwQKdcJn69H49sdN5SW60MXIC\n2l+y1xH+p2TDOWjiQ+SQPHhO8pEUU+lb6d4J87wlnSX5cZcRkz/yBE8g9UnMEyei7CkbFrrpe2kO\ntB36YxrPks8m/u/2z/uO/Eq/J5knrnpoMW1kQ2mRy+2LXCJtRX/b295W/TOcrH5tVf3Xvfdvevz7\ntz+e5N+3c95YVZ/fe/+vDvTVf+AHfmASYEaBTL/731Mw4eHVI22xGT0rpr4Z8NJ4UlAI85sBXwq2\nDFKjirNflwA1Oacf/iC+tjmwwka7OTo6mlyTXqSRGvtJ4MPKZ0rKGXCS8x8KbiS8JM0jYu8riaqI\nuwP6ob+JrC8lZEdH82dWE5lKlc1EhllFJUmkXLlyL8Kvg0RtKbi576Q3cY8IPf2DY2RF1VefJEOX\nPX3J/WiUqLTWZmMmKdN1DtapOOL32u3m31csffmR7ITEl3afbI46TmSUuMPVYgVtb0s+4/obBclE\nxtxenfimBIMklvenfziZ45xHeNtam+0aSJhC3ZG8ENtH8Tfp6hDZTOSA8WnpOTVPejk+kqL0DDK3\n+FK2KeEYyd8/aV/UD3V1yLZcPinOej/EHTa/j+7F2EM8c3mSO4z6HRUjuD074X0qsBNTSYqF5R7D\nGP/uE8OSf6Z5+/2rahZ7tOKtgtPt7e2kwL6UzLs8fQdI2uI7SsaI5STliXvRJpNe0jWJn5Lv+P2k\nB8fp5EucJwt2vfeJjFmsIY7KJljsVd/OnRmfkryY3KdEkLF6xOVSojXCWtqMJ9zEe+8nyZiy8nuP\n9JCwiivw7gt8BFEtrfwS//i87Ciucbxc0CL+J3ziNSkXcV2qjx/7sR+r/hlOVv9iPVox/euPf/+2\nqvqa3vvfsnPeVFXHVfWvVdVrquo7e+9vDX31t7/97bPJiRCS7KWVm1QtOGQwBHsSVl3noJUCK5Wf\nVlO8+f0ZqEbBxLfv+phpQCkhc3mMHD4Fcx5sDJKbzfxlU0z0lsaggwlt731CkgS8icQuJWhONh0w\n/Xzqoff51wO5PUmfyd5GwU7NA8MSGU66SoePJRER6bWqJoA52ipGEp4Cw9L4SJD8Z/Y/ssklGafx\nuV/6mBn8nYyQ3CWCL7/2MdKWEjHnvDmu5Hv0Q9mKjzEFAWIgk0MFJbcN9uN2qX7cH52Y06+4EpZW\nKYl5xDDaXkpGaFtO6H3niuvDSZDPkwm2y5z3GZHREZaObED2NtKvxuJ60Uqc45BseQkv0ji8JYLm\nNkq/9599fE5SUzJN33E/TYmm95PkleIRG3VF+1dxizixhN0jeY3sk7bt+hnFMM7z0Jxpm36d+x5x\nKOnC7+V9io9Rn7o/++b4lvSZfCbFMPICcQMmwt5SbKHcXRYpxuoc6iHpmXbC4i4bx8JVXY2Xxb3k\nn5Qh/5bi5gi3WYRln67LFJsZVzxxVx+jFdERVqkx5qe4kvjYUnxe4lP04bQq6b55yB9Gulri286L\nnZvQ/hIPOsRDaROMu86PaMsjXJbME095+9vfXv0z/IKl+2TBx1X1p+vRVuFnquonWms/3Hu/4Imq\n3hLYq2qyvWt/c/vZqwUK2Gk1Sk3K0WrhyPAkZE+4EvgksurNyZS/NpuJp89fivbtfAqwvrLJ/fEe\njJUwJgKoQ6uoBLdUafSqpSqf/tKI3h9ts3r48OFwqxgdgQ7gCaEKFe6E0rfO86/T4PbmVPlxObAa\nlAKDdKAXNmjM6Tlgt0EPfryPbEw24ElA0vGhllZOdrvdvgpKG01BXY1FFgYmtREItzZ9LXt6QUEK\n0FXz6jsDhZMXVR9pt1w91Zgof/qq9KzrdC/564ho8JNHWvVwWenTf04J/NK9JTMnc8JLBrPLy8s4\nfx8TizMeTJl0yE6ur6/3+KCXcjBJ8QCslTnqnGSYpNGJrsbktuR+6gfHrP7cVkYyd1nLLnSu7EYY\nwWRBcpAt+c6LkT9wq7UXA52gOSnUIbxx+yJh8OIvi7L65E6k1uZf4ZXGz/EwlnmiKHti835IlhjD\n/BwWdEhqNQ7JwfGROMlkhr5H36fvkgBKVz5mxXSPPUzGZDf+YjAvYHCniJNWybJquttF9xyRX/bn\nc2M/vffJKiZjvriD/3+3m37vsRdTE5b6ooRjP5Nw9xH3YcZmyk964PZT11f6WfdyrKKOqRcmzk76\n5RspkfGVspTU8XCbZCI1Sl7cV30M9JEUnxIOHR09eUFQ8vP7xE/qi7gpTOYOI/WZztecyOGoDCjY\nIAAAIABJREFU4xQf6d+ybX/TNXcu3t3dTb6m7+7ubvJ1f/qaI86bGKf7S5aOMRoX9cAVYx+f2xSv\nU1wgb9J9yZNdN0v2ziTc2yuZrD5fVV9kv39RVX0Y53yoqv5p7/2jVVWttXdX1TdU1TvZmT+z+hVf\n8RX15V/+5bNqzOM+ZoSGmXvVk22rUsiSY3NLoQNQuncKSqyIpApJ731CIpm0udONEgV3YAcPT0x9\npU7X8HoaJ0Es9aVDwUp9XV1d7d8GpjkosDJopyYdJJB1csZA4QAlefXe98mhmm9lo+Mw2Pm9ffxu\nG5xPqvg6iEiG3HbOQJUId2pLtkei5HOlPH3FW/KXLyRdce6jyt3Nzc2kaCLdSNfsj32oH5Iejdv1\nyQCz5IOUSyrkaHz+f+nG5eXY5H5IHxY+KSBw3lVPtvfIjrxYITvxgOgvhvE5icjprZejZMj9PRGn\nROh9zArQ3o+P4/z8fKgHvxeTUC9SeKAdJQcj35Ct+L2IeUyAHPN9fMRJJvHEQ9qExqz76y2OPhdP\ntEY+Q9Ko/3shUSSRfp+IousyxUTp8Zlnntlf48n00ht5nQhSv8QNkmVvTnLddj2B0r1cTrKbkf15\ngdq/71lvzqVdJPvz/qhvn4/jREqsiG/Jr6UnbdvU/RiHE9H1e2kMmqP0XFUTH3Hf5cqX60EYrOvP\nzs4mvqvtvIp95+fn9eyzz+7xzAtrHh+FecQqYb/bAXWcbJxxyzFevsZdSF6w0SMxIxvVPYhrTD5o\nRwlTnFd4bOSiBONTSoITx0t+7rjjc2Tim+SqT/cpycD1xkQzjaVq/ggRuZUXCVznKfH1z8TzJBN/\n9EZ9+1fO+DGKEbrei+jcru12LTv0l63RRshxKVOPKz5PPyRTcQL5u+4jGXC7f/Ir+pfL4fT0dIiB\nL7zwQr3wwgsHY/cruQ14W49esPRnq+ojVfXPav6CpS+vqu+tR6urZ1X1rqr613vvn0Zf/S1vectM\nOdz6yoRCjYCaHJ4BXMbkZD0lV/y5ak4ynKDKGAlQPFIlyre6CkT9/slx6bRMiKrme90PJTkeoB3U\nXDej7SH8G8G0Kr+YwPtIuvOKfHLc5MSJuCXbSWSdVSYmmYnAe5XaCZsHelZeGYxT8kCZEkB4DVep\nR+DOcST9sXEbXe99AnSpsp5ADP4/SRZGK5nc/i85J1vhGPxnztOTPQI6Pykbl6sHN9kB7W2UHHqF\nkr4mf0yJpyeLxM5EmLhtU7bh/pH8aSQL/5vPk3bpdqfz3fZlf7RdycTf3JlkqEZyqP7vg4s+h5Qs\nUt/SOfGCYxjhq8uO/stk9z6N8UKYw7f28rmmqvmzyx5TZUv3tR0SNMeLZF/Jlr1vv3cqBupcxl3a\nAH1E9kUyT13Rtj1+p8TTyXoqBPs1HA91SMzb7fJ3BnOctEnasWzO7zcqYNOW6QcpztIGyePoW+yH\nc5B/Enc4PrdbTzwpd78f+0q8JumKMV9yId6N4rsSXfc9vQCQibvjFIuern/HAh+b5MfHQEYY47+T\nr6X7Ur9pfN6n+xn5iR8s0sr+/aCdiHs5b3P88AUPYvkSpyTfl/xoO5Qf/YG6cqygDL0/zls+4npO\nHJC64PgcV8Wr2I/bAf3Ziy6KKdqRlrbt/8iP/Ej1P8g24NbaH62q/6yq/lTv/Q2ttddX1b/de//v\nl67rvd+21v7Dqnp7Pfnqmt9orf2Nx///7t77+1tr/6iq3l2PktXvZKKq9jmf8zkz5dDRndAQkBwo\nU7LFrVVVTxKV7fbRG03dUPTK6ZETeQKpajkBk0azlDQ50b24uKjen7zR0t+E7CQ0NY1D3zXG80bJ\nAwPPiHTJyZPBej+UkxcfFOyZcLtMUmAlaSRgMJFJJJrAIj170PRAcXp6OguiI4Klw6vd3v8owZBe\nPSmRPRB0E4HxwoYHaFXjExlICVRKyki4KC8Wa3wsrlP6QCIGsolEFkgYVMljAOSngz6Dgq9GOcj6\nC5Y80BNnkg+ngMMgQUIum+MLES4uLvYE1ZOLs7Oz/eqW+5H8SyvB1CNt3+cjHe52u73/+NvMExn1\n8TNwuf/5/Sgz19fIXo6PH31Prcbv9n91dRXJgHzVt8R64qPfuQ3Lx+uJsn52spoq1dJtVe0LI4lA\n0G79/+7DxAP3+6qa2K22nOnwl6RpniQrLFa57ByrNS7t4HEimV7IJpnf3d3t7TL9PxWGfDxq8nuR\nIGKE4o/7rI/Z4/Vut9vH7JSU+GM72h6Y5OQrUR4P0wsKPQ7qf0xKRoW0xGdSMkHfdtLuOOR+5gft\nK/EMXuO8RXjEorv7kidb3hLJT/IbxbB0nuTm29nFX1zHqeDlyaO/lNNjis9TPMF5j+zRFxeEJZ60\nqx99L3myKdmL+E4qDCzZiOyEB7EsYbOPxbF6iW87NmiFnZwwjZXjkX3R77SF9uTkZH8v7Vp0PPVH\nHSQDyVN6dNtx/D1kX+LJ5J2MlyP+7/OWfXniyTwnJfXcrpt2iiwl987tnUOSj7nPCKvSS/iEIywK\nnZ6e1vn5eXLtfTu4stpa+4Wq+u+q6u/2R8lqq6r/u/f+xxcvfBlba61/13d91yQBccd8fM7+5xGg\n6zxWP/gMDwM2f/a/sbpIY0mNQJLG7fckWdPPlEUinax6JccYHQ4arJq77KSHUbUpkd/7BpNEfhPo\nUjYkckl/iQAmOfsxuvchnVOvSc9ul6lala6hTHVNquo72JIEJXvj/UjEndylJE9JnMsx6ZirWgno\nki143xqjyyvdi3pKiULSUbrWbcpxhf2ojZJV2glXLVix97GlPtxuUqAfEV+Ow+XgcuUcEiYTB1ig\n83lydSlhqcvzPuf4+Ee45D8fwsVkAyKXTljZT7Ill0PyvTTm0Tz9mkQyiCncLZTwIeFNwhD/WbJY\nImXExxRzaF9c9XPZ+DxpX/fBZGKXxpTG6n6xhOOjQ30vzZ34O/JH1437EAsK/un3TH59H17gK+si\nz4fkTC6heabkxvtI8xrZpCcu9IHkjz629PPSNZIdVzt1HfE/6W/JVpbmkGyTsWgUs/zcUbxMtsxk\nSHPnjsOEDyy+pXnSj0e8ivJ1PPOCmIpfzicS/tMnUixKzX0z+WvSFRvHo+sS99bPSVeHxjSKJeQf\nzIWSrVJ+KWbchxeMMNH1/D3f8z3V/4AvWHpt7/0drbW/+/j3o6o6vcd1L2tT1k0lEixJaAViKShV\nPXntt5zPScfp6WmdnZ3tDxoziflut5s9GJ0IvRzMX0Tk12jlzY3FK3CqAHLLRjJGgpUqhV5R0hxG\nW7x0vlZ2NCdWoUk2Xc7SHcGM5JEr3GkOfr2DqPfloOYvwfHPBNR01BQ0l+aVyFxKXPx3Jxg+J+lE\nL6fhVkT9nHzASZvk6CuFWi0avTzA59Xao8LI7e1tXV5eTirBnINs+JlnnqnT09NI6FOg0P286OKB\nSGPwFeaEAd7vSD4833XvwZdFAyYCrsfNZvriGvXFV8KnMbotbDabvb1Kf45NFxcXM2Ihv/YVdgZN\nBRONRS/78SJUIv06tGLm/bitqMlffcUq2b/ux75SIE/z1Eqyz0mVcy/E+EvInAjzu1e9yq5xJ7xn\nEWi73daDBw8mq0TuE7766n7jK2xMHKpqOE+9oIoFDScHWmFMcteYhekp4Ui+Q0yT7C4uLibPcjqx\nJKa5rSfc1Vh8DvIlP1znwkQ+y5ZI2ui+TmAlH4+ptEtu5ee9HfOks0MYnZK9EUnUNYoRXiTkPEXe\nl16eJBky4SXZ1EqVJ50k0dRNiq1poUDnMQ5xdwufWXXfdBtyu3M8IBYkHsdiiXCHCZDmojgnn9Zc\nkk267YjDeb/u577S6/bCRn4oefn8nQcQzxzHGUeY/LTW6uzsbIIRtLmEJc4vxa91r2eeeaY2m01M\ngp3feiwUD5GdakeRYh3jhvNE2kDVkx02S8noUjxIsZA68vs6b9Hc+LZ63tvHQDun/LglmtyXccpx\n1WWTeHdV/kpM8nninY9N2Mh4s9Tus7L6U1X1d6rqf6uqP1VV/3FVfVN//P2pn4nWWuvf+73fW1XT\naiiDtYTEhNYBiwTeq1c0IvbtQcYB2xufwWEy7KtNfg3fBpyq5DRgNQ8Cfv+q6fMONzc3+4Cj5FgB\nz0Ga85Tc/WBRoGr+PJKDnkjjfaovDHAp0HOFj4GdhMsrPw7oh45UfeeqgtnpXh+0UdokD79Wn0nG\nBEcHYp1Pf7gP+SRwpYPjW0q8mEwtNRIe2Y4ftHUnR/ocAWyqdPqYGGydPMiPaDsEWdpkWrkk5jgR\nSdii/kmkSCQPFe3ch5lkUp88EtZSxvdpDJiJmFI+9BXXg+ZCW/Zz1NfIj/wgbic/0nyd6JIkqnnS\n5rZ0dDT9ui4VMSjLpD/6VUoEUktxY3S+xzQmuW4XnpRo3qPn3dTvKLGinbgeRHJSApRiP5O6VLTz\nGJHiPufpR/KTET56ow168VBzOuTDfq+E7Wojn3Vd0LeSLzpmOf/xsdBnU0uy9bH4mEYx0uU1Kmyl\nWOjzWiqOOH4v+T254dKR+CN1SzmkOJwa/8d4PuLIh7gzG+NSwm33WedjPtYUJ2i7S36dbE2FIr4N\nO+3KGsXEkQ/T7xMvTnNK/p94if9M23Y/GnG3hFVLdqP/c2Eu5T6Jm1I+S9jrGOwHOdsor/mhH/qh\n6n/AldX/qKr+UVV9RVV9vKreU1X//j2ue1nbxz/+8ShQ/SyCUTUFa9+u4lUFrqQmwrAEoKNlbRFd\nB0evpCXHlbHqMxElKlQgy+dZVMHSV1DoHD0zob78uSZfPTk7O9vL5Orqam/QdBqd789KjozV/8cg\n4wSv9x5XOFxXiaCqf+9b8/Zqla7xFW+RBT1rueS4nky7zrxy69Vbd2LOIQUvBk23Ew+srtMEWCMA\nY9CSblPgam3+nYOJpHnQSgRis9lMvgaD8qmaPsDPhMjJpl/jvqHrhQOub7ZUTWZ1myCrpv7dH0Y4\n4X6vuUmmTsR9LFxp01x9ZUPXOTHxeXnyMCK/o6BOAk870r0YtFJQ90Ra96LP8x4+T644yd4SZic9\nu91rTCIB0uPp6eneVlxX/tU6bk+UMYl91fQ5NRVZXJ9aVdAqgMs0EbNRwpHiyIgMpSTB/YjF1GTL\nS4mLVpSFcxcXFxO/SvjBAlki1Co0eGz13Qs+p1SgIOGi7VIWxMne+2SVWv7q2w79WSzdg6t5uk52\no1UPJkM+BulGiYfsJMmHRTJ/Tk3X00dl58Imjyv+DK/bkf8tkU/HOrclT2TSqhHty+fO5CvxNPqD\nxlc1xQZyJi9Kud6TffGeTNgSLtD+NRbHs1HxjfL2lhIpFnG4cNFam8RY8RbuxknJssue8Uz27ljP\nZNDtlnjpuiOX93t5XCM/YdFWjXjljXpNHNvHyGLrKPnyeOEr2inh9gKYz8t3TiwlppxDSkSJDa09\nWcHVex6o81RY87h7e3sbOS9lQT/yuXvhPekktXu/Dbi19qCqNr33T93rgpextdb6m970pomg02vZ\nE5jJEKvycx/69OSL2ya82kHDIhFMpJ9gTWMQUVXw82oVE2O/N2XBCo2CrR+siHPbbQLZZEScd1V+\ntXaq0HhLK6tqTEw4Tjo7ia33IxCkjtOcUqDimBzMnTA4+fRxp5bIOBMVNoKXj8nPcWAl6Kcxp+BP\n4jZqPpZR4uvnJqDjvXj/RDJoB0uFBQYTTya4cpMwhls5uV2KpKdq/oyog75vYaTuGIBTRdj1Tpkx\nmXD7chk7BnlQ8oBH7KqaEjWSHunTcUg2x+SLgTLZNufKYE0ZaE4uQ9qA8Nbn6gUyJSe0ZR7uaxq7\n2wmLgV54WTpcfiSYbl+jogqb+y51TgJdNd+Nw9jn548w1+0kJVa+aurkJeFMiiO8L4l78odRXKN/\npCPJXbpcKhSluEds02fy83R/9kVdjwodfi7lyWTDZcpiu/rdbOa7Bkbyc59PuMhkj2NO8Wop0XOC\nPMJFLwC4HpgIpvjNGOuyT0nJbvekEEiu4D7F66umBbBU7CVu+24Ncl4/UqKXMM77SfjBJIXc2O2L\nq9fpXqmRQ7h/UF/sm4l7wkraEn2ERUXdm/btNpGwvqomj0Tpa4ZY1GDRx2VPbNHP5E3UP3W5xB+I\nHQl/qZsRd3Z5jQq9fq/v//7vr/4HfBvws1X156rqa6vq9PEgeu/9bx+69uVsesau6tGk9UarpDi2\nZHQEx91ut387rhQ9IhMOdk5aVWGumu8Dv7198pwYqy8O4lU1IYeJtKbkxt/CWvXk+700V1V0/RwF\ncAZPjV/ORLBhVUky9oT/6OjJS3K0QktdJLJJXTmZcn16tbtqmnA4MeLWFCUeqm774QktVwE9MSTp\nV0LosmGQp0Onlsg7gY/FBW8j0HBy4KtWbsesFDOwy6ZIaJZetuArS/yUbXkiKrlrFcvn6eTDq7pp\nXBzjqHkSJbvyartkrTHTDzQGr/i6bTAZ9WAtPaTGAoHGMNJ3CjrJ3hiEKBvN7+TkpM7Pz/d6kB37\n81jehyqkLksGJPXt8mWgT4Wr5G8+BiamrbUJ7jiJpT9pzo5Hu91uj3mULeXm8YC4qLE55sh/1UgA\n0+foS+FJLLwv2scoZrj9c35uR9KZy4nj4b1l77IB2p+vurk/OPH27bGuZ58X7dd1y3N0b70JnwQ6\n2W0i4sRpjidxBsqZfMLl4ztviG2cLz9TEsCxSMZeIOcKm+7L5xZHMnAbZyLvxS/GGZJsj3ssOKWV\nad3fZZSSL5cLSTL9UbJU4cq5jmMVORyLLvIdxkG3R+5e0jydO9Dnkp2JE7kNseDk90qFUPcRJisp\ntqjJv32OTFq8iOTnsX//mXg94i3Eam+UlyeViYu6PZA70fbddnUdC5PyC2GabENj1VeDObepmhZ7\nGeec07osqLtUAOA1SefEPMdsj20s1tDXGMt0jubuf79Pu8824LdU1UVV/UJVXdf/z93bxey2rndd\n13jfNec7P9bOtgVTSEVKqyEeQCKkgE3ZVvlsBRsD3WFjQ9VEqUYPOKjWlIQe1dYQgnIACEE8EqPB\nqAf1AA8kQAKcNHa3tYEQ6m4RQ7S1e635zjn3mu/wYK3/8/6e3/O/xztb9mKzvJOR5/0Y4x73fX38\nr/913fcYz8w2M2+3HPsxtZVRrs6NITBweDXFLYlX+7qKGN7r16/nxYsXZ/c+eqieAMSVoIyTRpz/\nE/zbtg4Gdq8+zVxWyUk62C8T7lVVh2PgnPhCqryAIcT/+vq6ki0WBDhPB3VXYgJOngd/T3WKDmKy\n5eudPKc13RGE28+NiNOxLfv83SDR5mjb9nw8B8qbYGgCk2YCRJv0ChnBPC9V4jgMUA0UCaiZv0lD\nmyPPz2d8NiudK13YBtm/q76r4Er/beNpAdHJaUs8SFBzflutsyzcJ1/OE9+2bZNcJRClgNASSH79\ngnW8Iu/52TiU85wMOamifhvGGx/j/zc3N2e2a5t2UKaM+bUlbJHjkydPzraquTBFsppEjHpwoXTm\nfJsZSXb6a/bfKtNOiFph1L6XApETosyfBJrbtxqmUs7RM8dJkuREcmZOcYQvcXGS1Fa+THZNdGNT\nxEfHGSccnKcTR5JSc4lGEvOITewgSRdfNkKuka8ZSkKZF8pQ1ibTTLj5IhP6CPlGErL4DROC5iO0\niTRjM/2e9w0XYvE+8w62hD/ER3LEjuiTDdvj+/R3r9zHH+LDTpZjF6uYwUSFR0vqvJIfLmgbYxGQ\nfCx4G2yKDVgX7d7R67Nnz862+LqIYV+nTUUWrYBNv+ec6Ge2VR7GlBZr7UftHOqaMYC+Sl8gp3Vs\nMB/y191wR1bsjMln4qeTzPj0kydP5vr6+uJ7rRkLGRNvbm5Oc4oNJOe4u7t78KVyM5fJs+2FvDA6\njL8yZhjjWuGDsjROs1Bhvun4sWpv84Kl/23f9197eNLH3LZt23/oh34oP19MzEGqbePzYTByMjhz\nuc/eAZlELk5ytIUk4/QqJf+fc+xwVnAL2pSPZbWSWe5l4GUgb335b04cWvWlEZomCxMv/0w5ZPzt\nLXZOHha2dSEXByjqZiXb5nC2SycuBn0CKl+H7wTBQcDN5zlZbEDfglBLpDxfz8d9N3m6WmqfpJxX\nsp65fHbHpIxz5xw4ZpLXjNc7GywL2zp9r+mZsnFyyGBj3Gn6aoHNSYP9qOnb411hStO3A6DJVyN4\nlkubp1ubt5MQ+1k+V1jPQgLn1bC0JQKUnZNc2qCxlMcK/5vPNB16nm3OK7yzrELwScJMqJtubE9v\nc3/7TfRAeTDm5py2ukLdNTLVcIifGffKl/KzCVdk5vu7SEAfScLBRI3nsLjKe3t1jAUy2n/mkvmx\nqMDdLo6x9Ff7ePMd+gKLKo7Rxi1jcntG2tsgV32t/Jq+13CYuGO78OEiXubAg7LzatmKw63ssfEE\nJoxcAc+2UcanFR84mlcwzmNuHK01nke/ZtHpiEs0PdAmY+ueJ8eaexvbV5yXY3ezrpxMtwWtt+U2\nLc49JOO3wfJVIYVcx3mNZWXdGQuaL8Xvm++RV60w+chG932fP//n//zsv8gXLP3Jbdu+b2b+65n5\nOdzk/3mLa79s7d13352Zh5XMZkBhAGxO2pKCBuC5rgU6B+RU1nO88847p+Bze3t7tuWHhwnziowy\nmLkdgTb74fI+g1LbFrQCfQfiyDlBKUDcnhvKeFZtRd5JhB0cWYlK9YpfFM2Vl0YMV7bSHM5VJo6Z\npMxEg8E5jfc2Sdy2+0q4n7vyeSZfHrcJMwOZX0qWz1aMiDwjd87dZKMlCg7OWRU1uaMfperHwEsb\n5jYxB63YM98yTDLAtwceEdnoj9uqPdesHtBOYyPZZps55OtI6Lf5bPZle2z+bR/yPO2L8WMeTA6I\nRzzSf1tBM+lP5Zk+sHrhE+feXpTGZJW+fPTCoMyTOgrmRZc5h4E2q60M7LF/rppRxi9fvjz5LF/m\nRFxyUYpJLpttYNvuv+Cd/dDHWwyzD5O0Bnv8Mo3cn8VA34t4x35b47kZU8PG6NoJUfNLjv3169cn\nu8pBv2njIIl8mzjHHU2NT1A+sePMLSur8fuslLgRn/k1PrlPbNDj5T1c7GhkNjJOrKIPcx6M5fmb\neQrlmJ8bf3Hcpy9mHoxV7TqPr+nXPtz4Y0sMmECbtDv5il20RQrGwDYHY0rk7uSRvpG5xeY5J/9s\nLMhW4djXKmbkZxfQW7IVO0ps9f/bp3H67u7ubMWbOE05rhZomCSt5GG7IM55lTLzCR/JdcbPpj/i\nQnyfOxhcfLAu7Oe5N+8Xv7DvuYBCHk4uYlxIY0HAcZXcxtzB9tOSdOIFZWEsXbW3WVn9rpn5kzPz\ns/PhNuCPxrJ//eGFX8a2bdv+gz/4gzNz+Qp4V19IjEIEP+qD/V18mow2Q/dnU6TPa4nOQwkk58DE\nwEbPfkkQV6DadN0Cv+fQqkweM6vE3CrcVl0or1VhgONp1/CelMVqbk3erpqHWHLbnhOtBlgPydAE\nzFuCqGMekbvPIWE38AQQMg9uO2TF3a350UNt5RdsrXLmoO3zqDuC4ENjsoxj/9Rd8+G26mFC5Dl4\nddFjW9mc7cT25Xk3fLBdmGS2YDJzuZ3YK4cNL5quaG+r4oNl2IpCkeuRXfnejRQ6QXJiZWKSc+If\nbQuV7XH1ufL5h+zfNkD7o3zSh+X6kC2tijUNj1fY1eZy1JxUWp8taXLfxrOmC8eaq6urszklPjU8\n8z1XiVwOF7sZ74jTJJLN95usXNC0vFYys++t+A9XqHhe5kXcI26afObT2xepi8jDiXxiqrfB2/4o\n0227fMTpIduz7Bz3HUtaa/dgskCbbHzSzYmM8c/4ar1T5+zD92z3txwYM7iVk4sSM5fPbLOPlcz8\nvxbzWmHZ8Yu25O28SdLNZdr4LGuec4R17X9s9qvG921r5owrDtKw27ZCu2j3o587XrZc6AgTV7Gw\nHatCTItF5kwNg//Un/pTs/8iV1b/yMz8c/u+/9RbnPuxtWfPnlUhBXh/ocbqFoddVYhaM+B7q0Aj\n8waZjJ/PLRg0E4A++OCDU7U+indlZZUM5mAl1Kt6aQwSCRRc4Xjy5MkZ2LBCxNYIPQNPxsct20yM\nU8XN/54+fXrSjauBToydIOacrOx4SxeP2A1Jbiph27adktmMxytWjQD62YLYKXWda1slKpV1ytwk\nhzbGefHeeQ7CgEjCnK/ucNB5KJFZkem0FeliMkJSzZW4RgAbCUlQiH7tD00++TtXW1sy4eSIwbIF\nC69sNEA3mfFOjJk5PdvllRz2S6LpFXcWJzI2+9GquOW5NfmxOprVC47l+vr6DCPb1roWOG0TxIr8\nncE4/bpokWtIMqzfVpWmT3lsq0TH19gmTJiJN0eJmn08ccPEkvduvkg8fJvx2xecxDQfYWLHvoJb\nwdU3b96c6ac9fsMY1pJ0jp3YGP9z/GFfLPLQLthyfQqM0WnjEivymzkRa+OzlL9JbfyUsXqlL9rl\nEUZ6zJFN7v/q1asTx6BeUtx59OjRPHv27GI+q0QqYwoG+XwWaTMexpqM19jr5IaJrm0j92kveLKf\n5N6tsOY58eBKXHSW3Rh5GSjtyTtEVqtjGSOx0NwmvKolUsSF6DB9rOZJH7euGrZ7R0zuxXkxlqxe\nOGV7pZwZw1KIz7xdiHdMNX49JPeWMNJXrq/vv/5w1Sh/j6/xlhXuH/EW/35315+ZbbyDMuZ4iYEc\nG7nw3d3li8H2/fxFUt65FRnn/37ZbHDmSKZvk6z+7fnwBUtf0cYAm88GIjnXYOJrGokgaDrwskJJ\ngKeBc3z5maSlGQidiRXgZoh2KCcanCONncbNqhZXkXwkOIZAmDAEnGnQfr6GJDrgYh1mrmlXV+db\n63ieAwxbC5o8j0T35uZmnj9/fpYUB2Q/+OD8q0tmzr/fL32m3wCQEyJv1/V2T44xfXrLI++Tn51k\nNJtrstj3/ex7Ad0ctGiTnj/v5U8SP97bSYhXxe1HkUO2y5KMOrBGbpx7I9+tMNGA3Ak3X9/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4SOuZ4Hk6ZgvfWd67gzonG3Vfw8Oprt+LC90GZsk+QTSWqye8BYEV5AG2vj4643FzDTT3CXY6Xd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\neHFh5pwTtSJ74z9M7Pn4HnM1FslSJPju7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76zqpX/82fr0JXv6CL98J4NKDNePgcRu3IS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\nqpmZmZmZmQ0dB6tmZmZmZmY2dP4HmM2yQzXZnBYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1144fa510>"
]
}
],
"prompt_number": 823
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"resp_right_1 = convolve2d(stim, right_1, 'same');\n",
"resp_right_2 = convolve2d(stim, right_2, 'same');\n",
"resp_left_1 = convolve2d(stim, left_1, 'same');\n",
"resp_left_2 = convolve2d(stim, left_2, 'same');"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 847
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"4. Square the filter output"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"resp_left_1 = resp_left_1**2;\n",
"resp_left_2 = resp_left_2**2;\n",
"resp_right_1 = resp_right_1**2;\n",
"resp_right_2 = resp_right_2**2;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 848
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"5. Normalise the filter output"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"energy_right = resp_right_1 + resp_right_2;\n",
"energy_left = resp_left_1 + resp_left_2;\n",
"total_energy = sum(sum(energy_right)) + sum(sum(energy_left));\n",
"\n",
"RR1 = sum(sum(resp_right_1)) / total_energy;\n",
"RR2 = sum(sum(resp_right_2)) / total_energy;\n",
"LR1 = sum(sum(resp_left_1)) / total_energy;\n",
"LR2 = sum(sum(resp_left_2)) / total_energy;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 849
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"6. Sum the paired filters in each direction"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"right_Total = RR1 + RR2;\n",
"left_Total = LR1 + LR2;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 850
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"7. Calculate net energy as the R-L difference"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"motion_energy = right_Total - left_Total;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 851
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"8. Display filter response"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"energy_opponent = energy_right - energy_left;\n",
"xv, yv = energy_left.shape;\n",
"energy_flicker = total_energy / (xv * yv);\n",
"motion_contrast = energy_opponent / energy_flicker;\n",
"\n",
"mc_min, mc_max = motion_contrast.min(), motion_contrast.max();\n",
"peak = abs(mc_max) if abs(mc_max) > abs(mc_min) else abs(mc_min);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 862
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(20,4));\n",
"plt.subplot(2, 1, 1);\n",
"plt.imshow(stim, extent=[x_stim.min(), x_stim.max(), t_stim.min(), t_stim.max()], cmap=plt.cm.binary);\n",
"plt.title('stimulus');\n",
"plt.gca().axes.get_xaxis().set_ticks([]);\n",
"plt.subplot(2, 1, 2);\n",
"mc = motion_contrast[:-nt/4.+1, nx/4.:-nx/4.+1];\n",
"plt.imshow(mc, extent=[x_stim.min(), x_stim.max(), t_stim.min(), t_stim.max()]);\n",
"plt.title('response');\n",
"plt.xlabel('position');\n",
"plt.ylabel('time');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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+LKnmOfaNTiccp22iI25dUs/gw+or/aFbvdnyAdoJk5RuxYv24yJIJ0ee7+TT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mbSxF1VlZwrP852mED5+5Sr+ctCXMXvqj/KaRzHo9XlLLYxMfTZMpemBTzrMA/zktiBsqTPs65X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3XSgiT+S3SNvri+MLiglWvGDU+0zaZwUGIrBQ38cWrRcdLEVTbf8cp+tGjhmTqwy/PrK9Txfq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Yy8QAPgxohxjNV7DdMDwkQIByft5LC4oeTwMrm8+lasuoe2QGuqetcqWDfOBibB9V4QjliN\npm0et5VMSO+U5HmhAStJ7sicEmYlYHZZ7ugqwluF+aexGvfWWKvUbZXjrVLXKswPEJYICzxgpO11\nYBBH+ERV1n1R9zUNc9JjhfSKAq0WANcOIQl8DV7Ac1xzBNawptyDq0cIa2ZtlND9brtH7a3V+8ai\nqpA5oPwFZ4qhT4E5vNsqZnp7uq7SrZr7muzWK58p0CIiD+AnAfwQgF8F8LNE9NeY+WvmtP8HwFeZ\n+WMi+pcB/BUA37+5mGVM2XV1QxWxggbqTaY3KfYCT/RAU6/tAavHfKqslapF8nubfzspe6CqnZAt\n0Gr/r7XOtNf1SUBPWjMOjJWkluhznORhzu+AUAAtp4nElbBE22AMPZ8C276J5gMoi1Ur7O8KU20t\nWnpYWqqJrlfxp43/CRPLUAsOFB1WGmCjVxbnfdXqp48b03kBqbIZpI/qowKqnVnR/Ln+jZ5vTO3m\n6vtF7+ENF7fqqvlvmr9vHWhNVLXub/L2T+W/i0CfB9s1/r1ay7JfTqyjHzqGd1va4vWIdtc0W3Jf\nWajuSsFFOKz1ZZSSoDpEV+vUlLIh4NpvgysoPdHSEfVzlE5kDaQQS99z+x7sYe5bc6zUjabvOZ3H\nDKFkq3a18YGow/BTiYJpuf0pkuMmvcUemMr3imKxsWOot2bY/4ud8/Q3PWQNz+svJToPgVNOsJCt\ne5xSWcQUbCmluDBjr7bslefPwZVsCGH1A141fDPVz2FBkT6/T/c9ob9utu8Y6FstW3+qAaBRrHmF\n/pcseUr7G1b57FbkZMCkPqg1/cZq0QM8CC7rR9uZdM2K0ZuJu+sWFeBlxfQe0JK2jcsnUKElyz1m\n5ykJo884kiSezt/xBZNbMfoV07hiPARMx7XP1nhMqG8VtHacz2ioatgC6QHwA8Nnipp8F8cZcXpA\nHB3iRAgjYfWiOKwq9UCVQNQlkz4NhTDbEOvPbV12vtc31eZDsiHsF/P7C0bMNMHRBEkbQYgusW80\nabxVtLcuCD1m0p6s19aeIsvOr4gijbcgWedmc22xwjFiYFDwiBSlurCZIwqSCOqHLINE97S9Ylkd\nDg42CI38n4cjyXCYfVzBmcGTlXArg8iDnewh7Bw4MHKqi54iqL6RrUxnFH2ktG/HcEoZTOtwSTad\nqN4dy5XS/3ogSvuQMuTq7/3Xymdt0foKgK8z8y8DABH9FIAfBpCBFjP/b+b8/wbAv3v1iu2mcQ1U\n7VmpeoCqR/3r1R6o6nGw2/ux9w/0J5OlrtlJ2ANGe6Cqd9z6aVXWGRQrR0sDo3S/+kygrIFgu4gE\n1M7V2t97YdVbPrMVbmz/wBy397cHQG3ftf3okTXSOVDKEHNI14rGRmaxYDPkEvjJdKfEJ1dQGZVP\nbsM2W78S6+xbLSwtUEF/cW4XHEIBfgZkZOd/jahkgwEoLc9Yg1T7c027lZ9dhUMgC4kqNNd544zD\nMKiAK+58lwcb5WYDuvS8TZ+UwSKnpAUyg7G2T2J1vIl8pMEQlPduHYyp7kNrHROrrhX/6sW7wKu9\nFnnRR/VEYhGD7wucSovJmyuX7xhUUXDycQZerh7L2fnZAvm6RW8MmPfDUc/tgOr8/ql8jp22tcT1\nNuGWBuewXVN6IKNX7Jiya6CW2HzW+wxUWfei84BjBKuMoWYMou7Dal6YMOi1P5r+P7brp/aFrun2\ne1t0XtkK7FjyI0jzC6Vjp0BqCPBj8lcjq6RQQWZbgZTbDy6LLz07U6V0UMEmO+OrooGyhc+Gls++\n1s1rteudcyU0f1tbH9qxwMXKatKzbWeFSMeqYq02a6LPzZhygI8phayfjkI9nNiJlWth8ftauE6e\n3EbP3bNw2blwbR+xY51RfJgTTdUNLFa2IZoAR0AcSKoHwkBCJ0sBmVYnVqOVhgr4XAdU46Z902ot\naTYgRxVFkSZc3IrRrRgGCb01h4jVj1jdJO4Q0BAqnT6yrgQahbIHtHprT6//9xToPT/HprInocpB\nxj8FILgIigyKnAKPyvNs7X69+bktWwWHzEdLwM1XIJkTUDjiBODIM8saGF1MbJsgERxtNN685rkr\nQMuAN7Ofa1RbBVu6tuSQ9ww4T/CJPujgyrN3aN4WOO0d17f22Oby2QOt3wPg75rPvwLg+66c/68C\n+K+uXrFdRHogq0f/U5+qnkXqMd+p1oxvowy9CW2lXdBaUNXzB+oFX7h23FqqDqgnq53ELVghuxKY\nfu5Zm3qUnR49co/W1/aNLUXa7GtZWyvexszOKZJVBMYo4YHHWJIOGn8Ka72gJCj0SkVzVMtB8qcp\n0cGSgJoytVfZ2h/zo9mj82g/XAOZ2TpHBUSa4AQwuUF0scuWnNQquKooeN1+aITuDDLlxWWQ1STz\niybgTBHOe4Cr+7P9UjlHm+P2c9RrU+5XjXxdzm/63fZ/uzlWx5xBruYOy3lYrCOuycfiTPQoddJV\nrb9DzNr/fUtaveAX8Ma1tZTKWdINW61/f7t1263YhmQ2ochzGoH0mbJiwW2BQnW809+98d/7P6DM\nC0bxNWzP+TSFOp97glFrEdCiczhQAf3mGu0t8bU+6Clh9vqo12dI98fYrh97x8Yih5T3z3lJ1Klr\np9M1sxnLe0Kbas4L9Q95PJaRq4qCtJYYUJUjMsZ6DOaIv41FtJqyVNazKky0TxpCfe70Bwb6pjuu\ndfe9eSOwTOcm8sVUANWgTmccM6Fut1Kq5js/tDabAB8DhiitVhcZLgAU0hIUeGNNocjbccKNgGg7\nsB3vne/IQd5e5DyEYogYfUDwi/SRLz4+PeBpoW0NRuVzH1B1ANQOdbHNHTZjwkzpfEqhUGjE7CYs\n04g5TJjjhHUdEWePuAyI84A4e/AifltsffYDElOn7u832kt6fdyybawyWfd2pYAmy7lGIpQ9Hojs\nMAehZ84hyTdZ1hHFYqvOcNXisVOozGkZLnYuNxZlPd+lvIPEiI4l+qpa7o1Sz8oWbQCUrIyCXeLK\nPpK/Y0pUeoCdy8rQaJTMa2pdo2TWZ9PPZQ2pf9WW7frTbiJ1+ayB1htve0T0gwD+JIA/0j3hP/6L\n5Yp/+H3gD70vg1o3Fbs55otiu0Hq4I0og9hu4K3mYsLWYtWGb22F5vbpVXvR+gPt5We6xtO9VhNv\nXirLpBzKBkqDIv769gBAQ3MWLaqhDVoc1gOyLQ1zz4LVExz2FpbW4tZ5XvEPkEiIeiwUFolipa13\nhtGdqSwqpCIfZw2qaSMZzwI/ILBE14gBIlyuA3gZEBdfwvvPtHWgtQC0R/doS6tN02NrqWs1Yhlw\nAfASSl1aef/sIqLmj2uTdRtrV7coQMnUL+QFRkFWJfCQbAzehXQ+meuYACBpMbu+TLW30izGQBHw\nNQ9RlYNI+5u2ioOekqanENi8o7JzSi4xL+t/S081ltQSeU1aN8aS7HtcMYwLvF8l6hrEGX8g1bDv\nBQmI+fs9jZv2ma0toOpx71cMEglPHaAhfg7KuwcBWCHaSabCv8+V9teAazSRXjfXXf64QNj+X9pI\ntTfKtXgLrKo1nN7g2LR79ZqlLTbH7bntbzzWJz2FTKWc4YoeTT4F+tDErdnPLBZ/BuNvViVANrBJ\nbr+Aqk1NczZbVLkBURqZUxN/J1CVI09apkCPbt2hD0fHOVmqUjo1h5/cqpyr6RqEXmU7V6r+n1LZ\nRqy44IA6g9R1Mlyfnrj9v60vW6pO3kX7faFA9ma1+cxlZ3Oc3h+zfM8Mxwzi9LZYd0MAXCt5bAvz\nWV5Duk6I8IHAFBA1UBA5RKJiAaF0h9SuP6U3bM9aS1cNXSfYJDO9XGClreNPLjRi9QOWHJhjLOsf\ny30E+E20vTxes8KR8tguXWM0LWae6nDd9iZt/08VAVWb/j/tfSUPFnII9nX1KIrMslDs6FHzOlj7\nQ2tFtsxn3+c2t5X5W/3O+1DkCYvpG1mggCy5z5jXhrJGZMWtZWCYtSArfZly5FooSOutpT05vf2/\n/Hd6v1R/FwH8738d+LkP6vWoUz5roPWrAL7XfP5eiFWrKkT0BwH8JwD+WWb+qHulP/dvSdujX+lG\nEpq/0XXSWox6lMJrVLdrvNt2E7S/eW3Tuwa0ulFn2spJcIvJnM+gpIVUTbpo1jnToKyWzw7UnIA5\n+Jx0Oef80YdQAbUnDl8Dt2ievVdagbQFuTmhMidLHYvlamK4acVwWDBMK/yU8q44I6SmtkTaaU3l\nVlNitZWGwcycXFKKNiZqosVlAM8DeB7FgfZaouRrIX4fA1p2HL0pVdITMFB1LnsGJyEreC6mdk0e\nqMeka31ZODMgqtoafO0WEkpdlTOJTO6iawCvU4r1zFrOinBWUjIkYZ9TJ0Ygh+W95q/ZC05jQQLM\n8WOAOPc/gScCDk6iR00ATww+DIiHFXRY4Q4j/LRKAnC/IDqCRvBzCUip78i4EcbEL8ZqKq2erdVA\nbjXM2zAAWaucfC8AZAFJ+yAHsNCcgpqgXTW/e2O93fxasLM3/q9ZdxP/v4Rpj9A0HzaipY2GmH+v\nGl81kFchZpPHawPqFdhTHzBZILW3n7S1V2xf2c9twJIeMyBRpnMAH8+SmyenWZA2a37V7yEH9UAK\nA13bcuQ2OPefzb2T101rFU3+g9kfzfgUal/m4C5VWGjajpmqcp7uIAY8I/oATpr/6APikCxnA+UI\noapYk3BFa94H1J5ksz3WoCagiONtSIetdbofxmE/xEMBUXFz3F6jD77MORT6x+Z59q61AW4c4TjA\nx3QcIxwzXGTZTiLgYtwRaJPVnSgRECixxiRxdEhJvyOJn3POEUmDUBMr8GXDlRQg1aMfls81FO71\nICsY1KcnJ9HsGuWBPFZR27JZxAp46tceg0DXYQu3u4mhU2APW0UBNmBdXQqeNCQrjyq9XGc+pbUn\n7ev12stp/+GirDcKGEqtKmE0+I+kvkjjz1DzrLVbd6Qt0DSW4xz4S63cXqJGarTIWPINxhyl1wNN\nkKiun+/eGnxN1rdKMW1vfhD4Yz9Y9rW/8m+jVz5roPVzAH4/Ef1eAL8G4E8A+BF7AhH9AwD+SwB/\nkpm/vnulcZW2R0lpNxfbmSO2AlMrPLVWKaCvTWxLTzDoCV27QIurKEAw1qd2YOtmSGZgO0tHIt74\njOjNlcmN7JPRUjHiygia64hJfLAq9ccVYbq34SnQiua4pd70NP8KtLo8ZaEDCtiKoHGFG1e4YcHg\nF4x+Tlz7snlYraurekJfLVXH7fa18oAlDFjDmNqULHkewLNHvLh9ummv7UUl2hOw9vpr12qCGnR1\nLV6Uv+NKCEsCipee2PChFbCrwJUBfFomCZUASw2o0mz2CtJ6SWGrSIKmzfQCc2xDgkf1j7NJgqMD\nkwdTGXz5XevGUlBIPf979FhrkWmtXO176ilRbAS4BOh4Rbp3D2JkVQANensMcu20khEa89lS5P/t\nll0iq22FAtoIgA4RS+6OcqWulUxBRhBKDc9DAVc2afsefdj2OXf6r6XTtJSa1pfIC01WrTEtPbYK\nSJEBftxd0lqabJe6ZqMkZnCvm7pplVpEKG27RurnPY1rW3p7TO87tVrZtcDmOUt015riGnO4Y00e\nq/PNvv98e7mfjMWqoZjGKBbOkp+yAaat4rQ3x/b6YbPvUhIQ5bj4wWmb1g7nEGPSw4S0lhAhwsOT\nx0o1UNkDPy3w6SVX7821+m94c43+b9eief1dC95qkb0m6tVWtG1+x37YiaryCooAJ2DlQ4QLica4\nIrfUCqsRKPxt3ipbbNuZ99FLMI4wOKyDtMG7YoWiDniiFkgV22HvjfSA1F5pz9G10wKGotiqbY9t\nRrgVg4lXaZNI16FVNOz+uQm9P2PCGlnGeBgQFp8iNpt12bozWDkEwIb2nBSGbPqfBwaPEWEMOS+e\nGyQfHg8BflyLb3t+nftzQCyhdR9acJtbdvIeXUpl4z1CjAiUIgHFlMmKse/Oci3HoN2vWvnsTXDD\nI3rizxRoMfNKRH8awF9Nv/UTzPw1Ivqz6f9/HMCPAngPwF8medELM39le6fJXBXNThKArH63GxfS\nsXaMdRDm5vgxraGd/C1Fpatt5ULJyMdckl860SRaKoONUOVSvqAqCSYZQhuVKDFWM6Dq9Vag2i4i\nyE7E4mvkTMjoweSR8EbQpH6wjxaHWcvVXj+3i2mvtAJHFn6TZmZhgHxawAhg2c4CvNArKOVkccX/\npa31T9UTvF2AmRyYUABCimwTPUBjmtw9jfWA7bOrIkAFL6BMXPvMlaYJW8HT1tZ6smvl6rQZbFGj\nCGAzlmuQlSP8QEEVtmHVbfCNDiADDJBCEuhSXgulSORjFeDMcdaEW8Gu0pI32nGrxWvHoT6zBbgh\n9VELEnqA2L6fVoHQo8YagThTH1LIW3Yk+nTlkRvajhad9UPauCNWBDh4+M4WX29k+retKNfXnA5Y\neMTKY6M5lRrXAQhakdYH2moBbR/1LNvt2NbzbD86FEWUBmnwxZKvodXLehqrBJWW5iKKqLZPy4LU\ngnpyLE7mIYLIgULScEeJ+AVicGRZnyiBUIcCJJSq3tOStuyIXqHmuAeqMrhgc1wcxEnz1hkH8hIl\nzChJGCm/mavAZq3gaKh8mbZLJtlrWzvP286nPVDVAqouwEJZr/LaFc2+a1keRTFpg9zkNaop7YwS\nRYdL3wzYU3D0bRhbobMoAuvjvdpauLa1Iv8a8NQPXT9sbD69kBRFUaX3FznCMwkJIlmxKPmLYRWg\ntatsuTb+2/3SVJfyKo4uVt8rGGCjQIwuWcxSXiXB266k18jyEpmfpf695GMjbzEyOGHIseB5SjKD\n/G4kwuoSSPA+H587sSkfcMIZRzzgZOoN7nHCPW7yca89I+AiYYYR4UVxprKTjaLYY2/xzrzaKHMJ\nPDlgIsnHlvKrhskD41qSPydrsadQrb211U9fM2/aTTXWdE8h9z2HmKM9UwJcRWalEpuhF9SuB8R6\nwU32FK52f3ubQAsAmPmvA/jHmu9+3Bz/GQB/5ru6+DVhfe98C57sNXrAqq0baoZZ1HVhT5QVDSOd\nj40DvPM1kMpRmyO41XEAACAASURBVIx2qzfYalBVLw8yucoyDXP2d9157UamwuieoGnP6wFbe832\nbxzqW2qBSyCZCCiCXFwBrIS4CifZaahhm0jUF7BVNij1Zyn9pwJpWabSe9AoVc4jjAEDe4RpRVgG\nhGnAugzAIkBVtUesWqSZtoFU9ihrrS+L1fzblsxnPcdSZrV/Q/N3jwGDamHl5NtXLAVViHSlFGXw\nZC1W6fLmOPOlCVl4ywIcgEzJQqe1DrOV8yyV66rGPbgi7GWLgo4h2mqm7DPbOd6Cqh7A2gNt7Txo\nN6vW91A/ewAGeAZ4A0RLNDMVfWwySkv7aYW6Mq4L2JIhs9XmWk1w8VMYsMa6htWLooYBpijq6yE9\nvONiOe0JVL2yBx4yyGID/pPlKiVGVv+b1opVKNP9n8zDMVtnytjaWE3ZUFNta6hxKoTJRGCxrIDL\ns6Rx2h1Dtl/a9dF+Z63MO2kmSuRUA6YsdVwvaYGm+nokrXC2+sRiqeNkxeuCqLx22fmGer5dmz/t\nS9lbm2yrgL2dbwNnC6euXb3x4VzyQ9OWwlUQtB0/8sNiLy43zChBQHoCpAn/YfZ7+13v/7dWry3w\nqql/j1mv1J5iQdVjsf0WS9Bzksx54BXjKO0QAvzKGEKEX6OE9A4swGsPbLWKBx0H7V6l37dKStLx\nXLcA4MsGY64btmOwvR9rsbimGNiTaTqKTT4AfCDTEs7DiMs4SR0mXMYRd7jFPW5xh5t0LO1rPMMd\nbnGX2tc4Y8KtBE5Byo1FUax+A2FlD4cBMXoBH6sojKsO6slntr8d6mfO58n+q30jis5yHCIhBCdu\nHJ4kCEaKmFsYGS7LYW3haiaUVnNM2vyhtWLVJ2U8klxFW0tWD1jZ/9tTCPT6x/bjI+L0Zw60ftOL\nbmJ202EUEKBUhB7i3EOh10CW/azgKi/4utlFs+mVUavUprzP5hoRXeH8RjjjfFqoG7rAyi0w2oXb\nliJeNaJWdtJ0mTcfk09LVCtB7tdoNM5m16NUNSP3gOuh7NuFqhUmbLlGj2tBsd6W/oYzGtQgvG6K\nTgSN5NhbtI9sNr+SEaFn5bL9m03Xlq/tHcKYhFEWR9kQPdZFaIXrOmBdlFblUhJEKm0vIua1fBy9\nxbDcbN1nve/3qGyVxYvNd0WQKwIcV9G79qM0UqIQuUIlskErOtS/TE2KhJxAMvvA6PPTdrMD6om1\ntyn2zmvPt332yKK56X9tbW0pnjuWxyqJaAJbDMpJiykyEJAcxoccmnpP6JJbub5WADAbWFkvaiKS\nJSgly1lOmpvyZuXwven9jOb9aZje9v3Zvqr6jevvqnU1ZqtMidpYrFWAAgoBEJnyRiTrwI4HeJ3T\nCxXAsk7Xaj1FNW7TjVtaGlLL5vvqefv3kYvSdI1DerXPQY4rZUdlNU6rOT3yzFzSUFSg0s5F44NW\nqKIo2uJWIOkJz3vCqda9uWMplWi+b5UXlhrpWdJ2mLUrR0hM+XS8L8wRjTZbZsFWwXkNeLUgjGEs\n8u2rhQIwAJB8RiG1exr9noXrTaiJe35bNWkuGIvWti3eTOuOpWuFN8F6BiQF51T/1pb+GJJPF4tf\nV5CWEp3QuAP3hn4+hjlns64zgLRubkBe65Pf88+9ZtHoKevacdphM9ARoAOnnKYMHAF3G3G8nRFu\nKdc7usUdPcNruhVARc/wGs9wgwe8wlmJgybsR/FIm/yCA804jBdMPGOMM9bLiHAeER5SPY9gDdTV\n5mNr98t2ztk9TPdJhiRPJi9jOzpQ8HDDgNVGe+ZQ8lUi5ra3TrWqwghZf0Pywwoa8XZ1EohsHUTW\nUh9hu6dnBUznmTRmQ9sPvfWslRs+ZfncAK3hIB4EdkOpk8BycdQ3f9cKGjZCS2khQILTfmk2G7vx\nsJ5vtJh6PRUSmYXSVoO5tEFSWRly6EhCtTmK0oE7NI9yTs4B1B5r/5i2W5JQoD4yjPJb7AkUA3gs\n1KuKhqU0wuxoiH16QHtc9QkqLFctVjD/H833qmHJ2swiZLikvfQuYHQSLneg/cwd15x+7ebWMWKj\n4n2ndvUDlrFxug0jlmXEsky5jbMHXxz44oBZW9qmEdhb6Nv+07rn62XfQ69mWq1RTkSCBtPgBLKz\n8JnGfHSUhVsdz2VuEpgZQgWWm80CXrIEqOUpBxWwvpfWGmU0Z1cFtx542tuEe9WWa/Jwu6nasalt\n73hTub6GuZEs7AfhpYORqC8E55I2kKzQdd0KjnyU1r5GpEtvsN7YUgUJXQcq2EdG9A4+hirAQWsB\nUiCiy99mYpP5hpr1i8raboNX5DUQpt0pDBk30az3bXSuDRXORrTj+jgDjny8+cF6gNj/t+8c5vgN\nQBV6z2366De16G+kdZb1IZgANshIlZ36VUSx2r0JwCrDsvRH7hdT23m0UV6woUvCUARDyo8YSsLS\n7LBvUnsgljxgO0CG8kwoYKxHz92bXblr83cAmv9vrc3VK2muUc9tdOf99v73Am3Y/a9O2mxph62v\n1/WAGfXvdvuNhNVDnkHj9pnq1anu73Ld+llbAOl5xRgDhrhK5SAWtxDgQ5S6BriVQReGmwG6MGhm\nkE1ObANc9cBZK4zzzlidsUm/4x8Y/p4x3gG4kTqODzhMjOM443a6x/PpVbZovc6WLbVu3VZWr3u6\nwb0XuqHQDE84D0dcjkecnx1xCQec1yPiOiCuDnERN5G4OOSovYFSjtRqc6rnZzsvnYqUohjNPo/R\nIay+8pPNS8gja1eliNXjYKzrPZ9Ya1EHClPFQUDVmwKoVk7VZ++17fFO+fwArVGA1uBFcyJmyYDB\nr2WC5wUzNCJDsQxpKX25zTETOGl21VoBsViEkNpUZcD6XCVyn4IP2lgmbJJfbl+mBR3dDYYbLV4E\nJT8FVH4KaTMZBHTkTcXkjbK/q5o29YHRRKU5yksoUV1C8NmHS6LuSfS9DBJU4G81Rqop6tEFbNEB\n2/Ml6oV3h1CHPK3wwwJ/WDH5Cw7uggOdcXQXHOjScSudU3yiOm99z8EZwGbbsKx3q+8r+qZU/QFn\nd8R5OuLMB1z4iHmeEO4nrPdJw4RRzPuaJFI5xRf0Td49QaZHq2ktVrsRLLGN8DgAPBrBEg5KS/JG\nO7zxfaEicNh2q9goArlNPprzMGUNeyPAZz8QowTQCJm6OfRoSz3wz53vW4tsC9J65dqc7fnNZeog\nlX4eXPreSf43rEKRA+CSEiEnVm0Epj0Bx7ZWxNM30lv3eiKSWnNZI0D5Qt3I64R5f3XYXdS/zWg2\nJQXqBVgJAKvBVvWdApD0N/Ib5fp5LcvWqCQ4pBdU0wGd8TMqG3u2yNnWAvIWnLdjo7cJE5C4tuY7\nKuAKqMEXpQsTpwht5TtO4Cu3aMBXC0b3LF36PQOSDNs+X+mnEmWxnYe6RqBureLGntPSkK4V24cd\nwc7S9QuFPyb6fsiKtzqPVskFludQQytv/Z+2AKwV/mulhoKtawtGq+QAyhi2be8X0LQ94Fb+YgvO\ntoCsZx3v0xV7ysjH77BZA6qyhZatnNb7jZ6Pmg2tP0BojZOfcfB1li3Zmc1OzRcc4owpzDiEWY7X\nBe4hwp8j/JnhHyLcmWvgpW1rJQvYFn3IiFpOSks9ZgBnAPfAeFpAp4DpdMYzOKyDwz1uBEThBnep\nLf5a9vNpU8/+iIs/4DKVp154LDWOyQ/XI8bE0EltlW6hVaR1mCoAyncp6qEOALJ7QNtuviPj927X\n3s5x75pAjWw2ihwuSixX2GjWPaIK0mV9ffWzmQM2bsI3O6+/vZ3f1kVfmkcEpQz0IxZM1E8GuNXg\n9KWlHgEnUBKkqehxFj9iGdLgRGrXCeEyYp1HBBDWwLKxLKg1IS33c+3eShGSK845kiBMWSAulIgA\nmgLctMIPK0a/5Dp5CXXeRiDK/klphy1rwLYf9FlnnvLEDOcR63mSdh0Rgi8LxUOq553a48T2Si+X\n2CnVm9LSc6lgln46MUZacEP3eEaiAxI90N2mHnGu6gGXatxo7fWN5uWwoEq1SKphytxqeo5XpHfy\nDA/xBouLmJmxrIR4GYB7AK9SfZ3auzSGbJ2xBRGtBk2F+l4utmOnHprPnATcIYBcAI0RdAgYU06y\ncVgweslRtheBq9UKv2nZJ8M08zNRNS1tUwM0hMWL/9wyCJXgQsliSHKsm2Ev+XgLantWWgvAtFwD\num2OvDbJ+NGlvnfgAwAWSpP3wEgBg58xDnX2F62tc3tfMNyheNZbBRiEmljUuNeT0WVTHf8sJFph\nhKvaNnpfzp/SlgSgKmsWFLyXIEBvWjLY4iQWp+MSBY9KZMpQxhyCIjikzZiQ8w5m0IB+Bfpr+l7J\noMv8ZraAsrGEFvAl8gXXtEqlshNAMEEeshIk9Yj2ayfa5xv3a+d9FjCb+rjRREcFrw0lsUpqba3Z\nu/2lfdH0S+Orln2gK0tWkFxg2tKehUZHc29ds4DEWlf2QUYLmvS4/a63xuU+NxCjd17/unoHBf61\nd7B3z+WXyvEbj48GxrW9VNu2ts+xvSOg7S0LrizAauW/AqrqesJDte+f6AE3/h4nr1BFdu/Ti7NA\nFZZ6jBe4BxYrVKoZfLVBFno02j0XCUD2mov83bBGDEsELqvIUvfAZZhxGe9xHiZckl+XPoUGz6gl\nGWk3it/0eSbJHzZjwuxLzrE2KXQvmbQ1NKxxyAYINTYEDaoWHWzkVbYh1nsyzJu4SPQUmr3vhpjT\nHmEQg4S3NEb13zcB03J7ZRes/aK3sxEA/rudufG5AVrLh7cAADoMwOECOiTHvqHWi9jpqp/Z/LvV\nAPW1MhEODhFBFz1GoU2pFjfIAOKVhPeaQ2hiyxO1g8ci7HbiTZwSmkp1U4SfFvhpTXXBOC44JMtN\ntuC4GXWO9CVpd2r3doe4WewZZHVBWDFgxogzHXHGCQ+pPccjZj5iXhnzTIgPHuEOwCed+rpTrTZI\nj3taCSuIan0B4GWq6Zi/QAhfcODLAI5AHAlxeoD3ASf3gBf+E7xLH+Id+gjv4EO8g49yfRZf4zbe\n4zbe4Tbc4zbeYwpzohaUCpBEDnKi2Y/OYfEjLn7CeTjgMhxw9gfcuVt8jJf4GC/xCV7gY7zER3gH\n38F7uQIARwc6E+Jrj/DhCPoOA98B8KGp30l9eG/qQ+qzwEA0lSE0HkdpDJFYSwwgzfVZqs9N+zyN\nvwmJS84YTwummzOm0wXj6YLpdO5uWjWnfyvs63Gv9PShxR6tIo8zW2nZVhcaRfFht9ZhxGU9YB4n\nzKvUcBkkxDuL5ZXhZfG/YKsY0DGpx708aO3GoBz9ni+WgioFWIf0Po4oSoMTgFvIegF5fzQCPkaM\nvIq2lR/SlmmEAzxknr6traJAe7JXeuJlrRO2SUL3XeT1nJZKG+AqS2Vkt2vFqKyiLuYUFXsWOquR\nt+Opaomk6thiscapQ3VgXzj/QQSEYHIISloAKmHNI2S+2STMjwEuKy1utKqd77K1hprPXFoDpNRv\nrec/WSLYxprVcKVP7XGv9PYNa+PJNSUZrZK7JpCrfa6O7BryvYR/by1kti8NEMt7ZjrP699Qtdeq\nmO8Q8164Fxyi/f+WGqfH7RjcQgTpqT1QZftLFBwdxYUBXK39zK6T7Tuw57W//WmKHQOtBc7OqcxK\ngEOdBsGX9x4ceHUl19FKErRgpRxtdcNG2DMKqvVy4ByFlMYouTTHAD+tGKaAYZhxcBcc3VnkI3/G\nkc4dm48FWKW9oRTnj1LcP3+Poz/jeHvGMZ5x5DOO8QK/xKayBAHRqIsrxE+st17sgS9K5yfF6jCt\noBgw8gU3IKyOBChRkvRSaz23tsdTVg4X216d4PlijnermzA7hbLp98OIdRmxLAOWZcRKjAhf5vZC\nkmdUQ8tbC2BL4bOuIra2TBHrZ+U4M0VojHDjCj+ucJOk//HDisktGN2M0S3puM6oVpSXrZ/iipY+\n6zsz9v83QAu/LvB/PU2IJ4/5NMGdTnCn2pKjtbdYlsXDWnJ0uSx1ZY/AaQnWsMZhSMnghmTJGYTj\nurhEoXMCtnRSEVLekvRDzaARfnKAGwNoinBjEKvUMGMaZoxJkz35C070gBOdcXQPsliQ6DJaPcbU\n6CTUGdVWAlfDJsBjwdAR5U7ZCjPgGQgsC+vqES4D3OsA+pgFEHzb1O+k+nFTPwEQLkCcgTAD8SJt\nTzpxB8AfpHUHwE/ASwLeRVXj9zjgd48IDwMoTIAHbk4zMH0Cf2Acpwue+1d4Fx/iS/gmvohv4kv4\nJr6Eb+H2co/jqwtOr2ccX11wfD3D38cigKsVTn3CzOTmE4GfA/E5gV8Q4nPC/fGED6d38NH4Eh+O\n0n7LfQE3uMOYTHcLJGobZkK88wgfj5i/zcDfA/ANU38dwEcRmGepywzMC7D2vHUZG1OJG4HTAJx8\naW+9JFF4DymQCICjaMExRdBtBL3LcO8E3Eyv8Gx8jWfjK9wOMgo0iKzdkEpqyDLuLHdeW6vCKMKC\nr5atsBHuB41vVS3qukmcobROiZanWnFePeI8ICwj4nkA3xNw78APTqyESUu4AbHte7dgy1q92s1S\nLVjt4q/WQmsxtNxx5YwjaeSHCBwYdFwxTBcchnvc+DvcOLGN2mC+etxqbSfMXUFxq7dG7nP7DvRt\nFrtZ2bAvedM+VFrPBSM8Qq31TMcRDpFkFBBxTnTcRr/LK3D2O+vbNHtgqwcKijBarhYoicrawiE4\nj0BDprwCSXfBBI4AM2efgw0j4dNw/Fs/Pdc5LkaMRsjgnBIkR9EzYexL9Lx0bMBVBgnUp5nafm3F\n9F6phX0yIKHeaSNp36bv2MvndTD+e6lY/worcNt+1XkG06piKXqZPwEyh1j6DDH1GVCNFasE6lLO\nrvjytvvpPjgtCHsDThogVWxoteim84ggQTP0GvZaDiXQk/6aBVX2je6VnuoZzTGDsmuBHLsKRCmA\n5pQol9WVYvUCqjT6W26xbXs07q5lg9CmIuEBWKYDFlXQTgAdA4bDguG0YDjOGI4LxumS5KcHnLIs\ntQVehniXj4844+iSrOXVVnTGdEi7Emu7wHPAEKXqsWMGxZgDf0iL4o7JLMcdhUxMbiPkgoTTX4VS\n7l3ESCsCXbC6WklWB+efqjW9D6IO6KtT6++LleyYP8804eImzMMBHgGzi1jdAIaXmAVhKG6e+n5t\n7qreGtqujbZvkL4bxBhBml91inBTxDjOGMclt9NQFJFFKdna8fbrp7Fo7ZXPD9D6NWniYUA8DMDx\nkCO4PIyi1cAQkyUomuhUsUrE2JbMQw3KR00+HzmjdtG4cC+MLbAVvBR9j0ha6gh/WOFMHYdZtC3+\ngoMTf6Kje6i1Kil7wm3FxL1LE78M+yPO1VRSVO6QLDIoC2qAzxNLtR0XHE340NsMsFRIUyBGYGBh\n0AODXkGsL9+GEFP/Xmq13s/A3QLcL8DdLG3FjdO2U+Kt1GyCuQX4AKwT8HAAPpmAb0/AQ7IgRkiy\n3RHg5wP4RkzXTAQeCY4CRsy4wQOe4zXewUe4mR8wvgoYvxUwfjOAvs3FEncHEcBfo5j9jcWCbhj0\nAnAv2VjYLggvXsO9YIwvF5xe3GM8zBBrofhv3eEW53hCXEas5wNmBavfSX32DQD/bxrrHy8APgbY\nmrpeYbtLAcVklUxYfAMsqe/WW+D8DDif5P6PECsWABoZw2nG+HzG+N4Fw5dmTF88F8sffYx38BFe\n4uMUcFbGoB73FqWekFanvvTJYqpCfaEu6GKugkZMFi0V8CtlAEt9wAlnPmKZJ6wPE8LDiPVeWr53\n5V3uAavWomWtrT3H57ZYgbmi+qIALLUsPqsrPWO45yvcixX++QL3YsV4M+PG3eHW3+HW3csx7jrr\nQgFadrtste89C3b7Tnx6JypAy2fOjydt3/LfWpIqoZZk8jD1HfZb4belPFpiUQ0Kar/bHj+h/WsH\nn3AypT099RL7rIFXCgyWpDRT5VlLI93TxO5RXlrn+J6mVgWHJmG9S4EdnA8ZZPlO21LENeRzS5/v\n9TOhiNXavxsLYQNetxS3raVDA+gE9pnWG1OUsLgMybKh+6wCrU6fdi0bKAL3QDnqcOSSg4TJQ2P9\nEWKyciLRLNvxWGiAPSGqpRNugepWndFamBiUjyzIsqUHonpV53X7vj5tKfcrv76FjOa6OZgMwfo5\nFiul+qm74jur4OoxqnYbUtsGz6pvuO9e0VyTV4d1HRADYVk9aD3AH5Lf9HDE/XCDo0/yV95VynEn\nRXDl55XXXHVdoXofbOFOuyZXLdtx1dqKuX52815a5SXAEAbDCgfO1PIJCw4bBWa9/7auELZXjub4\ngDkdzxhxxIgZZzpiHA64uBXDsMLHgGWcEIYRwTOCIwTnZL4u5n21qVT2/M7z+lhXGiGuM4cAmla4\n5EajrjOjL5arlvnR0vALI6RYuMXNJubapzD0lVJt+fwALfUy2zjvi4aDvascz0OrOZRxuK29AA29\nF7+72Dd1YuAoSBvHCDoyhsOM43jGcXrAcZL25B42/kPFk6f2L7Iuj7e4w5HPmHjGIS6iSYkzxrjC\nhQgXWbK0R9HuhYEQB5czqC9+wAMVu8QDnXAHoWVGiP/RgDUt3kCEZNyYccCZj5jDQUKG3nvwaxJr\n1UepKu3tOywc48sZmM9AUIlWUYy2dyjSq0WtbVa5GQg3wHwjgjI7AV0qvD6HgJ0XkOSA8Fj8hMt0\nwj3fpGR+p7yUzpgw0goikoj1jiGp7M04sQE97B5G6TtbWDTzgw8YDzOOi0eIhFscjbVxLtQ6DqAY\nJcfIKuAVgYEQgRgh6vQHCLD6EMC3Uv0Efc/bZyjRM9IORV5WIndADtuv9LVb6St6hzE9X3Bzc4fT\ndIeb4TVu6A7v4sNcFXT1/Nxa+s2AFcWQXraOudmiNFSNAPhDlaCxduq9wT2nYz7hgW/wwCfM8wHz\nZcJyOWC5TJgvB/DZCfC+h7QP1PcTtL6Tdpip8KyWaIKsMXZN0Dlvq1qn7JqkViylBx4hY/UWwC2n\nFqCbgPFmxnh7xnRzwXRzweFwzn4CFlQpxNRxpAKebskrBoi1ejDisAp1VnzU87eBne0WVDbiLZ2k\np/vv+XVZ8bNMn8c88PqCraVs6XX0+Vv4F9PCr3qvvFWq0M++sBSWEWEeEOYBcRklAlfWwlOhu/Sq\n3UOsoo3MI7drRwvMbcCflIA5W6+yb8Ha+BY0dU+A67Q9oLuFy2x6uPS2/I9r3iaZN+YLCyQOCMGX\n3GsZYHnwMgDLYCxZqAXt1sdlr//s3hxgIpINCMEBa4QbBsTRI4wrAhaMziNgQYDDmOdDGTcWAK0Y\n8ggP8BtqtJ7ZizyovdhatbYqhD0o9d0BJ/tmVMv+aa/FzT0zSJL8EhdwRwYEEIMopVBJFuEYGXAM\nVsujhtduldE6/nv+sD3FVk/msmvwiALEUCjLFABe09qYoqfCw7xrUfRNOG6E8akCTy3vov5ufzWs\n+Ruq+PAIVUCWer5ubZ/WetIHyToCtxZUay2163Y73nTsyP5ezxW7WgApgxyJhY2cUQQMEStFrC6K\ne89AktRY81tp7cnc+p6tDN9J5YCBE1UwwI8hUwZHt8JrpRKXQJYJUbotGDd9pOyMPdqw3ZvqHbWm\nsffK5wdofZLazeaErfCjiraWwtGjejxG/9DjNuCAWqysc/skVgLnVkHapwC6XXGczrj1d3jmX+OZ\nf41busNzvKrqM7zutrd8h5v4gBt+wCk+4EZ9iWYJSepmhl+SwI76ntkDdADCgYGDUJOiozzoIjmo\nP9bFCLw5QR4/wx3fSsUN7tcbrMuIsExYlwFxdrXpPy+iBDgPuCndjHaWOq9YybP3ovT/snQKUKLE\njRMwjdLnyWKICLmPMxAuHvPhgPtwg1fxObxZMO1G+WJ4hdubB9y8+wD4M8ZTAN4JdVCK1yhRguzt\nHVD8nBLQ4+eEcOuwHgfM44SLS2Z141iq2nVOOcnYU7KAJI2sT8mUMpVJ+0mBlEWA2hKKs1W6GXoG\nDDfA8QY4HYQ6+BzAFwB8EcDvAvBlAF9mDC9WHJ5fcHu6w4vhEzzHxwlcFZD1Eh81VlWpdvHXDcFS\nilQgL0TXAhm2V1M1QglXe4dbnJcTLvMR58sJl4u08ezBDyR0QG17Vqm9ZIW2C3Vu6/C0ShXCVrhr\n157OGrAJOHIC6MSgU4C7WeFuAtxNgD8sOA6JiuLPOAxNRCzjC6cabLVIC1AdNxtCr/TE8F72nKUS\nK2r7eJNBJ1Grk/0kRWnN9KLUIlkOQGhScVync/WAV89qoKUVYGuxJYk4PGAJA9Y4YokJZIURcVGQ\nJblYsPjr2vY9IfDamOnkT2sBFjzghihBaIYVbgggBVZDnQOqZ7XsgVQ7HrSPtL+ttaRXWoFfx16P\n6lb7UKaxkfp4DR5rUAtWopMFl+iYHRDbs2TpvtL2t92rM/AiSR9hoiUKryMJoOTEb4yENrrSiBUL\nFtr6bdXUQXu8BbA1yFL7Aqob3wNafXG6/II99xpQ23t/3y1o+9R/R0COBMmEElWTiu+hozoPZwuy\nW8Fbr6ttz6K1ieiKDLpI2U6eTSJvpLyEIlwDhYIudOkmVPwV4XsriPc87ervCoiyDhz7oGxrmbaC\n/p6fYHmH1j5m528b6KLlpxTF2oQVY75bvZZdv7Wv8v8Ri8+cW+DGBf54QTC00kwtzfkzgW3uQS6t\nzeuZIo2Sj3BDgMtBLiQiuVDQAaZiMea8VgWsnb7tr6XWylhmW+nv2gJ5rXx+gNacWp1kizluNzdR\nY24B1JuALNVqo7mendBalVY2lnujkeEPAf64YLhZ4G8XnKZ73FISHel1ioj3Cs8SyHqBTyrAJfVV\n8Y3hC47hjFO44BBmjOsKCmUNy/fQgE32Dss4YB5GXMYB8zjg7A94jeeV3ewVnm8COXzML/FReAcf\nre/go/AOPllf4vX8HHzvRdBdkrMjUl8cIZhII+PdHYC7Sfo1g8CeKaHtaEI3/vhAYh14DuAFCWXv\nC5D2FiLoHCARIgAAGwVJREFUeiCSx0wTHuiEgZ6D04RRzb5aUd49fIR33vsI7774GPxlYAgXTPcB\ndAfQ61LR+uUAIkwrWy/hwXh0uEwH3E3P8NH4Eh8NL/BNfBHfwXv4GC/wGre4xwkXmrD4EevoEQ8O\nfKJ0HcqYErcEzAMQnyWfhSMQXiYrVxunnFBz0m4BdwMcHPDcAy8d8MKLX9vvTvXLAL4I0HuAu43w\ntwHjYcXoZwNKiwYcGTgJIJfp5ZpFR2pLB5wxdV2Oe3lA7uIt7sMN7sMN7qK06/2IeDdUFeeOtaoN\nXtEmfW4VKSrkthrWdgPfC89uh6cFWQcUq7ZRcNBhFb74NGOcZoyThB+eqOaMi8VqSVZlAa4rPDhZ\nAhcM8JjyZmv1l8B2Y2VQV8dq9bH72ebEOpH9bLStnN6l3RbKCYadJo11Qt8CUIV117FkhX8rJCpF\nrQcKesJnV2zhAUscsYYByzpiTVEqWROLr04Sils3yB6oslrWXulRXrpjiSuKIA0sAMsXwcENwfhg\nBfG/MsLAnkBlP5d+bembPYGs9PJ2ZPWDNtTHRmxJ4yWyL4mQc+30px7b+apKtL1i1+UIGVQhQRwG\nwB4cI2J0WELK6bN4LMOQgevg1mwlrEd+nXB3T+DtKQPsGNa+1XYvCl8Nnvp23n4g9nK9rehXj4nv\nthTlSR3cJqdHsNFEi9MRJDKmS1ImlXcdIOtmzyerBdo92c7OL5XNslzGQisbWCi3SWEhAvkK8hHs\ngegIK/n0k5SYAHuKi33Le6swao9tW/7GKpZqUFaA3db6Zc/bs/i3gL83n3sE7a2Koe8z3QZC0jsC\niiVswCqv2yfLp2f4GBBGvw2UYvMYqp91eq0KtGy+WGcjqqa9Rd2ClMoKApgIgcR61Zs1rTWqJ8v0\n3qe1Xl1T/rXl8wO01DjSTjK7hrRasFZAtotyGwlGSw9kWbBlhS2be8hqUAYxnTqDsmttmC6HRVOo\nfitnHNKkEcS30oDLMOMwHHGnOaB4SdnVI3yMcOK5LQ7HycE7OI+Fhg3v9iH5CilZUaxXt3iFFxnq\nKfx7FZ7j1fICd8tznOcbzOejhMhWXqYnESxvUTZG/fyAROEy7XqUavnUtn+139sw2FO6ZuPjgncg\nAOKdVG+BcHJYDgPOwxHeCR9MrQEzRtzjBh/jJV74T/DSf4IX0yd4Aak3Lx9wDBcc40XacMEQQx4j\nsodIP1/8iMswCYgdRty5m3SVl9Arfoh38S18Ed/CF/Ex3sEdnuHsTriMByzHEeHWg19Q8QcCih/V\nOwNwfgE8PAPOvwt4iEIxrJKxRbmpYQAGL633wDEBq/dQ2vcAfAnAl1jaLwJ4yeBkdeEJYC9Lh/rk\nDemmAnwVeEGPe8KZZT3r8V5sp7qecFluxHJ1lnq+nMBKA1TfKo2+2OYzsZpRqxXLNBLUoMquI3ug\nympHG01pPp4gwMq0bgo5QuiQIoYOw4KBVkmin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"text": [
"<matplotlib.figure.Figure at 0x115fd9b50>"
]
}
],
"prompt_number": 891
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
gfabieno/SeisCL | docs/notebooks/Inversion/ComputingGradient.ipynb | 1 | 615 | {
"cells": [
{
"cell_type": "markdown",
"id": "filled-needle",
"metadata": {},
"source": [
"# Computing the Gradient\n",
"\n",
"Under Development ..."
]
}
],
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| gpl-3.0 |
tpin3694/tpin3694.github.io | machine-learning/encoding_ordinal_categorical_features.ipynb | 2 | 5753 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Encoding Ordinal Categorical Features \n",
"Slug: encoding_ordinal_categorical_features \n",
"Summary: How to encode ordinal categorical features for machine learning in Python. \n",
"Date: 2016-09-06 12:00 \n",
"Category: Machine Learning \n",
"Tags: Preprocessing Structured Data \n",
"Authors: Chris Albon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminaries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load library\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Feature Matrix"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>High</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Score\n",
"0 Low\n",
"1 Low\n",
"2 Medium\n",
"3 Medium\n",
"4 High"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create features\n",
"df = pd.DataFrame({'Score': ['Low', \n",
" 'Low', \n",
" 'Medium', \n",
" 'Medium', \n",
" 'High']})\n",
"\n",
"# View data frame\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Scale Map"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create mapper\n",
"scale_mapper = {'Low':1, \n",
" 'Medium':2,\n",
" 'High':3}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map Scale To Features"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Score</th>\n",
" <th>Scale</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Low</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Low</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Medium</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Medium</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>High</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Score Scale\n",
"0 Low 1\n",
"1 Low 1\n",
"2 Medium 2\n",
"3 Medium 2\n",
"4 High 3"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Map feature values to scale\n",
"df['Scale'] = df['Score'].replace(scale_mapper)\n",
"\n",
"# View data frame\n",
"df"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
sophie63/FlyLFM | Notebooks/.ipynb_checkpoints/986_FlyLFMpaper-checkpoint.ipynb | 1 | 2222126 | null | bsd-2-clause |
UChicagoPhysics/SampleExercises | exercises/classicalMechanics/Velocity of Moving Object Under Decelerating Force.ipynb | 1 | 67218 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Velocity of Moving Object Under Decelerating Force\n",
"\n",
"- **PROGRAM**: Velocity of moving object under decelerating force\n",
"- **CREATED**: 4/14/2018"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Import packages.\n",
"import numpy as np\n",
"import matplotlib.pylab as plt\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Set folder to save images in.\n",
"import os\n",
"#os.chdir('Folder/Address/In/Here')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Rename the function 'figure' from the 'plt' library (a.k.a. the 'matplotlib.pylab' library) to make it more convenient to use.\n",
"fig = plt.figure()\n",
"\n",
"#Set up axes.\n",
"ax = fig.add_subplot(1, 1, 1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x118359d30>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Define the parameters in the problem. This way, they are located in one place in the code and can be easily changed to test different values.\n",
"k = 2\n",
"b = 3\n",
"v_0 = 15\n",
"\n",
"C = v_0**2 / (v_0**2 + b**2)\n",
"\n",
"#Pretend to plot the function as a smooth curve by plotting a thousand points close together.\n",
"t_f = 10\n",
"t = np.linspace(0, t_f, 1000)\n",
"#Above creates 1000 points at evenly-spaced locations between 0 and some final time, t_f.\n",
"\n",
"#Define the function.\n",
"#Assume v > 0. (An 'opposite' graph of the same shape would appear if a minus sign was added. One can try this.)\n",
"v = ( abs(b) * C * np.exp( -b**2 * k * t ) ) / np.sqrt( 1 - C * np.exp(-2 * b**2 * k * t))\n",
"\n",
"#Plot the function.\n",
"ax.plot(t, v, 'b-', label = 'Velocity')\n",
"\n",
"#Label the plot.\n",
"fig.suptitle('Velocity of Object with Decelerating Force $F = -mk(v^3 + a^2v)$')\n",
"ax.set_xlabel('Time, t (seconds)')\n",
"ax.set_ylabel('Velocity, v ($\\\\frac{meters}{s}$)')\n",
"\n",
"ax.legend(loc = 'upper right', fancybox = False, shadow = False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Save figure by inserting a filename below.\n",
"#plt.savefig()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Notes\n",
"- Make sure that comments are as concise as possible, while being as helpful as possible.\n",
"- Add observations about important values.\n",
" - If $v_0 = 0$, $v(t) = 0$ for all $t$. If initial velocity is 0, the object never moves.\n",
" - Increasing $k$ or $a$ makes the force against the particle greater, and it approaches 0 faster.\n",
" - The particle's velocity never reaches 0, but approaches 0 as time approaches infinity."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
xpharry/Udacity-DLFoudation | tutorials/sentiment_network/.ipynb_checkpoints/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Project 4)-checkpoint.ipynb | 19 | 324875 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n",
"\n",
"by Andrew Trask\n",
"\n",
"- **Twitter**: @iamtrask\n",
"- **Blog**: http://iamtrask.github.io"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What You Should Already Know\n",
"\n",
"- neural networks, forward and back-propagation\n",
"- stochastic gradient descent\n",
"- mean squared error\n",
"- and train/test splits\n",
"\n",
"### Where to Get Help if You Need it\n",
"- Re-watch previous Udacity Lectures\n",
"- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (40% Off: **traskud17**)\n",
"- Shoot me a tweet @iamtrask\n",
"\n",
"\n",
"### Tutorial Outline:\n",
"\n",
"- Intro: The Importance of \"Framing a Problem\"\n",
"\n",
"\n",
"- Curate a Dataset\n",
"- Developing a \"Predictive Theory\"\n",
"- **PROJECT 1**: Quick Theory Validation\n",
"\n",
"\n",
"- Transforming Text to Numbers\n",
"- **PROJECT 2**: Creating the Input/Output Data\n",
"\n",
"\n",
"- Putting it all together in a Neural Network\n",
"- **PROJECT 3**: Building our Neural Network\n",
"\n",
"\n",
"- Understanding Neural Noise\n",
"- **PROJECT 4**: Making Learning Faster by Reducing Noise\n",
"\n",
"\n",
"- Analyzing Inefficiencies in our Network\n",
"- **PROJECT 5**: Making our Network Train and Run Faster\n",
"\n",
"\n",
"- Further Noise Reduction\n",
"- **PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary\n",
"\n",
"\n",
"- Analysis: What's going on in the weights?"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3"
}
},
"source": [
"# Lesson: Curate a Dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "eba2b193-0419-431e-8db9-60f34dd3fe83"
}
},
"outputs": [],
"source": [
"def pretty_print_review_and_label(i):\n",
" print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n",
"\n",
"g = open('reviews.txt','r') # What we know!\n",
"reviews = list(map(lambda x:x[:-1],g.readlines()))\n",
"g.close()\n",
"\n",
"g = open('labels.txt','r') # What we WANT to know!\n",
"labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n",
"g.close()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"25000"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(reviews)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "bb95574b-21a0-4213-ae50-34363cf4f87f"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn t '"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reviews[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'POSITIVE'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lesson: Develop a Predictive Theory"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "e67a709f-234f-4493-bae6-4fb192141ee0"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"labels.txt \t : \t reviews.txt\n",
"\n",
"NEGATIVE\t:\tthis movie is terrible but it has some good effects . ...\n",
"POSITIVE\t:\tadrian pasdar is excellent is this film . he makes a fascinating woman . ...\n",
"NEGATIVE\t:\tcomment this movie is impossible . is terrible very improbable bad interpretat...\n",
"POSITIVE\t:\texcellent episode movie ala pulp fiction . days suicides . it doesnt get more...\n",
"NEGATIVE\t:\tif you haven t seen this it s terrible . it is pure trash . i saw this about ...\n",
"POSITIVE\t:\tthis schiffer guy is a real genius the movie is of excellent quality and both e...\n"
]
}
],
"source": [
"print(\"labels.txt \\t : \\t reviews.txt\\n\")\n",
"pretty_print_review_and_label(2137)\n",
"pretty_print_review_and_label(12816)\n",
"pretty_print_review_and_label(6267)\n",
"pretty_print_review_and_label(21934)\n",
"pretty_print_review_and_label(5297)\n",
"pretty_print_review_and_label(4998)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project 1: Quick Theory Validation"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import Counter\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"positive_counts = Counter()\n",
"negative_counts = Counter()\n",
"total_counts = Counter()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(len(reviews)):\n",
" if(labels[i] == 'POSITIVE'):\n",
" for word in reviews[i].split(\" \"):\n",
" positive_counts[word] += 1\n",
" total_counts[word] += 1\n",
" else:\n",
" for word in reviews[i].split(\" \"):\n",
" negative_counts[word] += 1\n",
" total_counts[word] += 1"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('', 550468),\n",
" ('the', 173324),\n",
" ('.', 159654),\n",
" ('and', 89722),\n",
" ('a', 83688),\n",
" ('of', 76855),\n",
" ('to', 66746),\n",
" ('is', 57245),\n",
" ('in', 50215),\n",
" ('br', 49235),\n",
" ('it', 48025),\n",
" ('i', 40743),\n",
" ('that', 35630),\n",
" ('this', 35080),\n",
" ('s', 33815),\n",
" ('as', 26308),\n",
" ('with', 23247),\n",
" ('for', 22416),\n",
" ('was', 21917),\n",
" ('film', 20937),\n",
" ('but', 20822),\n",
" ('movie', 19074),\n",
" ('his', 17227),\n",
" ('on', 17008),\n",
" ('you', 16681),\n",
" ('he', 16282),\n",
" ('are', 14807),\n",
" ('not', 14272),\n",
" ('t', 13720),\n",
" ('one', 13655),\n",
" ('have', 12587),\n",
" ('be', 12416),\n",
" ('by', 11997),\n",
" ('all', 11942),\n",
" ('who', 11464),\n",
" ('an', 11294),\n",
" ('at', 11234),\n",
" ('from', 10767),\n",
" ('her', 10474),\n",
" ('they', 9895),\n",
" ('has', 9186),\n",
" ('so', 9154),\n",
" ('like', 9038),\n",
" ('about', 8313),\n",
" ('very', 8305),\n",
" ('out', 8134),\n",
" ('there', 8057),\n",
" ('she', 7779),\n",
" ('what', 7737),\n",
" ('or', 7732),\n",
" ('good', 7720),\n",
" ('more', 7521),\n",
" ('when', 7456),\n",
" ('some', 7441),\n",
" ('if', 7285),\n",
" ('just', 7152),\n",
" ('can', 7001),\n",
" ('story', 6780),\n",
" ('time', 6515),\n",
" ('my', 6488),\n",
" ('great', 6419),\n",
" ('well', 6405),\n",
" ('up', 6321),\n",
" ('which', 6267),\n",
" ('their', 6107),\n",
" ('see', 6026),\n",
" ('also', 5550),\n",
" ('we', 5531),\n",
" ('really', 5476),\n",
" ('would', 5400),\n",
" ('will', 5218),\n",
" ('me', 5167),\n",
" ('had', 5148),\n",
" ('only', 5137),\n",
" ('him', 5018),\n",
" ('even', 4964),\n",
" ('most', 4864),\n",
" ('other', 4858),\n",
" ('were', 4782),\n",
" ('first', 4755),\n",
" ('than', 4736),\n",
" ('much', 4685),\n",
" ('its', 4622),\n",
" ('no', 4574),\n",
" ('into', 4544),\n",
" ('people', 4479),\n",
" ('best', 4319),\n",
" ('love', 4301),\n",
" ('get', 4272),\n",
" ('how', 4213),\n",
" ('life', 4199),\n",
" ('been', 4189),\n",
" ('because', 4079),\n",
" ('way', 4036),\n",
" ('do', 3941),\n",
" ('made', 3823),\n",
" ('films', 3813),\n",
" ('them', 3805),\n",
" ('after', 3800),\n",
" ('many', 3766),\n",
" ('two', 3733),\n",
" ('too', 3659),\n",
" ('think', 3655),\n",
" ('movies', 3586),\n",
" ('characters', 3560),\n",
" ('character', 3514),\n",
" ('don', 3468),\n",
" ('man', 3460),\n",
" ('show', 3432),\n",
" ('watch', 3424),\n",
" ('seen', 3414),\n",
" ('then', 3358),\n",
" ('little', 3341),\n",
" ('still', 3340),\n",
" ('make', 3303),\n",
" ('could', 3237),\n",
" ('never', 3226),\n",
" ('being', 3217),\n",
" ('where', 3173),\n",
" ('does', 3069),\n",
" ('over', 3017),\n",
" ('any', 3002),\n",
" ('while', 2899),\n",
" ('know', 2833),\n",
" ('did', 2790),\n",
" ('years', 2758),\n",
" ('here', 2740),\n",
" ('ever', 2734),\n",
" ('end', 2696),\n",
" ('these', 2694),\n",
" ('such', 2590),\n",
" ('real', 2568),\n",
" ('scene', 2567),\n",
" ('back', 2547),\n",
" ('those', 2485),\n",
" ('though', 2475),\n",
" ('off', 2463),\n",
" ('new', 2458),\n",
" ('your', 2453),\n",
" ('go', 2440),\n",
" ('acting', 2437),\n",
" ('plot', 2432),\n",
" ('world', 2429),\n",
" ('scenes', 2427),\n",
" ('say', 2414),\n",
" ('through', 2409),\n",
" ('makes', 2390),\n",
" ('better', 2381),\n",
" ('now', 2368),\n",
" ('work', 2346),\n",
" ('young', 2343),\n",
" ('old', 2311),\n",
" ('ve', 2307),\n",
" ('find', 2272),\n",
" ('both', 2248),\n",
" ('before', 2177),\n",
" ('us', 2162),\n",
" ('again', 2158),\n",
" ('series', 2153),\n",
" ('quite', 2143),\n",
" ('something', 2135),\n",
" ('cast', 2133),\n",
" ('should', 2121),\n",
" ('part', 2098),\n",
" ('always', 2088),\n",
" ('lot', 2087),\n",
" ('another', 2075),\n",
" ('actors', 2047),\n",
" ('director', 2040),\n",
" ('family', 2032),\n",
" ('between', 2016),\n",
" ('own', 2016),\n",
" ('m', 1998),\n",
" ('may', 1997),\n",
" ('same', 1972),\n",
" ('role', 1967),\n",
" ('watching', 1966),\n",
" ('every', 1954),\n",
" ('funny', 1953),\n",
" ('doesn', 1935),\n",
" ('performance', 1928),\n",
" ('few', 1918),\n",
" ('bad', 1907),\n",
" ('look', 1900),\n",
" ('re', 1884),\n",
" ('why', 1855),\n",
" ('things', 1849),\n",
" ('times', 1832),\n",
" ('big', 1815),\n",
" ('however', 1795),\n",
" ('actually', 1790),\n",
" ('action', 1789),\n",
" ('going', 1783),\n",
" ('bit', 1757),\n",
" ('comedy', 1742),\n",
" ('down', 1740),\n",
" ('music', 1738),\n",
" ('must', 1728),\n",
" ('take', 1709),\n",
" ('saw', 1692),\n",
" ('long', 1690),\n",
" ('right', 1688),\n",
" ('fun', 1686),\n",
" ('fact', 1684),\n",
" ('excellent', 1683),\n",
" ('around', 1674),\n",
" ('didn', 1672),\n",
" ('without', 1671),\n",
" ('thing', 1662),\n",
" ('thought', 1639),\n",
" ('got', 1635),\n",
" ('each', 1630),\n",
" ('day', 1614),\n",
" ('feel', 1597),\n",
" ('seems', 1596),\n",
" ('come', 1594),\n",
" ('done', 1586),\n",
" ('beautiful', 1580),\n",
" ('especially', 1572),\n",
" ('played', 1571),\n",
" ('almost', 1566),\n",
" ('want', 1562),\n",
" ('yet', 1556),\n",
" ('give', 1553),\n",
" ('pretty', 1549),\n",
" ('last', 1543),\n",
" ('since', 1519),\n",
" ('different', 1504),\n",
" ('although', 1501),\n",
" ('gets', 1490),\n",
" ('true', 1487),\n",
" ('interesting', 1481),\n",
" ('job', 1470),\n",
" ('enough', 1455),\n",
" ('our', 1454),\n",
" ('shows', 1447),\n",
" ('horror', 1441),\n",
" ('woman', 1439),\n",
" ('tv', 1400),\n",
" ('probably', 1398),\n",
" ('father', 1395),\n",
" ('original', 1393),\n",
" ('girl', 1390),\n",
" ('point', 1379),\n",
" ('plays', 1378),\n",
" ('wonderful', 1372),\n",
" ('far', 1358),\n",
" ('course', 1358),\n",
" ('john', 1350),\n",
" ('rather', 1340),\n",
" ('isn', 1328),\n",
" ('ll', 1326),\n",
" ('later', 1324),\n",
" ('dvd', 1324),\n",
" ('war', 1310),\n",
" ('whole', 1310),\n",
" ('d', 1307),\n",
" ('away', 1306),\n",
" ('found', 1306),\n",
" ('screen', 1305),\n",
" ('nothing', 1300),\n",
" ('year', 1297),\n",
" ('once', 1296),\n",
" ('hard', 1294),\n",
" ('together', 1280),\n",
" ('am', 1277),\n",
" ('set', 1277),\n",
" ('having', 1266),\n",
" ('making', 1265),\n",
" ('place', 1263),\n",
" ('comes', 1260),\n",
" ('might', 1260),\n",
" ('sure', 1253),\n",
" ('american', 1248),\n",
" ('play', 1245),\n",
" ('kind', 1244),\n",
" ('takes', 1242),\n",
" ('perfect', 1242),\n",
" ('performances', 1237),\n",
" ('himself', 1230),\n",
" ('worth', 1221),\n",
" ('everyone', 1221),\n",
" ('anyone', 1214),\n",
" ('actor', 1203),\n",
" ('three', 1201),\n",
" ('wife', 1196),\n",
" ('classic', 1192),\n",
" ('goes', 1186),\n",
" ('ending', 1178),\n",
" ('version', 1168),\n",
" ('star', 1149),\n",
" ('enjoy', 1146),\n",
" ('book', 1142),\n",
" ('nice', 1132),\n",
" ('everything', 1128),\n",
" ('during', 1124),\n",
" ('put', 1118),\n",
" ('seeing', 1111),\n",
" ('least', 1102),\n",
" ('house', 1100),\n",
" ('high', 1095),\n",
" ('watched', 1094),\n",
" ('men', 1087),\n",
" ('loved', 1087),\n",
" ('night', 1082),\n",
" ('anything', 1075),\n",
" ('guy', 1071),\n",
" ('believe', 1071),\n",
" ('top', 1063),\n",
" ('amazing', 1058),\n",
" ('hollywood', 1056),\n",
" ('looking', 1053),\n",
" ('main', 1044),\n",
" ('definitely', 1043),\n",
" ('gives', 1031),\n",
" ('home', 1029),\n",
" ('seem', 1028),\n",
" ('episode', 1023),\n",
" ('sense', 1020),\n",
" ('audience', 1020),\n",
" ('truly', 1017),\n",
" ('special', 1011),\n",
" ('fan', 1009),\n",
" ('second', 1009),\n",
" ('short', 1009),\n",
" ('mind', 1005),\n",
" ('human', 1001),\n",
" ('recommend', 999),\n",
" ('full', 996),\n",
" ('black', 995),\n",
" ('help', 991),\n",
" ('along', 989),\n",
" ('trying', 987),\n",
" ('small', 986),\n",
" ('death', 985),\n",
" ('friends', 981),\n",
" ('remember', 974),\n",
" ('often', 970),\n",
" ('said', 966),\n",
" ('favorite', 962),\n",
" ('heart', 959),\n",
" ('early', 957),\n",
" ('left', 956),\n",
" ('until', 955),\n",
" ('let', 954),\n",
" ('script', 954),\n",
" ('maybe', 937),\n",
" ('today', 936),\n",
" ('live', 934),\n",
" ('less', 934),\n",
" ('moments', 933),\n",
" ('others', 929),\n",
" ('brilliant', 926),\n",
" ('shot', 925),\n",
" ('liked', 923),\n",
" ('become', 916),\n",
" ('won', 915),\n",
" ('used', 910),\n",
" ('style', 907),\n",
" ('mother', 895),\n",
" ('lives', 894),\n",
" ('came', 893),\n",
" ('stars', 890),\n",
" ('cinema', 889),\n",
" ('looks', 885),\n",
" ('perhaps', 884),\n",
" ('read', 882),\n",
" ('enjoyed', 879),\n",
" ('boy', 875),\n",
" ('drama', 873),\n",
" ('highly', 871),\n",
" ('given', 870),\n",
" ('playing', 867),\n",
" ('use', 864),\n",
" ('next', 859),\n",
" ('women', 858),\n",
" ('fine', 857),\n",
" ('effects', 856),\n",
" ('kids', 854),\n",
" ('entertaining', 853),\n",
" ('need', 852),\n",
" ('line', 850),\n",
" ('works', 848),\n",
" ('someone', 847),\n",
" ('mr', 836),\n",
" ('simply', 835),\n",
" ('children', 833),\n",
" ('picture', 833),\n",
" ('face', 831),\n",
" ('friend', 831),\n",
" ('keep', 831),\n",
" ('dark', 830),\n",
" ('overall', 828),\n",
" ('certainly', 828),\n",
" ('minutes', 827),\n",
" ('wasn', 824),\n",
" ('history', 822),\n",
" ('finally', 820),\n",
" ('couple', 816),\n",
" ('against', 815),\n",
" ('son', 809),\n",
" ('understand', 808),\n",
" ('lost', 807),\n",
" ('michael', 805),\n",
" ('else', 801),\n",
" ('throughout', 798),\n",
" ('fans', 797),\n",
" ('city', 792),\n",
" ('reason', 789),\n",
" ('written', 787),\n",
" ('production', 787),\n",
" ('several', 784),\n",
" ('school', 783),\n",
" ('rest', 781),\n",
" ('based', 781),\n",
" ('try', 780),\n",
" ('dead', 776),\n",
" ('hope', 775),\n",
" ('strong', 768),\n",
" ('white', 765),\n",
" ('tell', 759),\n",
" ('itself', 758),\n",
" ('half', 753),\n",
" ('person', 749),\n",
" ('sometimes', 746),\n",
" ('past', 744),\n",
" ('start', 744),\n",
" ('genre', 743),\n",
" ('final', 739),\n",
" ('beginning', 739),\n",
" ('town', 738),\n",
" ('art', 734),\n",
" ('game', 732),\n",
" ('humor', 732),\n",
" ('yes', 731),\n",
" ('idea', 731),\n",
" ('late', 730),\n",
" ('becomes', 729),\n",
" ('despite', 729),\n",
" ('able', 726),\n",
" ('case', 726),\n",
" ('money', 723),\n",
" ('child', 721),\n",
" ('completely', 721),\n",
" ('side', 719),\n",
" ('camera', 716),\n",
" ('getting', 714),\n",
" ('instead', 712),\n",
" ('soon', 702),\n",
" ('under', 700),\n",
" ('viewer', 699),\n",
" ('age', 697),\n",
" ('days', 696),\n",
" ('stories', 696),\n",
" ('felt', 694),\n",
" ('simple', 694),\n",
" ('roles', 693),\n",
" ('video', 688),\n",
" ('name', 683),\n",
" ('either', 683),\n",
" ('doing', 677),\n",
" ('turns', 674),\n",
" ('wants', 671),\n",
" ('close', 671),\n",
" ('title', 669),\n",
" ('wrong', 668),\n",
" ('went', 666),\n",
" ('james', 665),\n",
" ('evil', 659),\n",
" ('budget', 657),\n",
" ('episodes', 657),\n",
" ('relationship', 655),\n",
" ('piece', 653),\n",
" ('fantastic', 653),\n",
" ('david', 651),\n",
" ('turn', 648),\n",
" ('murder', 646),\n",
" ('parts', 645),\n",
" ('brother', 644),\n",
" ('head', 643),\n",
" ('absolutely', 643),\n",
" ('experience', 642),\n",
" ('eyes', 641),\n",
" ('sex', 638),\n",
" ('direction', 637),\n",
" ('called', 637),\n",
" ('directed', 636),\n",
" ('lines', 634),\n",
" ('behind', 633),\n",
" ('sort', 632),\n",
" ('actress', 631),\n",
" ('lead', 630),\n",
" ('oscar', 628),\n",
" ('example', 627),\n",
" ('including', 627),\n",
" ('known', 625),\n",
" ('musical', 625),\n",
" ('chance', 621),\n",
" ('score', 620),\n",
" ('feeling', 619),\n",
" ('already', 619),\n",
" ('hit', 619),\n",
" ('voice', 615),\n",
" ('moment', 612),\n",
" ('living', 612),\n",
" ('low', 610),\n",
" ('supporting', 610),\n",
" ('ago', 609),\n",
" ('themselves', 608),\n",
" ('hilarious', 605),\n",
" ('reality', 605),\n",
" ('jack', 604),\n",
" ('told', 603),\n",
" ('hand', 601),\n",
" ('moving', 600),\n",
" ('dialogue', 600),\n",
" ('quality', 600),\n",
" ('song', 599),\n",
" ('happy', 599),\n",
" ('paul', 598),\n",
" ('matter', 598),\n",
" ('light', 594),\n",
" ('future', 593),\n",
" ('entire', 592),\n",
" ('finds', 591),\n",
" ('gave', 589),\n",
" ('laugh', 587),\n",
" ('released', 586),\n",
" ('expect', 584),\n",
" ('fight', 581),\n",
" ('particularly', 580),\n",
" ('cinematography', 579),\n",
" ('police', 579),\n",
" ('whose', 578),\n",
" ('type', 578),\n",
" ('sound', 578),\n",
" ('enjoyable', 573),\n",
" ('view', 573),\n",
" ('husband', 572),\n",
" ('romantic', 572),\n",
" ('number', 572),\n",
" ('daughter', 572),\n",
" ('documentary', 571),\n",
" ('self', 570),\n",
" ('modern', 569),\n",
" ('robert', 569),\n",
" ('took', 569),\n",
" ('superb', 569),\n",
" ('mean', 566),\n",
" ('shown', 563),\n",
" ('coming', 561),\n",
" ('important', 560),\n",
" ('king', 559),\n",
" ('leave', 559),\n",
" ('change', 558),\n",
" ('wanted', 555),\n",
" ('somewhat', 555),\n",
" ('tells', 554),\n",
" ('run', 552),\n",
" ('events', 552),\n",
" ('country', 552),\n",
" ('career', 552),\n",
" ('heard', 550),\n",
" ('season', 550),\n",
" ('girls', 549),\n",
" ('greatest', 549),\n",
" ('etc', 547),\n",
" ('care', 546),\n",
" ('starts', 545),\n",
" ('english', 542),\n",
" ('killer', 541),\n",
" ('animation', 540),\n",
" ('guys', 540),\n",
" ('totally', 540),\n",
" ('tale', 540),\n",
" ('usual', 539),\n",
" ('opinion', 535),\n",
" ('miss', 535),\n",
" ('violence', 531),\n",
" ('easy', 531),\n",
" ('songs', 530),\n",
" ('british', 528),\n",
" ('says', 526),\n",
" ('realistic', 525),\n",
" ('writing', 524),\n",
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" ('writer', 522),\n",
" ('comic', 521),\n",
" ('thriller', 519),\n",
" ('television', 517),\n",
" ('power', 516),\n",
" ('ones', 515),\n",
" ('kid', 514),\n",
" ('novel', 513),\n",
" ('york', 513),\n",
" ('problem', 512),\n",
" ('alone', 512),\n",
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" ('taken', 497),\n",
" ('sad', 497),\n",
" ('ways', 496),\n",
" ('richard', 495),\n",
" ('knows', 494),\n",
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" ('surprised', 491),\n",
" ('similar', 491),\n",
" ('taking', 491),\n",
" ('car', 491),\n",
" ('george', 490),\n",
" ('perfectly', 490),\n",
" ('across', 489),\n",
" ('sequence', 489),\n",
" ('eye', 489),\n",
" ('team', 489),\n",
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" ('room', 488),\n",
" ('due', 488),\n",
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" ('cannot', 487),\n",
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" ('beauty', 486),\n",
" ('famous', 485),\n",
" ('tries', 484),\n",
" ('myself', 484),\n",
" ('happened', 484),\n",
" ('herself', 484),\n",
" ('class', 483),\n",
" ('four', 482),\n",
" ('cool', 481),\n",
" ('release', 479),\n",
" ('anyway', 479),\n",
" ('theme', 479),\n",
" ('opening', 478),\n",
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" ('interest', 472),\n",
" ('happen', 471),\n",
" ('crime', 470),\n",
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" ('sets', 467),\n",
" ('group', 466),\n",
" ('stop', 466),\n",
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" ('message', 463),\n",
" ('sister', 463),\n",
" ('working', 463),\n",
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" ('nature', 461),\n",
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" ('thinking', 459),\n",
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" ('disney', 457),\n",
" ('society', 456),\n",
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" ('blood', 454),\n",
" ('upon', 453),\n",
" ('viewers', 453),\n",
" ('parents', 453),\n",
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" ('usually', 452),\n",
" ('tom', 452),\n",
" ('peter', 452),\n",
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" ('certain', 448),\n",
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" ('whether', 447),\n",
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" ('middle', 443),\n",
" ('god', 443),\n",
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" ('period', 440),\n",
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" ('earth', 437),\n",
" ('feature', 437),\n",
" ('stand', 436),\n",
" ('words', 435),\n",
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" ('obviously', 433),\n",
" ('elements', 433),\n",
" ('romance', 431),\n",
" ('jane', 430),\n",
" ('yourself', 427),\n",
" ('showing', 427),\n",
" ('fantasy', 426),\n",
" ('brings', 426),\n",
" ('america', 423),\n",
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" ('huge', 422),\n",
" ('unfortunately', 422),\n",
" ('indeed', 421),\n",
" ('running', 421),\n",
" ('talent', 420),\n",
" ('stage', 419),\n",
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" ('sweet', 417),\n",
" ('leads', 417),\n",
" ('japanese', 417),\n",
" ('poor', 416),\n",
" ('deal', 416),\n",
" ('personal', 413),\n",
" ('incredible', 413),\n",
" ('fast', 412),\n",
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" ('deep', 410),\n",
" ('hours', 409),\n",
" ('nearly', 408),\n",
" ('dream', 408),\n",
" ('giving', 408),\n",
" ('turned', 407),\n",
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" ('near', 406),\n",
" ('obvious', 406),\n",
" ('cut', 405),\n",
" ('surprise', 405),\n",
" ('body', 404),\n",
" ('era', 404),\n",
" ('female', 403),\n",
" ('hour', 403),\n",
" ('five', 403),\n",
" ('note', 399),\n",
" ('learn', 398),\n",
" ('truth', 398),\n",
" ('match', 397),\n",
" ('feels', 397),\n",
" ('except', 397),\n",
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" ('complete', 394),\n",
" ('clear', 394),\n",
" ('older', 393),\n",
" ('street', 393),\n",
" ('lots', 393),\n",
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" ('stewart', 391),\n",
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" ('fall', 390),\n",
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" ('present', 386),\n",
" ('situation', 386),\n",
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" ('boys', 364),\n",
" ('dog', 362),\n",
" ('recently', 362),\n",
" ('sexual', 362),\n",
" ('please', 361),\n",
" ('wouldn', 361),\n",
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" ('lack', 360),\n",
" ('forget', 360),\n",
" ('setting', 360),\n",
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" ('married', 359),\n",
" ('social', 357),\n",
" ('adventure', 356),\n",
" ('interested', 356),\n",
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" ('sees', 355),\n",
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" ('move', 354),\n",
" ('call', 354),\n",
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" ('dr', 353),\n",
" ('theater', 353),\n",
" ('animated', 352),\n",
" ('western', 351),\n",
" ('space', 350),\n",
" ('baby', 350),\n",
" ('leading', 348),\n",
" ('disappointed', 348),\n",
" ('portrayed', 346),\n",
" ('aren', 346),\n",
" ('screenplay', 345),\n",
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" ('hate', 344),\n",
" ('towards', 344),\n",
" ('noir', 343),\n",
" ('outstanding', 342),\n",
" ('decent', 342),\n",
" ('kelly', 342),\n",
" ('directors', 341),\n",
" ('journey', 341),\n",
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" ('effective', 340),\n",
" ('looked', 340),\n",
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" ('cold', 339),\n",
" ('storyline', 339),\n",
" ('fi', 339),\n",
" ('sci', 339),\n",
" ('mary', 339),\n",
" ('rich', 338),\n",
" ('charming', 338),\n",
" ('harry', 337),\n",
" ('popular', 337),\n",
" ('manages', 337),\n",
" ('rare', 337),\n",
" ('spirit', 336),\n",
" ('open', 335),\n",
" ('appreciate', 335),\n",
" ('basically', 334),\n",
" ('moves', 334),\n",
" ('acted', 334),\n",
" ('deserves', 333),\n",
" ('subtle', 333),\n",
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" ('inside', 333),\n",
" ('pace', 333),\n",
" ('century', 333),\n",
" ('boring', 333),\n",
" ('familiar', 332),\n",
" ('background', 332),\n",
" ('ben', 331),\n",
" ('creepy', 330),\n",
" ('supposed', 330),\n",
" ('secret', 329),\n",
" ('jim', 328),\n",
" ('die', 328),\n",
" ('question', 327),\n",
" ('effect', 327),\n",
" ('natural', 327),\n",
" ('rate', 326),\n",
" ('language', 326),\n",
" ('impressive', 326),\n",
" ('intelligent', 325),\n",
" ('saying', 325),\n",
" ('material', 324),\n",
" ('realize', 324),\n",
" ('telling', 324),\n",
" ('scott', 324),\n",
" ('singing', 323),\n",
" ('dancing', 322),\n",
" ('adult', 321),\n",
" ('imagine', 321),\n",
" ('visual', 321),\n",
" ('kept', 320),\n",
" ('office', 320),\n",
" ('uses', 319),\n",
" ('pure', 318),\n",
" ('wait', 318),\n",
" ('stunning', 318),\n",
" ('copy', 317),\n",
" ('review', 317),\n",
" ('previous', 317),\n",
" ('seriously', 317),\n",
" ('somehow', 316),\n",
" ('created', 316),\n",
" ('magic', 316),\n",
" ('create', 316),\n",
" ('hot', 316),\n",
" ('reading', 316),\n",
" ('crazy', 315),\n",
" ('air', 315),\n",
" ('frank', 315),\n",
" ('stay', 315),\n",
" ('escape', 315),\n",
" ('attempt', 315),\n",
" ('hands', 314),\n",
" ('filled', 313),\n",
" ('surprisingly', 312),\n",
" ('expected', 312),\n",
" ('average', 312),\n",
" ('complex', 311),\n",
" ('studio', 310),\n",
" ('successful', 310),\n",
" ('quickly', 310),\n",
" ('male', 309),\n",
" ('plus', 309),\n",
" ('co', 307),\n",
" ('minute', 306),\n",
" ('images', 306),\n",
" ('casting', 306),\n",
" ('exciting', 306),\n",
" ('following', 306),\n",
" ('members', 305),\n",
" ('german', 305),\n",
" ('e', 305),\n",
" ('reasons', 305),\n",
" ('follows', 305),\n",
" ('themes', 305),\n",
" ('touch', 304),\n",
" ('genius', 304),\n",
" ('free', 304),\n",
" ('edge', 304),\n",
" ('cute', 304),\n",
" ('outside', 303),\n",
" ('ok', 302),\n",
" ('admit', 302),\n",
" ('younger', 302),\n",
" ('reviews', 302),\n",
" ('odd', 301),\n",
" ('fighting', 301),\n",
" ('master', 301),\n",
" ('break', 300),\n",
" ('thanks', 300),\n",
" ('recent', 300),\n",
" ('comment', 300),\n",
" ('apart', 299),\n",
" ('lovely', 298),\n",
" ('begin', 298),\n",
" ('emotions', 298),\n",
" ('doctor', 297),\n",
" ('italian', 297),\n",
" ('party', 297),\n",
" ('la', 296),\n",
" ('missed', 296),\n",
" ...]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"positive_counts.most_common()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pos_neg_ratios = Counter()\n",
"\n",
"for term,cnt in list(total_counts.most_common()):\n",
" if(cnt > 100):\n",
" pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n",
" pos_neg_ratios[term] = pos_neg_ratio\n",
"\n",
"for word,ratio in pos_neg_ratios.most_common():\n",
" if(ratio > 1):\n",
" pos_neg_ratios[word] = np.log(ratio)\n",
" else:\n",
" pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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" ('academy', 0.60872983874736208),\n",
" ('fashioned', 0.60798937221963845),\n",
" ('melodrama', 0.60749173598145145),\n",
" ('peoples', 0.60613580357031549),\n",
" ('charismatic', 0.60613580357031549),\n",
" ('rough', 0.60613580357031549),\n",
" ('dealing', 0.60517840761398811),\n",
" ('fine', 0.60496962268013299),\n",
" ('tap', 0.60391604683200273),\n",
" ('trio', 0.60157998703445481),\n",
" ('russell', 0.60120968523425966),\n",
" ('figures', 0.60077386042893011),\n",
" ('ward', 0.60005675749393339),\n",
" ('shine', 0.59911823091166894),\n",
" ('brady', 0.59911823091166894),\n",
" ('job', 0.59845562125168661),\n",
" ('satisfied', 0.59652034487087369),\n",
" ('river', 0.59637962862495086),\n",
" ('brown', 0.595773016534769),\n",
" ('believable', 0.59566072133302495),\n",
" ('bound', 0.59470710774669278),\n",
" ('always', 0.59470710774669278),\n",
" ('hall', 0.5933967777928858),\n",
" ('cook', 0.5916777203950857),\n",
" ('claire', 0.59136448625000293),\n",
" ('broadway', 0.59033768669372433),\n",
" ('anna', 0.58778666490211906),\n",
" ('peace', 0.58628403501758408),\n",
" ('visually', 0.58539431926349916),\n",
" ('falk', 0.58525821854876026),\n",
" ('morality', 0.58525821854876026),\n",
" ('growing', 0.58466653756587539),\n",
" ('experiences', 0.58314628534561685),\n",
" ('stood', 0.58314628534561685),\n",
" ('touch', 0.58122926435596001),\n",
" ('lives', 0.5810976767513224),\n",
" ('kubrick', 0.58066919713325493),\n",
" ('timing', 0.58047401805583243),\n",
" ('struggles', 0.57981849525294216),\n",
" ('expressions', 0.57981849525294216),\n",
" ('authentic', 0.57848427223980559),\n",
" ('helen', 0.57763429343810091),\n",
" ('pre', 0.57700753064729182),\n",
" ('quirky', 0.5753641449035618),\n",
" ('young', 0.57531672344534313),\n",
" ('inner', 0.57454143815209846),\n",
" ('mexico', 0.57443087372056334),\n",
" ('clint', 0.57380042292737909),\n",
" ('sisters', 0.57286101468544337),\n",
" ('realism', 0.57226528899949558),\n",
" ('personalities', 0.5720692490067093),\n",
" ('french', 0.5720692490067093),\n",
" ('surprises', 0.57113222999698177),\n",
" ('adventures', 0.57113222999698177),\n",
" ('overcome', 0.5697681593994407),\n",
" ('timothy', 0.56953322459276867),\n",
" ('tales', 0.56909453188996639),\n",
" ('war', 0.56843317302781682),\n",
" ('civil', 0.5679840376059393),\n",
" ('countries', 0.56737779327091187),\n",
" ('streep', 0.56710645966458029),\n",
" ('tradition', 0.56685345523565323),\n",
" ('oliver', 0.56673325570428668),\n",
" ('australia', 0.56580775818334383),\n",
" ('understanding', 0.56531380905006046),\n",
" ('players', 0.56509525370004821),\n",
" ('knowing', 0.56489284503626647),\n",
" ('rogers', 0.56421349718405212),\n",
" ('suspenseful', 0.56368911332305849),\n",
" ('variety', 0.56368911332305849),\n",
" ('true', 0.56281525180810066),\n",
" ('jr', 0.56220982311246936),\n",
" ('psychological', 0.56108745854687891),\n",
" ('branagh', 0.55961578793542266),\n",
" ('wealth', 0.55961578793542266),\n",
" ('performing', 0.55961578793542266),\n",
" ('odds', 0.55961578793542266),\n",
" ('sent', 0.55961578793542266),\n",
" ('reminiscent', 0.55961578793542266),\n",
" ('grand', 0.55961578793542266),\n",
" ('overwhelming', 0.55961578793542266),\n",
" ('brothers', 0.55891181043362848),\n",
" ('howard', 0.55811089675600245),\n",
" ('david', 0.55693122256475369),\n",
" ('generation', 0.55628799784274796),\n",
" ('grow', 0.55612538299565417),\n",
" ('survival', 0.55594605904646033),\n",
" ('mainstream', 0.55574731115750231),\n",
" ('dick', 0.55431073570572953),\n",
" ('charm', 0.55288175575407861),\n",
" ('kirk', 0.55278982286502287),\n",
" ('twists', 0.55244729845681018),\n",
" ('gangster', 0.55206858230003986),\n",
" ('jeff', 0.55179306225421365),\n",
" ('family', 0.55116244510065526),\n",
" ('tend', 0.55053307336110335),\n",
" ('thanks', 0.55049088015842218),\n",
" ('world', 0.54744234723432639),\n",
" ('sutherland', 0.54743536937855164),\n",
" ('life', 0.54695514434959924),\n",
" ('disc', 0.54654370636806993),\n",
" ('bug', 0.54654370636806993),\n",
" ('tribute', 0.5455111817538808),\n",
" ('europe', 0.54522705048332309),\n",
" ('sacrifice', 0.54430155296238014),\n",
" ('color', 0.54405127139431109),\n",
" ('superior', 0.54333490233128523),\n",
" ('york', 0.54318235866536513),\n",
" ('pulls', 0.54266622962164945),\n",
" ('hearts', 0.54232429082536171),\n",
" ('jackson', 0.54232429082536171),\n",
" ('enjoy', 0.54124285135906114),\n",
" ('redemption', 0.54056759296472823),\n",
" ('madness', 0.540384426007535),\n",
" ('hamilton', 0.5389965007326869),\n",
" ('stands', 0.5389965007326869),\n",
" ('trial', 0.5389965007326869),\n",
" ('greek', 0.5389965007326869),\n",
" ('each', 0.5388212312554177),\n",
" ('faithful', 0.53773307668591508),\n",
" ('received', 0.5372768098531604),\n",
" ('jealous', 0.53714293208336406),\n",
" ('documentaries', 0.53714293208336406),\n",
" ('different', 0.53709860682460819),\n",
" ('describes', 0.53680111016925136),\n",
" ('shorts', 0.53596159703753288),\n",
" ('brilliance', 0.53551823635636209),\n",
" ('mountains', 0.53492317534505118),\n",
" ('share', 0.53408248593025787),\n",
" ('dealt', 0.53408248593025787),\n",
" ('providing', 0.53329847961804933),\n",
" ('explore', 0.53329847961804933),\n",
" ('series', 0.5325809226575603),\n",
" ('fellow', 0.5323318289869543),\n",
" ('loves', 0.53062825106217038),\n",
" ('olivier', 0.53062825106217038),\n",
" ('revolution', 0.53062825106217038),\n",
" ('roman', 0.53062825106217038),\n",
" ('century', 0.53002783074992665),\n",
" ('musical', 0.52966871156747064),\n",
" ('heroic', 0.52925932545482868),\n",
" ('ironically', 0.52806743020049673),\n",
" ('approach', 0.52806743020049673),\n",
" ('temple', 0.52806743020049673),\n",
" ('moves', 0.5279372642387119),\n",
" ('gift', 0.52702030968597136),\n",
" ('julie', 0.52609309589677911),\n",
" ('tells', 0.52415107836314001),\n",
" ('radio', 0.52394671172868779),\n",
" ('uncle', 0.52354439617376536),\n",
" ('union', 0.52324814376454787),\n",
" ('deep', 0.52309571635780505),\n",
" ('reminds', 0.52157841554225237),\n",
" ('famous', 0.52118841080153722),\n",
" ('jazz', 0.52053443789295151),\n",
" ('dennis', 0.51987545928590861),\n",
" ('epic', 0.51919387343650736),\n",
" ('adult', 0.519167695083386),\n",
" ('shows', 0.51915322220375304),\n",
" ('performed', 0.5191244265806858),\n",
" ('demons', 0.5191244265806858),\n",
" ('eric', 0.51879379341516751),\n",
" ('discovered', 0.51879379341516751),\n",
" ('youth', 0.5185626062681431),\n",
" ('human', 0.51851411224987087),\n",
" ('tarzan', 0.51813827061227724),\n",
" ('ourselves', 0.51794309153485463),\n",
" ('wwii', 0.51758240622887042),\n",
" ('passion', 0.5162164724008671),\n",
" ('desire', 0.51607497965213445),\n",
" ('pays', 0.51581316527702981),\n",
" ('fox', 0.51557622652458857),\n",
" ('dirty', 0.51557622652458857),\n",
" ('symbolism', 0.51546600332249293),\n",
" ('sympathetic', 0.51546600332249293),\n",
" ('attitude', 0.51530993621331933),\n",
" ('appearances', 0.51466440007315639),\n",
" ('jeremy', 0.51466440007315639),\n",
" ('fun', 0.51439068993048687),\n",
" ('south', 0.51420972175023116),\n",
" ('arrives', 0.51409894911095988),\n",
" ('present', 0.51341965894303732),\n",
" ('com', 0.51326167856387173),\n",
" ('smile', 0.51265880484765169),\n",
" ('fits', 0.51082562376599072),\n",
" ('provided', 0.51082562376599072),\n",
" ('carter', 0.51082562376599072),\n",
" ('ring', 0.51082562376599072),\n",
" ('aging', 0.51082562376599072),\n",
" ('countryside', 0.51082562376599072),\n",
" ('alan', 0.51082562376599072),\n",
" ('visit', 0.51082562376599072),\n",
" ('begins', 0.51015650363396647),\n",
" ('success', 0.50900578704900468),\n",
" ('japan', 0.50900578704900468),\n",
" ('accurate', 0.50895471583017893),\n",
" ('proud', 0.50800474742434931),\n",
" ('daily', 0.5075946031845443),\n",
" ('atmospheric', 0.50724780241810674),\n",
" ('karloff', 0.50724780241810674),\n",
" ('recently', 0.50714914903668207),\n",
" ('fu', 0.50704490092608467),\n",
" ('horrors', 0.50656122497953315),\n",
" ('finding', 0.50637127341661037),\n",
" ('lust', 0.5059356384717989),\n",
" ('hitchcock', 0.50574947073413001),\n",
" ('among', 0.50334004951332734),\n",
" ('viewing', 0.50302139827440906),\n",
" ('shining', 0.50262885656181222),\n",
" ('investigation', 0.50262885656181222),\n",
" ('duo', 0.5020919437972361),\n",
" ('cameron', 0.5020919437972361),\n",
" ('finds', 0.50128303100539795),\n",
" ('contemporary', 0.50077528791248915),\n",
" ('genuine', 0.50046283673044401),\n",
" ('frightening', 0.49995595152908684),\n",
" ('plays', 0.49975983848890226),\n",
" ('age', 0.49941323171424595),\n",
" ('position', 0.49899116611898781),\n",
" ('continues', 0.49863035067217237),\n",
" ('roles', 0.49839716550752178),\n",
" ('james', 0.49837216269470402),\n",
" ('individuals', 0.49824684155913052),\n",
" ('brought', 0.49783842823917956),\n",
" ('hilarious', 0.49714551986191058),\n",
" ('brutal', 0.49681488669639234),\n",
" ('appropriate', 0.49643688631389105),\n",
" ('dance', 0.49581998314812048),\n",
" ('league', 0.49578774640145024),\n",
" ('helping', 0.49578774640145024),\n",
" ('answers', 0.49578774640145024),\n",
" ('stunts', 0.49561620510246196),\n",
" ('traveling', 0.49532143723002542),\n",
" ('thoroughly', 0.49414593456733524),\n",
" ('depicted', 0.49317068852726992),\n",
" ('honor', 0.49247648509779424),\n",
" ('combination', 0.49247648509779424),\n",
" ('differences', 0.49247648509779424),\n",
" ('fully', 0.49213349075383811),\n",
" ('tracy', 0.49159426183810306),\n",
" ('battles', 0.49140753790888908),\n",
" ('possibility', 0.49112055268665822),\n",
" ('romance', 0.4901589869574316),\n",
" ('initially', 0.49002249613622745),\n",
" ('happy', 0.4898997500608791),\n",
" ('crime', 0.48977221456815834),\n",
" ('singing', 0.4893852925281213),\n",
" ('especially', 0.48901267837860624),\n",
" ('shakespeare', 0.48754793889664511),\n",
" ('hugh', 0.48729512635579658),\n",
" ('detail', 0.48609484250827351),\n",
" ('guide', 0.48550781578170082),\n",
" ('companion', 0.48550781578170082),\n",
" ('julia', 0.48550781578170082),\n",
" ('san', 0.48550781578170082),\n",
" ('desperation', 0.48550781578170082),\n",
" ('strongly', 0.48460242866688824),\n",
" ('necessary', 0.48302334245403883),\n",
" ('humanity', 0.48265474679929443),\n",
" ('drama', 0.48221998493060503),\n",
" ('warming', 0.48183808689273838),\n",
" ('intrigue', 0.48183808689273838),\n",
" ('nonetheless', 0.48183808689273838),\n",
" ('cuba', 0.48183808689273838),\n",
" ('planned', 0.47957308026188628),\n",
" ('pictures', 0.47929937011921681),\n",
" ('broadcast', 0.47849024312305422),\n",
" ('nine', 0.47803580094299974),\n",
" ('settings', 0.47743860773325364),\n",
" ('history', 0.47732966933780852),\n",
" ('ordinary', 0.47725880012690741),\n",
" ('trade', 0.47692407209030935),\n",
" ('primary', 0.47608267532211779),\n",
" ('official', 0.47608267532211779),\n",
" ('episode', 0.47529620261150429),\n",
" ('role', 0.47520268270188676),\n",
" ('spirit', 0.47477690799839323),\n",
" ('grey', 0.47409361449726067),\n",
" ('ways', 0.47323464982718205),\n",
" ('cup', 0.47260441094579297),\n",
" ('piano', 0.47260441094579297),\n",
" ('familiar', 0.47241617565111949),\n",
" ('sinister', 0.47198579044972683),\n",
" ('reveal', 0.47171449364936496),\n",
" ('max', 0.47150852042515579),\n",
" ('dated', 0.47121648567094482),\n",
" ('discovery', 0.47000362924573563),\n",
" ('vicious', 0.47000362924573563),\n",
" ('losing', 0.47000362924573563),\n",
" ('genuinely', 0.46871413841586385),\n",
" ('hatred', 0.46734051182625186),\n",
" ('mistaken', 0.46702300110759781),\n",
" ('dream', 0.46608972992459924),\n",
" ('challenge', 0.46608972992459924),\n",
" ('crisis', 0.46575733836428446),\n",
" ('photographed', 0.46488852857896512),\n",
" ('machines', 0.46430560813109778),\n",
" ('critics', 0.46430560813109778),\n",
" ('bird', 0.46430560813109778),\n",
" ('born', 0.46411383518967209),\n",
" ('detective', 0.4636633473511525),\n",
" ('higher', 0.46328467899699055),\n",
" ('remains', 0.46262352194811296),\n",
" ('inevitable', 0.46262352194811296),\n",
" ('soviet', 0.4618180446592961),\n",
" ('ryan', 0.46134556650262099),\n",
" ('african', 0.46112595521371813),\n",
" ('smaller', 0.46081520319132935),\n",
" ('techniques', 0.46052488529119184),\n",
" ('information', 0.46034171833399862),\n",
" ('deserved', 0.45999798712841444),\n",
" ('cynical', 0.45953232937844013),\n",
" ('lynch', 0.45953232937844013),\n",
" ('francisco', 0.45953232937844013),\n",
" ('tour', 0.45953232937844013),\n",
" ('spielberg', 0.45953232937844013),\n",
" ('struggle', 0.45911782160048453),\n",
" ('language', 0.45902121257712653),\n",
" ('visual', 0.45823514408822852),\n",
" ('warner', 0.45724137763188427),\n",
" ('social', 0.45720078250735313),\n",
" ('reality', 0.45719346885019546),\n",
" ('hidden', 0.45675840249571492),\n",
" ('breaking', 0.45601738727099561),\n",
" ('sometimes', 0.45563021171182794),\n",
" ('modern', 0.45500247579345005),\n",
" ('surfing', 0.45425527227759638),\n",
" ('popular', 0.45410691533051023),\n",
" ('surprised', 0.4534409399850382),\n",
" ('follows', 0.45245361754408348),\n",
" ('keeps', 0.45234869400701483),\n",
" ('john', 0.4520909494482197),\n",
" ('defeat', 0.45198512374305722),\n",
" ('mixed', 0.45198512374305722),\n",
" ('justice', 0.45142724367280018),\n",
" ('treasure', 0.45083371313801535),\n",
" ('presents', 0.44973793178615257),\n",
" ('years', 0.44919197032104968),\n",
" ('chief', 0.44895022004790319),\n",
" ('shadows', 0.44802472252696035),\n",
" ('closely', 0.44701411102103689),\n",
" ('segments', 0.44701411102103689),\n",
" ('lose', 0.44658335503763702),\n",
" ('caine', 0.44628710262841953),\n",
" ('caught', 0.44610275383999071),\n",
" ('hamlet', 0.44558510189758965),\n",
" ('chinese', 0.44507424620321018),\n",
" ('welcome', 0.44438052435783792),\n",
" ('birth', 0.44368632092836219),\n",
" ('represents', 0.44320543609101143),\n",
" ('puts', 0.44279106572085081),\n",
" ('fame', 0.44183275227903923),\n",
" ('closer', 0.44183275227903923),\n",
" ('visuals', 0.44183275227903923),\n",
" ('web', 0.44183275227903923),\n",
" ('criminal', 0.4412745608048752),\n",
" ('minor', 0.4409224199448939),\n",
" ('jon', 0.44086703515908027),\n",
" ('liked', 0.44074991514020723),\n",
" ('restaurant', 0.44031183943833246),\n",
" ('flaws', 0.43983275161237217),\n",
" ('de', 0.43983275161237217),\n",
" ('searching', 0.4393666597838457),\n",
" ('rap', 0.43891304217570443),\n",
" ('light', 0.43884433018199892),\n",
" ('elizabeth', 0.43872232986464677),\n",
" ('marry', 0.43861731542506488),\n",
" ('oz', 0.43825493093115531),\n",
" ('controversial', 0.43825493093115531),\n",
" ('learned', 0.43825493093115531),\n",
" ('slowly', 0.43785660389939979),\n",
" ('bridge', 0.43721380642274466),\n",
" ('thrilling', 0.43721380642274466),\n",
" ('wayne', 0.43721380642274466),\n",
" ('comedic', 0.43721380642274466),\n",
" ('married', 0.43658501682196887),\n",
" ('nazi', 0.4361020775700542),\n",
" ('murder', 0.4353180712578455),\n",
" ('physical', 0.4353180712578455),\n",
" ('johnny', 0.43483971678806865),\n",
" ('michelle', 0.43445264498141672),\n",
" ('wallace', 0.43403848055222038),\n",
" ('silent', 0.43395706390247063),\n",
" ('comedies', 0.43395706390247063),\n",
" ('played', 0.43387244114515305),\n",
" ('international', 0.43363598507486073),\n",
" ('vision', 0.43286408229627887),\n",
" ('intelligent', 0.43196704885367099),\n",
" ('shop', 0.43078291609245434),\n",
" ('also', 0.43036720209769169),\n",
" ('levels', 0.4302451371066513),\n",
" ('miss', 0.43006426712153217),\n",
" ('ocean', 0.4295626596872249),\n",
" ...]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# words most frequently seen in a review with a \"POSITIVE\" label\n",
"pos_neg_ratios.most_common()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[('boll', -4.0778152602708904),\n",
" ('uwe', -3.9218753018711578),\n",
" ('seagal', -3.3202501058581921),\n",
" ('unwatchable', -3.0269848170580955),\n",
" ('stinker', -2.9876839403711624),\n",
" ('mst', -2.7753833211707968),\n",
" ('incoherent', -2.7641396677532537),\n",
" ('unfunny', -2.5545257844967644),\n",
" ('waste', -2.4907515123361046),\n",
" ('blah', -2.4475792789485005),\n",
" ('horrid', -2.3715779644809971),\n",
" ('pointless', -2.3451073877136341),\n",
" ('atrocious', -2.3187369339642556),\n",
" ('redeeming', -2.2667790015910296),\n",
" ('prom', -2.2601040980178784),\n",
" ('drivel', -2.2476029585766928),\n",
" ('lousy', -2.2118080125207054),\n",
" ('worst', -2.1930856334332267),\n",
" ('laughable', -2.172468615469592),\n",
" ('awful', -2.1385076866397488),\n",
" ('poorly', -2.1326133844207011),\n",
" ('wasting', -2.1178155545614512),\n",
" ('remotely', -2.111046881095167),\n",
" ('existent', -2.0024805005437076),\n",
" ('boredom', -1.9241486572738005),\n",
" ('miserably', -1.9216610938019989),\n",
" ('sucks', -1.9166645809588516),\n",
" ('uninspired', -1.9131499212248517),\n",
" ('lame', -1.9117232884159072),\n",
" ('insult', -1.9085323769376259)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# words most frequently seen in a review with a \"NEGATIVE\" label\n",
"list(reversed(pos_neg_ratios.most_common()))[0:30]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transforming Text into Numbers"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih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\nAH0AAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"\n",
"review = \"This was a horrible, terrible movie.\"\n",
"\n",
"Image(filename='sentiment_network.png')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4nb799ttmyZIlLeslHQN9WpN91nx5boSslh0hMlL33XffbX19Jk2aJKcq/U1f\nYPGq2nSRC3rWvnXL0GNFQBH4IwJ/okAoAnkQgBDgqMuyWz74VjDQlCUM0oSF5w2WpbJbt25tSVrQ\ngUixfBg4koron5bsJC0/Kh11Zn0rJ84JA7t8du3aZT71qU/ZkPRRdVXtHP3Hrt5p+tC3NtI3Vdbf\nNzxVn76NgBKXvt3/hbUe6wfTFwgDMIHPCCJWlGDZgRThu8GO1bx9E98jSXAyGdgZOCA+cUI9SKdD\n4xfljwIB2rFjh10K3UniFYdpEdfwX9qzZ08RRXWtDCUvXYNeK64ZAkpcatah3WwOBIHNGAm4NnTo\nUHPjjTcadmaGcEBi2pGGsO4QDawrlIGDJstit2zZYubMmZOJWDBwMLCLRSWqPrHQhK+V+Zt2olsR\nAgEaN25cEUV5VQYrpHbu3OmVTlmUEfKS9n8hS12aRxGoKwLq41LXnvWkXVgwnnjiCWsFWL16tZ3e\nOeaYY+w3RCQsEJP333/f7mRMEDk+Z5xxhjn//PMbSfMO9BAXBg7XIpG3zIZyKQ+ERBVl4cFZmQG+\nqrt5t4KP/sGH6sknn2yVpFLn+b+gz5NYDCvVMFVWEegAAh/vQB1aRR9GAHLghmzngY1FZtu2bZGo\n4OjLdFCcBULeWuPSRBb+0UkGDIgLgyEreJjiylpWXD1JrmEhKbJuptGwTqj4jQD/F0pe/O4j1c5f\nBNTi4m/fqGYxCBRhqaCMV155xVx00UVdefMtw8rDTt5IVcP8t+pyiCaEtqenp1WSSp6HvGB1Kcri\nVkkQVGlFICUC6uOSEjBN7gcCYjXJ6isgxIf9fDjOWk5WNKgz6yqirHVWOV9dp1RkulLuxyr3kequ\nCHQKASUunUJa6ykcAR76spIpTeG85SLylks5DBydHDyKWkWUpt2a1k8E5D7s5P3nJxKqlSKQDAEl\nLslw0lSeIoCPihCRJCoyPcNAIYOF5JE33zRlSd6032VMEaXVoWrpGdTDfVa1NsTpK21T8hKHkl5T\nBP6IgBIXvRMqjQBTCHySPPCFMLSadhBCQ7qyBD11iig9uliohg8fnj5jhXIIeekEea4QLKqqItAL\nAV1V1AsSPVEGAgzYr732mtm/f7+NxUIdsuyZY6LgskxajlkZc/rppzctWbYXI/7wwBdLSsRl67+S\ndOUQpEZWLWXZlTmqfvdc0auI3LI5PvLII8369evDpyv/m5VofUG4l/G3gryIFTBPu/m/+9nPfmZ2\n795t90z64IMPmv7vqE8IIf+D/N8NHjy40JVuefTXvIpAFAK6qigKFT1XCAI8fInhQvyW9957zz4g\nR4wYYYYMGWJXiFCJLAUmLYMTH3nIvvHGGzbfxIkTzahRo5piuUQpKBYV9xoPbgaCLIMAOkFk5E3Y\nLTfLcZR+WcqJy0Mds2bNstswxKWr2rW6rpZq1Q/cs9y7We9b+b8jijIBCfm/g9QeccQRtsrw/x0n\nCVGwb98+w4aW/L/yPzd69Gi763orK2Ur/fW8IlAmAkpcykS3D5bNA5cH39y5c23reWhedtllmR7A\nFAB5ePXVV82iRYvsw5RB+eqrr45cvhx+2PPgR/IQjzzEx1b+0Z8idHHLa3UMBiwbhgBmHfhald3N\n82whwcDLbuR5+rObbUhbN32Z1FJI2U8//bS599577f9MUrLfSifunRdeeMEQ0JD/wW9+85tm8uTJ\nfQb7VrjoeU8QCOIiqCgChSDw1FNP9QSDSk9AVno4Llpef/11WzZ1BDsg9wSDc68qgge9Pc93MC3T\n63qWE9RD3Xkkb/4kdVMHn1NOOaXnX//1X5NkqUwa+lz6VNrZCUx9AKhdO4MXhZ5gmsd+yvi/A/dg\n+w4C6NjvqP87H3BSHfoOAgR0UlEEciHAgy0IzW8fnO0esrkq+igzdUCOeFjz0A7Lww8/HElqwunS\n/qbeLA/tsjChXPcj7bntttt6+NRFuL/o6yhx218UUY2qp9vnou4h2sv/AaSuDMISbjP1BVYXWx//\nYyqKQLcQUOLSLeRrUi8PMN7EsIB0WsTCwyAthIIHPMcMdmUI5aYZIEmbJn2cztTtDtSt0sobeKvr\nVTtP//LG307AOQk+7crx9bpLXqLu/U7pjR4QSUiM/N91qm6tRxEAAfVx8WTKrmpqMP+OHwv+LI8/\n/nhmH5a87WYu/qtf/ar5wx/+YIIBzpx99tm2yDJ9Siib9idxnMzjkEs9wWDcgCjNKieWXK9Zs6bh\n/NwopIIH7A6+ZMmS1G0BexHwCCwT8rOy37TpBz/4gXV472b/cv/PmDHD4EDfzf//ynakKp4LASUu\nueDrm5l5aI0ZM8Y2fu3atZGOsp1EhgH+1ltvNc8995xdTSOEIg9paKc/GAQWkNjBNG39UqbUnWew\nvf322w0bLlZ9l2gcTiHI27dvF1gyfbskEOdluUcyFdbFTDNnzjQvvfSSeeSRR8yxxx7bRU3+WDWr\nvdi1e/PmzZXFtOsgqgKpEVDikhqyvp1BSMuwYcO8GRTRieWaPNRXrVrV9BBNSx7S9i7lR1lCGCiR\ndm/55BcpckClfogPFpt2Okj9Pn6zl9QVV1xhLr300sLUCxPEqP4rrLICC+L+fvPNNw0vC7TBl36F\nXE6bNq3p/67AZmtRikAvBJS49IJET7RCwEfSEtZVyAvWEMgMOjOIl/mGHRXvpRVhcokKuks8jXA7\nivgNFkhVrS5gNXbsWGvZKjOOCP0X+GpYrIokj7bAgv64pKVMLLKqq+QlK3KaLwsCSlyyoNZH8/j+\n8JRuET0xXyMMTLydlvnAhxxBkiBILmlxB0V0KZOoUL4r6ER9VTXj49syZ86cQq0tLj5Rx/QhpFfE\nB2sM0zEPPfSQ2bp1a6n3sLQ56zcxX4i35LueWdun+fxBQImLP33htSY8lG655ZbS336LAuG8884z\nwRJtM3v2bFukSyaKqiNcDoMeDpN/9md/ZgYMGGAvd3vgY9BDJyFxYZ19/e2L3i7x7IY1RqxOVSGf\nBApkW4Enn3zS11tL9aoBAkpcatCJZTdB3ty7uYohbRujdC6DvITf0AmVDmnpNmFx8YLEMeUyffp0\n97S3x5AF8MPyUeYUX1oAwn1ddh9TH3XMmzfPTJo0Ka26XUmPzmeddZZdcVQVnbsClFaaCwElLrng\n6xuZcZAM4jY0rBdVaXXYdA2ZQfI6NUKARNy3cJcYdWJ6SnRo940ukBfM+Gy/4LNUaeBzrTF5VoC1\n6g9WhrHXUNWsF2Il4jvv/1orbPR830ZAiUvf7v+2rZeHkDi7ts3gWQJIFxvMibXBJRdJVXUHKPJE\n+alEkSLy4Vfjw8P7vvvuM//0T/9k/u3f/s0rK4bbB5AWltn7tGLN1S/umP53Y+5E3SNx+cPXKK/K\nq8Kq7hge7g/97RkCGodPEYhDgAiZnQgnHqdDnmtE+QyIQ1OETzcCaVTZAUlrisCaJDpoqzKJ5kp5\n3RR0ow1EOQaLbuvTCgui47J1RBK8W5Xhy3kwlw/3QFoBi25Eo06rZ6v0tDkY6nJHjQ52rLblUNaD\nDz7YqjpvzwckPJP+QVC/Rj7a3mkJYkD1iO7XXnttp6tvW1/nEWmrkv8JpEOj/pnirvnfsmYN6xI6\nnv1c3EGAgdEdvHnIyiAjg3wzEvG/yBMn1NcuTVz+rNei6mVA9I28oCfh4+tCWsL9Fb6/wtfDv2XQ\nB5cqC/canzwiz1O+qyiif9RYwTn5QFRc6TZxQRdXh2effdZVr+vHfxIAp1JTBPr162fk88QTT6Ru\nJcHcCOtddZk1a5ZdTuq2Y+fOnXbahKkjBNO+fNIsmxaTvlt2+JjyKJu6mA7phKAXn/CUBTFdcPbE\n52XTpk2dUCW2Dpkeeuedd2xgtTTYxxbs0UWmCuXekvuAe4EPfRSWu+66ywQDvtdLn8M6R/2+4YYb\nzMKFCzPf82zzQMA9hP9hlc4igD9cQLxspawo9Uq6Tp0qqEAci4671ummBjdaS0bfTpe6vPVJO//q\nr/6q53vf+561fIi1pQgrSNoyqBtsy5Qkdcgmfa4lqkydosoGO6w/ed/Ko8quyjnXGiP3pW8WsTxY\nYu3MMtXMVMWRRx5pn19811HyPJ87hQfTc6KnT1N1anHxikb6o8wLL7xgAvN95d/6QJQ3W94eeKvn\njVeW2Mrbb1bUKZcy0ojUjeNuGYJOvOHziRNC6BMbhCXuWF/K0idKB6wsrJhhiTZOw1WN7BvVtrTn\n6CfuIT4cf//73zfuSrW05fmWnu0aVq5cmVqtYJrC7N+/3+a7/vrrm/JjiRFL8re//W0b9fjOO+9s\nnOMaaYKptqZ87o9XX33VkFfK4XvgwIFt82G5njhxYlM+freyaJ9yyimNtOiESH5XnwkTJth0Ug7f\nrm6SNtzOPXv2yKXGt5vmoosuapznIKrdcfqPGjWqkf/+++9vHHf9oJPMzbVGBA23zlbBzdmkQmCS\najA8mHb4ulsGx2GBqbsskXpa1eXmJY9bdlweN12YhcZdoz6czdw2Us/ll19u5xNdfeTYLQ8syH/h\nhRc2YRTWgfKk3eHv8Fyq1BP+xucgy5tSuBxffvM2i6NxWHjjzWIByZpP6o/yP5FrWb7zlIfVJRg0\nreUjCxZJ9UVHqYv7q8y6kurkW7pgh/MePnUR+pxnEN9pxH3u8cxzxX2+8yx1n4fu844ywuMH5dxx\nxx0tn4/kZ9zZvXu3W6U9Dj+33bo45rkbFrcd8pxO8nx2/UsoWwS93HqlTLnOt1iqSOded3Fzy5Bj\n2hclLr6++Lr8f0SiNC7gHDeO23ABSb7DNwnpXeBdMMOdGb6h6VQ3r9ThfofzpNUPSKJuRoEq7lqW\nGydcntsW99j9p0nyjyH6tvqm7LoNLAzOUW2C1KR9sKadImqFM+WkrTtcFm2SaYbwtaS/KYMpG/qd\n77zlufVSthAWpg6Kws6toy7HOCjXDR/ahKN/UgkPzuF87Z6jrZ6LlJM0L+MIL8Ei4bHHrcM9pnxX\nws9vriV5Pofra1VmeMVPGDt+IxAOV89Wx2H9yesSvXB9XO+GlE5c4kiLgBe+ScI3l7Bm9yYIA+jO\niUq5Ud+U4UoW/Vw9wh3d6lrWG8ctL6o97jlhw0n+MVwMwsetrBPhdGX+dttQVD1x8+1pBos0aZPo\nDt5RhKrsvFHlowdv/JA8LFQcZ2kvbWL5NZYV7lG+s5QTpWOdz4FVN6WM/zvuoTS+VO7zn+dzWNzr\n4EUaGSP4dtvAdXlZDY8RPFvlGnWELSoM2CJumdQnpCb84hvW131+h8cKdJMPRMWVOOLiEgnGTldc\nbFxdXD04L4QmjFd4LKZsV5dwfW7dnTwu9b/EbTAd5HZc3DUAcIHmhnLTA57cqAKW22HhutyO5poM\n8G6Z4Txx11zd3DaF9XavuXnS3DhuvrCOYTLk/qOhC+nlQ3uSCm9HDPLdFPdBIQ+JvPrw8Gz1AMXq\nkcTKwMCelWTE6U+ZSep3yyB9XmuNW174mPuAQQcCw33EmzMERHAMf5OW+wbSw4e0TDeWqWNY5yr/\nhtiBcTeljP877gHuhaTCS6k8t8IvqJQRftaHxwKeF5Kfb3kuhp/pLmkR3dz2u4O0+xx2n+vkCz+H\npSy+4/K5Ooafz2Fd3TJbWVVI42IneobTR+FFW0WfsC7gJNf4Dud3devUcanOuT/60Y+Cdv5RAvJh\npk6dKj+ts2QAbON32PEnWAHSuMbyzfnz5zd+s3HeEUcc0fjNgRsWO1zXTTfd1FjWRdq33nqLL5NH\nP1tAwj84UMmyPrIsW7bMDB482OamHQ888IAJbhz7O7gpTPCPYI/Df8LtwvEquFEbydjcrAgJbnQb\nbbaIsoooI8oBLUu5YLxly5bIrLIMt91y5YBgtHV8jaygzclgoLfl4lybRMQJV/ROkidtmvPPP99u\n87B9+3br6Mj/IPvQtJL+/fvbZavoBk5Lly61OzuXqWMrXap4PiB45tBDD/VG9aL+73jGpXk2vfba\naw0MjjrqqMZx1EHwEthrLMC52X0u/vKXv7RZ2T5BhGfB6aefLj8b31/72tcaxzyLBYPTTjutcf6a\na64xOMCKAzDP4WDAbnwaCUs6YOwICFGj9Jdffrlx/MMf/rBxfOaZZ9rjXbt2Nc61wuvKK69spPnV\nr37VOOYgPNYyPnRbPl6mAu4NSNj1sLgeywzs/ONy0yHcVAzUkBZEBn46zCVA9mLw5/nnn5dDu69O\n48dHBz/5yU/Cp0we/XoVFnMi6Y0jbQ3fOFJ08OYrh43vk08+uXFc1wNirkQ9ZNK2N/wPGM7Pih8G\n3VYrheKuhcvK8psBnrqpp9UGfhCrwNLSUscs9SbJI7q1wiZJGZomHgHfXhiK+r9j64LVq1fHN965\nykalIocccogcRn5/4QtfiDzvEp5f//rXNg2rCkVkUJff8g35doUxCZk2bZpZvHhx49LNN99sjyEx\nSGCpMZCe8Coee7GEP9QnY+JPf/pTWwMkC7KFQFDk5TiwQNlz/GGcZLVSnLQjmW55ceWUea1Ui4sA\nSwMuvvjipuVdgCdWBmmg3CTyG9YcTuNaYiTdu+++K4f2m2VtSSSvfknqII3b0XLjuEvdOBbSQvpW\nN07SdlFGHsEqEcY9T3l587J0Vt588pbVLr8Qh3C6JIHmwnmy/kYHCSDnliHnlDy4qOhxWQgU9X+H\nNTGNyOCbJk/ZaSEBEEtepqPkscces2MclphOiGv5FCvL+vXrG1WzR1udpVTikhc4iEz4JuYtQKV8\nBNpZJ5Jo4MZbCBO1dr95EIhwD0B8eSh0gsDwhhiOaFrWFJG0MfwdjvcicVbkfDi9/lYEBIGq/t+J\n/u40iJxr9S3WlPB1mR7i/NFHH20vyzc/3OkVe/GjP+5LJqdkBoBjyMt3vvOdxpQQrg583Jc8LDGd\neEZhgZZ6eT5SZ+CThppWXIuSa0XCUuNOa0Ud00bfpVTi4t6AgYNPW8DCgyWMPyycC1tY3JuL9Pv2\n7Qtni/ydV7/IQiNO1vHGiWhmqaf45+ShEHVPFF0xb4hMyYi/S9lTRK30F7+XFStWWP+XtG+urcrV\n84pAUgQ6+X8nOh122GFy2NL6LAmYvgmPB/x2p3UGDRpkk7tT7bSLYGxhCVbCNU5BDCArlOe+aAkx\nwWWBD64AQiLI7LoGNAor4cANzEeQP3GXcKeJqPb4449v1A5hC89sNC4mPOiU5T9OnVKJi+vQlNZS\nQuRAeevmpqAzEG4496bkHMTFvXGifEQAW24+iWCYRz/qTSpF3zhJ682ajnll+efMWkbV82HZwJek\nk1NEYczEn2XSpElWFyFS4XT6WxGoEwKf/exnG81xLSeNk6GDYMVSg7xAMvjtilgfsNq648S3vvWt\nJvJCJF0Zc8gvDqu8ULv5wi/PvJQzLom4L6pyrt132NLTLj3X3eki19UgPE0E+ZKXdPT8yle+0vR8\nZ6x1x0eJ3is6hIkh5XVbSiUu55xzTqN9ODEJYeAkYFx33XUNMkFoZBEY4cyZM+WnXdkwd+7cxm86\nKcyW5SYjEW/mzz33XCM9UwzujSU3clb9GgUnPMh74ySsJjZZmn+MoUOHNvnlxBZc44s4yL7yyiul\nrCJqB1vYn6WV30u7coq4DmHC6nTPPfdYixcPxqgP/7OkYfPG8FRbEXr0hTLS/J9WBQ+mOdNYC90F\nB//xH//RtplYGiAWvJjyLZYHMuInKQMtL7isSBXBx3H48OGNMcgd/CnH3djRtW5AbqQ+6oQQiXCe\nMtMK4yNlhUlDXDnudJGbTsY395zbFvAZMmRIo91sNyDjIwSH7VFccY0OGBDCMxxu2k4dl7qqCABY\nQilOsHSOeGGHG+gCSx4BkhsBYAGL+TlhxLBld6UQN6h747k3k1uXeyNn1c8tL+kx7aMdiNw4UXmj\nbpyodGnPCfbBGv1eN2ZUWUU8QFk1xttIkZLnnwYr0qCPzMZJdMLicsYZZ9hBOM2DN0nZcWl40LOK\nJ+zPwm8hNGXrQz3sV8U01YsvvmiC+CL2rY03M/d/1W0H+HLfYBFlFQmm+SCui32wq0Oxi1T0MQOe\nG/YhOlX7s7793/EimmYwd1eb8qwkf6v/e8aE999/v4msCEIMsv/8z/8sP+03Uzt79+5tGiuaEgQ/\nGHMISeHW+Y1vfMP6kLikKJyP3xAPN19UGjmHfu3Kk7StviFUssKJNJQpRM3Nw1jH/2ar8Ze0jD1r\n1651s9ljd7HIyJEje13vyomyA8YEBCQ25H/Q6KbAdIHndlOwGwmig55x17geDtpD2e4n6NRGxEPS\nI2n1I0/QwY1yXf3aXSOtq0/4mHLRxxW3LgIBhcUtM/B4b7pMe8N1hIMLNWX46AeBsLodgC5Kr7zn\n0kTwJCAcH6STEV+pK3hQxzYVvQg+V4ZI8EHuG0L/87udPq30oC0SwI4gdkTSzVpWqzrqdJ4+Bae6\nCf2edgdw99lFgDdX3GdeQFzsM51nn/usCz+X3fwcU2Y4T0BY7FgUDPDh5I3flOvqJnVynvEpLO7z\nO6wT6YMX6Sa95fkcHsvC5cpv2iE68B2uQ9LJN3WGA7KiY1w+t71RY5CU3clvHGY7IgDjAgDI3Dhh\nINw0ABoW92bjRgsP9HSMm4Z62nUMdSTVj7RxN2PcNfKmvXHc8sJYUR56y41Lu12J+8dw04WPGRiD\nN/rw6cr/howxECeRMFkJ/05SRpo0DOhp6kibvp0u1M2gWRbBEELEfdUqenE7HfvCdf6X60buIC2Q\nlzTiDtzh55r7zIO4qJSHAGOIjC+MRb5Ix4iLLw1WPZIhwABW1lt9Mg2KT5V0UIgiEK4FpmjN8lhQ\n0DXPQEfdhGOHUKQdXLLggL4QSO6vKJyzlFmnPGnIdVXanaWvsXrwYsr/LN+uFUSJS+d63rXOiDWo\nc7W3rqlU59zgplOpKALMZYYdoCvaFKs2DqP4abQLP49vB3FcwoJPCU6qRa/syRufJY/TLpiQn1Vk\n+POweqlsoT6255gxY4YZO3ZsR5a3l92mIssnwviGDRuKLLKrZfH/RCTctD5O+ImII21gVTfBoNnV\ndvTFyoMXInPvvffapgfWlkS+kZ3CSYlLp5CuWD14puOYWQdhgMbpDOLSTgILRMsVELJEul0ZSa/L\nagtIUR4RJ14hQUnKYknnVVddZR555BGzYMGCtoQuSZlp0kCSWKmE4+95551XOCFMo4svaSHF3Av0\nSdEEuVttxMF74sSJmarHkTZwHbB5w3vZZSpQM6VCALIIaUTchS+pCikpsRKXkoCterFYXBgIeWOq\nupx66qn2je24446zgyUDZpQkCTTHEuk0BCGqHs5RF4NUOwtQq/zh85TFp1Xb3PQsW4YwbN682bCR\nYrcEfdGBtzliUhSBa7fakrVe2kyf8eF/jWXm4BJMo2Ut0qt8ixYtMu4qobTKkR9hZaobTiNtOZo+\nHQJYWyTYZzBd1LE9mJJq2Y9ZpKSJNV3fQkBi6fBGXmV5+umnrcmTQVLEHeAxSwuBYNBoJ0LmkqQN\nl8WbNNMyaU3n4XLiftO2Vps00qcMAligpM1xZXXqGnqtWrXKEhmxIHWq7k7Ww72DVU8kqp+wdK5b\nt65px3tJX6Vv7kOmA932Vkl/1dVfBJS4+Ns3XdeMhywDLAOtT4NcWmBOOukkM2fOHHPppZdGZoVM\nPPXUU434B/i4tCMlPJTTkg/wpK5ODMy8ydNnbjt8JS3SKUJeqn6/SXvk2yXJSe4t7hEIDfnc/pPy\nqvKN9QifnenTp1dFZdWzIggocalIR3VLTQYTgo5V9eGDtYWoy9u3b28JYZiEJHkrprBwvpYVBBei\niERc+iKuuUSJiLYPPfSQ2bp1q9ck1HdylaRf6GtM7SJpCS756C92aceRuYrC/wbWlrqR0Cr2RR11\nVuJSx14tsE0MfrwlxjmtFlhdoUXx5orvxMKFC1v6ctA+JO7NttVARPnkb2dB4SEeNSVQaGNbFIaO\nWJP+4R/+wfq1tNO1RTEdPY2zLo7Usqqko5VnqAyMGaBFiuhryqScNWvWpLbsiR7d/FZrSzfRr3/d\nSlzq38e5W4iT1o4dOyr39pdE7zRWEwGSPCL/+7//a1iBFTWVJgNaljduKT/vtwyA9913X8upsrx1\nFJ0fMghmrK7ppvNwXLvcewAfqTIIIb4uOKf6biUL4yR6x1k5w3n0tyKQBgElLmnQ6qNpGfywXBB7\noxOxPoqAmYEFUzXfrawpXMtLKsAmyj+GwZdrZQxoafBh6oW9RpYuXZomW9fTyhSfL4M2/ek6mea9\nb5ICjOUiCOBWGesT1kksZlW1FCXtF03XXQSUuHQX/8rUzgMJ0zUm8W4Pxu1AS2JlYCBCWpGadnW4\n16mP8sCFb3aUPuigg8yAAQO6NkWEfjKIxJE3tx2+HXd7ugHcRJI41UraIr+5nyBJVbCY8X8wZswY\n+8JQVZ+4IvtOyyoPASUu5WFbu5J5C542bZrXS1bl4UlskLhl3EVYW9wOFiLEW7nr49DKP8bNW9Zx\ntwf+vO2ijzrp4NnNvorDigCKBAtkiTT3ta8yZcoUa92rqkOxr7iqXr0RUOLSGxM9E4OAz6s+hLQc\neuihsf44RZMW4KJupozaTaW5b/Fl+Uagj1hbqr6qo0zyRZ8V7VQL9mXIv/zLv9ipWlYa+Wjx9Pm5\nUEZ/aJndRUCJS3fxr2Tt8pBavHixNw9RIS0AGhdcTSwjRUwRSedRJvUzoKQhReGBs8jpCPqof//+\nlfGNECzD32J1cf1LwmnS/O4UcUyjU7u0cn/t2bPHS4unPA/i/u/atVGvKwJpEFDikgYtTdtAgIeV\nL5FOebB/9atfNUcffbRdhRG1wkcUT0MsJE/cN5YNN9AbZAR9srwVk88doN0ppzgdwtfQgby0tUiC\nFq6nU78JIBi3pD1OjzCmnXKqjdMpzTX0R6QfZbrWB58X7jN8WhAlLRYG/dMhBD72j4F0qC6tpkYI\nsPkZRIHVRkcddZRhcOmGPPHEE9YP4rLLLrOD2yc+8YmWapRBWhhQDjvssEad1M8Sab7jdGlkcA4g\nQFhd5LN3717zy1/+0hKh3/3ud031ONl6Hb700ktG3s57XazgiU9+8pPm5Zdfbmy4F9cEBtO33nrL\nYsagz3QcJE4wjcvr27UwaUE/9tvif40NCD/88ENz9tlnd0Vt/peIRE0oADZAjHtZ6IqCWmmtEVCL\nS627t/zGYXGYMGGCOeaYY2y0T3kzLLtmBiiWZ2/YsMFaWVgyGmfliBoE8ujIgzvOIlI0SaK9rj9G\n3LQS1rAqRzsO9wt9h6XEtUa5aVyn2jL9htw6yz5ud79yHSsjMn/+/NzL+pO2h/vwu9/9riUr7Bjc\nzqcrabmaThFIhQCbLKooAnkQCMKb99x9991s1tlz44039gQDTJ7iYvNKXQFB6pk8eXIPvxG+g4G9\nZd5gt92W19JcoJ6kZSVNl6Z+SQvGlC8fwYHrAYmLxULKqNK32ybpg6i2V6lNrXSlb5P+D/F/x/9C\n2f936Bo4n9u6xo0bl1i/Vm3U84pAHgTU4pKK5mniOAR4C7zrrrvslE3wILXm7DgrSFxZ4WuUzTJL\n3i6HDx9uZs2a1estU6wSYT+Goqwf6EAdSdtEeqQTViixOnzsYx8zp5xyigkeCmEIK/2bN/s///M/\nN6wyqotVJapDstwz3JPsx4UfEP93WEDD/wNRdSU5R9nLly+3+1yRPquvUZK6NI0ikBSBP0maUNMp\nAu0QYIAmdkrwtmiTEkGTDxvGQR7SCoMx4cMpg6mRffv22YicEJioBzPz7JynLh64CAMBefMKuiBJ\nSQtpwQM9RBfOlSXoRdv/8Ic/mOCNuKxqulYuZOxP//RPbRvT9EHXFM5QcRbSQjXc9/J/xxQh/i/4\nwbDlRZb/O/TACZi4LJBEfIaWLFliNyr1dQuGDHBrlgojoBaXCndeFVQneBZ+KBs3brT7HTGoDho0\nyPpgoD9Ldnk47t+/3zbnwIEDNt22bdvs71GjRpnRo0ebkSNHpnIAhGjwQIdERZEcW3jCPzz84/xZ\n2hVD/rw6tKtDrkP0du7cGRt8T9JW6RsMsbbVNbhZVtLSqg/5vyOC84svvmg/bFqJM/3QoUMbWYYM\nGWJ2795tf7f6vzvttNM6YjFsKKUHikACBJS4JABJkxSDAJYHHExZ8cKDEsGKwl467gOVqaA459Ok\n2jzzzDPms5/9bCoriVu26JuXdFAOA1MnLAVYt5C6hVyvM3EpmrS49zDHch+3+7+DyBxxxBEduU/D\nOupvRSANAkpc0qClaSuDgAwGWF0gS2nJB/l54BdFNrAAMXWEPmVKXYkLfYFlrm6+O3KfdsIPqsz7\nTstWBDqJgPq4dBJtratjCDBFJEQB0sIbO4NfEsniz9KuXAiQu5y5XXq93oxA2YSvubbO/JL7TElL\nZ/DWWuqDgBKX+vSltuQjBKJ8SiAvvN3KG24rsMjLQFLGYCIEqlXder7vICAWuDLus76Dora0ryKg\nxKWv9nxN2w0xabWKSKZ95E3XhQBrjBCeMt/u0a0deXL10uM/IgBmdRnkhbSUeZ/pfaMI1BkBJS51\n7t0+2DaZImrVdLGmQFJEGBT5pPWDkfxpvqkfkpR02ipN2XVOS7/itF11UdJS9R5U/X1A4OM+KKE6\n1B8BBmp8PFjmLEsvo1otS6VZ4cC+LGnessViElWue443XZm2IWAbgc3EGuOmK+uYupLqmlYHlpdv\n3bo1bTbv0wfRcr3XsZ2CSlraIaTXFYFkCChxSYaTpsqAAFaMF154wQaRI54EsSSGDRtmY7gQ+TZK\nWLLJEunFixeb1atXG/YgIvYLmyjGkQvqajVFFFUP51il8vvf/77V5VLPExeGgSyuTVkUGDx4sMU7\nS16f8xBvhHuhqqKkpao9p3r7iIAuh/axVyquE1E3V65caa0rE7JufpwAABmPSURBVCdONASRO/XU\nUzMtBZZAWuxAO2DAADNnzpzIYHRpLRikl6BykB4sQkWTiHbdSL1IGqtSuzJpRx2XDRPFlUCE7Ehc\nNVHSUrUeU319R0CJi+89VCH9IBnslYK0Ihh5muMSovvuu68xiKUhLTJlFfZnaXU+j75J8qbRPUl5\npCHcOyHaw21Mmt/HdFjTNm/e3HFymRcLJS15EdT8ikBvBJS49MZEz6REAMsBkVrxX3EJRcpiEidn\nsGc/lkMPPdTMnDnTDtRJrBZJLCtlEIl2DSu6TjDB12X27Nntqq7EdQZ/9qvCQbdKoqSlSr2lulYJ\nAV1VVKXe8lBXrCC82eN/gPNtJ0z51Ld9+3YzduxYO32QZP8aBhGk3XQQZUMksMB0SopcIg05Y0+a\nH/zgB7YdnWpDmfU88cQThinHKgn3EGRalzxXqddU16ogoBaXqvSUh3ryZr9q1Sq7Y3O3piUgJNde\ne621vixfvjxyoGAQEX+WpDBSLoNOEktO0jLj0mV9Oyefu+IGEoTOVZ1aicKIqa+FCxeaquxMXLQF\nLQoTPacI9GUElLj05d7P2HasEVdffbV5//33zaOPPtqxwb2VuugzY8YM884775i1a9c2yEtev5Uk\nU0utdMpyPsmAFyYqrQjZ7bffbpedL1iwIIsq3uQRvyksbFWQJH1YhXaojoqAzwgocfG5dzzUDTIw\nZswYq5lLEnxQFQvQm2++ackLevJpNzXUTu+85Kdd+e516oIsuTozELrSiqi4aTimHKwu7QLyhfP5\n9nv8+PFmxIgRldjtWkmLb3eP6lNXBJS41LVnS2oXy1LDlo2SqspULOTlpZdeMo888og59thjM5UR\nlYlBKSlpiMqf9Nwzzzxjk7L0G8kzBQcWSFWtLmCOHxO+U777iihpsbea/lEEOoKAEpeOwFyPSph+\nIJCcb5YWF12mFiAtBJZL4rTr5m13XLTfi1hz3HrFOTgPYZHyqm51YSURxIUVaz6Lkhafe0d1qyMC\nSlzq2KsltAlCcNVVV9mVKp1yWM3aDAgB01llDHp5/F7CRIVAce60kNveogbDe+65x2zZsqVwEufq\nWsbxihUrzKJFi+zqsTLKL6rMovqpKH20HEWgLyCgxKUv9HLONjLgMk2CJaMqKzuwjvDGvmbNmlzT\nLVHQCQFpZxWRdFJGHFGRNPJN3rC/i1xL8005Z511lnVenjRpUpqsXUtbZt8V2SglLUWiqWUpAskR\nUOKSHKs+mxK/FmTp0qWVwqDst3YGLtfvBaLhBklLQ1SigGUALyIWCOWgJ74irSw8UfV34xxEqyxr\nWZHtUdJSJJpaliKQDgElLunw6nOpeUBXxUEyqnOwumBpKMPaAFF55ZVXzEEHHWT3UZIYKlF6ZD1X\n1ABJoMBp06Z5HzYfkvzBBx94PbVVVJ9kvSc0nyLQ1xFQ4tLX74A27a/SctSophRJvLBcRAV7y+P3\nEqWze66oKSPKdJeL+7hKx3f96AusVu2mCN3+02NFQBEoHgElLsVjWpsSixz0uwkK5OuSSy5JbXUJ\nExV3WijcnjIHNYgRUoRTtJADHwIHuhiKXr6uWCuSQLrt1mNFQBFIj4DuVZQes7Y5TjnlFNOvXz/7\nYZdeV+Q836+++qp7KfaY/VrcvLGJC7r4wAMPmFmzZnkfQ6Ndc9kSgBUq7QSi5n4gCrxdyyfOSsE1\n0pGfQa5IQQ/XdyZP2cR0GTZsmNUVYtZtASuIpQQOjMO4W7oqaekW8lqvIhCNQCWJC2RABnFIgkrx\nCPCwXrZsmR1Uii+9syXKSihIhSsuSeFYCIp8ZxlEyYuFRKwkbn15joUU5SlD8kJe2MUbCxIOzN0S\niBMrng455BBvYwMpaenW3aH1KgKtEfh460t6pS8jsHHjRjN58uRCpid8wPGv//qvLRFzdYEMlCGs\n3BHyUsT0jugI0WCwL2JlELt4v/7662bq1Klm3bp1hngvReoqOkd9QwbYEPPv/u7vzMMPP5x6Ci+q\nzDLOKWkpA1UtUxHIj0AlLS75m11uCT/5yU9MT0+P/TAwVFHWr19v34arqHtYZ4LnfelLX7JTc2JN\nKYu0SN1CAoqcjhELEANqEQIGW7duNSeeeKLd1wjyUlTZrfRjdRNWFoLi4ehaxmqvVnWnOa+kJQ1a\nmlYR6DACwQBbGdm2bVtPAE/kJ5i379WOBx98sIfzbh7O7d+/PzKtpLv88svt9TvuuKORVzJIGr7R\nh8+FF15o01E24tYp51rlf/bZZxv5KZOyOBeWxx9/vKEL6aIkTXuj8rvngoG3J/CrcE9V/rgbbQpW\nIfUElo1CsSu6PJQLSETPuHHjesDotttuK7TvweCpp57qCQiS/XDss6AveKgoAoqAnwjUcqro3Xff\ntdMczz//fDDGN8s111xjjjzySBOQAzN48ODmi86viy66yETld5LYt9Wbb77ZPZXqGBP9vHnzmvJQ\nJ5+AhFgzftPFFj+KaK9btFgJxGrgXstzjJ7EPdmxY4fdqPHll182AYlsKpK+OfPMM83RRx9tLQFn\nnHGGOeKII5rSZP0xfPhw87Of/axjUyLo6Trtxq1KStMmLCXik5MmX1xapp/Y24m+x4eMmDTnnnuu\ntYicfvrpqaensFgw3Ugfr1q1yoD9nDlzDFNUPotaWnzuHdVNEfgjArUkLvhmxJEOBsuLL77Y7Nq1\nyxDdNCyPPfZY+FTk7zykhQLDpMWtBIJ1wgknGAaNdpK3veHyIRgMNEUJ5dHWxYsXty2Svgnjz6qg\nW265JTeBGTFihNm9e3dXti2AbEAKIDJFEEKIBX40RZTldgoEBuddSAbEgylDsEe4J8AQGTJkSNP/\nzp49e8yBAwfMW2+9Zd544w1LTgMLjl2GfsMNNxSup1Wi4D9KWgoGVItTBEpCoFI+LgzigeHKWiME\nD5Z2cg6/EoRlwy5pwXLBdT7BdItks2/67u/GhY8OKDeYBmrkDV+X3zzUpfws/iyt9KN89gZqJ0W1\n162HwV0GKPd8luPnnnvOYDVJQlpalU9eyqCsPII1h4G1WyJOtWLRyqMHhKWoJdJRekCwsI6wzQP1\nbN682UAgEQgKfTJ//vzGZ+fOnfba6NGjrcWG/wksOPiwFE2ubEUF/xFnaumjgovX4hQBRaBIBIIH\nTOUEX44AA/vBn8SV4OHauBaQCveSPW6V1z1P2fiuRInUy7f4woTTJfVxaacfdQSDhC2+lY9L1vaG\ndXZ/33333T188gq+RAFZaPSHi12WY8qizKyCbwh+HN2WIv1eyvB36TY+na4fX666+XN1GkOtTxHo\nJAK1myp67bXXgjHxjxJlNRg1apRctkGvgkGkyeQtF5NM0bAPTh4hmmtY8O9whWmWOF+cotrr1olV\ngjfnvIJvA1M/rmDJCgifjd3BVFiU8PbOfjVMVbjWM8qizJtuuikqW2XOFen3UuQS6coAWKCiEm+n\nClahAputRSkClUagdsSFCJwi+LG0kyjiwuCaRPr3758kWcs0UU6nYZ8b9IuTItobLh/SEKVbOF27\n3xAPV5iau+yyy9xTkcdCGiEoRBd2/W0os+rERRpdhN8LJAjBP0OOpXz9jkdASUs8PnpVEfAVgUr5\nuPgKouoVjYBrLcEXKAlpCZcEiSGviFumnKvyt/hU5PF7oQxioqgkR0BJS3KsNKUi4BsCtSMurrXE\nda4N5t8aTrTucRGWhaydihNsWMIWlnb6ldFeQrAzRVWkDBo0KHNxefJmrrSDGZmm4BPekiCNCrJE\nOk2evppWSUtf7Xltd10QqN1U0WmnnWZ9V+ggfCVk2sHHDiN6KPFiXCHuhQirYNoRlzLaO3To0F6+\nKaJT1m9WpWRZdUV95K27FOH3UtYSabDHIsSSZ/yM9u3bZ/bu3dvUJZBd7humT/HJgkj5KEpafOwV\n1UkRSIdA5S0u7733XlOLzznnnMZvYqG4uzNjRbjuuuu82aCR2CaufixtRmeRK6+8Ug5bfpfVXpa8\n5hWccEWIzXLnnXeasEVJrkd9kxZ83LgubplReeLOMfAywPosDPgMrjLAptEVqw2+LnzyCmUQnn/K\nlCk2GN2ECRPMypUrbbEDBw60u4azc7h8xJmblwXOsQkqzutsI5ClLXn1j8oveqgjbhQ6ek4RqA4C\nlbe48AbIQ5IpE2K54EdBfAlxWoUIuGTA7RoesN2WOP0kbkacjmW0l+BieeKuiL4MXC7pIGAfn2Bb\nA3PooYfagU3Sut9YWN5///2mFUVyPc9KLsgYVgHfBZ8VBlmsHOIDk1Rn0ueJqktenKgXLlxoJIBc\nsAVA21gsYQsLxCdYqm02bNhgrS/odf3113ctcq6SlqR3kKZTBCqAQCfXXhdVF3v5BNA2fQLi0ig+\nIDNN+/+E0/KbuC2uuHFc3LLcNBy7ZRFbJUrIL+nC9ch5vt29kNzzHIfztYrjQv1Z2hult5wjpkXw\nVio/M38Tg0b2cQq3L8tvypK4NlmUIoZLsCopS9au5AksTpn2zMmST2Lc0O/E8Ckyrgn6dHOvIo3T\n0pXbVytVBEpDAIfVSgoDuxvcLIpskCY8cBL0LSq4HGllMI0qS0CSNHznJS4QDspwiQ765tlkMWl7\npT2tvhnAithoLnBA7tUHLoZJj2kXZeUR6ipyQM6jS9K8DPpZgswlHawpn00VhbDwu0wRAhPsg1TI\n/dVOV+7hqvV5uzbpdUWgryNQWeLS1zuu7PYH+x/1PPzww4VVAzF0CVpSwkIe8uYVLC3sTlxVgbyk\nJRXtCA/XwQRLVKcHd6w63ANFRGhu1aeQlrSYtSpLzysCioA/CPRDleABoqIINCGAY+a9995b+Ioe\nHKTZIRp/k5/+9Kfm17/+dVO9n/nMZ8zJJ59sV6cUuTP07bffbuuZPXt2U31V+oHPC6uPAutIYrVb\n+busWLHCxsfBQZz9hLohtAc/LnYCX7RoUaEB9CgbnDQoXzd6VutUBMpFQIlLufhWtnScK4niG7yJ\npxoofW0wS4Vx+k3r7Opbe3AypW+StiPKKXXmzJl26wQf8KAtM2bMMO+8845Zu3ZtIURDSYtvd63q\nowgUi0Dll0MXC4eWJgjwpnrjjTeaZcuWyanKfmM9GjBgQOLB3ueGYkXgkzRYHWkhB3wQSAsr7oi0\nm5T8lIkH9xk7UAdTgmbMmDENPbPWqaQlK3KaTxGoDgJqcalOX3VcUwbHsWPH2kGuyiZ3pp6mTZtm\nAr+djmNYZoX0D5ssJukb0gaO4Ja0FGXZKLptQqqy6qekpege0fIUAT8RUIuLn/3ihVbE5mCDw+XL\nl3uhTxYlsLbgxsWu4Aze7idLeT7lSROsjuB77Kz96KOPJiI63WjnggULrL/L1Vdfnbp6JS2pIdMM\nikBlEVCLS2W7rjOKM9BjdeGbaYeqyUknnWTmzJkTGfiMNrmCT48P0yeuTkmO2/m9MKhjmfFleiiu\nTUxpMWUULJc2SR2plbTEIarXFIH6IaDEpX59WniLMOF/8MEH1heh8MJLLJBw82vWrEm8MopBk8Hd\nlaRTMW6ebhyL7lERbM866yzrANut1UNp8YCIECGZvgu3J1yWkpYwIvpbEag/Akpc6t/HuVvIoMgA\nft9990VaLnJXUEIBRVkZKCeIBdKkYbvBtClxh39gRXLJFsvAd+zYYZ588skOa5KvOpZrs0R6+/bt\nLQsKt7VlQr2gCCgCtUJAiUuturO8xjBIMGXkwxLadq2EaJVpZQhPMbHU2qdpNMiWOOyiW1WXtGN1\n4Z6bPn16ry6nD3wmkL0U1hOKgCJQGAJKXAqDsv4FMfXy0EMPma1btzYGRh9bzYDH8lqcPTsh+JhA\nDlxxrR7u+U4do9Ott95qjjrqqMS+Ip3SLWk9QpaZvhMiRl4lLUkR1HSKQD0RUOJSz34trVV5l6yW\npthHBfuiX3iKqdOOvxAXrC3oceyxx5YNe2nljx8/3owYMaJhdVHSUhrUWrAiUBkElLhUpqv8UdQX\ncuAiwvTQ3LlzvY1TIs6zrs5lTjHRR0inrE5uu4o8hqhMnTrV+rooaSkSWS1LEaguAkpcqtt3XdWc\ngTHYuNAGNev2EmJIAUtokazBy7oBZtQUU1F+G5CiKvgjJcGdJe3XXHONue6665Ik1zSKgCJQcwQ+\nXvP2afNKQoA3eXxe8Cfp5moj3sJx4Jw4caKN1+L6QpTU9MKKxaE37NRLe1zJMsW0adMmG4+m24TS\nbUee42D3aruXUZ4yNK8ioAjUBwG1uNSnL7vSEiEORKa97bbbeg3EZSmFleW73/2uuf/++003dzgu\nq31SbtQUUzvHX6xh/fv3r6xTrrRdvvHTgSCHHaDlun4rAopA30JAiUvf6u9SWsvgin8JIeVnzZpl\nCNlepuWDMP7Ud8wxx1irT9hqUUojPSo07PiLau4UE1MrS5YsaTrnkfqZVKnT1FcmADSTIqAINBBQ\n4tKAQg/yIoD1Zf78+Wbbtm2WwLAipChSATl66qmnbFAy9Fy4cKE5//zz86pcm/wyxfThhx+ac845\np7KxW1p1yJQpU2xsnqpE/23VDj2vCCgC+RHQTRbzY6glfIQAb/1EaCVU+759++xyXAYcoqDiiJpW\nICtYVygDX49169ZZwkI0VSUtzWiCPZ+DDjrI4BOCYJmpiwwdOtTeU3Vpj7ZDEVAEsiOgFpfs2GnO\nNghAPFh5tH79erNhwwYzYMAAO71DXA6EnaddYQfjAwcOmLfeesu88cYbNlQ9g/All1xiLrjggsKs\nN26ddTuGJBIgcOnSpbVqGg7HixcvrtzWBbXqBG2MIuAJArqqyJOOqKMa+Llceumljf2NsABATvbv\n328JCtNKrgwaNMgMHDjQjB492nzjG9+olY+G284yjyF+WCfqJljcVBQBRUARAAElLnofdAwBlufW\nZYlux0DTiiwCOOfiO6WiCCgCioD6uOg9oAgoAt4jgJN3Fj8p7xumCioCikBqBJS4pIZMMygCioAi\noAgoAopAtxBQ4tIt5LVeRUARSIyAWlsSQ6UJFYHaI6DEpfZdrA1UBKqPAFFzZZl39VujLVAEFIE8\nCChxyYOe5lUEPEOAUP/E0FFRBBQBRaCuCChxqWvParv6JAKDBw82e/furV3bWVF04okn1q5d2iBF\nQBFIj4ASl/SYaQ5FwFsE2IBx9erV3uqXVTGCEhLjR0URUAQUASUueg8oAjVCgKB/WCbqFO6f7iGS\n8umnn16jntKmKAKKQFYElLhkRU7zKQKeIjBy5Ejz3HPPeapderUgYe+9954GL0wPneZQBGqJgBKX\nWnarNqovIzBq1Ci70WVdMHj11VfNxIkT69IcbYcioAjkRECJS04ANbsi4BsC7JyNlaIusU8WLVpk\nIGMqioAioAiAgBIXvQ8UgRoigIXirrvuqnzLfvzjH9tpIsiYiiKgCCgCINCvJxCFQhFQBOqFANaW\nL37xi+bnP/+5wWG3qjJ+/HgzYsQIM3369Ko2QfVWBBSBghFQi0vBgGpxioAPCLAp4fDhw83y5ct9\nUCeTDlhbiN9y9dVXZ8qvmRQBRaCeCKjFpZ79qq1SBKyfC3FdCJcPkamaqLWlaj2m+ioCnUFALS6d\nwVlrUQQ6jsDnPvc5c9ttt1VymmXFihXm7bffVmtLx+8arVAR8B8Btbj430eqoSKQGYHf/va3BqvL\nvHnzzKRJkzKX08mM4p+zZs0a66fTybq1LkVAEfAfASUu/veRaqgI5EIAX5GxY8eazZs3VyKI23nn\nnWfOPfdcM3v27Fzt1syKgCJQTwR0qqie/aqtUgQaCLC6aNasWWbChAkGC4zPMnPmTKuekhafe0l1\nUwS6i4BaXLqLv9auCHQMAUjBm2++adauXevlEmn027hxo9m6dauX+nWso7QiRUARiEVALS6x8OhF\nRaA+CCxYsMAMGzbMjBkzxjvLC6Rl1apV5vHHH1fSUp9bTluiCJSCgBKXUmDVQhUBPxFwyYsPO0gz\ndSWWoKr44PjZs6qVItB3EFDi0nf6WluqCFgEIC84v+IEu2nTpq6hwuohrD8yfcXybRVFQBFQBNoh\noMSlHUJ6XRGoIQI4vz7yyCPmqquuMlOmTOn41BFxWnAahkBhaanytgQ1vD20SYqA1wgocfG6e1Q5\nRaA8BNi4kL2MEGK93HPPPeVV9lHJLM3G0sOOz8Rp0dVDpUOuFSgCtUNAVxXVrku1QYpAegQgFPPn\nz7fRar/+9a/biLVFWkGefvpps3LlSrv3UJWC4aVHUnMoAopA2QgocSkbYS1fEagQAhCYBx54wCxb\ntsxMnjzZjB492owcOTLTVA5lPfvss+b+++83AwYMMDNmzDBf/vKXM5VVIQhVVUVAESgZASUuJQOs\nxSsCVUQAx9kXXnjBrFu3zqxevdr6orCUeuDAgWbIkCHm4IMP7tUsdnI+cOCA2bFjh81z4oknmnHj\nxpnLLrusEhF7ezVITygCioCXCChx8bJbVClFwC8EsJ7s2bPHEpMtW7ZEKjdo0KAGsTnuuOMquSN1\nZMP0pCKgCHiFgBIXr7pDlVEEFAFFQBFQBBSBOAR0VVEcOnpNEVAEFAFFQBFQBLxCQImLV92hyigC\nioAioAgoAopAHAJKXOLQ0WuKgCKgCCgCioAi4BUCSly86g5VRhFQBBQBRUARUATiEFDiEoeOXlME\nFAFFQBFQBBQBrxBQ4uJVd6gyioAioAgoAoqAIhCHgBKXOHT0miKgCCgCioAioAh4hYASF6+6Q5VR\nBBQBRUARUAQUgTgElLjEoaPXFAFFQBFQBBQBRcArBP4fntNQJrCufL0AAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"review = \"The movie was excellent\"\n",
"\n",
"Image(filename='sentiment_network_pos.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Project 2: Creating the Input/Output Data"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"74074\n"
]
}
],
"source": [
"vocab = set(total_counts.keys())\n",
"vocab_size = len(vocab)\n",
"print(vocab_size)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['',\n",
" 'inhabitants',\n",
" 'goku',\n",
" 'stunts',\n",
" 'catepillar',\n",
" 'kristensen',\n",
" 'senegal',\n",
" 'goddess',\n",
" 'distroy',\n",
" 'unexplainably',\n",
" 'concoctions',\n",
" 'petite',\n",
" 'scribe',\n",
" 'stevson',\n",
" 'sctv',\n",
" 'soundscape',\n",
" 'rana',\n",
" 'metamorphose',\n",
" 'immortalizer',\n",
" 'henstridge',\n",
" 'planning',\n",
" 'akiva',\n",
" 'plod',\n",
" 'eko',\n",
" 'orderly',\n",
" 'zeleznice',\n",
" 'verbose',\n",
" 'amplify',\n",
" 'resonation',\n",
" 'critize',\n",
" 'jefferies',\n",
" 'mountainbillies',\n",
" 'steinbichler',\n",
" 'vowel',\n",
" 'rafe',\n",
" 'bonbons',\n",
" 'tulipe',\n",
" 'clot',\n",
" 'distended',\n",
" 'his',\n",
" 'impatiently',\n",
" 'unfortuntly',\n",
" 'lung',\n",
" 'scapegoats',\n",
" 'muzzle',\n",
" 'pscychosexual',\n",
" 'outbid',\n",
" 'obit',\n",
" 'sideshows',\n",
" 'jugde',\n",
" 'particolare',\n",
" 'kevloun',\n",
" 'masterful',\n",
" 'quartier',\n",
" 'unravelling',\n",
" 'necessarily',\n",
" 'antiques',\n",
" 'strutts',\n",
" 'tilts',\n",
" 'disconcert',\n",
" 'dossiers',\n",
" 'sorriest',\n",
" 'blart',\n",
" 'iberia',\n",
" 'situations',\n",
" 'frmann',\n",
" 'daniell',\n",
" 'rays',\n",
" 'pried',\n",
" 'khoobsurat',\n",
" 'leavitt',\n",
" 'caiano',\n",
" 'sagan',\n",
" 'attractiveness',\n",
" 'kitaparaporn',\n",
" 'hamilton',\n",
" 'massages',\n",
" 'reasonably',\n",
" 'horgan',\n",
" 'chemist',\n",
" 'audrey',\n",
" 'jana',\n",
" 'dutch',\n",
" 'override',\n",
" 'spasms',\n",
" 'resumed',\n",
" 'stinson',\n",
" 'widows',\n",
" 'stonewall',\n",
" 'palatial',\n",
" 'neuman',\n",
" 'abandon',\n",
" 'anglophile',\n",
" 'marathon',\n",
" 'chevette',\n",
" 'unscary',\n",
" 'eponymously',\n",
" 'spoilerific',\n",
" 'fleashens',\n",
" 'brigand',\n",
" 'politeness',\n",
" 'clued',\n",
" 'dermatonecrotic',\n",
" 'grady',\n",
" 'mulligan',\n",
" 'ol',\n",
" 'bertolucci',\n",
" 'incubation',\n",
" 'oldboy',\n",
" 'snden',\n",
" 'plaintiffs',\n",
" 'fk',\n",
" 'deply',\n",
" 'franchot',\n",
" 'cyhper',\n",
" 'glorifying',\n",
" 'mazovia',\n",
" 'elizabeth',\n",
" 'palestine',\n",
" 'robby',\n",
" 'wongo',\n",
" 'moshing',\n",
" 'eeeee',\n",
" 'doltish',\n",
" 'bree',\n",
" 'postponed',\n",
" 'gunslinger',\n",
" 'debacles',\n",
" 'kamm',\n",
" 'herman',\n",
" 'rapture',\n",
" 'rolando',\n",
" 'tetsuothe',\n",
" 'premises',\n",
" 'bruck',\n",
" 'loosely',\n",
" 'boylen',\n",
" 'proportions',\n",
" 'grecianized',\n",
" 'wodehousian',\n",
" 'encapsuling',\n",
" 'partly',\n",
" 'posative',\n",
" 'calms',\n",
" 'stadling',\n",
" 'austrailia',\n",
" 'shortland',\n",
" 'wheeling',\n",
" 'darkie',\n",
" 'mckellar',\n",
" 'cushy',\n",
" 'ooookkkk',\n",
" 'milky',\n",
" 'unfolded',\n",
" 'degrades',\n",
" 'authenticating',\n",
" 'rotheroe',\n",
" 'beart',\n",
" 'neath',\n",
" 'grispin',\n",
" 'intoxicants',\n",
" 'nnette',\n",
" 'slinging',\n",
" 'tsukamoto',\n",
" 'stows',\n",
" 'suddenness',\n",
" 'waqt',\n",
" 'degrading',\n",
" 'camazotz',\n",
" 'blarney',\n",
" 'shakher',\n",
" 'delinquency',\n",
" 'tomreynolds',\n",
" 'insecticide',\n",
" 'charlton',\n",
" 'hare',\n",
" 'wayland',\n",
" 'nakada',\n",
" 'urbane',\n",
" 'sadomasochistic',\n",
" 'larnia',\n",
" 'hyping',\n",
" 'yr',\n",
" 'hebert',\n",
" 'accentuating',\n",
" 'deathrow',\n",
" 'galligan',\n",
" 'unmediated',\n",
" 'treble',\n",
" 'alphabet',\n",
" 'soad',\n",
" 'donen',\n",
" 'lord',\n",
" 'recess',\n",
" 'handsome',\n",
" 'center',\n",
" 'vignettes',\n",
" 'rescuers',\n",
" 'pairings',\n",
" 'uselful',\n",
" 'sanders',\n",
" 'nots',\n",
" 'hatsumomo',\n",
" 'appleby',\n",
" 'tampax',\n",
" 'sprinkling',\n",
" 'defacing',\n",
" 'lofty',\n",
" 'opaque',\n",
" 'tlc',\n",
" 'romagna',\n",
" 'tablespoons',\n",
" 'bernhard',\n",
" 'verger',\n",
" 'acumen',\n",
" 'percentages',\n",
" 'wendingo',\n",
" 'resonating',\n",
" 'vntoarea',\n",
" 'redundancies',\n",
" 'red',\n",
" 'pitied',\n",
" 'belying',\n",
" 'gleefulness',\n",
" 'bibbidi',\n",
" 'heiligt',\n",
" 'gitane',\n",
" 'journalist',\n",
" 'focusing',\n",
" 'plethora',\n",
" 'citizen',\n",
" 'coster',\n",
" 'clunkers',\n",
" 'deplorable',\n",
" 'forgive',\n",
" 'proplems',\n",
" 'magwood',\n",
" 'bankers',\n",
" 'aqua',\n",
" 'donated',\n",
" 'disbelieving',\n",
" 'acomplication',\n",
" 'immediately',\n",
" 'contrasted',\n",
" 'reidelsheimer',\n",
" 'fox',\n",
" 'springs',\n",
" 'toolbox',\n",
" 'contacting',\n",
" 'ace',\n",
" 'washrooms',\n",
" 'raving',\n",
" 'dynamism',\n",
" 'mae',\n",
" 'sky',\n",
" 'disharmony',\n",
" 'untutored',\n",
" 'icarus',\n",
" 'taint',\n",
" 'kargil',\n",
" 'captain',\n",
" 'paucity',\n",
" 'fits',\n",
" 'tumbles',\n",
" 'amer',\n",
" 'bueller',\n",
" 'redubbed',\n",
" 'cleansed',\n",
" 'kollos',\n",
" 'shara',\n",
" 'humma',\n",
" 'felichy',\n",
" 'outa',\n",
" 'piglets',\n",
" 'gombell',\n",
" 'supermen',\n",
" 'superlow',\n",
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" 'mutilating',\n",
" 'dupes',\n",
" 'launius',\n",
" 'widescreen',\n",
" 'joo',\n",
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" 'danniele',\n",
" 'converging',\n",
" 'hypothermia',\n",
" 'glorfindel',\n",
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" 'attentive',\n",
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" 'bombshells',\n",
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" 'lyoko',\n",
" 'southron',\n",
" 'destruction',\n",
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" 'lowly',\n",
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" 'limousines',\n",
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" 'malaysia',\n",
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" 'hasselhoff',\n",
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" 'slayer',\n",
" 'tangy',\n",
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" 'interrogates',\n",
" 'bismillahhirrahmannirrahim',\n",
" 'rowboat',\n",
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" 'astrotheology',\n",
" 'dekhiye',\n",
" 'valga',\n",
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" 'wipes',\n",
" 'hostilities',\n",
" 'sentimentalising',\n",
" 'documentary',\n",
" 'salesman',\n",
" 'virtue',\n",
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" 'balky',\n",
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" 'ily',\n",
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" 'functionality',\n",
" 'dissociated',\n",
" 'elk',\n",
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" 'tempe',\n",
" 'linoleum',\n",
" 'photogrsphed',\n",
" 'bottacin',\n",
" 'hipper',\n",
" 'titillating',\n",
" 'barging',\n",
" 'untie',\n",
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" 'gnat',\n",
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" 'fresco',\n",
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" 'obstinate',\n",
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" 'appelagate',\n",
" 'linkage',\n",
" 'enables',\n",
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" 'nativetex',\n",
" 'suprise',\n",
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" 'speeded',\n",
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" 'saddening',\n",
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" 'mos',\n",
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" 'neccessarily',\n",
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" 'tankers',\n",
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" 'donal',\n",
" 'crabby',\n",
" 'lifeless',\n",
" 'perfidy',\n",
" 'teresa',\n",
" 'bulldog',\n",
" 'cockamamie',\n",
" 'rasberries',\n",
" 'notethe',\n",
" 'captivity',\n",
" 'chiseling',\n",
" 'smaller',\n",
" 'clampets',\n",
" 'alerts',\n",
" 'tough',\n",
" 'wellingtonian',\n",
" 'aaaahhhhhhh',\n",
" 'dither',\n",
" 'incertitude',\n",
" 'florentine',\n",
" 'imperioli',\n",
" 'licking',\n",
" 'disparagement',\n",
" 'artfully',\n",
" 'feds',\n",
" 'fumiya',\n",
" 'tearfully',\n",
" 'lanchester',\n",
" 'undertaken',\n",
" 'longlost',\n",
" 'netted',\n",
" 'carrell',\n",
" 'uncompelling',\n",
" 'reliefs',\n",
" 'leona',\n",
" 'autorenfilm',\n",
" 'unfriendly',\n",
" 'typewriter',\n",
" 'shifted',\n",
" 'bertrand',\n",
" 'blesses',\n",
" 'tricking',\n",
" 'fireflies',\n",
" 'zanes',\n",
" 'unknowingly',\n",
" 'unnerve',\n",
" 'caning',\n",
" 'flat',\n",
" 'recluse',\n",
" 'dcreasy',\n",
" 'chipmunk',\n",
" 'dipper',\n",
" 'musee',\n",
" 'cousin',\n",
" 'shys',\n",
" 'berserkers',\n",
" 'eve',\n",
" 'conflagration',\n",
" 'irks',\n",
" 'restricts',\n",
" 'parsing',\n",
" 'positronic',\n",
" 'copout',\n",
" 'khala',\n",
" 'swiftness',\n",
" 'higginson',\n",
" 'imprint',\n",
" 'walter',\n",
" 'sundance',\n",
" 'whispering',\n",
" 'thematically',\n",
" 'underimpressed',\n",
" 'uno',\n",
" 'expressly',\n",
" 'russkies',\n",
" 'discos',\n",
" 'shaping',\n",
" 'verson',\n",
" 'prototype',\n",
" 'chapman',\n",
" 'trafficker',\n",
" 'semetary',\n",
" 'unrealistically',\n",
" 'lifewell',\n",
" 'rivas',\n",
" 'consequent',\n",
" 'katsu',\n",
" 'titantic',\n",
" 'jalees',\n",
" 'ranee',\n",
" 'shipbuilding',\n",
" 'gambles',\n",
" 'dispenses',\n",
" 'disfigurement',\n",
" 'bright',\n",
" 'cristian',\n",
" 'puertorricans',\n",
" 'constituent',\n",
" 'capta',\n",
" 'jewel',\n",
" 'erect',\n",
" 'farah',\n",
" 'despondently',\n",
" 'avoide',\n",
" 'inconnu',\n",
" 'headquarters',\n",
" 'sanguisga',\n",
" ...]"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(vocab)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 0., 0., 0.]])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"layer_0 = np.zeros((1,vocab_size))\n",
"layer_0"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih\nyGJVVsUIiIhU3AF1qJ43aqJvHnTQQTb2xL/8y7/UQS3pkAAB+u355583//RP/5Qgdb2SQJxwlL7m\nmmvqpZi0qRwBNlaEbJRJaipvpBToIKCpmQ4Ug3vgpmUgJE5uuukmw5u2pN4IELWUnW+bOr3BPYej\nNFOCmgqs973ma4f147/+67/MunXr/NOFHVPuySefbF5//XW7G3RhBaugWiIgi0gtu6W/SjElw4oZ\nSbMQcNaQppIQ0IZ8XH755eYrX/lKs8CXtkJACBSGgIhIYVA2uyDIiIKaNasP77jjDjN37txmKR2h\n7WWXXWZX/MhXJAIcnRICA4CAiMgAdHLSJhJw6p577kmaXOkqRIBBe9myZXZDuQrVKKRqrCIQ4fXr\n1xdSXpMKcc6XOO5yjAMozpjuw2+uRQnX+OBH4RxFx4wZMyLpgw8+aH1xXJl8f+ELX7D1jUj8hxOU\niY+Gnwd/njhdyIYOUfVzLao80ob9QEhHnUzLIOPHj7e/nXOqj5dNEPqDfocffvgwvWkrPidhYfqH\nuigTjMLYuzrD+fS7eARERIrHtNElYubHVC6pNwIM2oTmb4tfBffdo48+Wm/QS9Tue9/7nh10ia8y\nNDTU+UyaNMlccMEFlkjEVf+pT33KXtq1a5fZvn37sGSQAwLdsTTflbtjxw7zi1/8wtYX5ePBoH3s\nscdaYvjAAw908n3mM5+xU7iUmUYY6HGEJ9gePh9Oj3nz5plbbrnF1gUBQXbbbTd7/fHHH7e/Xfor\nr7zS/o77Q36IxO23326thK4O19Z99tkn1p+FjT8h9fw/uXzUz/8YZUr6gEAAvEQIjEDgBz/4wYhz\nOlEfBIKH5lBgvaqPQjk1CZxVh4LHXc5Smpc9GGhtu2n7nXfeGdmAYFWUTXP99dcPu37iiScOBQPs\nULCZ4LDz7gflcT0YjN2pYd/k43pAYIadp9xgr6IR510iVy/fvlx00UW2PP8cZVPWWWed5Z/uHLu2\nhdsQEAHbZvDxxeEVxoryu+ns2upj4eqIyxdXl6+PjotBQBaRPpC9JlaBqVxSXwQee+wxc+SRR9ZX\nwZSaYdnhLRwH3EGUYDA0s2bNimw6/RwM8tZ6EE7AGz/XooR4QLNnzzZ777131GWbj/xYPZxgIXni\niSdsXBqsE1HCcmvyJRHKxhKC9SNKXNvuu+++qMuJzm3atMncf//9XXUGI3RevHjxiDKJwRPV1nHj\nxhksKdu2bRuRRyeKRUBEpFg8VZoQKB0Bt8y6bWSRwZiYKIMowRt912Yfc8wxdiBl0PUFzKKIBj4P\nDLxEr40T8pHfXzH3zDPP2OSTJ0+Oy2YJMPmSCGWTlkE9Tm677bYRU0pxaaPOv/rqq/Z0N51dW92U\nj1/OwQcf7P8cccw0lqRcBEREysVXpQuBwhEg5sbEiRMLL7fqAhkQwj4OVevUr/r32GOPrlXtvvvu\n9jp+IL7stdde/s/OsUvHfeI7nIaPsVb8/Oc/7+Rj0MUKEEVuOomCgwMOOMD/GXv87LPPJk4bW0iP\nC/jXIFFWDT/rEUccYXbu3Omfsse98o3IoBOFIyAiUjikKlAIlIsAVoOxY8eWW0kFpfPWLDN4d+Ad\nweie6o9XAz+HjgNmMJsfeRzlsPrHEnQkBMpHQESkfIxVgxAoHIG4ZZKFV6QC+4LA22+/3bUeZylK\n2u/OgvLaa691LTd88S/+4i/slI5bxRK+7n7/6Ec/coddv0ePHm16pWVVTXgZb9dCQxc/8IEP2DO9\ndH7hhRcM+kjqh4CISP36RBoJgYFE4P3vf79ZtWrVQLYdZ8tugq8FUyZJHZQ/8pGP2OK2bNkSWyzL\ndJmq8ZfjHnLIITZ9N18diANTOkkE3xfSkidOVqxYYR1xs06ROB8PHLjjxOncyxcnLr/Ol4uAiEi5\n+Kp0ISAEEiLAjryDKgzWccHCcDyFqMStPInCDB8PVopcd911Juzg6tK7FSSXXHKJO2XOOOMM61xK\nyP04CwOrcZIKQdAgUHF5IEOsmPnEJz6RtMgR6SBnEAxiiHTTGT0+97nPjcivE9UjICJSfR9IAyEg\nBAYcgSBGiB1IsU74gylTFmeeeaYlFXHLe+Og+7d/+zcTxPqw5MInOQz+EARIShCPY8SKFojB97//\nfUOgNEiQE6wK5MO5NW7JsEvrviFE1A2RIi91O6HsKVOm2OkSdPXFTS3h7JpE2O4Ax12WgPs6u7ZS\nDnpktbok0UFpsiMgIpIdO+UUAkKgQATYBbotkWLTwsKqmZdeesnsu+++NgqpW91CdE/IAktc0wqD\nLo6oWFIeeuihzuoZLAMIMT6iyA1Ow88995whqiskyOlCuHV8SNI6txKdlKXE++23n7WOuPKI+Iol\ng3aHCYKLL0JU2fD0URQOrq3EBCFKqquDtrJ8mPYoSmoUcvU4N4q4aPVQRVoIASHQCwH2mPnqV79q\neGO89NJLeyVv1HWCmS1YsMAOmo1SPIeyWBkY4CEbUaQgR9HKKgQag8A7GqOpFK09AgwkPFgJMMQy\nzDfeeMOEneV44yW2AW+AEyZMsMcf+tCHat+2KhWEfAQh960KvPmxb0cb92XxpySqxFt1CwEh0F8E\nRET6i3fratuwYYPdwh2PdZbGYc7Fix2TLoNmOPonUUEJyMXcLUsL2cb+6aefthtOnXLKKTb/IDst\nuhvE4cRvcPTJGgP2m2++6ZK25puYF0cffXRr2qOGCAEhkAwBTc0kw0mpPAR4Q7/77rutGR3ywe6V\nJ5xwQub5fcpjLpxlfCwbxKntsssuy1yep2qjDn3y8d73vrdr+5kDh5C0ibSdfvrp5uyzzzannXZa\no/otj7KamsmDnvK2BQE5q7alJ/vQDgjDzTffbIj3wJ4Ua9asMZs3bzZs4Z7HyZDBlMEHhzq36RkD\nMc5s1NlmwUGTNrt2Y/ng0wtPVgd85zvfaRU0kFDCcEuEgBAYLARERAarvzO3loGSDbQcAYE0+NMF\nmQsOZWQAvvHGG+30DdEmIT3Lly8PpWr2T0c8+Ka9ScmH32qICCsB2iJggXWtFwFrS3tdO1ihwnqB\ntjmqYt079NBDXTP1LQS6IqCpma7w6CIIEIyIYEHEHcD60U9hgOIh/cEPftAsWrSosVMRtMNJEQQO\nSwp+OFik2iDcYz/72c/MTTfd1IbmDHwb6E/2xeGlQiIEeiEgItILoQG+zrQIAYeQr3/965W9raIH\nfig4aBINMuwAW8cuQme30gX9iiAf4Xbyxrlw4UJz/PHHhy817jdTcY888oh5z3ve04j+bRzAfVaY\ne5MAYmXc931uiqrrAwKamukDyE2swpEQggGtXbu2MhICdviQLF261EZNPO644wzWgDoK5mgsH3w4\ndlMuZT2MP/vZz9oVS3XEIo1ODz/8sCUf3GtMzfjWozTlKG09EHD9V9Z9X49WSosiEZBFpEg0W1KW\nT0LqZlpl0GJvDDYBq4NlJM1Kl6JvD/qJpb0sh26ybwVvz/Pnzx+2WobBTANZ0XdMf8rDyZyAe2n2\nxumPZqqlrgiIiNS1ZyrSq84kxEFSNRnBIuOCb/VaZut0LuMbEkQk0t/85jfWYlRGHWWXSV9ec801\nkb4uIiNlo198+Tw/cDCn75pMjotHRiV2Q0BEpBs6A3ht5syZhtUqrIqps+AMRyA0po36EUvDmZvB\nhAdtP+rshj9kCB34oA9LqZtmQWDQYiVW2Britxvc64C3r5OO4xGAWN5yyy3WYhmfSleEwHAERESG\n4zHQv4gRctddd5mNGzdWPtAm6QgCYBEqHv+RMsQnH3Ua5MODM8ubWVHUlH5zfQWZZAuAXqQX0sXb\nddXkz+mt73gEeJEhQvIgBaWLR0NXkiIgIpIUqZan42HPmycrPerge5EEbmcGvvfeewtZOUJ5Za90\nSdKubmkgIVGkCFJ2yCGHNGZennZMnTo1sQlfZKTbXVGPa0wVMlXZtoi/9UC33VqIiLS7fxO3joGM\nfT6atqMre92ce+65lkBkeWP2yUfU3jiJASw5odMzioRQtVulc+utt9b+bTSrriIjJd9kOYvHModV\nriwLZU71lL3GCIiI1Lhz+qWacxhsmmnf4ZOWRDEQstIEqTP5cO1DX4hIL0uVI2V1WVHk9Pe/aQex\naViqm2VFFmQEwilHSB/VehyztP4LX/hCIdbJerRIWvQLARGRfiFd43qilk/WWN0RqjE48RBkWiXO\nKkKaOqx0GaF8jxOQECTJwEvab33rW+bKK6+szfJmv3mOhOy333653prTYOLXr+PyEHD/g47gl1eT\nSm4jAu9oY6PUpuQIYA1BmuxchqWAHXvZEdifWvLJB/4vvSwKyVHrT0r0T/P2zyDwD//wD+Zd73qX\nJWZ1sow4EgJyONbmEUgZZIRPEoKWpy7lTYbAgw8+aP8Hk6VWKiEQQiDYcKmv8sADDwwFKtjPWWed\n1de6i6jM1592+OLaxXewk6h/qedxYKru4HLnnXf2TF9UgmnTpg2tXr26qOIqKyfYiXYoGJSG+Haf\nwAJSmT55K6YNafQnvS/0KfdhHfo2sFQNBZv0DV1++eW+irmPA+I1RNmS6hHgf099UX0/NFUDhXgP\nntaDKrxRrlq1ykyaNKkVELBPCdMvOHTyiZumqXtj3cqYpPrTj6xW8AULV0BObBRaIl1ikahCsLgx\nbcYKmSw+Id10xhrCB8uRpDoE8E1i5+SmWRyrQ0w1hxEQEQkjMkC/n3zySTNjxozGDth+V0E8cJR7\n7LHH/NONOoYsOBKSRnGmZBiQwwImlLdt2zYbOIzjfgnkiJgShOMn2Jo/ZVakDm7qSmSkSFTTlcX/\nHJtSSoRAVgRERFIid8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ\nVhiVSUggUwTGox0Em8OBOG2b0rSftCIjaRErLj39zQ7QJ5xwQnGFqqSBQ6AWRITtokeNGtX58Gb7\n1ltvxXbGpk2b7Nuvn2fMmDF222m3MiKc2U9Lfj4nnXSSrZP6kSRpcMry04Xr8X+vW7euUwd5qI9z\nWSSqzThook9WYVrmiCOOyJo9dz6HI+0oQpxpuGlvxxAQxOmfFAvyhadk4vKed955lhQQK8YREkzq\nRQmYMwWEU/AzzzxjV+0wFZN0eimvHo6MlEmy8urYxvzr1683gZ9ZpEWuje1Vm0pCoN/OLb6zJ86q\nfIKmjfjss88+Qzt27Bih3vXXXz8irZ+ffK+//vqIfH6acBnOOTRJGl9/0vvi57/66qtj9XT1+Xm7\nOauS3i87fExdaQXHsiASZ9pshaZ37TjxxBMLKzeYaqqFg2bSBtEPOF1mkbCDatIycIK95557bP8H\nFhPrRBoMKKn1oP5rr712WDlZ25JU9yTpsuKSpGylGY5AW5zdh7dKv/qNQKXLd++///5gLIqWgITY\neAgrV67sJMBycdVVV3V+Rx2Q7+STTzavvfaaDVYVlaZXGeRJkiaqbHfuuuuuc4cjvi+44AJz8MEH\nG6YSegkWFNJ3E+oaO3asmTVrVrdkw65t3brV7L///sPOVfXjiSeeKKzqCRMmGO6BJghv71k3dOs1\nJdOt/VgPsJDwwZLBPbZ48WLruEyYeO4L7iesjGHB2vHzn//cbjgYrIQxfDDNH3/88eGklf12vjFl\nTwlV1sCaVIxFDqsqy+YlQiAPApVPzQRvwyawYFifi127dhl+O/GJClMubJLlhMiMwRLZjq9GYBVw\nl+xAdMcdd3R+Rx2QPmB99hM3gCdJE1W2OxdYMjp1BJYUd9p+sz9KEvHb5WPFYBtYkzpFgE3ctFQn\nkXdAfsz0dRGmnoqQ8ePH26mBIsoqswxHJLJMXaSZkunVBqaDcCTFj4T/B+7TuXPnRpIQyrrooovM\nggULbFrilMybN69WJMS115GRqlYLOT3a/M09EyzJ7tv0W5uxHPi29dsEE57aCAbEYSoQfyPolM4n\nICf2ejhfVJwOf5qHKRpf/DJJFyVJ0oT18Mvx8wcEwr9kj8NTLK5tXIyammGKyS8zjBX5aadLg25J\n5aabbhriU6U4vflmesbHI6temOUxF9dVmBbJO3WQN39dsSlDL6a+wFxSPAJM7TKlJxECeRGo1CKC\nVWPvvfcOxqE/Svi3e8tnu3AnweAbOa3xmc98xiWxVhGmH6KEuAa9JEmabmWceuqpIy5/9KMfHXau\nm0MuCZlecoI1JIwN+6Scc845Lon5yU9+0jnudYCJHetBXsHR1Dmdpv3262Z6BjM/02+9cPHzNekY\nSwafPFMGzpLSpHZXqSsWHzCXZaTYXnAO4XWakiu2hSqtnwhUSkQOOOCAxG198803O2nDA7q7sPvu\nu7tD++1IzLCTwY9wuvB1fidJE5XPnQuTBs5DHHyJ08+lCSwE7tAwUEcN9L4vyttvv91Jn+QgrE+S\nPGWmefnll60/TJNjgcThw2CIpF0Z45dHGUlXyfj5Bv1YZKT4O4AXBlbLSIRAEQhUSkSKaIDKEAJ1\nR8C9PUYFHUuje1zgsjRlDGpaR0YcIRxUHIpqN4sIiKkkEQJFINAYIsKOnU6effZZdzjs27cgcKHK\nN34ccMMStoBEWU38PL5VBsfUYB6u6+fLX/6yn73rMasi4qauumbUxVQIMJUCAclLQjQlkwr2yMTO\nGiUyEglP4pPEnwFLh2fijEooBGIQqHT5boxOkadZVuiEFR+ssggvfw1iI7gkBj+ScePGdX73+wAf\nDAKY+eITKPTrRUQOOuigTnbyQmSKIlcszQwTt05lKQ5YNfH5z38+RY7eSXvh0ruEeqQoijz0Y0qG\nOl544QXrW8W9i7hluhxDpCZOnMih4X+R+4f/v6YNRrSDtvLJSw4tGAP4B2sIS78lQqAoBBpDRIgN\nwuANCUH+8R//0Xzta1/rkBGisfrLfX0nzqLASlNOOLYHEVD9eCBJ9INI4dCL7wTt/tSnPmW3UHcE\nizKJZukwCVbNpDKXFkFEnC5psCkzLVYerD1VCo6RRYY2Z0omj4NrHBZMGXEPEQti586dlmiwpPvs\ns8/ukGRXLwM3eiAsm9+4caO9F8k3efJku1UAG+01QRwZof1NI1JV48u9vWzZMhMEsqtaFdXfJgTy\nLrtJm99f/hq1jDYYVDvLUQOch0VXDS9/5XrUJyAsI5aC+unilrkmSePrT3pf/Py9jmmnL1HLd7ke\nri+uXPKnkbovc03TFj8tkT6rXJZMZFGWjBYlZSzVXb16dScaKnjlqYP2EqU1WPE0FAzwFnvONUEC\nC2OhfdWENufVkb4merFECBSJQGN8RIIB2EYO9QN8cS4sWE0ef/zxwqYwwuUn/R2EkY9NSqCzpNMP\nOISRvptgNVm7dm23JCOusfqCN+G2Ccu83RRCv9uG1QAp6i2b8opcJfPwww+bQw891FxzzTVm/vz5\n1sLB1JqzemTBC+sCZnqCm7HL7vbt263ObHxX9yWzbn8a50ycpf2Dlmf58uWt2ihz0Pqvru1tFBEB\nRBwyIRphQgIBYcAm9kYdpguIrxFYM+zUiut8YoGge1wkV5cu/E16nF/D5AYCQptfeumlxMTGlc0A\nwlx/mx7CDHyQK8Km91scjuBalDAVUkR56Mauu46AbN682ZQxjQKhYaM79MbPhH4ocmO9onD1yxEZ\n8dHofgwx5l4q497pXrOuth2BUZhX2t5ItS8aAfxLcDokxHcbhEEPosrbeT8Fp9Sse8bE6VmUoytv\nsJB2wraff/75fQ3HTX+ce+651odk0aJFfa07Dte480X79cTV0+TzbCOBXxlkUyIEikSgcRaRIhs/\n6GXhZIg5vQ3CoLdixYq+e/M7wpBlz5g43IuakoFoQkLoY8hmkTrG6e6fJ+omTrsE2psyZUqtrW9g\ng0WH/pREI4C18cwzz4y+qLNCIAcCIiI5wGt6VgYKTK1uWqHJ7fnwhz9sp71Gjx5tBxMGlDIHFd6g\nHQkpGre8UzLoxhYFrLYqcvVOlnYywLM5GtOI6FT3e01kJLqX3f9SHn+i6JJ1VggYo6mZAb8L2mJu\nZQrikUcesYOe36XuAerOFTGFgsWCwb4op1SnG995yQ16YX1AcGDutxXEVhzzB2fZSy65xE6dlYFd\nTLWZTufth0yV1jgT1jUCLOLcLBECRSMgIlI0og0rz00D5H0Lr7rZrAZZuHBhzy3peSP3I9yyKiWN\nQyh4IWnyJMWmiLJxSiUQWd1IiMOgaWSkCOLq2t7Ub8gtOEDOyrjvm4qL9C4OARGR4rBsbEm87SBN\ndULDGoIzJKtB0gqDPyTMCZFr497WITFYGMp6GOd9C8e69fTTT9eWhDiMm6In+tLn9HedLEsOx359\nQx5vueWWvjuB96t9qqd6BEREqu+DyjVwVhH8CeIG4cqVjFHAva3hkFnE/DXlgYMvzm+gzLfjvCTE\nrVBpylsrlps99tjDLF261Ie6lseDTkZmzpzZqMi5tbyJpFRXBEREusIzOBcJQMVg3u+lr3kR5iGJ\nlDmgYXHxY9MUQXj8duedknFk7N577+05NeXXW+Vx03Qu2xpWZV90q9u9pDCdOchWoW4Y6Vp+BERE\n8mPYmhJY1TB16tTGxBUp2wrgrCNh4oHVwZe8lpK81pB+kDG/vUUdu/7DAtWEQS4vYSwKt36WAwln\nX6EyiX4/26O66omAiEg9+6USrXjrg4w04c26bF0ZdCAiSaaq0CWrA2xeEkJ+yGNTBvPwjc0UDRF+\nm7IaY9DICM8DNhRlqb9ECJSFgIhIWcg2tNwmrGqAILBENdhorZQBLO9gQ/4kDrB56+EWYyBnx9ym\nRscFA1YuNWnVVhH91oTHgyP7/r3cBL2lY/MQEBFpXp+VrjHmWCJy4i+SxCJQukJeBZCQE044wXzs\nYx8rZZUPD9+iV8a4KR6vGZ0onuFpHz9Nr+OmW0Nc+5oYowIyktRi5trZtO+2xBhqGu6DqK+IyCD2\neoI2MzisXLmyVmTEWUIOOuggc8455xSySsaHgoE9r7+HX16347ADbJZ66aM27BXU1Ddv7kcISd3I\nerf7Ls01LFV1fBlJ0walbQYCIiLN6KdKtHTTNHXwGWGwYp+LSZMmdSwhRRIHyspjnUjTQVGmfdqX\nxs+EQZCYJ02a0uiGEb4Is2fPbtzOrm0lIzgSX3755Zli83TrZ10TAlEI/On/CyTqgs4JgQMPPNDs\nt99+ZtasWea3v/2t+fjHP14JKFgP2MX1i1/8ornqqqs6OvDGtm3bNvN///d/mVddMJC88sorfSMh\nKI9jKRYQX/baay/rK0Gb+KAX6SAtfH79618b0jj55je/af7nf/7Hhkx355r+vX79evP3f//3jWrG\nO9/5TsOHe4h+a4vcdttt1g+LiMUSIVA2ArKIlI1wC8rnbf2iiy6yLVmwYEHfBm0GYJww33jjDbNk\nyZLYeknHwJ3WRJ41X54uzWp5ccTE1X3TTTdZX5nzzjvPnWr0N32BRarJjpFZ+7ZuHce91iZrW93w\nlT4jEdDuuyMx0ZkQAgzwzBWzTJQPvgkMHGUJD0Ic5XjDZGkncQy6TZsQgpsPA0FScfqnJS9Jy49K\nR51Z35pxoAUD93nttdfMe97zntrvZhuFQ9Q5+s/tnBx1vQnn6Js092Dd2sT/HYJlatq0aaVtZVC3\ndkuf6hEQEam+DxqjAdYJpgsQBlQCaTGXXJRgeYHk8Da2a9cu+3ZMfIkkwa7cQM1A4B6ocXpRD8Lg\n108pyp8DQrNlyxa7dLefRKpsrPD/2bp1a9nVlFp+k8nInDlz7P/zihUrzNlnn10qTipcCPgIiIj4\naOi4JwIM+GyOh2PlhAkTrEMb88gQCEhJLxIQrgDigPWDMnBYZKvxZ555xsyfPz8TUWAgYKB2Fo+o\n+pwFJXytzN+0E92KEAgNb6xtE1YAvfrqq41vliMjaf8Xqm7422+/bZ3BV61aZadD2fYh7v+oal1V\nf7sQeEe7mqPW9AsBCAkWEj5YGB588EGzePFi+yBjOmX//fe30yoQi7BANNiqnp1iCUrGZ+HChcOi\nN+YZuLES8ABFL99ikKfMcBvS/EaXrFMyUfVgNRg7dmzUpUafmzhxoiWhjW7EH5SHjHD/QXqTWPTq\n1macwlk102+rYd1wkD79QUBEpD84t7oWBns/RDcPYCwmzz//fGS7cXxl+qWbhcC9VXZLE1n4H07y\nAOWNFPLBChWmlLKW1a2eJNewYBRZN9NWWA8k9UaA/4umkhFeDrB8SoRAPxAQEekHygNWh7NC5B18\nsSJgTcj6VsabKGWsW7fOnHTSSZX0QlVWmEoam7NSCCPTAm0SR0a4F7Pex/3GAxKydu3afler+gYY\nAfmIDHDn173pzqqRda7dzW+zHwvHWcvJilPRUzJZ9WhKviZOYSTB1hFzdz8myVNVGv7nmGJta19U\nhavq7Y6AiEh3fHS1YgR4iLuVOmlUwSSOuLdQymEg6OdgUNQqmTTtVtp6IuDuw37ef1mQWLNmzTC/\nqixlKI8QSIuAiEhaxJS+7whgsnfEIknlTIfw4HcPf5fHvZmmKcvlTfutKZm0iJlc03Dpa+t/Dnc/\n1pWMfPnLXy7Ul6n/CKvGpiIgItLUnhsgvTET80nyAHcEIM607AgK6coS9CxylUxZetatXCxIrJxp\nszgy0g8ynBZHR9TT5lN6IZAXATmr5kVQ+TsIMAC/8MILZseOHZ1lmG6ZLoncsl53zMqPI488MpEp\nmAe4s3R0KvQO8P9IujIGkoIjLeVhbYkjLV7xqQ6LXiUTrnyfffYxjz76aPh043/7m/41vjFdGsC9\nzP0KGSli8Of/7vvf/755/fXX7Z43xAPx/++ozxE8/gf5vxs3bpysH136SJf6i4D2mukv3q2rjYcp\nMURY7bBz5077wDv66KPN+PHj7RJdGuxWz5CWwYaPe2i++OKLNt/06dPN5MmTh8USiQLLWTz8azyI\nebBneaijE0TEvan65WY5jtIvSznd8lDH3Llzbdj9bumado0AWgixaQZBuGe5d7Pet+7/jii7BLjj\n/w6Suvfee1v4wv93nGRJ/fbt220Yd/5f+Z875ZRTbPyfogn5IPSh2lgMAiIixeA4UKXwAGU/imuu\nuca2m4fgGWeckemBSgGQgU2bNplFixZZUsIge/7550daKsIPbx7kSB4ikYfI2Mr/8KcIXfzy4o7B\ngDgsELqsA1lc2VWeZ8sABlJ2e87Tn1W2IW3d9GVSSx5lP/zww+aWW26x/zNJyXucTtw7Tz75pGF3\na/4HL7zwQjNjxoyBwT4OF52vAIEhiRBIgcDq1auHgkFiKCAfQxwXLd/5znds2dQR7DA7FAy2I6oI\nHtz2PN/BNMiI61lOUA9155G8+ZPUTR18Dj/88KFvfOMbSbI0Jg197vrUtbMfmNYBoF7tDIj/UDCt\nYj9l/N+BexBJdSgYgux31P9dHXCSDu1EwLSzWWpV0QjwoAoCHdkHYa+HZhF1Uwdkh4cvD+Gw3HPP\nPZEkJZwu7W/qzfIQLgsTyvU/rj3XXnvtEJ+2CPcXfR0lfvuLIp5R9VR9Luoeor38H0DSyiAg4TZT\nX2AVsfXxPyYRAv1AQESkHyg3vA4eSLwpYaHotzgLDIOuIwg8sDlm8CpDKDfNgEfaNOm76Uzd/sAb\nl9a9Icddb9p5+pc38l4Czknw6VVOXa/7ZCTq3u+X3ugBMYSUuP+7ftWtegYPAfmIVDAd1pQqmb/G\nDwR/kAceeCCzD0je9jKX/elPf9r87ne/M8GAZT7+8Y/bIsv0yaBs2p/EkTCPgyr1BINrB6I0q3hY\nIkwAKueU2CmkgQfsvrxkyZLUbQF7J+ARWA7cz8Z+06b77rvPOoBX2b/c/3PmzDE4lFf5/9/YjpTi\niREQEUkM1WAl5CE0ZcoU22j2najao54B+4tf/KLdN+app57qEIQ8JKBXj4JBYKHoOjimrd+V6erO\nM3h+6UtfMmyA1/TNyXDAhPBu3rzZwZLp2yd1OPMmIZGZKio50xVXXGG+/e1vm3vvvdcccMABJdfW\nu3hWMy1YsMCu0moqpr1bqRRVIiAiUiX6Na3bkZDDDjusNoMcOkGGeEivXLly2EMxLRlICzvlR1kq\nGPiQXm/h5HdS5ABJ/RAZLCq9dHD11/GbvYDOPvtsc9pppxWmXpjwRfVfYZUVWBD398svv2w3naMN\ndelXyOIll1wy7P+uwGarqAFHQERkwG+AcPPrSELCOjoygrUCcoLODMplvq1FxRuJI0A+8UD3MqdO\nwAJpqlUErKZOnWotT2Va3ei/wNfBYlUkGbQFFvTHJyFlYpFVXZGRrMgpXy8ERER6ITRg1+v+MHTd\n4fRkmgZhoOHtscwHOGQH0gPh8UmIP8ihS5nEg/J9QSfq86er/Ot1P8Y3ZP78+YVaQ3q1mT6ExDqp\ng7WE6Y+77rrLbNy4sdR72LU56zcxR4j3U3c9s7ZP+apBQESkGtxrWSsPmauvvrr0t9OiGn/ccceZ\nYEmxmTdvni3SJwdF1REuh0EMB8K//Mu/NKNHj7aXqx7IGMTQyZGysM51/V0XvX0iWYW1xFmFmkIm\nCTxHGPmHHnqorreW9GoYAiIiDeuwstR1b9ZVeumnbVuUzmWQkfAbNKGxISFVExAfL0gZUxxNCY/O\n4A9+WCbKnFLzMUpyHO7rsvuY+qjjuuuuM+edd14SFStPg85HHXWUXVHTFJ0rB00KdEVARKQrPINz\nEYfBIG5Ax7rQlJaHTcWQEySvkx+Exon/luwTnX5MBzkden2jC2QEsznh9ussTRrIfGtJnhVOcf3B\nyif2immadcFZcfjO+78Wh43ODw4CIiKD09exLXUPFef8GZuwphcgUWz45awBPllIqrI/4JAnys8j\niuSQD7+UOjyMb731VvOv//qv5j//8z9rZWXw+wASwrLwOq3I8vXrdkz/+zFfou6RbvnD1yivyaue\nmu4oHe4P/a4QgcGL4aYWhxEggmI/wkeH6y3qN1EgAyIwLAKkH6Eyqp6AdA2L0JkkemRcmUT7pLwq\nBd1oA1FwwaJqfeKwIHoqWwUkwTuujLqcB3P34R5IK2BRRbTitHrGpafNwdCVO6pwsCOwLYey7rzz\nzrjqBup8EECugwm49FvoB+rlE+yUXnr1/W9h6U0qpoLgja3TEeF/jm7Xiqm9f6W0JVQ4+3H4D3UG\nOn8w5qHpBg03aKdBmTzdhPp6pemWP+u1qHoZ4OpGRtCTcOFtISHh/grfX+Hr4d9uEAeXJgv3Gp88\n4p6nfA+SuIGeb4iHL1UTEXRx/XLiiSf6qpVy/CcBCJIGIzBq1CjjPg8++GDqlhAcjDDOTZe5c+fa\n5Y9+O1599VU7TcFUDYIp3X3SLPN1JnS/7PAx5VE2dTH90A9BLz7hKQJiiuD8iM/Ihg0b+qFK1zrc\ndMybb75pA3Wlwb5rwTW6yNScu7fcfcC9wIc+CstXvvIVEwzgtV6qG9Y56vdll11mFi5cmPmeJ6w/\nAdwQ/ocl9UHA9ccTTzxhsowtqVpSCr1pQaGODQZgjjAXdrvW76ajn/uEWXUvXdryVuba+bd/+7dD\nX/va16xlwllDirBSpC2DusG2TElSh9s0zbcUlalTVNlgh3Um71tzVNlNOedbS9x9WTeLVR4ssUZm\nmdoNticY2meffezzi+9BE/fc5jvts7sfWIX7h99liSwiwV0wqPLkk0+awFze+Lcy+o83T1aL8NbN\nG6lbEureTrP2MeVSRhpxdePIWoagE2/gfLoJIdOJTcGSbKwjZekTpQNWEFaEsKQYJ9qmRn6Nalva\nc/QT9xAfjm+77Tbjr8RKW17d0hOef8WKFanVCgZfs2PHDptv9uzZw/LzBu4svV/4whfstRtuuKFz\njmsXX3yx2bp167B8/g+sLYcffviwPJx76623/GTDjimPcl3dY8aMsdYA8rhzfIfL4HdYP9JxLqzj\n9OnTbVl+xWeeeaY95ywPfvspBwmm8YbpgJ5hCadZt27dsCSbNm0y4Om3BX1cvX5i7tFzzjnHnqKf\nHn/8cf9yscdFMhx8KXxrQaCptSYEjRhWTRA0q/MWDxMOX/fL4DgsMDPfmYZ64ury8ybVjzy+Dml9\nRHC+8tuIbmeddVYs6/XrAgvyMy/n2gVGYR0oz10Pfydl18zZZ3mT8TGt0zFvmzjehoU30iwWiqz5\nXP3M/6e1pri8Ud95ysMqEgyC1jKRBYsofaLOoaOri/urzLqi6m/CuWAH6SE+bRH6nGcQ32nEf+7x\nzPOFZ5h7rvEs9Z+H7rz7DuflGdotPc/TKAdMynFlhr+vv/76Ydf8MYuywunDv/3nd5Jnt99+ynJy\n0UUXdeqKsiJRj6ub674Vw7/m0vjf4ByWgHx0yivTV+SPLQxrkOJ32o4nPSA5EHwAwh0QvsnodD+v\nK8P/DudJqx9N9/9J/Juo17UsnR2uy2+Lf8wN7CTJzezSxn1TdtEDBVjTh/QpOkb1Fee4RhrSkqco\nYbCNahMkJe2DsigSQTlp6w7jQZucWT98LelvymCKhH7nO295fr2U7QgIpvqisPPraMsxDrtF41P1\n/x1twvE9qYQH73C+8DjgPwfDx+EBshsJcXl5BvmDNMdRzyqXPvztP7P853c4nf/b1Zfk2R1uv8Mn\nfD5MqHyiwrETn1D4OoWPw2MdOvtpwvW58vN+F0JEsnR8eMB2Het3qg8kDU16s1CGL1n08/UId07c\ntayd7Zfnd3rUMXUgSW5mH4PwcZz1IJwu6W/6z/8niNK92znyunsgaZ1R6brNV6d5+KdJG6VH+Bx4\nRxGkcLqo33nyRpWHHryRQ9qwIHGcpb3oxXJhLB/0Ld9ZyonSsc3nwKooqcv/HfdQGl8k//kfJhJg\nEx5wSeMGQcaB8PPPDfLhfP6zu9s1Xx/6x88XtoZw3T2rwlYUP1/4mnt2u76nHPdBN1/CurprYWLg\n10can0z59fljjI8l7fCxDBM0yvTzhuvjehGS+z8iDJivaLdrKO830L0du44BENfZrqGU7a7z7dcV\nvllcJ3TTods1Xze/nrDe/jU/T5rO9vOF20U7/DbTTl/8a7QnqfD2wqBdhKCj/w/g65TmmDJcv2XV\ni4dh3AMRqwSDZy9hoM5KGrqVTZlJ6vfLIH1ea4pfXviY+4BBBEJCX/FmC6FwOIa/Sct9A4nhQ1qm\n98rUMaxzk39D1MC4CKnT/x33APdCUvFfWsIvnJQRfjaHx4KwRcVd98v1Le1OL38M4bnrhOe1e1ZF\n5eOcu863q88vj+dXWPxne/j57JcXvhZuv1+u30YfOx8TdHHkzD/v6+7KJJ3//A7r4hOVKGxcOXm+\nczurfutb3wra9nsJlDSzZs1yP63zYNBRnd+3335755iDYIVD5zfLDRcsWND5zUZme++9d+c3B34Y\n5HBdV155pY3W6DK88sor9jCPfq6sJN84JLllaKRftmyZGTdunM1KO+644w4TdLb9HdzEsY4/4Xad\ndNJJJrjZbD7+sNlUERLcnDYaad6ycNKiz2lTXqEMygo7gqUpF4yfeeaZyCxu2Wiv5bUBYejpCBpZ\nQY+TwcBty8XZNIk4p1Snd5I8adMcf/zxNqz/5s2brTMc/4PsIxInu+++u11miW7gtHTpUrtzbpk6\nxunSxPMBYTN77rlnbtXr9n/HMy7Ns+mFF17oYLDvvvt2jqMOgsF8xFjgnq3h9H65RFsOy+TJkzun\neF7TH3xYouokKl/UOdLzvAoGYPvZvn27LQKHUJxicSb1xwRXft7vY445plPEf/zHf3SOn3322c7x\nJz7xCesQzYnXXnutcz4gXCOw9J1SSfiTn/ykk56D/fbbr/P7xRdf7BwXefCOvIVl6Xgajhx55JF2\nt1dICOI6jRvPJzT2YvDHv1mCNzh3uvP90ksvdY7dQR79XBlJvpN2tmtruLNdHVHt+sAHPuAu1+6b\nOCRhEkL/BSza7LHHHubggw+O1JkYHzy4gjfyYf1KWZQJscwiYfIaLoMVLQyicSthul0Ll5XlNwM2\ndVNP3IZqEKXAEhKrY5Z6k+RxusVhk6QMpemOQFEvAHX7vyNU/apVq7o33rvKxpFOeE50kwMOOKDb\n5WHX3BjCyZNPPnnYtagfkJCwjB8/Pnyq8xI54kJwgjICK4K54IILoi4Xfs5vF89LXoIhZt/73vc6\ndbGNgpPA4uEO7bPWrcLpnAwddCOUP/vZz0Kpi/mZm4hk6XhHRGgCb/v33XffsMHMt5S4ZobfkllW\nlUTy6pekDtIU1dlJ25VUr7h0WA1YdpdXIBK+8A+ZZNM1SCgC4eANYuLEiZ1iKDMrEekU0uXAEYHw\ngJskcFmXYlNdou6ofWrQASIS1i1V4UrcegTq9n+HtS+NhF9e0uStU1pISDDV1nmJdrph2R47dqxh\nFsAfg9z1PN+Mn7zo3X///bYYLCG8gC1evNj+xirsP0/z1NWvvH/Sr4ri6qEjwzclb8uS8hHoZT1I\nooFvpeKfLwkJCZfrLGPuvF+mO1f0N29w4YiXZU3JxOkejjfi4ny483H5dF4I+P8jTfq/cz2H1bQM\n8csNnEU70yZu+iT8HfUMxGoVlvAY5a7z4uWIBnWTjjq+/OUvR1r1Xb6838QFcoIlhLY68adlOMd0\nqhMITBiD8G9077fkJiJ5O56gR2HhXNgC4ltRSO/m48J5w7/z6hcuL+53Ezo7TveizvMGkFXy5M1S\nJ29wWB6cv0jZUzJxOjq/keXLl1v/kbRvlnHl6vzgIJDnfydP3jwI77XXXp3s3aYCOokSHhxxxBGd\nlElfaCEjzn+PzFE+ZlHnSEvAQCcXXnjhMP8LXrIdSXFpivomAJoTLCG+fv60DGkOOuggl9RgPUGv\nrJJmmixNHbmJSJaOdwoSzc2Zl7gRcKRBYJXOzOTSQkT8myXKx4JpDRcxDmchJI9+ru4k30V2dpL6\n8qZhXjYc8S9vmf4/Zdqy8uRNW5dLj+UBX4x+Tsm4ut238wc577zzrC6OGLnr+hYCvRDI87+TJ28v\nvbpdf9/73te5/OMf/7hznPfAd+TEZ8ONA5TLy60fNZWoq05cBFF+48fn5+PY+fa59FHfLKZwgzzT\nzZ/61KeikkWe86f2IxOETrrpGXfa6Rc1LYP/iHshZ2xFL//Zzzjsj53hKKtEq3biynG/C/sOzDK5\nhKU+gTKdj7+cNWj0sNgSQSM6dYWXDJEvvO6a374EJshOPdTpL/UML99lyRKSVT90de3y20SZcdf8\n8/7yXadHcJN0ykQvJ36+cJtJQ/1OFzDwxZ3nO6ynny587JZlhs+n/e0v7UIH+oG+TSqk9dtHGZSZ\nVVgemWZZcvDgGPrGN76Rtbpc+aKW87Jcl/P9FnAAO+4Lt0QXHMMfAqGRJvBRqETPfuNSdH0O37zl\n1u3/jvs2sOYlbpb/P8+zMiz+czvueeA/+xhrnPjPUz9N+Nh/BpM/fL3bb1dfeNzplsevD12j0ro0\nfvtJFyU+hq4sfzmvnydcnksf/ga7sPjjlj/mhtPl+R3dwpQlZul4n1TQUDd4+f9gYVCS3izhzsii\nn58nPMDHXcva2X55eYiIu6nczdytG4t6IEb9M6AHDxf6kutRH66Rxunsf4fx7taO8DUCbKXZYI3B\nl4G/34N/N8LRL32oB7yIawH+fDuiAS5RH9Jz70BQyJMnIFq47wbhN5imIcpxmNTt/y5tu8LPcvf8\nd+31n6VpiQhlxz1b3HMm6hnj1+nSue8w4XBEhG9/oHbp+WaMYyxy5yjDlygd3bM7rIufzx2DmSvb\nfXcjCnH3jMvLOOTaFVdHuJ9curzfhRCRtB2PtcI1nm//puh2jVopbAYAACaLSURBVMaGO8gvh2M6\nNwxWWv2oxycHvn69rmXpbL+utESk282MrnGS9sERVw5YR+kQ7pekv6P6L67uqPNpIjz6Az549Euo\nCwtEN0E3yEoZgjXDEQmIB7976ROnB21xAdEgJRCVrGXF1dGm8/QpOOWVuv3fpX0BoP3+cyM8gPrP\n+bRExGHLs9ivg2cQ5CDqGevycI363POKZzO6cN6d49sfYxizfMLh8lBmHIHhWjgf5VIX4ref83Hi\nt89/oY9LT51hndA3PMa5/PSLa3dcP7i0eb7jW5ih1KQd74MHCGEJW0vCLA0w/TQA1Q1MV35S/UhP\nea4Dwp3U7Rp503a2X17UPwn1O11oty/dbmY/XfiYgS6NKTWc3/+NDn6fOl3TflMGZeURBlgG1iQS\nJh/h30nKSJPGTX8kzZM2fa9yaR+DYFmEwREc7iusJpJoBPi/KIKs1en/DkILGUkj/mAbfq6lKacf\naf1nMAP+oIhPsBxJKqPthRKRMhRUmeUhwIBU5Fs3/6w+qUpKRMgTJntZW530IR9FOnwLSdb64/Ll\nsXCga56Bi7oJvw1BSDtYxLWn23n0hRByf0Xh3C3vIFxLQ5aT4FGH/7ssfY1VwU1rJHmbT4JF1jT+\nixTPI//ll5dDpyfPF9IOgtA/7hkOJmXKKAoPKpMMIAJXXHGFjXzKio0iBe/05557zgZ5Y437L37x\ni2HF/8Vf/IUhWixLnj/ykY8MW/I2LGHKHxs2bLDr93utBHDxQ4KBeUQNxPLgfJEhy6MCl42ouMeJ\nrGWAybnnnmumT59u5s+fX2i7eqhsWJIcvOkaljWyZYPk9wjcfPPNNvzAjTfeWCgkVf3f8f9EAL6A\n8KZuDytSXETSgFCVGnujm3K+Ht3ScS2wDGSKl9Sr3Lpd9zEpvc1lshyVXW8E2KiqqA24qm4p0wKz\nZ8+2/gq9dOn1lt7req/y/etYnPJYM/yy0lpV8N3ACpJ0qsqvq6hjdOYe41MUDkXpVkU5YMAqrdGj\nR7cGjyz+IT72zhpR9lu3X2fUse8bEpCCjjXAP677FFJUu7Kc861V/bAAaWomSy+1JA8PRQYqBoum\nC235q7/6K/uQh0jEkYm48377KauIKSvqoqwihfKStIE5ewb/ItpRhP7oU/RUYBF69aMM+oA+4+P6\nAyyqJIhFtjtvW/B1cYN9UVO0WduHH4TvF+H0wsEzyn8vaz11z0c/0HampPxpqrL01tRMgPYgC9Mz\nSNFm4n5j+vDDD5tbbrllWKRDoqU6IQCQm26JmpJx6dx3t+kblybu2wUpK3O/mG6b5tGnRHRcu3Zt\np81xuvbzPHqxWRtTZ20OY8+9409TRG1uyLTVI488MmxH8X72RVF1cR9OnTp1WHuLKlvlDA4CIiKD\n09eRLXXzu8GbWq0GrUhlu5w89NBDrQ/EaaedFpkKchBMRdldKknAXjO9CEmWsO/gSV39GGij/Ebq\nSkJcpzgy0vT7zbXHffukN8m9xT0CQSFfr/vQ1VHH79NPP90cffTR5tJLL62jetKpIQiIiDSko8pU\nk8GBEL9NfZhgDbnmmmvM5s2bY2EKk4okb60UFs4XW0FwIYoYdEtfxDWf+OAEedddd5mNGzfWmlTW\nnSwl6Rf6Opgm6yTNYv2iv9gjhNDgTRT+N7CGtI1UNrEvmq6ziEjTe7AA/RnMeIvDnNy0tzPeLI86\n6iizcOFCc/zxx0eiQfuQbm2LG1gon/y9LBw8lKNM8JEKFXwSHbH2/PM//7N5+umne+pacPWZimP3\n0MCHpTGracCYAddJEX1NmZSzZs0au+rEld2Ub1lDmtJT9ddTRKT+fdQXDdnxeMuWLY17O0uidxqr\nhgObPE7+93//13z0ox+NtDK4ASrLG7ErP++3G9BuvfVWEzc1lbeOovND7sDs3nvvjSWQRdeZtjz/\nHsDHqBcZTVs+6fEVWbRoUe2tWOG2Ob27WSHDefRbCMQhICISh8yAnWcww7IwZ84cU3RckbKgZKDA\nNMx3nLWDa3lJAthE+ZcwmHKtjAEqDWZMdbCV+tKlS9Nkqzytm1Kry1QS/ek7mea9b5ICjGUhWHnS\nGOsQ1kMsWk215CTtF6XrHwIiIv3DuvY18YDBVIwJuurBtRdYSawADCxIHEnpVYd/nfooD1z4JmDb\nu9/9bhPEg6hsSgb93KDQjYz57ajbcdXmfXBzksTJ1KUt8pv7CdLTBIsW/wdTpkyxLwBN9Skrsu9U\nVjEIiIgUg2NrSuEt9ZJLLqn1Ekv3MAwCIHVddlyENcTvWEdseGv2fQTi/Ev8vGUdVz2Q520XfdRP\nh8cq+6obVi4CLkt6ua/rKjNnzrTWt6Y62NYV10HXS0Rk0O+AiPbXeVWDIyF77rlnV3+WokkIMFE3\nUzS9pq78t+yyfAvQx1lDmr5qoUwyRZ8V7WQK9mXIv//7v9upUVbS1NEiWefnQhn9oTL7h4CISP+w\nblRN7qGzePHi2jwUHQkByG7BupzloogpGddplEn9DBBpSE54ICzS/E8fsV9P0/dxcVYR3z/D4Z7l\nu19EMItucXnc/bV169ZaWiTd86Db/11c23ReCPRCQESkF0IDfJ2HT10iYfKg/vSnP232228/u8rA\nRUmN6p40RCEqf/gclgfqc8QGcoE+Wd5ayecPuP4UT7jebr/Rgby01enVLX3drxGQrtsS7G76hzHt\nl5NpN53SXEN/xPWjmx6tg88I9xk+IYhIiIVBf0pA4E//XyAllKsiW4BAsNmRHfhZTbPvvvsaBosq\n5MEHH7R+BGeccYYdrN75znfGqlEGCWGA2GuvvTp1Uj9Levnupksng3cAocEq4j7btm0zP/7xjy2x\n+fWvfz2sHi/biMNvf/vbxr09j7jYwBPvete7zLPPPmu453oJg+Mrr7xiMWMQZ/oLUuYw7ZW/TtfD\nJATdDjzwQPu/NmvWLPPb3/7WfPzjH69EZf6XWA7O0vXbb789cvl6JYqp0tYhIItI67q0+AZhETjz\nzDPN/vvvb4gG6d7ciq9peIkMOCwnfuyxx6wVhCWO3awQUQ/14SWm+8WDuJvFomjSQ3t9f4Zu0zhY\nq5ocDTfcE/QdlgzfWuSn8Z1My/S78ess+7jX/cp1rIDIggULci9DT9oe7sOvfvWrlnxcd911PX2i\nkpardEIgFoGydtNTue1CgF1fb7rpJrsjIzupBgNGaQ10dQWEZ2jGjBmdHWw5HwzUsfUm2ZU2NrN3\ngXqSlpU0nVd84kMwpnz3QS8n7HjaDQuXrknffptcH0S1vUltitOVvk36P8T/Hf8LZf/foWvgjG3r\nmjZtWmL94tqo80IgKQImaUKlEwIgwMOTB2LAbO13kYMhZbuHLg/CqEHeDVDh3ohKG06T5Dc6pGkT\n6fn0Q9CLdr700ksW/37U2c86zj333KGrrrrKtjFNH/RTxyLqynLPcN/7/3dF3e+0h7L5v4MI8lm/\nfn0RzVQZQiAxAn8SayrRBSEQgQDTMjfeeGPHhE6ERT5M2TBVkVYwuRMumjKYiti+fbuN2Eicgiin\nQ3wsOE9dmJARTNjkzSvognSb/gnXAR7o4XQJXy/yN3rR9t/97ncmIGpFFl2Lsg4//HDzZ3/2Z7aN\nafqgFsonVKLXdExcMdz37v+OKTn8R/DZYouDLP936IFTLHFBmOrC52bJkiV248i4PZvidNN5IZAX\nAfmI5EVQ+Q3BmPDjCN6k7H41DJJjx461PgzAwxJTHnY7duywaO3atcume/755+3vyZMnm1NOOcVM\nmjQplUMcxIEHdPCGGUlabOEJ//Aw7+YP0qsY8kcRp175slyHuL366qtdg7llKbfqPGCIL0Rbg2Vl\nJSFx/cL/HRF+2eiQD5sIsqpswoQJnSzjx483r7/+uv0d9393xBFH9M3vq6OYDoSAh4CIiAeGDvMj\ngGUgMKvbFR08+BCsHOyF4j8gJ06caK0YWBTyyDe/+U3zvve9L5UVw6/P6ZuXRFAOA00/3uSxPiFt\nC7HdZiJSNAnx72GO3X3MSqpu/3cQk7333rsv92lYR/0WAnEIiIjEIaPztUfAPdyxikB+0pIJ8vMA\nL4o8YKGBWKFPmdJWIkJfYDkLJpbLhK/vZbv7NC/p7rviqlAI9AkB+Yj0CWhVUzwCTMm4gR8Swhs1\ng1kSyeIP0qtcCA2ESJINgbIJXDat8uVy95lISD4clbvdCIiItLt/W9u6KJ8MyAhvn+4NNK7x5GVg\nKGNwcIQorm6dHxwEnIWsjPtscFBUSwcBARGRQejllrURohG3SsZNs7g3Ub/pWEscgSnz7RvdepEh\nXy8d/x4BMGvLoO1ISJn3me4bIdAWBERE2tKTA9QONyUT12Rn7YB0OGGQ45PWj8TlT/NN/ZCepNNE\nacpuc1r6FSfmpotISNN7UPr3G4F39LtC1ddeBBh48ZFgWa5bKhjVWre0Fw9+9tVI8xbsLBpR5frn\neBN10yR/+qd/av76r/+6MKdUv564YywzSXWNKyPuPMuhN27cGHe5seeDwFqN1d0pLhLikNC3EEiO\ngIhIcqyUMgIBrAxPPvmkDUrmYhkcdthhNobI3LlzI3IYu7SXJb2LFy82q1atMkE0Rxugi03t3NRK\nVEbqipuSiUrPOVZh/OY3v4m7XOp54pIwMHVrUxYFxo0bZ/HOkrfOeYh3wb3QVBEJaWrPSe+qEdDy\n3ap7oKH1E5VxxYoV1voxffp0Q1CyD3/4w5mWrrrATOzwOXr0aDN//vzI4GZpLQykd0HKIDFYbIom\nBb26j3qRNFafXmXSjjYucyXKJ4Ht2PG1aSIS0rQek751QkBEpE690QBdIA3BnhdW0zjCkKcZPsG5\n9dZbO4NSGhLipojC/iBx5/PomyRvGt2TlEcawnsTkjvcxqT565gOa9dTTz3Vd7KYFwuRkLwIKv+g\nIyAiMuh3QML282ZPJE/8P3yCkDB76mQM3uynseeeexq2vGfgTWJVSGL5KIMY9Gpg0XWCCb4i8+bN\n61V1I64zmLPfEA6rTRKRkCb1lnStKwJaNVPXnqmRXlgpePNm/h5n1H6Yzqlv8+bNZurUqdZcn2T/\nEQYFpNf0C2VDDLCQ9EuKXNIL2WJPkfvuu8+2o19tKLOeBx980DDF1yThHoIca4luk3pNutYRAVlE\n6tgrNdKJN++VK1faHXGrmgaAYFx00UXWOnL33XdHPvgZFJw/SFL4KJdBJImlJWmZ3dJlfXsmn7+i\nBFKDzk2dyojCCIvXwoULTVN2fi3awhWFic4JgUFBQERkUHo6ZTuxFpx//vnm5z//ufn617/et8E6\nTk30mTNnjnnzzTfN2rVrO2Qkr99HkqmcOJ2ynE8ygIWJRxzBYgt4lkmzPXyTxfkdYQFrgiTpwya0\nQzoKgbogICJSl56okR4M7lOmTLEa+YN+HVTEQvPyyy9bMoKefHpNxfTSOy+Z6VW+f526ID++zgxs\nvsQRDz8Nx5SDVaRXgLdwvrr9Pv30083RRx/diN2ERULqdvdInzYgICLShl4suA0sowxbHgquIldx\nkJFvf/vb5t577zUHHHBArrL8zAwySUmAny/t8Te/+U2bhaXKSJ4pL7BAmmoVAXP8gPA9qruvhUiI\nvdX0RwgUjoCISOGQNrtAzP0EJqubJcRHFVM+JIRAZUmcWP28vY6L9htx1ha/Xucsm4eAuPKabhVh\npQxEhBVZdRaRkDr3jnRrOgIiIk3vwQL1Z4A/99xz7UqMfjlwZlWfAZ7pozIGsTx+I2HiQeAxfxrG\nb29Rg9vNN99snnnmmcJJma9rGcfLly83ixYtsqujyii/qDKL6qei9FE5QqBtCIiItK1HM7aHAZRp\nCSwNTVm5gPWCN+o1a9bkmt6IgswRil5WC5fOldGNeLg07pu8YX8Rdy3NN+UcddRR1pn3vPPOS5O1\nsrRl9l2RjRIJKRJNlSUEohEQEYnGZeDO4heCLF26tFFtL/utmoHI9xuBOPhBt9IQjyhgGZCLiEVB\nOeiJr0WcBSaq/irOQZzKsmYV2R6RkCLRVFlCIB4BEZF4bAbmCg/cpjgMRnUKVhEsAWVYAyAezz33\nnHn3u99t98FxMTyi9Mh6rqgBj8Bzl1xySe3DpEN633777VpPJRXVJ1nvCeUTAoOEgIjIIPV2TFub\ntHwyqglFEiksC1HBw/L4jUTp7J8raoqGMv3lzXVchVJ3/egLrEq9puT8/tOxEBAC+RAQEcmHX+Nz\nFzmIVwkGZOrUU09NbRUJEw9/GibcnjIHKYgOUoSTsBvs6xCIzsfQ6VXXFVlFEkK/3ToWAkKgOwLa\na6Y7PrFXDz/8cDNq1Cj7YRdUX9x5vjdt2uRf6nrMfht+3q6JC7p4xx13mLlz59Y+hkOv5hICnhUY\nvQTi5X8Y+Hn7dZ9uVgSukY78DFpFCnr4vid5yiamyGGHHWZ1hWhVLWAFUXSB6LphXJWuIiFVIa96\nhYAxtSUiDO5uUGbQlxSPAA/fZcuW2UGi+NL7W6Jb6QNJ8MUnHRw7wuG+swyK5MWC4awYfn15jh3J\nyVOGywsZYZdkLDw49FYlECFW9Oyxxx61jU0jElLV3aF6hcDvEXiHgBhcBNavX29mzJhRyHRAHVD8\nxCc+YYmVrwuDexnCyhRHRoqYTnE6QhwYvItY+cIuyd/5znfMrFmzzCOPPGKIN1Kkrk7nqG8GdzYo\n/PznP2/uueee1FNmUWWWcU4kpAxUVaYQSIdAbS0i6ZrR/9QvvfSSGRoash8e9E2URx991L6tNlH3\nsM4EY/vYxz5mp8KctaMsEuLqdoN6kdMfzkLDAFmEgMHGjRvNIYccYvelgYwUVXacfqzewQpCkDUc\nP8tYzRRXd5rzIiFp0FJaIVAiAsFgWit5/vnnh4LmRn6Cee8Rut55551DnPfzcG7Hjh2RaV26s846\ny16//vrrO3ldBpeGb/Thc+KJJ9p0lI34dbpzcfkff/zxTn7KpCzOheWBBx7o6EK6KEnT3qj8/rlg\nIB0K/BL8U40/rqJNwSqbocDyUCh2RZeHcgEpGJo2bdoQGF177bWF9j0YrF69eiggPPbDcZ0FfcFD\nIgSEQPUIRI92FeqVlIhANBw58ImDO95nn32GXn/99WEtYRB31yEi4fwusUvDt09U+O1IR1IicvXV\nV3fq9Mv1y3L1diMiWdrryo365iHMgNQ2YaANppwqaRbkgQGuKCmDjKAbfX/55Zfb+/LYY48dCqZO\nMg3KjnxQFvcS2NedgNB+kRBQkAiB+iDQWB8RfBueeOKJYDyPlmDgNieffLJ57bXXDNEvw3L//feH\nT0X+vuqqqyLPJz153XXXxSa94IILzMEHH2yOPPLI2DTuQt72unLc91tvvWUmTpzoflbyPX36dOP6\nIfiXKEQHtpMPCGglYeqZBmGahumVYGDO3R6Cp+GHUkRZvjL4n+DMOn/+fIOfEFN0AWG2SbgnwBAZ\nP378sP+drVu3ml27dplXXnnFvPjii2bLli0mIB922fRll11WuJ5WiYL/aDqmYEBVnBAoAIHa+Ygw\nKDMoBZaHTvMC64M9h18GwjJXn4SQljx8AqtCJx9kxP/dufCHA8oNLDCdvOHr7jcPaVd+Fn+QOP0o\nn71deklR7fXrYbB2A45/vqrj4C21kKoDS5gdKAspLEMhzsm0CL8RCEhRS3qjmgJhwqGVsP7U89RT\nTxmWQSMQjsWLF5sFCxZ0Pq+++qq9dsoppxhWtfE/we7H+IAUTZZsRQX/cc7Fro8KLl7FCQEhkBWB\n4GFSS2EKJGiT/TAN4kvwsOxcY+ojLHF5/fOUzTRQlLh6+Xa+JOF0aaZmwnnDegQPfZskbmoma3vD\n9fq/b7rppiE+VQrYOqzj+iKtfkxnMEVQtWD+L2pqpahyqsakyvrxhWqbP1SVeKpuIVAkArWziAQD\nU0954YUXOmmi3uonT57cuU4Qpbi37SRTIuxjkkeI9hmWj370o8NOMU3STYpqr18H5nWsB3UR97Zd\nF33y6oG1gamaIoKfuSW9eXUa1Pwu3ksTrDaD2kdq92Aj0EgiArlwgh+IC3zmvsMDbBQRYVomiey+\n++5JksWm2XvvvUdcC/usROnnZyqivX55HLPpWJRu4XT9+v2lL33J4IPQNoGMuCmBrG0reklvVj2a\nmE8kpIm9Jp0HDYFGEpFB66Q6t9ePgOuIYNJv56hK+5xz8Q033NA6QuJ8EvL4jVBGsNqlzrdC7XQT\nCaldl0ghIRCJQCOJiG/N8J1NgzmrjlOpf1zlmz9OoWEJW0B66VdGewm53WtKKKx32b8hI6xSwjrS\nNmFagE84BH2adrqpnjR5BjWtSMig9rza3UQEGrl894gjjrAbaAE4vgVJfD2q6hyiS5500knDqn/2\n2Wc7v5lG6kVEymjvhAkTrBWio4gOSkfA9xvptstvN0XKWtJLnVhsmB6DEG7fvt1s27ZtmCqQV+4b\npivHjRtnfWCGJajJD5GQmnSE1BACCRFohEVk586dw5pzzDHHdH4Ti8Pf/Za3/IsvvrjjN1L1hnnE\nEfH1YykuOjs555xz3GHsd1ntZYlm24SBlAGzzpLHbwSrCrEwigjTThmEY585c6YN/37mmWeaFStW\nWOjGjBljd2VmZ2b3YdkuAvnnHFNwOHMTNt4N/jZBhX+cHnJMrbATVLUQSIlAIywivKHx0GOKglgi\nZ5xxho1t4Jw4Gdj9wd3HgAdm1dJNPxe3oZuOZbSXYFXEicgrrFBieqxICTvzpikbcsVbe90Fnw8G\nTawQzockqc6kdzsJJ83jpyMv8XUWLlzYCUgWhHzvGQsEAuULRCZYWmwee+wxax1Br9mzZ9vYJH66\nfh2LhPQLadUjBApGoMi1wEWWxV4sQVOHfQIi0qkiICcjQrSH0xOvwxc/fodflp+GY78cYntECfld\nunA97jzf4RDx/rVwvrg4ItSfpb1RertzxFQI3hrdz9Z8VxniPQuIgb9Q5vDqafdKcTFW6HdiyBQZ\nV4N2VLnXjOKEZLn7lEcI1AOB2k7N4FcRDNSxsS7wq1i3bp1NE+wZE4zvfxQiofKWniUK6h9LKeaI\nMOa8fWLNcYK+AdFKpV/R7XWm6zwrOVx76vS9atUqc+CBB9ZJpa664DdCX6R1YiUfH2cF6FYJlgsc\ngKdOnWqj6bL65tJLL+1pAelWZvgauhCldfPmzTZ0/DXXXGOnbfpxf7k63D0d1k2/hYAQqDcCo+BD\n9VZR2pWFAL4BbNde123a07Z7w4YNJtiAzQ6GafPWIT1kJK0Ta68pGq5DyPfff3/ry9HPwRrfkc9/\n/vMmsL5Y4lMGxpAQ2gQRkggBIdBMBGprEWkmnM3SGufD5cuXN0vpLto+99xz1uehS5JaX8rixNpt\nSS99ixVkzpw5dk+YfpIQgMbqgvVlzZo11iG2CAdbvwNFQnw0dCwEmouALCLN7bvcmjMw4BgazK8X\naqbPrVjGAljaysZtaZ0/M1ZXWjamW+ibpO1w0zM+0bjiiivMypUra4EHbYEMvfnmm2bt2rWFWC9E\nQkq7/VSwEOg7ArKI9B3y+lSIOZupjGXLltVHqYyasAx19OjRiQfvjNX0JRuEgk9SvxHSMtjzQSAh\nrCjDGpGUzJTZMO4zdvjFT2rKlCkdPbPWKRKSFTnlEwL1REAWkXr2S9+0YrDDfM+g1eR59g984APm\nkksuMTNmzOgbdv2oiP5J6jdCWhyjISFFWR6KbqMjSVn1EwkpukdUnhCoHgFZRKrvg0o1wMdg4sSJ\n5u67765UjzyVYw3B55qYJgzG/idPuXXIm8ZvhGBuTMd8/etfry2pvPHGG81+++1nzj///NTwioSk\nhkwZhEAjEJBFpBHdVK6SDNxYRfj2/QzKrbW40g899FC7ZJTlo2GhTb7gE1OH6QpfpyTHvfxGGKSx\nnNRlOqZbm5hCYorm2GOPNfPmzeuWtHNNJKQDhQ6EQOsQEBFpXZdmaxAm87ffftvO5WcroZpcLBFl\nVQZOqkmEQZDB2pekUx9+niqOne5YSXzhPMuwcQhtylJsiAXh4em7cHv8tnEsEhJGRL+FQLsQEBFp\nV39mbg2DGQPyrbfeWlmI7rTKF2UFoJzwjsi9Bse0uhaZHiuPT54IVrZlyxa7RLfIesoui+XFixYt\n6hr3JdzWsnVS+UJACPQfARGR/mNe2xp56DNF04QlsGVbAcJTOiwNrtO0FeTJORejW1OXYGMV4Z4j\n5khY6IM6E8KwvvotBIRANgRERLLh1tpcTHXcddddZuPGjZ2Bro6NZQBjOSjOj/0QfDQY7H3xrRL+\n+X4do9MXv/hFs++++yb2teiXbknrceQ3vGpLJCQpgkonBJqPgIhI8/uw8BbkXWJZuEKhAuuiX3hK\np9+OsBARrCHoccABB4RQas7P008/3e6B46wiIiHN6TtpKgSKQEBEpAgUW1hGXQZ7H1qmY9hMra5x\nMtAv7Ahb5pQOfYT0yyrk90WRxxAP9sNhwzyRkCKRVVlCoBkIiIg0o58q0ZKBbv369TZIVtVLXhnk\nWfKJZA2GVQWIUVM6Rfk9QHKa4M+TBHeWYF9wwQXm4osvTpJcaYSAEGgRAu9oUVvUlIIR4E0bnxH8\nMapcTcNbMg6N06dPt/FCnJNmwc0tpTgcXMNOrrTHlyxTOuw0DDmsmiD67chzPG3aNLsXTZ4ylFcI\nCIFmIiCLSDP7ra9aOyJA5NJrr712xMBaljJYQb761a+a22+/3Vx33XWNiZGRFo+oKZ1ejrBYq3bf\nfffGOqmGMcLPBcIbdggOp9NvISAE2oeAiEj7+rSUFjFY4p9BCPG5c+faEN1lWiYI2059+++/v7XK\nhK0KpTSyRoWGHWFRzZ/SYSpjyZIlw87VSP1MqrRpqikTAMokBAYUARGRAe34rM3GOrJgwQLz/PPP\nW0LCioeiSAJkZ/Xq1TbIFfotXLjQHH/88VlVbV0+N6Xz29/+1hxzzDGNjR0S1zEzZ860EWKbEh02\nrh06LwSEQDoEtOldOrwGPjVv5Q899JANzb19+3a7fJQBhCiZOGamFcgH1g/KwFfikUcesQSEFRQi\nIcPRBHs+7373uw0+FQiWk7bIhAkTDPeURAgIgcFCQBaRwervwlsLkWBlzaOPPmoee+wxM3r0aDud\ncvTRR9u62NnXF3aI3bVrl3nllVfMiy++aEOTM6ieeuqp5oQTTijMuuLX2bZjSB8B55YuXdqqpuGA\nu3jx4saFqm9VJ6gxQqACBLRqpgLQ21QlfiLseut2vuUNHbKxY8cOSziYxvFl7NixZsyYMeaUU04x\nn/vc51rl4+C3s8xjiBzWg7YJFjGJEBACg4eAiMjg9XmpLW7TktJSgVLhIxDAWRXfI4kQEAKDhYB8\nRAarv9VaIVBbBHB6zuJnVNsGSTEhIAQSISAikggmJRICQkAICAEhIATKQEBEpAxUVaYQEAKpEZA1\nJDVkyiAEWoGAiEgrulGNEALNR4Coqm5ZcvNboxYIASGQFAERkaRIKZ0QqAkChHZXvI2adIbUEAJC\nIDcCIiK5IVQBQqC/CIwbN85s27atv5X2oTZWzBxyyCF9qElVCAEhUCcERETq1BvSRQgkQIAN8Vat\nWpUgZbOSEOSOGDMSISAEBgsBEZHB6m+1tgUIEEQOy0GbwrvTLUTaPfLII1vQQ2qCEBACaRAQEUmD\nltIKgZogMGnSJLNu3bqaaJNfDUjVzp07DQHxJEJACAwWAiIig9Xfam1LEJg8ebLdeLAlzTGbNm0y\n06dPb0tz1A4hIARSICAikgIsJRUCdUGAnYmxIrQl9saiRYsM5EoiBITA4CEgIjJ4fa4WtwQBLAhf\n+cpXGt+a7373u3ZaBnIlEQJCYPAQGDUUyOA1Wy0WAs1HAGvIhz70IfODH/zA4MDaVDn99NPN0Ucf\nbS699NKmNkF6CwEhkAMBWURygKesQqBKBNgkbuLEiebuu++uUo1cdWMNIX7I+eefn6scZRYCQqC5\nCMgi0ty+k+ZCwPqJEFeE8OgQk6aJrCFN6zHpKwSKR0AWkeIxVYlCoG8IsNz12muvbeS0xvLly80b\nb7wha0jf7hZVJATqiYAsIvXsF2klBBIj8Ktf/cpgFbnuuuvMeeedlzhflQmdf8uaNWusn0uVuqhu\nISAEqkVARKRa/FW7ECgEAXwtpk6dap566qlGBAU77rjjzLHHHmvmzZtXSPtViBAQAs1FQFMzze07\naS4EOgiwembu3LnmzDPPNFhI6ixXXHGFVU8kpM69JN2EQP8QkEWkf1irJiFQOgIM8i+//LJZu3Zt\nLZf0ot/69evNxo0ba6lf6R2kCoSAEBiBgCwiIyDRCSHQXARuvPFGc9hhh5kpU6bUzjICCVm5cqV5\n4IEHREKae4tJcyFQOAIiIoVDqgKFQLUI+GSkDjv0MlXkLDVN8WGptgdVuxAYLARERAarv9XaAUEA\nMoIzKE6hGzZsqKzVrI7BOuOmi7S7bmVdoYqFQG0REBGpbddIMSGQDwGcQe+9915z7rnnmpkzZ/Z9\nqoY4ITjRQoiwhDQ5DH2+nlBuISAEuiEgItINHV0TAg1HgI3k2IsGIdbIzTffXHqLWEqMJYYddYkT\notUxpUOuCoRAoxHQqplGd5+UFwLJEYAgLFiwwEYz/exnP2sjmhZppXj44YfNihUr7N4xTQqulhxB\npRQCQqAMBEREykBVZQqBGiMAIbnjjjvMsmXLzIwZM8wpp5xiJk2alGnqhLIef/xxc/vtt5vRo0eb\nOXPmmE9+8pOZyqoxZFJNCAiBEhEQESkRXBUtBOqMAI6kTz75pHnkkUfMqlWrrC8HS3/HjBljxo8f\nb3bbbbcR6rNT7q5du8yWLVtsnkMOOcRMmzbNnHHGGY2I6DqiQTohBIRA5QiIiFTeBVJACNQDAawb\nW7dutUTjmWeeiVRq7NixHaJy4IEHNnLH38iG6aQQEAKVISAiUhn0qlgICAEhIASEgBDQqhndA0JA\nCAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUhICJSGfSqWAgIASEgBISA\nEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJCQERE94AQEAJCQAgIASFQGQIiIpVBr4qF\ngBAQAkJACAgBERHdA0JACAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUh\nICJSGfSqWAgIASEgBISAEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJC4P8Di13nEo+f\nAH0AAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='sentiment_network.png')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'': 0,\n",
" 'inhabitants': 1,\n",
" 'goku': 2,\n",
" 'stunts': 3,\n",
" 'catepillar': 4,\n",
" 'kristensen': 5,\n",
" 'goddess': 7,\n",
" 'offing': 49797,\n",
" 'distroy': 8,\n",
" 'unexplainably': 9,\n",
" 'concoctions': 10,\n",
" 'petite': 11,\n",
" 'paramilitary': 24759,\n",
" 'scribe': 12,\n",
" 'stevson': 13,\n",
" 'senegal': 6,\n",
" 'sctv': 14,\n",
" 'soundscape': 15,\n",
" 'rana': 16,\n",
" 'immortalizer': 18,\n",
" 'rene': 67354,\n",
" 'eko': 23,\n",
" 'planning': 20,\n",
" 'akiva': 21,\n",
" 'plod': 22,\n",
" 'orderly': 24,\n",
" 'zeleznice': 25,\n",
" 'critize': 29,\n",
" 'baguettes': 25649,\n",
" 'jefferies': 30,\n",
" 'uncertainties': 61695,\n",
" 'mountainbillies': 31,\n",
" 'steinbichler': 32,\n",
" 'vowel': 33,\n",
" 'rafe': 34,\n",
" 'donig': 68719,\n",
" 'tulipe': 36,\n",
" 'clot': 37,\n",
" 'hack': 12526,\n",
" 'distended': 38,\n",
" 'cornered': 37116,\n",
" 'impatiently': 40,\n",
" 'batrice': 12525,\n",
" 'unfortuntly': 41,\n",
" 'lung': 42,\n",
" 'scapegoats': 43,\n",
" 'pscychosexual': 45,\n",
" 'outbid': 46,\n",
" 'obit': 47,\n",
" 'sideshows': 48,\n",
" 'jugde': 49,\n",
" 'kevloun': 51,\n",
" 'quartier': 53,\n",
" 'harp': 61948,\n",
" 'unravelling': 54,\n",
" 'antiques': 56,\n",
" 'strutts': 57,\n",
" 'tilts': 58,\n",
" 'disconcert': 59,\n",
" 'dossiers': 60,\n",
" 'sorriest': 61,\n",
" 'craftsman': 49412,\n",
" 'blart': 62,\n",
" 'dependence': 37120,\n",
" 'sated': 61698,\n",
" 'iberia': 63,\n",
" 'sagan': 72,\n",
" 'frmann': 65,\n",
" 'daniell': 66,\n",
" 'rays': 67,\n",
" 'pried': 68,\n",
" 'khoobsurat': 69,\n",
" 'leavitt': 70,\n",
" 'caiano': 71,\n",
" 'attractiveness': 73,\n",
" 'kitaparaporn': 74,\n",
" 'hamilton': 75,\n",
" 'massages': 76,\n",
" 'horgan': 78,\n",
" 'chemist': 79,\n",
" 'audrey': 80,\n",
" 'yeow': 55655,\n",
" 'jana': 81,\n",
" 'dutch': 82,\n",
" 'pinchot': 24773,\n",
" 'override': 83,\n",
" 'dwervick': 63223,\n",
" 'spasms': 84,\n",
" 'resumed': 85,\n",
" 'tamale': 66259,\n",
" 'calibanian': 49636,\n",
" 'stinson': 86,\n",
" 'widows': 87,\n",
" 'stonewall': 88,\n",
" 'palatial': 89,\n",
" 'neuman': 90,\n",
" 'abandon': 91,\n",
" 'lemmings': 65314,\n",
" 'anglophile': 92,\n",
" 'ertha': 61706,\n",
" 'chevette': 94,\n",
" 'unscary': 95,\n",
" 'spoilerific': 97,\n",
" 'neworleans': 67639,\n",
" 'metamorphose': 17,\n",
" 'brigand': 99,\n",
" 'cheating': 41603,\n",
" 'clued': 101,\n",
" 'dermatonecrotic': 102,\n",
" 'grady': 103,\n",
" 'mulligan': 104,\n",
" 'ol': 105,\n",
" 'incubation': 107,\n",
" 'plaintiffs': 110,\n",
" 'snden': 109,\n",
" 'fk': 111,\n",
" 'deply': 112,\n",
" 'franchot': 113,\n",
" 'henstridge': 19,\n",
" 'cyhper': 114,\n",
" 'verbose': 26,\n",
" 'mazovia': 116,\n",
" 'elizabeth': 117,\n",
" 'palestine': 118,\n",
" 'robby': 119,\n",
" 'wongo': 120,\n",
" 'moshing': 121,\n",
" 'mstified': 12543,\n",
" 'eeeee': 122,\n",
" 'doltish': 123,\n",
" 'bree': 124,\n",
" 'postponed': 125,\n",
" 'debacles': 127,\n",
" 'amplify': 27,\n",
" 'kamm': 128,\n",
" 'phantom': 18893,\n",
" 'boylen': 136,\n",
" 'rolando': 131,\n",
" 'premises': 133,\n",
" 'bruck': 134,\n",
" 'loosely': 135,\n",
" 'wodehousian': 139,\n",
" 'onishi': 70389,\n",
" 'encapsuling': 140,\n",
" 'partly': 141,\n",
" 'stadling': 144,\n",
" 'calms': 143,\n",
" 'darkie': 148,\n",
" 'wheeling': 147,\n",
" 'ursla': 15875,\n",
" 'subsidized': 49420,\n",
" 'mckellar': 149,\n",
" 'ooookkkk': 151,\n",
" 'milky': 152,\n",
" 'unfolded': 153,\n",
" 'degrades': 154,\n",
" 'authenticating': 155,\n",
" 'writeup': 12548,\n",
" 'rotheroe': 156,\n",
" 'beart': 157,\n",
" 'intoxicants': 160,\n",
" 'grispin': 159,\n",
" 'cannes': 61718,\n",
" 'antithetical': 70398,\n",
" 'nnette': 161,\n",
" 'tsukamoto': 163,\n",
" 'antwones': 44205,\n",
" 'stows': 164,\n",
" 'suddenness': 165,\n",
" 'vol': 61720,\n",
" 'waqt': 166,\n",
" 'camazotz': 168,\n",
" 'paps': 55042,\n",
" 'shakher': 170,\n",
" 'terminate': 63868,\n",
" 'kotex': 56419,\n",
" 'delinquency': 171,\n",
" 'bromwell': 25214,\n",
" 'insecticide': 173,\n",
" 'charlton': 174,\n",
" 'nakada': 177,\n",
" 'titted': 24791,\n",
" 'urbane': 178,\n",
" 'depicted': 54491,\n",
" 'sadomasochistic': 179,\n",
" 'hyping': 181,\n",
" 'yr': 182,\n",
" 'hebert': 183,\n",
" 'waxwork': 12990,\n",
" 'deathrow': 185,\n",
" 'nourishes': 24792,\n",
" 'unmediated': 187,\n",
" 'tamper': 37143,\n",
" 'soad': 190,\n",
" 'alphabet': 189,\n",
" 'donen': 191,\n",
" 'lord': 192,\n",
" 'recess': 193,\n",
" 'watchably': 61023,\n",
" 'handsome': 194,\n",
" 'vignettes': 196,\n",
" 'pairings': 198,\n",
" 'uselful': 199,\n",
" 'sanders': 200,\n",
" 'outbursts': 72891,\n",
" 'nots': 201,\n",
" 'hatsumomo': 202,\n",
" 'actioned': 18292,\n",
" 'krimi': 24797,\n",
" 'appleby': 203,\n",
" 'tampax': 204,\n",
" 'sprinkling': 205,\n",
" 'defacing': 206,\n",
" 'lofty': 207,\n",
" 'verger': 213,\n",
" 'tablespoons': 211,\n",
" 'bernhard': 212,\n",
" 'goosebump': 64565,\n",
" 'acumen': 214,\n",
" 'percentages': 215,\n",
" 'wendingo': 216,\n",
" 'resonating': 217,\n",
" 'vntoarea': 218,\n",
" 'redundancies': 219,\n",
" 'strictly': 57081,\n",
" 'pitied': 221,\n",
" 'belying': 222,\n",
" 'michelangelo': 53153,\n",
" 'gleefulness': 223,\n",
" 'environmentalist': 24803,\n",
" 'gitane': 226,\n",
" 'corrected': 66547,\n",
" 'journalist': 227,\n",
" 'focusing': 228,\n",
" 'plethora': 229,\n",
" 'his': 39,\n",
" 'citizen': 230,\n",
" 'south': 55579,\n",
" 'clunkers': 232,\n",
" 'pendulous': 55991,\n",
" 'mounds': 24805,\n",
" 'deplorable': 233,\n",
" 'forgive': 234,\n",
" 'proplems': 235,\n",
" 'bankers': 237,\n",
" 'aqua': 238,\n",
" 'donated': 239,\n",
" 'disbelieving': 240,\n",
" 'acomplication': 241,\n",
" 'contrasted': 243,\n",
" 'muzzle': 44,\n",
" 'amphibians': 72141,\n",
" 'springs': 246,\n",
" 'reformatted': 49443,\n",
" 'toolbox': 247,\n",
" 'contacting': 248,\n",
" 'washrooms': 250,\n",
" 'raving': 251,\n",
" 'dynamism': 252,\n",
" 'mae': 253,\n",
" 'disharmony': 255,\n",
" 'molls': 72979,\n",
" 'dewaere': 12569,\n",
" 'untutored': 256,\n",
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" 'blushed': 61831,\n",
" 'prototype': 971,\n",
" 'lifewell': 976,\n",
" 'trafficker': 973,\n",
" 'crucifixions': 62188,\n",
" 'unrealistically': 975,\n",
" 'rivas': 977,\n",
" 'consequent': 978,\n",
" 'katsu': 979,\n",
" 'titantic': 980,\n",
" 'jalees': 981,\n",
" 'ranee': 982,\n",
" 'gambles': 984,\n",
" 'dispenses': 985,\n",
" 'disfigurement': 986,\n",
" 'bright': 987,\n",
" 'cristian': 988,\n",
" 'subculture': 37268,\n",
" 'capta': 991,\n",
" 'jewel': 992,\n",
" 'erect': 993,\n",
" 'avoide': 996,\n",
" 'inconnu': 997,\n",
" 'headquarters': 998,\n",
" 'babbling': 1000,\n",
" 'pac': 1001,\n",
" 'performace': 1003,\n",
" 'dorrit': 1004,\n",
" 'runners': 1005,\n",
" 'sentimentality': 1006,\n",
" 'marred': 1007,\n",
" 'commemorative': 1008,\n",
" 'helpers': 1012,\n",
" 'chiles': 1011,\n",
" 'snowy': 1013,\n",
" 'cheddar': 1014,\n",
" 'neath': 158,\n",
" 'outshine': 1016,\n",
" 'nadu': 1019,\n",
" 'wellbeing': 1020,\n",
" 'envisioned': 43779,\n",
" 'fanaticism': 1021,\n",
" 'morrisette': 12687,\n",
" 'sesame': 1024,\n",
" 'gran': 1023,\n",
" 'marlina': 1025,\n",
" 'artificiality': 1030,\n",
" 'coinsidence': 1027,\n",
" 'founders': 1028,\n",
" 'dismissably': 1029,\n",
" 'dracht': 66299,\n",
" 'scavengers': 1031,\n",
" 'neese': 12685,\n",
" 'pangborn': 1034,\n",
" 'elmore': 1039,\n",
" 'bristol': 71162,\n",
" 'lillies': 1035,\n",
" 'parkers': 1036,\n",
" 'skipped': 1038,\n",
" 'clipboard': 1042,\n",
" 'jucier': 1041,\n",
" 'haifa': 1043,\n",
" ...}"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"word2index = {}\n",
"\n",
"for i,word in enumerate(vocab):\n",
" word2index[word] = i\n",
"word2index"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def update_input_layer(review):\n",
" \n",
" global layer_0\n",
" \n",
" # clear out previous state, reset the layer to be all 0s\n",
" layer_0 *= 0\n",
" for word in review.split(\" \"):\n",
" layer_0[0][word2index[word]] += 1\n",
"\n",
"update_input_layer(reviews[0])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 18., 0., 0., ..., 0., 0., 0.]])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"layer_0"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_target_for_label(label):\n",
" if(label == 'POSITIVE'):\n",
" return 1\n",
" else:\n",
" return 0"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'POSITIVE'"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels[0]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_target_for_label(labels[0])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'NEGATIVE'"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels[1]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_target_for_label(labels[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project 3: Building a Neural Network"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"- Start with your neural network from the last chapter\n",
"- 3 layer neural network\n",
"- no non-linearity in hidden layer\n",
"- use our functions to create the training data\n",
"- create a \"pre_process_data\" function to create vocabulary for our training data generating functions\n",
"- modify \"train\" to train over the entire corpus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Where to Get Help if You Need it\n",
"- Re-watch previous week's Udacity Lectures\n",
"- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (40% Off: **traskud17**)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time\n",
"import sys\n",
"import numpy as np\n",
"\n",
"# Let's tweak our network from before to model these phenomena\n",
"class SentimentNetwork:\n",
" def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n",
" \n",
" # set our random number generator \n",
" np.random.seed(1)\n",
" \n",
" self.pre_process_data(reviews, labels)\n",
" \n",
" self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n",
" \n",
" \n",
" def pre_process_data(self, reviews, labels):\n",
" \n",
" review_vocab = set()\n",
" for review in reviews:\n",
" for word in review.split(\" \"):\n",
" review_vocab.add(word)\n",
" self.review_vocab = list(review_vocab)\n",
" \n",
" label_vocab = set()\n",
" for label in labels:\n",
" label_vocab.add(label)\n",
" \n",
" self.label_vocab = list(label_vocab)\n",
" \n",
" self.review_vocab_size = len(self.review_vocab)\n",
" self.label_vocab_size = len(self.label_vocab)\n",
" \n",
" self.word2index = {}\n",
" for i, word in enumerate(self.review_vocab):\n",
" self.word2index[word] = i\n",
" \n",
" self.label2index = {}\n",
" for i, label in enumerate(self.label_vocab):\n",
" self.label2index[label] = i\n",
" \n",
" \n",
" def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
" # Set number of nodes in input, hidden and output layers.\n",
" self.input_nodes = input_nodes\n",
" self.hidden_nodes = hidden_nodes\n",
" self.output_nodes = output_nodes\n",
"\n",
" # Initialize weights\n",
" self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n",
" \n",
" self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n",
" (self.hidden_nodes, self.output_nodes))\n",
" \n",
" self.learning_rate = learning_rate\n",
" \n",
" self.layer_0 = np.zeros((1,input_nodes))\n",
" \n",
" \n",
" def update_input_layer(self,review):\n",
"\n",
" # clear out previous state, reset the layer to be all 0s\n",
" self.layer_0 *= 0\n",
" for word in review.split(\" \"):\n",
" if(word in self.word2index.keys()):\n",
" self.layer_0[0][self.word2index[word]] += 1\n",
" \n",
" def get_target_for_label(self,label):\n",
" if(label == 'POSITIVE'):\n",
" return 1\n",
" else:\n",
" return 0\n",
" \n",
" def sigmoid(self,x):\n",
" return 1 / (1 + np.exp(-x))\n",
" \n",
" \n",
" def sigmoid_output_2_derivative(self,output):\n",
" return output * (1 - output)\n",
" \n",
" def train(self, training_reviews, training_labels):\n",
" \n",
" assert(len(training_reviews) == len(training_labels))\n",
" \n",
" correct_so_far = 0\n",
" \n",
" start = time.time()\n",
" \n",
" for i in range(len(training_reviews)):\n",
" \n",
" review = training_reviews[i]\n",
" label = training_labels[i]\n",
" \n",
" #### Implement the forward pass here ####\n",
" ### Forward pass ###\n",
"\n",
" # Input Layer\n",
" self.update_input_layer(review)\n",
"\n",
" # Hidden layer\n",
" layer_1 = self.layer_0.dot(self.weights_0_1)\n",
"\n",
" # Output layer\n",
" layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n",
"\n",
" #### Implement the backward pass here ####\n",
" ### Backward pass ###\n",
"\n",
" # TODO: Output error\n",
" layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n",
" layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n",
"\n",
" # TODO: Backpropagated error\n",
" layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n",
" layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n",
"\n",
" # TODO: Update the weights\n",
" self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n",
" self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n",
"\n",
" if(np.abs(layer_2_error) < 0.5):\n",
" correct_so_far += 1\n",
" \n",
" reviews_per_second = i / float(time.time() - start)\n",
" \n",
" sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n",
" if(i % 2500 == 0):\n",
" print(\"\")\n",
" \n",
" def test(self, testing_reviews, testing_labels):\n",
" \n",
" correct = 0\n",
" \n",
" start = time.time()\n",
" \n",
" for i in range(len(testing_reviews)):\n",
" pred = self.run(testing_reviews[i])\n",
" if(pred == testing_labels[i]):\n",
" correct += 1\n",
" \n",
" reviews_per_second = i / float(time.time() - start)\n",
" \n",
" sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n",
" + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n",
" + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n",
" \n",
" def run(self, review):\n",
" \n",
" # Input Layer\n",
" self.update_input_layer(review.lower())\n",
"\n",
" # Hidden layer\n",
" layer_1 = self.layer_0.dot(self.weights_0_1)\n",
"\n",
" # Output layer\n",
" layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n",
" \n",
" if(layer_2[0] > 0.5):\n",
" return \"POSITIVE\"\n",
" else:\n",
" return \"NEGATIVE\"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress:99.9% Speed(reviews/sec):587.5% #Correct:500 #Tested:1000 Testing Accuracy:50.0%"
]
}
],
"source": [
"# evaluate our model before training (just to show how horrible it is)\n",
"mlp.test(reviews[-1000:],labels[-1000:])"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n",
"Progress:10.4% Speed(reviews/sec):89.58 #Correct:1250 #Trained:2501 Training Accuracy:49.9%\n",
"Progress:20.8% Speed(reviews/sec):95.03 #Correct:2500 #Trained:5001 Training Accuracy:49.9%\n",
"Progress:27.4% Speed(reviews/sec):95.46 #Correct:3295 #Trained:6592 Training Accuracy:49.9%"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-62-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# train the network\n",
"mlp.train(reviews[:-1000],labels[:-1000])"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n",
"Progress:10.4% Speed(reviews/sec):96.39 #Correct:1247 #Trained:2501 Training Accuracy:49.8%\n",
"Progress:20.8% Speed(reviews/sec):99.31 #Correct:2497 #Trained:5001 Training Accuracy:49.9%\n",
"Progress:22.8% Speed(reviews/sec):99.02 #Correct:2735 #Trained:5476 Training Accuracy:49.9%"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-64-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# train the network\n",
"mlp.train(reviews[:-1000],labels[:-1000])"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n",
"Progress:10.4% Speed(reviews/sec):98.77 #Correct:1267 #Trained:2501 Training Accuracy:50.6%\n",
"Progress:20.8% Speed(reviews/sec):98.79 #Correct:2640 #Trained:5001 Training Accuracy:52.7%\n",
"Progress:31.2% Speed(reviews/sec):98.58 #Correct:4109 #Trained:7501 Training Accuracy:54.7%\n",
"Progress:41.6% Speed(reviews/sec):93.78 #Correct:5638 #Trained:10001 Training Accuracy:56.3%\n",
"Progress:52.0% Speed(reviews/sec):91.76 #Correct:7246 #Trained:12501 Training Accuracy:57.9%\n",
"Progress:62.5% Speed(reviews/sec):92.42 #Correct:8841 #Trained:15001 Training Accuracy:58.9%\n",
"Progress:69.4% Speed(reviews/sec):92.58 #Correct:9934 #Trained:16668 Training Accuracy:59.5%"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-66-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# train the network\n",
"mlp.train(reviews[:-1000],labels[:-1000])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Understanding Neural Noise"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih\nyGJVVsUIiIhU3AF1qJ43aqJvHnTQQTb2xL/8y7/UQS3pkAAB+u355583//RP/5Qgdb2SQJxwlL7m\nmmvqpZi0qRwBNlaEbJRJaipvpBToIKCpmQ4Ug3vgpmUgJE5uuukmw5u2pN4IELWUnW+bOr3BPYej\nNFOCmgqs973ma4f147/+67/MunXr/NOFHVPuySefbF5//XW7G3RhBaugWiIgi0gtu6W/SjElw4oZ\nSbMQcNaQppIQ0IZ8XH755eYrX/lKs8CXtkJACBSGgIhIYVA2uyDIiIKaNasP77jjDjN37txmKR2h\n7WWXXWZX/MhXJAIcnRICA4CAiMgAdHLSJhJw6p577kmaXOkqRIBBe9myZXZDuQrVKKRqrCIQ4fXr\n1xdSXpMKcc6XOO5yjAMozpjuw2+uRQnX+OBH4RxFx4wZMyLpgw8+aH1xXJl8f+ELX7D1jUj8hxOU\niY+Gnwd/njhdyIYOUfVzLao80ob9QEhHnUzLIOPHj7e/nXOqj5dNEPqDfocffvgwvWkrPidhYfqH\nuigTjMLYuzrD+fS7eARERIrHtNElYubHVC6pNwIM2oTmb4tfBffdo48+Wm/QS9Tue9/7nh10ia8y\nNDTU+UyaNMlccMEFlkjEVf+pT33KXtq1a5fZvn37sGSQAwLdsTTflbtjxw7zi1/8wtYX5ePBoH3s\nscdaYvjAAw908n3mM5+xU7iUmUYY6HGEJ9gePh9Oj3nz5plbbrnF1gUBQXbbbTd7/fHHH7e/Xfor\nr7zS/o77Q36IxO23326thK4O19Z99tkn1p+FjT8h9fw/uXzUz/8YZUr6gEAAvEQIjEDgBz/4wYhz\nOlEfBIKH5lBgvaqPQjk1CZxVh4LHXc5Smpc9GGhtu2n7nXfeGdmAYFWUTXP99dcPu37iiScOBQPs\nULCZ4LDz7gflcT0YjN2pYd/k43pAYIadp9xgr6IR510iVy/fvlx00UW2PP8cZVPWWWed5Z/uHLu2\nhdsQEAHbZvDxxeEVxoryu+ns2upj4eqIyxdXl6+PjotBQBaRPpC9JlaBqVxSXwQee+wxc+SRR9ZX\nwZSaYdnhLRwH3EGUYDA0s2bNimw6/RwM8tZ6EE7AGz/XooR4QLNnzzZ777131GWbj/xYPZxgIXni\niSdsXBqsE1HCcmvyJRHKxhKC9SNKXNvuu+++qMuJzm3atMncf//9XXUGI3RevHjxiDKJwRPV1nHj\nxhksKdu2bRuRRyeKRUBEpFg8VZoQKB0Bt8y6bWSRwZiYKIMowRt912Yfc8wxdiBl0PUFzKKIBj4P\nDLxEr40T8pHfXzH3zDPP2OSTJ0+Oy2YJMPmSCGWTlkE9Tm677bYRU0pxaaPOv/rqq/Z0N51dW92U\nj1/OwQcf7P8cccw0lqRcBEREysVXpQuBwhEg5sbEiRMLL7fqAhkQwj4OVevUr/r32GOPrlXtvvvu\n9jp+IL7stdde/s/OsUvHfeI7nIaPsVb8/Oc/7+Rj0MUKEEVuOomCgwMOOMD/GXv87LPPJk4bW0iP\nC/jXIFFWDT/rEUccYXbu3Omfsse98o3IoBOFIyAiUjikKlAIlIsAVoOxY8eWW0kFpfPWLDN4d+Ad\nweie6o9XAz+HjgNmMJsfeRzlsPrHEnQkBMpHQESkfIxVgxAoHIG4ZZKFV6QC+4LA22+/3bUeZylK\n2u/OgvLaa691LTd88S/+4i/slI5bxRK+7n7/6Ec/coddv0ePHm16pWVVTXgZb9dCQxc/8IEP2DO9\ndH7hhRcM+kjqh4CISP36RBoJgYFE4P3vf79ZtWrVQLYdZ8tugq8FUyZJHZQ/8pGP2OK2bNkSWyzL\ndJmq8ZfjHnLIITZ9N18diANTOkkE3xfSkidOVqxYYR1xs06ROB8PHLjjxOncyxcnLr/Ol4uAiEi5\n+Kp0ISAEEiLAjryDKgzWccHCcDyFqMStPInCDB8PVopcd911Juzg6tK7FSSXXHKJO2XOOOMM61xK\nyP04CwOrcZIKQdAgUHF5IEOsmPnEJz6RtMgR6SBnEAxiiHTTGT0+97nPjcivE9UjICJSfR9IAyEg\nBAYcgSBGiB1IsU74gylTFmeeeaYlFXHLe+Og+7d/+zcTxPqw5MInOQz+EARIShCPY8SKFojB97//\nfUOgNEiQE6wK5MO5NW7JsEvrviFE1A2RIi91O6HsKVOm2OkSdPXFTS3h7JpE2O4Ax12WgPs6u7ZS\nDnpktbok0UFpsiMgIpIdO+UUAkKgQATYBbotkWLTwsKqmZdeesnsu+++NgqpW91CdE/IAktc0wqD\nLo6oWFIeeuihzuoZLAMIMT6iyA1Ow88995whqiskyOlCuHV8SNI6txKdlKXE++23n7WOuPKI+Iol\ng3aHCYKLL0JU2fD0URQOrq3EBCFKqquDtrJ8mPYoSmoUcvU4N4q4aPVQRVoIASHQCwH2mPnqV79q\neGO89NJLeyVv1HWCmS1YsMAOmo1SPIeyWBkY4CEbUaQgR9HKKgQag8A7GqOpFK09AgwkPFgJMMQy\nzDfeeMOEneV44yW2AW+AEyZMsMcf+tCHat+2KhWEfAQh960KvPmxb0cb92XxpySqxFt1CwEh0F8E\nRET6i3fratuwYYPdwh2PdZbGYc7Fix2TLoNmOPonUUEJyMXcLUsL2cb+6aefthtOnXLKKTb/IDst\nuhvE4cRvcPTJGgP2m2++6ZK25puYF0cffXRr2qOGCAEhkAwBTc0kw0mpPAR4Q7/77rutGR3ywe6V\nJ5xwQub5fcpjLpxlfCwbxKntsssuy1yep2qjDn3y8d73vrdr+5kDh5C0ibSdfvrp5uyzzzannXZa\no/otj7KamsmDnvK2BQE5q7alJ/vQDgjDzTffbIj3wJ4Ua9asMZs3bzZs4Z7HyZDBlMEHhzq36RkD\nMc5s1NlmwUGTNrt2Y/ng0wtPVgd85zvfaRU0kFDCcEuEgBAYLARERAarvzO3loGSDbQcAYE0+NMF\nmQsOZWQAvvHGG+30DdEmIT3Lly8PpWr2T0c8+Ka9ScmH32qICCsB2iJggXWtFwFrS3tdO1ihwnqB\ntjmqYt079NBDXTP1LQS6IqCpma7w6CIIEIyIYEHEHcD60U9hgOIh/cEPftAsWrSosVMRtMNJEQQO\nSwp+OFik2iDcYz/72c/MTTfd1IbmDHwb6E/2xeGlQiIEeiEgItILoQG+zrQIAYeQr3/965W9raIH\nfig4aBINMuwAW8cuQme30gX9iiAf4Xbyxrlw4UJz/PHHhy817jdTcY888oh5z3ve04j+bRzAfVaY\ne5MAYmXc931uiqrrAwKamukDyE2swpEQggGtXbu2MhICdviQLF261EZNPO644wzWgDoK5mgsH3w4\ndlMuZT2MP/vZz9oVS3XEIo1ODz/8sCUf3GtMzfjWozTlKG09EHD9V9Z9X49WSosiEZBFpEg0W1KW\nT0LqZlpl0GJvDDYBq4NlJM1Kl6JvD/qJpb0sh26ybwVvz/Pnzx+2WobBTANZ0XdMf8rDyZyAe2n2\nxumPZqqlrgiIiNS1ZyrSq84kxEFSNRnBIuOCb/VaZut0LuMbEkQk0t/85jfWYlRGHWWXSV9ec801\nkb4uIiNlo198+Tw/cDCn75pMjotHRiV2Q0BEpBs6A3ht5syZhtUqrIqps+AMRyA0po36EUvDmZvB\nhAdtP+rshj9kCB34oA9LqZtmQWDQYiVW2Britxvc64C3r5OO4xGAWN5yyy3WYhmfSleEwHAERESG\n4zHQv4gRctddd5mNGzdWPtAm6QgCYBEqHv+RMsQnH3Ua5MODM8ubWVHUlH5zfQWZZAuAXqQX0sXb\nddXkz+mt73gEeJEhQvIgBaWLR0NXkiIgIpIUqZan42HPmycrPerge5EEbmcGvvfeewtZOUJ5Za90\nSdKubmkgIVGkCFJ2yCGHNGZennZMnTo1sQlfZKTbXVGPa0wVMlXZtoi/9UC33VqIiLS7fxO3joGM\nfT6atqMre92ce+65lkBkeWP2yUfU3jiJASw5odMzioRQtVulc+utt9b+bTSrriIjJd9kOYvHModV\nriwLZU71lL3GCIiI1Lhz+qWacxhsmmnf4ZOWRDEQstIEqTP5cO1DX4hIL0uVI2V1WVHk9Pe/aQex\naViqm2VFFmQEwilHSB/VehyztP4LX/hCIdbJerRIWvQLARGRfiFd43qilk/WWN0RqjE48RBkWiXO\nKkKaOqx0GaF8jxOQECTJwEvab33rW+bKK6+szfJmv3mOhOy333653prTYOLXr+PyEHD/g47gl1eT\nSm4jAu9oY6PUpuQIYA1BmuxchqWAHXvZEdifWvLJB/4vvSwKyVHrT0r0T/P2zyDwD//wD+Zd73qX\nJWZ1sow4EgJyONbmEUgZZIRPEoKWpy7lTYbAgw8+aP8Hk6VWKiEQQiDYcKmv8sADDwwFKtjPWWed\n1de6i6jM1592+OLaxXewk6h/qedxYKru4HLnnXf2TF9UgmnTpg2tXr26qOIqKyfYiXYoGJSG+Haf\nwAJSmT55K6YNafQnvS/0KfdhHfo2sFQNBZv0DV1++eW+irmPA+I1RNmS6hHgf099UX0/NFUDhXgP\nntaDKrxRrlq1ykyaNKkVELBPCdMvOHTyiZumqXtj3cqYpPrTj6xW8AULV0BObBRaIl1ikahCsLgx\nbcYKmSw+Id10xhrCB8uRpDoE8E1i5+SmWRyrQ0w1hxEQEQkjMkC/n3zySTNjxozGDth+V0E8cJR7\n7LHH/NONOoYsOBKSRnGmZBiQwwImlLdt2zYbOIzjfgnkiJgShOMn2Jo/ZVakDm7qSmSkSFTTlcX/\nHJtSSoRAVgRERFIid8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ\nVhiVSUggUwTGox0Em8OBOG2b0rSftCIjaRErLj39zQ7QJ5xwQnGFqqSBQ6AWRITtokeNGtX58Gb7\n1ltvxXbGpk2b7Nuvn2fMmDF222m3MiKc2U9Lfj4nnXSSrZP6kSRpcMry04Xr8X+vW7euUwd5qI9z\nWSSqzThook9WYVrmiCOOyJo9dz6HI+0oQpxpuGlvxxAQxOmfFAvyhadk4vKed955lhQQK8YREkzq\nRQmYMwWEU/AzzzxjV+0wFZN0eimvHo6MlEmy8urYxvzr1683gZ9ZpEWuje1Vm0pCoN/OLb6zJ86q\nfIKmjfjss88+Qzt27Bih3vXXXz8irZ+ffK+//vqIfH6acBnOOTRJGl9/0vvi57/66qtj9XT1+Xm7\nOauS3i87fExdaQXHsiASZ9pshaZ37TjxxBMLKzeYaqqFg2bSBtEPOF1mkbCDatIycIK95557bP8H\nFhPrRBoMKKn1oP5rr712WDlZ25JU9yTpsuKSpGylGY5AW5zdh7dKv/qNQKXLd++///5gLIqWgITY\neAgrV67sJMBycdVVV3V+Rx2Q7+STTzavvfaaDVYVlaZXGeRJkiaqbHfuuuuuc4cjvi+44AJz8MEH\nG6YSegkWFNJ3E+oaO3asmTVrVrdkw65t3brV7L///sPOVfXjiSeeKKzqCRMmGO6BJghv71k3dOs1\nJdOt/VgPsJDwwZLBPbZ48WLruEyYeO4L7iesjGHB2vHzn//cbjgYrIQxfDDNH3/88eGklf12vjFl\nTwlV1sCaVIxFDqsqy+YlQiAPApVPzQRvwyawYFifi127dhl+O/GJClMubJLlhMiMwRLZjq9GYBVw\nl+xAdMcdd3R+Rx2QPmB99hM3gCdJE1W2OxdYMjp1BJYUd9p+sz9KEvHb5WPFYBtYkzpFgE3ctFQn\nkXdAfsz0dRGmnoqQ8ePH26mBIsoqswxHJLJMXaSZkunVBqaDcCTFj4T/B+7TuXPnRpIQyrrooovM\nggULbFrilMybN69WJMS115GRqlYLOT3a/M09EyzJ7tv0W5uxHPi29dsEE57aCAbEYSoQfyPolM4n\nICf2ejhfVJwOf5qHKRpf/DJJFyVJ0oT18Mvx8wcEwr9kj8NTLK5tXIyammGKyS8zjBX5aadLg25J\n5aabbhriU6U4vflmesbHI6temOUxF9dVmBbJO3WQN39dsSlDL6a+wFxSPAJM7TKlJxECeRGo1CKC\nVWPvvfcOxqE/Svi3e8tnu3AnweAbOa3xmc98xiWxVhGmH6KEuAa9JEmabmWceuqpIy5/9KMfHXau\nm0MuCZlecoI1JIwN+6Scc845Lon5yU9+0jnudYCJHetBXsHR1Dmdpv3262Z6BjM/02+9cPHzNekY\nSwafPFMGzpLSpHZXqSsWHzCXZaTYXnAO4XWakiu2hSqtnwhUSkQOOOCAxG198803O2nDA7q7sPvu\nu7tD++1IzLCTwY9wuvB1fidJE5XPnQuTBs5DHHyJ08+lCSwE7tAwUEcN9L4vyttvv91Jn+QgrE+S\nPGWmefnll60/TJNjgcThw2CIpF0Z45dHGUlXyfj5Bv1YZKT4O4AXBlbLSIRAEQhUSkSKaIDKEAJ1\nR8C9PUYFHUuje1zgsjRlDGpaR0YcIRxUHIpqN4sIiKkkEQJFINAYIsKOnU6effZZdzjs27cgcKHK\nN34ccMMStoBEWU38PL5VBsfUYB6u6+fLX/6yn73rMasi4qauumbUxVQIMJUCAclLQjQlkwr2yMTO\nGiUyEglP4pPEnwFLh2fijEooBGIQqHT5boxOkadZVuiEFR+ssggvfw1iI7gkBj+ScePGdX73+wAf\nDAKY+eITKPTrRUQOOuigTnbyQmSKIlcszQwTt05lKQ5YNfH5z38+RY7eSXvh0ruEeqQoijz0Y0qG\nOl544QXrW8W9i7hluhxDpCZOnMih4X+R+4f/v6YNRrSDtvLJSw4tGAP4B2sIS78lQqAoBBpDRIgN\nwuANCUH+8R//0Xzta1/rkBGisfrLfX0nzqLASlNOOLYHEVD9eCBJ9INI4dCL7wTt/tSnPmW3UHcE\nizKJZukwCVbNpDKXFkFEnC5psCkzLVYerD1VCo6RRYY2Z0omj4NrHBZMGXEPEQti586dlmiwpPvs\ns8/ukGRXLwM3eiAsm9+4caO9F8k3efJku1UAG+01QRwZof1NI1JV48u9vWzZMhMEsqtaFdXfJgTy\nLrtJm99f/hq1jDYYVDvLUQOch0VXDS9/5XrUJyAsI5aC+unilrkmSePrT3pf/Py9jmmnL1HLd7ke\nri+uXPKnkbovc03TFj8tkT6rXJZMZFGWjBYlZSzVXb16dScaKnjlqYP2EqU1WPE0FAzwFnvONUEC\nC2OhfdWENufVkb4merFECBSJQGN8RIIB2EYO9QN8cS4sWE0ef/zxwqYwwuUn/R2EkY9NSqCzpNMP\nOISRvptgNVm7dm23JCOusfqCN+G2Ccu83RRCv9uG1QAp6i2b8opcJfPwww+bQw891FxzzTVm/vz5\n1sLB1JqzemTBC+sCZnqCm7HL7vbt263ObHxX9yWzbn8a50ycpf2Dlmf58uWt2ihz0Pqvru1tFBEB\nRBwyIRphQgIBYcAm9kYdpguIrxFYM+zUiut8YoGge1wkV5cu/E16nF/D5AYCQptfeumlxMTGlc0A\nwlx/mx7CDHyQK8Km91scjuBalDAVUkR56Mauu46AbN682ZQxjQKhYaM79MbPhH4ocmO9onD1yxEZ\n8dHofgwx5l4q497pXrOuth2BUZhX2t5ItS8aAfxLcDokxHcbhEEPosrbeT8Fp9Sse8bE6VmUoytv\nsJB2wraff/75fQ3HTX+ce+651odk0aJFfa07Dte480X79cTV0+TzbCOBXxlkUyIEikSgcRaRIhs/\n6GXhZIg5vQ3CoLdixYq+e/M7wpBlz5g43IuakoFoQkLoY8hmkTrG6e6fJ+omTrsE2psyZUqtrW9g\ng0WH/pREI4C18cwzz4y+qLNCIAcCIiI5wGt6VgYKTK1uWqHJ7fnwhz9sp71Gjx5tBxMGlDIHFd6g\nHQkpGre8UzLoxhYFrLYqcvVOlnYywLM5GtOI6FT3e01kJLqX3f9SHn+i6JJ1VggYo6mZAb8L2mJu\nZQrikUcesYOe36XuAerOFTGFgsWCwb4op1SnG995yQ16YX1AcGDutxXEVhzzB2fZSy65xE6dlYFd\nTLWZTufth0yV1jgT1jUCLOLcLBECRSMgIlI0og0rz00D5H0Lr7rZrAZZuHBhzy3peSP3I9yyKiWN\nQyh4IWnyJMWmiLJxSiUQWd1IiMOgaWSkCOLq2t7Ub8gtOEDOyrjvm4qL9C4OARGR4rBsbEm87SBN\ndULDGoIzJKtB0gqDPyTMCZFr497WITFYGMp6GOd9C8e69fTTT9eWhDiMm6In+tLn9HedLEsOx359\nQx5vueWWvjuB96t9qqd6BEREqu+DyjVwVhH8CeIG4cqVjFHAva3hkFnE/DXlgYMvzm+gzLfjvCTE\nrVBpylsrlps99tjDLF261Ie6lseDTkZmzpzZqMi5tbyJpFRXBEREusIzOBcJQMVg3u+lr3kR5iGJ\nlDmgYXHxY9MUQXj8duedknFk7N577+05NeXXW+Vx03Qu2xpWZV90q9u9pDCdOchWoW4Y6Vp+BERE\n8mPYmhJY1TB16tTGxBUp2wrgrCNh4oHVwZe8lpK81pB+kDG/vUUdu/7DAtWEQS4vYSwKt36WAwln\nX6EyiX4/26O66omAiEg9+6USrXjrg4w04c26bF0ZdCAiSaaq0CWrA2xeEkJ+yGNTBvPwjc0UDRF+\nm7IaY9DICM8DNhRlqb9ECJSFgIhIWcg2tNwmrGqAILBENdhorZQBLO9gQ/4kDrB56+EWYyBnx9ym\nRscFA1YuNWnVVhH91oTHgyP7/r3cBL2lY/MQEBFpXp+VrjHmWCJy4i+SxCJQukJeBZCQE044wXzs\nYx8rZZUPD9+iV8a4KR6vGZ0onuFpHz9Nr+OmW0Nc+5oYowIyktRi5trZtO+2xBhqGu6DqK+IyCD2\neoI2MzisXLmyVmTEWUIOOuggc8455xSySsaHgoE9r7+HX16347ADbJZ66aM27BXU1Ddv7kcISd3I\nerf7Ls01LFV1fBlJ0walbQYCIiLN6KdKtHTTNHXwGWGwYp+LSZMmdSwhRRIHyspjnUjTQVGmfdqX\nxs+EQZCYJ02a0uiGEb4Is2fPbtzOrm0lIzgSX3755Zli83TrZ10TAlEI/On/CyTqgs4JgQMPPNDs\nt99+ZtasWea3v/2t+fjHP14JKFgP2MX1i1/8ornqqqs6OvDGtm3bNvN///d/mVddMJC88sorfSMh\nKI9jKRYQX/baay/rK0Gb+KAX6SAtfH79618b0jj55je/af7nf/7Hhkx355r+vX79evP3f//3jWrG\nO9/5TsOHe4h+a4vcdttt1g+LiMUSIVA2ArKIlI1wC8rnbf2iiy6yLVmwYEHfBm0GYJww33jjDbNk\nyZLYeknHwJ3WRJ41X54uzWp5ccTE1X3TTTdZX5nzzjvPnWr0N32BRarJjpFZ+7ZuHce91iZrW93w\nlT4jEdDuuyMx0ZkQAgzwzBWzTJQPvgkMHGUJD0Ic5XjDZGkncQy6TZsQgpsPA0FScfqnJS9Jy49K\nR51Z35pxoAUD93nttdfMe97zntrvZhuFQ9Q5+s/tnBx1vQnn6Js092Dd2sT/HYJlatq0aaVtZVC3\ndkuf6hEQEam+DxqjAdYJpgsQBlQCaTGXXJRgeYHk8Da2a9cu+3ZMfIkkwa7cQM1A4B6ocXpRD8Lg\n108pyp8DQrNlyxa7dLefRKpsrPD/2bp1a9nVlFp+k8nInDlz7P/zihUrzNlnn10qTipcCPgIiIj4\naOi4JwIM+GyOh2PlhAkTrEMb88gQCEhJLxIQrgDigPWDMnBYZKvxZ555xsyfPz8TUWAgYKB2Fo+o\n+pwFJXytzN+0E92KEAgNb6xtE1YAvfrqq41vliMjaf8Xqm7422+/bZ3BV61aZadD2fYh7v+oal1V\nf7sQeEe7mqPW9AsBCAkWEj5YGB588EGzePFi+yBjOmX//fe30yoQi7BANNiqnp1iCUrGZ+HChcOi\nN+YZuLES8ABFL99ikKfMcBvS/EaXrFMyUfVgNRg7dmzUpUafmzhxoiWhjW7EH5SHjHD/QXqTWPTq\n1macwlk102+rYd1wkD79QUBEpD84t7oWBns/RDcPYCwmzz//fGS7cXxl+qWbhcC9VXZLE1n4H07y\nAOWNFPLBChWmlLKW1a2eJNewYBRZN9NWWA8k9UaA/4umkhFeDrB8SoRAPxAQEekHygNWh7NC5B18\nsSJgTcj6VsabKGWsW7fOnHTSSZX0QlVWmEoam7NSCCPTAm0SR0a4F7Pex/3GAxKydu3afler+gYY\nAfmIDHDn173pzqqRda7dzW+zHwvHWcvJilPRUzJZ9WhKviZOYSTB1hFzdz8myVNVGv7nmGJta19U\nhavq7Y6AiEh3fHS1YgR4iLuVOmlUwSSOuLdQymEg6OdgUNQqmTTtVtp6IuDuw37ef1mQWLNmzTC/\nqixlKI8QSIuAiEhaxJS+7whgsnfEIknlTIfw4HcPf5fHvZmmKcvlTfutKZm0iJlc03Dpa+t/Dnc/\n1pWMfPnLXy7Ul6n/CKvGpiIgItLUnhsgvTET80nyAHcEIM607AgK6coS9CxylUxZetatXCxIrJxp\nszgy0g8ynBZHR9TT5lN6IZAXATmr5kVQ+TsIMAC/8MILZseOHZ1lmG6ZLoncsl53zMqPI488MpEp\nmAe4s3R0KvQO8P9IujIGkoIjLeVhbYkjLV7xqQ6LXiUTrnyfffYxjz76aPh043/7m/41vjFdGsC9\nzP0KGSli8Of/7vvf/755/fXX7Z43xAPx/++ozxE8/gf5vxs3bpysH136SJf6i4D2mukv3q2rjYcp\nMURY7bBz5077wDv66KPN+PHj7RJdGuxWz5CWwYaPe2i++OKLNt/06dPN5MmTh8USiQLLWTz8azyI\nebBneaijE0TEvan65WY5jtIvSznd8lDH3Llzbdj9bumado0AWgixaQZBuGe5d7Pet+7/jii7BLjj\n/w6Suvfee1v4wv93nGRJ/fbt220Yd/5f+Z875ZRTbPyfogn5IPSh2lgMAiIixeA4UKXwAGU/imuu\nuca2m4fgGWeckemBSgGQgU2bNplFixZZUsIge/7550daKsIPbx7kSB4ikYfI2Mr/8KcIXfzy4o7B\ngDgsELqsA1lc2VWeZ8sABlJ2e87Tn1W2IW3d9GVSSx5lP/zww+aWW26x/zNJyXucTtw7Tz75pGF3\na/4HL7zwQjNjxoyBwT4OF52vAIEhiRBIgcDq1auHgkFiKCAfQxwXLd/5znds2dQR7DA7FAy2I6oI\nHtz2PN/BNMiI61lOUA9155G8+ZPUTR18Dj/88KFvfOMbSbI0Jg197vrUtbMfmNYBoF7tDIj/UDCt\nYj9l/N+BexBJdSgYgux31P9dHXCSDu1EwLSzWWpV0QjwoAoCHdkHYa+HZhF1Uwdkh4cvD+Gw3HPP\nPZEkJZwu7W/qzfIQLgsTyvU/rj3XXnvtEJ+2CPcXfR0lfvuLIp5R9VR9Luoeor38H0DSyiAg4TZT\nX2AVsfXxPyYRAv1AQESkHyg3vA4eSLwpYaHotzgLDIOuIwg8sDlm8CpDKDfNgEfaNOm76Uzd/sAb\nl9a9Icddb9p5+pc38l4Czknw6VVOXa/7ZCTq3u+X3ugBMYSUuP+7ftWtegYPAfmIVDAd1pQqmb/G\nDwR/kAceeCCzD0je9jKX/elPf9r87ne/M8GAZT7+8Y/bIsv0yaBs2p/EkTCPgyr1BINrB6I0q3hY\nIkwAKueU2CmkgQfsvrxkyZLUbQF7J+ARWA7cz8Z+06b77rvPOoBX2b/c/3PmzDE4lFf5/9/YjpTi\niREQEUkM1WAl5CE0ZcoU22j2najao54B+4tf/KLdN+app57qEIQ8JKBXj4JBYKHoOjimrd+V6erO\nM3h+6UtfMmyA1/TNyXDAhPBu3rzZwZLp2yd1OPMmIZGZKio50xVXXGG+/e1vm3vvvdcccMABJdfW\nu3hWMy1YsMCu0moqpr1bqRRVIiAiUiX6Na3bkZDDDjusNoMcOkGGeEivXLly2EMxLRlICzvlR1kq\nGPiQXm/h5HdS5ABJ/RAZLCq9dHD11/GbvYDOPvtsc9pppxWmXpjwRfVfYZUVWBD398svv2w3naMN\ndelXyOIll1wy7P+uwGarqAFHQERkwG+AcPPrSELCOjoygrUCcoLODMplvq1FxRuJI0A+8UD3MqdO\nwAJpqlUErKZOnWotT2Va3ei/wNfBYlUkGbQFFvTHJyFlYpFVXZGRrMgpXy8ERER6ITRg1+v+MHTd\n4fRkmgZhoOHtscwHOGQH0gPh8UmIP8ihS5nEg/J9QSfq86er/Ot1P8Y3ZP78+YVaQ3q1mT6ExDqp\ng7WE6Y+77rrLbNy4sdR72LU56zcxR4j3U3c9s7ZP+apBQESkGtxrWSsPmauvvrr0t9OiGn/ccceZ\nYEmxmTdvni3SJwdF1REuh0EMB8K//Mu/NKNHj7aXqx7IGMTQyZGysM51/V0XvX0iWYW1xFmFmkIm\nCTxHGPmHHnqorreW9GoYAiIiDeuwstR1b9ZVeumnbVuUzmWQkfAbNKGxISFVExAfL0gZUxxNCY/O\n4A9+WCbKnFLzMUpyHO7rsvuY+qjjuuuuM+edd14SFStPg85HHXWUXVHTFJ0rB00KdEVARKQrPINz\nEYfBIG5Ax7rQlJaHTcWQEySvkx+Exon/luwTnX5MBzkden2jC2QEsznh9ussTRrIfGtJnhVOcf3B\nyif2immadcFZcfjO+78Wh43ODw4CIiKD09exLXUPFef8GZuwphcgUWz45awBPllIqrI/4JAnys8j\niuSQD7+UOjyMb731VvOv//qv5j//8z9rZWXw+wASwrLwOq3I8vXrdkz/+zFfou6RbvnD1yivyaue\nmu4oHe4P/a4QgcGL4aYWhxEggmI/wkeH6y3qN1EgAyIwLAKkH6Eyqp6AdA2L0JkkemRcmUT7pLwq\nBd1oA1FwwaJqfeKwIHoqWwUkwTuujLqcB3P34R5IK2BRRbTitHrGpafNwdCVO6pwsCOwLYey7rzz\nzrjqBup8EECugwm49FvoB+rlE+yUXnr1/W9h6U0qpoLgja3TEeF/jm7Xiqm9f6W0JVQ4+3H4D3UG\nOn8w5qHpBg03aKdBmTzdhPp6pemWP+u1qHoZ4OpGRtCTcOFtISHh/grfX+Hr4d9uEAeXJgv3Gp88\n4p6nfA+SuIGeb4iHL1UTEXRx/XLiiSf6qpVy/CcBCJIGIzBq1CjjPg8++GDqlhAcjDDOTZe5c+fa\n5Y9+O1599VU7TcFUDYIp3X3SLPN1JnS/7PAx5VE2dTH90A9BLz7hKQJiiuD8iM/Ihg0b+qFK1zrc\ndMybb75pA3Wlwb5rwTW6yNScu7fcfcC9wIc+CstXvvIVEwzgtV6qG9Y56vdll11mFi5cmPmeJ6w/\nAdwQ/ocl9UHA9ccTTzxhsowtqVpSCr1pQaGODQZgjjAXdrvW76ajn/uEWXUvXdryVuba+bd/+7dD\nX/va16xlwllDirBSpC2DusG2TElSh9s0zbcUlalTVNlgh3Um71tzVNlNOedbS9x9WTeLVR4ssUZm\nmdoNticY2meffezzi+9BE/fc5jvts7sfWIX7h99liSwiwV0wqPLkk0+awFze+Lcy+o83T1aL8NbN\nG6lbEureTrP2MeVSRhpxdePIWoagE2/gfLoJIdOJTcGSbKwjZekTpQNWEFaEsKQYJ9qmRn6Nalva\nc/QT9xAfjm+77Tbjr8RKW17d0hOef8WKFanVCgZfs2PHDptv9uzZw/LzBu4svV/4whfstRtuuKFz\njmsXX3yx2bp167B8/g+sLYcffviwPJx76623/GTDjimPcl3dY8aMsdYA8rhzfIfL4HdYP9JxLqzj\n9OnTbVl+xWeeeaY95ywPfvspBwmm8YbpgJ5hCadZt27dsCSbNm0y4Om3BX1cvX5i7tFzzjnHnqKf\nHn/8cf9yscdFMhx8KXxrQaCptSYEjRhWTRA0q/MWDxMOX/fL4DgsMDPfmYZ64ury8ybVjzy+Dml9\nRHC+8tuIbmeddVYs6/XrAgvyMy/n2gVGYR0oz10Pfydl18zZZ3mT8TGt0zFvmzjehoU30iwWiqz5\nXP3M/6e1pri8Ud95ysMqEgyC1jKRBYsofaLOoaOri/urzLqi6m/CuWAH6SE+bRH6nGcQ32nEf+7x\nzPOFZ5h7rvEs9Z+H7rz7DuflGdotPc/TKAdMynFlhr+vv/76Ydf8MYuywunDv/3nd5Jnt99+ynJy\n0UUXdeqKsiJRj6ub674Vw7/m0vjf4ByWgHx0yivTV+SPLQxrkOJ32o4nPSA5EHwAwh0QvsnodD+v\nK8P/DudJqx9N9/9J/Juo17UsnR2uy2+Lf8wN7CTJzezSxn1TdtEDBVjTh/QpOkb1Fee4RhrSkqco\nYbCNahMkJe2DsigSQTlp6w7jQZucWT98LelvymCKhH7nO295fr2U7QgIpvqisPPraMsxDrtF41P1\n/x1twvE9qYQH73C+8DjgPwfDx+EBshsJcXl5BvmDNMdRzyqXPvztP7P853c4nf/b1Zfk2R1uv8Mn\nfD5MqHyiwrETn1D4OoWPw2MdOvtpwvW58vN+F0JEsnR8eMB2Het3qg8kDU16s1CGL1n08/UId07c\ntayd7Zfnd3rUMXUgSW5mH4PwcZz1IJwu6W/6z/8niNK92znyunsgaZ1R6brNV6d5+KdJG6VH+Bx4\nRxGkcLqo33nyRpWHHryRQ9qwIHGcpb3oxXJhLB/0Ld9ZyonSsc3nwKooqcv/HfdQGl8k//kfJhJg\nEx5wSeMGQcaB8PPPDfLhfP6zu9s1Xx/6x88XtoZw3T2rwlYUP1/4mnt2u76nHPdBN1/CurprYWLg\n10can0z59fljjI8l7fCxDBM0yvTzhuvjehGS+z8iDJivaLdrKO830L0du44BENfZrqGU7a7z7dcV\nvllcJ3TTods1Xze/nrDe/jU/T5rO9vOF20U7/DbTTl/8a7QnqfD2wqBdhKCj/w/g65TmmDJcv2XV\ni4dh3AMRqwSDZy9hoM5KGrqVTZlJ6vfLIH1ea4pfXviY+4BBBEJCX/FmC6FwOIa/Sct9A4nhQ1qm\n98rUMaxzk39D1MC4CKnT/x33APdCUvFfWsIvnJQRfjaHx4KwRcVd98v1Le1OL38M4bnrhOe1e1ZF\n5eOcu863q88vj+dXWPxne/j57JcXvhZuv1+u30YfOx8TdHHkzD/v6+7KJJ3//A7r4hOVKGxcOXm+\nczurfutb3wra9nsJlDSzZs1yP63zYNBRnd+3335755iDYIVD5zfLDRcsWND5zUZme++9d+c3B34Y\n5HBdV155pY3W6DK88sor9jCPfq6sJN84JLllaKRftmyZGTdunM1KO+644w4TdLb9HdzEsY4/4Xad\ndNJJJrjZbD7+sNlUERLcnDYaad6ycNKiz2lTXqEMygo7gqUpF4yfeeaZyCxu2Wiv5bUBYejpCBpZ\nQY+TwcBty8XZNIk4p1Snd5I8adMcf/zxNqz/5s2brTMc/4PsIxInu+++u11miW7gtHTpUrtzbpk6\nxunSxPMBYTN77rlnbtXr9n/HMy7Ns+mFF17oYLDvvvt2jqMOgsF8xFjgnq3h9H65RFsOy+TJkzun\neF7TH3xYouokKl/UOdLzvAoGYPvZvn27LQKHUJxicSb1xwRXft7vY445plPEf/zHf3SOn3322c7x\nJz7xCesQzYnXXnutcz4gXCOw9J1SSfiTn/ykk56D/fbbr/P7xRdf7BwXefCOvIVl6Xgajhx55JF2\nt1dICOI6jRvPJzT2YvDHv1mCNzh3uvP90ksvdY7dQR79XBlJvpN2tmtruLNdHVHt+sAHPuAu1+6b\nOCRhEkL/BSza7LHHHubggw+O1JkYHzy4gjfyYf1KWZQJscwiYfIaLoMVLQyicSthul0Ll5XlNwM2\ndVNP3IZqEKXAEhKrY5Z6k+RxusVhk6QMpemOQFEvAHX7vyNU/apVq7o33rvKxpFOeE50kwMOOKDb\n5WHX3BjCyZNPPnnYtagfkJCwjB8/Pnyq8xI54kJwgjICK4K54IILoi4Xfs5vF89LXoIhZt/73vc6\ndbGNgpPA4uEO7bPWrcLpnAwddCOUP/vZz0Kpi/mZm4hk6XhHRGgCb/v33XffsMHMt5S4ZobfkllW\nlUTy6pekDtIU1dlJ25VUr7h0WA1YdpdXIBK+8A+ZZNM1SCgC4eANYuLEiZ1iKDMrEekU0uXAEYHw\ngJskcFmXYlNdou6ofWrQASIS1i1V4UrcegTq9n+HtS+NhF9e0uStU1pISDDV1nmJdrph2R47dqxh\nFsAfg9z1PN+Mn7zo3X///bYYLCG8gC1evNj+xirsP0/z1NWvvH/Sr4ri6qEjwzclb8uS8hHoZT1I\nooFvpeKfLwkJCZfrLGPuvF+mO1f0N29w4YiXZU3JxOkejjfi4ny483H5dF4I+P8jTfq/cz2H1bQM\n8csNnEU70yZu+iT8HfUMxGoVlvAY5a7z4uWIBnWTjjq+/OUvR1r1Xb6838QFcoIlhLY68adlOMd0\nqhMITBiD8G9077fkJiJ5O56gR2HhXNgC4ltRSO/m48J5w7/z6hcuL+53Ezo7TveizvMGkFXy5M1S\nJ29wWB6cv0jZUzJxOjq/keXLl1v/kbRvlnHl6vzgIJDnfydP3jwI77XXXp3s3aYCOokSHhxxxBGd\nlElfaCEjzn+PzFE+ZlHnSEvAQCcXXnjhMP8LXrIdSXFpivomAJoTLCG+fv60DGkOOuggl9RgPUGv\nrJJmmixNHbmJSJaOdwoSzc2Zl7gRcKRBYJXOzOTSQkT8myXKx4JpDRcxDmchJI9+ru4k30V2dpL6\n8qZhXjYc8S9vmf4/Zdqy8uRNW5dLj+UBX4x+Tsm4ut238wc577zzrC6OGLnr+hYCvRDI87+TJ28v\nvbpdf9/73te5/OMf/7hznPfAd+TEZ8ONA5TLy60fNZWoq05cBFF+48fn5+PY+fa59FHfLKZwgzzT\nzZ/61KeikkWe86f2IxOETrrpGXfa6Rc1LYP/iHshZ2xFL//Zzzjsj53hKKtEq3biynG/C/sOzDK5\nhKU+gTKdj7+cNWj0sNgSQSM6dYWXDJEvvO6a374EJshOPdTpL/UML99lyRKSVT90de3y20SZcdf8\n8/7yXadHcJN0ykQvJ36+cJtJQ/1OFzDwxZ3nO6ynny587JZlhs+n/e0v7UIH+oG+TSqk9dtHGZSZ\nVVgemWZZcvDgGPrGN76Rtbpc+aKW87Jcl/P9FnAAO+4Lt0QXHMMfAqGRJvBRqETPfuNSdH0O37zl\n1u3/jvs2sOYlbpb/P8+zMiz+czvueeA/+xhrnPjPUz9N+Nh/BpM/fL3bb1dfeNzplsevD12j0ro0\nfvtJFyU+hq4sfzmvnydcnksf/ga7sPjjlj/mhtPl+R3dwpQlZul4n1TQUDd4+f9gYVCS3izhzsii\nn58nPMDHXcva2X55eYiIu6nczdytG4t6IEb9M6AHDxf6kutRH66Rxunsf4fx7taO8DUCbKXZYI3B\nl4G/34N/N8LRL32oB7yIawH+fDuiAS5RH9Jz70BQyJMnIFq47wbhN5imIcpxmNTt/y5tu8LPcvf8\nd+31n6VpiQhlxz1b3HMm6hnj1+nSue8w4XBEhG9/oHbp+WaMYyxy5yjDlygd3bM7rIufzx2DmSvb\nfXcjCnH3jMvLOOTaFVdHuJ9curzfhRCRtB2PtcI1nm//puh2jVopbAYAACaLSURBVMaGO8gvh2M6\nNwxWWv2oxycHvn69rmXpbL+utESk282MrnGS9sERVw5YR+kQ7pekv6P6L67uqPNpIjz6Az549Euo\nCwtEN0E3yEoZgjXDEQmIB7976ROnB21xAdEgJRCVrGXF1dGm8/QpOOWVuv3fpX0BoP3+cyM8gPrP\n+bRExGHLs9ivg2cQ5CDqGevycI363POKZzO6cN6d49sfYxizfMLh8lBmHIHhWjgf5VIX4ref83Hi\nt89/oY9LT51hndA3PMa5/PSLa3dcP7i0eb7jW5ih1KQd74MHCGEJW0vCLA0w/TQA1Q1MV35S/UhP\nea4Dwp3U7Rp503a2X17UPwn1O11oty/dbmY/XfiYgS6NKTWc3/+NDn6fOl3TflMGZeURBlgG1iQS\nJh/h30nKSJPGTX8kzZM2fa9yaR+DYFmEwREc7iusJpJoBPi/KIKs1en/DkILGUkj/mAbfq6lKacf\naf1nMAP+oIhPsBxJKqPthRKRMhRUmeUhwIBU5Fs3/6w+qUpKRMgTJntZW530IR9FOnwLSdb64/Ll\nsXCga56Bi7oJvw1BSDtYxLWn23n0hRByf0Xh3C3vIFxLQ5aT4FGH/7ssfY1VwU1rJHmbT4JF1jT+\nixTPI//ll5dDpyfPF9IOgtA/7hkOJmXKKAoPKpMMIAJXXHGFjXzKio0iBe/05557zgZ5Y437L37x\ni2HF/8Vf/IUhWixLnj/ykY8MW/I2LGHKHxs2bLDr93utBHDxQ4KBeUQNxPLgfJEhy6MCl42ouMeJ\nrGWAybnnnmumT59u5s+fX2i7eqhsWJIcvOkaljWyZYPk9wjcfPPNNvzAjTfeWCgkVf3f8f9EAL6A\n8KZuDytSXETSgFCVGnujm3K+Ht3ScS2wDGSKl9Sr3Lpd9zEpvc1lshyVXW8E2KiqqA24qm4p0wKz\nZ8+2/gq9dOn1lt7req/y/etYnPJYM/yy0lpV8N3ACpJ0qsqvq6hjdOYe41MUDkXpVkU5YMAqrdGj\nR7cGjyz+IT72zhpR9lu3X2fUse8bEpCCjjXAP677FFJUu7Kc861V/bAAaWomSy+1JA8PRQYqBoum\nC235q7/6K/uQh0jEkYm48377KauIKSvqoqwihfKStIE5ewb/ItpRhP7oU/RUYBF69aMM+oA+4+P6\nAyyqJIhFtjtvW/B1cYN9UVO0WduHH4TvF+H0wsEzyn8vaz11z0c/0HampPxpqrL01tRMgPYgC9Mz\nSNFm4n5j+vDDD5tbbrllWKRDoqU6IQCQm26JmpJx6dx3t+kblybu2wUpK3O/mG6b5tGnRHRcu3Zt\np81xuvbzPHqxWRtTZ20OY8+9409TRG1uyLTVI488MmxH8X72RVF1cR9OnTp1WHuLKlvlDA4CIiKD\n09eRLXXzu8GbWq0GrUhlu5w89NBDrQ/EaaedFpkKchBMRdldKknAXjO9CEmWsO/gSV39GGij/Ebq\nSkJcpzgy0vT7zbXHffukN8m9xT0CQSFfr/vQ1VHH79NPP90cffTR5tJLL62jetKpIQiIiDSko8pU\nk8GBEL9NfZhgDbnmmmvM5s2bY2EKk4okb60UFs4XW0FwIYoYdEtfxDWf+OAEedddd5mNGzfWmlTW\nnSwl6Rf6Opgm6yTNYv2iv9gjhNDgTRT+N7CGtI1UNrEvmq6ziEjTe7AA/RnMeIvDnNy0tzPeLI86\n6iizcOFCc/zxx0eiQfuQbm2LG1gon/y9LBw8lKNM8JEKFXwSHbH2/PM//7N5+umne+pacPWZimP3\n0MCHpTGracCYAddJEX1NmZSzZs0au+rEld2Ub1lDmtJT9ddTRKT+fdQXDdnxeMuWLY17O0uidxqr\nhgObPE7+93//13z0ox+NtDK4ASrLG7ErP++3G9BuvfVWEzc1lbeOovND7sDs3nvvjSWQRdeZtjz/\nHsDHqBcZTVs+6fEVWbRoUe2tWOG2Ob27WSHDefRbCMQhICISh8yAnWcww7IwZ84cU3RckbKgZKDA\nNMx3nLWDa3lJAthE+ZcwmHKtjAEqDWZMdbCV+tKlS9Nkqzytm1Kry1QS/ek7mea9b5ICjGUhWHnS\nGOsQ1kMsWk215CTtF6XrHwIiIv3DuvY18YDBVIwJuurBtRdYSawADCxIHEnpVYd/nfooD1z4JmDb\nu9/9bhPEg6hsSgb93KDQjYz57ajbcdXmfXBzksTJ1KUt8pv7CdLTBIsW/wdTpkyxLwBN9Skrsu9U\nVjEIiIgUg2NrSuEt9ZJLLqn1Ekv3MAwCIHVddlyENcTvWEdseGv2fQTi/Ev8vGUdVz2Q520XfdRP\nh8cq+6obVi4CLkt6ua/rKjNnzrTWt6Y62NYV10HXS0Rk0O+AiPbXeVWDIyF77rlnV3+WokkIMFE3\nUzS9pq78t+yyfAvQx1lDmr5qoUwyRZ8V7WQK9mXIv//7v9upUVbS1NEiWefnQhn9oTL7h4CISP+w\nblRN7qGzePHi2jwUHQkByG7BupzloogpGddplEn9DBBpSE54ICzS/E8fsV9P0/dxcVYR3z/D4Z7l\nu19EMItucXnc/bV169ZaWiTd86Db/11c23ReCPRCQESkF0IDfJ2HT10iYfKg/vSnP232228/u8rA\nRUmN6p40RCEqf/gclgfqc8QGcoE+Wd5ayecPuP4UT7jebr/Rgby01enVLX3drxGQrtsS7G76hzHt\nl5NpN53SXEN/xPWjmx6tg88I9xk+IYhIiIVBf0pA4E//XyAllKsiW4BAsNmRHfhZTbPvvvsaBosq\n5MEHH7R+BGeccYYdrN75znfGqlEGCWGA2GuvvTp1Uj9Levnupksng3cAocEq4j7btm0zP/7xjy2x\n+fWvfz2sHi/biMNvf/vbxr09j7jYwBPvete7zLPPPmu453oJg+Mrr7xiMWMQZ/oLUuYw7ZW/TtfD\nJATdDjzwQPu/NmvWLPPb3/7WfPzjH69EZf6XWA7O0vXbb789cvl6JYqp0tYhIItI67q0+AZhETjz\nzDPN/vvvb4gG6d7ciq9peIkMOCwnfuyxx6wVhCWO3awQUQ/14SWm+8WDuJvFomjSQ3t9f4Zu0zhY\nq5ocDTfcE/QdlgzfWuSn8Z1My/S78ess+7jX/cp1rIDIggULci9DT9oe7sOvfvWrlnxcd911PX2i\nkpardEIgFoGydtNTue1CgF1fb7rpJrsjIzupBgNGaQ10dQWEZ2jGjBmdHWw5HwzUsfUm2ZU2NrN3\ngXqSlpU0nVd84kMwpnz3QS8n7HjaDQuXrknffptcH0S1vUltitOVvk36P8T/Hf8LZf/foWvgjG3r\nmjZtWmL94tqo80IgKQImaUKlEwIgwMOTB2LAbO13kYMhZbuHLg/CqEHeDVDh3ohKG06T5Dc6pGkT\n6fn0Q9CLdr700ksW/37U2c86zj333KGrrrrKtjFNH/RTxyLqynLPcN/7/3dF3e+0h7L5v4MI8lm/\nfn0RzVQZQiAxAn8SayrRBSEQgQDTMjfeeGPHhE6ERT5M2TBVkVYwuRMumjKYiti+fbuN2Eicgiin\nQ3wsOE9dmJARTNjkzSvognSb/gnXAR7o4XQJXy/yN3rR9t/97ncmIGpFFl2Lsg4//HDzZ3/2Z7aN\nafqgFsonVKLXdExcMdz37v+OKTn8R/DZYouDLP936IFTLHFBmOrC52bJkiV248i4PZvidNN5IZAX\nAfmI5EVQ+Q3BmPDjCN6k7H41DJJjx461PgzAwxJTHnY7duywaO3atcume/755+3vyZMnm1NOOcVM\nmjQplUMcxIEHdPCGGUlabOEJ//Aw7+YP0qsY8kcRp175slyHuL366qtdg7llKbfqPGCIL0Rbg2Vl\nJSFx/cL/HRF+2eiQD5sIsqpswoQJnSzjx483r7/+uv0d9393xBFH9M3vq6OYDoSAh4CIiAeGDvMj\ngGUgMKvbFR08+BCsHOyF4j8gJ06caK0YWBTyyDe/+U3zvve9L5UVw6/P6ZuXRFAOA00/3uSxPiFt\nC7HdZiJSNAnx72GO3X3MSqpu/3cQk7333rsv92lYR/0WAnEIiIjEIaPztUfAPdyxikB+0pIJ8vMA\nL4o8YKGBWKFPmdJWIkJfYDkLJpbLhK/vZbv7NC/p7rviqlAI9AkB+Yj0CWhVUzwCTMm4gR8Swhs1\ng1kSyeIP0qtcCA2ESJINgbIJXDat8uVy95lISD4clbvdCIiItLt/W9u6KJ8MyAhvn+4NNK7x5GVg\nKGNwcIQorm6dHxwEnIWsjPtscFBUSwcBARGRQejllrURohG3SsZNs7g3Ub/pWEscgSnz7RvdepEh\nXy8d/x4BMGvLoO1ISJn3me4bIdAWBERE2tKTA9QONyUT12Rn7YB0OGGQ45PWj8TlT/NN/ZCepNNE\nacpuc1r6FSfmpotISNN7UPr3G4F39LtC1ddeBBh48ZFgWa5bKhjVWre0Fw9+9tVI8xbsLBpR5frn\neBN10yR/+qd/av76r/+6MKdUv564YywzSXWNKyPuPMuhN27cGHe5seeDwFqN1d0pLhLikNC3EEiO\ngIhIcqyUMgIBrAxPPvmkDUrmYhkcdthhNobI3LlzI3IYu7SXJb2LFy82q1atMkE0Rxugi03t3NRK\nVEbqipuSiUrPOVZh/OY3v4m7XOp54pIwMHVrUxYFxo0bZ/HOkrfOeYh3wb3QVBEJaWrPSe+qEdDy\n3ap7oKH1E5VxxYoV1voxffp0Q1CyD3/4w5mWrrrATOzwOXr0aDN//vzI4GZpLQykd0HKIDFYbIom\nBb26j3qRNFafXmXSjjYucyXKJ4Ht2PG1aSIS0rQek751QkBEpE690QBdIA3BnhdW0zjCkKcZPsG5\n9dZbO4NSGhLipojC/iBx5/PomyRvGt2TlEcawnsTkjvcxqT565gOa9dTTz3Vd7KYFwuRkLwIKv+g\nIyAiMuh3QML282ZPJE/8P3yCkDB76mQM3uynseeeexq2vGfgTWJVSGL5KIMY9Gpg0XWCCb4i8+bN\n61V1I64zmLPfEA6rTRKRkCb1lnStKwJaNVPXnqmRXlgpePNm/h5n1H6Yzqlv8+bNZurUqdZcn2T/\nEQYFpNf0C2VDDLCQ9EuKXNIL2WJPkfvuu8+2o19tKLOeBx980DDF1yThHoIca4luk3pNutYRAVlE\n6tgrNdKJN++VK1faHXGrmgaAYFx00UXWOnL33XdHPvgZFJw/SFL4KJdBJImlJWmZ3dJlfXsmn7+i\nBFKDzk2dyojCCIvXwoULTVN2fi3awhWFic4JgUFBQERkUHo6ZTuxFpx//vnm5z//ufn617/et8E6\nTk30mTNnjnnzzTfN2rVrO2Qkr99HkqmcOJ2ynE8ygIWJRxzBYgt4lkmzPXyTxfkdYQFrgiTpwya0\nQzoKgbogICJSl56okR4M7lOmTLEa+YN+HVTEQvPyyy9bMoKefHpNxfTSOy+Z6VW+f526ID++zgxs\nvsQRDz8Nx5SDVaRXgLdwvrr9Pv30083RRx/diN2ERULqdvdInzYgICLShl4suA0sowxbHgquIldx\nkJFvf/vb5t577zUHHHBArrL8zAwySUmAny/t8Te/+U2bhaXKSJ4pL7BAmmoVAXP8gPA9qruvhUiI\nvdX0RwgUjoCISOGQNrtAzP0EJqubJcRHFVM+JIRAZUmcWP28vY6L9htx1ha/Xucsm4eAuPKabhVh\npQxEhBVZdRaRkDr3jnRrOgIiIk3vwQL1Z4A/99xz7UqMfjlwZlWfAZ7pozIGsTx+I2HiQeAxfxrG\nb29Rg9vNN99snnnmmcJJma9rGcfLly83ixYtsqujyii/qDKL6qei9FE5QqBtCIiItK1HM7aHAZRp\nCSwNTVm5gPWCN+o1a9bkmt6IgswRil5WC5fOldGNeLg07pu8YX8Rdy3NN+UcddRR1pn3vPPOS5O1\nsrRl9l2RjRIJKRJNlSUEohEQEYnGZeDO4heCLF26tFFtL/utmoHI9xuBOPhBt9IQjyhgGZCLiEVB\nOeiJr0WcBSaq/irOQZzKsmYV2R6RkCLRVFlCIB4BEZF4bAbmCg/cpjgMRnUKVhEsAWVYAyAezz33\nnHn3u99t98FxMTyi9Mh6rqgBj8Bzl1xySe3DpEN633777VpPJRXVJ1nvCeUTAoOEgIjIIPV2TFub\ntHwyqglFEiksC1HBw/L4jUTp7J8raoqGMv3lzXVchVJ3/egLrEq9puT8/tOxEBAC+RAQEcmHX+Nz\nFzmIVwkGZOrUU09NbRUJEw9/GibcnjIHKYgOUoSTsBvs6xCIzsfQ6VXXFVlFEkK/3ToWAkKgOwLa\na6Y7PrFXDz/8cDNq1Cj7YRdUX9x5vjdt2uRf6nrMfht+3q6JC7p4xx13mLlz59Y+hkOv5hICnhUY\nvQTi5X8Y+Hn7dZ9uVgSukY78DFpFCnr4vid5yiamyGGHHWZ1hWhVLWAFUXSB6LphXJWuIiFVIa96\nhYAxtSUiDO5uUGbQlxSPAA/fZcuW2UGi+NL7W6Jb6QNJ8MUnHRw7wuG+swyK5MWC4awYfn15jh3J\nyVOGywsZYZdkLDw49FYlECFW9Oyxxx61jU0jElLV3aF6hcDvEXiHgBhcBNavX29mzJhRyHRAHVD8\nxCc+YYmVrwuDexnCyhRHRoqYTnE6QhwYvItY+cIuyd/5znfMrFmzzCOPPGKIN1Kkrk7nqG8GdzYo\n/PznP2/uueee1FNmUWWWcU4kpAxUVaYQSIdAbS0i6ZrR/9QvvfSSGRoash8e9E2URx991L6tNlH3\nsM4EY/vYxz5mp8KctaMsEuLqdoN6kdMfzkLDAFmEgMHGjRvNIYccYvelgYwUVXacfqzewQpCkDUc\nP8tYzRRXd5rzIiFp0FJaIVAiAsFgWit5/vnnh4LmRn6Cee8Rut55551DnPfzcG7Hjh2RaV26s846\ny16//vrrO3ldBpeGb/Thc+KJJ9p0lI34dbpzcfkff/zxTn7KpCzOheWBBx7o6EK6KEnT3qj8/rlg\nIB0K/BL8U40/rqJNwSqbocDyUCh2RZeHcgEpGJo2bdoQGF177bWF9j0YrF69eiggPPbDcZ0FfcFD\nIgSEQPUIRI92FeqVlIhANBw58ImDO95nn32GXn/99WEtYRB31yEi4fwusUvDt09U+O1IR1IicvXV\nV3fq9Mv1y3L1diMiWdrryo365iHMgNQ2YaANppwqaRbkgQGuKCmDjKAbfX/55Zfb+/LYY48dCqZO\nMg3KjnxQFvcS2NedgNB+kRBQkAiB+iDQWB8RfBueeOKJYDyPlmDgNieffLJ57bXXDNEvw3L//feH\nT0X+vuqqqyLPJz153XXXxSa94IILzMEHH2yOPPLI2DTuQt72unLc91tvvWUmTpzoflbyPX36dOP6\nIfiXKEQHtpMPCGglYeqZBmGahumVYGDO3R6Cp+GHUkRZvjL4n+DMOn/+fIOfEFN0AWG2SbgnwBAZ\nP378sP+drVu3ml27dplXXnnFvPjii2bLli0mIB922fRll11WuJ5WiYL/aDqmYEBVnBAoAIHa+Ygw\nKDMoBZaHTvMC64M9h18GwjJXn4SQljx8AqtCJx9kxP/dufCHA8oNLDCdvOHr7jcPaVd+Fn+QOP0o\nn71deklR7fXrYbB2A45/vqrj4C21kKoDS5gdKAspLEMhzsm0CL8RCEhRS3qjmgJhwqGVsP7U89RT\nTxmWQSMQjsWLF5sFCxZ0Pq+++qq9dsoppxhWtfE/we7H+IAUTZZsRQX/cc7Fro8KLl7FCQEhkBWB\n4GFSS2EKJGiT/TAN4kvwsOxcY+ojLHF5/fOUzTRQlLh6+Xa+JOF0aaZmwnnDegQPfZskbmoma3vD\n9fq/b7rppiE+VQrYOqzj+iKtfkxnMEVQtWD+L2pqpahyqsakyvrxhWqbP1SVeKpuIVAkArWziAQD\nU0954YUXOmmi3uonT57cuU4Qpbi37SRTIuxjkkeI9hmWj370o8NOMU3STYpqr18H5nWsB3UR97Zd\nF33y6oG1gamaIoKfuSW9eXUa1Pwu3ksTrDaD2kdq92Aj0EgiArlwgh+IC3zmvsMDbBQRYVomiey+\n++5JksWm2XvvvUdcC/usROnnZyqivX55HLPpWJRu4XT9+v2lL33J4IPQNoGMuCmBrG0reklvVj2a\nmE8kpIm9Jp0HDYFGEpFB66Q6t9ePgOuIYNJv56hK+5xz8Q033NA6QuJ8EvL4jVBGsNqlzrdC7XQT\nCaldl0ghIRCJQCOJiG/N8J1NgzmrjlOpf1zlmz9OoWEJW0B66VdGewm53WtKKKx32b8hI6xSwjrS\nNmFagE84BH2adrqpnjR5BjWtSMig9rza3UQEGrl894gjjrAbaAE4vgVJfD2q6hyiS5500knDqn/2\n2Wc7v5lG6kVEymjvhAkTrBWio4gOSkfA9xvptstvN0XKWtJLnVhsmB6DEG7fvt1s27ZtmCqQV+4b\npivHjRtnfWCGJajJD5GQmnSE1BACCRFohEVk586dw5pzzDHHdH4Ti8Pf/Za3/IsvvrjjN1L1hnnE\nEfH1YykuOjs555xz3GHsd1ntZYlm24SBlAGzzpLHbwSrCrEwigjTThmEY585c6YN/37mmWeaFStW\nWOjGjBljd2VmZ2b3YdkuAvnnHFNwOHMTNt4N/jZBhX+cHnJMrbATVLUQSIlAIywivKHx0GOKglgi\nZ5xxho1t4Jw4Gdj9wd3HgAdm1dJNPxe3oZuOZbSXYFXEicgrrFBieqxICTvzpikbcsVbe90Fnw8G\nTawQzockqc6kdzsJJ83jpyMv8XUWLlzYCUgWhHzvGQsEAuULRCZYWmwee+wxax1Br9mzZ9vYJH66\nfh2LhPQLadUjBApGoMi1wEWWxV4sQVOHfQIi0qkiICcjQrSH0xOvwxc/fodflp+GY78cYntECfld\nunA97jzf4RDx/rVwvrg4ItSfpb1RertzxFQI3hrdz9Z8VxniPQuIgb9Q5vDqafdKcTFW6HdiyBQZ\nV4N2VLnXjOKEZLn7lEcI1AOB2k7N4FcRDNSxsS7wq1i3bp1NE+wZE4zvfxQiofKWniUK6h9LKeaI\nMOa8fWLNcYK+AdFKpV/R7XWm6zwrOVx76vS9atUqc+CBB9ZJpa664DdCX6R1YiUfH2cF6FYJlgsc\ngKdOnWqj6bL65tJLL+1pAelWZvgauhCldfPmzTZ0/DXXXGOnbfpxf7k63D0d1k2/hYAQqDcCo+BD\n9VZR2pWFAL4BbNde123a07Z7w4YNJtiAzQ6GafPWIT1kJK0Ta68pGq5DyPfff3/ry9HPwRrfkc9/\n/vMmsL5Y4lMGxpAQ2gQRkggBIdBMBGprEWkmnM3SGufD5cuXN0vpLto+99xz1uehS5JaX8rixNpt\nSS99ixVkzpw5dk+YfpIQgMbqgvVlzZo11iG2CAdbvwNFQnw0dCwEmouALCLN7bvcmjMw4BgazK8X\naqbPrVjGAljaysZtaZ0/M1ZXWjamW+ibpO1w0zM+0bjiiivMypUra4EHbYEMvfnmm2bt2rWFWC9E\nQkq7/VSwEOg7ArKI9B3y+lSIOZupjGXLltVHqYyasAx19OjRiQfvjNX0JRuEgk9SvxHSMtjzQSAh\nrCjDGpGUzJTZMO4zdvjFT2rKlCkdPbPWKRKSFTnlEwL1REAWkXr2S9+0YrDDfM+g1eR59g984APm\nkksuMTNmzOgbdv2oiP5J6jdCWhyjISFFWR6KbqMjSVn1EwkpukdUnhCoHgFZRKrvg0o1wMdg4sSJ\n5u67765UjzyVYw3B55qYJgzG/idPuXXIm8ZvhGBuTMd8/etfry2pvPHGG81+++1nzj///NTwioSk\nhkwZhEAjEJBFpBHdVK6SDNxYRfj2/QzKrbW40g899FC7ZJTlo2GhTb7gE1OH6QpfpyTHvfxGGKSx\nnNRlOqZbm5hCYorm2GOPNfPmzeuWtHNNJKQDhQ6EQOsQEBFpXZdmaxAm87ffftvO5WcroZpcLBFl\nVQZOqkmEQZDB2pekUx9+niqOne5YSXzhPMuwcQhtylJsiAXh4em7cHv8tnEsEhJGRL+FQLsQEBFp\nV39mbg2DGQPyrbfeWlmI7rTKF2UFoJzwjsi9Bse0uhaZHiuPT54IVrZlyxa7RLfIesoui+XFixYt\n6hr3JdzWsnVS+UJACPQfARGR/mNe2xp56DNF04QlsGVbAcJTOiwNrtO0FeTJORejW1OXYGMV4Z4j\n5khY6IM6E8KwvvotBIRANgRERLLh1tpcTHXcddddZuPGjZ2Bro6NZQBjOSjOj/0QfDQY7H3xrRL+\n+X4do9MXv/hFs++++yb2teiXbknrceQ3vGpLJCQpgkonBJqPgIhI8/uw8BbkXWJZuEKhAuuiX3hK\np9+OsBARrCHoccABB4RQas7P008/3e6B46wiIiHN6TtpKgSKQEBEpAgUW1hGXQZ7H1qmY9hMra5x\nMtAv7Ahb5pQOfYT0yyrk90WRxxAP9sNhwzyRkCKRVVlCoBkIiIg0o58q0ZKBbv369TZIVtVLXhnk\nWfKJZA2GVQWIUVM6Rfk9QHKa4M+TBHeWYF9wwQXm4osvTpJcaYSAEGgRAu9oUVvUlIIR4E0bnxH8\nMapcTcNbMg6N06dPt/FCnJNmwc0tpTgcXMNOrrTHlyxTOuw0DDmsmiD67chzPG3aNLsXTZ4ylFcI\nCIFmIiCLSDP7ra9aOyJA5NJrr712xMBaljJYQb761a+a22+/3Vx33XWNiZGRFo+oKZ1ejrBYq3bf\nfffGOqmGMcLPBcIbdggOp9NvISAE2oeAiEj7+rSUFjFY4p9BCPG5c+faEN1lWiYI2059+++/v7XK\nhK0KpTSyRoWGHWFRzZ/SYSpjyZIlw87VSP1MqrRpqikTAMokBAYUARGRAe34rM3GOrJgwQLz/PPP\nW0LCioeiSAJkZ/Xq1TbIFfotXLjQHH/88VlVbV0+N6Xz29/+1hxzzDGNjR0S1zEzZ860EWKbEh02\nrh06LwSEQDoEtOldOrwGPjVv5Q899JANzb19+3a7fJQBhCiZOGamFcgH1g/KwFfikUcesQSEFRQi\nIcPRBHs+7373uw0+FQiWk7bIhAkTDPeURAgIgcFCQBaRwervwlsLkWBlzaOPPmoee+wxM3r0aDud\ncvTRR9u62NnXF3aI3bVrl3nllVfMiy++aEOTM6ieeuqp5oQTTijMuuLX2bZjSB8B55YuXdqqpuGA\nu3jx4saFqm9VJ6gxQqACBLRqpgLQ21QlfiLseut2vuUNHbKxY8cOSziYxvFl7NixZsyYMeaUU04x\nn/vc51rl4+C3s8xjiBzWg7YJFjGJEBACg4eAiMjg9XmpLW7TktJSgVLhIxDAWRXfI4kQEAKDhYB8\nRAarv9VaIVBbBHB6zuJnVNsGSTEhIAQSISAikggmJRICQkAICAEhIATKQEBEpAxUVaYQEAKpEZA1\nJDVkyiAEWoGAiEgrulGNEALNR4Coqm5ZcvNboxYIASGQFAERkaRIKZ0QqAkChHZXvI2adIbUEAJC\nIDcCIiK5IVQBQqC/CIwbN85s27atv5X2oTZWzBxyyCF9qElVCAEhUCcERETq1BvSRQgkQIAN8Vat\nWpUgZbOSEOSOGDMSISAEBgsBEZHB6m+1tgUIEEQOy0GbwrvTLUTaPfLII1vQQ2qCEBACaRAQEUmD\nltIKgZogMGnSJLNu3bqaaJNfDUjVzp07DQHxJEJACAwWAiIig9Xfam1LEJg8ebLdeLAlzTGbNm0y\n06dPb0tz1A4hIARSICAikgIsJRUCdUGAnYmxIrQl9saiRYsM5EoiBITA4CEgIjJ4fa4WtwQBLAhf\n+cpXGt+a7373u3ZaBnIlEQJCYPAQGDUUyOA1Wy0WAs1HAGvIhz70IfODH/zA4MDaVDn99NPN0Ucf\nbS699NKmNkF6CwEhkAMBWURygKesQqBKBNgkbuLEiebuu++uUo1cdWMNIX7I+eefn6scZRYCQqC5\nCMgi0ty+k+ZCwPqJEFeE8OgQk6aJrCFN6zHpKwSKR0AWkeIxVYlCoG8IsNz12muvbeS0xvLly80b\nb7wha0jf7hZVJATqiYAsIvXsF2klBBIj8Ktf/cpgFbnuuuvMeeedlzhflQmdf8uaNWusn0uVuqhu\nISAEqkVARKRa/FW7ECgEAXwtpk6dap566qlGBAU77rjjzLHHHmvmzZtXSPtViBAQAs1FQFMzze07\naS4EOgiwembu3LnmzDPPNFhI6ixXXHGFVU8kpM69JN2EQP8QkEWkf1irJiFQOgIM8i+//LJZu3Zt\nLZf0ot/69evNxo0ba6lf6R2kCoSAEBiBgCwiIyDRCSHQXARuvPFGc9hhh5kpU6bUzjICCVm5cqV5\n4IEHREKae4tJcyFQOAIiIoVDqgKFQLUI+GSkDjv0MlXkLDVN8WGptgdVuxAYLARERAarv9XaAUEA\nMoIzKE6hGzZsqKzVrI7BOuOmi7S7bmVdoYqFQG0REBGpbddIMSGQDwGcQe+9915z7rnnmpkzZ/Z9\nqoY4ITjRQoiwhDQ5DH2+nlBuISAEuiEgItINHV0TAg1HgI3k2IsGIdbIzTffXHqLWEqMJYYddYkT\notUxpUOuCoRAoxHQqplGd5+UFwLJEYAgLFiwwEYz/exnP2sjmhZppXj44YfNihUr7N4xTQqulhxB\npRQCQqAMBEREykBVZQqBGiMAIbnjjjvMsmXLzIwZM8wpp5xiJk2alGnqhLIef/xxc/vtt5vRo0eb\nOXPmmE9+8pOZyqoxZFJNCAiBEhEQESkRXBUtBOqMAI6kTz75pHnkkUfMqlWrrC8HS3/HjBljxo8f\nb3bbbbcR6rNT7q5du8yWLVtsnkMOOcRMmzbNnHHGGY2I6DqiQTohBIRA5QiIiFTeBVJACNQDAawb\nW7dutUTjmWeeiVRq7NixHaJy4IEHNnLH38iG6aQQEAKVISAiUhn0qlgICAEhIASEgBDQqhndA0JA\nCAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUhICJSGfSqWAgIASEgBISA\nEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJCQERE94AQEAJCQAgIASFQGQIiIpVBr4qF\ngBAQAkJACAgBERHdA0JACAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUh\nICJSGfSqWAgIASEgBISAEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJC4P8Di13nEo+f\nAH0AAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='sentiment_network.png')"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def update_input_layer(review):\n",
" \n",
" global layer_0\n",
" \n",
" # clear out previous state, reset the layer to be all 0s\n",
" layer_0 *= 0\n",
" for word in review.split(\" \"):\n",
" layer_0[0][word2index[word]] += 1\n",
"\n",
"update_input_layer(reviews[0])"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 18., 0., 0., ..., 0., 0., 0.]])"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"layer_0"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"review_counter = Counter()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for word in reviews[0].split(\" \"):\n",
" review_counter[word] += 1"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[('.', 27),\n",
" ('', 18),\n",
" ('the', 9),\n",
" ('to', 6),\n",
" ('i', 5),\n",
" ('high', 5),\n",
" ('is', 4),\n",
" ('of', 4),\n",
" ('a', 4),\n",
" ('bromwell', 4),\n",
" ('teachers', 4),\n",
" ('that', 4),\n",
" ('their', 2),\n",
" ('my', 2),\n",
" ('at', 2),\n",
" ('as', 2),\n",
" ('me', 2),\n",
" ('in', 2),\n",
" ('students', 2),\n",
" ('it', 2),\n",
" ('student', 2),\n",
" ('school', 2),\n",
" ('through', 1),\n",
" ('insightful', 1),\n",
" ('ran', 1),\n",
" ('years', 1),\n",
" ('here', 1),\n",
" ('episode', 1),\n",
" ('reality', 1),\n",
" ('what', 1),\n",
" ('far', 1),\n",
" ('t', 1),\n",
" ('saw', 1),\n",
" ('s', 1),\n",
" ('repeatedly', 1),\n",
" ('isn', 1),\n",
" ('closer', 1),\n",
" ('and', 1),\n",
" ('fetched', 1),\n",
" ('remind', 1),\n",
" ('can', 1),\n",
" ('welcome', 1),\n",
" ('line', 1),\n",
" ('your', 1),\n",
" ('survive', 1),\n",
" ('teaching', 1),\n",
" ('satire', 1),\n",
" ('classic', 1),\n",
" ('who', 1),\n",
" ('age', 1),\n",
" ('knew', 1),\n",
" ('schools', 1),\n",
" ('inspector', 1),\n",
" ('comedy', 1),\n",
" ('down', 1),\n",
" ('about', 1),\n",
" ('pity', 1),\n",
" ('m', 1),\n",
" ('all', 1),\n",
" ('adults', 1),\n",
" ('see', 1),\n",
" ('think', 1),\n",
" ('situation', 1),\n",
" ('time', 1),\n",
" ('pomp', 1),\n",
" ('lead', 1),\n",
" ('other', 1),\n",
" ('much', 1),\n",
" ('many', 1),\n",
" ('which', 1),\n",
" ('one', 1),\n",
" ('profession', 1),\n",
" ('programs', 1),\n",
" ('same', 1),\n",
" ('some', 1),\n",
" ('such', 1),\n",
" ('pettiness', 1),\n",
" ('immediately', 1),\n",
" ('expect', 1),\n",
" ('financially', 1),\n",
" ('recalled', 1),\n",
" ('tried', 1),\n",
" ('whole', 1),\n",
" ('right', 1),\n",
" ('life', 1),\n",
" ('cartoon', 1),\n",
" ('scramble', 1),\n",
" ('sack', 1),\n",
" ('believe', 1),\n",
" ('when', 1),\n",
" ('than', 1),\n",
" ('burn', 1),\n",
" ('pathetic', 1)]"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"review_counter.most_common()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project 4: Reducing Noise in our Input Data"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time\n",
"import sys\n",
"import numpy as np\n",
"\n",
"# Let's tweak our network from before to model these phenomena\n",
"class SentimentNetwork:\n",
" def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n",
" \n",
" # set our random number generator \n",
" np.random.seed(1)\n",
" \n",
" self.pre_process_data(reviews, labels)\n",
" \n",
" self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n",
" \n",
" \n",
" def pre_process_data(self, reviews, labels):\n",
" \n",
" review_vocab = set()\n",
" for review in reviews:\n",
" for word in review.split(\" \"):\n",
" review_vocab.add(word)\n",
" self.review_vocab = list(review_vocab)\n",
" \n",
" label_vocab = set()\n",
" for label in labels:\n",
" label_vocab.add(label)\n",
" \n",
" self.label_vocab = list(label_vocab)\n",
" \n",
" self.review_vocab_size = len(self.review_vocab)\n",
" self.label_vocab_size = len(self.label_vocab)\n",
" \n",
" self.word2index = {}\n",
" for i, word in enumerate(self.review_vocab):\n",
" self.word2index[word] = i\n",
" \n",
" self.label2index = {}\n",
" for i, label in enumerate(self.label_vocab):\n",
" self.label2index[label] = i\n",
" \n",
" \n",
" def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
" # Set number of nodes in input, hidden and output layers.\n",
" self.input_nodes = input_nodes\n",
" self.hidden_nodes = hidden_nodes\n",
" self.output_nodes = output_nodes\n",
"\n",
" # Initialize weights\n",
" self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n",
" \n",
" self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n",
" (self.hidden_nodes, self.output_nodes))\n",
" \n",
" self.learning_rate = learning_rate\n",
" \n",
" self.layer_0 = np.zeros((1,input_nodes))\n",
" \n",
" \n",
" def update_input_layer(self,review):\n",
"\n",
" # clear out previous state, reset the layer to be all 0s\n",
" self.layer_0 *= 0\n",
" for word in review.split(\" \"):\n",
" if(word in self.word2index.keys()):\n",
" self.layer_0[0][self.word2index[word]] = 1\n",
" \n",
" def get_target_for_label(self,label):\n",
" if(label == 'POSITIVE'):\n",
" return 1\n",
" else:\n",
" return 0\n",
" \n",
" def sigmoid(self,x):\n",
" return 1 / (1 + np.exp(-x))\n",
" \n",
" \n",
" def sigmoid_output_2_derivative(self,output):\n",
" return output * (1 - output)\n",
" \n",
" def train(self, training_reviews, training_labels):\n",
" \n",
" assert(len(training_reviews) == len(training_labels))\n",
" \n",
" correct_so_far = 0\n",
" \n",
" start = time.time()\n",
" \n",
" for i in range(len(training_reviews)):\n",
" \n",
" review = training_reviews[i]\n",
" label = training_labels[i]\n",
" \n",
" #### Implement the forward pass here ####\n",
" ### Forward pass ###\n",
"\n",
" # Input Layer\n",
" self.update_input_layer(review)\n",
"\n",
" # Hidden layer\n",
" layer_1 = self.layer_0.dot(self.weights_0_1)\n",
"\n",
" # Output layer\n",
" layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n",
"\n",
" #### Implement the backward pass here ####\n",
" ### Backward pass ###\n",
"\n",
" # TODO: Output error\n",
" layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n",
" layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n",
"\n",
" # TODO: Backpropagated error\n",
" layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n",
" layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n",
"\n",
" # TODO: Update the weights\n",
" self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n",
" self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n",
"\n",
" if(np.abs(layer_2_error) < 0.5):\n",
" correct_so_far += 1\n",
" \n",
" reviews_per_second = i / float(time.time() - start)\n",
" \n",
" sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n",
" if(i % 2500 == 0):\n",
" print(\"\")\n",
" \n",
" def test(self, testing_reviews, testing_labels):\n",
" \n",
" correct = 0\n",
" \n",
" start = time.time()\n",
" \n",
" for i in range(len(testing_reviews)):\n",
" pred = self.run(testing_reviews[i])\n",
" if(pred == testing_labels[i]):\n",
" correct += 1\n",
" \n",
" reviews_per_second = i / float(time.time() - start)\n",
" \n",
" sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n",
" + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n",
" + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n",
" \n",
" def run(self, review):\n",
" \n",
" # Input Layer\n",
" self.update_input_layer(review.lower())\n",
"\n",
" # Hidden layer\n",
" layer_1 = self.layer_0.dot(self.weights_0_1)\n",
"\n",
" # Output layer\n",
" layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n",
" \n",
" if(layer_2[0] > 0.5):\n",
" return \"POSITIVE\"\n",
" else:\n",
" return \"NEGATIVE\"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n",
"Progress:10.4% Speed(reviews/sec):91.50 #Correct:1795 #Trained:2501 Training Accuracy:71.7%\n",
"Progress:20.8% Speed(reviews/sec):95.25 #Correct:3811 #Trained:5001 Training Accuracy:76.2%\n",
"Progress:31.2% Speed(reviews/sec):93.74 #Correct:5898 #Trained:7501 Training Accuracy:78.6%\n",
"Progress:41.6% Speed(reviews/sec):93.69 #Correct:8042 #Trained:10001 Training Accuracy:80.4%\n",
"Progress:52.0% Speed(reviews/sec):95.27 #Correct:10186 #Trained:12501 Training Accuracy:81.4%\n",
"Progress:62.5% Speed(reviews/sec):98.19 #Correct:12317 #Trained:15001 Training Accuracy:82.1%\n",
"Progress:72.9% Speed(reviews/sec):98.56 #Correct:14440 #Trained:17501 Training Accuracy:82.5%\n",
"Progress:83.3% Speed(reviews/sec):99.74 #Correct:16613 #Trained:20001 Training Accuracy:83.0%\n",
"Progress:93.7% Speed(reviews/sec):100.7 #Correct:18794 #Trained:22501 Training Accuracy:83.5%\n",
"Progress:99.9% Speed(reviews/sec):101.9 #Correct:20115 #Trained:24000 Training Accuracy:83.8%"
]
}
],
"source": [
"mlp.train(reviews[:-1000],labels[:-1000])"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress:99.9% Speed(reviews/sec):832.7% #Correct:851 #Tested:1000 Testing Accuracy:85.1%"
]
}
],
"source": [
"# evaluate our model before training (just to show how horrible it is)\n",
"mlp.test(reviews[-1000:],labels[-1000:])"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
bbglab/adventofcode | 2016/ferran/day7.ipynb | 1 | 4536 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Chellenge 7\n",
"\n",
"## Challenge 7.1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"myinput = '/home/fmuinos/projects/adventofcode/2016/ferran/inputs/input7.txt'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def fourlyndromes(mystr):\n",
" for i in range(len(mystr)-3):\n",
" if (mystr[i] == mystr[i+3]) and (mystr[i+1] == mystr[i+2]) and (mystr[i] != mystr[i+1]):\n",
" return True\n",
" return False\n",
"\n",
"def separate_brackets(mystr):\n",
" bracket_dict = {'[': set([]), ']': set([])}\n",
" state = ']'\n",
" myword = ''\n",
" for char in mystr:\n",
" if char == '[' or char == ']' or char == '\\n':\n",
" if myword != '':\n",
" bracket_dict[state].add(myword)\n",
" myword = ''\n",
" state = char \n",
" else:\n",
" myword += char\n",
" return bracket_dict\n",
" \n",
"def tls_count(myinput):\n",
" tls_counter = 0\n",
" with open(myinput, 'rt') as f:\n",
" for line in f:\n",
" bracket_dict = separate_brackets(line)\n",
" inside = False\n",
" for word in bracket_dict['[']:\n",
" if fourlyndromes(word):\n",
" inside = True\n",
" continue\n",
" outside = False\n",
" for word in bracket_dict[']']:\n",
" if fourlyndromes(word):\n",
" outside = True\n",
" continue\n",
" if (not inside) and outside:\n",
" tls_counter += 1\n",
" return tls_counter"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"115"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tls_count(myinput)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Challenge 7.2"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def threelyndromes(mystr):\n",
" myset = set([])\n",
" for i in range(len(mystr)-2):\n",
" if (mystr[i] == mystr[i+2]) and (mystr[i] != mystr[i+1]):\n",
" myset.add(''.join(mystr[i:i+3]))\n",
" return myset\n",
"\n",
"def transform(aba):\n",
" return aba[1]+aba[0]+aba[1]\n",
"\n",
"def ssl_count(myinput):\n",
" ssl_counter = 0\n",
" with open(myinput, 'rt') as f:\n",
" for line in f:\n",
" bracket_dict = separate_brackets(line)\n",
" inside = set([])\n",
" for word in bracket_dict['[']:\n",
" inside = inside.union(threelyndromes(word))\n",
" outside = set([])\n",
" for word in bracket_dict[']']:\n",
" for aba in threelyndromes(word):\n",
" outside.add(transform(aba))\n",
" if len(outside.intersection(inside)) > 0:\n",
" ssl_counter += 1\n",
" return ssl_counter"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"231"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssl_count(myinput)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:adventofcode]",
"language": "python",
"name": "conda-env-adventofcode-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
scott-maddox/obpds-binder | examples/degenerate_pn_diode.ipynb | 1 | 33651 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from ipywidgets import interactive, fixed\n",
"from obpds import *\n",
"\n",
"layers = [\n",
" Layer(1*um, InAs, 1e15/cm3), # p-type layer\n",
" Layer(1*um, InAs, -2e18/cm3), # n-type layer\n",
" ]\n",
"\n",
"d = TwoTerminalDevice(layers=layers,\n",
" Fp='left',\n",
" Fn='right')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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6Dh06+OnTpxcZR+7nh4aG+ueee+6Y5f/zn//4rl275nnme++9lyf2kSNHeu+9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\nm7cYM5mCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f9c9e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Simulate and compare the equilibrium band profiles under different approximations\n",
"import matplotlib.pyplot as plt\n",
"_, ax1 = plt.subplots(figsize=(10,8))\n",
"ax1.set_ymargin(0.05)\n",
"ax1.set_ylabel('Energy (eV)')\n",
"ax1.set_xlabel('Depth (nm)')\n",
"\n",
"solution = d.get_equilibrium(approx='boltzmann')\n",
"x = solution.x*1e7 # nm\n",
"ax1.plot(x, solution.Ev, 'r:')\n",
"ax1.plot(x, solution.Ec, 'b:', lw=2, label='Parabolic Boltzmann')\n",
"\n",
"solution = d.get_equilibrium(approx='parabolic')\n",
"x = solution.x*1e7 # nm\n",
"ax1.plot(x, solution.Ev, 'r--')\n",
"ax1.plot(x, solution.Ec, 'b--', lw=2, label='Parabolic')\n",
"\n",
"solution = d.get_equilibrium(approx='kane')\n",
"x = solution.x*1e7 # nm\n",
"ax1.plot(x, solution.Ev, 'r-')\n",
"ax1.plot(x, solution.Ec, 'b-', label='Non-parabolic Kane')\n",
"ax1.plot(x, solution.Ef, 'k--')\n",
"\n",
"ax1.legend(loc='best')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
zhouqifanbdh/liupengyuan.github.io | chapter2/homework/computer/3-29/201611690595-322-ex4.ipynb | 27 | 1620 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入要输入的整数个数，以回车结束：3\n",
"请输入一个整数，以回车结束：2\n",
"请输入一个整数，以回车结束：4\n",
"请输入一个整数，以回车结束：3\n",
"3\n"
]
}
],
"source": [
"m = int(input('请输入要输入的整数个数，以回车结束：'))\n",
"i = 0\n",
"max1 = 0\n",
"max2 = 0\n",
"k = int(input('请输入一个整数，以回车结束：'))\n",
"n = int(input('请输入一个整数，以回车结束：'))\n",
"if(k > n):\n",
" max1 = k\n",
" max2 = n\n",
"else:\n",
" max1 = n\n",
" max2 = k\n",
"\n",
"while i < (m-2):\n",
" w = int(input('请输入一个整数，以回车结束：'))\n",
" if((w >= max2) and (w < max1)):\n",
" max2 = w\n",
" elif(w >= max1):\n",
" max1 = w\n",
" i = i+1\n",
" \n",
"print(max2)\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
}
| mit |
arve0/FY3490 | morphology-gw-chap9/tophat.ipynb | 1 | 1434684 | null | mit |
freedomtan/tensorflow | tensorflow/lite/g3doc/performance/post_training_integer_quant_16x8.ipynb | 7 | 18889 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "c8Cx-rUMVX25"
},
"source": [
"##### Copyright 2020 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "I9sUhVL_VZNO"
},
"outputs": [],
"source": [
"#@title 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": {
"id": "6Y8E0lw5eYWm"
},
"source": [
"# Post-training integer quantization with int16 activations"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CGuqeuPSVNo-"
},
"source": [
"\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/lite/performance/post_training_integer_quant_16x8\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_integer_quant_16x8.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_integer_quant_16x8.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/tensorflow/lite/g3doc/performance/post_training_integer_quant_16x8.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
" \u003c/td\u003e\n",
"\u003c/table\u003e"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BTC1rDAuei_1"
},
"source": [
"## Overview\n",
"\n",
"[TensorFlow Lite](https://www.tensorflow.org/lite/) now supports\n",
"converting activations to 16-bit integer values and weights to 8-bit integer values during model conversion from TensorFlow to TensorFlow Lite's flat buffer format. We refer to this mode as the \"16x8 quantization mode\". This mode can improve accuracy of the quantized model significantly, when activations are sensitive to the quantization, while still achieving almost 3-4x reduction in model size. Moreover, this fully quantized model can be consumed by integer-only hardware accelerators. \n",
"\n",
"Some examples of models that benefit from this mode of the post-training quantization include: \n",
"* super-resolution, \n",
"* audio signal processing such\n",
"as noise cancelling and beamforming, \n",
"* image de-noising, \n",
"* HDR reconstruction\n",
"from a single image\n",
"\n",
"In this tutorial, you train an MNIST model from scratch, check its accuracy in TensorFlow, and then convert the model into a Tensorflow Lite flatbuffer using this mode. At the end you check the accuracy of the converted model and compare it to the original float32 model. Note that this example demonstrates the usage of this mode and doesn't show benefits over other available quantization techniques in TensorFlow Lite."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2XsEP17Zelz9"
},
"source": [
"## Build an MNIST model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dDqqUIZjZjac"
},
"source": [
"### Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gyqAw1M9lyab"
},
"outputs": [],
"source": [
"import logging\n",
"logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"import numpy as np\n",
"import pathlib"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "srTSFKjn1tMp"
},
"source": [
"Check that the 16x8 quantization mode is available "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "c6nb7OPlXs_3"
},
"outputs": [],
"source": [
"tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eQ6Q0qqKZogR"
},
"source": [
"### Train and export the model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hWSAjQWagIHl"
},
"outputs": [],
"source": [
"# Load MNIST dataset\n",
"mnist = keras.datasets.mnist\n",
"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
"\n",
"# Normalize the input image so that each pixel value is between 0 to 1.\n",
"train_images = train_images / 255.0\n",
"test_images = test_images / 255.0\n",
"\n",
"# Define the model architecture\n",
"model = keras.Sequential([\n",
" keras.layers.InputLayer(input_shape=(28, 28)),\n",
" keras.layers.Reshape(target_shape=(28, 28, 1)),\n",
" keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),\n",
" keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
" keras.layers.Flatten(),\n",
" keras.layers.Dense(10)\n",
"])\n",
"\n",
"# Train the digit classification model\n",
"model.compile(optimizer='adam',\n",
" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=['accuracy'])\n",
"model.fit(\n",
" train_images,\n",
" train_labels,\n",
" epochs=1,\n",
" validation_data=(test_images, test_labels)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5NMaNZQCkW9X"
},
"source": [
"For the example, you trained the model for just a single epoch, so it only trains to ~96% accuracy."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xl8_fzVAZwOh"
},
"source": [
"### Convert to a TensorFlow Lite model\n",
"\n",
"Using the Python [TFLiteConverter](https://www.tensorflow.org/lite/convert/python_api), you can now convert the trained model into a TensorFlow Lite model.\n",
"\n",
"Now, convert the model using `TFliteConverter` into default float32 format:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_i8B2nDZmAgQ"
},
"outputs": [],
"source": [
"converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
"tflite_model = converter.convert()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F2o2ZfF0aiCx"
},
"source": [
"Write it out to a `.tflite` file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vptWZq2xnclo"
},
"outputs": [],
"source": [
"tflite_models_dir = pathlib.Path(\"/tmp/mnist_tflite_models/\")\n",
"tflite_models_dir.mkdir(exist_ok=True, parents=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ie9pQaQrn5ue"
},
"outputs": [],
"source": [
"tflite_model_file = tflite_models_dir/\"mnist_model.tflite\"\n",
"tflite_model_file.write_bytes(tflite_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7BONhYtYocQY"
},
"source": [
"To instead quantize the model to 16x8 quantization mode, first set the `optimizations` flag to use default optimizations. Then specify that 16x8 quantization mode is the required supported operation in the target specification:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HEZ6ET1AHAS3"
},
"outputs": [],
"source": [
"converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
"converter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zLxQwZq9CpN7"
},
"source": [
"As in the case of int8 post-training quantization, it is possible to produce a fully integer quantized model by setting converter options `inference_input(output)_type` to tf.int16."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yZekFJC5-fOG"
},
"source": [
"Set the calibration data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y3a6XFqvHbYM"
},
"outputs": [],
"source": [
"mnist_train, _ = tf.keras.datasets.mnist.load_data()\n",
"images = tf.cast(mnist_train[0], tf.float32) / 255.0\n",
"mnist_ds = tf.data.Dataset.from_tensor_slices((images)).batch(1)\n",
"def representative_data_gen():\n",
" for input_value in mnist_ds.take(100):\n",
" # Model has only one input so each data point has one element.\n",
" yield [input_value]\n",
"converter.representative_dataset = representative_data_gen"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xW84iMYjHd9t"
},
"source": [
"Finally, convert the model as usual. Note, by default the converted model will still use float input and outputs for invocation convenience."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yuNfl3CoHNK3"
},
"outputs": [],
"source": [
"tflite_16x8_model = converter.convert()\n",
"tflite_model_16x8_file = tflite_models_dir/\"mnist_model_quant_16x8.tflite\"\n",
"tflite_model_16x8_file.write_bytes(tflite_16x8_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PhMmUTl4sbkz"
},
"source": [
"Note how the resulting file is approximately `1/3` the size."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JExfcfLDscu4"
},
"outputs": [],
"source": [
"!ls -lh {tflite_models_dir}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L8lQHMp_asCq"
},
"source": [
"## Run the TensorFlow Lite models"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-5l6-ciItvX6"
},
"source": [
"Run the TensorFlow Lite model using the Python TensorFlow Lite Interpreter."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ap_jE7QRvhPf"
},
"source": [
"### Load the model into the interpreters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Jn16Rc23zTss"
},
"outputs": [],
"source": [
"interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))\n",
"interpreter.allocate_tensors()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "J8Pztk1mvNVL"
},
"outputs": [],
"source": [
"interpreter_16x8 = tf.lite.Interpreter(model_path=str(tflite_model_16x8_file))\n",
"interpreter_16x8.allocate_tensors()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2opUt_JTdyEu"
},
"source": [
"### Test the models on one image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AKslvo2kwWac"
},
"outputs": [],
"source": [
"test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)\n",
"\n",
"input_index = interpreter.get_input_details()[0][\"index\"]\n",
"output_index = interpreter.get_output_details()[0][\"index\"]\n",
"\n",
"interpreter.set_tensor(input_index, test_image)\n",
"interpreter.invoke()\n",
"predictions = interpreter.get_tensor(output_index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XZClM2vo3_bm"
},
"outputs": [],
"source": [
"import matplotlib.pylab as plt\n",
"\n",
"plt.imshow(test_images[0])\n",
"template = \"True:{true}, predicted:{predict}\"\n",
"_ = plt.title(template.format(true= str(test_labels[0]),\n",
" predict=str(np.argmax(predictions[0]))))\n",
"plt.grid(False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3gwhv4lKbYZ4"
},
"outputs": [],
"source": [
"test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)\n",
"\n",
"input_index = interpreter_16x8.get_input_details()[0][\"index\"]\n",
"output_index = interpreter_16x8.get_output_details()[0][\"index\"]\n",
"\n",
"interpreter_16x8.set_tensor(input_index, test_image)\n",
"interpreter_16x8.invoke()\n",
"predictions = interpreter_16x8.get_tensor(output_index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CIH7G_MwbY2x"
},
"outputs": [],
"source": [
"plt.imshow(test_images[0])\n",
"template = \"True:{true}, predicted:{predict}\"\n",
"_ = plt.title(template.format(true= str(test_labels[0]),\n",
" predict=str(np.argmax(predictions[0]))))\n",
"plt.grid(False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LwN7uIdCd8Gw"
},
"source": [
"### Evaluate the models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "05aeAuWjvjPx"
},
"outputs": [],
"source": [
"# A helper function to evaluate the TF Lite model using \"test\" dataset.\n",
"def evaluate_model(interpreter):\n",
" input_index = interpreter.get_input_details()[0][\"index\"]\n",
" output_index = interpreter.get_output_details()[0][\"index\"]\n",
"\n",
" # Run predictions on every image in the \"test\" dataset.\n",
" prediction_digits = []\n",
" for test_image in test_images:\n",
" # Pre-processing: add batch dimension and convert to float32 to match with\n",
" # the model's input data format.\n",
" test_image = np.expand_dims(test_image, axis=0).astype(np.float32)\n",
" interpreter.set_tensor(input_index, test_image)\n",
"\n",
" # Run inference.\n",
" interpreter.invoke()\n",
"\n",
" # Post-processing: remove batch dimension and find the digit with highest\n",
" # probability.\n",
" output = interpreter.tensor(output_index)\n",
" digit = np.argmax(output()[0])\n",
" prediction_digits.append(digit)\n",
"\n",
" # Compare prediction results with ground truth labels to calculate accuracy.\n",
" accurate_count = 0\n",
" for index in range(len(prediction_digits)):\n",
" if prediction_digits[index] == test_labels[index]:\n",
" accurate_count += 1\n",
" accuracy = accurate_count * 1.0 / len(prediction_digits)\n",
"\n",
" return accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T5mWkSbMcU5z"
},
"outputs": [],
"source": [
"print(evaluate_model(interpreter))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Km3cY9ry8ZlG"
},
"source": [
"Repeat the evaluation on the 16x8 quantized model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-9cnwiPp6EGm"
},
"outputs": [],
"source": [
"# NOTE: This quantization mode is an experimental post-training mode,\n",
"# it does not have any optimized kernels implementations or\n",
"# specialized machine learning hardware accelerators. Therefore,\n",
"# it could be slower than the float interpreter.\n",
"print(evaluate_model(interpreter_16x8))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L7lfxkor8pgv"
},
"source": [
"In this example, you have quantized a model to 16x8 with no difference in the accuracy, but with the 3x reduced size.\n"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "post_training_integer_quant_16x8.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
zkbt/timingtransits | SuperSeriousNotebookForRealDataAndStuff-Backup.ipynb | 1 | 276956 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This is a new notebook for use with real data. We totally won't try to divide by zero this time."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import batman\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"% matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def chisqa (data, model):\n",
" chi = 0\n",
" \n",
" for i in range(len(data)):\n",
" num = (data[i]-model[i])**2\n",
" denom = model[i]\n",
" summ = num/denom\n",
" chi = chi + summ\n",
" chisum = np.sum(chi)\n",
" return chisum\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x10f48dbd0>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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p7x58XjR8rF5d2JiZQj4r2koA+B3wxIm+terLL3MPtPzGN/z1mQAfJnJd0sEM\n2HPP8Hm0leZb3/LjWKLh66mn0us8fkh0Pg45xI9/CU4g2Fz47dkzvG5V166Z255OnXKPM5LS6wC/\n3aQ9K7fwEZyZNNdhmB1NqbpdpHBBXbPC34QJwJo1/nGPHplH2GQzcWJhhxxH7bJL5vcLxpNED/Eu\nxIkn+vU3epK6XIKBxEFLT7QsTU1a95OmnCetUuoxH+Ug12nTOyK1fPC+ezR8tHS+lLZQVZX7nCH5\neOEF380Rt2RJ/mPh6ur8rVjF1FcwXiUe/ipx3WdS+BCqcmv5qDRBy0eSR6uUm+Aw3J/9LNnPzdby\n0Z7qf8yY7Gfp7d8/+bIUI17/7anuOwKFD6FS+OBSy4fvbmC0urG7XSpNvG7V8sGl8CGt0tEOta00\nCh882bpdpO1cdpkPmsGg13j9a91PlsKHUCl8cGnAKY9+eSerb1/gmmvC56p/LvVySauo5aN9U8sH\nj8YccKn+uVTd0irnntu69yt8cKnlg0fN/lxq+eBS+JBW+eUvWzdYT+GDSy0fPNr5cSn8cSl8CJXC\nB1dQ/4zzTFQ6NftzBfWt8Meh1V2oouEjeC7JiXe7aAeYHLV8cMVPb691P1mqbqFSyweXul341OzP\nofDHpfAhVAofXFVVCh9M0fCnuk+WxnxwKXwIlcIHl5mOdmGKhg81+ydLLR9cWt2FSuGDS90uXNEB\nv6r7ZGnAL5eqW6gUPrh0ng8udbvwqOWDS+FDqBQ+uNTywaXwwaMxH1wKH0Kl8MEVDDjVeT44FD54\n1PLBpfAhVAofXPEBp+r3TpbCB4/GfHCpuoVK4YNLYz64ouu/6j5ZavngUvgQKoUPri5dgHXrtANk\niY+5keRozAeXwodQKXxwqeWDS90uPOp24VJ1C5XCB1d0Axx9LslQtwuPul24FD6ESuGDT4fa8qjl\ng0dXteVS+BAqhQ8uneeDS2M+eKJXtdWYj+QpfAiVwgdXdAMcfS7JUMsHj8Z8cKm6hUrhgy/6y1sb\n4GRpzAePxnxwaVMjVAofXOp24VLLB4/CB5fCh1ApfHApfHBpzAePzvPBpfAhVAofXDrUlkstHzwa\n88Gl6hYqhQ8+tXzwaMwHj7pduBQ+hCr6D68NQPLU7cKllg8ehQ8uhQ+h0gaAS+GDS2M+eDTmg0vh\nQ6gUPsqDzvPBoZYPHo354FJ1C5XCB1f8l7c2wMnSmA8ebXu4tKkRKm0AuNTtwqWWDx5te7gUPoRK\nG4DyoPDmmeRqAAAdSklEQVTBoTEfPBrzwaXwIVQKH1xq+eBSywdP0MXY1KQxHwyqbqFS+OBS+ODS\nmA8ebXu4FD6EShsALoUPLrV88Gjbw6XwIVTaAJQHHWrLofDBozEfXAofQqXwwaWWDy4NOOXReT64\nVN1CpfDBpfN8cGnMB4+2PVza1AiVrqpaHlT/HOp24VH44FL4ECqFDy51u3ApfPBozAeXwodQBf/w\nGvDIofDBpTEfPBrzwaXqFiqFDy6FDy6N+eBRtwuXwodQKXyUB+0AOdTtwqPwwaXwIVQKH1zBzk/1\nz6HwwaMxH1wKH0Kl8MGlbhcujfng0ZgPLlW3UCl8lAed54NDYz54gnVdLU8c2tQIlcIHl1o+uNTt\nwhPd9qj+k6fwIVQKH1wKH1wKHzwa88Gl8CFUCh9cCh9c0W4XSZbGfHBVswsglU3hozwofHBozAeP\nDrXlUvgQKoUPLrV8cCl88Ch8cCl8CJXCB5fO88Gl8MGj8MGlXi6hUvgoD9oBcmjMB098wKnGfCRL\nLR9CpfDBFe920QY4WWr54FHLB5fCh1DFw4ckS2M+uBQ+eBQ+uBQ+hEotH3wKHzwKHzwKH1xqZBUq\nhQ+u6AY4+lySoTEfPBrzwaXqFiqFDy51u3DpDKc8avngKip8mNkZZvaema0xs5lmtmse879lZqvN\nbK6ZHRN7vdrMfmtm76SW+aqZjY3Nc7GZNcVubxVTfikfCh9cCh9cCh88Ch9cBY/5MLOfArgWwMkA\nXgZQB2C6mQ11zi3PMv9pACYCOAnAKwBGAfgfM/vEOfdoaraJAI5MzTMfwP4AHjKz0c65OZHFvQFg\nXwDBavJ1oeWX8qLwUR5U/xwKHzy6qi1XMS0fdQBucc792Tk3D8CpAFYDOCHH/Een5r/fObfQOXcP\ngFsBnBebZ6JzbnpqnpsBPAbgv2LL+to5t8w593Hq9kkR5ZcyovDBpUNtuTTglCe67dGYj+QVVN1m\n1hnAcABPBdOccw7AkwBG53hbVwBrY9PWAhhpZp0i83wVm2cNgO/Epg0xs/+Y2btmdqeZbV5I+aX8\nKHxwqduFK6j/lSuBFSvYpaks6nbhKjTr9QPQCcDS2PSlAAbkeM90ACeZWQ0AmNkIACcC6JxaXjDP\n2WY22Lz9ABwKYNPIcmYCOA7AWPjWlq0BPGdmPQv8DlJGFD74FD54gvBxzz3AggXs0lQWhQ+uJM7z\ncRmA/gBmmFkVgCUApgA4F0BwaqlfwnfFzEtNexfA7Yh05TjnpkeW+YaZvQzgfQCHA7gj14fX1dWh\nd+/eadNqa2tRW1vbqi8lpaHwwaVDbbl0qC2Pwkeovr4e9fX1adNWrlzZpp9ZaPhYDmA9fJiI6g8f\nKjI459bCt3yckppvMYBTAKxyzi1LzbMcwKFm1gVAX+fcYjO7EkDO3wLOuZVm9jaAwc0VePLkyaip\nqcnry0nyFD641O3CpfDBo/N8hLL9IJ81axaGDx/eZp9ZUHU75xoBNMAfcQIAMDNLPX+xhfeud859\nlBojcgSAR7LMsy4VPDoDOAzA1FzLM7MN4IPH4kK+g5QXhQ8uhQ8uhQ8etXxwFdPtch2AKWbWgPBQ\n2x7wXSkws0kABjrnxqeeDwEwEsBLADYCcDaAHQEcGyzQzEYC2AzAbACDAFwMfzjtNZF5roEPLO+n\n5r0UQCOA9LYiaVcUPviCX36A6j9pQfg44ghgaXwknbQphQ+ugsOHc+5eM+sHYAJ8N8psAGODLhT4\ngafRo1A6wR8yOxQ+LDwDYIxzblFknm4ALocfRPoFgEcBHO2c+zwyzyAAdwPoC2AZgOcB7Oac0xjx\ndkzhg0tjPrh0ng+eePio5G4XhqIGnDrnbgJwU47Xjo89nweg2UEXzrnn4FtDmptHI0Q7IIUPLp3n\ng0vhgyc+5kP1nyxtaoRK4YNPYz54FD541O3CpfAhVAofXOp24VL44FH44FL4ECqFDy4d7cKl8MGj\nMR9cqm6hUvjgiocPSZau7cKjMR9cCh9CpfDBp1/ePAp/PLqqLZfCh1ApfHBF6191nzx1u/BE133V\nf/IUPoRK4YNLOz8u1T+PxnxwqbqFSuGDTxtfHoUPHo354NLmRqgUPriiG2DVffIUPnh0qC2XwodQ\nKXxwaefHpaNdeBQ+uBQ+hErhg08bXx4d7cKjMR9cqm6hUvjgUssHl+qfR2M+uBQ+hErhg0tjPrgU\nPnjU7cKl8CFUCh9cwc5Pv/w4FD54FD64FD6ESuGDTxtfHoUPHo354FJ1C5XCB1d0A6yNb/J0tAuP\nxnxwaXMjVAofXPrlzaWjXXjU7cKl8CFUCh982vjyKPzxKHxwKXwIVXQDEH0uydDOj0v1z6Or2nIp\nfAiVWj64dKgtl8IHj7Y9XAofQqUNAJcOteVS+ODTmBsOhQ+hUvjg086PR0e7cCn88Sh8CJXCB5e6\nXbh0tAuXwgePwodQKXxwRTe+Os9H8rTz41L982hzI1QKH3za+PJo58el+udR+BAqhQ8ubXy5VP9c\nqn8ehQ+hUvjg0pgPLu38uDTgl0fhQ6gUPvi08+PRzo9LA355qtkFkMqm8MGl83xwRcOHJE/hj0fh\nQ6gUPrjU7M+lnR+X6p9H4UOoFD64NOaDSzs/LjNte1gUPoRK4YNP5/ngUfjgUvjgUfgQKoUPLnW7\ncCl8cFVVAevX+8eq/2Tpt45QKXxwqduFS+GDSy0fPGr5ECqFDz61fPDoaBcu1T+PwodQKXxwqeWD\nSy0fXGr54FH4ECqFD65o/avuk6fwwaXwwaPwIVQKH1wKH1wKH1wKHzwKH0Kl8FEe1O3CofDBpfDB\no/AhZUEbAI5o+NN5PpKn8MGl8MGjzY1QqeWDS90uXAofXAofPAofQqXwUR4UPjh0VVUuHWrLo/Ah\nVNFDPSV5OtSWS2eY5VLLB4/Ch1Cp5YMrGOexfr3qnkHhg0vhg0fhQ6gUPrg05oNL4YNL4YNH4UOo\nFD64gvpWyweHwgeXmS4sx6LwIVQKH1xq+eDS0S5cVVXa9rAofAiVwgeXzvPBpaNduNTtwqPNjVAp\nfHCp5YNL3S5cOtSWR+FDqBQ+uBQ+uBQ+uNTywaPwIVQKH1wacMql8MGl8MGj8CFUCh9cavngUvjg\nUvjgUfgQKoUPLoUPLh3twqXwwaPwIVQKH1wKH1w62oVL4YNH4UOoFD64FD641O3CpfDBo/AhVAof\nXDrPB5fCB5cOteXR5kaoFD641PLBpfDBpZYPHoUPoVL44FL44FL44FL44FH4ECqFDy6d54NLR7tw\nKXzwKHwIlcIHl1o+uHS0C1dVla5qy6LwIWVB4YND4YNL3S5cavngUfgQOm0AeBQ+uBQ+uLTt4VH4\nEDptAHgUPrgUPrh0qC2PwofQKXzw6DwfXAofXNr28GhzI3TaAPCo5YNLR7twadvDo/AhdNoA8Ch8\ncOloFy5te3gUPoROGwCeoKtF5/ngULcLl7Y9PAofQqcNAI9aPrgUPri07eFR+BA6bQB4FD64FD64\notseSZbCh9ApfPAofHApfHBpwC+PwofQKXzwKHxwaefHpW0Pj8KH0GkDwKPzfHDpaBcubXt4tLkR\nOvW78qjlg0vdLlwKHzwKH0IXbAD0z588hQ8uhQ8uXdWWR+FD6BQ+eII613k+OBQ+uNTywVNU+DCz\nM8zsPTNbY2YzzWzXPOZ/y8xWm9lcMzsm9nq1mf3WzN5JLfNVMxvb2s+V9kHhg0ctH1wKH1y6sBxP\nweHDzH4K4FoAFwPYBcAcANPNrF+O+U8DMBHAbwHsAOASADea2YGR2SYC+BmAMwAMA3ALgIfM7NvF\nfq60HwofPAof5UHhg0MtHzzFtHzUAbjFOfdn59w8AKcCWA3ghBzzH52a/37n3ELn3D0AbgVwXmye\nic656al5bgbwGID/asXnSjuh8MGj8MGllg8uhQ+egsKHmXUGMBzAU8E055wD8CSA0Tne1hXA2ti0\ntQBGmlmnyDxfxeZZA+A7rfhcaScUPngUPrii4UOHOidP4YOn0NW9H4BOAJbGpi8FMCDHe6YDOMnM\nagDAzEYAOBFA59TygnnONrPB5u0H4FAAm7bic6WdUPjg0Xk+uILwofWfQ+GDpzqBz7gMQH8AM8ys\nCsASAFMAnAsgOLvDL+G7Yualpr0L4HaUoEulrq4OvXv3TptWW1uL2tra1i5aSkThg0dHu3Cp24VL\n4cOrr69HfX192rSVK1e26WcWGj6WA1gPHyai+sOHigzOubXwLR+npOZbDOAUAKucc8tS8ywHcKiZ\ndQHQ1zm32MyuBLCg2M8NTJ48GTU1NXl+PWFQ+OBR+OBS+OBS+PCy/SCfNWsWhg8f3mafWVBDq3Ou\nEUADgH2DaWZmqecvtvDe9c65j1JjNY4A8EiWedalgkdnAIcBmNraz5Xyp/DBozEfXAofXDrUlqeY\nbpfrAEwxswYAL8MfhdIDvisFZjYJwEDn3PjU8yEARgJ4CcBGAM4GsCOAY4MFmtlIAJsBmA1gEPzh\ntAbgmnw/V9ovhQ8ehQ8uhQ8utXzwFBw+nHP3ps6tMQG+22M2gLFBFwr8ANDNI2/pBH/I7FAAjQCe\nATDGObcoMk83AJcD2BrAFwAeBXC0c+7zAj5X2imFDx6FDy6FDy6FD56iBpw6524CcFOO146PPZ8H\noNlBF8655+BbQ4r+XGm/FD54FD64dKgtl8IHj1Z3oVP44FH44NKhtlwKHzwKH0Kn8MGj83xwqduF\nS1e15dHmRugUPnjU8sGl8MGllg8ehQ+hU/jgCVo7dJ4PDoUPLh1qy6PwIXQKHzxq+eBS+OBSyweP\nwofQKXzwBHWunR+HjnbhUvjg0eoudAofPNE6V/0nTy1PXAofPAofQhf80+ufP3kKH1xqeeJS+OBR\n+BA6hQ8ehQ8uhQ8uhQ8ehQ+hU/jgida5xhwkT+GPS+GDR5sboVP44NHOj0v1z6VDbXkUPoRO4YNH\nOz8u1T+XWj54FD6ETuGDRzs/LnV7cSl88Gh1FzqFDx6FDy7VP5fCB4/Ch9Dpn55HOz8u1T+XwgeP\nwofQqeWDRzs/LtU/l65qy6PwIXQKHzza+XGp/rnU8sGj8CF0Ch88GvDIpfDBZRa2fHTqxC1LpdHm\nRugUPni08+NS/XNF67xzZ145KpHCh9ApfPBo58ellicurf88Wt2FTuGDJ7rDU/0nTzs/LtU/j8KH\n0AX/9OpzTV60qVkb3+Rp58el+udR+BA6tXzwqOWDSzs/LtU/j8KH0AX/9OrzTp42vlyqfy7VP482\n90Kn8MGjlg8u7fy4VP882twLnbpdeHS0BZfqn0vhg0eru9Cp5YNHG18u1T+X6p9Hm3uhU/jgUbcL\nl3Z+XKp/Hm3uhU7hg0cbXy7VP5fqn0ebe6HTmA8etXxwaefHFV3/9eMnWapuoVPLB492flyqfy7V\nP48290Kn8MGjjS+X6p9L9c+jzb3QqduFR90uXDrUlkvhg0eru9Cp5YNHOz8u7fy4VP882twIncIH\nj1o+uLTz41L982hzL3TqduHRxpdL9c+l+udR+BA6tXzwaOPLpW4vLq3/PFrdhU7hg0cbXy6FDy6t\n/zxa3YVO4YNHG1+uaJ136sQrR6XS+s+jzb3QacwHVxD6VP/JU8sHl8IHj1Z3oVPLB5fCH4/CB5fC\nB49Wd6FT+OAK6l31nzyFDy6FDx6t7kKnX95cqn8ehQ8u1T+Pqlvo1PLBpfDBo50fl06yx6PVXegU\nPrg04JRH4YNL3S48Wt2FLvinf/RRbjkqlVo+eBQ+uLTO82h1FzptdLlWr/b32hAnT+GDS8GbR6u7\n0FVXs0sggDbADAofXAofPFrdha5zZ3YJBNAGmEHhg0vhg0eru9AF4WOffbjlqHTa+SVP4YNL4YNH\nq7vQLVjg759+mluOSqcNcPIUPrgUPni0ugvdv/7FLkFlGzjQ32sDnDyFDy6FDx6t7iIVrmdPf68N\ncPIUPriC+l+3jluOSqTVXaTCBZdyV/hInsIHl9Z5Hq3uIhVu3jx/rw1x8hQ+uLTO82h1FxEA2hAz\nKHxwaZ3n0eouIgC082NQ+OBS+ODR6i50Z53FLoEAOtkbwxtvhI8VPpKnOudR1QvdY4+xSyCATnPP\n8MUX4WPtCKWSaHUXuuBoC+FSy0fyouu+wkfyJk5kl6ByaXUXOm10y8P69ewSVJ7ouq//g+StWcMu\nQeXS6i502uiWh4ceYpeg8ih8SKXS6i50r7/u70eO5Jaj0kXHH0jyFD6kkmh1l7KxahW7BJWtSxd2\nCSrPqFHhYx32mbwTT2SXoHIpfAjd6NH+PrjGiHCo/pO39dbhY+d45ahUvXqxS1C5FD6ELhjo+Pbb\n3HJUugMOYJeg8uhoFy7VOY+qXujee8/ff/45txyVbsst2SWoPNGdn36FJ09dXTwKH0J38cX+fvvt\nueWodPoVmDyd44ZL6zyPql7oBg/294cfzi1HpdOOMHna+XGp/nlU9ULX1OTv1QTKpQ1x8lTnXBrk\ny6NVX+iCDYA2xFy9e7NLIJKs4IePJE+be6ELNgAKHxzB4Z5B95dIpdA2h0dVL3QKH1wa6yEiSdPm\nXugUPkSEobqaXYLKpaoXun32AcaMAcaPZ5dEJHl33w0MGMAuRWWaOZNdgspV1G9NMzvDzN4zszVm\nNtPMds1j/rfMbLWZzTWzY7LMc5aZzUvNs8jMrjOzrpHXLzazptjtrWLKL+WlVy/ghReATTdll6Qy\n6YJ+XLW1wN57s0tRmSZNYpegchUcPszspwCuBXAxgF0AzAEw3cz65Zj/NAATAfwWwA4ALgFwo5kd\nGJnnSACTUsvcHsAJAA5PvS/qDQD9AQxI3b5TaPlFJN1tt+nU9lKZdk39bNZg6+QV0+1SB+AW59yf\nAcDMTgVwIHxguDrL/Een5r8/9XxhqqXkPACPpqaNBvC8c+6e1PNFZvZXAPHfZF8755YVUWYRyaFb\nN2DIEHYpRDiuvhoYN45dispTUMuHmXUGMBzAU8E055wD8CR8gMimK4C1sWlrAYw0s2Cc/YsAhgfd\nN2a2DYADEIaTwBAz+4+ZvWtmd5rZ5oWUX0REJOqcc4ChQ9mlqDyFdrv0A9AJwNLY9KXw3SDZTAdw\nkpnVAICZjQBwIoDOqeXBOVcP3+XyvJmtA/BvAM84566KLGcmgOMAjAVwKoCtATxnZroQuIiISDuS\nxNEul8GP05hhZlUAlgCYAuBcAE0AYGZ7AbgQPlS8DGAwgBvMbLFz7nIAcM5NjyzzDTN7GcD78GND\n7sj14XV1degdO3VjbW0tamtrS/HdRERE2rX6+nrU19enTVu5cmWbfqa5Ak5un+p2WQ3gMOfcw5Hp\nUwD0ds4d0sx7O8GHkMUATgFwpXOuT+q15wDMdM6dG5n/KPixIhs0s8yXAfzDOXdRltdqADQ0NDSg\npqYm7+8oIiJS6WbNmoXhw4cDwHDn3KxSL7+gbhfnXCOABgD7BtPMzFLPX2zhveudcx+lxogcAeCR\nyMs9AHwde0vQKpL1cmNmtgF8C8niQr6DiIiIcBXT7XIdgClm1gDfRVIHHx6mAICZTQIw0Dk3PvV8\nCPxRKy8B2AjA2QB2BHBsZJmPAKgzszmp+YYAmADg4VRYgZldk5rvfQCbAbgUQCOA9LYiERERKWsF\nhw/n3L2pc3pMgO9GmQ1gbOQQ2AEAokehdALwXwCGwoeFZwCMcc4tisxzGXxLx2XwwWIZgIcB/Doy\nzyAAdwPom3r9eQC7OedWFPodREREhKeoAafOuZsA3JTjteNjz+cBaHbQhXMuCB6XNTOPRoiKiIh0\nALqUl4iIiCRK4UNEREQSpfAhIiIiiVL4EBERkUQpfIiIiEiiFD4kQ/w0u9Iy1VlxVG+FU50VR/VW\nXhQ+JIP+SQunOiuO6q1wqrPiqN7Ki8KHiIiIJErhQ0RERBKl8CEiIiKJKur06u1ENwCYO3cuuxzt\nzsqVKzFrVsmvoNyhqc6Ko3ornOqsOKq3wkT2nd3aYvmWumhsh2NmRwK4i10OERGRduwo59zdpV5o\nRw4ffQGMBbAQwFpuaURERNqVbgC2AjC9La4e32HDh4iIiJQnDTgVERGRRCl8iIiISKIUPkRERCRR\nCh8iIiKSKIUPERERSVS7Cx9mtoeZPWxm/zGzJjM7OPJatZldZWavmdkXqXn+ZGabxpbR1cxuNLPl\nZrbKzO43s02S/zbJaG2dmdk3zOwGM5tnZqvN7H0zu97MenG+UTJKsa7Flvd4fDkdTanqzMxGm9lT\nqflWmtmzZtY12W+TnBJt1/qb2V/MbHFqvgYzOzT5b5OM5uos9frFZjY3VRefmNk/zGxkbJ6K2hcA\nra+3Uu0P2l34ANATwGwApwOIHyfcA8DOAC4FsAuAQwBsB+Bvsfl+B+BAAIcB+C6AgQAeaLsi07W2\nzgYC2BTA2QB2BDAewP4A/l+blpqvFOsaAMDM6gCsz7KcjqbVdWZmowE8DmAagBGp2x8ANLVlwclK\nsa79BcAQAD8E8E0ADwK418y+3XbFpmquzgBgPoAz4Otid/hzPj1h/hxQgUrbFwCtr7fS7A+cc+32\nBr8xOriFeUbAb/QHpZ73AvAVgEMi82yXWtZI9ncqxzrLMc+PAawBUMX+TuVeb/A7jkUANslnOR3l\nVmydAZgB4BJ2+dthva2CPxtldL7lAE5gf6cyqbMNU/PtnXpe0fuCYustxzwF7w/aY8tHofrAp7vP\nUs+Hw1/T5qlgBufcfPidw+jES1ee4nWWa57PnXMd+ddooTLqzcy6w5/m/3Tn3MesgpWxtDozs40B\njAKw3MxeMLMlqS6X3ZmFLEPZ/kdfAPDTVLO4mdkRALoCeJZQvrJiZp0BnAJfX3NSk7UvaEGOesum\n4P1Bhw4fqT7iKwHc7Zz7IjV5AIB1zrnPY7MvTb1W0XLUWXyefgB+DeCWJMtWzpqpt8kAnnfO/Z1T\nsvKVo862Sd1fDL9+jQUwC8BTZrZt8qUsP82saz8F0AXACvhf9H+E/1W/IPlSlgczO9DMVsFfYuOX\nAPZzzn2Seln7ghxaqLf4vEXtDzps+DCzagD3wf86OJ1cnHYhnzozsw0BPArgDfg+6IqXq95SA7n2\nAVBHKlrZamZdC7ZJNzvn/uycm+OcOxu+H/qEhItZdlr4H70cQG/4dW44gOsA3GdmOyZayPLyNIBv\nw7dkTIOvj37cIrULedVba/YHHTJ8RP5BNwfw/divgyUAumQZmds/9VpFaqHOgnk2ADAdvgnuUOfc\n+mRLWX5aqLe94X/JrzSzRjNrTE1/0MyeTrioZaOFOlucup8be9tcAFskULyy1Vy9mdk28IMET3DO\nPeuce905dxmAV1LTK5Jzbo1zboFz7mXn3M8AfA3gxNTL2hfk0EK9AWj9/qDDhY/IP+g2APZ1zn0a\nm6UBviL3jbxnO/gN24ykyllO8qizIOE+AT+o6GDn3LpkS1l+8qi3SQB2gv8FEdwA34x5fFLlLCct\n1ZlzbiGAj+AH/kUNBfB+EmUsR3msaz3gW0PiO4D16IDb+Vaogh8HA2hfUIhovZVkf1BdurIlw8x6\nAhgMwFKTtkkdSvYJ/K+mB+CPLvghgM5m1j813yfOuUbn3OdmdhuA68zsU/gR4jcAeME593KS3yUp\nra2z1Ir2D/hLLB8FoI9ZsCgs66iDTkuwrn0M4OPYMgHgA+dch9yRtrbOUo+vAXCJmb0Gf0jgcfBh\n5LBEvgRBCeptHoB3AdxqZufAj/s4BMD34A8l7XBaqLMVAC4C8DB8/fUD8HP4w0TvA4BK3BcAra+3\nku0P2If6FHFo0J7wh/2sj91uB7BllteC59+NLKMrgN/DH4a2KlWpm7C/W7nWWer98fcG82zB/n7l\nWm85lrkeHfhQ21LVGYBz4Vs6VgF4HsBo9ncr93oDsG1qW7Y4VW+vAjiS/d1IddYVPrB9AP/r/EMA\nDwGoiS2jovYFpai3Uu0PLLUwERERkUSoL1BEREQSpfAhIiIiiVL4EBERkUQpfIiIiEiiFD5EREQk\nUQofIiIikiiFDxEREUmUwoeIiIgkSuFDREREEqXwISIiIolS+BAREZFE/X9d0D3Zy7+CFwAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d33ba90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import astropy.io.ascii\n",
"table = astropy.io.ascii.read('kepler1b.txt')\n",
"time = table['time']\n",
"flux = table['flux']\n",
"nflux = flux/np.median(flux)\n",
"uncertainty = table['uncertainty']\n",
"plt.plot(time, nflux)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.06666417 0.06415853 0.06162956 0.05908018 0.05651547 0.05393816\n",
" 0.0513529 0.04876179 0.04616929 0.0435791 0.04099612 0.03842453\n",
" 0.03587083 0.03334145 0.03084372 0.02838619 0.02597661 0.02362419\n",
" 0.02133843 0.01912973 0.01700802 0.01498519 0.01307203 0.01128025\n",
" 0.00962059 0.00810445 0.00674209 0.00554467 0.0045219 0.00368379\n",
" 0.00303916 0.00259466 0.00235476 0.00232275 0.00249899 0.00288115\n",
" 0.00346481 0.00424318 0.00520875 0.00635308 0.00766699 0.00913932\n",
" 0.01075909 0.01251529 0.01439721 0.01639272 0.01848994 0.02067621\n",
" 0.02294065 0.02527313 0.02766356 0.03010254 0.03258112 0.03509167\n",
" 0.03762615 0.04017887 0.0427431 0.0453151 0.04788933 0.050463\n",
" 0.05303135 0.05559181 0.05814087 0.06067549 0.06319163 0.06568592\n",
" 0.0681543 0.07059209 0.07299567 0.07535964 0.07768126 0.07995166\n",
" 0.08216702 0.08432193 0.0864121 0.08843143 0.09037549 0.09223875\n",
" 0.09401656 0.09570363 0.09729534 0.09878769 0.10017592 0.10145793\n",
" 0.10263051 0.103693 0.10464355 0.1054827 0.10621101 0.10683089\n",
" 0.10734415 0.10775497 0.10806898 0.10829186 0.10842908 0.10848763\n",
" 0.10847476 0.10839785 0.10826442 0.10808252 0.1078594 ]\n"
]
}
],
"source": [
"params = batman.TransitParams()\n",
"params.t0 = 0.0 #time of inferior conjunction\n",
"params.per = 2.47061317 #orbital period\n",
"params.rp = 0.1281 #planet radius (in units of stellar radii)\n",
"params.a = 7.903 #semi-major axis (in units of stellar radii)\n",
"params.inc = 83.872 #orbital inclination (in degrees)\n",
"params.ecc = 0.0 #eccentricity\n",
"params.w = 0.0 #longitude of periastron (in degrees)\n",
"params.u = [0.1, 0.3] #limb darkening coefficients\n",
"params.limb_dark = \"quadratic\" #limb darkening model\n",
"\n",
"\n",
"plotting = False\n",
"\n",
"\n",
"t00 = np.arange(-.8, -0.7, 0.001)\n",
"chiSq = np.array([])\n",
"for i in t00:\n",
"\n",
" # set the model t0 to one value of t00\n",
" params.t0 = i\n",
" m = batman.TransitModel(params, time)\n",
" modelflux = m.light_curve(params)\n",
" chiSq = np.append(chiSq, chisqa(nflux, modelflux))\n",
"\n",
" if plotting:\n",
" plt.plot(time, modelflux, color='orange', linewidth=3)\n",
" plt.scatter(time, nflux)\n",
" plt.title(\"t0 = {}, chi^2 = {}\".format(i, chiSq[-1]))\n",
" plt.show()\n",
"#plt.scatter(t00, chiSq)\n",
"mint00 = t00[np.argmin(chiSq)]\n",
"print chiSq\n",
"#plt.title(\"Mininum t00 = {}\".format(mint00))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 7.21114782e-02 8.21374405e-02 9.50341157e-02 1.11199709e-01\n",
" 1.30992180e-01 1.54692635e-01 1.82332359e-01 2.14015802e-01\n",
" 2.50386369e-01 2.91973640e-01 3.39303841e-01 3.92921559e-01\n",
" 4.53395408e-01 5.21321616e-01 5.97325161e-01 6.82061352e-01\n",
" 7.76217214e-01 8.80511885e-01 9.95697666e-01 1.12256156e+00\n",
" 1.26192578e+00 1.41464840e+00 1.58162461e+00 1.76378790e+00\n",
" 1.96211057e+00 2.17760512e+00 2.41132560e+00 2.66436873e+00\n",
" 2.93787427e+00 3.23302769e+00 3.55106038e+00 3.89325085e+00\n",
" 4.26092637e+00 4.65546517e+00 5.07829677e+00 5.53090435e+00\n",
" 6.01482551e+00 6.53165500e+00 7.08304569e+00 7.67071095e+00\n",
" 8.29642641e+00 8.96203128e+00 9.66943069e+00 1.04205976e+01\n",
" 1.12175769e+01 1.20624853e+01 1.29575159e+01 1.39049400e+01\n",
" 1.49071095e+01 1.59664599e+01 1.70855140e+01 1.82668843e+01\n",
" 1.95132765e+01 2.08274939e+01 2.22124396e+01 2.36711208e+01\n",
" 2.52066539e+01 2.68222677e+01 2.85213076e+01 3.03072402e+01\n",
" 3.21836596e+01 3.41542915e+01 3.62229990e+01 3.83937891e+01\n",
" 4.06708178e+01 4.30583967e+01 4.55609989e+01 4.81832683e+01\n",
" 5.09300263e+01 5.38062797e+01 5.68172289e+01 5.99682775e+01\n",
" 6.32650408e+01 6.67133562e+01 7.03192953e+01 7.40891741e+01\n",
" 7.80295405e+01 8.21473089e+01 8.64495314e+01 9.09436534e+01\n",
" 9.56374634e+01 1.00538904e+02 1.05656412e+02 1.10998840e+02\n",
" 1.16575309e+02 1.22395291e+02 1.28469017e+02 1.34806692e+02\n",
" 1.41419314e+02 1.48318298e+02 1.55515633e+02 1.63023812e+02\n",
" 1.70855968e+02 1.79025863e+02 1.87547932e+02 1.96437328e+02\n",
" 2.05709966e+02 2.15382571e+02 2.25472733e+02 2.35998944e+02\n",
" 2.46980707e+02 2.58438711e+02 2.70394317e+02 2.82870610e+02\n",
" 2.95891677e+02 3.09483042e+02 3.23671701e+02 3.38486241e+02\n",
" 3.53956949e+02 3.70115922e+02 3.86997346e+02 4.04637375e+02\n",
" 4.23074577e+02 4.42350000e+02 4.62507353e+02 4.83593288e+02\n",
" 5.05657695e+02 5.28753870e+02 5.52938830e+02 5.78273764e+02\n",
" 6.04824359e+02 6.32661170e+02 6.61860148e+02 6.92503163e+02\n",
" 7.24678537e+02 7.58481826e+02 7.94016445e+02 8.31394571e+02\n",
" 8.70738080e+02 9.12179625e+02 9.55863865e+02 1.00194886e+03\n",
" 1.05060766e+03 1.10203016e+03 1.15642518e+03 1.21402288e+03\n",
" 1.27507764e+03 1.33987126e+03 1.40871684e+03 1.48196317e+03\n",
" 1.56000009e+03 1.64326464e+03 1.73224846e+03 1.82750663e+03\n",
" 1.92966823e+03 2.03944911e+03 2.15766739e+03 2.28526238e+03\n",
" 2.42331797e+03 2.57309138e+03 2.73604934e+03 2.91391360e+03\n",
" 3.10871855e+03 3.32288583e+03 3.55932039e+03 3.82153756e+03\n",
" 4.11383124e+03 4.44150175e+03 4.81116792e+03 5.23120243e+03\n",
" 5.71235181e+03 6.26863741e+03 6.91869924e+03 7.68785931e+03\n",
" 8.61139105e+03 9.73991058e+03 1.11486791e+04 1.29545712e+04\n",
" 1.53492175e+04 1.86696236e+04]\n"
]
}
],
"source": [
"rp0 = np.arange(0.1, 1.8, 0.01)\n",
"chiSq2 = np.array([])\n",
"\n",
"for j in rp0:\n",
" params.rp = j\n",
" m = batman.TransitModel(params, time)\n",
" modelflux2 = m.light_curve(params)\n",
" chiSq2 = np.append(chiSq2, chisqa(nflux, modelflux2))\n",
"print chiSq2"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"t00 = np.arange(-.8, -.7, 0.001)\n",
"chiSq = np.array([])\n",
"rp0 = np.arange(0.01, 0.2, 0.01)\n",
"\n",
"chichi = np.zeros([len(t00), len(rp0)])\n",
"\n",
"for i in range(len(t00)):\n",
" thist = t00[i]\n",
" \n",
" # set the model t0 to one value of t00\n",
" params.t0 = thist\n",
" m = batman.TransitModel(params, time)\n",
" modelflux = m.light_curve(params)\n",
" chiSq = np.append(chiSq, chisqa(nflux, modelflux))\n",
" chiSq2 = np.array([])\n",
" for j in range(len(rp0)):\n",
" print i, j\n",
" thisrp = rp0[j]\n",
" params.rp = thisrp\n",
" m = batman.TransitModel(params, time)\n",
" modelflux2 = m.light_curve(params)\n",
" chichi[i,j] = chisqa(nflux, modelflux2)\n",
"\n",
"#print chiSq\n",
"#print chiSq2\n",
"#print chichi\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(101, 19)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XfGyEPwQ5oxt31O6Lh6Qekrlv2j0m75jIQ2k2sZG4Jm+pwEP5NSPk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\nISh2QgghZEJQ7IQQQsiE+P9ZXXQs3Aq/KwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10be1d910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.imshow(np.log(chichi), cmap='gray', vmax = -2.999, aspect = 'auto')\n",
"\n",
"plt.colorbar()\n",
"chichi.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"row, column = np.unravel_index([np.argmin(chichi)], np.shape(chichi))\n",
"tbest = t00[row]\n",
"rbest = rp0[column]\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(122.6, 122.8)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Pt1Yq6pJigLyTWRpFRO7OnIQkRptujKjAnhAiIrIW0XMsamqVvSHVOdwvhFqm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f46de90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def f(xdata, rp, t0):\n",
"params = batman.TransitParams()\n",
"params.t0 = t0 #time of inferior conjunction\n",
"params.per = 2.47061317 #orbital period\n",
"params.rp = rp #planet radius (in units of stellar radii)\n",
"params.a = 7.903 #semi-major axis (in units of stellar radii)\n",
"params.inc = 83.872 #orbital inclination (in degrees)\n",
"params.ecc = 0.0 #eccentricity\n",
"params.w = 0.0 #longitude of periastron (in degrees)\n",
"params.u = [0.1, 0.3] #limb darkening coefficients\n",
"params.limb_dark = \"quadratic\"\n",
"\n",
"m = batman.TransitModel(params, xdata)\n",
"modelflux = m.light_curve(params)\n",
"\n",
"\n",
"plt.plot(time, nflux, color='black', linewidth=3)\n",
"plt.plot(time, modelflux, color='orange', linewidth=3)\n",
"plt.xlim(122.6, 122.8)\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"make imshow plot of t0 vs rp and an imshow plot of t0 vs per\n",
"\n",
"comment EVERY single line of code"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"per00 = np.arange(2, 6, 0.1)\n",
"chiSq = np.array([])\n",
"rp0 = np.arange(0.01, 0.2, 0.001)\n",
"\n",
"chichi2 = np.zeros([len(t00), len(rp0)])\n",
"\n",
"for i in range(len(per00)):\n",
" thisp = per00[i]\n",
" \n",
" # set the model t0 to one value of t00\n",
" params.per = thist\n",
" m = batman.TransitModel(params, time)\n",
" modelflux = m.light_curve(params)\n",
" chiSq = np.append(chiSq, chisqa(nflux, modelflux))\n",
" chiSq2 = np.array([])\n",
" for j in range(len(rp0)):\n",
" print i, j\n",
" thisrp = rp0[j]\n",
" params.rp = thisrp\n",
" m = batman.TransitModel(params, time)\n",
" modelflux2 = m.light_curve(params)\n",
" chichi2[i,j] = chisqa(nflux, modelflux2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(101, 19)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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2+U3A4VV1X6fKUmCPrvp3JHk18FHgZOBu4K2dr59NWU4zEl8OPAu4j2bk/8FB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\nS5I05gxzSZLGnGEuSdKYM8wlSRpz/z9ifHpwwW3jmwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10db12550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.imshow(chichi2, cmap='gray', aspect='equal')#, extent=????)\n",
"plt.colorbar()\n",
"chichi.shape"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"make this curve with scipy optimize curve fit"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy.optimize as spicy"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "<batman.transitmodel.TransitParams object at 0x10f46df10> is not a Python function",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-68c36dec6cf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mspicymodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspicy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurve_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnflux\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muncertainty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/matt/anaconda/lib/python2.7/site-packages/scipy/optimize/minpack.pyc\u001b[0m in \u001b[0;36mcurve_fit\u001b[0;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0;31m# determine number of parameters by inspecting the function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_util\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgetargspec_no_self\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_getargspec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 629\u001b[0;31m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvarargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvarkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefaults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_getargspec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 630\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\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[1;32m 631\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unable to determine number of fit parameters.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/matt/anaconda/lib/python2.7/site-packages/scipy/_lib/_util.pyc\u001b[0m in \u001b[0;36mgetargspec_no_self\u001b[0;34m(func)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0mpython\u001b[0m \u001b[0;36m2.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0munder\u001b[0m \u001b[0mpython\u001b[0m \u001b[0;36m3.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 325\u001b[0m \"\"\"\n\u001b[0;32m--> 326\u001b[0;31m \u001b[0margspec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetargspec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 327\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0margspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'self'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[0margspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\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/Users/matt/anaconda/lib/python2.7/inspect.pyc\u001b[0m in \u001b[0;36mgetargspec\u001b[0;34m(func)\u001b[0m\n\u001b[1;32m 814\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim_func\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 815\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 816\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{!r} is not a Python function'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\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 817\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvarargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvarkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 818\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mArgSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvarargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvarkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: <batman.transitmodel.TransitParams object at 0x10f46df10> is not a Python function"
]
}
],
"source": [
"spicymodel = spicy.curve_fit(f = params, xdata = time, ydata = nflux, sigma = uncertainty)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
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"nbformat": 4,
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| mit |
PaulSoderlind/JuliaTutorial | Tutorial_03b_DataContainers.ipynb | 1 | 22604 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Containers\n",
"\n",
"This notebook shows how to combine data into different types of \"containers\" (arrays, dictionaries, tuples, ...) inside your program. \n",
"\n",
"(This notebook does not discuss DataFrames, see [DataFrames.jl](https://juliadata.github.io/DataFrames.jl/stable/).)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Packages and Extra Functions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"printyellow (generic function with 1 method)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Printf\n",
"\n",
"include(\"jlFiles/printmat.jl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Arrays\n",
"\n",
"are used everywhere in finance and statistics/econometrics."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vectors, Matrices and High-dimensional Arrays\n",
"\n",
"can be created in many ways: the code below demonstrates just a few of them. See the tutorial on Arrays for (many) more details.\n",
"\n",
"To access an array element, just do `A[2]` or similarly. Also, you can change an array element as in `B[2,1] = -999.`\n",
"\n",
"Notice that `D = [A B]` creates an independent copy, so later changing `B` does not affect `D`. However, if we define `E = B`, then a change of `B` will affect both itself and `E`.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 100 \n",
" 101 \n",
"\n",
" 1 2 \n",
" 0 10 \n",
"\n",
" 100 1 2 \n",
" 101 0 10 \n",
"\n"
]
}
],
"source": [
"A = [100,101] #a vector\n",
"printmat(A) #or display(A)\n",
"\n",
"B = [1 2; #a matrix\n",
" 0 10]\n",
"printmat(B)\n",
"\n",
"D = [A B] #a 2x3 matrix\n",
"printmat(D)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A[2] is 101\n",
"\n",
"B is now\n",
" 1 2 \n",
" -999 10 \n",
"\n",
"\n",
"D is not affected\n",
" 100 1 2 \n",
" 101 0 10 \n",
"\n"
]
}
],
"source": [
"println(\"A[2] is \",A[2]) #access an element\n",
"\n",
"B[2,1] = -999 #change an element\n",
"println(\"\\nB is now\")\n",
"printmat(B)\n",
"\n",
"println(\"\\nD is not affected\")\n",
"printmat(D) #D is not changed when B is"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4×3×2 Array{Float64, 3}:\n",
"[:, :, 1] =\n",
" 0.641239 0.778994 0.585005\n",
" 0.250815 0.388669 0.856596\n",
" 0.0243288 0.364745 0.403632\n",
" 0.481117 0.539046 0.0905029\n",
"\n",
"[:, :, 2] =\n",
" 0.242738 0.21676 0.245096\n",
" 0.36651 0.920883 0.988267\n",
" 0.0474819 0.194145 0.837609\n",
" 0.819833 0.347735 0.0161131"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"C = rand(4,3,2) #a 4x3x3 array\n",
"display(C)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Arrays of Arrays (or other types)\n",
"\n",
"You can store very different things (a mixture of numbers, matrices, strings) in an array. For instance, if `a` is a vector, `str` is a string and `C` is a matrix, then `x = [a,str,C]` puts them into a vector.\n",
"\n",
"If you later change elements of the matrix `C` then it will affect `x` (discussed at the end of the notebook).\n",
"\n",
"Try first allocate an array of arrays and later fill it, use, for instance, `y = Vector{Any}(undef,3)` can be filled with any sort of elements, while `y = Vector{Array}(undef,3)` can be filled with arrays."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 \n",
" 2 \n",
" 3 \n",
"\n",
" Hazel\n",
"\n",
" 11 12 \n",
" 21 22 \n",
"\n",
"\n",
"2nd try:\n",
" 1 \n",
" 2 \n",
" 3 \n",
"\n",
" Hazel\n",
"\n",
" 11 12 \n",
" 21 22 \n",
"\n"
]
}
],
"source": [
"a = 1:3\n",
"str = \"Hazel\"\n",
"C = [11 12;21 22]\n",
"x = [a,str,C] #element 1 of x is a\n",
"foreach(printmat,x) #loops over the elements of x\n",
"\n",
"println(\"\\n2nd try:\")\n",
"y = Vector{Any}(undef,3)\n",
"y[1] = 1:3\n",
"y[2] = \"Hazel\"\n",
"y[3] = [11 12;21 22]\n",
"foreach(printmat,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tuples and Named Tuples\n",
"\n",
"are very useful for collecting very different types of data (a number, a string, and a couple of vectors, say). \n",
"\n",
"Once created, you cannot change tuples (they are immutable). (Exception: *changing elements of an array* that belongs to the tuple will affect the tuple too.)\n",
"\n",
"Tuples are often used as inputs or outputs of functions.\n",
"\n",
"The next few cells show how to create tuples and named tuples, how to extract parts of them and what happens when you to try to change them."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(1:3, \"Hazel\", [11 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(a = 1:3, str = \"Hazel\", C = [11 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(a = 1:3, str = \"Hazel\", C = [11 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(a = 1:3, C = [11 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(a = 1:3, str = \"Hazel\", C = [11 12; 21 22], abc = 3.14, x2 = \"a\")"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = 1:3 #how to create tuples and named tuples\n",
"str = \"Hazel\"\n",
"C = [11 12;21 22]\n",
"\n",
"t = (a,str,C) #a tuple, or tuple(a,str,C)\n",
"display(t)\n",
"\n",
"nt = (a=a,str=str,C=C) #a named tuple, (a2=a,str2=str,C2=C) would also work\n",
"display(nt)\n",
"\n",
"nt_b = (;a,str,C) #also a named tuple, names are given by variables\n",
"display(nt_b)\n",
"\n",
"nt_c = nt[(:a,:C)] #create a new named tuple as a subset of another one\n",
"display(nt_c)\n",
"\n",
"nt_d = merge(nt,(abc=3.14,x2=\"a\")) #merge named tuples to create a new one\n",
"display(nt_d)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a2 and str2 are: 1:3 Hazel \n",
"\n",
"t[3] is [11 12; 21 22]\n",
"\n",
"nt.C is [11 12; 21 22]\n",
"a3 is 1:3\n",
"\n",
"(in Julia 1.7+) C and a are:\n",
" 11 12 \n",
" 21 22 \n",
"\n",
" 1 \n",
" 2 \n",
" 3 \n",
"\n"
]
}
],
"source": [
"(a2,str2,C2) = t #extract the tuple into variables (\"destructuring\")\n",
"println(\"a2 and str2 are: $a2 $str2 \\n\")\n",
"\n",
"println(\"t[3] is \",t[3],\"\\n\") #can index into (tuple) t\n",
"\n",
"println(\"nt.C is \",nt.C) #we can use nt.C as a name (nt is a named tuple)\n",
"\n",
"(a3,str3...) = t #n Julia 1.6+, str3 will be a tuple \n",
"println(\"a3 is \",a3)\n",
"\n",
"(;C,a) = nt #in Julia 1.7+, extract some symbols like this\n",
"println(\"\\n(in Julia 1.7+) C and a are:\")\n",
"printmat(C)\n",
"printmat(a)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(1:3, \"Hazel\", [11 12; 21 22], 3.14)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(a = 1:3, str = \"Hazel\", C = [11 12; 21 22], abc = 3.14)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#t[1] = -999 #cannot change the tuple, uncomment to get an error\n",
"#t[4] = 34 #cannot add elements like this, uncomment to get an error\n",
"t = (t...,3.14) #add an element like this\n",
"display(t)\n",
"\n",
"#nt.a = -999 #cannot change the named tuple, uncomment to get an error\n",
"#n.D = 34 #cannot add elements like this, uncomment to get an error\n",
"nt = (;nt...,abc=3.14) #add an element like this or by using merge()\n",
"display(nt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a Tuple Dynamically (extra)\n",
"\n",
"when the values and (perhaps also the names) are created dynamically in the program.\n",
"\n",
"Suppose `values` and `names` in the next cell may differ in length from one run of the program to the next. Using `tuple(values...)` and `NamedTuple{names}(values)` allows you to still create tuples/named tuples."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1:3, \"Hazel\", [11 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(a = 1:3, b = \"Hazel\", c = [11 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"values = [a,str,C]\n",
"\n",
"t2 = tuple(values...) #or (values...,)\n",
"display(t2)\n",
"\n",
"names = (:a, :b, :c) #should be a tuple of symbols (:a) \n",
"nt2 = NamedTuple{names}(values) #or (;zip(names,values)...)\n",
"display(nt2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dictionaries\n",
"\n",
"offer a flexible way to collect different types of data. Dictionaries can (in contrast to tuples) be changed. Also, changing elements of an array that belongs to the dictionary will affect the dictionary too.\n",
"\n",
"A dictionary is organised as (key,value) pairs, where the key is the name of the element. You can loop over the elements (see below) and also change/add elements in a loop."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Dict{Symbol, Any} with 4 entries:\n",
" :a => -999\n",
" :verse2 => \"Stardust\"\n",
" :str => \"Hazel\"\n",
" :C => [11 12; 21 22]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Dict{Symbol, Any} with 5 entries:\n",
" :a => -999\n",
" :abc => 3.14\n",
" :verse2 => \"Stardust\"\n",
" :str => \"Hazel\"\n",
" :C => [11 12; 21 22]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"D[:C] is [11 12; 21 22]\n"
]
}
],
"source": [
"a = 1:10\n",
"str = \"Hazel\"\n",
"C = [11 12;21 22]\n",
"\n",
"D = Dict(:a=>a,:str=>str,:C=>C) #dictionary, \"a\" instead of :a works too\n",
"#D = Dict([(:a,a),(:str,str),(:C,C)]) #alternative syntax\n",
"\n",
"println(\"D[:C] is \",D[:C])\n",
"\n",
"D[:a] = -999 #can change an element\n",
"D[:verse2] = \"Stardust\" #can add an element this way\n",
"display(D)\n",
"\n",
"D_b = merge(D,Dict(:abc=>3.14)) #merge Dicts\n",
"display(D_b)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a: -999\n",
"verse2: Stardust\n",
"str: Hazel\n",
"C: [11 12; 21 22]\n"
]
}
],
"source": [
"for (key,value) in D #loop over a dictionary\n",
" println(\"$key: $value\")\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## From a Dict to a NamedTuple and Back Again (extra)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(a = -999, verse2 = \"Stardust\", str = \"Hazel\", C = [11 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Dict{Symbol, Any} with 4 entries:\n",
" :a => -999\n",
" :verse2 => \"Stardust\"\n",
" :str => \"Hazel\"\n",
" :C => [11 12; 21 22]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nt = (;D...) #create a named tuple from a dict\n",
"display(nt)\n",
"\n",
"D2 = Dict(pairs(nt)) #create a dict from a named tuple\n",
"display(D2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Potential Pitfall in Adding to a Dict (extra)\n",
"\n",
"If you have created a dict with only numbers by \n",
"```\n",
"D = Dict(:aa=>1)\n",
"``` \n",
"then you cannot add eg. a string by `D[:cc] = \"hello\"` since `D` is only set up to accept variables of the type `Int`. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dict{Any, Any} with 2 entries:\n",
" :aa => 1\n",
" :cc => \"hello\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"D = Dict(:aa=>1)\n",
"#D[:cc] = \"hello\" #error since D only accepts Int\n",
"\n",
"D = Dict{Any,Any}(:aa=>1) #this works\n",
"D[:cc] = \"hello\"\n",
"display(D)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a Dictionary Dynamically (extra)\n",
"\n",
"See below for examples.\n",
"\n",
"Remark: if you have the names as an array of strings (`names = [\"a\",\"b\",\"c\"]`), but want symbol names (`:a` etc), then use `Symbol.(names)`."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dict{Symbol, Any} with 3 entries:\n",
" :a => 1:10\n",
" :b => \"Hazel\"\n",
" :c => [11 12; 21 22]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"names = (:a, :b, :c) #or [\"a\",\"b\",\"c\"]\n",
"values = [a,str,C]\n",
"\n",
"D = Dict(zip(names,values))\n",
"display(D)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dict{Any, Any} with 3 entries:\n",
" :a => 1:10\n",
" :b => \"Hazel\"\n",
" :c => [11 12; 21 22]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"D = Dict() #empty dictionary\n",
"for i = 1:length(values) #loop\n",
" D[names[i]] = values[i] #add this to the dictionary\n",
"end\n",
"display(D)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance Tips (extra)\n",
"\n",
"Named tuples are often faster to create than Dictionaries. However, Dictionaries are easier to change afterwards."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Your Own Tailor Made Data Type\n",
"\n",
"It is sometime conventient to define your own `struct` as a container. The `struct` command creates an immutable type (you cannot change it, except for elements of arrays that belong to it). There is also a `mutable struct` approach."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x1: MyType(1:10, \"Hazel\", [11 12; 21 22])\n",
"x1.s: Hazel\n"
]
}
],
"source": [
"a = 1:10\n",
"str = \"Hazel\"\n",
"C = [11 12;21 22]\n",
"\n",
"struct MyType #change to `mutable struct` to be able to change it later\n",
" x #can be anything\n",
" s::String #has to be a String\n",
" z::Array #has to be an Array\n",
"end\n",
"\n",
"x1 = MyType(a,str,C) #has to specify all arguments\n",
"\n",
"println(\"x1: \",x1)\n",
"println(\"x1.s: \",x1.s)\n",
"\n",
"#x1 = MyType(1:10,10,[1;2]) #error since 10 is not a string\n",
"#x1.x = 3 #error since we cannot change"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Potential Pitfall in Using Arrays in Structures (extra)\n",
"\n",
"It is also possible to specify array types (for instance, `z::Array{Float64}` instead of just `z::Array`). This has the effect of converting (if possible) an input array to Float64. While this might have its uses, it also comes with a potential drawback: the conversion breaks the link between the input array and the array inside `MyType`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#A Potential Pitfall when Using an Array in another Data Container (extra)\n",
"\n",
"Suppose you create an array of arrays (or a tuple or a dictionary) called `y`, and that the array `C` is one of the elements.\n",
"\n",
"If you later change *elements* of `C` then it will affect `y` as well (and vice versa). This happens with *arrays*, since they are designed to conserve memory space. For instance, even if `C` is a very large array (several GB, say), creating `y = [\"hello\",C]` will require very little additional memory space.\n",
"\n",
"If you want an independent copy, use `copy(C)`, for instance, `y = [\"hello\",copy(C)]`.\n",
"\n",
"In contrast, if you change the shape of `C` then it will *not* affect `y` (but you don't save any memory)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"3-element Vector{Any}:\n",
" 1:10\n",
" \"Hazel\"\n",
" [-999 12; 21 22]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(1:10, \"Hazel\", [-999 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Dict{Symbol, Any} with 3 entries:\n",
" :a => 1:10\n",
" :str => \"Hazel\"\n",
" :C => [-999 12; 21 22]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"MyType(1:10, \"Hazel\", [-999 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = 1:10\n",
"str = \"Hazel\"\n",
"C = [11 12;21 22]\n",
"\n",
"x = [a,str,C]\n",
"t = (a,str,C)\n",
"d = Dict(:a=>a,:str=>str,:C=>C)\n",
"e = MyType(a,str,C)\n",
"\n",
"C[1,1] = -999 #changing an element of C affects x,t,d,e\n",
"\n",
"display(x)\n",
"display(t)\n",
"display(d)\n",
"display(e)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(1:10, \"Hazel\", [-999 12; 21 22])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"C = 0 #changing the shape of C does not affect x,t,d\n",
"display(t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"@webio": {
"lastCommId": null,
"lastKernelId": null
},
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Julia 1.7.2",
"language": "julia",
"name": "julia-1.7"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
vadim-ivlev/STUDY | coding/Cracking the code interview.ipynb | 1 | 14856 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1.1 Is Unique**: Implement an algorithm to determine if a string has all unique characters. What if you cannot use additional data structures?"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.88 µs ± 12.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
]
}
],
"source": [
"# from IPython.core.debugger import set_trace\n",
"\n",
"def has_unique_characters(str=''):\n",
" '''\n",
" Checks if a string has all unique characters\n",
" \n",
" >>> has_unique_characters('abc')\n",
" True\n",
" >>> has_unique_characters()\n",
" True\n",
" >>> has_unique_characters('abac')\n",
" False\n",
" '''\n",
"# 1/ time: 1.74 µs ± 14.5 ns ---------------------------\n",
"# unique_chars = dict.fromkeys(str,1)\n",
"# return len(unique_chars)==len(str)\n",
"\n",
"\n",
"# 2/ time: 1.04 µs ± 7.39 ns using dictionary ----------\n",
"# unique_chars ={}\n",
"# for char in str:\n",
"# if char in unique_chars:\n",
"# return False\n",
"# unique_chars[char]=1\n",
"# return True\n",
"\n",
"\n",
"# 3/ 1.4 µs ± 19.1 ns using a set -----------------------\n",
"# char_set =set()\n",
"# for char in str:\n",
"# if char in char_set:\n",
"# return False\n",
"# char_set.add(char)\n",
"# return True\n",
"\n",
"# 4/ 3.16 µs ± 131 ns using a bit vector ----------------\n",
"# bit_vector =0b0\n",
"# pos_a=ord('a')\n",
" \n",
"# import pdb; pdb.set_trace()\n",
"# for char in str:\n",
"# pos_char=ord(char)-pos_a\n",
"# if bit_vector & (1 << pos_char):\n",
"# return False\n",
"# bit_vector |= (1 << pos_char)\n",
"# return True\n",
"\n",
"# 5/ 1.88 µs ± 12.2 ns Array -----------------------------\n",
" a =[0]*40\n",
" pos_a=ord('a')\n",
" \n",
"# import pdb; pdb.set_trace()\n",
" for char in str:\n",
" pos_char=ord(char)-pos_a\n",
" if a[pos_char]:\n",
" return False\n",
" a[pos_char] =1\n",
" return True\n",
"\n",
"\n",
"\n",
"# print(has_unique_characters('qwertyuuidfsdgsg'))\n",
"\n",
"\n",
"\n",
"%timeit has_unique_characters('qwertyuuidfsdgsg')\n",
"# import doctest; doctest.testmod()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
" \n",
"\n",
"---\n",
"**1.2 Check Permutation:** Given two strings,write a method to decide if one is a permutation of the\n",
"other.\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"# def isPermutations(s1,s2):\n",
"# len1 = len(s1)\n",
"# len2 = len(s2)\n",
" \n",
"# if len1 != len2:\n",
"# return False\n",
" \n",
"# ss1=sorted(s1)\n",
"# ss2=sorted(s2)\n",
" \n",
"# for i in range(0,len1):\n",
"# if ss1[i] != ss2[i]:\n",
"# return False\n",
"# return True\n",
"\n",
"\n",
"def isPermutations(s1,s2):\n",
" if len(s1) != len(s2):\n",
" return False\n",
"\n",
" char_counts={}\n",
"\n",
" \n",
" # fill it \n",
" for c in s1:\n",
" char_counts[c] = char_counts.get(c,0) +1\n",
"\n",
" # empty it\n",
" for c in s2:\n",
" char_counts[c] = char_counts.get(c,0) -1\n",
"\n",
" # check if all are 0\n",
" for c in char_counts:\n",
" if char_counts[c] != 0:\n",
" return False\n",
"\n",
" return True\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"print( isPermutations('abcdefffgh', 'dcabefghff') )\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.3 URLify : \n",
"Write a method to replace all spaces in a string with '%20 You may assume that the string has suf cient space at the end to hold the additional characters, and that you are given the \"true\" length of the string. (Note: if implementing in Java, please use a character array so that you can perform this operation in place.)\n",
"\n",
"|EXAMPLE||\n",
"|:---|:---|\n",
"|Input: |\"Mr John Smith \", 13|\n",
"| Output: | \"Mr%20John%20Smith\"|"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def urlify(ss=\"Mr John Smith \", true_len=13):\n",
"# 574 ns ± 47.7 ns\n",
"# return s.strip().replace(' ','%20')\n",
"\n",
"# 760 ns ± 34 ns\n",
"# return '%20'.join(s.strip().split(' '))\n",
" \n",
"# 6.54 µs ± 191 ns\n",
"# n_spaces = ss.count(' ',0,true_len)\n",
" n_spaces = 0\n",
" for i in range(0,true_len):\n",
" if ss[i]==' ':\n",
" n_spaces +=1\n",
" s=list(ss)\n",
" for i in range(true_len-1,0,-1):\n",
" if s[i] != ' ':\n",
" s[i+n_spaces*2]=s[i]\n",
" else:\n",
" s[i+n_spaces*2]='0'\n",
" s[i+n_spaces*2-1]='2'\n",
" s[i+n_spaces*2-2]='%'\n",
" n_spaces -= 1\n",
" return ''.join(s)\n",
"\n",
"\n",
"urlify()==\"Mr%20John%20Smith\"\n",
"\n",
"# %timeit urlify() \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.5 One Away: \n",
"There are three types of edits that can be performed on strings: insert a character, remove a character, or replace a character. Given two strings, write a function to check if they are one edit (or zero edits) away.\n",
"#### EXAMPLE\n",
"```\n",
"pale, ple -> true \n",
"pales, pale -> true \n",
"pale, bale -> true \n",
"pale, bake -> false\n",
"```\n",
"\n",
"1h"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True True\n",
"True True\n",
"True True\n",
"True True\n",
"False False\n"
]
},
{
"data": {
"text/plain": [
"TestResults(failed=0, attempted=5)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def first_diff(old, new):\n",
" for i in range(0, min( len(old), len(new) )):\n",
" if old[i] != new[i]:\n",
" return i\n",
" return i+1\n",
"\n",
"\n",
"\n",
"def inserted1(old, new):\n",
" if len(new)-len(old)!=1:\n",
" return False\n",
" \n",
" i = first_diff(old,new)\n",
" \n",
" if old[i:] == new[i+1:]:\n",
" return True\n",
" return False\n",
"\n",
" \n",
" return True\n",
"\n",
"\n",
"\n",
"\n",
"def deleted1(old, new):\n",
" if len(new)-len(old)!=-1:\n",
" return False\n",
" \n",
" i = first_diff(old,new)\n",
" \n",
" if old[i+1:] == new[i:]:\n",
" return True\n",
" return False\n",
"\n",
"\n",
" return True\n",
"\n",
"\n",
"\n",
"def updated1(old, new):\n",
" if len(new)-len(old)!=0:\n",
" return False\n",
" \n",
" i = first_diff(old,new)\n",
" \n",
" if old[i+1:] == new[i+1:]:\n",
" return True\n",
" return False\n",
"\n",
"\n",
" \n",
"def zero_or_one(old, new):\n",
"# if old == new :\n",
"# return True\n",
" \n",
"# if inserted1(old,new) \\\n",
"# or updated1(old,new) \\\n",
"# or deleted1(old,new):\n",
"# return True\n",
"\n",
"# return False\n",
"\n",
"\n",
"# len_diff = len(new)-len(old)\n",
"# checks = {0:updated1, 1:inserted1, -1:deleted1}\n",
"# return checks.get(len_diff, lambda o,n: False)(old,new)\n",
"\n",
"\n",
" len_diff = len(new)-len(old)\n",
" \n",
" if len_diff == 0: return updated1(old,new)\n",
" elif len_diff ==1: return inserted1(old,new)\n",
" elif len_diff == -1: return deleted1(old,new)\n",
" else: return False\n",
"\n",
"\n",
"print (True, zero_or_one('pale', 'pale'))\n",
"print (True, zero_or_one('pale', 'ple'))\n",
"print (True, zero_or_one('pales', 'pale')) \n",
"print (True, zero_or_one('pale', 'bale'))\n",
"print (False,zero_or_one('pale', 'bake')) \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
">>> zero_or_one('pale', 'pale')\n",
"True\n",
">>> zero_or_one('pale', 'ple')\n",
"True\n",
">>> zero_or_one('pales', 'pale') \n",
"True\n",
">>> zero_or_one('pale', 'bale') \n",
"True\n",
">>> zero_or_one('pale', 'bake') \n",
"False\n",
"\"\"\"\n",
"\n",
"\n",
"import doctest; doctest.testmod()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.6 String Compression: \n",
"Implement a method to perform basic string compression using the counts of repeated characters. For example, the string aabcccccaaa would become a2blc5a3. If the \"compressed\" string would not become smaller than the original string, your method should return\n",
"the original string. You can assume the string has only uppercase and lowercase letters (a - z)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x1a3b2c1d4 2e3 1\n",
"xaa\n"
]
}
],
"source": [
"char=''\n",
"count=0\n",
"\n",
"def compr(c):\n",
" global char,count\n",
" \n",
" if char == c:\n",
" count+=1\n",
" return ''\n",
" \n",
" old_char, old_count = char, count\n",
" char, count = c, 1\n",
" \n",
" if old_count>0:\n",
" return '{}{}'.format(old_char,old_count)\n",
" \n",
" return ''\n",
" \n",
"\n",
"def compress(string):\n",
" s=''\n",
" for c in string:\n",
" s+=compr(c)\n",
" \n",
" s+=compr('')\n",
" \n",
" return string if len(string) <= len(s) else s\n",
"\n",
"\n",
"\n",
"print(compress('xaaabbcdddd eee '))\n",
"print(compress('xaa'))\n",
"\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.7 Rotate Matrix: \n",
"Given an image represented by an NxN matrix, where each pixel in the image is 4 bytes, write a method to rotate the image by 90 degrees. Can you do this in place?\n",
"\n",
"\n",
"<img src='rotate90.png' width='400'>"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"code_folding": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 2 3 4 5\n",
" 6 7 8 9 10\n",
" 11 12 13 14 15\n",
" 16 17 18 19 20\n",
" 21 22 23 24 25\n",
"\n",
"rotated:\n",
" 21 16 11 6 1\n",
" 22 17 12 7 2\n",
" 23 18 13 8 3\n",
" 24 19 14 9 4\n",
" 25 20 15 10 5\n",
"\n"
]
}
],
"source": [
"N = 3\n",
"m = [\n",
" [1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9]\n",
"]\n",
"\n",
"\n",
"def generate_m(N):\n",
" '''Generates a square matrix NxN'''\n",
" k = 1\n",
" m = []\n",
" for i in range(0, N):\n",
" n = []\n",
" m.append(n)\n",
" for j in range(0, N):\n",
" n.append(k)\n",
" k += 1\n",
" return m\n",
"\n",
"\n",
"def print_m(m, N):\n",
" \"Pretty prints matrix\"\n",
" for i in range(0, N):\n",
" for j in range(0, N):\n",
" print('{0:>4d}'.format(m[i][j]), end='')\n",
" print()\n",
" print()\n",
"\n",
"\n",
"def rotate(l, n):\n",
" '''Rotates list to the right'''\n",
" return l[n:]+l[:n]\n",
"\n",
"\n",
"def rotate_m(m, N):\n",
" for i in range(0, N//2+N % 2):\n",
" for j in range(0, N//2):\n",
" cp = [m[i][j], m[N-j-1][i], m[N-i-1][N-j-1], m[j][N-i-1]].copy()\n",
" [m[i][j], m[N-j-1][i], m[N-i-1][N-j-1],\n",
" m[j][N-i-1]] = rotate(cp, 1)\n",
"\n",
"\n",
"N = 5\n",
"m = generate_m(N)\n",
"print_m(m, N)\n",
"\n",
"print('rotated:')\n",
"rotate_m(m, N)\n",
"print_m(m, N)\n",
"\n",
"[x for x in dir() if not x.startswith('__')]\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": "3.6.2"
},
"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": 2
}
| mit |
ricklupton/ipysankeywidget | examples/Linking and Layout.ipynb | 1 | 31782 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linking and Layout\n",
"\n",
"> [<i class=\"fa fa-2x fa-paper-plane text-info fa-fw\"> </i> Simple example](./Simple%20example.ipynb)\n",
">\n",
"> [<i class=\"fa fa-2x fa-space-shuttle text-info fa-fw\"> </i> Advanced examples](./More%20examples.ipynb)\n",
">\n",
"> <i class=\"fa fa-2x fa-link text-info fa-fw\"> </i> Linking and Layout\n",
">\n",
"> [<i class=\"fa fa-2x fa-image text-info fa-fw\"> </i> Exporting Images](./Exporting%20Images.ipynb)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from ipysankeywidget import SankeyWidget"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> <i class=\"fa fa-info-circle fa-2x fa-fw text-primary\"></i> This uses the base [ipywidgets](https://github.com/ipython/ipywidgets) for layout and data, but you can use any widgets!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import (\n",
" VBox,\n",
" HBox,\n",
" IntSlider,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"links = [\n",
" {'source': 'start', 'target': 'A', 'value': 10},\n",
" {'source': 'A', 'target': 'B', 'value': 10},\n",
" {'source': 'C', 'target': 'A', 'value': 10},\n",
" {'source': 'A', 'target': 'C', 'value': 10},\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "790a475697554bbfbee5867c7fa8d68f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"SankeyWidget(links=[{'source': 'start', 'target': 'A', 'value': 10}, {'source': 'A', 'target': 'B', 'value': 1…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sankey = SankeyWidget(links=links)\n",
"sankey"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> <i class=\"fa fa-gears fa-2x fa-fw text-info\"></i> A convenience factory function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def slider(link, i, sankey):\n",
" value = IntSlider(description=\"{source} → {target}\".format(**link), min=0, max=10, step=1, value=10)\n",
"\n",
" def _change(change):\n",
" sankey.links[i][\"value\"] = value.value\n",
" sankey.send_state()\n",
" \n",
" value.observe(_change)\n",
" \n",
" return value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build up a slider per link to control the value:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"sliders = [slider(link, i, sankey) for i, link in enumerate(links)]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ecbee913b4e44d32abc1c78d4dc8a9da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(SankeyWidget(links=[{'source': 'start', 'target': 'A', 'value': 10}, {'source': 'A', 'target': …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"box = HBox(children=[sankey, VBox(children=sliders)])\n",
"box"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
"00a23a8e78c6408aa952ec9fc96ad35b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"096faac070cc4679b7a31e158589f524": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "start → A",
"layout": "IPY_MODEL_c20a7f4732574151b1ce6c4f9a2e1e50",
"max": 10,
"style": "IPY_MODEL_ca9103a303a641c7ad843cbc9b417a52",
"value": 10
}
},
"0ae2cd3147cb4d3783d51cb7837f47e7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "C → A",
"layout": "IPY_MODEL_51dd9f11aa11461f876b483aa6b7ddec",
"max": 10,
"style": "IPY_MODEL_195bfa2a5c47491c883ba150dc68c5cc",
"value": 10
}
},
"12a554a0c0224ee39414d2c8647b349e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"195bfa2a5c47491c883ba150dc68c5cc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"2cc0e2e252334c9b96cbff3f581874da": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"30b65cdd28f649688fcd5f8fb16b2704": {
"model_module": "jupyter-sankey-widget",
"model_module_version": "^0.2.3",
"model_name": "SankeyModel",
"state": {
"_model_module_version": "^0.2.3",
"_view_module_version": "^0.2.3",
"layout": "IPY_MODEL_97baf4291f73405d91e6ada159832abc",
"links": [
{
"source": "start",
"target": "A",
"value": 10
},
{
"source": "A",
"target": "B",
"value": 10
},
{
"source": "C",
"target": "A",
"value": 10
},
{
"source": "A",
"target": "C",
"value": 10
}
],
"nodes": [
{
"id": "A"
},
{
"id": "B"
},
{
"id": "start"
},
{
"id": "C"
}
],
"order": [],
"png": 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| mit |
sudhanshuptl/Fun-Tool | Learn/ExtractEmail_&_remove_from databse.ipynb | 1 | 8214 | {
"metadata": {
"name": "",
"signature": "sha256:53e9c7993b1a8d16f3842da2579de65e4efb6075ec0672338f97a4f9d397c9be"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Extract Email from deleviry failure mail & remove it from database"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import re\n",
"import pprint,sys"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filename = \"email1\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f=open(filename,'r')\n",
"data=f.read()\n",
"f.close()\n",
"print len(data)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"74901\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mailRegex = re.compile(r'([\\w.]+(@|\\(at\\))\\w+(\\.|\\(dot\\))((\\w){2,7}(\\.|\\(dot\\))(\\w){2,5}|(\\w){2,7}))')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res=mailRegex.findall(data)\n",
"print len(res),\"emails found\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"487 emails found\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Removing duplicates & extras\n",
"ls=[]\n",
"for email in res:\n",
" if email[0] != \"[email protected]\" and email[0] != \"[email protected]\":\n",
" if email[0] not in ls:\n",
" ls.append(email[0])\n",
"print \"total email to remove \",len(ls)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"total email to remove 82\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pprint.pprint(ls)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['[email protected]',\n",
" 'CAG3Hmb8Ysvb7QswrjNauKNfA_NasRC8YLg4Le86RYqknDOTS7w@mail.gmail.com',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]',\n",
" '[email protected]']\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"R you sure to remove\"\n",
"check=raw_input(\"y/n\")\n",
"if check=='n':\n",
" sys.exit()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"R you sure to remove\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"y/ny\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Removing from database"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pymongo import MongoClient"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"client=MongoClient()\n",
"\n",
"db=client.Emaildb\n",
"inidata=db.data.count()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for email in ls:\n",
" try:\n",
" db.data.remove({\"email\" : email})\n",
" except:\n",
" print \"email not found\"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print inidata-db.data.count(),\"Documents removed\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 Documents removed\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
slock83/FaceDetect | NN Playground-VariX.ipynb | 1 | 409067 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Neural network \"playground\"\n",
"\n",
"## imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T18:35:49.070000",
"start_time": "2016-11-19T18:35:39.097000"
},
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from cStringIO import StringIO\n",
"import matplotlib.pyplot as plt\n",
"import caffe\n",
"from IPython.display import clear_output, Image, display\n",
"import cv2\n",
"import PIL.Image\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T18:35:49.090000",
"start_time": "2016-11-19T18:35:49.088000"
},
"collapsed": false
},
"outputs": [],
"source": [
"os.chdir(\"start_deep/\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Caffe computation mode"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CPU"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T18:35:49.991000",
"start_time": "2016-11-19T18:35:49.092000"
},
"collapsed": true
},
"outputs": [],
"source": [
"caffe.set_mode_cpu()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GPU\n",
"\n",
"Make sure you enabled GPU support, and have a compatible (ie. nvidia) GPU"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T20:22:43.238000",
"start_time": "2016-11-19T20:22:43.233000"
},
"collapsed": true
},
"outputs": [],
"source": [
"caffe.set_device(0)\n",
"caffe.set_mode_gpu()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Network loading and tests"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T18:35:50.797000",
"start_time": "2016-11-19T18:35:49.993000"
},
"collapsed": true
},
"outputs": [],
"source": [
"net = caffe.Net('deploy.prototxt', \"facenet_iter_200000.caffemodel\", caffe.TEST)\n",
"#training = caffe.Net('facenet_train_test.prototxt', \"facenet_iter_200000.caffemodel\", caffe.TRAIN)\n",
"#solver = caffe.SGDSolver('facenet_solver.prototxt')\n",
"#test_net = solver.testnets[0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T18:35:50.817000",
"start_time": "2016-11-19T18:35:50.800000"
},
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def showarray(a, fmt='jpeg'):\n",
" a = np.uint8(np.clip(a, 0, 255))\n",
" f = StringIO()\n",
" PIL.Image.fromarray(a).save(f, fmt)\n",
" display(Image(data=f.getvalue()))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T18:35:51.080000",
"start_time": "2016-11-19T18:35:50.821000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAkACQBAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APn+iiiiiiiiiiiiiiii\niiiiiiiiv//Z\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"36\n",
"36\n",
"[[ 0.20703125 0.30078125 0.3046875 ..., 0.3359375 0.78125 0.9609375 ]\n",
" [ 0.21875 0.34765625 0.36328125 ..., 0.3671875 0.7734375 0.96875 ]\n",
" [ 0.24609375 0.41015625 0.44140625 ..., 0.3515625 0.703125 0.9609375 ]\n",
" ..., \n",
" [ 0.22265625 0.3515625 0.640625 ..., 0.296875 0.4765625\n",
" 0.61328125]\n",
" [ 0.24609375 0.3671875 0.65234375 ..., 0.1953125 0.22265625\n",
" 0.2109375 ]\n",
" [ 0.26171875 0.37109375 0.640625 ..., 0.2421875 0.2421875\n",
" 0.23046875]]\n",
"{'prob': array([[ 0.01360568, 0.98639435]], dtype=float32)}\n"
]
}
],
"source": [
"#im = np.array(PIL.Image.open('train_images/0/137021_102_88_72_72.pgm'))/256.0\n",
"im = np.array(PIL.Image.open('train_images/1/image000619.pgm'))/256.0\n",
"im_input = im[np.newaxis, np.newaxis, :, :]\n",
"net.blobs['data'].reshape(*im_input.shape)\n",
"net.blobs['data'].data[...] = im\n",
"showarray(im)\n",
"print (len(im))\n",
"print (len(im[0]))\n",
"print (im)\n",
"output = net.forward()\n",
"print(output)\n",
"if output['prob'][0][0] >0.9:\n",
" print \"visage\""
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-08T08:49:34.007000",
"start_time": "2016-11-08T08:49:34.001000"
},
"collapsed": false
},
"source": [
"The cell bellows checks that opencv and its python bindings are properly installed"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-16T17:38:38.060000",
"start_time": "2016-11-16T17:38:38.055000"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.4.13\n"
]
}
],
"source": [
"print cv2.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Face detection in image\n",
"\n",
"This block performs the actual processing of an image (specified in the code btw), and checks for faces in it\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T20:23:46.948000",
"start_time": "2016-11-19T20:22:47.677000"
},
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"starting processing\n",
"(919, 440)\n",
"(706, 338)\n",
"(543, 260)\n",
"(417, 200)\n",
"(320, 153)\n",
"(246, 117)\n",
"(189, 90)\n",
"(145, 69)\n",
"adding overlay\n"
]
},
{
"data": {
"image/jpeg": 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biJchpY1I6gsKAJaKiF1bn/lvH/32KDcwD/ltH/30KYyWio/Oi/56J/30KBNETtEiE+gI\nzQBJRQORR0oEFFFFAwooooAKO1GaKACiiimIKKKSgAzTW5pc0xnC5J6DmplsIP40+lS1CjAlSCOl\nTVcdhIKKKKoYUlLSUCClpKKYBRRRTEA4zRzRRQISilopiYmBSAYFOxQBxQFhMUYpcUUBYTFIRxTq\nMUBYaqKowBTsUuKKB2ExS0UUDsJgbvqOlLgUEfMPpS0AkJgelKBilxRSKSEwPSlwM0tHc0DsGKXA\noxS0ikhMUAUtFA7BRiilpFWEpMU6koFYwIXAt1ZS0h64JDH6HFWQZCAdg+m6n7cD0opSbZzpWG/v\nCOUA991L8/8AdGP96orq5hsVXzn+ZuijGahGp2xAP7zn2H+NRdDLfzdNq/8AfX/1qUbvRf8Avr/6\n1U/7Tt+mH/T/ABp66jbHq+36j/Ci4Fj5s4wv/fX/ANaj5sDhT+NRfa7bqJgf+At/hS/bIP75/wC+\nD/hRcZL8+ei/99f/AFqX58fdX/vr/wCtUa3ELH/WKPqcU8yxdTNF/wB9j/GgAUuccJz/ALX/ANan\nYbH8OfrTBPDn/XR/g4p6srfdIP05oATDn+5+Z/wpQH9U/WnBGPRW/KjawOCCPwoAZ8/A3p7cGkxK\nejx4/wB05pksZcc9RyvOOf8AP9alQkxjIwfTNTcBpSTtKn4qaTZJgbpFz7L/APXpxPNLQA3a3/PQ\nf98f/XpGyCAJDzx93/69OpjY3L60MBNuAf3hye+z/wCvTCv/AE1b/vkf404moycUgEK/9NWz/uj/\nABqMqf8Ans+f90U4kVj6r4k0zSFf7VcoJF/5ZBhvP4E0XAb4oGPB+tHzHb/QpxyoH8Br5Nk5NfRm\no+PNB1jwvqlpFdmK4ltZlSOVNuSVIAz0z+NfPNxbyQ53rwDjIOaE00JJ8zK+ABUJ6mns2aZTLHKc\nEH0NfTHmxSxJKpkxIoYHd0yPpXzN2r6WsdLvLbSbK3ufIjuIYI45Ua5jyrhQGH3uxBrKpVp02lN2\nuNpuJdsCu/OX/Mf4Vqps9H/76rPs7OVSCXg/C4Q/1rTW3cH70X/f1f8AGs/rNH+Zf18hcrHbEI4D\nZ93b+hqVUi4DKSTyfnbH86asL/3ov+/q/wCNSiJ8D5k/7+L/AI0fWqP8y/r5ByvsPVY8cbhjsG/x\nqRQncN/30aaImx/CcDs4P9aAa0p1YVL8jvYTViYLHj7p/wC/jf40qxRg5xJ/39f/ABqMNx/gacmR\nn/HNaiJlSLghWI7EyP8AyzTjtPVefXc3+NMDelLu5pgG1P7p/wC+2/xpdif3T/303+NJmjvQApVO\nPk/8eP8AjQNoOfLX9T/Wmk9KM8e9ACkq3WNPypCE7RJ+VIPrRmkAFYyOYo8/7oo2Rkk+Wn/fIoJp\nB3oAdwBjauPYUFUPPlp/3yKKTPNAC4X+4n/fIpcL/dT/AL5FIDmigBBjn5E/BRS54xgflSZ4ooAX\nPHQfkKXdnsPyFMB5pc0AOz7D8hSA47D8hSUZoAUkEchT9QKOCPur/wB8ikooAdkD+Ff++RS7sdh+\nVNzRQIdv9MD6Cl3E/wD6qZnpRmgB2fYflRn2H5U0mkzxSGPLZ9PwFODkDrUBmQZyw46+1ODAjIPU\nUXAmEjAY3fpS+Yx4zUYNLmgCTzG9acJGx1H5VED70oOfegCXcf8AIpRIw71GDS54pgSiRsdaN7ev\n6UwH3paAHbj64rjvFfxJ03wxJ9m2G7uh96NX2hPqcHn2rd8Q6uug+H73VGTd9njLKuerEgKPzIrw\nuLRLjWCdS1Ji09wfMOewPTjtWVWqqauzajRdV2Rr3Pxu12WV1tLCyijJ+UOjOwH13AH8q1tG+M8y\nFE1rTG25+eaA4IHYhT1/MVlWHhq0Vx5oQnA6CtzWPCljeaRJFtUSbDiQDkGub6429jreASWrPTtO\n1K11WxhvbG4Se3lG5JE6H/A+3UVc3H1zXzt8PfE934X1s2twxFjNMI5Y2bC8nbuBPAIOCT6fhj6F\nRgwBHTFdkZqRwSi4uzJM+v8AKl3HFNz6UVQh+fSjcfWm5ozQA7cwOM0u4+tRk4f8KUGi4Em4+tGT\n60zNFFxj8+5pNx3Ac0lNJ/er/umi4XJMn1pc03NANFwuOyfWjcfWkzRTuFw43ZCjPrijPsPypM/M\nPpS0XC40xRscmNCfUqKTyYv+eUf/AHwKfRTuFxnkw/8APKP/AL4FHkQ5z5Mf/fAp1L3oC4gRAciN\nAfUKKTyoh/yyj/74FOooAb5Uf/PKP/vkUGKInmND/wABFOzRRcLjQiA4EaD3CinAAHoKTvS0CuNM\ncZ6xofqopPLjx/q0/wC+RTzSGi4Ddif3F/Ko2jRiQ0aEdCCOKl71Gep+tS9gQir+8j46KetT9Ow/\nKoV+8n0NTZrRbEi59hSUUVQxNoz3/M0tFFABx6frRRRTAPwpCM9h+NLQKYgxRilopgJjmkx606ig\nBMUYpaWgLCUUtJQAUUvaigdhuPc0uPc0tFArBRilopFWExzmlxS0UDsJS/jS0UhpCYPtS4NA70tB\nSQYopaKRVhOgpaQ9DSjoKACiiigAooooAxnb3rPlgvJdSglil226EGRQxy3XtnH6H/F0g1JoCUFm\nJcf3mKj9PTn8Md81ahyuQSM98dKtnMjG8UWs0iRTwo7soPyr3rmVuL9Rh7S+H+4B/wDEmvRZOYUJ\n7E1BtGegrPlTDZnAnUpkPzQap/3wP/jVJ/bhXGYdQH+9CT/7IK79hFGu5+nsKVY43UEx4yMkMOR7\nGjlQa2ucD/bpJ/5ar/vW5/xFL/b0a8teBfY23/2yu7a3iP8AyzX8qYbOA9Yk/KjkQrnDf8JNaIfm\nv4/xiA/9qGnp4ntf4buH8Wx/LNdmdOtTnNvH/wB8iom0uwOQbePI6jZmpcENHMr4jB+5dWv4zSD+\nSGn/ANvMTzc2pHtLKf8A2nW+dD0yQZ+yQkf7oqCTwto8g+ewtz9UFHIBjjXo+Myxn6OT/MCpF15T\ngqqn33xD+bCrreDtDP8AzD4AfZAKYfB2jAcWqr9OKOQZCNfnUZVFA9ftFuP/AGesDxPrGuzfZW02\naZCmTIsV8iZHHHD4/Qnj6g2tT0zw7p9/BaTR3CNOkknmq+2NQnDZOck+oUEgAk4HNZuh6LZaxNc2\nl3bXVneW4DSRi5kOAxOOeAemQQSGBzx0EuNgWrOu07xDJPfxRNK75OCGJwePyrqhllDYPIz0rjtI\n8K2elXYnRpZHHA8xy2Ppk11sTZiX24qbWZWlhxLAfcb8qYXbcMo4A9QKcTTGNMQhLE/6t/wFRkv/\nAM8nH4U4nJ61GTxikBwPxG8UT6H9mgtpvKlkVtwYdVPA/HP9K8wutO1bVmEskVxnOOQckn61r+JV\n/tD4rC3eVpUilQKGbcBgAkfnmu4u7+0sYgZEkcDGREvtXLXnJStE68LSg4c8+p5Vf+F7vS4fNG5y\nRlhXO3BLkxzDGeORgivcH1bTprUzvHiI8ZYd6w9Xi0rVtIuDCY2ZUOAVwVOOozWdOrK/vI1q0Y2v\nE8SlRo2KsMGo6s3uPtBI/H69KrV3I4Q7V9HyTtPc3zsQSt9dKOR0WZ1/pXzhXusV1v1DWk7Q6teI\nf+/zN/WuebtiF/hf5of2GdTYA8cqP+BCpdU1zTtDsmuL67iTA4jDZdz6Kvf/ADmucvNY/sjRbq84\nLRr8uf7x4H86810zw1qGvO11e3kgZzlS/wAxP5mtJVIwV5BTpzqPlgdzD8XbVrza9hILfzDhgw3F\nM8HHrjHGfx9PQtK1qw1y1+06ZeQXMakq3LKyn3UrkfjXiN58Ob2B8R3cL4GV6ihpdX8I2FjNFKCy\n3cryIMmOQbYwAw79D/TFZOunZQ3Yq9OpSS03dvz7eh9BQGXzDxDjY3SRs/dP+zSKbgjlYQfaVv8A\n4msHwzqlhr2hQ6p9lhiR45GdG2nYVBz/ACzWLfePPDllciLCSc4LrGNv4eprOEqntZ6LaPfz8jF+\n22svvf8Akd6DLkcRf99t/wDE09TLu/5YgH/bbP8A6DXGWfinRLyVVhFo2eillBP046+2a6eO3tJY\n1ZIIsMMj92M1up1H0X3v/IX7/svvf+RfYyqwH7k/8DYH/wBBpylyOfLX/gRP9KprZ2wyDBAec8xr\nn+VLZAJblVAAEkgAA4A3mqjOfMoyS1v36WBTmpqM0tb7N9Ld15l7B/vR/mf8KAGzy8f6/wCFR5pc\n1qakgB3jMiHHoDQQuPvfpTM0vUUALj35owP735ClKkUmDSAMDHX9KQj0fB91z/WlKmk2kUAJhv8A\nnoMf7n/16QiTtIv/AH7P/wAVTsHPWjB9qAGhZOMyofpGf/iqCrn/AJaqP+2f/wBlTsEd6MGgBNpA\n4k/Ep/8AXpcHP3//AB3/AOvRz3qFru1VmDXUIYHBBkGRUynGPxOxE6kIfE0vVpfmyfH/AE0H/fH/\nANemkHtIP+/f/wBeoPt1p/z9Qf8AfwUfbbT/AJ+4P+/gqfb0v5l96/zI+sUf5196/wAyfB7yD/vj\n/wCvS4HZj/3zVf7baf8AP3B/38FH220/5+4P+/go9vS/mX3r/MPrFH+dfev8yyB/t/8Ajv8A9ejG\nP4vx2/8A16rfbrTH/H3B/wB/BR9ttMf8fcH/AH8FHt6X8y+9f5h9Yo/zr71/mWP+B4/4D/8AXpSB\n3c/gv/16q/brT/n7g/7+CkN9acf6XB/32KXt6X8y+9f5h9Yo/wA6+9f5lk4B+Vzj/aXP9aYGOT84\n4/2Ov61Ab20/5/IP++6pXus21mEwTMGz/qfmI6df89qXt6f8y+9f5h9Yo/zr71/mXbzUbWwRWuLg\nJuOF/dkk/gD9K8i8c/EkxaxCNAu3Jt2+ZxuCSdQVK5wRg9euRkY4JzvGmt69rGrSm10q9ktVGyEN\naMQFxycEHnOeetY2ieCLi7hM+owzRFv4ChVv16VE8VSgruS+9f5mlOpSm7Kcf/Ao/wCZX1Px5qer\nOqyN5MG3DW8cjlHPXJBJHXB9OOldZ4a8c+Ib+SCKa5hjsbdSX8tAGIXsQvPp0xmuQ1vwjJYxFrZp\nJo+4IyRWJo92LDVLZpTL5SSqZFX0B9M1UZqavE6Z0pU3aSPq3zpW5SaLb2zEf/iqeHm4/ex/QRn/\nAOKrKguo57WCeCTzIZI1dG9QRVhJTj2q02YmkrNx+8U/8A/+vUqtn+P/AMd/+vVBJQSAWAz0zVgM\nQcZqkwsWwf8Ab/8AHf8A69OBboSufYVVV6lD88n8adxE2TjqM/T/AOvS5PqPy/8Ar1xviP4kaF4d\neW3eVru9jBzBBzg+jMeB+pHpXBXPxyvGnH2XTLaKLjiRmdvzG3+VMNz0n4hwvceANXRAzMI0kwo7\nK6sfyAJrzN/ENpZQJDKk0jooV/LTIU45Brf0v4i2vi/S7zQr1Esr29tpIYZGkxEzMpAGeoyT0OfT\nJrkb3RrG2iMjnaijAViWOfTbmuavytK52YRSbfKaMvii2tYYrqGJplfOBuA6Yzn06ir1v8Rre5jW\nO4sJFLEKRC4c8+3HrVCwfTBpZ00uWfcSieWxAY59jxz34q74dm02SULMri5hPEb4wp9cDAP5VypJ\nfZPQlFys+Y5LX7Wf/hJjBFauiSr5qpNtQgH6nGcj1z+NfQ+guz+H9PLyB5Ps0YdgOCwUZ/XNedX+\nm/btSa4iSOV5oPspR16qzfNz9CRXo8drJGoRL64CjgDbH/8AE1vSqu7io3t6fqePipWm1GLdvNdr\nl87sfKwz7rn+tCNuUHI59v8A69Uxbzf9BC4/75j/APiabHbzFB/p9x0/ux//ABNdHtJ/yfijm9pP\n+R/ejQ/H9P8A69BBB6j8v/r1R+zTf8/9z/3zH/8AEUfZpv8An/uf++Y//iKOef8AJ+KD2k/5H96L\n+V465xjNITwcH8x/9eqP2eb/AJ/7j/vmP/4ilW2mz/yELn/vmP8A+Irnq1KrnGMU1e/8r2t39Rqp\nPfkf3ouq4ZQcE55zTtw9GrMtb2KK0H2q6jDK8i5kZVJCsQPTsKuQ3EU6b4ZEkX+8jAj9KqEakoqX\ntHr5RNKdRTipJbk+V7Bh+IpCQSDhsj6UzdRmq9nU/wCfj+6P+RV12JM+mfxpA/1/Co9+KQP70uSp\n/wA/H90f8h3XYm3fX8aN3uah3Ub/AHoozlzSjJ3s10S6X6A0OuZ47e3eaWZIo0BZnccADr3rzHWv\ni88Fy0Oj2KXCK2BNMCA30UH+v4VF421abxD4hTw/bXHl2Vswa5ZW++3ce+OmPXPtVKw8CaMHL3L3\nN0pH3Hl2j/xwA/rTqV+XQ3p0HNXJLX4xaoso+2aXamI8ERl1bP4k16boWv2XiGxF1YXKuucMhTDI\nfRhmuOsvCuhaexe1sVDf9NJGfA9gxrn9bgn8K6nD4h0aQxRhwtxCD8jA9iO4P6detKniLvUc8NZX\ni9T2Qbs8uv8A3z/9elII+6w/4EM/1rO03Uk1LTba9iyEnjEgGemR0q4JK6eY5Lkp3AcMCfpQC/8A\ns0wPShs07jHZf/Zpct7UwuqqWYhQBkk8AV574h+L2jaTM1vp8L6jKv3nRtkY+jYJP4DHvTuCVz0U\ng9jg00lh2X868ftvjgzTYn0ZPLJ4KT8j9Oa9J0DxHYeIrEXVlLkdHQ/eQ+hFLmQnpubGWx/D+Z/w\npCTj+H8z/hSE0hPFMBct6L/30f8ACmk4PP6Ugams3NKT0GhwJ3JjHQ9TUuWHZf8Avr/61Vg3zRir\nGauL0JFzJn7qf99H/Cg7+wT/AL6P+FU9T1ODSrF7q4bCrwB3Y9gK801rxvezwees8tvCzFUjhfbk\nDHfacnnvgcGsq2IhStfqKTaSdr30/rbser5K9h+dKGyOMV4VH4v1KN1zeakF3gAG76k/8BzXa6F4\n4ub2aOC4MMLvIEEkiFgecZOCPc1EcZFu3K/w/wAyXKa+w/w/+SPQh+P5UcVlx3OoSRLIkkBUoGwL\ndi3XHTfn1P4U5ZtSbbmS3UkFmU27ZA5wcb8np9eRW3tv7r/D/MLz/kf4f/JGnxSYxWfG2qOwLS2q\nK3AJgbPc9N/1qa3la5tLO5cAF0V229BlCeB9T9aqNVSfLZr1/wCHY4zvPkaadr9P82W6KhWZt5Ux\nEDICksOR/j1/L3pwkUSEAsecHgkA+ntWtzTlaJKKKWmSJjijJPYilrJ8Q+INP8MaVPqepSlII8BV\nXl5G7Ko7k/8A6+KB27Gt+FHPpXg1x8YvFetXEo0TSFhtQdqssLSuPq3TP4VPpPxU8UaHfIniiyln\nsnIBlNuI3X3BACn6H8xUc8b2uNxklex7lS4qpp2o2mrWMV7ZTLNbyjKuv9fQ+oPIqxJJHDG0srrH\nGgLO7kAKB1JJ6CqEtdh+KMV51q3xQQXLW2hWf2rBx9plyEJ9l4JHvkfSqVp4s8VrN51wBLH18sRL\ngflz+tZutBPlb1NlRm1ex6lQP/rVh6P4mttTYQP+6ucDKNxk/j/Kt3rVme4UvamuVWNmYhVAySeg\nFeBfEP4sXV5ey2Gh3EkNhEdvmxMVeU9zkchfQfifZ9Lsqx78GB6VWbUrFZWi+1RGRWCMobJVvQ46\nH618pR+NvEF7p8+nS3872cqMzq7bixA3Dk+hANYY1O7VuZpGHuxrF1dbRNYwiqSnNtNt7JPa3dru\nfaSSIzMFYEqcEA9DTsivl/wfehL2GZoYbxhlmguEDq69x83frXv+laX4Y1bTory30XTCjggj7LGS\nrDgqeOoPFV77V9PxFCeHb5by+5f5nRZorK/4RnQP+gHpn/gJH/hR/wAIxoH/AEA9M/8AASP/AApe\n/wCX4m1qHeX3L/M1e1Hasr/hGNA/6Aemf+Akf+FH/CMaB/0A9M/8BI/8KPf8vxC1DvL7l/matFYu\ngWtvZ3et29rBFBCl8u2OJAqjNvCTgDjrW1Ti7q5FWChNxXl+KT/UMj1pNw9R+dLRVGepyi6Hax3I\nu2aaWdcYeSTcetaA4Oaa0bv96MH6kULEyH7gUH0FWcxIxzAPrUYNPIIgweoP9ajFQuoMcQGHIBqG\nFcOCCzKwOeeM7s9PfJ/KlxPuJDx7PdDn8805ssoAYqfVcf1zRa5SbSIMTrFGoZmMeGkYn7+Acgfi\nAfx/CpJDKRtjbGQMORnB9MemP8insNyFdxzjG4cH9KaEG3DAH68/zpWK5xSZNxbaT0UDdjjuen+c\ne9Eqho+2V+YZOMEe/wDnjNLwOAMCjJpkX6kEryKkjxITkLtG05znnj6YoiknLqGUhQq5Zhgk4OeP\nrj9alOKY4G0kKM9elKxSktrA7EzooLqBknA4bjvxXMa89yl1svdWvtNtpTst7m12eSGPAEgKFlOT\n13bTjgqTitC11DVL2zt7uOwswk0ayKGvXyAQCM4i96kefWCP+PKx/wDAxv8A41UKot0n9xvPCyT5\nZON1p8SOQ0TT9dm099C1m3j1DT7eR4IpwRGytHwpLZJ24PBUbgykHjNdLoXhix0CNfJ3SzKrRiVi\nRhC2cBc7V6DO0DJGcVYM+r8YsbH/AMDH/wDjVIbnV/8Anxsf/Ax//jVLmXZ/cL2Ev5o/+BI0SauQ\nudmPQ45rnmuNX/58rL/wMf8A+NVZtrjVypA0/Tzjnm8f/wCNVMpq+z+4Pq8rfFH/AMCRtkn0ppOK\nzvtGtAcafpw+l6//AMappuNawf8AQLD6/bX/APjVTzrs/uD6vL+aP/gSL4YGQDOeajdgO9UDc6zj\nH2CwH/b4/wD8aqFptW/6B+n/APgW3/xqlzLs/uH9Xl/NH/wJHm+paJbWnxHmulwQzNIVfJxlVOef\n9pjV2+03S5y0koLsMkjGSPp6fhV/xMJYbpry5tbRZHUBtlyzHA/4AOK4hZ45J2bO0E58vzSufyGa\n4qsnzN6nbTpqMFFOP3o6C5/sv+yGtfMVSzAgZ5X/AGfbr0rLGlraW7vFeO0JRhtJyMEe/wDTFLd3\nitZEhYos8eZ5pYj9M1gDXisph3SSD0VeSfXrn9KmEm1pccoXvdx/8CRwd9A1vdNG55HNVq62fSId\nU1CaSSSaOVzwpj249KoXXh1LX791I3+7CD/7NXbGtHb9DkeGl3j/AOBIwa9Z+3CLX/FEG7BGs3RH\nPq5/wrzJrWDypzHNMXiUMVeELkbgOoY+td605Txt4tUHH/E0mP1/ePWcmnXXo/zRnUpShHW2vZ3J\nvFd8r+G5oY5Vd3nUYDZI96vabrNhpkEKTy4YADABOPxqpeqLjSGUkYWVWIz1xmpJNN0a7gFzLmNu\n/PBNZ1+VtKRvhVJJuPU3Z9asUTzXuFCHoSazNdlg1Hw48sDrIgZsY/4DUV5aaXdWdrAJI1VUxuRu\neveszxBJHp3h22t7ObzFkmk3MH6kBOO/tWCim423v/mLHNrlvtzL8pC+FNTurZJdJSU/Zza3s5UD\nq32WT/DpU2l+EDehbvUIyVI+RWJHFXfh/oRa+TU7uRNgtbg7B7xOpz+BNdPc+INOXbaIzeZnABQg\nH6ZFZzqSdaah2X6m9GnFK8zIvvBOh2VpBew21+1y8RkKQXixKcOy4G6Nv7tXfCHj+wW9j0me2v4t\nzbFNxeJJhs/9c1NLrWqyIulIpdI3tT9xRnPnSDkngdK4TxPa/YNQh1K2dSJcNlezD/I/I0YWEqkb\nzk769X3ZjW5L6H0WJEXnyJh+f/xNUH1nSdOj/wBNuo7Us74E86qT8x9QO9eYeM/iVeW6R2GkSCGR\nolaacfeBZQdq56devX8q818Q3M02piWWRnd7a3ZmY5JJhQk57mux0I88fels/tPyPNlJ+1irfzfo\nfT1nr2j6hIY7O+huG67YZg5/ICrvnw4ztfH+favj1bhlYFGIZTkEHBr1XwJ8Wp7ONrDxBcTzQqMx\nXBJdx/sn1HvWvsF/NL/wJm12e2G7th1Dj8/8KZLqEMcDyIjMVx8rNt49ckdhz+FefzfGfw8k2BDf\nyLjlgi8fm31qzYfFnwpqcjwvPNaPt+R7uHKk/wDASf6fWn9Xj3l/4Excz/pHazz2DQNI03IJ/ehg\nTHngkcfLx3/E+tVg2l7C6X92QgwzfbZGU/XJIHP4/gcFbRN1vG1stj5JXKtEuVYdiMcY/GnvatKA\nJltJMcrmDofzq/YxX25f+BP/ACJ+X4FS6vfseovbtKVtmUgfaLrZhlODtIy3pkZxyPpWnb3ds0KG\nCRpY8fKwJfP4nr+dV762mmumZobGZFY7RLESRz65Pt27UyOTU1ZvMhs2X+EJKykfXKnP6VVCTqUI\nNvWyv9yE1ZsLphNJIbu2uJbdSNipkrjHJKjqc/XtijypnuLnZBsDNhbg/fUbQeBjkZz1I+mOKST7\nXIx8y3R17R/aSFxjBBGzkfXNPia7VpGMEYMjbm/fE9gP7vtW12luvvFZDra5kN60DQXSYV2DuQVc\nBscckjqOoHXv2ddNcvGv2UsJVZnQkZVsZAVuehz+me1QzQNdQNBPbwPExyQZCec5znbwafGkkQj/\nAHMQEabFIkJIHHqPYUXW4ymmpyXljqbJHPBPCzqsZZSQwjB7EjqfWr1jKkVuwy5zK5+Y5/iPrULi\nbbIRboHeRXJWbBJGP9noQoB+pqexYm2yyhWMj5AOcHeeM1jVf7yNvP8AQya/ex9H+haE8bdHH4nF\nODp/fXH1qFzEiF5NoVRklscCuH1nxnCDIlj5ax8gSMuS3fj24NKU1FXZ0KLex3bA+dG6shABBz7k\nf4VEqGOOGNZwSWO4556E15jB49ubYutyiSor8EfKQv07nNdp4d8R2evQ7REEmUcgjg/SlGtFkSoP\nc6FwRGeeT/dPOKatvIqoqsigA7gD3Pp9KaYYjzsA+lM8iPtkfjWlxOFxkdndpn98vAOD1JPv64GP\n8ikaG7dtwmSMZYHPJ68Y7f570rRJ6t/31Wbq1/Y6XYyXV3MIoYxyzE0czI9hFK13b1ZalEsaRqtw\niKoAIxktj3rzT4oX1xZ6Pw+77TJz8oKjAx3HB6c5qnq3xU0xVA0+zlmfPzGU7Vx7YyT+lcf4k8bj\nxBarbLZ+QgOT+83E/oKWpapq9zlYA91con8TsBzXr+lXcVs1lp6Auy4XcXVB7/eI/Lqe2eK5Dw1D\npk13bXCPiW34aFzy2QfmH4+ld48tpJeW37stIZFI46c9a8zGTTi010f5M7pQthqjT+zL/wBJZFHq\ndnwHkKPnBVhyCenFYXiM6LZ22naidJsb6S7uJkklmeYYCLEQAEkUZ+duoPar0z6ffRJF9oG9s5Uk\nhgDjPoeeKoeMNJuZvDWi21mokWO7um3ZwFBWE9e3JNZNQ5optq79NLPtY68Q5OJ6RoF0lxoliy21\nkqIuI9jysoA3AAEtngYHP+FbK+c7KRDYttOeGkHb6/WvO/Bt0ukaTHbalqCA+YSsZ+7GPY+9dzC8\nVwokVwwPRkNdkaNO3/Bl/wDJHkyg76v+vuL7RP5bt9iiL9VG9+e+Pve5qWKS6y2y0t1UHCjLD0z3\n+tVo3uYh8ku4f3WqwtxMy5GFPcFTVKhT8/vl/wDJCcPP+vuNBGbAzCme/J/xrnvGvioeFtF86OGJ\n7uZtkKOW257k4IPT37ir0940SHfLz6CvPPiEBfaMJsF5YJAyknoDxj+VH1en5/fL/wCSNE23YxrD\nTrbXY7qa90Gyjkltp5RNHLclw4iZlPzSkE5A6iuNuPDcnmsIwWAPpXoFre30OoywQxuscdldfKFX\n7wt5COe/OOlc9e6lf+TAYAUZ85bYO3B9a4qLnGrJJ6adW+/ds7nCFttjBt9Jufs8kqxOVjOCw7Yr\npUSXV7OKZZQZhgnecgt0JPr61NpN/e3VpcQ3EbktExU7RknHtgVW0KxuToWpXsbENamNhFwdynO7\n+n5Hv06neWnUmLUHdbF22e4gaNGZ4rw4BaONXQfgUB/8erYj0ZrO5W/urozyycMfLCBT7Af1rmT4\nrkTaPs2XHcYxV8a5e6qiBiqKDwg61ChLqbyrQtpuegaSqXF/bsN7CNwcLnqPm6Dn+H+dduk2RkJL\n/wB+2/wrg/DObcQTP1L8n/gJ/wAam+IniO80TRIVsXVLm7dolck7lGByvHUZHOeOK0w7XNL1X5Hl\nTTdafy/9JLHiT4m6N4ema1jV76+XIaGI4VCDyHbsevAB6c4rndO+MyC426jpZSAgkNbyFnUduCAD\n27j+lcdY+C5JoyZHJd/mZiP61eg+HlxcStGl4wXtnrmreJheyOlYWbVz1nw9430bxNI8VhLKtwnJ\ngmjIfHqMZBH0P1rod+Ozf98mvE7vwLqvhYw63p90JLi1ZXJj+U9QDx0IIJB9a9d0q/kvtKtLtw0b\nzQpIVz0JAOK1jNS2MJ05QdmX92eAGP8AwE159468VX8V4+i6UzQyIitcTr95cjIUfgRXdSSMVwDk\n9s8815FfsZNXLMeZba3ZnY8n9xHXNiJONSDXaX6G2GpqpKzMp4Dc3bNMZJJPLiyxJyTsXn+dW0t9\nZ0aIajplzPEyYZo88EfTuK2oLaNZkcquSq859gK1nIMKxkL5Z4Jrlo1JcqaO3DUIPDxT7f5nQeEP\nFCeJ9GFyIylxERHcRqOjY6j2NdBiQ/8ALNq8j8Cs2m+NrmCFmEFyjDb7jkfyP513fiHXjpVtCiEG\n6uX8uHdg7eCWY+oAH5kV6MZpq55soWnyo0NV1yw0WISajcx2+QWVXcb3A67VzlvwFZEHj/w1PMIh\nqYjY/wAU0TxL+LOoA/E1iQeRdPvkjR5CdzSPyzH1J79vyArZitLFbQymGPzOn3QciuZ4pN2SOxYF\n2u2dHHOJYRLHuaNlDK6qSGBxgjHX8KhlvAg5juB/2wf/AAriYZI/COo3eoQyvHo8sAea1B/dxSed\nEnmKMcfK5JA64+mOn1K8MSfeOadCSlObXdf+ko5alNwfKzyW3aWXxxr8sZ3+XNIyK3HLOTzUklrr\nN1Nul8yNlbIYysAB/ujgd+aWPNp4nvpvL2/aHkdnLZLnfn+TVq3+oxDTX3TtCz/KjAZO76VnUk+a\n6PQo01a0mJrVjfzpZzWcsh2x/OolZST6ggGs7XZNTXwncNfqcxFWG5slgSB+PWrGizfZbkCfUpna\nVdsUcgIyevcA9KuanIL7yrRwrJJINwZscD5uv4U4ys7FTgnr1Oz8Go1j4Q0yCYTb/J3kGJsjcS2O\nnvW6t7H/AHZ/+/En/wATVPTWkg0u0hLyAxwop3E54UDmrazSB2HmNj3Ymu5PQ8hqzsTR3KyMFAlB\nPrE4/mKcLgLwyS7hwR5Tf4VH57ngu2Pc0vnsB94gD3qriPJPir45kNzJ4dsZikKAG7YDBZuuz6Dj\nPvkdq4Xw94fuNYZZ3G2FjhAe4HU1m3SXPijx1qCL8rXF1LI2f4csSa9Ek0sabLbWUVjqzbIYi00e\norEnKKxAQRngFiOfSuLFV+RqC3f9d0dlGF+mxlah4PSKykuII/4iAPYVT8JeIJfDuvJKXYQk7ZVP\ndc9fw/xrt9TIbQ4Bb2mouXB3rFeojf8AfRiP9K4vV7DTdPsv7Rn0XWVGVVw2pxnGehz5R78VjRry\n6xf4f/JGmIpJx2PohJg6Ag9RmkaUetZXh67SfwzpMqCQI9lCyiRwzAFAcE4GT74H0FaJn+v5/wD1\nq7Pbv+SX3L/5I89K63FMy/3hmm+cjE4YGjz/AG/l/hWbrWvRaLpsl5MucYVI9wBkY9BnH4/hSddr\n7Evw/wDkilG73I9U8U6PocsMeoXixSMMhAjMTn2ANQD4h+GCuRqRJxnH2eUH9VryzUbqw1z+1bvy\nbs3sMKzZmullQjzY02kCMYA3569q46a6jaQjzd79Sd2APappYtzbSVrd/v7s1dFLqepeNvFFnrkt\numnXDS28Sbj8rLlieeGAzgAfnXMm5VrO1RBv3Suq7ug4Xn8jWHp18scipNLGY8Y2s4P5Cta6vYot\nJiliJKCRwJAhPZe/41jiouTjLz/RkvenHz/SRKH33JQoGxyM9iBgVfjKYjgb/lpD37HJwPf0/Csb\nTx59hLf+a4jtz+9ymAgNa2narpt7EJYll82Ehsso+bt2J7n9adKDOmpGyudRqnxM/seztLdopri6\nCAvIrqisOx6E59arW/xlhlMaT6PIFz8zpfMDj1A2jP5iuLutCuNa8QvIz+XbKFQDrnAAP65rp7Tw\nbo9pEpaEO+Pvvya6HiuV23IWGuux6Ho/iXRNZsBd2891EMH5J5XRsjty2D+BI6+lXNOu9OGnWyve\norrEgYG5I52jtmvKL6zl0i+ttY0tleK0UoyLhlKkk/MO4JJH1969Ei8TaHp1hbCWdBIIgGCRlmyO\nuQBnqD1+tKnWqVJqV0tH37+p5LVZ1Va20raPpK3fyNma+09YJHjvY2dVJVftZGT2HWpUvtNTBF/D\nn3us/wAzXMyfEfw8rGJp5kkJwqtGQT/h+OK6K01iwurJruK4TyFUsz7uFA6k12J1Hs1+P+ZbVdO1\n19z/AMyddSsA+TqVuR6GVMVVl8VaNBK0b3TEr12QSOPwIUg14f41+IU+uai8UTslhE2IYgcFz03H\n/P098DR7u8udZs38lBGZ0ztySBuHfFcVbGVoXcUml6/5nTPD1qdKU5NXSbtZ9E338j6X03XtL1cZ\nsL2KY4yVBw35HmvHPHcx8YfExdHaVv7O01csgPDPwG/XA/Cuejl1ZUtsxrA4YYlV2RwOm0EHv+Nb\n2l2U3nSsHzdNH8k3dgfU+tVUxnu2tqzthhrPU6y1t7azt0gjjVFQYULgCrSPpNyjWeptbtDKpUxy\nuAT9M1wMkF7b3i3d6tnaRxH/AFwIBc+hY4/Wti5SLUdWgvtJ1XTCTGqvEGWR0I4PAOQCefxrCitb\nnTPRW69Dc8PRN4K8Xro3nb9Pv0DQknueFP8AvcbTjqAD2qHxtrcuv6kdFsZgNPgbFw6H/WyDque4\nHp6g+gqt8Top7DTPDmsK3NvI0TlV5KtgjAORxtPHvWfaiGztrZSDEiLk56n/ADmu7E1HCn7u7OKl\nBKrd9df8zRtNNgt0QJF9zv71fiUiVT/CDyK5m58YWcdylvaN5uWALAjaPxzmpH8UGwvjHPBKSBu/\nden1rzacJc2p6cqkeV6ncX2lQ6host5EjRvECV2HB6c81t+G7u4l06MTuJOuxifmIHr69ufeszQd\nastT0VraMNHO4JMZU5PAzx17jtznjNads9vHcQRwkBYV+dl6Y2nr6dB/kV7VKPunjVpJtNbmR8U9\nYk0fwBfyQnEtxi3UjsG6/wDjoI/Gvmmx0jUNbAFv8kecZx196+l/iTpqa94E1K2huEEsKfaFAYc7\nBuI/Fc4/CvLfAtrD/wAI/bu7KmSxJb13Gs8ZN06Cce5rSip2T8zk18GanYwea7RuuGTCk5ywwP1I\nrNXw1etceWw2knGO9ewajJaQ2gfzk2iWJic9hIuazV1nw7cX6j7UsjBuyEqPxArzaFapKWqO2vRp\nqhH/ALe/Q4Pw609tqa2Uaqk0cnLuoIIweDntXceItR1TSPDsVyN9sHkWMpDIwST5T8w9QeOvpmo/\nFfhJLK7tvENjIzW0jAFoj0PYg/55rqDBp+t+GEheNAzfvihUjJzjcB7knOOK9iDtBtnkOn+/ilqm\ncz4J8b6lZygzPK9uOPK3EjBPLBf8K9n0fXLbVVZY5YmkTr5TEg/mP0rg/CPgfT4NTMjy+ZDjcISv\nQ/WrGq+HG0TxlY3WjXMluLg7pIWJZGAPIAyO1TSftFqXKnKnzNPY9JopF+6KWg1MrSf+Qlrv/X8v\n/pPDWrWVpP8AyEtd/wCv5f8A0nhrVqIfD9/5s3xH8T5L/wBJiFFFFWYGEsCtk7pBk54kb/GpQoQY\nBY/7zE1Qv729tZoY7TTWu1dWLOJAoQgZGc9jz6/Q1A+oarscLo48zYSrfaAUDbcgHgH2yB19BzVn\nKapz5TVAKjt7m5mk2S2LQJ5YYuZAw3cfL69zz7e9KDUdR2Gu8STKHKhzwueM9Rx6nk/nQWlDKoIO\nScluMfl/9ekkdkXKj6nGcfh35wMe9Is6FUJO0twAQQc4z0PtUmivva4iPcGP95HtPLHkk4IJwMdw\ncClLyvFGyptzhmUnB9cdPw7UNLmESJkggEYGTj6fSj7RH3cf0/z/AIH0oHq9kCecGw4BGT8+Rz6c\nf56VFcQ+akuCd7EAMV5A4yPpxUvmoQcMMK2DweOM/wAqbFN5kKSEFdyhiOuMjNGgrta2EeaYq5EX\nGflwQMj8emf84qMtcjcRtChflB5ycnvnPQD86nzngc9s+tV1mLRbgpZ2G4LjGM9s9P1oC/kU9BZf\n7B04A5P2WIkenyCtEmsnRnYaBpo3D/j1i6KcgbB27np+dXI7qGVV8t87hkEg89e5+h/Kog/cXovy\nN8VCTr1Hb7UvzZMTTSciqwuWKIDjzgoZ0APcdB7+nr+omLcVVzncWtwJqe1f5iPUVTZ6ms3/AHij\npnI6UpCRp54pucpnrknpQAf7w/L/AOvQVyFUMeOAABUgRNUTHANZ2peJND0p5VvdZtYXiGXjZwzj\n/gIOc+2K5O4+J2k3mq2+laSJrp7iUReey+VGAe4z8x+hApPRNhH3mordnP8AjrxK09+8AZhbxHbt\nxjketZGiaRc6xmd7iOCJOdj8E/hmuzh8G21xdC71DBEbNsiHQ8nknuT1NZmuWf2SWU28FszMRsJj\nHC4xjI5HU9D3rz5z5n2PRhQVPzKF1oVyuVaQSJ/CewH9K5nUNPudHdrmOOF1PUMma6C31qbTNqal\nII4pM7RIeR/9b0J//XHrN9a3mmSyQSBtoyfaiCaduhVSMbXRiwX8d8kW6MIWPC+nr+H+FU9alMEy\nKZAVI9MY/Gs23uy80SoPmRflA79q6S208a2q6eIXe9ZWbeSqqoUFiWZiAMAE8+laS5afvPY5uXm2\nOWe2a6W6MA3u8QXAPX51/wADXa3+mwWvjLxDO85aWe/nOMfKuZWPr1qhaeFbzTrZg8+nmaY4G3Ub\nfCgc8nfhRweTx0HXGek1DRbTV9avLq21iRJLqeSYRxyWTldzE4H+l9s9cVxVcXTjV5lLS3RN9V5G\n04fu4K3R/wDpTOeu5prVDF5imOTqNuP/AK9Ri6kjtF8o4YDiumf4aQXO121idH7hktif0ua0LX4c\nWUMMiS6u7hhxlIBg/wDf40nmGHa1bfyf+RCpzW2h5HceY07rjbjkqKs3okj8O2C/NuN1OcDr9yGv\nRtP8C6O9/KJ9SeUqcFPMtR/6Dck/pXRT+F9GtbEDywLZG4ZI4pTvYc5xIcZCjnvj25KmaUeaMVff\ns+zMKlBycHf7X6SPPPA2r30F69hcAJE9ndMkjcGMiCQ59xxWymmCzJnmu5DG+NwaUku3Yn/J61oy\n6b4ftLiW+SW9eb7NNAE8hVU74mj/AL5wPm9K4+XU2t4TDJmSO3YhQecjqB+RqoSdSpKcU0nbpbud\nsVGOk9bbHXa1d6fc/wBjzLIHJtSUKc9JpQTke4NZXiDybrSD5jb0iAYAHsKytYmuZLDw/LbqkRNi\n5wW2/wDLzP0AosRc3No8F0VHmLtC+5Hc08PFxgnfq/8A0plTleL904i4uN8hPdjXU3Phm41CaOf7\nqm0t8Z9RCg/pWJpulSXetRWkyleQxH+z1r0DWb+SwnSKKeGBUjQAMhbPyiumvNqpGMd9f0PPhTXt\nYuX97/204608G3U7EyERj9aq6r4auNNzKoLRqMkiu607ULi4EnmpGxTnKd/zqleanJqNtPAkFvtw\nVYCbL/lRCrUctTrnSpqOiKHh7wzp/iPSDK88dqbdC08jZx94KOiserL0qy/w90sZKa/aAe6Tf/Gq\np+C9Qaw0PxBmISYtFkClsf8ALxCuP1/SteLxFAmgre/ZcDznQqwVjkBT1I/2q5KssQqslCTtdJbd\nVfqZTqQUI+7dvTt39ex6L8OylpojaS2rW97JaHgxJKoVGztHzKPQ9Pp2rsdq/wDPVP1/wry74eaq\nmp3eoNNEsJ2R7BGAhxlupXGfxr0MQxMMhpv+/wA/+NdMFiGtZf8ApP8Akcc5VE/gX/gX/ANGVVMr\n/vEHzHsf8Kj2r/z1T9f8Kr3EMZuJSTNy56SsO/1rnPFOv2PhzTjI5me6kBEMQnbJPqeeBWeGjiPY\nwtLov5ey8hTnUTfuL/wL/gGtqniHRdFdU1HVbeByMhSGY/kAcVl/8LB8Jk4/tyE/9sJv/iK8dbUN\nY16eSZhbyNnG+S0ikJP1KmrM/guW41a9ZXEULTuUVRgKu44AHpiq9pNS5ZTa/wDAX+hrShVnPllC\n2l979bdke6LqVnLb+fDcxyRbVbeucYZQ65z0yrA/jXD6n8VdOtbmS3ghlk2kqX425Hf3/wA81554\n0tL7RtWhkR5FjaxtYt6nAYLbxqR+YrO8P6JBrc7NPcMET+AdTW1KvehGpJ9DRUW58p1tz8Vr+O4Z\noY4xC/QSknPPseOPrXU6H4/ha+FreuqCSOOVeMbS6q+Ce/3uvHSuQl8HaMYGULIH7OWyRXI+IvM0\nrXPKWQsFt4FBHfESLn9KmNVVKi5fP9DOtQdOrC/aX6Ht3iLVnutOiiRcJOx+YKcEZ4A/x+navP7m\n0Y7ZViZipLNjIC+344qja+I5NR0y3gdlV0bapVsYPHzFf89K6yDRrYQk+ZIC2CXDkGoqyfNqdVGl\ndaHKS2UsrsI4yry4yxGAfQn+prV0cX2l30N3ACVRxkE8EehwenFbyWVp5eyQB177z1qhq2zTrU3d\nngGP7yg5BB46e1QpN6I1nR5Vc9VtboXVpDcKNqyorgegIzUhfHNcx4Wvd/hiweW6jJMfqOOen1AI\nqXXNYfTtKlntFa6uPuxRx/N8x7kDt/8Aq712+2gtJSV/Vf5nlzq04ScZSSfqv8yt4p8b6d4bUxyk\nz3hAK2yHBx6k4wB+vtXjnjLxvL4qmjjaJ7W1jGUhVt2W9WOBk06TSvEN5cz3VzYTyzSklpJYyTn2\nrJPhvWGky2mXnX/ng3+FL6zS6SX3r/MXtaPWa+9f5mQllNO4ECNMT2Vcn8qtN4c1pFydMvD6YhY/\nyrZt/D2t2UqXENldLIp3DbE2R+lem6b4hYww/btOvYpyoL7LSRlz+A79fasp4uK+Fp/Nf5m0KuGf\nxVEvnH/M8OH2myn+XzIpl47qwr0HwxqkmsNas7FJ7eVBIAoIcZ4+n/1q9DvdP8P6yjtdWVvI4xud\no9rg9Rk8EVwI019G8WBdOVfsct3HnB6KxU4+gzUV6kalJ97P8maVVyUZyi7pxl/6SypbzSRyGRjG\nYz94mPY2fT3q9qOpFvC1i+4qv226B/BIP8a5bUNWvJLeL7THsIb5cjGa6nTNNt7zwhpUsyeaFvLl\n8N0JKQdfyrGvFRcJS7/pI6nJ1JWRzscst+xENnJcsOmASB+FdHoV7rGg3I80PGkjf6tvun2x2NdN\nprNbqFihjjXHRcAVneMmUaDPK42tGyupHY5H+NaKq5OyKlQUYts7CTxDCLa2NsFkubhSyxseEA4L\nNjtnj3pBJczNmS6lORghW2j8hXH+Fpd9obx8b5gCuew/yc11dvNufhDU1asr2TIo0o2uy01o0kfD\nupA9cj8jXL6xHJJBNayKBKu1h3HX72O468V2EV0yghYyawNcRJDDcZCupKEjup7fng06NSV+VvRj\nqU4r30tUY1rd5vTE8YwbO6yxX7o+zyf4Vx2oX07PulMccQA2bomUMR/vf55rr7WSIarLHN937JdM\nCe4+zyZ/QmuD1LU9MilOZGuWBxtC5UVFKD9vNW7fqX7Vct27HS6NqIfrGI1AyWHpW7BqGnaFozC6\nuE828YRQxu22TYxwpIPO0ZY5PGB16V5PceJbp0aK3VYIyu045bGMYzWTLNJPI0ksjO7HLMxySfXN\ndsKDvdnLUr3XKj1PUNAgkJkCkDOTitfQdOtY1G1S/qTXnmieOr3S0WG7gW+gUYUM+1wOw3c8fUGt\n5/it5dqyWGiRwzE/flnLj8gB/Os3SqrTdGiq03q9z0vUdUs/D+km/uARFD82xPvN2AH4sBn3ryS1\n1698Q69EbvzGiluWkhhBJjiycsFHQdBn16nmsttZ1DW49ZutQuXmk+xKBngKPPi4A6AV1PgyOO60\nS0lKkNZzyjfnPLDoPT7/AP46KlR9kpt76fkc9GXta815r/0lHexXVuqrE1xEj4+4XGfyrRs5oxMo\nMuFPevPtT8MTXU4kS5TYzZ+ZRn88Zrfn8ONe+FVjt59ssbDczdCKxUY6WZ6aUktUd7qEyf2Jch2D\nxmJvmXntVXwVqD6h4O0+5k+/teInPXY7Jn8dua5nwx4evrDT5xc3MRE0RjCIiryeATg4rb8N2Z07\nQobRZZY4IwDDsCqSGUMxYEN8xcvnnHpXZTktkcVeErXfQ6Ke5MUTygZKKWA9cc145r8V5cagslrP\nHGrWlsdvmHj9xHwMV6hcIs9tLCZZiHRk+ZlxyMc4WvJvEy3VlqMkLMYjHaWsUij5iCLePI4+nasc\nRf2sPSX6DwiTbuXZtPvptLE6X7LLiIBS2Fx5a559c5NWLO11yW0aOSUrGf8AlqlwS4/PpWKZVSZU\nn1SaG1e1hBiEbFSpjUc8EDPPpV601C6GmGEuGQEbZc/eA6H61hTuoI6sKoyoQ9P8zV0ATaZ4nguS\n012rSpGWYqMLJ8gY8fMcn26Gtfx1O39t2CuwEUUDurHsSfm/RVpPDFlHqF3FdkDFqoRjt5H8SqCe\nM5O7/wDXVTx8+/X7WBmPlNaAjOM7i7g/oF/ya6k3yO5hOEfbJRKtl4lsYp1jKXGSflZoyoP0Nakf\niuwMwtGgu2kHVkiJA+prkLXSlg1C0Z5if3ysN8pJwDk1raho1m2vGRr8RtOfMChiNw5BwR1HtXMo\nwudyU+W3U6jXhb3nhTUWVg6m2Axj1kQ5/MUy/wBSabTbSZzlpIVds98gGk8qJPD19bwJv/cgiMyE\nhj5iYGT0yePxrlbrUoY/JtLm9kSed1EEBKlsMFAHsN27HStKEl7Sa81/6SjhxFJ2cn0/Uy9SleXX\nY5xwI4HYMB1O5Rgn6Gp9P8S2sUgW4JC98gVHK3l6hNpzEkooOSO+AT/MVh6hphDHBIFaTUZPUmlz\nRjpudsfEujx2zrb+WjnJ2gdTVvwPdz33ia4m5EEVpj6lnGP0Q15xYaSxlxuLZ9K9U8FwwLbyRR3r\nxTqcNHGsZOAB/eBP8Q9ufenCEYvQmtUnKOp3IPFPMioXd2CqBksTgAfWqa25I/4/7z/viP8A+Jri\nvijqM9vptjpdrPKG1JzG+QMFF256DPJYdPQ10HKld2KPiz4sNayPa+H443wCDeSrkZ/2FPp6nI9u\n54KPx94pjuRONZui45wzBl/75I2/pXRX/gO0k0vfaSN9oRR3zuOeeKs+FPACRSSXetQK2w4jhY5D\ne5x/KsVioW0Ol4WV0cVomvDT9aN3fKx37suvYs24nH1zx7169qmv2YtYz9pVI5LaHazdt0SkZHbg\ng81wnxH0GysUt7uzhEe9irqowAccUJrllpmoxpeKV32Fid4Gcf6LFXJXiqk4TitbS/Q1hJ0Z8reh\nf02/uLO5ilu9deW2BJ8vZ8rZB6EjBx7Uvi7xHZ6jpMmlofMaQLudRwuCCOfwqlqGs2F4n2fRd4vJ\nwd84z+7TqxOOT6ADknpzU+m/DbVJFE11OkJcfcb52X6n1rWNlaUtC6knLSBs6T8QrrT9EtbNrGIC\n1hSCNhIeQq7c4xznHaul8PeP7PV5hZ3aG0uzwN33HPsex9v1qna/DSwmjgkv7mRzGu0xxnapqLWf\nhtaPZy3GhrIs8YybfOd4H9z0bHY8HpwTkdFOrzOxyvDO10d8Hrzb4uSzLaaciNsV/MOR6jb/AI1r\n+BtZnvLWayv5Xe5t8YkI++h6fiMc/UVm/Eiyk1JYIeNiAGIKDvZmzu9sYUdfTrVVWorUmhFylZeZ\nz3gbSLfUNI1MyqZPNgEbbuhAljbH6V1EPg7TI7Uqtrb/ADdtuT+dYmmaPc2Wnan9gvLiECxRQgYt\ntfzkLEAEdRx+JqnpNv4jvLqSJdRbcqMQ00J25HQdQcn6nHccivMpu9SbT6r8jvhBxSTR2Gi+C9EF\nwz3WmQzKgPBJH8q5RLS10q5msWga5tmuZwqI2MH5MYPYdaZo3iHxJa3zK01vKjDa8eckA/XA/NhV\nKVtlrD9tOHlupmBDFSQdvOf/AK5+tdlX+HGL7/5nFWdqsH/eX5SNy01jRYpJNKms51WUbGRYsj88\n5OPWrFodJTR7kQ74XhcGSOU/6vaQxP0wOtYUdjILqEyo7RYyJWuCSR9c5H0qlrrj+xrmK3Y7rl/m\nLOSQgOf/AGXGPc06KSjc66kry5dyq+vTo6rEZU84Fv3WN3PueldNooubyymtL6Uu7ofLd2J2/WuN\nsLpBDCzADCjJPtVu61vRzOhunabYNuyMkqR3z2P61m4tyskEZRSu2bWp38/hXTordpIJJHjaMKqf\ne+Yknr0wR+OaxrzV7t9du4fNYIlw6KF6kAkVm+JNQttS1DTZbcFIJbRVRTxjEjr/ADFd+D4f0Wea\n6vU82aSRnIjXcQSc8+nWsdmrrWz/APSjzcMlOqrdpf8ApbOWsdBuLm680SMz7vmyM11kvh/UjF54\nZWQR7JRHkZX1NXbTxFosls00KmCNP7y1v6HrOk6xpl+LO7V5YoWcx9Dgd66qDlN8rO6pCMPePJ7D\nwpHa+I3k1QL9jiTepmOFOemea7i2utKKCOz+zlQcL5KjAP4VmSTz6la+RBdiC4jlK+ZjJwPTP4du\n1S6RaW2k3bxy3AkmmTlpGBJbsBwOc1zYyNrq/T9GKo28LUdvsy/9JZ0VhbaF4x0640vc4cIdsnlF\nQpA4IYjHFeOXPjTU7S2+wQ3CeZE8im6Q5ZwT2P8AUfnXV+L7JfD+iahdWFzcPJITbEpMyhAxwxZc\n4IIyv415JDb3d0/7iB5Ae4Xj8674RhKHvLYK8pxdl1LN1qFxdSGSeWSVj1aRixP4mruh3xiuCPOM\neR1DBffqcY/OpovCtzJEHmkCE9qb/YvkBkEhbIq4Vo30OWVGclqdTc+PbrXfD66DqC/aFgnEsFyr\nkEADAByCSOc9j/KugQTajbWwuQrbkxxnB46MfXBz+NeZWU4WdMx4YLsPH8Xv+FeqQwzppVnLPBcQ\nxiAP5vlgqSxwBkHAJ64JBwM4FZ4y/s+cvD1o05e/slp96/zGSWNlZ3CSTzKgQhjtYYVc+gUfrk/z\nrU1A6Ve2ttepPDKzkq2JMOnXB4IOCB9K5+6ksbVDDd213diZskrAGAPb+LipNNuLSKBoItJ1GdGB\nwwtwuz3yW6VxUam90/uOyeIjpaMrf4Wen+C7OAeXeRuHYAx5ySQPQk9RWr5AbxZMqxZQQ5fCYVgw\nO5Se5yEP0Ncv4NkntB/owDIxztkbZj8s12Npc381xdMLa1IDgD9+wx8q552HP6f1r0qNZOC0f3M4\nJ4iKcvdev91l5VUEg2IwSRkBSCOnP4V84a94T1K2ubq0WA7IZpFjGHbA3nGAvAGOfxr6OEuo/wDP\nra/+BLf/ABuvPfGt3JpOqLLcQ7Wu1BTyfnHGARk7fr+NXWrt0pKMX32fQWHrQvaSl/4Czh9M0C7X\nwxcw3YzctNGsZJ5UMyr/ADNU7PwhdLOhaWQbSc+VMVyfU8dRW/Jr1tAI4ZVuG3Sxyl1Th9rhtvXj\npTri9CMbwNNDFI3Cug79s5FeZCq7NpO/od861JUoxcZW1+y/I9A8O6VFeeFpdNvy8kbdSTz6gisu\n52wxXRjZBp8B8uAYAIY4BUYHPHPP93/ZrQ8J3l3cQOqRRSBF2/vJCnYHsG9a5TVILW/8YPb3KxeV\nDNsW0Lfug3QkZ/ntrulWXslo/uZxUMRFSk1F6f3WaWjeL9LsNQSCbzC54JXGB9ea6HxQftF3o91a\n/vWxLs2dTuULx+efwFcVH4WstO8TLZosKTKQQjkMmD06j09q9LSyvEe3kW1tA0QwdtwwDjjGfk7Y\nH5VdGqo6Wf3MitW1fuvVfys1YwVhUN1CgGn1UguJ5Lia3nhjjdERwUkLghiw7qP7tW62jNTV0OnN\nSjePp9xlaT/yEtd/6/l/9J4a1aytJ/5CWu/9fy/+k8NatKHw/f8AmzqxH8T5L/0mIUUUVZgY/akJ\nyaUqe4pAue36VZzWEz1qnnBOPWrexgMkY6981Sf/AFhrN7jBiAhLEKAM5Pb3pvlxvsLZJXo2efrU\ndypktZUXqykCmGSctHhNmCd2fmB7DHtzk9OlS2aRTaumTxcKWxgsc8jB/wA5zSBIypUqGI+Viw69\n/wCv61HBNJIqswXaVBBAIz+B/CnYb5sk8uCMenHH6GgTvd3JGVG6gHBBH1FACr0UD6CqkbTkeYuM\nmXaw3EjAYgkZ6dvyqaEyFP3g2np6n3Oe/r2oTCUWt2SsfWolRUPy8cYA7Coo1uPlaSQc53JtHHp0\n/H9KWTzvMXZtILgkkdF7jr19/ccdwXDl6XKGjIDoGmMuAwtYuSM/wj/D9KtyRKVXYqgqykYHTBH+\nFUtFE39jaacp5X2SLgD5s7B19v8ACrU6SSiWLojDbu9iCDgfXH+HGTEH7i9F+SN8Sn9Ynr9qX/pT\nHMgxhQqkjGQMYFIzH1qMeaCnmd85x2P+HH15pXqrnPJNDGaprR8Sr/vY/PiqzHmnQyBJAfQg0pbC\nRtyzR28Ek8zhIo1LuzdFAGSTXzp44+Il/wCJrqS3geS20xThLcNjeM53PjqeBx0HbuT7B8R797Dw\nHqLK/lvKqwocjLbmAYdf7u6vmNmzMT709kQvelr0JjITySasaa9x/adr9lZxceavllOu7PGKp16D\n8N/CstxqtprN4pS3iZpYFPBkKY+Yf7IYqPc59DWU9ItnRSV5o9H8RasNM0thGSxRMDJ5PHeuGh1e\n9kkE91BIwBB8oDH59wPetfxpOIz5TctIcpnpgVzb615sMNtIF2om3f0K/jXDJdDvpT3bZe1HUbPU\noRaWduYV3AyptC5IHHI64x+lcrqMT6baTRM+SeASOSP8/wCelX7N547yQRSLLApzu9TV++0U6rEk\n1zuQ7SY41GM+5rSDUdOhnUXOrrcxfA9jBLqQv72VI4bflVZgu8+grqdLjhOs3zxXLMPsl4xAkyOb\neToOnesK4sWsdMSMrGVzzhyCPwxVvw3GytezFAqrp94FAPX/AEeSsMU705yv0/yK5VGKjYpaNZy6\n7rH2GQmLzFO5xzgA5P6A16Rb2Wj6HbCGAwxnqzM3zsfWsHw/pqWuoQXEaBDI5AH+zsfHPf1/GrGu\naSL9HQSvuGdoXbgfmM0OSm+yOiUHCnBLez/9KNeO5hkf5HBB561LJOGTaPmz1x2rmfDej3dg0zSS\nKYv4VPOD+lUjYa2+pSvJdFIgcIF3Dj2xxn604Wu0mTO9ldETyTxeMLmC1U5ki3EDnB9a02TVpvB1\nzFbyst2mqxFvMuBnZ5UndsZGe31qmsos9ZurqNQ8oiCFm6jA5NW7TXpJfDt9dbY3C3tuFjDhSPkn\n7nvxWOKjKXK494/r/mcs6Kkoq9mnfv3/AMzO1GfVrNvJJsW4BZpbuNOv1bNZMNtKWZrhrJDIxbBv\noenb+Krd7qdrqd+PtVmYjtxhjnGKzlXT9Ut2igP7yElcE84z1+lbKM0tX+H/AATKUa17qov/AAH/\nAIJv6xCsWn6Jie1yLJhzeRKD/pE3q3P1HvT/AAxZSarqTxytb+THDI37mdJiWI2jhGJGC2c+1c7r\n2kEad4eDchNPdcZ/6epz/WtfwReQaNqJ3KRHJGUO0ZxyDn9KzoU37NNS6vp/efmTJ4h395f+A/8A\nBNCLRJLfVXvpnj+0EsMGRB8p46Z68frWjqUlo/LlDhVzhs9h3FZmsGL/AISI6ha3SzWtzD86rIGM\nUmeQV6qT6HB61RlGWZ4/mI5Ck8GlOlJVLyd/lY3oKcnGpKV7X0tbe1+r7GrZ3GnxRMvmwxu4+7u5\nA9/SmwWenO7SRJHv+9vU559QaoRK88H2qcFGRdxAtkZQfQHzM/1qGa9mht/tcyKku0DYOhP/AOqr\nULHXOa6mjp9ppMWj6uiE5SxVZCDjOLiE5/MCst7qxj0CGBrZzHLNKCoQnBCp/iOag0yR5tG8ReVg\nSfYUIz0P+kwVAXkHhu3eWB3k8+UZSdRjKx842nP09uvNZRh+8d39pf8ApJxV5awsuq/KRq6L9nt3\nAsndZm5zuweK9C8PeIJpp/sV4pEqjKyFhhh/nP8AnmvLdBYJdmSZ/wDVrgtjua3/AO2be3uIpYZd\n7RuCSnOB1NdUJSUrF1acHHm6nr9zJi6mGf42/nXgXxM1S6HjW8glYFUVFjXOQqlAePzP4mvbZbwS\n3l1sXhJ5EOT3DEf0ryz4oeH5b3UrLVYI/lIEMwGOCD8p9+Dj8K0wrXsIX/lX5I5LNzsjW8MWscGh\nW6LF8zpuc45JPPNa5G2ZjjGSa5nU7fU5beM2DfulHKq7KR/3zU6LqT3OqRTXEoTe7QFW6Dcf5cV5\n7Sc+Zv8Aq53XaxCVvsv80bvimyhv4TbzIGzbRYz/AAnylwfavHdO1d9GvJ9kYLplCCcd69Im0e8/\ntqOaR12CztAflyxIt4wec+orlL7Rba31q8vpmIikchVHXOASf51phJRVFReun+YThNtSWg+fXL+5\n0b7TCixOX2nHOKz9dtLrVJY3Y7nW1gO44HJiUkfnS3evW1rCLeO3PB3DJxird3rCNdpDhctbQN0w\neYkP9a1SlGUWl3/Q56vLKvBN9Jf+2nO6Cu66liZB5qkEZzkAHkdcelelalb6pKoFrIoh7bC24fqB\nXmX2prPxQk9vgsJVO3pz6V6HJf3EaKrBo0dFcJgqVyASMHnjp+Fa1k7qRvRcGuVlh9OkudJjgkum\nW5/jfdgH2qe10ldOsXWaV5QyFdpkDKc/gP5VhfbbZbrzQL5znIGGC/0/WrUl6ZGjVWZUZhhWAzj3\nxmsUnsdVSMUrs7jwpZLpukQRqoVpN8jgdM5UfyUVg/E27aFdIUEhZJJAff7n+Jrr0KxmMIoVApCg\ndAOK5bx7aLeafZXAj3TW9ypjGepPb8SBW9LRST7/AKI8SnLmqzl3l+kS/a2FuLcHy1Pyr17YFT2u\nl2PnFjaxMTydwrg7m/1tNSWOFLhfmwUaUspH06Y+laupLqtzp8cll9oVyoZgMqSPbbyf0rk9nZq7\nPWU9Nix49022tNOS/tbKKLyjiTyUC5U98Djr/Oua0DS5dXspp4rsRrG+HUgHtkHkfhWmsGrHRLuE\nxIImhYyPIGygx78lj0H+css7Q2Oj2tpEGSW4T94p2xt5hbGOSCxzwBya3UrKxmoXlzPYl1XQZ5LQ\nSQs8qqYVe3SQosiCPBPHfj8sVS0ize11O3iWYrGsyu0LoTsyQQoYnk9e2OR1zxrteX1levBcSsB5\nUbeW0Ww8INxJIHfPTp+lVtM1a2aW3lvrvZJdTqbWFk8zhnOFU4O3qpPIHJ6ms4qUsO0+z/JnBLlW\nCcl/K/ykZHiPSnmgt1MIKliqsTjaRgkED1yKt6jZ3MnhPTY7GdoVW8uOEOMjZBj+v51xEWp3z3t3\ndrNK3nNyJXMhIzxknqQO9dnNfC28KWRd9ub66Gf+AQdarEwcJQS7/ozsp1I1N9CTTdO1GfRpx9vZ\nbhGAT06fzpf7Mv20m4t9VvZp4JFx5Z5YMORg5NZ2j65LHK0Ty2iWzHJZnA4H49a0NV8QLa2n2hB5\ngRhtV8jcc8j8s0Wney6m0nDluyxAz6ZopFmBI8WEQN8xAwOuO/P5/lWba634iW8QNdxSB3VfLAG7\nkcnA6YrV8J6hBqc2yEKWdDIyM4UKwUs+S2AAACetaVyLWC58+KTSwAcMV1K1VvpkyCsZVo05OM7X\nJUYy1TsZPiLWtds9RS1tZxExUEtlRuzjjnirtjPqF3ppOpBmdZgEdQAXHP4en+Rzo311ZXmxlOkz\nrHjPmalan8VPmH8c4qn40Q3vhnyrGfTVMmFy2p2yLjg9S+D0NKOIp+7G6Xn/AEhzjFXle5lT6nbP\ndXMUbB3bT75VIPHFpKxIP4gfifSvJicmu80XQL46uz/aNKKjSryP5dXtXOTaSqOBJnHIyegGSSAC\na54eE9R/5+dG/wDB1Z//AB2uqlXoxqz95fZ/XyOCV2kYgFP21qXvh3UNOsDeymzkt1lWJntr6C42\nuwYqCI3YjIRuvoazRXbCpGorwd0ZtCbfalVSTwCa1NE8O6v4ivPsuk2E11KPvbB8qA55ZjwvQ9SM\n12Fh8NvFOmeJtO+w3UQjkYldUsXMscYAw/QA9MrggBjx3NXYDL8AeH08Q3+oWl1O1pYG1zcXQXKx\nKrrIck8AlY2xn0JwcU+O903RfGj2vh6+nn0WWSOMPPwZMgZJBAxhiccA4rtPF+q+H1s9V8J6YwtL\ne3j+0XNzHGuxp90afMiKMH7oJTCjLfKcCvIrGPz9Rt4h1klVB9SQKySV5p+X5GVF2qza7r8ker6j\neSKYzI7rDnGVxjPuSQAOe5qddRu7Wxkgv72SK0mIWMIsTFyfu8qSfQ8UCSFJXt7ghQfulhwfY1qa\nXHa27sbm9gaNfmAKgkcdc158Wux7t21e5Tl8U3Hhzw2LqdEncOqxRvkB2J7+gwCfwrp9G1sazoNl\nqIxvniDOFUgB+jAA9twIrzD4mSxm30hY8qCZmCH+78gU/oa0fAWv21n4SkS9uEQQXDBdzDIUgEAD\nqfmLGu2nC0E+p51eo5St0PSftHqa8y8du1t42laUlIZoIHU+o8pRn8wfyq7qHxFsLbK2kLXDgsDl\nwBx0IxnI/I1y/jjVptV1yCV40QnT7RgF/wBqBHI/N6zqR/ewv2l/7aKk3F3OiN1ZxlWGpvFJ5ERM\nZPylfLUjjIqqL0Ss+CWD9z2HtXLX7PHqULJEGYWttj2/cpW5p7mCIXN7x/dQ8Fj6Y/x//VhTgowT\nOjC1pToQj5f5nrPhF4x4fjRWBkR2EoB5VicgH/gJWsX4g6aZLaDVog7SQYilAPAjJJB/Bj/497Vx\nGieMtQ0/UblIbqAQzSFgs0JMZbABYlfmBwo6Z7cVo+JPilKlqNOh0+DzbgeXNI0olTYRg7dpxnnu\nSBjHNdvI7WOXntO6MG8MkzoCVeFv4y+3Fa8FnJNp6xQrAyxneWMnzg+xGQT9cfjXPrqbacxR7cSo\nORmr03j4JaCCKyiifbjIrnUJPSOx3qtGOsnqdQ+tf2b4d1eWJxK9vbIygnqfPiAz+JryLVdUutX1\nGS+u5N88hySBgDHTFdJb6t9u0HxGhj2/6Ajk5zn/AEq3/wAa40g9e1Vh4KNSp6r/ANJRx1arl6HW\nadrE+pMbm5lU3duUw+OZBjHPvgdfaupiudP1KEeVe2zuf4DIA3/fJwf0rzvRJRHqKoSNsg28+vUf\n4fjTNZtfs+oyAA7X+ce+ev65raVNS8iIVXFvqejvPp+lQO017aow6r5oLfkOa5LXNTuo57O6t7qV\nJA7yI6OVKE7RxjkHAx17CubtIBPdwxYzucA/TvV7WWzdhR/Co/P/ADinCCiwqVHPQ9L8LfF4oEtf\nEMZcdBdwqN3X+NehHuvPHQ1Y+KF9Bcv4X1WwkF1bs8yiSJtyk/JgfX73Ht7V43vHQ1p6PKz3SWxl\nYRmRZQu75S68jI+m4fjTkla5EFZno2l+P0t3EN3pzRqxwrBw1aOo/EaOwk+zRWEkrkbsMdgA9yax\nv7G05o1uWcZI4AIA5Pr9a3NattFmnhaWSJmMaoxhcZxt6EDt9a89ezvdI9RKdrXOe8Q6w/iPwtdT\nG28h7d1fb5m8HnHBwPWq7aXNc6pFdPCrRrpVqUEqBlZvsCYOCMHDY/Kug1OKxk0n+zrZlEUmA7E8\nAZ7k1q3NvGDDHCQ0a2MKrjsPs6gf0/Osq82rcq6S/wDbTjxcV7KXM+n+Rw/h3UJW1j7N5dplXDP5\ndrEnyrz2X+9sP4V6/YqJo1LSvgjI+dgK8U0iCa28ZXojjLgK3yggHnGMZ/xroJm1NZNw8yN854bb\n+uf6VpUoQcloFPD0Vf3F/XzPWVjgGQ1y49P3rf41b02G3adl8+UsByFnYfyNeWPp2u3eiWt0LiRk\nlZg655XHTPsf61teGLK8ilXfdujRn5diMnHoSeT+A79a0o0acXdpDq4ak42UP6+8qazdPovxLjgj\n2x29zIA2AA/zcn5/vHJO7rySa6jXkSGyaXcRtTLbyWz8yjAyePvVy/xB0oQapHqkfmu6QCQFnLfM\np7knPQVteIZnn8P3IEcu4Q9WjK5O9PUe1a4mlTSVl1X5+pxToxpVITgrO8fz9TFhllaw1QwO5P2V\ncbF3H/XR9APbNc/Hrd9p0sbWF7cRW6nLrNbkjOeSeM9u9TeGdYWzg1c3OSsVorMPbzoh/WtAav4b\nuGjkTUJBJnOwjP8ASuKjFxrTVuq/I9pTUou7sR6Tq1yb6W5hlSWKU7iyqAM98Y/Om65pw1XT4z5x\n3B3dS/vg4/Dt7VHfahBJKS9xFbx9C8jBQM9Mn1Na9ujNYKAuQVBBZc9veuitzpJrucVbmnKPJrZp\n9ujXn3OI0/Rbr7UFvLyQxqfuBiM/jXRXsdtdQbYziEDygwwFG3v+fH4Vbe0mUqpEI3ZJYwJ0+mKj\nurgR6fOiLGqLG2P3SkHAJ6YxXTR55xbcfx/4ByVatWEklH/yb/gHmWptJI8xhy1uHwHC8Nis+Fdo\nJYda9C8JpDrFp595bWkhSTZsFpEMD8FrtU0PR8jbptsOMACFB/SuaWJlCTjy/j/wDeNPEVFzcq/8\nC/8AtTyG8smubDS8bVxatjPr50ldBYPbXfizUdO1WRViNzK8E0hJCEMTtPIGCP8ADvXdXGmabEm4\nWVuvlqQgES8Dk4HHqSa8reaUa09xMnmt55dhjG45yR070Yfmqq7Vt/zbOXD06kJKpNWWq3v9p+S7\nHqx8K2F54SureKdYAjLKGc4yOnfp/wDXFReBvBP2fXRdvcNG0WQY9oKujKQRuBz37ivN18SIlxLG\nlzqEMcylRCjtjn+Htke1eieENVu9O0e/vbuQC3igG1n6ZPA/DJFdNBctkenWakmvuOVMb2N1eTC4\n8zbKFXaODu3e/H3RS28vm6vZyTWMrTCddtwZAoAJAOPUe1dNYXfgHUdFbTJL/wAm7lkyl1LEVZJO\nQvzYxtwOc4Bzng9OR0jxDHHqFrZtbZdp1jD5yOTjNZYylLkc12f5Myq4inKhVhF6JS/9JkT+Jkkm\n8NXbNZPa27Qq0a+YHBYMBkY7Y9a5O11SLSYxH5Bl2ADOcfpXqC6kPEnge+gW2hify2O9uwAzx78V\n54mjQ3kZkEkaEj5twJJ/Wqnyxj72xrJ8+sOmg5PE0bwNKlmeOozxVGLXy92jvZqI85ILYJHsa2Lb\nT7UafNAZokZpOrkenUmqSWEBYM84JUbdvDADPY+lTTcFqkKcZtWudDr2i2d3pVrrGnqwMzhG4wDx\nnn0I71dvfFN+NFk0J0T7NaRLNGY8/vP3igOCf4cMSOBwRnPWtXS5IrbwFqOnyrDuVSylwMIW249u\nQevvXDv9hRtYitpJJjDaiKIg7shZEyc98sRjHYV24nWkrdbfmjzKmlPme6a/9KidFp99BfWpie5C\nOvXPFbWjCzhkeNbxiZABkvkGvMr63kNvFdqCEkAJx2PetHQAvnox6g9Sa4adJJ3TPXliGo2se4aZ\nJbQswh6Km0sP4mNcXc+OdV8IfFObTrq6e40a5ZHMb8+WGVeUPUYI6dDzx3re0m8iS1UFwFXlmPTj\n3ryfx1q0eqeNWnjwVARVI9Bx/j+dd9OVtjkqJOm+bc+mdP13R9Vfy7LUbaeXG4xLKPMA916j8RWX\n430E6zoL+QrNc24Z41HJYY5Ue5wPyrw6wSS61We/gYqYm3xuvBDDnIP1FeqaT8W9Fdks9WEtrdKq\n75VjLxMSO2MsM9eRgetbwd9jGFXlldnkcsRhUmNbyRG4G6Qso9uvB7c5qW3gUxpJ5B87plgPlH0H\n860PGoD+JL650u9imtp5DIGicOjswzwRxwx/SsbS7e/WXfNMBnqBzXn1IxhVlFPTQ9SrJujDlXf9\nD1bw3qQtBbxq2HdgZFHpkf0Ark/FtgV8a3zO6QSfaC6MWYHB+YEYVvWpdDSSG9SVpC3I611/jjQI\ntRtbXWre3ae4ICSRpks+ATkAdSADx6VvSftIuKOWnaEmpdUcraJfR61Zm4tEhUYIkEu8uo/iIwMf\nzr2iydpLOJ2zuZcmvK9GmsZHE94Hj8gfvEkBBUDsc9K0tN+JsDwXd/JCTY/a2t4UBAZUVU5H1JJx\n71tTpu9uphUqa83TY76P/kMXP/XvF/6FJVyua0TxTpOtXc13a3IEbRxx4lG0hgXJHPHRh+ddICCM\nggj2qKSaTv3f5mOHlGSlZ9X+Zl6T/wAhLXf+v5f/AEnhrVrK0k/8TLXf+v5f/SeGtWnD4fv/ADZ3\n4j+J8l/6TEKKKMirMDyI6n4w1lDdrc22k6fJwjzssanuPmILHI7jg0x9T8aaNGLzz7fV9PQfO8JW\nRcdzkANwAeTkCtC/8JaxqZN1cXkBnZvuMTtVfQEA/liskxyafe6ethcbLtImSTaDtYiRh/wIfh0A\nNJwfc5Pr7UrOklH8TsfDviSz8RWnnQZjlTAmhY5MZ/qPQ1dl4krmYr7QPDxg1FLAw3OpybHKL9w5\nG7OT8o74HXjjjjp7jiU0SUkk5GkpQlJ+z2GZqOOXzAflK4JHOOxI/pSg+tQmBXJJYjkkFcZBORn8\nif8A61S7jjbqTKyKgCkBe3pz6fnTlwBxwOvpVUW3lxqsTsCuMFjngADp3+6P1qdAVBBOTuJ/M5oT\nYNLuDyoiszsAFBJJ7Y6/yNNkl2HAKZB+bLY2/KT/AEqCW0WZn3Svhjll4wf0zTmtoXkaR1LOwwSS\neeMfhx6UrstKHcX7UnmRoWA8xdynOO/Ax68/zpZpxHs5GCTu9htJz+lCxxnIwrKeCDyMDt+lNVl8\nsISCVABGc4OP/r0ai9290Z2j3KjQtMUA82sYz0AOFH8zV8TK77VByRnoRx/kVR0ZkOgaeuQc20eQ\ne/yL/wDWqwSJHbK52tgEj2H9f5VEL8i9Eb4rl9vU06y/NkVzNMpJiMe3ftw+fYDp3JP0+lVzcNIy\nhZYi5cthHJBUcdcepFWWVAclR97dyO/rVBo3UOA8WCu0ARdB6delNpmSlG1rD0uC8UbMyFiozszg\nnHbNPiDO5A7jr2qsgAGEQAkkDA5PYfoBV5z9lt1Vwu1h8zD1qoR5tzKcktip4x059bs7eCLVJtOM\nZZ2miySegxwR6569q5WDwOkckYn8W67c5jy3k3Pl5JPBGd2BgdP1HSug1O6ka1lUnkIdp9c//qqA\nTwWsckjMh2AZJxjrxn8/1qIUKcnJuKevZdl5HBCEJTnKUU9eqT6LumX7PTbSylS2s1mfagNxPdTy\nTkDsAGJAY+2MdfSsvXbw22u2zRqEhKlGVRhc5649ax7/AOI0Fjuh03S57sDJaeRzErMe4GCT+OOl\nVdP1aXxbYPJMsEF7BKD5CZ5UDhuSc9SPw96KuGpODSivuX+R0UqNLnUnBW9F/kW/Ec5ksysVzPA3\nUPDIVIP4da8+e68QmQomq3jKP4vPf/GvRZ9O85cvzx61z2oGyspALyZLa2J2gnJMh9AAM49T9K4a\nVOnJ8vKvuX+R11MLQjq4L7l/kY93da7baCbmLU7pjnBzKxOPXOa7RprkeHbG5WWUytbRsx3nLHaO\nfeqDCFbdHVkaAjtyrKf5059ejjt1iaHaijamF2gAdsVriMLSja0V9y/yIo0qDUk6a/8AAV/kcrrO\nsTz69e2iRoVS4dSSOwYitPRJFMlzGMZaxvcge1tJ/jVXW0gi1W+mREUmVyxHUncf61F4VLveXcrd\nP7PvAPxtpTXFUSeF07L/ANtNMLJ+wgn/ACr8kWvB+uXtxq4s7qUOkIzGdoBwFYckDngjrXW6hP8A\nZNOaXO2RjXmOk3H2PWZJ1JAEfbjqyj+tek65f2Aezs5NLtJ1e3hdzLJMCS0asfuyAd/SnWlyVFFR\nvf06LzPT526NPvr/AOlMwYvFMenxNApEgY7mZhyTWjpmp/bYZ5FbEZO4DP3eOf1qoZ9JguhaL4Xs\nSXGOZJznP/bSqera/aeHmlsrfw/pwM2PlMtzwMc5/e+/bFOMpPSMH98f8zKVSS1kyC41JLmaR4T+\n7I2qT3H+c1oRamdN8JX01v1a8tldVwRzHPztPHas6x1bTDEY5PD2mJ8uQFkuT/7Wq5BPpzeFtTZt\nA00xC+tiUEl0Ax2T4J/fZyOcYIHJyDxjOtKWl4P4o9V39TJyZx2o6om55F2meQclVC498DgVgrI8\ncgkjdkcHIZTgito+INO3H/ik9H+vm3n/AMfp8er2U24ReD9JfaCx2yXhwPX/AF9egpzS/hv74/5n\nLJ3d7l/V9bnTR/DfnqJd+nOxbODn7VcD+QFUE8TJbtvt7clx03twP8a6DVPsGp+EIZDolnbSW+h/\naIJIXnzEf7SaMqN0jAgh2PIJy3XAAHCQpF5qeeW8vcN+zrjviscG4zptONrN/m30KcnfRnQ+GZZb\n7VruWck+ZGS5A4yWH/163DeLaSFZwSvYjrVqztbKwUJYrm0dQ6S7smTIHJ/H29ajv7UTo20Aj3pV\nGpSv0Z1U9I2W6I4bvTS+77ZNjPCHpRqX+k2jjkHG4DuBTNL0yJJd5jGR0JqyipdeI3smz5Yt93B5\nB3dvzH5VLsnp0LblJJSMrQ7owad4iEgIC6ehPv8A6VAP60NcxSaFabJnw91N8mep2xf5/GtGS0tL\nKPxHatMCTpqMzY+6DdQYB9+lZMkUNn4ctXZ1wLmYqR3ysXT8qzTTqN+a/wDSWcVZtShr9r9JFy0s\nFaK4iluACyjChsDfT4bKOO3LF9/THzdwfTJ7/SuVyP7cijI+SSVcjpwSM1as9bhEw86FUBPJUsf6\n11ypyT01OuDpzp80mo9Or6X6HtV/dSJrGooOFF1LgZ/2zWZql0rafI0ivIkfz7F5OR6UzVZUm13V\nfJumIW9mUhQPlYOQRyKqZl7zy4+i/wCFY4a7w8Fb7K/JGPJBS5vaL7pf5FW58QSadZOYwGFY+oeI\ntTj1zUILQkJHNIp4B3YYj09uKm1QNDM6SIWVhuG5eveqWs6osGp3ccT5lSd1YuOThiM8YrNRtUty\n30f5o2/duum6n2X0l3Xkbtxr2oR+ILeDa6+Zp9ozlhwSbaMn6c8Vg+ItX893tAArxDceO7CtXUbx\nptatLZZWkk+xWrKzAZG6CMgcDoAcevHUnmuPt3+16tMLm3gDyg/OGf5j2/iowkL01Ll2X+Zc+T4f\naL7pf5GWJoUi3OhkkPXJ6VoavMv9sWrKoQ/ZbXp7wJVXVrBoI42W3VGLuDsLEEYXHUn1NXbu1H9r\nwTzsscSWdqcseuII66m05Rfr+h59eEoYiEb30b+9LuZ9mBJ4hQyRecTPkR5xvOeBmvT9cjM0NrOA\nVl2nr1xxwa8xe9trfVFu4IxK0cm9d+dpOcg44NdPpmuXOqhZruRmflCeAoORjA7cY+taVE3G5VN2\nmiWWbT45PMnEgl7jOP0qzbt58omKERj7ue9NuYxIQzKrEevak3lI93QAZrnS0OlyfU9BsNba/gM6\nGN2U7XTup7g/l1p189nqFqYbkNHyGUnJwQcgg15O93d3OgSx2InN6moRv+4zu+ZZsYxzng1v6Zfe\nK/KC6pYq8YHDMhEn/joI/MA1rCPxev6I82lpKbX836I6i6uzIYreCFNx5ZgAMjr1qX+0Z7+FbSSy\nhttmdrtJnnp8oxyPrWTPaJNb2zXMKlXUEo4ztPaorTTAk6h4rTygcnLZLemRXJaKdj2YvmVzei1Y\nNo9xGUj2RK33cEHAz2rn7m/h1tdzXcUEyYAVh8oHYAf561f1GETaZdvG20EKFI6E7hzXIWvh1ZJp\nL3ULpY7KL5pHzjI9BWlOCabZlOrKL0LPi/Vl06zFjDIrXVxCnmsnQLsUY/ED8j9K5vRpjJrWgLv3\nbbiIHJ6fvf8ADFaepaU2q6pZPBa5WTToHk2vjnYBkZ6cYGOemasaT4PmttasrgFlEVxHJtYqeAwJ\n5B/pWsVGGGa8n+TPHnNywjb/AJX+UjngiQ2EKcebI3Y8gf8A666jVZBF4Z09jwp1C75/7Z29czqO\nhapo5jubiEvCjhmkjO4de/pXWRRx654UtFtLqxMkV5cM6T3kULAMkODh2BOdp/KoxjScJS2v+jPQ\npbMxtPltIZfON4AR0GwDP41H4mVjZwTEtmSRjg9hitHSvCVwL3zJJdJO05G3VLY4/KSrXifQLua3\nto47jSwyuSN+q2y5GOesgz2qI4iiqitL+vuLm5ShqZPgHUxband2zD5Xsbt89wVtpT/KodKttNmE\ntzdq0i+Ycrk4x+FbPhDwXqEHiLzbl9PETWd2jKmo27k7reRcYVye+c9AASaz28G+IdMmdbeXTnib\nv/adsufqDIKn6xQdedppXUf18iIXSTauie9tPD82n3E1pE0RRTgKzAE9B1J71heIdT87T7GyRCEj\nTcSRw3GBj16Gt6y8J+IdTdbVn06O3ZgZHTUrZ8D6LISa6PUfh5bazMLeK6toTYwCKJkvYSGC8kMN\nxI+Zjz/OrWKoQfvTv+n4FSvNaKx5v4T/AOQxcf8AYM1D/wBJJqxB1r0Ww8Cahol9dXhurC4thYXs\nYMV3E0hL20iqNgYkkswGBnrXnksMsEhjmjeN14KupBH4VvQqQqVZyg7q0f1Od7G9aH/ihtW/7CVl\n/wCirqug+HujaFe6frura1ayXg05YPKtllKK/mMwJOOTjAPUd81zlmf+KF1b/sJ2X/oq6rofhRdR\nL4jvLCaMzfbbKRIoCcLJKuHXcewwrc1dDSU7/wA36RFLY6qTxdqmppDo2lQ2+mWk0ojjt7RPLVS+\nARxjgsd3TIz1qvc6nrfgrUBax6tBPMVLSJG3mIjYKgHOCSBzgjAz0pvgu6TTvFaJcC0KBXWSSbBW\nMKNxdWzgH5eG9CfWuZ8aeKV1jxBcTWwiMKM0UUiKVDoGJDEHuc+30r6OtUpUpqCiuS1/U5Ixclfq\ndt44Gm3fhDT5ra20631DVbNpZJwI4DI4khLAscZOcnn+7Xl8Oj3UVxE4nsBtcNuGoQcYOf79Zk1x\nLOwMrs2BgZPQVBkg5rwKkG5ylB2T8v8AglqnNScoy3t0vsrd0eyBBf2aiZrdm28lZ0P6g0/RPD1q\nt6JHk8xR/CJA2PyNcFo3iyK3dVvrdtuMFocc/gf8a6dfiBotjCxs7S6lm6BXVUX88n+VcKo1ouyf\n4f8ABOzmqNazX/gP/BHfFHSzd3enXNtNAhWMxlJp44RgHORvIz15/CuQtNHuI2kf7Tp5xGwXF9Cc\nsRgfx8dc/hWdrusXmu3rXV44LYwqLwqL6D2rPgOEOOvrXVCnVUbOX4f8E5pqq3fmX/gP/BOgGiXK\nQN/pNgCQBj7bD/8AF+1asGo64kttHO/h6WNI0hDzJp7uFVQi5YgscAD1PFcqJfMjAY8Hmr93YpNF\nbyWi4ZpNoO7PPGBjHB/E1FSg5/G0/l/wQSrX0n+H/BO01XxI2lCKTFhLI0aBY47C2YkhF3fPsOAO\nemQOg4HGDL441G9l3Ja6apbAVDpls2OMHkx96x7ed5FcTgMBF5UbegJycfhkfj7UjKiIFRQAOhHX\n86ilgqMFZxTf9eZum4wUIvRI3JfFd1FDABDpRZVYuBpVr94k/wDTP0C9Kot4qv5D5n2bSODn/kD2\nhx/5DrHdSxqu02yYKD8netfqtH+Vf18w5pWtc7q48XXH9kfavI0tnI2gHTLY5J/7Z/jV/wANx3mq\n2y3V5HpR3HIjXRrTge/7qvPIraa7mitbc5aWQBVLADJ9zxXoCW+saNYx3MN7FJbBcuLdBIikY3Bm\n9sgE/dyRgmuWtg4KNoJf18zenVUpXmjo9U0u3m0SezjfS7JrnakjrbW8BdAQ23KqD95VOPavLdb8\nPXGjshmjGyQfJIpyrfQ1217aXrx2erXaQtPOoaKMRu6Bcd+PvZ6g+2O+Oh1bS4tR8MNaJYRQAAtu\nSFYwWA+VlAGR3J6ZLHPQGow7jQW++5tUpqo7KNjxGN3hkSVCVdCGU+hHQ11niqNL7S7PV4tgEvDK\noAxu5xx1wcj696y202a0iVrqFdsq5Uex/lWnafZ5fC+oWLHMsQ82DIGcZGVB784P516SaeqOCScX\nqZvhW0FzrkWQCEBYg9D2x+Wap6s/n6ndSxx7Y/MOAo4AzgVs+GP9E03Ub8hDhdihgcqQM5GMV0vh\nqbQG0KaG7junusEq4mULt68KMHjnLZHuaqzexlOrCknKd7eR5iwBrV8N+HLvxDqYtoHWKJcGWZ+k\nY/qT2Hf8zUeuJZxarcCxnEttuOxtu049x0z644z04re8J+JdN0a1FsYHkubh2aeRsKqqAdoHUt34\nOOTSd0jTf4TUu7S5s7i604OCY2K5fjcvYj6jBpdNtJRHIkc1sh6uyhmOPTqAKqzanPqe2/B2S8hB\n1woJwppw17VZVFsohiU8Mw5z+FccottqJ3wqRUfeR0/hy2h1PVpLVlV4lhZpTyMgjaMehywP4VsM\nws/Esmly/ODbW6qQP4lgQHH1GfyFc9oepN4elWVLQ3ckwMQQy7CSfmznBz93p710eo37ajqX2hLK\nO2nt7KNpWaXMqFoEcfLgEYL7c5/KlVik1btL/wBtPNxjbpTbOMeF9P8AGl1JINocLhs8Y6fzXFdL\nPqFsbNRtCynjeeQvvWX4smtpI7G6AUXxiwTu4ABOQfU7gT7c+tc4NXF3EtrKDsVg2O//AOqniIXl\ndHp0ZcsU31Oni8bXz2/2AmBYCdrAoeF74Ocg+9dFoeuTbjCz+ZGo+WVuuD0z71xlouniRUj06N0J\nwTIxI/HJqWe9g02aT7PGiI3BVOg/wrJT1VjSW251vi7X7a00pzdBnE9u0EYUZJYgjv06jn9K4jTP\nE2o62Y1v7hZFhu4xGBGkYG6ObPCgDnaKo+L72a4uNPtJXI32wlWM8AFmIx9cAVzoiZdBvlYFWF5b\n5B4I+SauurFuCv1a/M8vFTXu26NfmeiXOlo2n668Yx5lkqnH/XxCf6Vx1loYefKySBgfpSaHrGpx\naP4iH22VxHpyFPM+faftUA757E1gnXNUfj7ZIvvGAh/MAVjh6cozmr9V/wCknROrGWrR0nit7ew0\n60sVKvK8nmuM8gAEDP1z+lVdH8SrYRXI8+5jkmjKhhhypJByCeQePfr7CuZk3Pud2LN1JY5Jpg6V\n2KKtZmLld3R07zz6hdxXkmrvJdI2UkZ8lQOwB7e3Su2stTuNVgSK5sPs8q5MjDlGXB5APrnpz096\n8lDYrV07xFqGmDbDOWiPWN+VP+H4Yqk2iGtbnVaRe3GgWLrbQCd5ZXK7gRkDgcD6ZrpNG1fVdXs7\nqX7P5TwruIY9ayvD+radeWwEwTa45jOCUYcY+nQ1sJrdvpsjJDaSukybQYwMYry6ms2mtT16SXKm\npaEVreapdtEJZJoXVSJI1tlZGO44OS2fu4qTUtGtri5jvSXDxgbkRFyxzwfvD+tSrrc1qiySRRoH\nOPKLDcv+NR3msSWtu14sId0YEIDjPPHP6/hRThK7SbW/9bHLPDRjtOVr7XXe/wDKWoILB5p4rydZ\nrkqYx5yDemcEHvzjHIx3z1o8a2UFn4CIeWSK2WeIF4B5jEbWABUlRjpznsPWuBsPtd3q899cysZH\nJcgDAz2GPSvVdLgtdc8H3uk6u4V79ljiJ6gj5tw+gXP4Y712UKWqjzP8P8jlrRbldzlb1X/yJ4VJ\nLpB+VdR1FT7WKf8Ax6uos7Sxsbi0upLi7d7Z0ZlNsoJKkHn94cfrVHV/htqdrNIdKlXU4UyTsXZI\nADjOwnnPYAk10P8AZ08sMczRSRyso86GRSrI2OQQeRUYqjKCs27f15GdLCxqKUHN6ruv8hmjXz21\np5Py+WeGB6GuanuWsNQnEIDDedqt2Hb9K6WLTZ3cKEIGcVleJbfT0iiMEm++Q4fyzlQvP3vfPT8f\nas4z5pWPRcOWJi/YprsSXDCJGZskeag5/wB3OalWO7QpG23YOmKzmuYFIBeQ+xY8VradfWkYZthb\njgeldUUc8n9508uoGLwdfQpHtV5ETLHJC5yBn6KK5bSmVl1U+ZtBtVBIPT99HXXaJpkHivTr/TLp\nzCZcPA4PCOM4J9Rzz9fpXFW2m3GlprcYkjmMdsF/dtkHE8Wf0zVV43pqXp+aOKv/AA2uzX4tC2vi\nZrR5rW6txc2xclf4WUE5q3ba7o0MokT7RH7Mmf5GsDUPLkuEmiUqsigspH3W6EVTbA4A5odKN20d\nUaskkjv7zxtDPZtDZ+YIkALFhjzD6fT/AArkXvGlvJbucne7ljmpdLsWNvLesuYbc4Ho0h6D3x1P\n0qfTPD1/r12baxgLlVBeRuETJ6sfpnjr1wDVRjZWREqjlqzZs/F0NtH9n0+KUlhjfIAvP0yat+Gd\nG13xzqyQQxCysmk/eXbx5BKg8A8biOeBj3xXS+H/AIeaXpKRy6gft10uCAQREp+n8XPrwfSuru76\naysLia1JjlggYxlf4SF4Ap89lZGdrnGrp6GwaG2B2JK6IGJ3YDHBPHUjnp36Cm2yzIShVXbpkuR/\nSvS9R0m3162XVdGES3qRqJbZjhZQBwM9mA4DYwRwe23lLfQrnU7xxZ285uEfbLDImzyW44c9B1zx\nkkcqGrhqU5c7kup6tHEQ9ko2Vl38/mjNju5rNS7JCqqNxZpSAB/3zXUaV/wk3iXTY5UjjsdKQYDT\nzNE06H7xQ7SQCMfMV6Hjqao3Hh6xsb5V1OT+0LmBhI8KcQxt1VMdWPclu23Cjca1TrF5cSia4lJi\nBwEHCj8K6cPSad2znrYpN+7Bfj/8kXtUYalpS6RNpFhHbInlxPHqDqYwBgEHyf5j6151r3g3WtC0\nBBFF9qsULSyTWZ83aT1LAhSBgDnGPftXX3F0ZHDBh17elaGk6tPbSqqSYBPRuldqnZ8rbt/XkYvE\naK8I2Xr/APJHjtlOttotsv8AaH2cPcSndIhOflj4wucdf1rs/DPxEu9NkS1S8trtTxiWUwoPxccV\n12u+BdH8W3DpIn9nXSqJUkt1AVnYsGZl/iyFXuDx1rxLxP4Z1DwjrZsb3GSA8ciH5ZFPcfkaVGnL\nlk4ybV3p8/Q56M4SjKUYR0b79/8AEfSXhXUvtv265mEMcl5cCZFjkLrgRInDYGfuE8evWulr5o8I\neJbnSZlVZcJnLKTw31H51734c8Q2+tW3yNiRQMqTVezSjeJSxcqlS1Td9tu36G5RRRUHQeW6hqV2\nl9NaG3t7qeJvLdE3AyxkDhQGPPUHqefY1DDq91DqQ22ttZ3V1MI1SUuWRGYsSwLep44GcnFS2Mnh\nrxBp1ytsY11K4DMsVzJh1kwcBT/EM88Z9/SmXVn4c0bRUi1mSKO+T53WGTdMTzgAA9Mcen061tKn\nLm5banjpTi+ZvT1IvHpmNnpdjLIk15Lc7kaOMoCAMYxk92Heu1u+Js9jXDeHILrxX4l/t+6iaPT7\nXi1RjnJHTHrg8k+vH07m7+8pFGIXLGMeq/U6qK0cu7v8tkV84FV3lmEsqouSFUrnpzkfjjGf85qb\nPFQNcxrN5eHLE7TtUkDvzj6j8x745WdML30VxSLnzCwMWNpUBs8nnB4P+7x9aWOVzKyMV4A4B6HH\nP8x+Y9eK6albyquwvliAAUI6nAOen/6j6GhJDJC0qAKxZscZyM4HfHQDvilfsaOMre8izIGOQrBS\ne/pTFVkZTvyoXH1Pr+n61Wae4aZwIgqB1wWB5Uk5x7jj9aaHnDJvzy5B5HIweT6dB+Z9qLk8rtq0\nWlQRlirNtOTsJJGT35NRbXUALIOvPy9eOe/XODk+lQzXM0ckKCDd5hbJDcLgZHOO/wDSkMs5BxDj\n5u7DOOvT9Oo60aAua1yvo4K6Np7LgA2seQByTtHf/P1qxJEHTGdvU8Huef581S0dpv7G0/cqFTAg\n4OCBt4Pv2/P2q07nzdoDZwMknA71ELci9EbYq/1ipr9qX5sbgIDgAZJOFGBUDtyaa73BViVQDAIX\nPPfIz09B/nhrGqOaS11LNphd8xUsR8q8dD3NS6jcKsSqBkkciqz3kGnaf5txII0Y5JYce3FVDe21\n0A9lcLPEeTgn9M81vFWRhLV2KN5kRMASVz8vvwcj8qr6e4mEnmEHBAIzkEgYx+dLOfMSSInHzDYf\nwJ/pVPSGcW4Ei7XBO4ehyRWdPeXr+iMaO8/X9EWr/TIJxs2Lycn37Cuf1DRRHCzW42zIRIpHqD/9\nc11JYY4I6Vm3r4dgv3inH5itTXbYbceILSCxWWZm3kf6pRk5rjtatpvEM8dxJD5A2hFTduwuep9S\nSfT0rq/7PS5eOa5RAIl3PgfeC5OT78jmtnQdPjeV9VuAMIcRJ2Lev4dB759KwVONOL5VububqSvL\noZmk+EpbPR41Eq24XJVZgZGbPqMgJ345rnNfs7qOJ9wj4ORsVtp/X+f613ep6gqs+X4APWuW1TX4\nWjCfZ5XVxwoAG4+nr+lc86cd+poqkm9NjhtfMo1zURuJzdyAD/gRq54acwXksLOS5sL53GeB/osu\nB/n1rcufDxvNXvprn5d9w/l7T0GTg/XpRpXhWGy1lppbicwPbXEDFEDEebE8eQCRnG/PUdK53RlL\nC2S+yvyRnhppUoei/JHDWLGZ7hh18sY/77Wu71yIy3EHzEOLG0wfT/Ro6avhLSLIpHFf358w7XJs\nVyBjdkDzeTkDjjgk9sHV1+LSIbyMXFxq8ZW0tl/d6ZvU/uEA+YSYzjqOxyMnrWFSb9vFqL69H2R6\nTlH2ME30f/pTMK1jvBCzyXJ2qOCF5/OuP1RHN7uZ2YtltzHJNejxQadfWz2tvc6vCmOZ5dORQR7B\npgT9ccVBP4K0d2WY6lfhEwMfYEIx/wB/q6oVLJ+7L/wFnLUkm1qcFapd3V2jQoW2DBx0r0GXw1qc\nng28isUW4eW7t2ZFYBlQJNk4PX7wAwTmtvTdA0S1laGG6uSU6g2if/HK2kmtbeya2t5rh3eRX3PE\nEAChh2Y5+8KJpVOVKL3XRrZkOdkfOmp25tdQlhKlSpA2kYI4qshZWDKSCOhFewfEnQ49R0VdUhRf\ntNrjzCo+8h/w4/WvIc9q7pRsZRlc7yW5mm8HYkldx/wje7k55/tYDP1wBXBiu3z/AMUf/wBy1/7l\nq4gV52BS9/1/zLlpY6PwvqCwzSW1wryxEZVN+APXt7101tq2lq5t7m8EMo42yAj6HPTFeeWs/wBn\nuo5eynn6d60dcj3GG5A4cFTgdx/+v9K6qlJT0ZVOq6b0O/8At+iWy7n1K3I7eW4Y/kMmuV1DVp9O\nu5r63CiWbKKWH3V/ziuc0+PzdQgTGRvBP0HJ/lWj4hcebBEMcKW/M4/pUQoRi+5VSvKVraEmjzST\n6Z4pllcvI2nIWYnkn7Xb0kWn3t/4as/sllcXPl3lxu8mJn25SHGcDjoaTQv+QP4m/wCwYn/pXb1h\nVCi5VJ8vRr/0k5qsJSUXF6p3/P8AzN6PRtY/tK1uJNJvkRHUu7W7gKAeSTjpiqX/AAjuuf8AQG1H\n/wABX/wqDTj/AMTO1/67J/MU/S9Hvta1BbLT7d5pm7DoB6k9AK0/ec2628/8zW1b6utV8T6P+VeZ\n2Wqp4h034g67cW2mag9rLqVwWCW7lXUyNgjjH0NdJNFqK2S3EcFwWchVTyjuHuVxkYHqK6A/D/Sb\nXxJqmq38S3txc30s6LIPkjDOxA29+vOc9sAVR8cWk17pTSKSTEBKFHouc/oTWGGjUdCG3wro+y8z\nGftuZ2a+5/5jvGOim5cT2ih1kAUpHyUIGOg7Y79K818RaJeSa9fSRcrJcyEZ46sa9ittdth4Ugut\nQjNz58IXyh1kbHP0Hv27c4rmpY4so6x4R/mUE7sD0zjk9KHGcH7TQ2pQlOpeo1oraX736sqLoTW+\nsQXLndM9jYwRr23fZ4k6/WuX8VeH57DVb+W2t2+wx3TorJkiIZ+VW9DgjGev4GvR9Q1WPRdYsNRn\nsmuo/IgWNFfaRmFAWHHJwTxx+vDvCErT3+sXoZit3IJY88ELuYKfyAP41eWylCkpvqv8y69m1FdD\nx6XUJIdPQS/vHZ3ALdsBfz61V8Qyl9RjJOSbS1P/AJAjr2LWPCWkeJLqWKWAWriFJElt1CbXJkyS\nuMHOBnPPy9a848beE77S7pbuMG5sVgii85FPy7EVPnH8Odue/Uc1p7rqppW+L9CMU37anf8Al/RH\nGk5rZ0S6SKOaNgx5DDB+v/1qxgKsWcnlXK+jcGtmtBHo1nqOk6hbhvtaQyLxIszBCD+PH5VBeX9k\n839nWUkckh+aScMPKiX1LdO4/H8q4a6QGZhnG75qhJ2jA4FYKgk73NnXbWx38XiKw0HRbltCgWV1\nnijlnmUjzSwkOcA5wNnHTqfqYdE8Zy3+pTw6i20Tpth8scI2egGepGQCc84965iwNtNo95aT3sNr\nI88MqGZXIYKsgP3Fbn5xS21jZw3UMp1zT8I6twk/Y5/55VC5E5KV9+z8vI4KdVU5y5u/Z9l5M9Xt\nJ1msVXAdosxMDz04H14xWdFZW51ESNaE4P3S5KH8Ka+oWml6kb26ureO1umyQFlJ9iBs579xU3/C\nW+GhLuGprj/rhJ/8TWLaUna9vR/5HZHGUnHV/hL/AORNXXJYl0kpImfMZflU+nzduccdvWvINeuI\n7jVbpLS5mksUlcW4kdiAmeMZ5HGK9Ng1H+1rO71IXdoIIYz5YDOQu0E5IZATyOw7AcmvMzptrn/k\nN2H/AHxP/wDG66Kc4Rj/AMB/5GE8VCT6/c/8jr7jxPbabJDFFAJM2sDAbtoTMakDGDnjB/8A1VDZ\neKprvXbCJLdI0kuY1OSWOCwHtXJ6xNDLqAMEyzRpBBH5iggMViRTjIBxkHqKl0BmPiLSxn/l8h/9\nDFLlX1Z3X2X+TOVprCP/AAv8pG7B4zn3CO7topImGCFznH4kg1zuvW1tZX0cumy/uLhPMCKfuckY\n/Ss0XLq3zckcVe0mNZ7wzyDIT7oI4zXTJRWqOxXW5NYR6s67kk8iNv43UZP0GM1pQWSRSebIzTT9\n5JOT+HpVgS5OTTlBfk1g5XJc2zX8MMUvL1wxMiWV1InGcYt5Rz7cj9K0bRbfVNoinjZm/hLYYfhW\nXoMf/E0l97C8/wDSaSo7ayRY1PU4Gc1ySipVZPyj/wC3GsK3JE7F7KPRrEKGiinkYIhbsT3JHoP6\nVlyX32BJrO3jZZZVCyyMQcDngYHTB9+nvxjeUoueMcDGRnj/ADxVtEAPPXvWqgows9bk1Kzk7rQs\nI4WPaO1MeSG4VYrmKOYA5CyoGA/A0hOMEHmqd2Rt8xe3pUpamBYuvD+m3HhLUooYxau99bOWj6Fg\nk+Pl6Y5PTFct4M0idfHNrFLdNZ/Y5BNJMgBIVSMbd3B3Egc8fNyCOK6a2uCfDOqfOSFv7bGT0+Se\nsp/EE2kWt+tsime4iUJJkgoQTyMdcAsR74PanhpSTmvP9Imt3oh/xL1nS9Q8Qt/ZJV1UEXEqDAeT\nJzg/xAY698nsBXD9Rmk6gg0iH5SD2r0HJuyY7C44phU7qev3RTu1IY1V4p4HHFCj5K0dI0LVdbkM\nemafcXTA4PlRkgfU9BQIzH5FNhGM5qaeGSCVo5V2upwR6UwcHFAxFbauPQkVp6bfyWlwjo2CjBx9\nRzWesRcSEdAy9OvOe34U+RPIn2Zz068dqN9ATs7jxLhiRxk4wKkVzudfTkVVY7T+OafDJmX8MUhl\nk4ZcjmoWiU9qftZGytBIb2NAEPlkMArFTnIOehr0L7Zbvo0bTl3uGHWRiwX3we9cEeRhvwNb+lX6\nyQfY5wDgcAjIYVz4mLcbo6MPJRlqdpY6VeNaL/aGoFElj/cwXF4sZkxk/LHuySMdwKn0rULxYJLa\n9LiNSQokxu2+9Z2kz29k+9pRGGOWC9W9zVjUdUt7h9tqN5PAauB+9okdydldsw/FOp211bWtrbOp\nMTN5oU9GGFx7dCfxrlRcNDMpU46qfxFWb1Nl7crn/lq38zWZMTuAPXNenSgoQSR5tWTlJtj/ALes\nOjtaBcSM+WYHt9PXj9TWeZ2KBQx2joM9KS5GHBBzuGcelQ5xWhkSF8imxD9+npuFIO1GOaAOu0uV\nIv8ARpXCljlAT1PpWzaW4+0lgBuHrXK6xGJNOjnVcZKt+BH/AOqtjw8t7bac08tzIS/CRseUUY7n\n1z0Hp9RWMqTlrFnVhqiTfMr2Tf3I1bh7hr+CQRZ+zOHTJIBPXkAEkcdq6GbQtcn1QXaWl3ci4sYn\n+0x27FN32VVwO+SAAT/eHQdKxUba2egK8k/zpunvJdalGykhPLlwC23OY2HJz64qsTg2oc8Hqk97\n9bdvQ46+ObpyvFW/woP+ET12S/Hm6JqZBDDeYHI5BH92p4PB2rX9lFI+k6lbzRLs2NauDxwMkjnj\nHOKkg0ua4jnUeSrqAY/LnQ7SOR0PHbmuaeHVdG1EXFpNYFHGHjlvYQG/Nx+lctfne04/+TGlLM6S\n/l+5f5HQJ4a8TWzBF0K+dAeCI2IP/jtW7fwjrU84nudLvRg7tjW7sM/TAqrb+JkYf6TaWqMP+eeq\nWzfzcUyfV7nVka2gtLKGEfff+0YZCfQYVuK54+0X2o/+Tf5nQ8zo21cfuX+RF4l0DVL6aK9udC1P\nIYK0iWsiLFGv8TZBAAHPp79at6d4T1O60ue11PQ9SB86LY62zhiAr4PTtnHPqK5i/trufVLQNcWj\nQ2rKFUXsJI5BIC7s57YxmtiDTptL8LQzK8UpkaJmCur8BZARlTggEjB7fhXVGpU5lFSi9V0l39Tj\nxdelWowkrX5raK3RPt0NIeAbyw0nXXRLiOOeyWNRdQmPB+0QsBuxgnCn0rzy/wBHvdNcC6t3jB6N\n1U/Qjg11UniSTTtMSUQ/azJIyKkzYKAc/Lwc9fXj3zWgNctNRsFD2nlQSYEkcjZCjPO44Htgj+fF\nelSoRbk5PV9r20VurZk6kla55uR8rfSougro9U0C4tfMmgQy2ZAdJFIPynnt/Os3R9Ni1PWbexnv\nIbOKV9r3Ev3UHX8+w6ckcjrWcouLszZNNXMzs3NNDVo6vpo0rVZ7NLqG7WJgBNAcq3Gf/rH3qgLd\nsnJAFIZd0m9e11CPYM7yEK+ua72ICeMTEXEqlSvloSQpx1AyOaxvDug6bc6C+oLI0moxXBjaN3Cq\ni+Wzqw+pUjk9jx3rZT7Rbr5tpLs3gEgjIauOvZu6OvDtxNmK2jdXuGsUtyBgfKF2j0FU71ntdCmu\nRGJGMqoobsMHP9KrWU97eXAW7c7Ac7QODWzqsBk0YpGuT5qnHc4Vjj8smrpQ6sdWpzP0OFj1q5t5\nSUhhB9Dk/wBa6Twrrk02svealOohtbaTy1xgByMDGB169fSueuIVY5GKrpOYS0QICN8z/hnH866K\nbUZXRx1LyVrneQ67BHGxDEAnczMwHPuaytQ+ILQp5Nqi3BH/AD2yUH07/lj61wt7ftcSHDHYPujN\nUmkO0nuelaSqOQlFLU0NQ17UdQZlnu5HUnlAdqf98jitLT7VRoUkzkAMc5/4FiudRcLzXZ2KCXwk\nykZBhkwPcFv8KwaVrIpybkmzlri1+c5Ug/SpbeLYOAcVU+03CLtD5UdA3NW9Mkub2+jhLYiHzPtG\nOB/nFTyy2NfaQWp01jeyaTYzXYdo3ETbSvUEjA/UiucsJnNnrLFiSbMZP1ni/wAat+I7rAitUyP4\n2/p/X9Koabk2Gsf9ea/+j4qqq/3VvT80cVb4G+7X5oZbXgksjbzIrlOYywzgdxQbO2njZ7aQpLj/\nAFcnr7GqA+VqduK8g81rfubWOyk8pdD0zRbYRFpZQZJFJPz9MHIH97nr257D0rTRbabpkdrbIEiT\np6se5Pua8Y8PXCrrls8z42sdue5xxXq1vOZGRARjdz+VRJ9CUrP0N1GyhkYk44H1pz4MBVujdc96\nrRuJSP7o5AqeRsIFyRnnmkWiHR7m4tIIJUl/fxrsYg9WHBH5itjWfGGoParBbokU8nyLsYkliOvs\nOCfoOtc9p5EZuIsjMc7kn03fP/7PUVm5u7qS9YHy8bYR6L/e/HAP0AoQtti5aW6wQCMEseSzMeWY\n8kn3NSOdx2KflWlU5YDPSmp/HgZ5q09yRGB8xVB4x+VRz3JgjzGcszhFJ9TQx/0gnsar3l0gkVEQ\nFw4VT1AJH+H86OozrRqU0F3ZEMN7wJkj2J/xqr8UNGi8ReBzqKg/aNObzQyrk7Dww/kfwrMmujLf\nJzjywIyPdVUH9c113haVNUtL+xuBvhkUoy56qwII/KunC1OVt9m/z1/A5MK+WXldr8T5ulzbyxyJ\nIskLKpEi5xnHIPof8816N8M9ZIuJpRIF8rDyn/Y5/rx9WFeb61DP4e1y906Q7vKkaGVD91wrEf04\nNX/DGpm3g1AxAxRvGqk9S3zqQCfTjP4VtJezrOHT9DWvS0t2Z9YWlwt1bJMh4YVNXIfD7UzqGiFS\ncmM4rr6xnHlk0jpoVHOmpPc4CXwrZHV4dTi0wwXMMokxBOAshz3UjHbPG089zkVDqHhrTtQ1d9Rv\n7KWW4IA8hrgCMhR94Ywe2ME/hXTl7jOQYv8Avk/41E5nJLYgJxjJQ/41ftpb3OdU49iul0LazjSK\nzWONV4RXUBF4AwB2+lLLIJbcSAfxFRz6HrU+2Vo1BW3wQP8AlmcevrUM0ZS2wdoAJwFGBWM2mjQq\n5qCW1ilySoyxBPGc4IP64AqXNQyp5mO5HQdueP8APQ+9Qy4vUdJNsLM7KsYHLE98jr6f59Kg+1oG\nbcwHzEKCD2x1/PP0NLEgMaOxZm2g5J7/AE6UpRC27Yuc7s46n1qdSk4LcPN2opchWPX64yf61Aby\nHaWQs/zbSAOnT147j86leNJF2soYdcEU3YqlmVQC33iB1/zk0O4k49Su10HQ7GbLNx8oJTjGffkf\nrj0p3nySsFRDHjkmQdsccA/zI6VMTzTS2OvSizG5LsU9J/5Aljk/8u8f/oIqd8ZDHqBjNVdKbGjW\nP/XvH/6CKmdsmph8C9F+SNcV/vFT/FL82Ru1RMacxJyahk4HNM5ytq0QvL1YTLOFCAIiYAAwM5NY\nlz4XeJvO0m8a2ugc/NgI31GP6V1M8Kxu1y7qoaNME9Bx1rGkvQX2QtsXP+tkBLf8BXqfr0FdJh1K\nstvqBt3E9uROrK48vLBuG5BHaobCV5GkG3L7zwBzWhFqUCK2ws+xgGLnJGcnJ9Oh/T1rOuLmfUZS\nouLaNSciNIy59skdD9TWFPeXr+iM6W8/X9EW2nJJX3xVeFln1OQHoi4wPrRaWwkJ82+iZkONypyf\nbrx+ZpItNngnllS5jO5wVKsQRWlzbqSXVyrDyUOWcjoO3JI/QD8a27wjT9Nhtw2REvVeMk8k/mTW\nLpUbXGrzPMrbLaMksT1c9PrwM1n+JtWYsbePnIyRnr9axqT6msI6W7lC91WV5DI+1o0PToDWXpDH\nUr6C6kPAnKxx9gANxNUzFe6w+0ZW3H3mA5x7etamiIkFxMBwsZ2RgehA5/ICsoQv7zNG0tEdi8e+\n4k46OePWpBEuD8v1xULTZu5EUgtubPPTmpQSVKq2DjqBWtD+FH0X5I46H8GHovyRDcxxma1G3A83\n0/2Gq9qttG19GXAK/ZocEgH/AJZr1PWuGfU9VWZVkczmCUklFKAsGxszjBJAPT+9Wxr+v3EWqw2/\nyIWhtTklQFzBGzJlseo+Y46n0FZP/eI/P8ondP8Ag0/R/wDpTNtIFGcBcDpSsGjBkRdytw6jpXNR\n+L4Hck28m3HyAMGZjxxgdOTwe9a2lazFqsLvAHRkYBo2GCD7frXXc5gulEcAvbdzugIyO5ToQfpW\ni5DOsikgEcn61BPGkgkYZjYghhn5WB659qxrS9bbHah8MNyMD3A4H6UAtTo1EdxaSW86ZjlUo4Hd\nTkH+dfP2o2rWOo3Fq+N8MjIceoOK9ztbgE8NkdBg15T4+sxa+KZ5FQrHcKsy575HzH/voNSnqgjp\nIt+HfEeoR+HdatsWckVlpg8gT2MEpUG8hJUlkJZcyMcEkAnOMgYzP+Es1H/n20b/AMEtn/8AGqj0\nH/kD+J/+wYn/AKV21Yma82nh6Lq1LxW6/L1N23ZHQx+KNUmkWOO00dmbgAaLZ/8Axqtz/hIr42f2\nO7j0gSR4cMuk2rAKe23y8fp3rkLGINHPMXdGiAKFTjmuq+xWVvYeaLqE3IyB+8G9jjOSM55PFazw\ndOKT5VZ/13Mp4qFNWabd/kSWOs3H20NE2jyqFJ+XRrRSO3aPPes/VfFmof2hKqwaSVTCjdpFox9+\nsfrTreVMSuQPMUkE98Vg38bYjuGkLNPljx0/zming6bi3yrT+u43X9o00uXyNOPxlq0SSJHHpKJK\nuyRV0e0AdchsH91yMqpx6gelRHxZqI/5dtG/8Etn/wDGqxKQ0fVaH8i/r5lczOksPFWovqNspt9H\nwZUBxo1oD1Hfyq9I+F3iM39rd28kWnxXiyA5hsYYt6kHHCIASCD27145pv8AyE7T/rsn/oQra8FX\nn2XxFEol8ppVKI/91xyvHfJGPxqHhaPN8K/r5nRd+wXq/wAkfREGo3P22S1vPLaQKZElMCAuPpjr\nz9Kp6lcTfZGJhtmAOHVraMgg8AYK/Sn206X8Fjd4xIrjIBzjdlSPz4qW7TLzRuMoq/OB/ESCatYS\nhaygv6+ZzuTOf8N35vftFjPDaB7Q9rGEAxknkALjnpxWveWcvlukX2LecFUNjCcE9QPl6VymlM9n\n4paHzHLfMgBHBx8wHqOgzXS6nrC6Sn2sjzJtr+WnXdJ8oX8M/pmpeFot25V/XzGptK9zm/EE/wDa\nWu2mjcNcR7VlKYVY1UDgbQACdo6AY4ro7Ffs8k7jbkhEwBjpk/zNYPhLTS9zPrNxJ5ks7sokBz5h\n43N+LE/981v3eIniPd5sHJ7f5NbRjGKUYqyQrvqRWp26tcjHH2ZDj/gUlSzrGbArKEYOD5gZcjb0\nP1z0/HFZs+pW2m6hcNcyGNTCisQD03SE/oarXHiGwnvAHuo1i2ibJO3eWGV6+x/Wsb++v+3v0Hiv\n49P/AA/ojx3xbo6aPrUkUKlbeQeZEDzgHt+BzWFnmvSvH0UV5pCXcbK5ikyrA5yD1H8vyrzSulO6\nJL09x9oEbEAMqBSRVQscEU8cKBUTfeNCAkzninBaavWpe9AHU34+1+HlmL7mCpJkdz0P8z+Vcv7V\n2eg2b3/heYo8K7C8O0uAxLDIOPxP5Vx6RmS5SJfvOwUfU0IS3OofNj4VASRkZ4gOOM7iCQfwJrlc\nV13iuNrTTrJDtKXH71CoPQcY5A7k+3HBrk8Zo6AiM9a0PDrn/hJNKz3vIv8A0MVQfjJ9qteHT/xU\n+kj/AKfIf/QxWdb+FL0f5MyxH8Gfo/yZnhFcZOc1s2QFuqR4xzk/Ws2xj8yRf9k7q0N22RCPUU5v\noas0Vb5ulXkJXAxkmszzzE6hQNxPX0FX4Dj5z1PPPYVkyGjpvDMEUupTCQuCLG6xsGScwOP5E9+1\nZrXEcDCPk84+YYx/9erHh+8hsdSS6li3I8bwN8+z5XUpknBxjdnoelWJZtJk18acdGvjczyBQw1C\nPBY9OfJ/WsLSU5PlbTttbpfzXcaV0iuUEVvFcs2TMzBRkHGOPw/r/NY7mJvlD4+oPNaGq32j2c4g\nk0q7MlsPJPl6hH82DjJPlYJ6Vmp4g0HcBJpWpICe1+h/9o051JPRQl+H/wAkJx8xxmV1ZVblTg+x\nqjcyEqzKfmH3lPQ1rPf6E0IdLC/dQOSNQTp7fuaqXN5oCjLabqXHcagn/wAZrNVX/I/w/wDkhKPm\nUobhI/CesTN91b60wD6+XccVyN3cPKqsxO48/QVu6rrGmSaPcaZYWF3B5tzHO8k92sv+rWRQABGu\nP9YecnoK5y6IyoHpW+Gi/elJWu+vovXsaNIrZw1HRm9CKRqRj0PbFdgx6n5RT15qMcIK0NJt7O7v\nBDeXEsKvgIURCCxIGCXdQoxn5ieP1ouI6PwHptjPqUl9qsBntLZGZIBg+dJwFBB52jIJODjjIIzX\nY6DbW93qL2rwwEyKIwJlEcUavjcwUAEc54wo471p+Go7Cz0ueDRp7TyJ2Hnw3EhniXKkENsdtrEf\ngcDmq50CGPUopJpVvInwoEsf7tu/pgjjg+55rzZ1ZSqNPTs+v3HVBUlScpK77f0tDz/xxpVxb3kO\noOpMd0i4fk5IRSAc99rJk9Cc4rk2+UZ9DXuNxcppM06382IZD8qTOhJ+Xg/M6DgDHfsMEDjyjxRe\n6feXifYbQQeShikZWUiVgT8/y8d/U9K66VRv3bbGM1bW9zKgDNLIq85jyQSRkDn+maLht13n1VT1\n9hUSSshUq2NylSR6HrRyJMkYyOK3IJJxlQ1QRyBJOT3qyTviI9Kot980MEaP2g5wFBoDA9Rg1Wt/\nNnmjhhieSWRgiIg3FieAAB1Nen6D8HdXvrcT6ncxWGeREB5r/jggD8z7ilsM873cdcCtDTLOR5I5\n2DKCT5RYcNjOefrgfn6V7Npvw10jRpBMsRuZwcrLP8xXp0HQdOuM+9T6z4ZTUbdEwFkiOYyRx9D7\nHipk9NEOO55Yw4LGOQqp5ZRuFaVoQsPnG3uTEn3pBEdi+7N0ArZl0gW0giureRGDB8CRlG4cBhg4\n/EUy4sYPsrW8EB+c/wAUjuB7gEkA+9cLnS2aZ2KNXe6ON1FNJm1GeOd52Rmz9otmDBCTy2wgZ6E4\n3D69qxNR02bTblVkkSWKRBJDNGcrKhyAw/EEEdiCO1exaLZeR4dvLK5QNHdJ9nhjlTcjSM2EYj/Z\nYg57cntWl4j+F3h6+gsLXTy+nGedkVkJlCkxu5OGOcYjxgEDvxzntpTUonJNWla586yHcxPvTf61\n2njD4a654Sja6lRbrTw2BdQZIXJwN4PKnp7ZIGTXFkVoSC9ad3pq/epTwaBHW2MS3+jQJKQVIAYe\nyt0/IVfjdmyFyB5wXj06VlaFuTSJHYYBf5TnrnH+BrRt3VEjd22J5ykkDOOT/wDWrRRtFeq/MKL9\n6X+GX5F+7k2o0f8Az0YJjvjq38v1qS1yNpDbScjPt0Ndf4e8FaZqum2+r6hc3UkRZiIYwEUbWYHe\nxzkEKORt61B4x0/Q9L1Cwh0ZYxG0bF9s7SAEqAB8xPdW79x7V086ehypdivonmF5g+NpK4G3HP1r\nj/FWnwyaML2CQkRzmNlZDwQWU4bG3GR0zmu20K5khuJGjSIpjBLRhhkf1rio7j+0dA121O3dFJJO\nqtJgBS4OFXHJzk9emayqJGlM4fbXY2Vgml+E31GVnWV2wi7AQzHoCc5HHsawdF059V1m0so13GWQ\nDHY89Pb0rovFEj3WoWehwZZoMI+EOTITyMdTgk/XtXOa7uxzFlltTtiTk+cpP516X4P2X3hWCJky\nIS8TZ7/Nu/8AZx+Vc9L4H1DTPIvpQwhEv3mTI4I6lSQvUdcckCr3w9u9i39oxHBSVVPbsx/9BopO\n9S3ka1bfVoyi01zPZp9F2MzUrX7PqBtJR8qOTE+eBuGCCPwHP+ROAq2TIR2HH4gVueIbaI3CSsOS\noGMf7ROf1rAunCSyRht2OpxyScE/rmu2OjOOWtmXbCXzNJktXcgOjx7j9D/SuJuIxBcyxkhijFSR\n0yK6S2u/ka3VNwy8hbPTt/T9awteXZeR3CgBZl5x0yOD/SorK8blUnZtFMsTzmkyahVixyM1KMnr\nXIbGloFzHba5avMMxFwrZPHPQ/gcGvRrqK3sZl+1TC3imJELTBkViBzywGOvfrXk5rr72aTWPDCs\n8ru6KGwzcBl4PH0z+dTKKkrMcZOErnW28EOPOE8XlDnzC424+vSse88VRWWsWf2K+jM1sZJkdMMo\ncIdpJ6HnHHNeatkGiKJ5C7L/AAKWJpQhyvc0nV5laxrwanIjYc7l9P8ACm390jhRERyMtgVkRyNu\nwakJzWjMuoobrSp8zDPQVETjNPjbj2pATnsK7Twyxm0Xym+4sjR/gcH+tcSDk57V1PhSc/ZriI9F\ncN+Yx/7LSZMjl24bB6g4rqfDtnHb6fJeS8eZk7scBF/ya568hL6xLBGOWmKqP+BcV0utXSWmkLaR\nEjKiMD2HU/59abHLV2OVvJ2uruWZuC7Zx6DsKuab/wAeGr/9ei/+j4qfYaFe6koeJQFbO3IJZsdc\nKASatJpdxp9hrBk2sn2ULuU9G8+Lgg8g+xrGtNctvNfmhYilP2blbt93MtbXvb5HPvjIppOWx6U0\nv82Pxozx7mtyhwYqwYHBByCK9U8OXw1CyWbI5QKQOx7/AMq8pzxXWeCdS+z3z2jnCyDcufUdf0/l\nUy7ky7nqUEgPy+g6VOz7nVc9KzrZhvB7dqsq+6R259OKSGVbgSNqZtwhMNyo3kdtud2fqNq/ifSt\nCHlWIGMdsVWSJm1HzvMO1YSuztyQc/pU8JARzimHUeCR04pGYIWGfpULS464pz/NGG7YxxVwVyWV\nZLj/AEuMAnk8j2rOinSe7jCggpdP5rE9SB/hgVajGdQBxnAqlbQiCS4foxlkYj/eOAfyWmlqD2Nd\nJjKjzMcF5XP54rr/AAPKYrtwT98AfSuORCLeBCByxz39K6LQ7hbedSMhlYYIOMClh3dteb/M5Y/A\n2v5n+Z538ctLFn43FxGnF7brLx6jKn/0HP41zmk2sdj4cNze71E9wBHCvDSbRyeei/N1wcnj1x6V\n+0BbiSw0W8TOS0kbEdx8pA/nXkL+d9ntoYoXZY152r3PNdtV/BN9vy0Our71n3Pc/hVrUl2Wiwkc\nKuFSNBgDIP4kn1OT+Vet14R8L4106G3kup1SWe5G2JGDMwHrg/KOvXk+nevds1FTVJmeEfxLzMfY\nf7yfmf8ACmsmcjK06k7ViWNYEABQvAA5J/wqvcBjA24ADOeDmrJqGfJhcCk9gMnOaY2GGCT+BIoP\nXntTSaWgwJ5ppNBamMcUABbmmFxTSTk1GW96QyQtTSTUe7B9qN2elICnpf8AyB7H/r3j/wDQRVh/\naq2l/wDIHsv+uCf+giotW1ez0i1M93MFGDtX+Jz6AVEPgXovyN8U/wB/U/xS/wDSmWc4JqKQnmsF\ndV1TVYDJaRJZxt91nG5yP5D9aYdO10xNIdbw4+6DEpB/SsniKadrgsPUavY09au410+FTIA4OCvf\nHrjrjpXMyXkjxeVaXKIznLSmQBvwGc1aS7lNlc2+sxRGZI3aKYD5XYDIx6NnFc8Q7KiTKDKwyE53\nY+vr7H8+ldtOanFNHJODhJpm7bwQ2unSAiaQGRSxXGW4b1I4psuuWdopihilU4y2QM/zrHgaH7BK\nkjzxLI6OjJFuPyhhg/MPX17VEunx3IJF3eAf7VsnP/kSub2qpuSknv28kc8OaMpJxe/Z9kXLPWrU\nRY3OrM5JLjitGG+ikieb7bGqhuORjHOcnOPSueTRopMg6lcqvp9lUf8AtSrMfhzSiMyXt07DkF0A\nH4gGq9r5P7mX7aPZ/cza07xXYwW80E0cn7yVnE6rnsANw7cAfnWLeqmpTCSORSJmxlT0XvyPaqGs\ntp8jx2C3t3EqjAVLYMADz1Mn0/Ksb+0NLsnVbLU7+Ixnl1skO785a5nUu9n9zN1iI78r+5noEMeI\nsA4KD5CBnaB2+lcxb3RTULkKwG+QjgdAOwqTSfFNpPILWS6nkYj77WKpn64lP8q0tG07Tbe/LmaZ\n9wLAywqB9c7jWk8TGKtZ/cwg3J3s/wDwFmzJLKl5OQg2CVh0x3NTmUwxbmPzHtRcXEDTbd2dx3D6\nE81Df2sNpgXmrWcPyo5UpOxUOoZc7YyOhHesKeKUYRhLR283sl2TOijhvZUYOfZffZGPeRGNYpTd\nxxK8srFpI9y8lc4B4boQfQsKtahcWD68sd1IqGOws/JliJj/AOWCk4K4xw30wBWDNYpOSZfEVjMC\nuwHZdYjUHOeYemeOM8t06kbup6LE90k/9p2oX7HaLvFvdMMC3jHUQkc8H1wRkA8VnPFQVaLjd6Po\n+0fI6XR5ow9p7q1/NlGbTba2BihlmmjkjV08mIswcHh8jj14zjr04rf09njsFeaNIbl/mm2KMM3q\ncd+lYOnwWlndRpB4l07a0gIieK5wSeDz5PHb8hnoK65LS3YEjVrMj/dmH/tOuyGLpS7/APgMv/kT\njq0XTfcrvfeQm6eBioywki5BA68Z/TrXLXzxyawhglBhJMisp9f/AK1dNqNstvY3NzBf2k0sUEsw\njDSrvEaF2HMeOinqa4Dw7OrsrlmUsOSprZVoTuovbya/NIyUXe53VmYljVenoD/WuS+Jtput9PvV\nQnBaJ39OhUf+hV1tvGu0H5T7g/nmuf8AF+qwahokmnafbvfyF1/eQKXSIjnqOpxx+JrRtW1FrfQ4\nfQf+QP4n/wCwYn/pXbVh10GjQywaT4nSWN42/sxOHUg/8fdtXP1x0v4lT1X/AKSjZ7Iv6cwbzYCc\nGRePrVlZU8uK2Ls0i53BhwPaqdlbwzCVpL2O2dBmMOG+c+mQOPxq7JaJcOsX22x8zey+bvYDCjg8\njoc8fTnFdbalCz3RhOneQqOTayrCRvd8ZH4f/XqrfyKzpEpysS7asS27W8GIbm1xllOyTJ4AOfoc\n4B9RWYylTg9aOZRhyrqNR1uN70jUopD3rM0LWm/8hK0/67J/6EKjt55La5iniYrJE4dWHYg5FP03\n/kJ2n/XZP/QhUKRySNtRGYnsBUfa+R0P+Av8T/JHvOlaiq6LFeRKSiyR3CgEHCNgsD9P6V073SXe\n5lyuOXDdQTj+lcD4KiuovCMlpdwgTjesEbEDehGQDg9cluvtW/YXYl3DOA7Dd7Hbz+v8qE7GDWph\n3Fz5Pi4XLrJv+0BVIORh+B7DofzNQ+Jp1uNa+xuzOsEYRUwcNIR1P0yD2pviic2WtQ3CjKKIpAN2\nM7XPX3qnp5nvdR3z/NJuLBwwxncCeR2wScU2+pKR3unSwaZp0EJbiNMAgdWPP8ya57xX4njtJIoL\nQh5xGrbj0X6j1rV1XYtpIIkOEiyD7rya8cH2nVtQlkDgM7/xe5qdlcpK7Ld8818gmnkaWWSVjuck\nkk7auXNtBcPHJ/aVrGVghjaN1lypWJVIOEI6g1cbw9dwCJEKu4G7I7buP/ZTTrrwo8cRfzCXON3H\nU1yOcZO/Nbc6sVhJzlCUXa0V26rzMe4hWPT5bZ9csvJkGcFZuD68R1gjS7PP/Ie07/vi4/8AjVau\ns6ZcWluN8ZMfUNXMYw1dEIyaup/gjhlRqRdnN/cjXOm2nH/E90//AL4uP/jVRnTLTd/yHdO/74uP\n/jVUe3Som+9Vck/5vwRHs5/zv7kaw0y0zn+3dP8A++Lj/wCNVINNtP8AoOaf/wB8T/8AxqsfdjpT\n8nijkn/N+CD2dT+d/cj0HwWtnaveQtqljOHVXCiOQ7cZBPzoOeR0rIstEt28TLDFqlpI6SttjVJt\n2QTj/lnjrjvVfwZerZeKLZ3VyrAhvLxu4+bgHg/d6d+nFbegwTReMtUvY0jMtsxzHIu7LNk4wceh\n7in7Odvj/BEuFRP439yNG90eDVPFkNjHdaanlKY/IZ3CowyzHcRtPAJ5bBP4Cruq+CtOk024a1v7\neWSEFm3SRkouBtIEYz35GDx6Vwf2mafxBLcwFJAkyuhbav3T8pxnpgcitu9vdb1KxVBps8yQ7CzQ\nxOwZhuw2Rwqn+6OOOKfsb6uX4Iyn9bjKKhN8vX4fxVtfvOdm0u1UMp1uwBHHKT//ABul0m2sLHWL\nG8l12xaOC4jlYLHPkhWBOP3XXisuZiwZm6nmqlRKlKScXJ2fkjWVGc4uLm7PTZFuxbbKPfircucE\nVnwHa2R61pS43ZHQ1ctzoZGt028OwBZRjn1rbs5GeMBznI5zXNSkxybu2enrWvZ3OR196iS0JaNU\nPLDf2bKWZRIByff/AOvXRrZxjxXcrN5W1IFXGBtDP830HGf8OK5OWc/ZeDgnkGtQa3cySSTpKoku\nQA46kAAKuck8jAPt2qovQV9CO+uFuZAURtrS7NyoFBUHg8e1VptPeSZ3kPljd8uB1rR2Ztlx26VL\nKu+2B7gVlza6E8xl2ULQiQyoudw25AyPxpt5IGUjjnjFSzcxE+lZd25x7EUbsa1Zks5LPnr0qOds\nyY9BikDZkYn1yaY5ySfWt0WDdKYw+TPoae3KZphPyY96oY/+GlQ80h6URnNAGhZazqmmKy2GpXdq\nrnLCCdkDH3wavHxhrrIqNqDFgMecUXzeuf8AWY3Zz3zmsM9aQdaTinugu1sWby6uLqUyXE8krkkl\npHLEk8k81WPMZp8nIFMQ8EUxDF+4p9DTtxMgz24pE/1ZX0Oaa3+sNAyzGwzz3qq/EhqVR3NPtbOS\n/wBThs4ceZPIsaZ4GScDPtQB6t8IfD1vEP8AhIL6ANucRW7E/wCqBLBnx9QR7YPXNezCbybgRv8A\ndPFc5oVhBb26aeiEW5tliUN/sjAz6nFXTK32TZISZYCY3JOTwOCfqMVle7uNG5NFjtlT0NVJbbJ4\nFUPD2qhgthM3fEZJ/SuhMQGQcDFaLUDGk09J0CyxLIM9GOPxqG30W1ic50+UejsykfoxP6VrNcxJ\nLsUM4BwzorMF9QSoOD7HFPW8hVQ0wMaEZ37WKAe7bdo/OplCMt0UpSWzOM1xZbfV9MKRYjSVmJ7L\n8hX+TH8u3WtfS1ur/VYJ5beWK3tonYGSRDvdsAYCseih+v8AeHvW3c2kF6h4U446dKdY2i2sLKB1\nPaqSSWhL1ZHeLFPbS208aSwyoY5EcZDKRgg+1fKnjDw8/hrxFdafy0KnfA5/ijPK9hz2PuDX1Tdn\nhua8r+JPh+01q0h1F5TFcW6mPeozuUnIBHoOf++qbaitRr3tDwtetOwWcKBkk4ApvRiKt6bH5l/H\nxnb835f/AF6aV2S9Dpk8m20/a7HERXAA68YpJj/oBxxyv9aRwWtrlB97y9wz7c1GHEmllx3Kn+db\nVNkvNBht5f4ZfkSW74kZGCkDoxFX7cATwkyNg7gF6diP54rK3FJzjvWxahZonfAJCZx75WtFsc7O\nm0G3kiTzXJ2OB/MnNeeWF8q3t95gytzDJwB/EPmH8jXoOmn7Lo11ctg+WpbIOe2f6mvI5WYbWHUc\n1nVZdJaM6PwyIrZLq+nUGNUKKSMgHGcfXjj6Gm6Fq81l4kj1W2ES3McnmRB1BUH0xkfhWWLx4rE2\nqH5JQC/v3FNsbyWxm82JYWbaRiaFJV5/2WBGfeseaxTV013PYdU+IUuo2jxJdXNxNco8JSa2jIiQ\nkA5xgrkZweT34xiuH8MXyN4z2qyJHdBoiznAxjOfzUfnWfbeJL6a7ijeLT9rkRnbp8CkAnsQgwfQ\n9R2xTNFu3vfFdnM6Qo2cERRLGvCEfdUAdv69aanepp/WrHCgqWEtzN3k99fso9E8QWLSaclySGjB\n2blwR3PGfxridQIE8aKfmaE5yc5JJ9PwrrtS/e6bIshGAAwYjOO2ePrXKTxxPFHcyyLsJMed435x\nxhc5I68gYFdb2MEULJ8GcfxeSf5modciD6HDOAcpOV/AjP8AT9akLK32ydFCK2I1GOcZ5qS+Tf4Z\ndTnOdw/A/wD66W8bDWkjlo2AFTg8VTB5qdGzXEzoJs10nhWV5nksUALMQyj9Dz+VczkYq1ptybTU\nIZgSMHBIOOv+c0Clewmq2TWOpXFsylTG5GD1x2q7bWyw6DLOw+Z1Yj+VanjmIzahb6kI1X7ZGGIU\nk/NjkZIHPOPwqlKXfRltYiWcxAlVHI7mhg2tGc6i45p9CspGM8+9IaBkchxRv4Cj8aJKYvWmBZLA\nJW54XnIurmPBwYw35HH9a55j0FaGiSmLUA2QAysvP0yP1ApClsasMAl8Tyufuxr5h+pH+J/SqutX\nBlvRHk7YxgDOavRP5K3N0WUF8D6gD/69c/I5kkZ26kkmq2BbtnrfgHWNLsNLubJtKtpbmSXcHkn2\nM67cgIB83GD0Hc9s4o+NdUstUTUWhsmgmNoZJZRMHSYGePZjAAyPm9euK4e1vdOhWPE9/BKowJI1\nVyvrtO5Tg88VcV9PfRtTFrdTs/2NC8T2wjVT50WcMHJb06D8KK017P7vzR51TCyjUdXm000su8eu\n5yRJLGpByaiHD1MvApHpMO9T2lw9pdRTp95GBqHFLQI9i0u9S5tkmTkFd2a0rXJj3EDmuN8EPJJp\ncxfO1W8tc9+5H6iu3txtQdvY1HkJCW0DRz3MrFsPgKMAYAH156+1SxH92wI71KT05qNOEbrT6DKU\nwODjgZ9aswnfEy8E9RUFwMZI798UtvJtYD1GKtaSE9UVxhbv5sVXUmTUJIxxlgB7jAP8ya1p7BFM\nM81/aweaC6o4kJxuK/woR1B71Xt9Pt21KRk1ezJ8sHaEmyO3/POsJYqmm7P8Jf5D5XYuWoEoLkZW\nNmA/IVds2VZssBt+lC2EcMIhGoWqjqzESYJ9vl/zmpYIbS1uEd9UtG2nO1lkwf8Ax2ow+Jpq++76\nS7/4TnpxbjL1f5kfxeikf4f2lwqB5LS5jdgV3bVKsMnPbO0V8/xPcaner5sjOfUn7o/oK+i7zQV1\n2LWorU2U9teWrqsnmP5kZOHA2iPkeYoPXOOgznPi1vp+nWivDHr2iGViMu8d4SuPQeTj88131cTT\ncFq9P7suv/bptFPksumh0/g+5J1u0WLJiSRY0/OvpIdK+b9FvtR0PWn0+aaGNra4aKZrWNYg+1ip\nBKqCy5z1r6PhYPCjAghlBzVuSnTjKOqf/DmdDSpJPy/UyfmP/LKT/vmm5bP+rf8A75NP70yRmCMQ\nT0PeoLGliekch/4AaZJ5m1gIn6Hkrx0qYEmNTk8gUZ+Yd+aBnPOcOw96YTTpeJWHfNRE1mthikio\n2bBpSc81GTTAYzGmFutOYg1G2KQCE0m7vTGJBpQcLSGZDzeT4ZtHZmVVjg3FGKnblc8jpxmvLrzU\n7jVNUEjndEXwiyfvCq54GWya9D1Ys3gjagLMbaIADkk/LXJ+D7CO5upLqZAyQKCqkZBbt/WuVtKF\n32X5Hq1JTjUfI7e9P/0r0Owgs4vscB3TBivQTuP0zSvbxpxuuP8AwIf/ABrnvEOryoSn2p7aNepR\nefx6n8qqaBe3k14YDdSzAjIWQDp1B6VxexSXMdMMZUlK13/XyOhuLWFo2BMp+srH+tY8NrC2j6jK\nzlr2CQRE56g4+Yj3Bx+dVdQ1+9S+eG2MGE4dXB7e9X/C1pNqMes3koC+d5cQQHPIOT/Su3B07Ss0\nceMxVSUbqT/r5F+DRoGjsoiOkLON2SuQV/h6Y5NU7uya2dhHuR+vXK5/GujlXZd2qDGRBIMfilUr\nyVF45d/Ra74wjZq3X/I4cTUlJwk3q4r85HOwNPuYOgGASTnAqSQh4QD8oIyzbsACrN0n7hru52xo\nnT5iCT2Awea4vW7zzoZZI5ZlyMbQ52nt0pvRHNuynfXa3YuJoMlnbau7g7c1gPC4YjjP1qTzWA64\nxURkOSc9ea5ktTfodP4J0lNR1sRseVQsfp0/rXReLfLs2gS2fAQFeK43w1rP9kakZyeGjKH8f/ri\nr13qEmoXSkZZSclj0FZyi3PXY6IzjGCtub+k6n50EfmcshOM10XiOwt7+5aUwSzz/YLeSNUQlVAh\nTLEge/Qn6cmuNtzGAqNB16ENjNdjeulrcSXgOoQsljaorRE7Cfs8fBI+oH/665pr2daMvKX/ALaF\nSr7SFuxy8GuXA0o5sWjt4VeMAk+Tlsjp3cB8g57V6jpkaX+gQPHcWaBrKALKtxHuQiBASwJ4IIIw\nR2FcZpVzay6ZDARYCPz3aRbiV0z8rD5lEZA4wOM84454evh2LULB4rSztTksFngiudoOT38jHH1/\nKtXQbqc8ZWa8r7pea7F16zdCEGu7/E273TDq0G65vbcW0MgMRXU4cNwcMSGwDycf/X4y4tHvVkbZ\nfab5YbCmTUISSM8dH69PSnafo0Edosc1k6lMqymQlQwPIwTwcggirDRRQAtbpBED/ETz+Fbwp1+s\n1/4D/wDbHC3HoiheadqDaj9m+2aYy/2ffblGoQKSTayqCQZM4yeT0ABJwBmvPbbQNas23W99pCH2\n1qzx/wCja1Y5PO8Z3siyFtmm36knOci0mrjZzG1wwjUKpxwDkA45/Wublre3n762j9n18y425UdL\nPpHiC9UJc6ppkqA5CHXLQqPoPNxXV+DtCvLWwmEk+mnMufk1O3cdB3WQ15UOODXSeFr94DcW+c5A\ndf5H+lFaFdw+Nf8AgP8A9sa0bKR0XiO0eyi8QzXFzp4E2nRwxIl/DJI7/aoGwEVy33VY5xjivN60\n9ake61eQgEnheKt2FjFaOktwqux6DqF/CrpRdJNyd2/K3S3mS1zMw9pHUGjFdeJEuhvuUgAY5UbB\nn8+tRmC0hlyixBh03oGU/nWiqp6MTg0ZusWvk6ZokguJJVltGdVY5Ef71wVX0Gcn6knvWRXaw2kU\nURYwWz+fledkm3nPA52fhin6h4Zis7YTMYHWSJ3AQjKkJkZ/MVoiG7bnDDrUtqiSXaJIMqTzzioh\n1qxYruvox70S0TKj8SPTdN0jR49OdrewiM/lkqzDewOO2c4P0qQWNodTE4hYbNvAib+WKZoG8A5+\n6K2rFzJeTIF3YKngZx1/z+FeZBydTc9SUoRo8so31vvbpbsy6kkBywWdSQxOLeT14/hrndVuXi1F\n0tGkCMTKCQVOT1ODzjrXW3NrdzWpitLhIZGGFATcWGe57f0rzOx1TUT4lWG6jaSdXKR7GBBAz0PQ\njryOOc13KMrbnDz0b/A//Av/ALUseIzPqE9ihmRyFEYcjb39DyTz2rqNGsLWxt4o3juPlyM/Z3J5\n75C1SNy73qqlohlDYZHcFcdckjkEHGMY71Zmk1m5eNJJUijxz5ZIyOwpe8+oc9H+R/8AgX/2preI\n7+yk0ySJDIJfJkRA0DpkkcDkCvLfD1m7avGHZTCmXcRyBicdOFJPU11F5DcJN8zFvf3qa2to4Wgu\nMIJpY1LbeMDA4x9QazrSlGNr7m9BUJSvyvT+9/wBZ/EUlhO3k2LzM4CAZwflyfT3/Sr1vrnn6VJq\nD2pCx8sOtWLt9MW1E1xhmAxgHqar2N5p0kEsYuIhuODETgn2rjVrLQ753k73srJL5GdceJLbXdIu\nbb7HJHKY3CEgEZA9vwry6K3kvb4QoVDtnG44HAz/AEr2qW2sE0aaaywWKFVUnJB9K8WlC22pSBSS\nqMQDXdh2nex5eJi01cRoJUYoy/MOoBzVd+Grp9MsPtSeYR8uBzWXrcMcMyBFwe5rSNROXKZSptR5\njLFPXk0wdacnWtTMt2d0bLUba5VmXypFbK9cA84rrf7QtrWy1i4M7faJ5WMbZyZAQBhuOxPX61xD\n1oagk8VraLOjI7LvIcYJDYYH6EEEeoINBLVykDuA5qQMwQgE1Xzxx2qaM5HNAxrHMRJ61AAScCrU\nSB3EbcgnFdrpGlW7x7IYFEnqBz+dZzqKG5pCm5bHCRMFb5sgd8CtMSxSRBUbcRVC+ga1v7iBuscj\nIfwOK3/D1mDbGUjljSqSSjzCjDmdjDuFPJqeyy6gA8nitTU7aLY5Eagj0GKz9LQCMyH3xSUlKNxT\njy6Gnt3DHboBWtNYtGllKIliintxJGgYsThmRicgdWRiMZABxng1kpcxGaSJckocE9jXZDSJRaaT\nc3js0V3GVt2AO7aoHy4APyg9wP4uc0crsZWM+NQbcD2pVyYipxWjfacbC2RwxzvKOpzleARwQDgg\n9fas1SOR7Vk01uQZ0oIYr6isa7PyMp7citm44IPoaxL04yRVR3KiZ1rAJ3bLYwfSopFG5gvQE1Ys\nmC3DD15qqWw3Fbrc1a0QL0Kmo/b3qXc3BIFRscuDjHtVCFbpToulMbrinp92gBe9FApTwBQIc3KC\no04NS4+Sox96gC1pdnHe6g8EkrRRiOSVnVN5ARGc4GRn7uOoqSSHRN+f7Q1AcdrFP/j1SaKMarJ7\n2V1/6IkrKdTuFYtOU2rtWt+vkzC0p1GuZpJLa3W/kze0iw0S51eyha9v5VluI0Mb2ioGywGCRKSA\nfUV13gyHR7vVJbyzs9r2yACTyXTaz5UDmd85G7jH4+vDeHST4l0oEH/j8h5/4GK734eW6ReGLu8T\nImluwhz02ooKn83asZ0uadnJ7Lr5vsjGWH56tpTlt3t1fZI9PKyQSqzB0cfdYMAoH021VvL+Rboz\nvDctDIuyWTA6jkckD3AwD161NBcW9/GsrQzq/BIjuCoz7A5pmvXVtDpZtbhpt0kiJtjmZnTcwUFy\nCMde/X3pfVY2+J/eafUo/wA8v/AmWbC2L26SwOhYENxn9PlIP4EitS8+3yQEkvK6rzGrqxIPAwNg\n/wDrgEVmWmoKhCjAA7Ck1Sf7TG8akbp41RMngOpJA+rAkfh70LDL+Z/eL6lH+aX/AIExy2Nzf/vb\nwMNjBMsrIQOTjAUnuAO3YA9pbjTJEIlty8UkKFsGRt5AwMKQi89uprK0vXbbT91rOd2JCTtJAQgY\nOfXnngcHnntoz+IreDdbW0u6Uq6nauxVbPfgY78EEZbk9ar6rH+Z/ex/Uo/zS/8AAmWdNvJ7p3ME\n20I+H/eKBljhSPkIxkc+pYHjmtjytS/57f8AkVf/AI1XMaFIFQ/aQVklZGwfvqqNu3EnnkgAD6kc\nV0p1CPPD59qFhV/NL7xPBx/nl/4EypeQ3wX55M5/6aKf/aYrktaiE+h6jHMN0Yhd3TBO7aN2MAqT\n93pkfUV0fiDxCmlaTJeGATqhUMofaQCwGRwfWsqORHkZ8ho/MO0cEMM05YWKXxS+8Fg4/wA8v/Am\neD/bfDGf+PL/AMlJf/kutGxj0C6sZ7i2jlt3jljjLx2zAkMHOMNOwP3evGMd88czrumnSNdvdPJZ\nhBMyKzDBZc8H8Rg1q6CD/wAI9fHp/pcH/oEtH1dRcWpPddTKthVTScZy3XXzNpYtOinR/td5/wCA\nq/8AxyqN/Yw6dbLHFcPLDNtlQvHsKgtIuMAnP3aljIkIU8n8qPECeZplgIzGriIE73C5AklHUn3r\nqrpwcHzNq/X0v2OrCRkqyipN3Ul0/l9EZ0xc/cRiB/EBx+dWdMjkgaaaQocDYRndnOf8KYuRbBme\nHOP+eq8/jmptLiYQXRnZCZGjxtdWAAznv71rzx7m/wBVr2+B/wBfM6bVJ0sfA0zK3zS/IvbOeP5V\n5ts3Hn0r0zUoY30Ozt/LLpvJUyRcEgDpuGD97t6j2qomhR3USxnSnkdhgGKDa34bRgnGeoNZ1KsX\npcunhayj8D/r5nLeKNIuNF1xtPuvsvnQwQBjaqQh/dIQeQDuIIJOOSTWMBhjXo1hZwXPi6FvFdvP\nemR1W5+0O/mspGATtIbIGMY64FZHiLw5HptzeLHatFHy0BmDRnbvAB+fHbI5/nWfPHuafVaz2i/6\n+ZzFj/yELb/rqv8AMVb8Ovs1+1Y9AT/I1Da2zx3kEjyQBVkVifPTgA/Wl0HB1+zViAC+CT9KcGnO\n6FXpyhh0pq2r/JHpWpKBp8xI4CgkiuRnYSJBtQ5R2IOc56eo9q7G6tZPsU/mt1Q5BPX0rjJHEW3A\nBQMRkfjXorY85Gd5my2Iwcu2APxNaVzGWsFg7mMjn3FZVsftN7FAqZUHJOelbNwwaZjkYyRilT1H\nLRnCgc1KPlH1pHG2Z1PZiKQfM2K4XudJKOBS9qTvR2pAdU8jX/hKOTyzm1YsXHRmyM8fQ5z9KraV\nq4sIppTcBN0Swsn99TyQRjp8o6YqjotyqvJbyuqxyrg7s9+DjFZsly6gwyxRPtPUrgg/UYNPpYzl\nDmjy3IrmUT3MkqxrGHYkIucD861bXTDcQKehx1rHRdzqvqcV2UIENqD6Csa03FKx1UYKTszmb2wm\ntDlsFfUGqadc+lamqXXmYHvWazAnA6VdNtx1JqJJ2QKC7VYy0YXYcMDkGmxIFGTUgGW5qjMvXs7L\nbQwYIJUM3+frVDBI4610PjKwbTde+zSySyT+RDLM0wIk8ySNXbdknnLHPvmsiKILEXMm0+3WiTsP\nltoMj064mIVYyCegPU/hWrbaTdWthq3mKhDWihSjg/8ALaI9M5H4021trqdVjtgXfGCR2H1roNPt\nLieC+gJUMtsqIhHXEiE5/IfTNY1Zx5Pu/NGVeL9n81+cTz1Y3afYqEvn7oHNbFvoN5KMuFjH+0ef\nyFbkukQpdo3KzEZCnr9PqKsoGRfmOfQ0Vako7HXTpxk9TlrnTDbusZnGScbsU+30tklZZDuI5zVr\nUEFzPsIyOpqbSLYpHLGo5LhQDVRk2tSKkUttjtvDloLXSIEAClsucH1PB/LFdJEeB9KzbVFjjRFH\nyqoAq+j7RimYrYlyC+Tn8TQxwvHSmhxuxQzDBGK0WwPchnO9fU1UjYqcehq7jIPeqEoMbg+tElpc\nEXddJ+y6Y+Bg2x/9GyVU0N7VdQmlvLtLWAQgvM/8IB7Z4z83fP0q5q2H0XTW67YCc+3myVy50lte\nmW1TBZA8oz32IWI/IVjQ1pr1f/pUimu/9bHWTeLvCVkBNb217rEb7o0Ij+VnABbcHxgYZOi9j+NF\n/iwNMtmNn4Qt7YKMjdcAj/vkIP51Y0XSbeHTIkeNepfGPXH+ArR1nwTBfeFry6aMqFjZsr1wKnC1\nHOTil1f5hSpRUXf+aX5lLw/8eLO81G1t7/RI7be4V7lJjhQe4Xaf515TO+l3mtXU/m3UcTzMU2Qq\n/wAueOrrWne6HZ6be2z2UeyKe1L7nJJBCEMfzBqz4L8Bah4i1GJYng+zAgzSxzo5jX3UNkdO+K9W\n81FwfX9DF1Eovs7Hp3h/wXZ674n1y+uWuTBHqEw5RUDP5hJA5OR78GvWAqqAoAAAwKitoEt4dsSo\nisS5AHVmOSfzNOPmZ/1if98H/GuPDO+Hpr+7H/0mJpGHI23u/wDMzqa4JRvXBqQMR0C/98ik3ZHK\nqfqorYgjjBWCMH720ZoxTixJHC4A/uimjBc5RcBSR8o65A/rQPdnP3vy3ko9ScfnVYnirmqAC8Jw\nADjgfSqJrNDQjNUbNzSmmNTGITUZbmnN3qI1IATSbs0hphz1pMDFnLDw5aMq79ogJjAyXGRwPxxV\nDw7FALu78tNoIVmXsGxyPzzV2cSP4bt4ozhnSFc/iuP1xTyyWylE27lAVmC43Ed68+ab+5Ht1ZLX\n/HU/9KItUsbKRX+0YC9yDg1naVcaPZN5kTRRtnC7myze+Ki1J5Z3yqlz2Qd6xru4lY7J9NlUgdfM\nX+XNQoJ6EqdtkWb3RtP1XUZZoZVMhOflP54966nwvHHZadcROyhRLwP4j8orj7FzKoaGB4XVs5Zc\nZ/HvXRaEA9zcTO2cYUjPQ9a7MKn7Tc4sVy+z0RpXku2+icggGGTAH1SqLlIopLq7Yx28fYdWPYD3\nqe8mVr9CXCqsb5Y9hlaxdXujKtuMlFdisCnqij7zn/aPb0ruW8vX/I5K3w0/8K/ORTl+16xdC5mX\nZAp2xQ9lH+NVtZsYksZ02pvdflCjuewA/wA/pnRa6SKJEjX5AMDFZMs3n3EkpJJ3ELnsP8ik11Zj\n5I8+mOCV755qInNdbPosd/cmVYj1+Yg4B+tQX0MFnafZ40j8x2yxXsB71lymqkVtC8MXurj7TsMd\nmpOZT/FjqBXUiwXTv3aKGUdD6itbwfDLB4YQuPlldnj5/hzj+amkvYQWPHGfyotpcbetijDDDLkQ\nSLFIfvRSY2n6Zrb1mMQ6jE0lmc/Y7X54m2nIgQcDpxWE1tvBU4/HjH41tapc3VrqEMasZIRZWvyb\nyhB+zx9GH9ciuWf8aHpL/wBtK6EKy2/mRukVwfm+bEeCflOOVxn8e2auWjxxI8dvaXscbncRG4TB\n9iCD+tUf7QBaPzJLqEKcnzcSAcEcEY9atx30RGU1KHP/AFzfP8q6Ibv+uhpV/h0/R/8ApTLKWNrJ\ny1hMCOTubHP50sqRxwssUQXjBCD5j9TUa3cUoAaWac9gikD8uP1p00zfZzti8pQPu9T/APWH41sm\nczOC0yHZ4nvsOGLWOo9ARj/RZvUfyrkJkMT7OOD1r0/RZ4v7TunQgk2V6QA7HpbydMAY/P8AmK4P\nVm3AgxqGB+9jBP8AWuKP8efpH/240XwozThxvOM9DW54a0m9vLlru1+zLBA6RTPcXUUKguGwAXYZ\nJCscD0rChYK2Dja3BroLJTH4M1UHqNUsv/Rd1RiJSULR3bS77sqLtK5p3HhS+8yR1n0rezbif7Wt\nR+H+sqZPBmr3UTPHLp7gf3NTtm/9qVzbz7i2Mlzz9BXpmg29nF4dtzPHIzOMhYk3M5/KuOr7eCXv\nL/wH/gnTSgp3OcsfAOt3EqsXsMZxkajA38nNdXJ8L4jaNILqNrgLx/pMYXP/AH1Wna6jpzsLeGWa\nKReAkibf6Vdk1CC02+ZPKc/dVBvLH2AFYc+Jb+L/AMl/4J0qjBRPOYvDt/aXixXE+mCPfht+p22V\nHrjzATT9asZ7CR4nKSRy28kkUkcquHT51zlWYdVbjPUUnjiaFtRt2SCRN6Ev5qFWzng4P1rTvbrT\nLfR9G85V3NpNwoZrlCMlrgD5cZ++cg59uozXZSnVjKKk7p36W6X7s4KsVr5Hko61e0hN2op7VR6s\na2PDcXm6kK66rtFsVJXmj0exj8m0Bxgkc1c0ORjPcuTtVCOT3P8An+lQE+XbcnoKx4dYlgkmihcf\nI+44HOcV5+GTc7ndiXaFj0OKYeTJv4DnGR7jp68/n61V1fR9J1Yo11GDMowjKxDDGPT/ACKxrHVW\nvbZQxX5cZI4/D/6wrWguBkKSRxwOBx/Su9HnsyV8KW+nsJNNvLiNt25objEqEd8AbSPrmtW2QwTh\nn2MFAB2jbzj05x+dKXZT8p4HUKePzqk14wJLggZOSOB+ZoEWL2CVyDFah2K5yxAAJ7muSn0rUbW/\nu76WIQoyoEjRgRxw2ME45wfxNbMXiaC5votPspI7m5IOdrnaoHcnHPHYfpV6WJ5AfNIJK7TgYFYV\n6sYrle5tQpyb5lscVJBLqVzGFuNoGTx0zV6LR7QuXg2LccbWaYNz+CjP04qhfJcaPfNLCgKE52kc\nfhVm38SQuT5dpiY9wnU1laX2djrhOFve3KV/fTaVBdW4m3SM5IZcjBB6iuFnbfcsSep5NdVrUUSN\nOk10ou1AaZCQMZAIA9fwrkXILk9RmuulFJHFUm5S1PQfDiB7PH4VzvimDyrpfqa3/CUvmQgdyMiq\nHjiLY8TeprlpO1Zo6aqvSTOOqRBnp1qOrtgAZeRnANd5xEBjfH3W/Kt3Vr21n8LaDZwxSJc2yzfa\nMDCFmkypx/e2gAk84CDooq3BpcDxFzqNorbd2wpJknGcfcxnt1x796lvLG2iVQtzaXI5GYUZcf8A\nfSj1/T6UriOQCNyNppYTzXY3mj2MFvbSw3SyNJjfFtwVPXHv6dulccPlmI7ZoBNPYt2wH2pD6mvV\nfCdqscHmkcnpXk1s22Zc9jXsvhwY0uNunGa4sZsjswvU8q8XwiHxVqCjAzJu49wD/Wt7w8inTY8H\nnFc/4snW48S3jr/f2n8OP6Vs+HJCLRBntVVr+yiTSt7RkGsfuonA71StoSsEcI+8cD8TV7Wzm6RP\nfNRQnYyuOikGqp/CjGu/esddp2ha7fWcdzaaNEBKPNjneW02YbnlSm48MeCxIz7V6Vb6I0tnYR3H\nlkWi48uARqrMRwcgAjHPAzk4OOMVzugan4ei0LTknuNJSRbWPzBJLAH3BRnI65+vNbSaz4YbDG/0\nckY/5bQcfpXZFpGTVzn/ABnazae489MxTlPLkJ3ZIHPIPc5OMccYrkj94c9RW/491PT7oWEen6ha\nSqpZmgtSjAH+8Sp4POMfWucjYPGDnpWFa1zORnzNlSO9Yl6eta92cSMPfNZMyebLtJ4qI7lRM61O\nLo+4qJVZ32ohLewzW1BbopKoi59cZq5cIILbau1ZCOqDGavn1NuXQ5vaUPzKQfemsuRuHWthka5g\nBcBvc9RVaSy22/mrllPX2q1JMlpozOpqQcR0zBViKd/CBVCHJSnrSgdxTSeaBEn8FRj71SD7tRHh\n6ANXRh/xMHP/AE6XX/oiSsqTGQea1dFOb2b/AK87r/0RJWQCS2fSso/xJfL9TKH8WXov1NTw6c+J\nNKz/AM/kOP8AvsV6X4K1HSE8G2VnLf2cd0N7MjyhTkuxGckdsV5n4fH/ABUulEf8/kOf++xVbR/+\nQpD6ZqZfG35fqxwV69vJfmz3OHxLpenMU+3Rrwf9WjSfkQCB+lc74l8ew3OmfYbOxmy7oTPKQpBV\ngeFGc9BySPpUbWqizDkDgZrn/sLanqsMCfdGXc+g6fzrGnX53ax6E6ChG53NvezXdpHKhMfmMgJz\nyNzAH+dSwyySFYXklaOWcICWODHtDZwT15HPTuAeKW3tGitPITBcFWGTgEgg9cH0q5a211ewPauq\n+YoBXN23GDnj93XQjlJLm1tXkj+0QtLKZWjWUPy0asFyxH3jgjqB/jAUtRL5iwpHPM74mcmbBBzu\nwcDJAYjj+H8rdhdzrEdMu4rjfnEckbIxXgDGWZfT9aivoJreQiSykVFUqC0SNlc9OGPGOK0ArQan\nLFCp82MvIGdy8ZB+Xg7iXz7cA/gK1YrsyMqhtrEdPQ/WshwobzQ7sWwGZo3TAyW5yNp6gcc8mq82\np29mxkkuIkCkZy36fX29qL2Fa43W7uS/EWnuG8uaQbx0OFwcZ+uP1rTinCtDhS0cbBV5wDyMn/P9\na47UfElk14JLebzQJHwFUggHHHOPSn2nim5uYpkht47d4sYYfOec88/TpUVKiSuXGnKTsjlPiZFK\nvji9lkjCJMEeMDptChePxUj8Kg0RP+KbufmwTeQH/wAdlrH1rUr7VNTlmv7mSeUMVBc52jJ4A7D2\nHFbmkAHw1cjGQLiDj/gMtN/Z9V+Zy4v4V6r8y4WVcFiCenyrzVbxK4GmWHyjm3P1/wBbJVuCRvIw\npRgOfmJBH41U18iW103Jzm3PHb/XSVvW+OHz/Ixl/Eh6v8jDRXfyYckhQMj3rqtPto/Jj3Ejc4A+\ng7/zFc9aqRKT681vyyPaWsBdMFRuB56HJH860WiubvV2NO6v4Vtp4JZGEkdwzRgDIP7uFRn0Hyt0\n9PfNZi3cjAsucYyeetZE9xJdSMqnDs+4mhLks0ixKxIGAccH8K4ZybZ1RikrGwlwd4cHZKOQVPI9\n6i1LUbrUFuIriTzXeMIHc88EHr/wHFU2SVoY2hYmbHzLj/PvUMcyscsv3eCPT8aUWxuK3MhwVJVh\ngjjFXNEAOsW4YZBJH6GoL3MkrTdicfjRpz+XqVs2ekq/zreJjPZnrEsitboJG+UY69TXF6goOn3O\nyTBVgxGCOc44/M11kn7wxknhcE1zdxH5sl1b7cq4ZQpH8RBAP5812/ZOeJnaVGsQeQHc2zGQasyo\nfmJx1NVNNTyoRgHvwKuzBivJHA7VVPYUtzjrsbbyb/fP86jToTVjVBt1GUepB/QVCqMQAAT9K4p/\nEzqWwop1KYZIxl42Ue4xTagAjBMqAHBJxTbpStw+7rnmp7Vgt3CxjWQCQHYwJDc9Djn8qseIYooN\nauI7csbXIe2DAgiJxvTgk4+Vh3P1oBFC0AN1Hnpmu2SNZLIj1FcRbMEuEJ6Zrt9OfzLbHcCubE9G\ndWHerOR1FDFcBT2qGORDwQAa1tdt9kiyY6HBrI+UnkVtTd4oxqxtJo7PQNJW+0qEwxb7mSeTI3bf\nkCrjknHXdWufDst/qSJHaReY7gN5YV0zn0XIH0HHpR4SvLW38Pwxm7ignZGAMivwC7ZwVViDgL27\n1fWys2eM219b+azqAz3Ei4JYDPMI4Gcn2B69KtXMehQ8T+FbPSLyeC2ufNEKDYZFG7oODx+VLFpG\np6jqOotY22nLbW88kcaNZw/NhiMA7O3vXRXcCeJHeC7mtft0gKQTW15CyyZOANjMrjj3z7VUtNaf\nSlvBGkRZru4bdK+1f9a4/GuLEpuS0vp+qHTpU6uIjGaurP8ANGxoOhX8Ec8dxawAjPlyJCiKc+yg\nfjWva+H1Dyfu4yWyMiFQME55wOf/AK1c/oXirU9almt4fLVo1LFkJC/rWfceIPEI1AxCW7Pl9Vgj\nXJ+mRmuZYeMm1Jf1956E8HhZU2uRNfP/ADPQtQ8FWkuhvqNrDby3UKs5SaBWyR6HgjvXAG6X+xZb\nr+z7BnizgfZ8LjOMYzx3r0fwJ4gvbpn03ULefGNyySL7dDxzXF69oMyajq+nWciQWgdt0smQiAnI\n3YBPsOP05rvjgsPKF+X8X/8AJHmPLcNdWh17y/8AkjjLHWk1C/mt30fSgvkStuS32tkISOc8dKNO\nmga9CDTrVPm3Ehpew93rO0S3e31q7ilUrJHFOjqeoIjcEfnV7TCBdyMOwwK5oUKcarjFaadX5+ZC\nwlKEpxStt1l5/wB47G2kQx/6mMewLf41ZEi/881z+P8AjVC2IEfJqwsnYCun2UP6b/zH7CHn97/z\nLKum77ijH1/xqQuuOVX9f8arLjOTmpQcg8GrjRh/Tf8AmJ0Kfn97/wAwEiAn5Ez+P+NQXTKAD5MR\n/wC+v8akI754FNl+ZRz9OKv2EGv+C/8AMn2EL9fvf+Y67lH9m2aGJG3WrMFJOB+9kGBzntn8fpXP\n2l6yX8KwWsKNIdu5Wkzg8H+L0JrY1FvKGkr2a1df/I0prH0WPdqjOeRCD+v+TXDThGNG/m+r/ml5\nm8cNCUra/fL/ADOuSUArGo4zgYruNRljh8A3G+URLLCYiSM8sdvT8a4CLBmQcZJrT+JGoPb+GtGs\nEwDcTGVseiAf1b9K68tglodVSMaWkNErv+r3Oc0fT/7ftrTStO2vKrzQyT3VpGSq7Rjg7tuMk8Nz\nn6V7L4f8P2HhnR4dN0+MLEgyzH7zt3Zj3JrivhfYmK61u7ljK5umWM/qf6V6OWFenWk27HBQSd5P\nvoCfcX6CmHqaEbCqMN0HY00tgkEN/wB8muLB/wC70/8ADH/0lG1V3ZRJppOaDH/02k/75WmiMD/l\nrIfwFb2MxT78UgIG4c5K/wBRUYy7squQF4JxzSiHacmVycY6CkBlarC7Soy4xjnnnvVP7DM390fU\n1uyWyyACRg4HOGjBqleaVZXFvIssShGHzGKJVfHsfWp5XfQd7I4jUfFOm6bey2jmeeaJtrCCInBH\nUc4rT07VdK1CxjuWvFtd2f3Vz8jrg45FYvhnwpZa02oPJNeCKKcpGXIBIxkgnBBPTpXRL8PtFjxv\neaQ9wTQ9dhp6ajZL3Q1HOs22fbmua1TxRBZzotpCt6hXLMswjwckY5B9Afxrrf8AhCdAGf8AQYyP\neR/8a5jxBceAvDbGOeFZ7peDb20sjMp/2vmAHToTn2pOLE2SaZ4n0a6tg18txaTlivkjEuRxg5Hr\nnp7V0Ags2jDqLgKezqFJ/DqPxrjPClzHqdw+ox2UdrbyyYt4c7ygAwTu785rtdRKQwp5bFvXI6Vy\nVKrjdI7KVBStzGBfaRZw20Yia4A86FFUzuQAXUdM1R1fQLizYyRmV1IyFMjbv1PNaV/dpFYCUuFZ\nZomDE424kU5/SoBq9o/3ruA/WQVwqSvqe5yYmdKLg29X59vJnGJdwLMVm8xSOmXYfhRI+nzMGLDI\n9WJ/nVrxXbWtzF9qs54Wl6OiuCXHr9a4gWV00mBCQPU8CtVCm9bnNz42Ltyy+5//ACJ0N5qVvAuV\ncgAd3OBWDD4jnivJHj2rHn3DN7Eg1X1a2uY7dYIoZZXfliiFgB6Z9ayEtNQUY+x3GPQxN/hXRT9m\nldNf18zmm8bJ6qX3P/5E65/FCTRPM8bjYQpxKX65Pc/7NZ914hEm2XcSQeNwBIH41iyW08WmzebD\nJGWmjxvUrnh/Ws5i2eRzWkLO9u/+RGIqV4cik2nyry6y7o6tvFSREKY2cDB68foapr4oZTzbow+m\nK56ir5Y9jn+sVv5n/XyNm58S3s3yR7Y4v7m0MP1p096kFxJE9yWaNihxYxY4OPWsSrOpf8hS7/67\nP/6EazlBOVjpp4mpGk5PV3S19G/I9b0yCWPQ7NljEsckKuPLmZCARnJQcDr2/wDr1SuZFBbIIA65\nkJx+dW0nhtfCGmzSRqJHtY+cckBQOtczc35KHzByxyB2A96yqrlsolRxtRttpfj/AJlqa8RU4LJg\nZJPb61Y1ieYXCBZZI/8ARbZ8gmTgwoRy3QkHOOg6DgCsLcZAGPC5yBjJY+prcu9S0/UjAtxo2pGU\nQRQs0N+kav5capkKYSQDtz1rimmpxdm9/wBPNG31ypbZfj/mZsevmG5Xe8kyL0VowR/6F/StuLUo\n5EDQWmMnkq4x+VUIbbRJvlTRtVJHX/iZx/8AxitCODSdNMSvpmqqJc4I1GNun/bAetbwr8uijL8P\n8znqVZVNZxT+/wDzLqXjFgoVACM5IJI/PA/SnXUr/Y3coSCMFmOCfpS213ov2pPNsNQRVRgVluFJ\nJyNpBEY7BuMen46k8+kS2wL2V4ikYXN2gP4Ax11wrys/ck/u/wDkjkxMI+61ZXV9L9369jyrwzc3\nMfiC8RpWBXT9QyucgEWs3b8KwZL24m/1j7vwFdXf6toGl6veyWel6g129vPbiSTUIzGDLE0ZYqIQ\nTgOT94c1xlRT5pVJTcWr23t0v5vuZ9EKK6W0ff4D1Jv4l1Kyz9PLucVzQro9LwfBOrKejalZD/yF\ndU8RtH/FH8xozbWCW6vT5HTHzE9AM4r2r+zWfTYIoJmhWKMJgDk4ry7wvqFpYzTQXqMY58BWUZKy\nA/L+HP8AKvVn1NViDdCV5Fc2Jb5rdjuoKKhe+5j2+j3BukkuJmbb947QpJzxwOlX9U0m5uLhJLKY\nw7cZG0Nke2TVF7m6uDJPBOImHyoCuR9cetWIrrUooPPnuFaRMYQLgdeee+RWScnLmOm0eWxyvxEg\ne2h09nl3SHeCcY+X5SO9crr/ADpHhn/sGv8A+ldzXT/Ey/juXsEH+sCMxX0Bx/ga5jXv+QP4Y/7B\nj/8ApXc10rek/N/+ks8yr8cjEHSug8Ipu1Bvauf7V13ga1aSW4mx8q4Ga2xDtTY8Mr1EdffSCO2O\negGc15mdXZdQmnUEq7dAccdq6/xbetb6c6KeX+Qfj/8AWrzyssLC0bmmKleVjrtJ8V29lMTKkoQj\nBCgZrY/4TTTmIeSR2/uoqkbfrnFec0da6eVHKemf8J3aNGd0UpVegEgJ/LNcprXia/1h2VnMVv2h\nQ8fj6msVAAKUkdxTSEWtM1GbStTt72E5eJw2D3HcfiMiva7LUbPWtOS7spA6kfMv8SHuCOxrwhhg\niren6le6ZP59lcPDJ0O08MPQjofxrCtQVSz6m1OryadD12+sDeKV2bj2qlpehmC6MjwgEHOSK5zT\nfiVd2oH2ywiuSAAGjcxk+54I/LFJqvxKvr23aGxtUs9ww0u8u/4HAA/LNZKlNaGrqQ3Oe8XOsnia\n9ZGDYcAkeoAB/XNYdSvkkknJPWoq7ErKxyt3Z2fgyXJ25+ZWx+FWPHkWLa3kA/j/AKVi+EJ/L1Qx\n/wB4Z/Kup8a2/maD5gHKMD+uK4pe7iEdq96geajrWnpIBvAp5BBrOAxV7TJo4LsSSjKAcrnGfbNd\npxHd3J00W7+TE0bLgkm5tnGM5OFAUk4zgetZjXeivkbdRB7EJGaq3evaZJIfI0m3hj7K08jt09dw\n/lUelz2t1qNvaAITcSLEGfJ2lmAyACM0pSUU2+hMmoxcnshY9z3IIUmMN8pYYOPeuYk4kBrtF1LQ\n8PsuZ13DHFsvHIPeX2rMTRNMmwW1C9j9nskB/wDRtZe3S3T+5maqr+V/czGt4pJZf3Yzg8+1emad\n4mtrXTRaiFiy4UyFlA6fX61Q0rR7Ge1mNvPciFQqNI1sqquT/v8APHWt6b4fiKzDreyFUO5kEQO4\nfTNctWvTm7ST+5nTTqyirqMv/AWeW6lZXE11NcjEm9ixC8kVteHTi2APUVqpYaZGGjN1db0zz9nX\nIx1H36nhtLIrLLbzyGREDkNAEB+ZV5wx5+b0oq1048rT+4VOvGE7yTV/J9TlPEkrJqCFTgrzU9nP\nHOwVgASOlV9UAn1/Yw3ACnNbbcMjYYdK6I/AkKtrJl9rKENnykJ9xmhYoU4MEP8A3wKW0uvOXy5D\ntkH61JLHtOev0ouzHVDFWJXLCONT/sKB/KrVq4DFSTiqQYA5zxUkTFJxzwaTdxM0ptA1m+iW4s9I\nv7iF/uyQ2zup7HBAweRWePCPidrgAeHtV/GzkA/MitPxKc6ZoR7/AGFv/SiasXw/IV8RW6gcEnn8\nDXPCdaUHNW69H0bXfyN6UVzJF+38JeJV3MdA1TvwbOT/AArQtPBvinUY9k2kXkS9B5sLqf1FdUul\nQy4ljiRHPUhcZ9629Oh8kqFIUDisvbV3ty/c/wDM9BYZdTzY+C/EFnI8L6LqEiFuHitXbH5DpUEv\nhHXoLFo10LVZSc/8uUmR6dq9M8RaJBq2nSKERp8fIwx1HOK828P29w/j3R4AgiaC/gMiOwGAJFJ6\n1cp1405VLx0Tez6Jvv5GE6fK7JHBSLuG4dRTM4re8PeHLvXZ8Kwht1PzSsM/gB3NO8X+GX8M6kka\nu01tMm+GUrjPqD7g/oRXpc8eblvqc3JLl5raGGo4pKYr4HOaUHIqiCYDio3XuKcDx1prS9gM0AaG\niZGoSehsrr/0RJWcq5GcVoaGWfUJQQMfY7r/ANESV1EXw+mm0OK5juNtyUDGNxx06Z7VzupGFR83\nl+pNKnKdWfL2X6nL6B8vijSQDwbyH/0MVDokPnaioHVRuFWNHieDxhpkMqlXS+iVgexDijwwAdYU\nHuh/mKKj96TX8v6sdNf7TZ9l+bPUFjFxpm0/eKdRWZ4TCf2pdh2DSDCFO+0df5/pWnFuSHg9qZpm\nltfTP5dsI5YiXjnUjL89D79fwFcWG+Jnp4r4Ubcku2UbcgJxV4mdDHeQbSOvy8g06zuThYLu0344\nyo56elSC5sbVyYpGCvw8LKePfPQV3o4Ce8gTUbUXdvhZVHzDuDTrLUF1GE2dyQlyg+RmOA3tSWk0\nMchMEysD1Wqes2itG9zayfOqlsITu/DHOaHVjH4nYidSEPjkl6tL82jznxtr92t9cafBL5cEPyFR\nwWbuc/0riRLLt8tmY4JIHbP+R+lbq6LrF5eNI+mXibmJ/eQNgfiRXQWPw9vblg15cxImw98H1x+d\nYyxNJbyX3r/MtVqD2nH71/mcXEHV1B+ZmUlcc81La3s1qS8UmOfm7g13K/Di5dATdWsZA6CQc89P\n5VgXnhLUrKV4/sksyY6wJuH6dan6xRktZL71/mP29JbVI/8AgS/zOO1GJhdPLg7ZGJ/GtzRww0G9\nUlkYXUHsfuS1U1HR9YizO2nXwSMb2drdgFA7k4rZtr6Wbw1NNuZf9Ih5U4wdsuf1FdEZxlFcrvqv\nzOPEVIzScGnqtmn18rkDXi2ykSskhK4wBz+NVdblle20eT5PK+zHITgZ86X8av3ep2wHlljISOQq\nZx+NZ+vXMZsNL8qL5WtidzdR+/l4x+FdFV+9D5/kTJe/D1f5EUDqCzdRitfU7qaC1mjDsGhjCBgc\nHhgP5GsCNtsK+pFdBcvDc3N1C7HH3SSc/wAYorawt/W6O/BtxrqS8/8A0mRgxand5GbiU5P9416n\n4YsUmsEknt1cyKDiUbiPzrgbHTBp+uIpkWWMoeQPukjofeuil8S6jalY4YoQgGNvlHt75rzq0FOy\nij0KWIrwfvzl97/zOxtNBtzcO7QqeT8jKCB+FYHivQPstrNc20rRv1CISB+VS3GsahHogvY0TzC2\n3yy2dp/yKr2mq6hqdqy38kbxbSeIdoU9AM0qVPq0a1MTUt8ctfN/5nCNLc/ZnIurjeATkSH/ABqh\nPM7PYSSOztsBLMcn/WNV2905rTSDfNccuxj8vGNpBHH5ZrMlPGn5/wCef/tR664xipKy/qxySqVZ\nUpqcm1Zbtv7Xm2emhWmEQH3SBmsfWWkttSCqMdCORjOP8a2tMlT7OmBmRkGc9qydfSOSeObIOfly\nenHX+deitUeRExdPRwNgD4GenIqzM6hsFhgdTVa2PzyY5UMRww9fSpL6QLFJkDHShT5Ytlct5WMl\n7T7bqDyxruU4x+Arbs0NsFDhto6FDjFbuleC5mtYZ47tIJ3QNsYg5yPStf8A4Qy7mX57qFJQOfkO\nD9a8WtX55M9Wnhpct0Yi3YkVlwrkjDbhkke9UpvDNlfTRyqptgXAfb0I74HY4rRu/DWrafIZfKWV\nV6tAc8e4p9peAjLcDGHHp6H/AD/jTpK2sTOpFrSSODm0u6t5jg26Mp4DXMYYc9xuyD/KqP2aW4vv\nJaSPzG5LtICOmeWziut8R2tlqMi3wmEGBtkfaSp7AnA49CfpxXMTWdnFvaPU7eRl6BRIC30yn867\nb3VzmasyhLG0E7RkqSjYypyD9CK6vQ7jci+4rkWOTnn8a09Iu/Jm2E8ZyKyrR5omlKVpHSa3aCSB\n/XFcYASduOlddeaxC8Qij5buxHFZ6PGoyu1Sf9nANZ0FKK1LruMnoblnZ2qWsH2xry3Hkpt8u2Eg\nPyjPV175qeSDS1j3W11fyP6PbKg/MO38qyLUxz5DxIeecn/CrV5aX1vbK+m6teW8WcGI3L9T2BHG\nPrXUldXObyLUSJNMsZ1HyUPV5i2F/wC+QT+QrtLWysWN0LyP5luZXV/QFieM5HevLLiTXoIC731z\nJGnJ3Slh1xnB+tdvLrCajqN/Zxlo5LeaSJs9zvbB+nauDFRbkrdv1RrhJRjik5fyv80b+n+ItF0u\n7dSjqpwMt/EoPr+FF34i0y6n863tjcWP8XnRAMhz25PHSuVl02YyxvdxqYVUBfLALEe4YgfzrVTS\n/tUIFtcGKLALqUQk+3CjH5mojZRseq5u90tD1Dwpe2ktsJLQKCCOBjpWjqdhd3MciQyRoJXleYHo\n8bKBg8dfftivP/CkslhshjJx3FL8XtQvrXQNPubO7kg85pIH2Ngsh25H45/Su+h70LHI5/vbs4Cz\nuBe+INTvVGFnN1IPoUc0aOCzMw/iY/4VHoUflRMx/wCfeY/+Qmq1osW1UB64rljZ1ZW8v1OSrJyq\nzfez/wDSjpIjtTAp6tjk9PWo1fA7ijr0zj3rpILSk9QakViOTg9sVmlnU53NipoixyQ2SKqImXOj\nEbvl9KaTx1xUSPlsEjd6GnsSM4xn+VakkWvfLa6TJnAEBz9POkqloqbTcyN1MmPwH/660ry60+e0\nt4bu0uJHhjaMGG5VAQXZuhQ/3vWrViumLYrILK8UOS2Ddqev/bOvHqTlCnyOD3fbu338zsoq8rku\nnIZr6Ncd+tVfHZOo+Kre2iy62UKRkKMgE/Mfx5x+FdV4WtLO7vg0NtcLt53STBh+iCuT1zUrCz+I\nd3E+m3ssrztukF4ip97qF8vP4bjXpYGUlG/s5fcv/kjHFSvGVmem+CmQaQyDG/dl8HvgD+ldGSfW\nuO8FTRyvemGOaJWIZVlkDggkgEYUY6dOa60lvUflXRWqyU2nTl9y/wDkjloNezsmKrHYvPYUm/3p\npYKuPSqzTjcef1owtNxowjLdJL7kkOc7ydhjLkdaaFwOeal2t6L+dIVbPQfnVgVoxmWfPQMP5CpC\nKVYiHLFlGevNLINu3kcnA570hkZFVb5/Kspn7qhP6VcKMPT86yfEUv2bQ7qQ9AuOtMmT0ZS8Foi+\nHIZVHMsk7n/vtgD+QFbR61S8PxeT4a0tFHAtlZhnuyAn9c1T8Wa+nhnw/c6jIoMijZCjEHfIeg68\njufYGpjsXUai35HAfFDx7daZdHRNLnETbAbiaMneCf4Ae3GCSOecZGDXik87O/JqTUbyW7u3nmka\nSWVi7sxySSckmn6VYpqOqJBK5SIAs7KMnAGcD3PQe5rGpPq9jSjTbait2e7+F7eKHw5pjjC5tYzh\nRjJ2jPT35rSvHAjJ3E+mST/OsvSwI0gswGVIIkTDnnAUdcd6j1TVEs4ZGcpx0DtgV5UYQlTTtq/+\nCaYbDU3BSnH+rvzH+ZITxtI+gqzCxyARj8MVxp8VgEMrWjY6qrkH8M1tDWNml/bmTCj3pRowT1R2\n/V6DXwr+vmaepLugb5VwRyGUNn8683h8Uq2v2WnwwQFZLqOKQm3jHBYAjpXVw+JY9QVk8pA3QATg\nt+WK8vsUKePbRScldSQZ/wC2orWVCm6c3bZP8mebj6NONOTjFL3X+T8zDlleaRpJGLOxySe9NHSk\nxThXoFDlOBT8hhg1H2pAelIAZdpptScEYNRkYODTAKtaj/yFLv8A67P/AOhGqtWtRGdVux/03f8A\n9CNT9r5G6/gP/EvyZ3l69xJpWlwyEssNpFhVGP4AR+hFQWVkb+9igI+Z+a1tCsH/ALFEcMaT3CIU\n4BwOAc84PGGq9pNjJb68rjlxH8wxjBrhqT1a7HTCkrRkupZXw9DaqNke+T1Izj6Csa9SZWkSOCMr\nxubzMn8+ldzcwG4hMZdlLjnGM1y8vh2aN5ldpmUqNjsAdpz1yMVnBJ7mkk1sWtGsIjCjxqQc8I2C\nVPq3rUHi2W3GnIqTxGSGRRtWUFhng54HrTx9q0fRZpot8soYKMDt3OKwvEdyJNIiWZcyeepaQptL\nLgkj+XetqcU9TOs7JouabEsrq/mSDcpyUBB7d+fX1rUktAqMAowwwWZsk/XPWuOTxTLFbB7e1iAh\nwhEjEghsknjGPuiqc/ibVr0ssU+F6lIkAYcdQeuPxrupXSd+/wDkceJs1C38q/ORi+JI2j124DDG\nSCMemBWTXY2PhqfV3S+1S4kS3I+XJzJIPbPQe/8A+utwabpVmB9ls4kKn7xG5vrk5qZTSdkYp2Vj\nzu3sLu7G63tpZFBwWVCQPqe1dPa6Lew+DNUjkQI51GzYDdu6R3Ppn1FbdxNIwYs75A4+Y0+Ms/hf\nUhkkfb7Xqf8ApncVy4ibtH/FH8xxZmeFdAluNft5ZSnkxnzXTr06frXZXwH2hsfdJ4FZvgx1ENyx\nxvyB+FWtSnIlYqeV5wfSsKk3Ko0zvppRppowtZ02a8eN4p5EiXIZFJ4PY4HJqz4c0WSwE9/dzu24\nYRHbOF9T6dKha9spm2zzTRODyEOM1e1RVl8Ptb2sksZmG3dJ94+v4Y4/GtI8z91lznCMOZLU898Q\n6odX1eW5BPlj5Ix6KOn+P41Pr3/IH8Mf9gx//Su4rOvbGexl2TIQOzAcN9K0de/5BHhj/sGP/wCl\ndxW9RJVKaXd/+knnp3uzECs7BFBLHgADk16b4bsW0vSFV12u/wAzZHc/5xWL4esIbOGCWRf9IueQ\nSOi9eK6uaZUtyoPOOtYYirzPlWx6GGo8i5nucb43ukkNrEn3uWbn8K5CtXxDMZtYlyeFAA/LNZVd\ndJWgkcVWXNNsKVetJTl6VoZkgNDHKkd6QGkfpmgQ77xpwHNMQ5Oaf/FQAppOlOpp6UANIqJhVu3t\npLqTy4hk4JJPAAHUmlvdPuLPHmpgN0PrSur2K5Xa/Qn8Ovt1u35wCSD+Ven31supaPPbZ5dCF471\n5Np0jRajbspwRIv869b0yUNGPyrixeklJHbhbODizyK6srqzfbcQSRnPG5SAfp61DvYV694kOmw6\nYzahGGRuAP4ie2PevKZYDyyH5c8A9RXTRqOpG9jmrU1Tdrlc4POa1PDm3/hJtK/6/If/AEMVl8qe\nVrT8Ohv+Em0k7ePtsPOP9sUV/wCFL0f5M5MR/Bn6P8mZ4kMbAquCDmtWyluL26CxoXcjhR3NZKo7\nHGDXbeC9KRp47ov80chRgB2Knn/PrRVtGPMzqpR5pWR3+gLpWkQNa3dygknC7kdSFbAx3GM/jXTW\niaXZYcTIkZGAXk4UegyeB9K4ufwuzzeab2SSL0Y9Of8AIrd1jQm1DRraCBdk0SDp1b1/l+teekm9\nz0bNLYyfHfh4XqjWNIlhkZP9akbA7h68d64zT7jZa6hDOCkgtwdw7fvYxj8yK73R9J1eyiHmtALf\nBDrjB/DrXA6tbSxX+vlI2MSwDaQO7TxNj/PpW01eFvT80edjFaF13X/pUTEglV9budy7sqACavP5\nQHzKVrm7qO4tLkMGZGIHKnFWodXuI1/fYkHbjB/MV0uF1dGU029TQmjyA8fBXkVagujLFyuWHBFU\nYNXsX4mjkiJ/iAyP0/wqcC1fEtvfWwB/vuFP5HmoafVEWJWKt0Xn0phO3gjmqlxqNtCo2yCZ/SMH\nH4k/0zWc+qys3ACr6U1BsFFna+IGLaNoBP8Az4tn/wACJqxNEBOtwbWKt82G9DtI/rUmu6oRoXhx\nQpy+nM3PT/j6uB/Ss/StR8i7iuVUEocMp9DWNCL9j/4F/wClSNY6TTfkdqkWvWtwXW5kaM9QVO0f\nnW1rmkalcJF9luZghUFwGxnP5VRfUbmYiNkVExx6sfatv+3ft0RVYns9qY82fgA/Q8H86yTd72PY\nUY8trk3h7SLmzRJ5558quNjlTn8jVnR9GjTxh/bLvCqS3MabXAz1UAg9iXwB69KzdL1C+uCytMjR\nA8OnRh6j8qwNQ167k+Jnh/SIriRbOO/tDJEDhXcyq2T68EflU1lKVGov7svyZlUlGFmYnhDV4xcr\nADsHYeldr4l0+11vw3cQTTxxyRL5sMrHAVgPX0PSvEoZ5beVZInKuOhFaL6re3ceye5kdR/CW4/K\nuyVD3+ZOxyLELktJXKEkZjbB5x3FR8ZqyRkcg1E2PSuk5Rm7t1pyrk8A0gbFSJgnrj6mgDb8MCOH\nWRJMC0a29wzKB1HkvkV3cfjPSmt8CYpgdHXFee6KyLfy4bc32O6+n+okrJ3OxyeK5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G2Q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3rE9auyRpGlFasdOhkOSx3epPWofuqVNMkvCeMAVB5xzmk2i7omcY3sP4kOfqKyYX8uVX9D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\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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"orig_scale = 4\n",
"\n",
"def process_chunk(chunk):\n",
" im_input = chunk[np.newaxis, np.newaxis, :, :]\n",
" net.blobs['data'].reshape(*im_input.shape)\n",
" net.blobs['data'].data[...] = imtmp\n",
" return net.forward()['prob'][0][1]\n",
"\n",
"def get_face_prob(chunk):\n",
" return process_chunk(chunk)\n",
"\n",
"def add_outline(img, i, j, scale, confidence):\n",
" color = (255,255,255)\n",
" if confidence > 0.985:\n",
" color = (0,255,0)\n",
" elif confidence >0.96:\n",
" color = (255,255,0)\n",
" else:\n",
" color =(255,0,0)\n",
" for x in range (int(i*scale), int((i+36)*scale)):\n",
" img[x][int(j*scale)]=color\n",
" img[x][int((j+36)*scale-1)]=color\n",
" for y in range(int(j*scale), int((j+36)*scale)):\n",
" img[int(i*scale)][y]=color\n",
" img[int((i+36)*scale-1)][y]=color\n",
" return img\n",
"\n",
"img = PIL.Image.open('test_vis4.jpg')\n",
"w, h = img.size\n",
"img = img.resize((int(w/orig_scale),int(h/orig_scale)), PIL.Image.NEAREST)\n",
"imbase = img\n",
"img = img.convert('L')\n",
"w, h = img.size\n",
"scale = 1\n",
"print \"starting processing\"\n",
"found_pairs = []\n",
"next_scale = 1.3\n",
"next_i = 4\n",
"while w >=36*2 and h >36*2:\n",
" img = img.resize((int(w/next_scale),int(h/next_scale)), PIL.Image.NEAREST)\n",
" w, h = img.size\n",
" print img.size\n",
" im = np.array(img)\n",
" scale*=next_scale\n",
" \n",
" i = 0\n",
" j = 0\n",
" last_result = 0\n",
" while i < int(h-36):\n",
" next_i = 4\n",
" while j < int(w-36):\n",
" imtmp = np.array(im [i:i+36, j:j+36]/256.0)\n",
" face_prob = get_face_prob(imtmp)\n",
" if face_prob > 0.5:\n",
" next_i = 2\n",
" while j < int(w-36) and face_prob > last_result:\n",
" imtmp = np.array(im [i:i+36, j:j+36]/256.0)\n",
" last_result = face_prob\n",
" face_prob = get_face_prob(imtmp)\n",
" j += 1\n",
" if last_result > 0.92:\n",
" next_i = 1\n",
" matched = False\n",
" \"\"\"print last_result\n",
" print \"visage trouvé @ %i , %i\"%(i, j)\n",
" showarray(imtmp*255)\"\"\"\n",
" for pair in found_pairs[:]: # copy the list to remove while working in it\n",
" if (abs(pair[0]*pair[3] - i*scale) + abs(pair[1]*pair[3] - j*scale)) < 20*scale :\n",
" matched = True\n",
" if pair[2] < last_result:\n",
" found_pairs.remove(pair)\n",
" found_pairs.append((i, j, last_result, scale))\n",
" if not matched:\n",
" found_pairs.append((i, j, last_result, scale))\n",
" j+=36\n",
" last_result = 0\n",
" \n",
" j+= 4\n",
" i+=next_i\n",
" j = 0\n",
"print \"adding overlay\"\n",
"for pair in found_pairs:\n",
" imbase = add_outline(np.array(imbase),pair[0],pair[1],pair[3], pair[2])\n",
"showarray(imbase)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Redraw\n",
"\n",
"Allows you to define a new threshold (provided it's more aggressive than the original), without calculating everything. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T19:07:14.604000",
"start_time": "2016-11-19T19:07:14.341000"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"adding overlay\n"
]
},
{
"data": {
"image/jpeg": 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biJchpY1I6gsKAJaKiF1bn/lvH/32KDcwD/ltH/30KYyWio/Oi/56J/30KBNETtEiE+gI\nzQBJRQORR0oEFFFFAwooooAKO1GaKACiiimIKKKSgAzTW5pc0xnC5J6DmplsIP40+lS1CjAlSCOl\nTVcdhIKKKKoYUlLSUCClpKKYBRRRTEA4zRzRRQISilopiYmBSAYFOxQBxQFhMUYpcUUBYTFIRxTq\nMUBYaqKowBTsUuKKB2ExS0UUDsJgbvqOlLgUEfMPpS0AkJgelKBilxRSKSEwPSlwM0tHc0DsGKXA\noxS0ikhMUAUtFA7BRiilpFWEpMU6koFYwIXAt1ZS0h64JDH6HFWQZCAdg+m6n7cD0opSbZzpWG/v\nCOUA991L8/8AdGP96orq5hsVXzn+ZuijGahGp2xAP7zn2H+NRdDLfzdNq/8AfX/1qUbvRf8Avr/6\n1U/7Tt+mH/T/ABp66jbHq+36j/Ci4Fj5s4wv/fX/ANaj5sDhT+NRfa7bqJgf+At/hS/bIP75/wC+\nD/hRcZL8+ei/99f/AFqX58fdX/vr/wCtUa3ELH/WKPqcU8yxdTNF/wB9j/GgAUuccJz/ALX/ANan\nYbH8OfrTBPDn/XR/g4p6srfdIP05oATDn+5+Z/wpQH9U/WnBGPRW/KjawOCCPwoAZ8/A3p7cGkxK\nejx4/wB05pksZcc9RyvOOf8AP9alQkxjIwfTNTcBpSTtKn4qaTZJgbpFz7L/APXpxPNLQA3a3/PQ\nf98f/XpGyCAJDzx93/69OpjY3L60MBNuAf3hye+z/wCvTCv/AE1b/vkf404moycUgEK/9NWz/uj/\nABqMqf8Ans+f90U4kVj6r4k0zSFf7VcoJF/5ZBhvP4E0XAb4oGPB+tHzHb/QpxyoH8Br5Nk5NfRm\no+PNB1jwvqlpFdmK4ltZlSOVNuSVIAz0z+NfPNxbyQ53rwDjIOaE00JJ8zK+ABUJ6mns2aZTLHKc\nEH0NfTHmxSxJKpkxIoYHd0yPpXzN2r6WsdLvLbSbK3ufIjuIYI45Ua5jyrhQGH3uxBrKpVp02lN2\nuNpuJdsCu/OX/Mf4Vqps9H/76rPs7OVSCXg/C4Q/1rTW3cH70X/f1f8AGs/rNH+Zf18hcrHbEI4D\nZ93b+hqVUi4DKSTyfnbH86asL/3ov+/q/wCNSiJ8D5k/7+L/AI0fWqP8y/r5ByvsPVY8cbhjsG/x\nqRQncN/30aaImx/CcDs4P9aAa0p1YVL8jvYTViYLHj7p/wC/jf40qxRg5xJ/39f/ABqMNx/gacmR\nn/HNaiJlSLghWI7EyP8AyzTjtPVefXc3+NMDelLu5pgG1P7p/wC+2/xpdif3T/303+NJmjvQApVO\nPk/8eP8AjQNoOfLX9T/Wmk9KM8e9ACkq3WNPypCE7RJ+VIPrRmkAFYyOYo8/7oo2Rkk+Wn/fIoJp\nB3oAdwBjauPYUFUPPlp/3yKKTPNAC4X+4n/fIpcL/dT/AL5FIDmigBBjn5E/BRS54xgflSZ4ooAX\nPHQfkKXdnsPyFMB5pc0AOz7D8hSA47D8hSUZoAUkEchT9QKOCPur/wB8ikooAdkD+Ff++RS7sdh+\nVNzRQIdv9MD6Cl3E/wD6qZnpRmgB2fYflRn2H5U0mkzxSGPLZ9PwFODkDrUBmQZyw46+1ODAjIPU\nUXAmEjAY3fpS+Yx4zUYNLmgCTzG9acJGx1H5VED70oOfegCXcf8AIpRIw71GDS54pgSiRsdaN7ev\n6UwH3paAHbj64rjvFfxJ03wxJ9m2G7uh96NX2hPqcHn2rd8Q6uug+H73VGTd9njLKuerEgKPzIrw\nuLRLjWCdS1Ji09wfMOewPTjtWVWqqauzajRdV2Rr3Pxu12WV1tLCyijJ+UOjOwH13AH8q1tG+M8y\nFE1rTG25+eaA4IHYhT1/MVlWHhq0Vx5oQnA6CtzWPCljeaRJFtUSbDiQDkGub6429jreASWrPTtO\n1K11WxhvbG4Se3lG5JE6H/A+3UVc3H1zXzt8PfE934X1s2twxFjNMI5Y2bC8nbuBPAIOCT6fhj6F\nRgwBHTFdkZqRwSi4uzJM+v8AKl3HFNz6UVQh+fSjcfWm5ozQA7cwOM0u4+tRk4f8KUGi4Em4+tGT\n60zNFFxj8+5pNx3Ac0lNJ/er/umi4XJMn1pc03NANFwuOyfWjcfWkzRTuFw43ZCjPrijPsPypM/M\nPpS0XC40xRscmNCfUqKTyYv+eUf/AHwKfRTuFxnkw/8APKP/AL4FHkQ5z5Mf/fAp1L3oC4gRAciN\nAfUKKTyoh/yyj/74FOooAb5Uf/PKP/vkUGKInmND/wABFOzRRcLjQiA4EaD3CinAAHoKTvS0CuNM\ncZ6xofqopPLjx/q0/wC+RTzSGi4Ddif3F/Ko2jRiQ0aEdCCOKl71Gep+tS9gQir+8j46KetT9Ow/\nKoV+8n0NTZrRbEi59hSUUVQxNoz3/M0tFFABx6frRRRTAPwpCM9h+NLQKYgxRilopgJjmkx606ig\nBMUYpaWgLCUUtJQAUUvaigdhuPc0uPc0tFArBRilopFWExzmlxS0UDsJS/jS0UhpCYPtS4NA70tB\nSQYopaKRVhOgpaQ9DSjoKACiiigAooooAxnb3rPlgvJdSglil226EGRQxy3XtnH6H/F0g1JoCUFm\nJcf3mKj9PTn8Md81ahyuQSM98dKtnMjG8UWs0iRTwo7soPyr3rmVuL9Rh7S+H+4B/wDEmvRZOYUJ\n7E1BtGegrPlTDZnAnUpkPzQap/3wP/jVJ/bhXGYdQH+9CT/7IK79hFGu5+nsKVY43UEx4yMkMOR7\nGjlQa2ucD/bpJ/5ar/vW5/xFL/b0a8teBfY23/2yu7a3iP8AyzX8qYbOA9Yk/KjkQrnDf8JNaIfm\nv4/xiA/9qGnp4ntf4buH8Wx/LNdmdOtTnNvH/wB8iom0uwOQbePI6jZmpcENHMr4jB+5dWv4zSD+\nSGn/ANvMTzc2pHtLKf8A2nW+dD0yQZ+yQkf7oqCTwto8g+ewtz9UFHIBjjXo+Myxn6OT/MCpF15T\ngqqn33xD+bCrreDtDP8AzD4AfZAKYfB2jAcWqr9OKOQZCNfnUZVFA9ftFuP/AGesDxPrGuzfZW02\naZCmTIsV8iZHHHD4/Qnj6g2tT0zw7p9/BaTR3CNOkknmq+2NQnDZOck+oUEgAk4HNZuh6LZaxNc2\nl3bXVneW4DSRi5kOAxOOeAemQQSGBzx0EuNgWrOu07xDJPfxRNK75OCGJwePyrqhllDYPIz0rjtI\n8K2elXYnRpZHHA8xy2Ppk11sTZiX24qbWZWlhxLAfcb8qYXbcMo4A9QKcTTGNMQhLE/6t/wFRkv/\nAM8nH4U4nJ61GTxikBwPxG8UT6H9mgtpvKlkVtwYdVPA/HP9K8wutO1bVmEskVxnOOQckn61r+JV\n/tD4rC3eVpUilQKGbcBgAkfnmu4u7+0sYgZEkcDGREvtXLXnJStE68LSg4c8+p5Vf+F7vS4fNG5y\nRlhXO3BLkxzDGeORgivcH1bTprUzvHiI8ZYd6w9Xi0rVtIuDCY2ZUOAVwVOOozWdOrK/vI1q0Y2v\nE8SlRo2KsMGo6s3uPtBI/H69KrV3I4Q7V9HyTtPc3zsQSt9dKOR0WZ1/pXzhXusV1v1DWk7Q6teI\nf+/zN/WuebtiF/hf5of2GdTYA8cqP+BCpdU1zTtDsmuL67iTA4jDZdz6Kvf/ADmucvNY/sjRbq84\nLRr8uf7x4H86810zw1qGvO11e3kgZzlS/wAxP5mtJVIwV5BTpzqPlgdzD8XbVrza9hILfzDhgw3F\nM8HHrjHGfx9PQtK1qw1y1+06ZeQXMakq3LKyn3UrkfjXiN58Ob2B8R3cL4GV6ihpdX8I2FjNFKCy\n3cryIMmOQbYwAw79D/TFZOunZQ3Yq9OpSS03dvz7eh9BQGXzDxDjY3SRs/dP+zSKbgjlYQfaVv8A\n4msHwzqlhr2hQ6p9lhiR45GdG2nYVBz/ACzWLfePPDllciLCSc4LrGNv4eprOEqntZ6LaPfz8jF+\n22svvf8Akd6DLkcRf99t/wDE09TLu/5YgH/bbP8A6DXGWfinRLyVVhFo2eillBP046+2a6eO3tJY\n1ZIIsMMj92M1up1H0X3v/IX7/svvf+RfYyqwH7k/8DYH/wBBpylyOfLX/gRP9KprZ2wyDBAec8xr\nn+VLZAJblVAAEkgAA4A3mqjOfMoyS1v36WBTmpqM0tb7N9Ld15l7B/vR/mf8KAGzy8f6/wCFR5pc\n1qakgB3jMiHHoDQQuPvfpTM0vUUALj35owP735ClKkUmDSAMDHX9KQj0fB91z/WlKmk2kUAJhv8A\nnoMf7n/16QiTtIv/AH7P/wAVTsHPWjB9qAGhZOMyofpGf/iqCrn/AJaqP+2f/wBlTsEd6MGgBNpA\n4k/Ep/8AXpcHP3//AB3/AOvRz3qFru1VmDXUIYHBBkGRUynGPxOxE6kIfE0vVpfmyfH/AE0H/fH/\nANemkHtIP+/f/wBeoPt1p/z9Qf8AfwUfbbT/AJ+4P+/gqfb0v5l96/zI+sUf5196/wAyfB7yD/vj\n/wCvS4HZj/3zVf7baf8AP3B/38FH220/5+4P+/go9vS/mX3r/MPrFH+dfev8yyB/t/8Ajv8A9ejG\nP4vx2/8A16rfbrTH/H3B/wB/BR9ttMf8fcH/AH8FHt6X8y+9f5h9Yo/zr71/mWP+B4/4D/8AXpSB\n3c/gv/16q/brT/n7g/7+CkN9acf6XB/32KXt6X8y+9f5h9Yo/wA6+9f5lk4B+Vzj/aXP9aYGOT84\n4/2Ov61Ab20/5/IP++6pXus21mEwTMGz/qfmI6df89qXt6f8y+9f5h9Yo/zr71/mXbzUbWwRWuLg\nJuOF/dkk/gD9K8i8c/EkxaxCNAu3Jt2+ZxuCSdQVK5wRg9euRkY4JzvGmt69rGrSm10q9ktVGyEN\naMQFxycEHnOeetY2ieCLi7hM+owzRFv4ChVv16VE8VSgruS+9f5mlOpSm7Kcf/Ao/wCZX1Px5qer\nOqyN5MG3DW8cjlHPXJBJHXB9OOldZ4a8c+Ib+SCKa5hjsbdSX8tAGIXsQvPp0xmuQ1vwjJYxFrZp\nJo+4IyRWJo92LDVLZpTL5SSqZFX0B9M1UZqavE6Z0pU3aSPq3zpW5SaLb2zEf/iqeHm4/ex/QRn/\nAOKrKguo57WCeCTzIZI1dG9QRVhJTj2q02YmkrNx+8U/8A/+vUqtn+P/AMd/+vVBJQSAWAz0zVgM\nQcZqkwsWwf8Ab/8AHf8A69OBboSufYVVV6lD88n8adxE2TjqM/T/AOvS5PqPy/8Ar1xviP4kaF4d\neW3eVru9jBzBBzg+jMeB+pHpXBXPxyvGnH2XTLaKLjiRmdvzG3+VMNz0n4hwvceANXRAzMI0kwo7\nK6sfyAJrzN/ENpZQJDKk0jooV/LTIU45Brf0v4i2vi/S7zQr1Esr29tpIYZGkxEzMpAGeoyT0OfT\nJrkb3RrG2iMjnaijAViWOfTbmuavytK52YRSbfKaMvii2tYYrqGJplfOBuA6Yzn06ir1v8Rre5jW\nO4sJFLEKRC4c8+3HrVCwfTBpZ00uWfcSieWxAY59jxz34q74dm02SULMri5hPEb4wp9cDAP5VypJ\nfZPQlFys+Y5LX7Wf/hJjBFauiSr5qpNtQgH6nGcj1z+NfQ+guz+H9PLyB5Ps0YdgOCwUZ/XNedX+\nm/btSa4iSOV5oPspR16qzfNz9CRXo8drJGoRL64CjgDbH/8AE1vSqu7io3t6fqePipWm1GLdvNdr\nl87sfKwz7rn+tCNuUHI59v8A69Uxbzf9BC4/75j/APiabHbzFB/p9x0/ux//ABNdHtJ/yfijm9pP\n+R/ejQ/H9P8A69BBB6j8v/r1R+zTf8/9z/3zH/8AEUfZpv8An/uf++Y//iKOef8AJ+KD2k/5H96L\n+V465xjNITwcH8x/9eqP2eb/AJ/7j/vmP/4ilW2mz/yELn/vmP8A+Irnq1KrnGMU1e/8r2t39Rqp\nPfkf3ouq4ZQcE55zTtw9GrMtb2KK0H2q6jDK8i5kZVJCsQPTsKuQ3EU6b4ZEkX+8jAj9KqEakoqX\ntHr5RNKdRTipJbk+V7Bh+IpCQSDhsj6UzdRmq9nU/wCfj+6P+RV12JM+mfxpA/1/Co9+KQP70uSp\n/wA/H90f8h3XYm3fX8aN3uah3Ub/AHoozlzSjJ3s10S6X6A0OuZ47e3eaWZIo0BZnccADr3rzHWv\ni88Fy0Oj2KXCK2BNMCA30UH+v4VF421abxD4hTw/bXHl2Vswa5ZW++3ce+OmPXPtVKw8CaMHL3L3\nN0pH3Hl2j/xwA/rTqV+XQ3p0HNXJLX4xaoso+2aXamI8ERl1bP4k16boWv2XiGxF1YXKuucMhTDI\nfRhmuOsvCuhaexe1sVDf9NJGfA9gxrn9bgn8K6nD4h0aQxRhwtxCD8jA9iO4P6detKniLvUc8NZX\ni9T2Qbs8uv8A3z/9elII+6w/4EM/1rO03Uk1LTba9iyEnjEgGemR0q4JK6eY5Lkp3AcMCfpQC/8A\ns0wPShs07jHZf/Zpct7UwuqqWYhQBkk8AV574h+L2jaTM1vp8L6jKv3nRtkY+jYJP4DHvTuCVz0U\ng9jg00lh2X868ftvjgzTYn0ZPLJ4KT8j9Oa9J0DxHYeIrEXVlLkdHQ/eQ+hFLmQnpubGWx/D+Z/w\npCTj+H8z/hSE0hPFMBct6L/30f8ACmk4PP6Ugams3NKT0GhwJ3JjHQ9TUuWHZf8Avr/61Vg3zRir\nGauL0JFzJn7qf99H/Cg7+wT/AL6P+FU9T1ODSrF7q4bCrwB3Y9gK801rxvezwees8tvCzFUjhfbk\nDHfacnnvgcGsq2IhStfqKTaSdr30/rbser5K9h+dKGyOMV4VH4v1KN1zeakF3gAG76k/8BzXa6F4\n4ub2aOC4MMLvIEEkiFgecZOCPc1EcZFu3K/w/wAyXKa+w/w/+SPQh+P5UcVlx3OoSRLIkkBUoGwL\ndi3XHTfn1P4U5ZtSbbmS3UkFmU27ZA5wcb8np9eRW3tv7r/D/MLz/kf4f/JGnxSYxWfG2qOwLS2q\nK3AJgbPc9N/1qa3la5tLO5cAF0V229BlCeB9T9aqNVSfLZr1/wCHY4zvPkaadr9P82W6KhWZt5Ux\nEDICksOR/j1/L3pwkUSEAsecHgkA+ntWtzTlaJKKKWmSJjijJPYilrJ8Q+INP8MaVPqepSlII8BV\nXl5G7Ko7k/8A6+KB27Gt+FHPpXg1x8YvFetXEo0TSFhtQdqssLSuPq3TP4VPpPxU8UaHfIniiyln\nsnIBlNuI3X3BACn6H8xUc8b2uNxklex7lS4qpp2o2mrWMV7ZTLNbyjKuv9fQ+oPIqxJJHDG0srrH\nGgLO7kAKB1JJ6CqEtdh+KMV51q3xQQXLW2hWf2rBx9plyEJ9l4JHvkfSqVp4s8VrN51wBLH18sRL\ngflz+tZutBPlb1NlRm1ex6lQP/rVh6P4mttTYQP+6ucDKNxk/j/Kt3rVme4UvamuVWNmYhVAySeg\nFeBfEP4sXV5ey2Gh3EkNhEdvmxMVeU9zkchfQfifZ9Lsqx78GB6VWbUrFZWi+1RGRWCMobJVvQ46\nH618op8QfFH2GS1i1W6SGTIZhISx/E8j8Kwvt9yX+d2fPXcahzj0IvKx9qJIjMwVgSpwQD0NOyK+\navAfiG/0u6iezuZGxl3tmY7HXuMdM8fpX0TpWpwatp0V5bsCjggjOSrDgqfcHiqtdXCFW75WXc0U\nUUjYO1HaiigAopB1NLQAZHrSbh6j86WigNTlF0O1juRds00s64w8km49a0Bwc01o3f70YP1IoWJk\nP3AoPoKs5iRjmAfWowaeQRBg9Qf61GKhdQY4gMOQDUMK4cEFmVgc88Z3Z6e+T+VLifcSHj2e6HP5\n5pzZZQAxU+q4/rmi1yk2kQYnWKNQzMY8NIxP38A5A/EA/j+FSSGUjbG2MgYcjOD6Y9Mf5FPYbkK7\njnGNw4P6U0INuGAP15/nSsVzikybi20nooG7HHc9P8496JVDR9sr8wycYI9/88ZpeBwBgUZNMi/U\ngleRUkeJCchdo2nOc88fTFEUk5dQykKFXLMMEnBzx9cfrUpxTHA2khRnr0pWKUltYHYmdFBdQMk4\nHDcd+K5jXnuUutl7q19pttKdlvc2uzyQx4AkBQspyeu7accFScV0xIyOf1prnPc0EvXc4DRNP12b\nT30LWbePUNPt5HginBEbK0fCktknbg8FRuDKQeM10uheGLHQI18ndLMqtGJWJGELZwFztXoM7QMk\nZxWwW6YppelYdxxNXIXOzHocc1ms/NWrVgVIIBxg1Etw6Fsk+lNJxS7sDgAfQUwscH+eKQDQwMgG\nc81G7Ad6cXPTj8qgbb/dX8qQHk+paJbWnxHmulwQzNIVfJxlVOef9pjV2+03S5y0koLsMkjGSPp6\nfhWz4ritLe6N44BkdQG9QB2+lcCktrJMXMjpk58oMRn8ua4at+dtndSnFQUYo3bn+y/7Ia18xVLM\nCBnlf9n269Kyxpa2lu7xXjtCUYbScjBHv/TFS3t3bGwJRhGTxvYkkf1rm/7fkW5aAbpIuxA+Zvep\nhzNaDnUi27o4S+ga3umjc8jmq1dZPpMeqajPJI8scj8qrJt57Vk3nh+5tD99HHtmu2NSL0OSUbGT\nXrP24Ra/4og3YI1m6I59XP8AhXk7KUYqwII7GvSGnKeNvFqg4/4mkx+v7x6yn/HX+F/+lIX2WTeK\n75X8NzQxyq7vOowGyR71e03WbDTIIUnlwwAGACcfjVS9UXGkMpIwsqsRnrjNSSabo13ALmXMbd+e\nCayr8raUjrwqkk3Hqbs+tWKJ5r3ChD0JNZmuywaj4ceWB1kQM2Mf8BqK8tNLurO1gEkaqqY3I3PX\nvWZ4gkj07w7bW9nN5iyTSbmD9SAnHf2rBRTcbb3/AMxY5tct9uZflIXwpqd1bJLpKSn7ObW9nKgd\nW+yyf4dKm0vwgb0Ld6hGSpHyKxI4q78P9CLXyandyJsFrcHYPeJ1OfwJrp7nxBpy7bRGbzM4AKEA\n/TIrOdSTrTUOy/U3o04pXmZF94J0OytIL2G2v2uXiMhSC8WJTh2XA3Rt/dq74Q8f2C3sekz21/Fu\nbYpuLxJMNn/rmppda1WRF0pFLpG9qfuKM586Qck8DpXCeJ7X7BqEOpWzqRLhsr2Yf5H5GjCwlUje\ncnfXq+7Ma3JfQ+ixIi8+RMPz/wDiaoPrOk6dH/pt1HalnfAnnVSfmPqB3rzDxn8Sry3SOw0iQQyN\nErTTj7wLKDtXPTr16/lXmviG5mm1MSyyM7vbW7MzHJJMKEnPc12OhHnj70tn9p+R5spP2sVb+b9D\n6es9e0fUJDHZ30Nw3XbDMHP5AVd8+HGdr4/z7V8ercMrAoxDKcgg4Neq+BPi1PZxtYeILieaFRmK\n4JLuP9k+o9619gv5pf8AgTNrs9sN3bDqHH5/4UyXUIY4HkRGYrj5Wbbx65I7Dn8K8/m+M/h5JsCG\n/kXHLBF4/NvrVmw+LPhTU5Hheea0fb8j3cOVJ/4CT/T60/q8e8v/AAJi5n/SO1nnsGgaRpuQT+9D\nAmPPBI4+Xjv+J9arBtL2F0v7shBhm+2yMp+uSQOfx/A4K2ibreNrZbHySuVaJcqw7EY4x+NPe1aU\nATLaSY5XMHQ/nV+xivty/wDAn/kT8vwKl1e/Y9Re3aUrbMpA+0XWzDKcHaRlvTIzjkfStO3u7ZoU\nMEjSx4+VgS+fxPX86r31tNNdMzQ2MyKx2iWIkjn1yfbt2pkcmpqzeZDZsv8ACElZSPrlTn9KqhJ1\nKEG3rZX+5Cas2F0wmkkN3bXEtupGxUyVxjklR1Ofr2xR5Uz3Fzsg2BmwtwfvqNoPAxyM56kfTHFJ\nJ9rkY+Zbo69o/tJC4xggjZyPrmnxNdq0jGCMGRtzfviewH932ra7S3X3ish1tcyG9aBoLpMK7B3I\nKuA2OOSR1HUDr37OumuXjX7KWEqszoSMq2MgK3PQ5/TPaoZoGuoGgnt4HiY5IMhPOc5zt4NPjSSI\nR/uYgI02KRISQOPUewoutxlNNTkvLHU2SOeCeFnVYyykhhGD2JHU+tXrGVIrdhlzmVz8xz/EfWoX\nE22Qi3QO8iuSs2CSMf7PQhQD9TU9ixNtllCsZHyAc4O88ZrGq/3kbef6GTX72Po/0LQnjbo4/E4p\nwdP764+tQuYkQvJtCqMktjgVw+s+M4QZEsfLWPkCRlyW78e3BpSmoq7OhRb2O7YHzo3VkIAIOfcj\n/ColQxxwxrOCSx3HPPQmvMYPHtzbF1uUSVFfgj5SF+nc5rtPDviOz16HaIgkyjkEcH6Uo1osiVB7\nnQuCIzzyf7p5xTVt5FVFVkUAHcAe59PpTTDEedgH0pnkR9sj8a0uJwuMjs7tM/vl4BwepJ9/XAx/\nkUjQ3btuEyRjLA55PXjHb/PelaJPVv8Avqs3Vr+x0uxkuruYRQxjlmJo5mR7CKVru3qy1KJY0jVb\nhEVQARjJbHvXmnxQvriz0fh932mTn5QVGBjuOD05zVPVvippiqBp9nLM+fmMp2rj2xkn9K4/xJ43\nHiC1W2Wz8hAcn95uJ/QUtS1TV7nKwB7q5RP4nYDmvX9Ku4rZrLT0Bdlwu4uqD3+8R+XU9s8VyHhq\nHTJru2uEfEtvw0Lnlsg/MPx9K7x5bSS8tv3ZaQyKRx05615mMmnFpro/yZ3ShbDVGn9mX/pLIo9T\ns+A8hR84KsOQT04rC8RnRbO207UTpNjfSXdxMkkszzDARYiAAkijPzt1B7VemfT76JIvtA3tnKkk\nMAcZ9DzxVDxhpNzN4a0W2s1Eix3d027OAoKwnr25JrJqHNFNtXfppZ9rHXiHJxPSNAukuNEsWW2s\nlRFxHseVlAG4AAls8DA5/wAK2V852UiGxbac8NIO31+ted+DbpdI0mO21LUEB8wlYz92Mex967mF\n4rhRIrhgejIa7I0adv8Agy/+SPJlB31f9fcX2ify3b7FEX6qN7898fe9zUsUl1ltlpbqoOFGWHpn\nv9arRvcxD5Jdw/utVhbiZlyMKe4KmqVCn5/fL/5ITh5/19xoIzYGYUz35P8AjXPeNfFQ8LaL50cM\nT3czbIUcttz3JwQenv3FXp7xokO+Xn0FeefEIC+0YTYLywSBlJPQHjH8qPq9Pz++X/yRom27GNYa\ndba7HdTXug2UckttPKJo5bkuHETMp+aUgnIHUVxtx4bk81hGCwB9K9Atb2+h1GWCGN1jjsrr5Qq/\neFvIRz35x0rnr3Ur/wAmAwAoz5y2wduD61xUXONWST006t9+7Z3OELbbGDb6Tc/Z5JVicrGcFh2x\nXSokur2cUyygzDBO85BboSfX1qbSb+9urS4huI3JaJip2jJOPbAqtoVjcnQtSvY2Ia1MbCLg7lOd\n39PyPfp1O8tOpMWoO62Lts9xA0aMzxXhwC0caug/AoD/AOPVsR6M1ncrf3V0Z5ZOGPlhAp9gP61z\nJ8VyJtH2bLjuMYq+NcvdVRAxVFB4QdahQl1N5VoW03PQNJVLi/t2G9hG4OFz1HzdBz/D/Ou3SbIy\nEl/79t/hXB+Gc24gmfqX5P8AwE/41N8RPEd5omiQrYuqXN27RK5J3KMDleOoyOc8cVph2uaXqvyP\nKmm60/l/6SWPEnxN0bw9M1rGr318uQ0MRwqEHkO3Y9eAD05xXO6d8ZkFxt1HSykBBIa3kLOo7cEA\nHt3H9K46x8FyTRkyOS7/ADMxH9avQfDy4uJWjS8YL2z1zVvEwvZHSsLNq56z4e8b6N4mkeKwllW4\nTkwTRkPj1GMgj6H610O/HZv++TXid34F1XwsYdb0+6Elxasrkx/KeoB46EEEg+teu6VfyX2lWl24\naN5oUkK56EgHFaxmpbGE6coOzL+7PADH/gJrz7x14qv4rx9F0pmhkRFa4nX7y5GQo/Aiu6kkYrgH\nJ7Z55ryK/YyauWY8y21uzOx5P7iOubEScakGu0v0NsNTVSVmZTwG5u2aYySSeXFliTknYvP86tpb\n6zo0Q1HTLmeJkwzR54I+ncVtQW0azI5VclV5z7AVrOQYVjIXyzwTXLRqS5U0duGoQeHin2/zOg8I\neKE8T6MLkRlLiIiO4jUdGx1Hsa6DEh/5ZtXkfgVm03xtcwQswguUYbfccj+R/Ou78Q68dKtoUQg3\nVy/lw7sHbwSzH1AA/MivRjNNXPNlC0+VGhquuWGixCTUbmO3yCyq7je4HXauct+ArIg8f+Gp5hEN\nTEbH+KaJ4l/FnUAfiaxIPIun3yRo8hO5pH5Zj6k9+35AVsxWlitoZTDH5nT7oORXM8Um7JHYsC7X\nbOjjnEsIlj3NGyhldVJDA4wRjr+FQy3gQcx3A/7YP/hXEwyR+EdRu9QhlePR5YA81qD+7ik86JPM\nUY4+VySB1x9MdPqV4Yk+8c06ElKc2u6/9JRy1Kbg+Vnktu0svjjX5Yzv8uaRkVuOWcnmpJLXWbqb\ndL5kbK2QxlYAD/dHA780sebTxPfTeXt+0PI7OWyXO/P8mrVv9RiGmvunaFn+VGAyd30rOpJ810eh\nRpq1pMTWrG/nSzms5ZDtj+dRKykn1BANZ2uyamvhO4a/U5iKsNzZLAkD8etWNFm+y3IE+pTO0q7Y\no5ARk9e4B6Vc1OQX3lWjhWSSQbgzY4HzdfwpxlZ2KnBPXqdn4NRrHwhpkEwm3+TvIMTZG4lsdPet\n1b2P+7P/AN+JP/iap6a0kGl2kJeQGOFFO4nPCgc1bWaQOw8xse7E13J6HkNWdiaO5WRgoEoJ9YnH\n8xThcBeGSXcOCPKb/Co/Pc8F2x7ml89gPvEAe9VcR5J8VfHMhuZPDtjMUhQA3bAYLN12fQcZ98jt\nXC+HvD9xrDLO42wscID3A6ms26S58UeOtQRfla4upZGz/DliTXokmljTZbayisdWbZDEWmj1FYk5\nRWICCM8AsRz6VxYqvyNQW7/rujsowv02MrUPB6RWUlxBH/EQB7CqfhLxBL4d15JS7CEnbKp7rnr+\nH+NdvqZDaHALe01Fy4O9Yr1Eb/voxH+lcXq9hpun2X9oz6LrKjKq4bU4zjPQ58o9+Kxo15dYv8P/\nAJI0xFJOOx9EJMHQEHqM0jSj1rK8PXaT+GdJlQSBHsoWUSOGYAoDgnAyffA+grRM/wBfz/8ArV2e\n3f8AJL7l/wDJHnpXW4pmX+8M03zkYnDA0ef7fy/wrN1rXotF02S8mXOMKke4AyMegzj8fwpOu19i\nX4f/ACRSjd7keqeKdH0OWGPULxYpGGQgRmJz7AGoB8Q/DBXI1Ik4zj7PKD+q15ZqN1Ya5/at35N2\nb2GFZszXSyoR5sabSBGMAb89e1cdNdRtIR5u9+pO7AHtU0sW5tpK1u/392auil1PUvG3iiz1yW3T\nTrhpbeJNx+VlyxPPDAZwAPzrmTcq1naog37pXVd3QcLz+RrD06+WORUmljMeMbWcH8hWtdXsUWkx\nSxElBI4EgQnsvf8AGscVFycZef6Ml704+f6SJQ++5KFA2ORnsQMCr8ZTEcDf8tIe/Y5OB7+n4Vja\nePPsJb/zXEduf3uUwEBrW07VdNvYhLEsvmwkNllHzduxPc/rTpQZ01I2VzqNU+Jn9j2dpbtFNcXQ\nQF5FdUVh2PQnPrVa3+MsMpjSfR5AufmdL5gceoG0Z/MVxd1oVxrXiF5Gfy7ZQqAdc4AB/XNdPaeD\ndHtIlLQh3x99+TXQ8Vyu25Cw112PQ9H8S6JrNgLu3nuohg/JPK6NkduWwfwJHX0q5p13pw062V71\nFdYkDA3JHO0ds15RfWcukX1trGlsrxWilGRcMpUkn5h3BJI+vvXokXibQ9OsLYSzoJBEAwSMs2R1\nyAM9Qev1pU61SpNSulo+/f1PJarOqrW2lbR9JW7+RszX2nrBI8d7GzqpKr9rIyew61Kl9pqYIv4c\n+91n+ZrmZPiP4eVjE08ySE4VWjIJ/wAPxxXRWmsWF1ZNdxXCeQqlmfdwoHUmuxOo9mvx/wAy2q6d\nrr7n/mTrqVgHydStyPQypiqsvirRoJWje6YleuyCRx+BCkGvD/GvxCn1zUXiidksImxDEDguem4/\n5+nvgaPd3lzrNm/koIzOmduSQNw74rirYytC7ik0vX/M6Z4etTpSnJq6TdrPom+/kfS+m69perjN\nhexTHGSoOG/I814547mPjD4mLo7St/Z2mrlkB4Z+A364H4Vz0curKltmNYHDDEquyOB02gg9/wAa\n3tLspvOlYPm6aP5Ju7A+p9aqpjPdtbVnbDDWep1lrb21nbpBHGqKgwoXAFWkfSblGs9Ta3aGVSpj\nlcAn6ZrgZIL23vFu71bO0jiP+uBALn0LHH61sXKRajq0F9pOq6YSY1V4gyyOhHB4ByATz+NYUVrc\n6Z6K3Xobnh6JvBXi9dG87fp9+gaEk9zwp/3uNpx1AB7VD421uXX9SOi2MwGnwNi4dD/rZB1XPcD0\n9QfQVW+J0U9hpnhzWFbm3kaJyq8lWwRgHI42nj3rPtRDZ21spBiRFyc9T/nNd2JqOFP3d2cVKCVW\n766/5mjaabBbogSL7nf3q/EpEqn+EHkVzNz4ws47lLe0bzcsAWBG0fjnNSP4oNhfGOeCUkDd+69P\nrXm04S5tT05VI8r1O4vtKh1DRZbyJGjeIErsOD055rb8N3dxLp0YncSddjE/MQPX17c+9Zmg61Za\nnorW0YaOdwSYypyeBnjr3HbnPGa07Z7eO4gjhICwr87L0xtPX06D/Ir2qUfdPGrSTaa3Mj4p6xJo\n/gC/khOJbjFupHYN1/8AHQR+NfNNjpGoa2ALf5I84zjr719L/EnTU17wJqVtDcIJYU+0KAw52DcR\n+K5x+FeW+BbWH/hH7d3ZUyWJLeu41njJunQTj3NaUVOyfmcifA2rQ24YGOQA9FJzWevhq9a48tht\nJOMd69qaayt1Z3nQIBktmsNdZ8O3F+o+1LIwbshKj8QK86jWqSeqN6lGnGNzg/DrT22prZRqqTRy\ncu6ggjB4Oe1dx4i1HVNI8OxXI32weRYykMjBJPlPzD1B46+maj8V+Eksru28Q2MjNbSMAWiPQ9iD\n/nmuoMGn634YSF40DN++KFSMnONwHuSc44r14O0G2ec6f7+KWqZzPgnxvqVnKDM8r2448rcSME8s\nF/wr2fR9cttVVljliaROvlMSD+Y/SuD8I+B9Pg1MyPL5kONwhK9D9asar4cbRPGVjdaNcyW4uDuk\nhYlkYA8gDI7VNJ+0WpcqcqfM09j0mikX7opaDUBRRRQAUUUUAYSwK2TukGTniRv8alChBgFj/vMT\nVC/vb21mhjtNNa7V1Ys4kChCBkZz2PPr9DUD6hquxwujjzNhKt9oBQNtyAeAfbIHX0HNWcpqnPlN\nUAqO3ubmaTZLYtAnlhi5kDDdx8vr3PPt70oNR1HYa7xJMocqHPC54z1HHqeT+dBaUMqgg5JyW4x+\nX/16SR2RcqPqcZx+HfnAx70izoVQk7S3ABBBzjPQ+1SaK+9riI9wY/3ke08seSTggnAx3BwKUvK8\nUbKm3OGZScH1x0/DtQ0uYRImSCARgZOPp9KPtEfdx/T/AD/gfSger2QJ5wbDgEZPz5HPpx/npUVx\nD5qS4J3sQAxXkDjI+nFS+ahBwwwrYPB44z/KmxTeZCkhBXcoYjrjIzRoK7WthHmmKuRFxn5cEDI/\nHpn/ADioy1yNxG0KF+UHnJye+c9APzqfOeBz2z61XWYtFuClnYbguMYz2z0/WgL+RIGXewBy3Vh6\nUpNQO7jgsM55wvT8O/b86ZHdQyqvlvncMgkHnr3P0P5UXQuST1sTE00nIqsLliiA484KGdAD3HQe\n/p6/qJi3FK4OLW4E1Pav8xHqKps9TWb/ALxR0zkdKUhI088U3OUz1yT0oAP94fl/9egrkKoY8cAA\nCpAiaomOAaztS8SaHpTyre6zawvEMvGzhnH/AAEHOfbFcncfE7SbzVbfStJE109xKIvPZfKjAPcZ\n+Y/QgUnomwj7zUVuzn/HXiVp794AzC3iO3bjHI9ayNE0i51jM73EcESc7H4J/DNdnD4Ntri6F3qG\nCI2bZEOh5PJPcnqazNcs/skspt4LZmYjYTGOFxjGRyOp6HvXnznzPsejCgqfmULrQrlcq0gkT+E9\ngP6VzOoafc6O7XMccLqeoZM10FvrU2mbU1KQRxSZ2iQ8j/63oT/+uPWb61vNMlkgkDbRk+1EE07d\nCqkY2ujFgv475It0YQseF9PX8P8ACqetSmCZFMgKkemMfjWbb3ZeaJUHzIvygd+1dJbaeNbVdPEL\nvesrNvJVVUKCxLMxAGACefStJctP3nsc3LzbHG3tq91MjQLvdjt+U9a76/02C18ZeIZ3nLSz385x\nj5VzKx9etULTwle2Fs+bjTPNkPT+07YYH/fyuk1DRbTV9avLq21iRJLqeSYRxyWTldzE4H+l9s9c\nVyVsZTVRNS0s1om+qfYIw021Oeu5prVDF5imOTqNuP8A69Ri6kjtF8o4YDiumf4aQXO121idH7hk\ntif0ua0LX4cWUMMiS6u7hhxlIBg/9/jUPMMO1q2/k/8AItU5rbQ8juPMad1xtxyVFWb0SR+HbBfm\n3G6nOB1+5DXo2n+BdHe/lE+pPKVOCnmWo/8AQbkn9K6Kfwvo1rYgeWBbI3DJHFKd7DnOJDjIUc98\ne3JUzSjzRir79n2ZhUoOTg7/AGv0keeeBtXvoL17C4ASJ7O6ZJG4MZEEhz7jitlNMFmTPNdyGN8b\ng0pJduxP+T1rRl03w/aXEt8kt6832aaAJ5Cqp3xNH/fOB83pXHy6m1vCYZMyR27EKDzkdQPyNVCT\nqVJTimk7dLdztiox0nrbY67WrvT7n+x5lkDk2pKFOek0oJyPcGsrxB5N1pB8xt6RAMAD2FZWsTXM\nlh4flt1SImxc4Lbf+XmfoBRYi5ubR4LoqPMXaF9yO5p4eLjBO/V/+lMqcrxfunEXFxvkJ7sa6m58\nM3GoTRz/AHVNpb4z6iFB/SsTTdKku9aitJlK8hiP9nrXoGs38lhOkUU8MCpGgAZC2flFdNebVSMY\n76/oefCmvaxcv73/ALacdaeDbqdiZCIx+tVdV8NXGm5lUFo1GSRXdadqFxcCTzUjYpzlO/51SvNT\nk1G2ngSC324KsBNl/wAqIVajlqdc6VNR0RQ8PeGdP8R6QZXnjtTboWnkbOPvBR0Vj1ZelWX+Hulj\nJTX7QD3Sb/41VPwXqDWGh+IMxCTFosgUtj/l4hXH6/pWvF4igTQVvfsuB5zoVYKxyAp6kf7VclWW\nIVWShJ2uktuqv1Mp1IKEfdu3p27+vY9F+HZS00RtJbVre9ktDwYklUKjZ2j5lHoen07V2O1f+eqf\nr/hXl3w81VNTu9QaaJYTsj2CMBDjLdSuM/jXoYhiYZDTf9/n/wAa6YLENay/9J/yOOcqifwL/wAC\n/wCAaMqqZX/eIPmPY/4VHtX/AJ6p+v8AhVe4hjNxKSZuXPSVh3+tc54p1+x8OacZHMz3UgIhiE7Z\nJ9TzwKzw0cR7GFpdF/L2XkKc6ib9xf8AgX/ANbVPEOi6K6pqOq28DkZCkMx/IA4rL/4WD4TJx/bk\nJ/7YTf8AxFeOtqGsa9PJMwt5GzjfJaRSEn6lTVmfwXLcatesriKFp3KKowFXccAD0xVe0mpcsptf\n+Av9DWlCrOfLKFtL73627I90XUrOW38+G5jki2q29c4wyh1znplWB/GuH1P4q6da3MlvBDLJtJUv\nxtyO/v8A55rzzxpaX2jatDIjyLG1jaxb1OAwW3jUj8xWd4f0SDW52ae4YIn8A6mtqVe9CNST6Gio\ntz5Trbn4rX8dwzQxxiF+glJOefY8cfWup0Px/C18LW9dUEkccq8Y2l1V8E9/vdeOlchL4O0YwMoW\nQP2ctkiuR8ReZpWueUshYLbwKCO+IkXP6VMaqqVFy+f6Gdag6dWF+0v0PbvEWrPdadFEi4Sdj8wU\n4IzwB/j9O1ef3Nox2yrEzFSWbGQF9vxxVG18RyajplvA7Kro21SrYwePmK/56V1kGjWwhJ8yQFsE\nuHINRVk+bU6qNK60OUlspZXYRxlXlxliMA+hP9TWro4vtLvobuAEqjjIJ4I9Dg9OK3ksrTy9kgDr\n33nrVDVtmnWpu7PAMf3lByCDx09qhSb0RrOjyq56ra3QurSG4UbVlRXA9ARmpC+Oa5jwte7/AAxY\nPLdRkmP1HHPT6gEVLrmsPp2lSz2itdXH3Yo4/m+Y9yB2/wD1d67fbQWkpK/qv8zy51acJOMpJP1X\n+ZW8U+N9O8NqY5SZ7wgFbZDg49ScYA/X2rxzxl43l8VTRxtE9raxjKQq27LerHAyadJpXiG8uZ7q\n5sJ5ZpSS0ksZJz7Vknw3rDSZbTLzr/zwb/Cl9ZpdJL71/mL2tHrNfev8zISymncCBGmJ7KuT+VWm\n8Oa0i5OmXh9MQsf5Vs2/h7W7KVLiGyulkU7htibI/SvTdN8QsYYft2nXsU5UF9lpIy5/Ad+vtWU8\nXFfC0/mv8zaFXDP4qiXzj/meHD7TZT/L5kUy8d1YV6D4Y1STWGtWdik9vKgkAUEOM8fT/wCtXod7\np/h/WUdrqyt5HGNztHtcHqMngiuBGmvo3iwLpyr9jlu484PRWKnH0GaivUjUpPvZ/kzSquSjOUXd\nOMv/AEllS3mkjkMjGMxn7xMexs+nvV7UdSLeFrF9xVftt0D+CQf41y2oateSW8X2mPYQ3y5GM11O\nmabb3nhDSpZk80LeXL4boSUg6/lWNeKi4Sl3/SR1OTqSsjnY5Zb9iIbOS5YdMAkD8K6PQr3WNBuR\n5oeNJG/1bfdPtjsa6bTWa3ULFDHGuOi4ArO8ZMo0GeVxtaNldSOxyP8AGtFVcnZFSoKMW2dhJ4hh\nFtbG2CyXNwpZY2PCAcFmx2zx70gkuZmzJdSnIwQrbR+Qrj/C0u+0N4+N8wBXPYf5Oa6u3m3Pwhqa\ntWV7JkUaUbXZaa0aSPh3UgeuR+Rrl9YjkkgmtZFAlXaw7jr97HcdeK7CK6ZQQsZNYGuIkhhuMhXU\nlCR3U9vzwadGpK/K3ox1KcV76WqMa1u83pieMYNndZYr90fZ5P8ACuO1C+nZ90pjjiAGzdEyhiP9\n7/PNdfayRDVZY5vu/ZLpgT3H2eTP6E1wepanpkUpzI1ywONoXKiopQft5q3b9S/arlu3Y6XRtRD9\nYxGoGSw9K3YNQ07QtGYXVwnm3jCKGN22ybGOFJB52jLHJ4wOvSvJ7jxLdOjRW6rBGV2nHLYxjGay\nZZpJ5GklkZ3Y5ZmOST65rthQd7s5ale65Uep6hoEEhMgUgZycVr6Dp1rGo2qX9Sa880Tx1e6Wiw3\ncC30CjChn2uB2G7nj6g1vP8AFby7VksNEjhmJ+/LOXH5AD+dZulVWm6NFVpvV7npeo6pZ+H9JN/c\nAiKH5tifebsAPxYDPvXklrr174h16I3fmNFLctJDCCTHFk5YKOg6DPr1PNZbazqGtx6zdahcvNJ9\niUDPAUefFwB0ArqfBkcd1olpKVIaznlG/OeWHQen3/8Ax0VKj7JTb30/I56Mva15rzX/AKSjvYrq\n3VVia4iR8fcLjP5Vo2c0YmUGXCnvXn2p+GJrqcSJcpsZs/Moz+eM1vz+HGvfCqx28+2WNhuZuhFY\nqMdLM9NKSWqO91CZP7EuQ7B4zE3zLz2qr4K1B9Q8Hafcyff2vETnrsdkz+O3Ncz4Y8PX1hp84ubm\nIiaIxhERV5PAJwcVt+G7M6doUNossscEYBh2BVJDKGYsCG+YuXzzj0rspyWyOKvCVrvodFPcmKJ5\nQMlFLAeuOa8c1+K8uNQWS1njjVrS2O3zDx+4j4GK9QuEWe2lhMsxDoyfMy45GOcLXk3iZbqy1GSF\nmMRjtLWKRR8xBFvHkcfTtWOIv7WHpL9B4RJt3Ls2n302lidL9llxEApbC48tc8+ucmrFna65LaNH\nJKVjP/LVLglx+fSsUyqkypPqk0Nq9rCDEI2KlTGo54IGefSr1pqF0NMMJcMgI2y5+8B0P1rCndQR\n1YVRlQh6f5mroAm0zxPBclprtWlSMsxUYWT5Ax4+Y5Pt0Na/jqdv7bsFdgIooHdWPYk/N+irSeGL\nKPULuK7IGLVQjHbyP4lUE8Zyd3/66qePn36/awMx8prQEZxncXcH9Av+TXUm+R3MJwj7ZKJVsvEt\njFOsZS4yT8rNGVB+hrUj8V2BmFo0F20g6skRIH1Ncha6UsGoWjPMT++VhvlJOAcmtbUNGs214yNf\niNpz5gUMRuHIOCOo9q5lGFzuSny26nUa8Le88KaiysHU2wGMesiHP5imX+pNNptpM5y0kKu2e+QD\nSeVEnh6+t4E3/uQRGZCQx8xMDJ6ZPH41yt1qUMfk2lzeyJPO6iCAlS2GCgD2G7djpWlCS9pNea/9\nJRw4ik7OT6fqZepSvLrsc44EcDsGA6ncowT9DU+n+JbWKQLcEhe+QKjlby9Qm05iSUUHJHfAJ/mK\nw9Q0whjgkCtJqMnqTS5ox03O2PiXR47Z1t/LRzk7QOpq34Hu577xNcTciCK0x9SzjH6Ia84sNJYy\n43Fs+leqeC4YFt5Io714p1OGjjWMnAA/vAn+Ie3PvThCMXoTWqTlHU7kHinmRULu7BVAyWJwAPrV\nNbckf8f95/3xH/8AE1xXxR1Ge302x0u1nlDak5jfIGCi7c9BnksOnoa6DlSu7FHxZ8WGtZHtfD8c\nb4BBvJVyM/7Cn09Tke3c8FH4+8Ux3InGs3Rcc4Zgy/8AfJG39K6K/wDAdpJpe+0kb7Qijvncc88V\nZ8KeAEikku9agVthxHCxyG9zj+VYrFQtodLwsro4rRNeGn60bu+Vjv3ZdexZtxOPrnj3r17VNfsx\naxn7SqRyW0O1m7bolIyO3BB5rhPiPoNlYpb3dnCI97FXVRgA44oTXLLTNRjS8UrvsLE7wM4/0WKu\nSvFVJwnFa2l+hrCToz5W9C/pt/cWdzFLd668tsCT5ez5WyD0JGDj2pfF3iOz1HSZNLQ+Y0gXc6jh\ncEEc/hVLUNZsLxPs+i7xeTg75xn92nViccn0AHJPTmp9N+G2qSKJrqdIS4+43zsv1PrWsbK0paF1\nJOWkDZ0n4hXWn6Ja2bWMQFrCkEbCQ8hV25xjnOO1dL4e8f2erzCzu0NpdngbvuOfY9j7frVO1+Gl\nhNHBJf3MjmNdpjjO1TUWs/Da0ezluNDWRZ4xk2+c7wP7no2Ox4PTgnI6KdXmdjleGdro74PXm3xc\nlmW005EbYr+Ycj1G3/GtfwNrM95azWV/K73NvjEhH30PT8Rjn6is34kWUmpLBDxsQAxBQd7M2d3t\njCjr6daqq1Fak0IuUrLzOe8DaRb6hpGpmVTJ5sAjbd0IEsbY/Suoh8HaZHalVtbf5u23J/OsTTNH\nubLTtT+wXlxCBYooQMW2v5yFiACOo4/E1T0m38R3l1JEuotuVGIaaE7cjoOoOT9TjuORXmU3epNp\n9V+R3wg4pJo7DRfBeiC4Z7rTIZlQHgkj+Vcolpa6VczWLQNc2zXM4VEbGD8mMHsOtM0bxD4ktb5l\naa3lRhtePOSAfrgfmwqlK2y1h+2nDy3UzAhipIO3nP8A9c/Wuyr/AA4xff8AzOKs7VYP+8vykblp\nrGixSSaVNZzqso2MixZH55ycetWLQ6Smj3Ih3wvC4Mkcp/1e0hifpgdawo7GQXUJlR2ixkStcEkj\n65yPpVLXXH9jXMVux3XL/MWckhAc/wDsuMe5p0UlG511JXly7lV9enR1WIyp5wLfusbufc9K6bRR\nc3llNaX0pd3Q+W7sTt+tcbYXSCGFmAGFGSfard1rejmdDdO02wbdkZJUjvnsf1rNxblZIIyilds2\ntTv5/CunRW7SQSSPG0YVU+98xJPXpgj8c1jXmr3b67dw+awRLh0UL1IBIrN8SahbalqGmy24KQS2\niqinjGJHX+YrvwfD+izzXV6nmzSSM5Ea7iCTnn061js1da2f/pR5uGSnVVu0v/S2ctY6DcXN15ok\nZn3fNkZrrJfD+pGLzwysgj2SiPIyvqau2niLRZLZpoVMEaf3lrf0PWdJ1jTL8Wd2ryxQs5j6HA71\n1UHKb5Wd1SEYe8eT2HhSO18RvJqgX7HEm9TMcKc9M813FtdaUUEdn9nKg4XyVGAfwrMknn1K18iC\n7EFxHKV8zGTgemfw7dql0i0ttJu3jluBJNMnLSMCS3YDgc5rmxkbXV+n6MVRt4Wo7fZl/wCks6Kw\nttC8Y6dcaXucOEO2TyioUgcEMRjivHLnxpqdpbfYIbhPMieRTdIcs4J7H+o/Our8X2S+H9E1C6sL\nm4eSQm2JSZlCBjhiy5wQRlfxrySG3u7p/wBxA8gPcLx+dd8IwlD3lsFeU4uy6lm61C4upDJPLJKx\n6tIxYn8TV3Q74xXBHnGPI6hgvv1OMfnU0XhW5kiDzSBCe1N/sXyAyCQtkVcK0b6HLKjOS1OpufHt\n1rvh9dB1BftCwTiWC5VyCABgA5BJHOex/lXQIJtRtrYXIVtyY4zg8dGPrg5/GvMrKcLOmY8MF2Hj\n+L3/AAr1SGGdNKs5Z4LiGMQB/N8sFSWOAMg4BPXBIOBnArPGX9nzl4etGnL39ktPvX+YySxsrO4S\nSeZUCEMdrDCrn0Cj9cn+damoHSr21tr1J4ZWclWxJh064PBBwQPpXP3UljaoYbu2u7sTNklYAwB7\nfxcVJptxaRQNBFpOozowOGFuF2e+S3SuKjU3un9x2TxEdLRlb/Cz0/wXZwDy7yNw7AGPOSSB6Enq\nK1fIDeLJlWLKCHL4TCsGB3KT3OQh+hrl/Bsk9oP9GAZGOdsjbMflmuxtLm/muLphbWpAcAfv2GPl\nXPOw5/T+telRrJwWj+5nBPERTl7r1/usvKqgkGxGCSMgKQR05/CvnDXvCepW1zdWiwHZDNIsYw7Y\nG84wF4Axz+NfRwl1H/n1tf8AwJb/AON15741u5NJ1RZbiHa12oKeT844wCMnb9fxq61dulJRi++z\n6Cw9aF7SUv8AwFnHaB4YvJvD93a3QBuScxknlVxWZZ+ELpZ0LSyDaTnypiuT6njqK6i28UW+mzqJ\nI55N4OWVeGzxjgnBqC4vQjG8DTQxSNwroO/bORXnRq+5dJ39DqdakvdcZW/ws9A8O6VFeeFpdNvy\n8kbdSTz6gisu52wxXRjZBp8B8uAYAIY4BUYHPHPP93/ZrQ8J3l3cQOqRRSBF2/vJCnYHsG9a5TVI\nLW/8YPb3KxeVDNsW0Lfug3QkZ/ntrslWXslo/uZz0MRFSk1F6f3WaWjeL9LsNQSCbzC54JXGB9ea\n6HxQftF3o91a/vWxLs2dTuULx+efwFcVH4WstO8TLZosKTKQQjkMmD06j09q9LSyvEe3kW1tA0Qw\ndtwwDjjGfk7YH5VdGqo6Wf3MitW1fuvVfys1YwVhUN1CgGn1UguJ5Lia3nhjjdERwUkLghiw7qP7\ntW62jNTV0OnNSjePp9wUUUVRYUUUUAY/akJyaUqe4pAue36VZzWEz1qnnBOPWrexgMkY6981Sf8A\n1hrN7jBiAhLEKAM5Pb3pvlxvsLZJXo2efrUdypktZUXqykCmGSctHhNmCd2fmB7DHtzk9OlS2aRT\naumTxcKWxgsc8jB/znNIEjKlSoYj5WLDr3/r+tRwTSSKrMF2lQQQCM/gfwp2G+bJPLgjHpxx+hoE\n73dyRlRuoBwQR9RQAq9FA+gqpG05HmLjJl2sNxIwGIJGenb8qmhMhT94Np6ep9znv69qEwlFrdkr\nH1qJUVD8vHGAOwqKNbj5WkkHOdybRx6dPx/Slk87zF2bSC4JJHRe469ff3HHcFw5elxXQEgr94fx\nEZ/z0H5VFJEpVdiqCrKRgdMEf4UAXBdC5i27RuCA53d+vb/CmTpJKJYuiMNu72IIOB9cf4cZKbCz\nvuOZBjChVJGMgYwKRmPrUY80FPM75zjsf8OPrzSvRcUk0MZqmtHxKv8AvY/PiqzHmnQyBJAfQg0p\nbCRtyzR28Ek8zhIo1LuzdFAGSTXzp44+Il/4mupLeB5LbTFOEtw2N4znc+Op4HHQdu5PsHxHv3sP\nAeosr+W8qrChyMtuYBh1/u7q+Y2bMxPvT2RC96WvQmMhPJJqxpr3H9p2v2VnFx5q+WU67s8YqnXo\nPw38Ky3Gq2ms3ilLeJmlgU8GQpj5h/shio9zn0NZT0i2dFJXmj0fxFqw0zS2EZLFEwMnk8d64aHV\n72SQT3UEjAEHygMfn3A961/Gk4jPlNy0hymemBXNvrXmww20gXaibd/Qr+NcMl0O+lPdtl7UdRs9\nShFpZ25hXcDKm0LkgccjrjH6VyuoxPptpNEz5J4BI5I/z/npV+zeeO8kEUiywKc7vU1fvtFOqxJN\nc7kO0mONRjPua0g1HToZ1Fzq63MXwPYwS6kL+9lSOG35VWYLvPoK6nS44TrN88VyzD7JeMQJMjm3\nk6Dp3rCuLFrHTEjKxlc84cgj8MVb8NxsrXsxQKq6feBQD1/0eSsMU705yv0/yK5VGKjYz9J0+48Q\na1HZkmIHLSv1wo64r0u3stH0O2EMBhjPVmZvnY+tZPhbTUtbmW4jQIZFwB6L257+v40a5pIv0dBK\n+4Z2hduB+YzTlJTeuiLjTcFpua8dzDI/yOCDz1qWScMm0fNnrjtXM+G9Hu7BpmkkUxfwqecH9KpG\nw1t9SleS6KRA4QLuHHtjjP1oha7SZc72V0RPJPF4wuYLVTmSLcQOcH1rTZNWm8HXMVvKy3aarEW8\ny4GdnlSd2xkZ7fWqayiz1m6uo1DyiIIWbqMDk1btNekl8O311tjcLe24WMOFI+Sfue/FY4qMpcrj\n3j+v+ZyzoqSir2ad+/f/ADM7UZ9Ws28kmxbgFmlu406/Vs1kw20pZmuGskMjFsG+h6dv4qt3up2u\np34+1WZiO3GGOcYrOVdP1S3aKA/vISVwTzjPX6VsozS1f4f8EylGte6qL/wH/gm/rEKxafomJ7XI\nsmHN5EoP+kTerc/Ue9P8MWUmq6k8crW/kxwyN+5nSYliNo4RiRgtnPtXO69pBGneHg3ITT3XGf8A\np6nP9a1/BF5Bo2oncpEckZQ7RnHIOf0rOhTfs01Lq+n95+ZMniHf3l/4D/wTQi0SS31V76Z4/tBL\nDBkQfKeOmevH61o6lJaPy5Q4Vc4bPYdxWZrBi/4SI6ha3SzWtzD86rIGMUmeQV6qT6HB61RlGWZ4\n/mI5Ck8GlOlJVLyd/lY3oKcnGpKV7X0tbe1+r7GrZ3GnxRMvmwxu4+7u5A9/SmwWenO7SRJHv+9v\nU559QaoRK88H2qcFGRdxAtkZQfQHzM/1qGa9mht/tcyKku0DYOhP/wCqrULHXOa6mjp9ppMWj6ui\nE5SxVZCDjOLiE5/MCst7qxj0CGBrZzHLNKCoQnBCp/iOag0yR5tG8ReVgSfYUIz0P+kwVAXkHhu3\neWB3k8+UZSdRjKx842nP09uvNZRh+8d39pf+knFXlrCy6r8pGrov2e3cCyd1mbnO7B4r0Lw94gmm\nn+xXikSqMrIWGGH+c/55ry3QWCXZkmf/AFa4LY7mt/8Atm3t7iKWGXe0bgkpzgdTXVCUlKxdWnBx\n5up6/cyYuphn+Nv514F8TNUuh41vIJWBVFRY1zkKpQHj8z+Jr22W8Et5dbF4SeRDk9wxH9K8s+KH\nh+W91Ky1WCP5SBDMBjgg/Kffg4/CtMK17CF/5V+SOSzc7I1vDFrHBoVuixfM6bnOOSTzzWuRtmY4\nxkmuZ1O31OW3jNg37pRyquykf981Oi6k9zqkU1xKE3u0BVug3H+XFee0nPmb/q53XaxCVvsv80bv\nimyhv4TbzIGzbRYz/CfKXB9q8d07V30a8n2RgumUIJx3r0ibR7z+2o5pHXYLO0B+XLEi3jB5z6iu\nUvtFtrfWry+mYiKRyFUdc4BJ/nWmElFUVF66f5hOE21JaD59cv7nRvtMKLE5facc4rP120utUljd\njudbWA7jgcmJSR+dLd69bWsIt47c8HcMnGKt3esI12kOFy1tA3TB5iQ/1rVKUZRaXf8AQ56vLKvB\nN9Jf+2nO6Cu66liZB5qkEZzkAHkdcelelalb6pKoFrIoh7bC24fqBXmX2prPxQk9vgsJVO3pz6V6\nHJf3EaKrBo0dFcJgqVyASMHnjp+Fa1k7qRvRcGuVlh9OkudJjgkumW5/jfdgH2qe10ldOsXWaV5Q\nyFdpkDKc/gP5VhfbbZbrzQL5znIGGC/0/WrUl6ZGjVWZUZhhWAzj3xmsUnsdVSMUrs7jwpZLpukQ\nRqoVpN8jgdM5UfyUVg/E27aFdIUEhZJJAff7n+Jrr0KxmMIoVApCgdAOK5bx7aLeafZXAj3TW9yp\njGepPb8SBW9LRST7/ojxKcuarOXeX6RL9rYW4twfLU/KvXtgVPa6XY+cWNrExPJ3CuDub/W01JY4\nUuF+bBRpSykfTpj6Vq6kuq3OnxyWX2hXKhmAypI9tvJ/SuT2dmrs9ZT02LHj3Tba005L+1soovKO\nJPJQLlT3wOOv865rQNLl1eymniuxGsb4dSAe2QeR+FaawasdEu4TEgiaFjI8gbKDHvyWPQf5yyzt\nDY6Pa2kQZJbhP3inbG3mFsY5ILHPAHJrdSsrGaheXM9iXVdBnktBJCzyqphV7dJCiyII8E8d+Pyx\nVLSLN7XU7eJZisazK7QuhOzJBChieT17Y5HXPGu15fWV68FxKwHlRt5bRbDwg3Ekgd89On6VW0zV\nrZpbeW+u9kl1OptYWTzOGc4VTg7eqk8gcnqazipSw7T7P8mcEuVYJyX8r/KRkeI9KeaC3UwgqWKq\nxONpGCQQPXIq3qNncyeE9NjsZ2hVby44Q4yNkGP6/nXERanfPe3d2s0rec3IlcyEjPGSepA712c1\n8LbwpZF325vroZ/4BB1qsTBwlBLv+jOynUjU30JNN07UZ9GnH29luEYBPTp/Ol/sy/bSbi31W9mn\ngkXHlnlgw5GDk1naPrkscrRPLaJbMclmcDgfj1rQ1XxAtrafaEHmBGG1XyNxzyPyzRad7LqbScOW\n7LEDPpmikWYEjxYRA3zEDA6478/n+VZtrrfiJbxA13FIHdV8sAbuRycDpitXwnqEGpzbIQpZ0MjI\nzhQrBSz5LYAAAJ61pXItYLnz4pNLABwxXUrVW+mTIKxlWjTk4ztclRjLVOxk+Ita12z1FLW1nETF\nQS2VG7OOOeKu2M+oXemk6kGZ1mAR1ABcc/h6f5HOjfXVlebGU6TOseM+ZqVqfxU+YfxziqfjRDe+\nGfKsZ9NUyYXLanbIuOD1L4PQ0o4in7sbpef9Ic4xV5XuZU+p2z3VzFGwd20++VSDxxaSsSD+IH4n\n0ryYnJrvNF0C+Ors/wBo0oqNKvI/l1e1c5NpKo4EmccjJ6AZJIAJrnh4T1H/AJ+dG/8AB1Z//Ha6\nqVejGrP3l9n9fI4JXaRiAU/bWpe+HdQ06wN7KbOS3WVYme2voLja7BioIjdiMhG6+hrNFdsKkaiv\nB3Rm0Jt9qVVJPAJrU0Tw7q/iK8+y6TYTXUo+9sHyoDnlmPC9D1IzXYWHw28U6Z4m077DdRCORiV1\nSxcyxxgDD9AD0yuCAGPHc1dgMvwB4fTxDf6haXU7WlgbXNxdBcrEqushyTwCVjbGfQnBxT473TdF\n8aPa+Hr6efRZZI4w8/BkyBkkEDGGJxwDiu08X6r4fWz1XwnpjC0t7eP7Rc3Mca7Gn3Rp8yIowfug\nlMKMt8pwK8isY/P1G3iHWSVUH1JArJJXmn5fkZUXarNruvyR6vqN5IpjMjusOcZXGM+5JAA57mp1\n1G7tbGSC/vZIrSYhYwixMXJ+7ypJ9DxQJIUle3uCFB+6WHB9jWppcdrbuxub2Bo1+YAqCRx1zXnx\na7Hu3bV7lOXxTceHPDYup0Sdw6rFG+QHYnv6DAJ/Cun0bWxrOg2WojG+eIM4VSAH6MAD23AivMPi\nZLGbfSFjyoJmYIf7vyBT+hrR8Ba/bWfhKRL24RBBcMF3MMhSAQAOp+Ysa7acLQT6nnV6jlK3Q9J+\n0eprzLx27W3jaVpSUhmggdT6jylGfzB/KruofEWwtsraQtcOCwOXAHHQjGcj8jXL+ONWm1XXIJXj\nRCdPtGAX/agRyPzes6kf3sL9pf8AtoqTcXc6I3VnGVYam8UnkRExk/KV8tSOMiqovRKz4JYP3PYe\n1ctfs8epQskQZha22Pb9ylbmnuYIhc3vH91DwWPpj/H/APVhTgowTOjC1pToQj5f5nrPhF4x4fjR\nWBkR2EoB5VicgH/gJWsX4g6aZLaDVog7SQYilAPAjJJB/Bj/AOPe1cRonjLUNP1G5SG6gEM0hYLN\nCTGWwAWJX5gcKOme3FaPiT4pSpajTodPg824HlzSNKJU2EYO3acZ57kgYxzXbyO1jl57TujBvDJM\n6AlXhb+MvtxWvBZyTaesUKwMsZ3ljJ84PsRkE/XH41z66m2nMUe3EqDkZq9N4+CWggisoon24yK5\n1CT0jsd6rRjrJ6nUPrX9m+HdXlicSvb2yMoJ6nz4gM/ia8i1XVLrV9RkvruTfPIckgYAx0xXSW+r\nfbtB8RoY9v8AoCOTnOf9Kt/8a40g9e1Vh4KNSp6r/wBJRx1arl6HWadrE+pMbm5lU3duUw+OZBjH\nPvgdfaupiudP1KEeVe2zuf4DIA3/AHycH9K870SUR6iqEjbINvPr1H+H40zWbX7PqMgAO1/nHvnr\n+ua2lTUvIiFVxb6no7z6fpUDtNe2qMOq+aC35DmuS1zU7qOezure6lSQO8iOjlShO0cY5BwMdewr\nm7SAT3cMWM7nAP071e1ls3YUfwqPz/zinCCiwqVHPQ9L8LfF4oEtfEMZcdBdwqN3X+NehHuvPHQ1\nY+KF9Bcv4X1WwkF1bs8yiSJtyk/JgfX73Ht7V43vHQ1p6PKz3SWxlYRmRZQu75S68jI+m4fjTkla\n5EFZno2l+P0t3EN3pzRqxwrBw1aOo/EaOwk+zRWEkrkbsMdgA9yaxv7G05o1uWcZI4AIA5Pr9a3N\nattFmnhaWSJmMaoxhcZxt6EDt9a89ezvdI9RKdrXOe8Q6w/iPwtdTG28h7d1fb5m8HnHBwPWq7aX\nNc6pFdPCrRrpVqUEqBlZvsCYOCMHDY/Kug1OKxk0n+zrZlEUmA7E8AZ7k1q3NvGDDHCQ0a2MKrjs\nPs6gf0/Osq82rcq6S/8AbTjxcV7KXM+n+Rw/h3UJW1j7N5dplXDP5drEnyrz2X+9sP4V6/YqJo1L\nSvgjI+dgK8U0iCa28ZXojjLgK3yggHnGMZ/xroJm1NZNw8yN854bb+uf6VpUoQcloFPD0Vf3F/Xz\nPWVjgGQ1y49P3rf41b02G3adl8+UsByFnYfyNeWPp2u3eiWt0LiRklZg655XHTPsf61teGLK8ilX\nfdujRn5diMnHoSeT+A79a0o0acXdpDq4ak42UP6+8qazdPovxLjgj2x29zIA2AA/zcn5/vHJO7ry\nSa6jXkSGyaXcRtTLbyWz8yjAyePvVy/xB0oQapHqkfmu6QCQFnLfMp7knPQVteIZnn8P3IEcu4Q9\nWjK5O9PUe1a4mlTSVl1X5+pxToxpVITgrO8fz9TFhllaw1QwO5P2VcbF3H/XR9APbNc/Hrd9p0sb\nWF7cRW6nLrNbkjOeSeM9u9TeGdYWzg1c3OSsVorMPbzoh/WtAav4buGjkTUJBJnOwjP9K4qMXGtN\nW6r8j2lNSi7uxHpOrXJvpbmGVJYpTuLKoAz3xj86brmnDVdPjPnHcHd1L++Dj8O3tUd9qEEkpL3E\nVvH0LyMFAz0yfU1r26M1goC5BUEFlz2966K3Okmu5xVuaco8mtmn26Nefc4jT9FuvtQW8vJDGp+4\nGIz+NdFex211BtjOIQPKDDAUbe/58fhVt7SZSqkQjdkljAnT6YqO6uBHp86IsaosbY/dKQcAnpjF\ndNHnnFtx/H/gHJVq1YSSUf8Ayb/gHmWptJI8xhy1uHwHC8Nis+FdoJYda9C8JpDrFp595bWkhSTZ\nsFpEMD8FrtU0PR8jbptsOMACFB/SuaWJlCTjy/j/AMA3jTxFRc3Kv/Av/tTyG8smubDS8bVxatjP\nr50ldBYPbXfizUdO1WRViNzK8E0hJCEMTtPIGCP8O9d1caZpsSbhZW6+WpCARLwOTgcepJryt5pR\nrT3Eyea3nl2GMbjnJHTvRh+aqrtW3/Ns5cPTqQkqk1Zare/2n5LserHwrYXnhK6t4p1gCMsoZzjI\n6d+n/wBcVF4G8E/Z9dF29w0bRZBj2gq6MpBG4HPfuK83XxIiXEsaXOoQxzKVEKO2Of4e2R7V6J4Q\n1W707R7+9u5ALeKAbWfpk8D8MkV00Fy2R6dZqSa+45UxvY3V5MLjzNsoVdo4O7d78fdFLby+bq9n\nJNYytMJ123BkCgAkA49R7V01hd+AdR0VtMkv/Ju5ZMpdSxFWSTkL82MbcDnOAc54PTkdI8Qxx6ha\n2bW2XadYw+cjk4zWWMpS5HNdn+TMquIpyoVYReiUv/SZE/iZJJvDV2zWT2tu0KtGvmBwWDAZGO2P\nWuTtdUi0mMR+QZdgAznH6V6gupDxJ4HvoFtoYn8tjvbsAM8e/FeeJo0N5GZBJGhI+bcCSf1qp8sY\n+9sayfPrDpoOTxNG8DSpZnjqM8VRi18vdo72aiPOSC2CR7Gti20+1GnzQGaJGaTq5Hp1JqklhAWD\nPOCVG3bwwAz2PpU03BapCnGbVrnQ69otnd6Va6xp6sDM4RuMA8Z59CO9Xb3xTfjRZNCdE+zWkSzR\nmPP7z94oDgn+HDEjgcEZz1rV0uSK28Bajp8qw7lUspcDCFtuPbkHr71w7/YUbWIraSSYw2oiiIO7\nIWRMnPfLEYx2FduJ1pK3W35o8yppT5numv8A0qJ0Wn30F9amJ7kI69c8VtaMLOGR41vGJkAGS+Qa\n8yvreQ28V2oISQAnHY960dAC+ejHqD1Jrhp0kndM9eWIajax7hpkltCzCHoqbSw/iY1xdz451Xwh\n8U5tOurp7jRrlkcxvz5YZV5Q9Rgjp0PPHet7SbyJLVQXAVeWY9OPevJ/HWrR6p41aePBUBFUj0HH\n+P51305W2OSok6b5tz6Z0/XdH1V/LstRtp5cbjEso8wD3XqPxFZfjfQTrOgv5Cs1zbhnjUclhjlR\n7nA/KvDrBJLrVZ7+BipibfG68EMOcg/UV6ppPxb0V2Sz1YS2t0qrvlWMvExI7Yywz15GB61vB32M\nYVeWV2eRTRNCpMUd9MrcAeYzAe3XjHvmpreBTGknkHzumWA+UfQfzq/433N4jvrnR76KW3nlMgaJ\nwyOW5OCPckVj6Xb36y75pgM9QOa4KsYwm4p6HdKTaXKj1bw3qQtBbxq2HdgZFHpkf0Ark/FtgV8a\n3zO6QSfaC6MWYHB+YEYVvWpdDSSG9SVpC3I611/jjQItRtbXWre3ae4ICSRpks+ATkAdSADx6VtS\nftIuKM6doSal1Rytol9HrVmbi0SFRgiQS7y6j+IjAx/OvaLJ2ks4nbO5lya8r0aaxkcT3gePyB+8\nSQEFQOxz0rS034mwPBd38kJNj9ra3hQEBlRVTkfUknHvW1Om726mFSprzdNjvo/+Qxc/9e8X/oUl\nXK5rRPFOk61dzXdrcgRtHHHiUbSGBckc8dGH510gIIyCCPaopJpO/d/mY4eUZKVn1f5i0UZHrRWh\n0BRRRkUAeRHU/GGsobtbm20nT5OEedljU9x8xBY5HccGmPqfjTRoxeefb6vp6D53hKyLjucgBuAD\nycgVoX/hLWNTJuri8gM7N9xidqr6AgH8sVkmOTT73T1sLjZdpEySbQdrESMP+BD8OgBpOD7nJ9fa\nlZ0ko/idj4d8SWfiK086DMcqYE0LHJjP9R6GrsvElczFfaB4eMGopYGG51OTY5RfuHI3ZyflHfA6\n8cccdPccSmiSkknI0lKEpP2ewzNRxy+YD8pXBI5x2JH9KUH1qEwK5JLEckgrjIJyM/kT/wDWqXcc\nbdSZWRUAUgL29OfT86cuAOOB19Kqi28uNVidgVxgsc8AAdO/3R+tToCoIJydxP5nNCbBpdweVEVm\ndgAoJJPbHX+Rpskuw4BTIPzZbG35Sf6VBLaLMz7pXwxyy8YP6ZpzW0LyNI6lnYYJJPPGPw49KV2W\nlDuL9qTzI0LAeYu5TnHfgY9ef50s04j2cjBJ3ew2k5/ShY4zkYVlPBB5GB2/Smqy+WEJBKgAjOcH\nH/16NRe7e6GG7Q7NoOG7kEYOQB+ppRMrvtUHJGehHH+RQWjYFcqQ3UZznp/9aoSRI7ZXO1sAkew/\nr/KlqHu9iK5mmUkxGPbv24fPsB07kn6fSq5uGkZQssRcuWwjkgqOOuPUirLKgOSo+9u5Hf1qg0bq\nHAeLBXaAIug9OvSk0ylKNrWHpcF4o2ZkLFRnZnBOO2afEGdyB3HXtVZAAMIgBJIGByew/QCrzn7L\nbqrhdrD5mHrVQjzbmU5JbFTxjpz63Z28EWqTacYyztNFkk9Bjgj1z17VysHgdI5IxP4t125zHlvJ\nufLySeCM7sDA6fqOldBqd1I1rKpPIQ7T65//AFVAJ4LWOSRmQ7AMk4x14z+f61EKFOTk3FPXsuy8\njghCEpzlKKevVJ9F3TL9nptpZSpbWazPtQG4nup5JyB2ADEgMfbGOvpWXrt4bbXbZo1CQlSjKowu\nc9cetY9/8RoLHdDpulz3YGS08jmJWY9wMEn8cdKq6fq0vi2weSZYIL2CUHyEzyoHDck56kfh70Vc\nNScGlFfcv8jopUaXOpOCt6L/ACLfiOcyWZWK5ngbqHhkKkH8OtefPdeITIUTVbxlH8Xnv/jXos+n\necuX549a57UDZWUgF5MltbE7QTkmQ+gAGcep+lcNKnTk+XlX3L/I66mFoR1cF9y/yMe7utdttBNz\nFqd0xzg5lYnHrnNdo01yPDtjcrLKZWto2Y7zljtHPvVBhCtujqyNAR25VlP86c+vRx26xNDtRRtT\nC7QAO2K1xGFpRtaK+5f5EUaVBqSdNf8AgK/yOV1nWJ59evbRI0KpcOpJHYMRWnokimS5jGMtY3uQ\nPa2k/wAaq62kEWq30yIikyuWI6k7j/WovCpd7y7lbp/Z94B+NtKa4qiTwunZf+2mmFk/YQT/AJV+\nSNHwPr15PqNzZXUodY4f3R2gHAOOSBzx6102oT/ZNOaXO2RjXlmn3f2HXklUkBsg44r07XL+wD2d\nnJpdpOr28LuZZJgSWjVj92QDv6VVeXJNJRvf06ep3U5txXcwYvFMenxNApEgY7mZhyTWjpmp/bYZ\n5FbEZO4DP3eOf1qoZ9JguhaL4XsSXGOZJznP/bSqera/aeHmlsrfw/pwM2PlMtzwMc5/e+/bFEZS\nekYP74/5hKpJayZBcaklzNI8J/dkbVJ7j/Oa0ItTOm+Er6a36teWyuq4I5jn52njtWdY6tphiMcn\nh7TE+XICyXJ/9rVcgn05vC2ps2gaaYhfWxKCS6AY7J8E/vs5HOMEDk5B4xnWlLS8H8Ueq7+pk5M4\n7UdUTc8i7TPIOSqhce+BwKwVkeOQSRuyODkMpwRW0fEGnbj/AMUno/1828/+P0+PV7KbcIvB+kvt\nBY7ZLw4Hr/r69BTml/Df3x/zOWTu73L+r63Omj+G/PUS79Odi2cHP2q4H8gKoJ4mS3bfb25Ljpvb\ngf410GqfYNT8IQyHRLO2kt9D+0QSQvPmI/2k0ZUbpGBBDseQTluuAAOEhSLzU88t5e4b9nXHfFY4\nNxnTacbWb/NvoU5O+jOh8Myy32rXcs5J8yMlyBxksP8A69bhvFtJCs4JXsR1q1Z2tlYKEsVzaOod\nJd2TJkDk/j7etR39qJ0baAR70qjUpX6M6qekbLdEcN3ppfd9smxnhD0o1L/SbRxyDjcB3Apml6ZE\nku8xjI6E1ZRUuvEb2TZ8sW+7g8g7u35j8ql2T06FtykkpGVod0YNO8RCQEBdPQn3/wBKgH9aGuYp\nNCtNkz4e6m+TPU7Yv8/jWjJaWllH4jtWmBJ01GZsfdBuoMA+/SsmSKGz8OWrs64FzMVI75WLp+VZ\npp1G/Nf+ks4qzalDX7X6SLlpYK0VxFLcAFlGFDYG+nw2UcduWL7+mPm7g+mT3+lcfeSmG/cjoTux\n9auWeroJ8zFlUkZ2812OlJbM2VaElZo9uv7qRNY1FBwoupcDP+2azNUulbT5GkV5Ej+fYvJyPSl1\nO5il1zVPJkD7b2ZWx2YOQRVUTN3HFZYZXw8E/wCVfkjn5nGd0ULnxBJp1k5jAYVj6h4i1OPXNQgt\nCQkc0ingHdhiPT24qbVgY55FdDtYbhlfxqjr2phNVvEglKSLcODwP7xrJQSqWt0f5o29reum39l/\nmjeuNe1CPxBbwbXXzNPtGcsOCTbRk/TnisHxFq/nu9oAFeIbjx3YVsanffaNdsoEO51sLQjPfdbx\nnP61w1skl3rNx9pYbpgec988YowdO9JStsv8zSpUekUyiJoUi3OhkkPXJ6VoavMv9sWrKoQ/ZbXp\n7wJWbf6dPazEFSVzwRWrd2o/teCedljiSztTlj1xBHXXLl54vyf6HDJy9tBev6GfZgSeIUMkXnEz\n5EecbzngZr0/XIzNDazgFZdp69cccGvMXvba31RbuCMStHJvXfnaTnIOODXT6ZrlzqoWa7kZn5Qn\ngKDkYwO3GPrVVE3G5tTdpollm0+OTzJxIJe4zj9Ks27efKJihEY+7nvTbmMSEMyqxHr2pN5SPd0A\nGa50tDpcn1PQbDW2v4DOhjdlO107qe4P5dadfPZ6hamG5DR8hlJycEHIINeTvd3dzoEsdiJzepqE\nb/uM7vmWbGMc54Nb+mX3ivyguqWKvGBwzIRJ/wCOgj8wDWsI/F6/ojzaWkptfzfojqLq7Mhit4IU\n3HlmAAyOvWpf7Rnv4VtJLKG22Z2u0meenyjHI+tZM9ok1vbNcwqVdQSjjO09qitNMCTqHitPKByc\ntkt6ZFclop2PZi+ZXN6LVg2j3EZSPZErfdwQcDPaufub+HW13NdxQTJgBWHygdgB/nrV/UYRNpl2\n8bbQQoUjoTuHNcha+HVkmkvdQuljsovmkfOMj0FaU4JptmU6sovQs+L9WXTrMWMMitdXEKeaydAu\nxRj8QPyP0rm9GmMmtaAu/dtuIgcnp+9/wxWnqWlNquqWTwWuVk06B5Nr452AZGenGBjnpmrGk+D5\nrbWrK4BZRFcRybWKngMCeQf6VrFRhhmvJ/kzx5zcsI2/5X+UjngiQ2EKcebI3Y8gf/rrqNVkEXhn\nT2PCnULvn/tnb1zOo6FqmjmO5uIS8KOGaSM7h17+ldZFHHrnhS0W0urEyRXlwzpPeRQsAyQ4OHYE\n52n8qjGNJwlLa/6M9ClszG0+W0hl843gBHQbAM/jUfiZWNnBMS2ZJGOD2GK0dK8JXAvfMkl0k7Tk\nbdUtjj8pKteJ9Au5re2jjuNLDK5I36rbLkY56yDPaojiKKqK0v6+4ublKGpk+AdTFtqd3bMPlexu\n3z3BW2lP8qh0q202YS3N2rSL5hyuTjH4Vs+EPBeoQeIvNuX08RNZ3aMqajbuTut5FxhXJ75z0ABJ\nrPbwb4h0yZ1t5dOeJu/9p2y5+oMgqfrFB152mldR/XyIhdJNq6J7208PzafcTWkTRFFOArMAT0HU\nnvWF4h1PztPsbJEISNNxJHDcYGPXoa3rLwn4h1N1tWfTo7dmBkdNStnwPoshJro9R+HltrMwt4rq\n2hNjAIomS9hIYLyQw3Ej5mPP86tYqhB+9O/6fgVK81orHm/hP/kMXH/YM1D/ANJJqxB1r0Ww8Cah\nol9dXhurC4thYXsYMV3E0hL20iqNgYkkswGBnrXnksMsEhjmjeN14KupBH4VvQqQqVZyg7q0f1Od\n7G9aH/ihtW/7CVl/6Kuq6D4e6NoV7p+u6trVrJeDTlg8q2WUor+YzAk45OMA9R3zXOWZ/wCKF1b/\nALCdl/6Kuq6H4UXUS+I7ywmjM322ykSKAnCySrh13HsMK3NXQ0lO/wDN+kRS2Oqk8XapqaQ6NpUN\nvplpNKI47e0Ty1UvgEcY4LHd0yM9ar3Op634K1AWserQTzFS0iRt5iI2CoBzgkgc4IwM9Kb4Luk0\n7xWiXAtCgV1kkmwVjCjcXVs4B+XhvQn1rmfGnildY8QXE1sIjCjNFFIilQ6BiQxB7nPt9K+jrVKV\nKagorktf1OSMXJX6nbeOBpt34Q0+a2ttOt9Q1WzaWScCOAyOJISwLHGTnJ5/u15fDo91FcROJ7Ab\nXDbhqEHGDn+/WZNcSzsDK7NgYGT0FQZIOa8CpBucpQdk/L/glqnNScoy3t0vsrd0eyBBf2aiZrdm\n28lZ0P6g0/RPD1qt6JHk8xR/CJA2PyNcFo3iyK3dVvrdtuMFocc/gf8AGunX4gaLYwsbO0upZugV\n1VF/PJ/lXCqNaLsn+H/BOzmqNazX/gP/AAR3xR0s3d3p1zbTQIVjMZSaeOEYBzkbyM9efwrkLTR7\niNpH+06ecRsFxfQnLEYH8fHXP4Vna7rF5rt611eOC2MKi8Ki+g9qz4DhDjr611Qp1VGzl+H/AATm\nmqrd+Zf+A/8ABOgGiXKQN/pNgCQBj7bD/wDF+1asGo64kttHO/h6WNI0hDzJp7uFVQi5YgscAD1P\nFcqJfMjAY8Hmr93YpNFbyWi4ZpNoO7PPGBjHB/E1FSg5/G0/l/wQSrX0n+H/AATtNV8SNpQikxYS\nyNGgWOOwtmJIRd3z7DgDnpkDoOBxgy+ONRvZdyWumqWwFQ6ZbNjjB5Mfese3neRXE4DAReVG3oCc\nnH4ZH4+1IyoiBUUADoR1/OopYKjBWcU3/XmbpuMFCL0SNyXxXdRQwAQ6UWVWLgaVa/eJP/TP0C9K\not4qv5D5n2bSODn/AJA9ocf+Q6x3UsartNsmCg/J3rX6rR/lX9fMOaVrXO6uPF1x/ZH2ryNLZyNo\nB0y2OSf+2f41f8Nx3mq2y3V5HpR3HIjXRrTge/7qvPIraa7mitbc5aWQBVLADJ9zxXoCW+saNYx3\nMN7FJbBcuLdBIikY3Bm9sgE/dyRgmuWtg4KNoJf18zenVUpXmjo9U0u3m0SezjfS7JrnakjrbW8B\ndAQ23KqD95VOPavLdb8PXGjshmjGyQfJIpyrfQ1217aXrx2erXaQtPOoaKMRu6Bcd+PvZ6g+2O+O\nh1bS4tR8MNaJYRQAAtuSFYwWA+VlAGR3J6ZLHPQGow7jQW++5tUpqo7KNjxGN3hkSVCVdCGU+hHQ\n11niqNL7S7PV4tgEvDKoAxu5xx1wcj696y202a0iVrqFdsq5Uex/lWnafZ5fC+oWLHMsQ82DIGcZ\nGVB784P516SaeqOCScXqZvhW0FzrkWQCEBYg9D2x+Wap6s/n6ndSxx7Y/MOAo4AzgVs+GP8ARNN1\nG/IQ4XYoYHKkDORjFdL4am0BtCmhu47p7rBKuJlC7evCjB45y2R7mqs3sZTqwpJyne3keYsAa1fD\nfhy78Q6mLaB1iiXBlmfpGP6k9h3/ADNR64lnFqtwLGcS2247G27Tj3HTPrjjPTit7wn4l03RrUWx\ngeS5uHZp5GwqqoB2gdS3fg45NJ3SNN/hNS7tLmzuLrTg4JjYrl+Ny9iPqMGl020lEciRzWyHq7KG\nY49OoAqrNqc+p7b8HZLyEHXCgnCmnDXtVlUWyiGJTwzDnP4Vxyi22onfCpFR95HT+HLaHU9WktWV\nXiWFmlPIyCNox6HLA/hWwzCz8SyaXL84NtbqpA/iWBAcfUZ/IVz2h6k3h6VZUtDdyTAxBDLsJJ+b\nOcHP3envXR6jftqOpfaEso7ae3so2lZpcyoWgRx8uARgvtzn8qVWKTVu0v8A2083GNulNs4x4X0/\nxpdSSDaHC4bPGOn81xXSz6hbGzUbQsp43nkL71l+LJraSOxugFF8YsE7uAATkH1O4E+3PrXODVxd\nxLayg7FYNjv/APqp4iF5XR6dGXLFN9Tp4vG189v9gJgWAnawKHhe+DnIPvXRaHrk24ws/mRqPllb\nrg9M+9cZaLp4kVI9OjdCcEyMSPxyalnvYNNmk+zxoiNwVToP8KyU9VY0ltudb4u1+2tNKc3QZxPb\ntBGFGSWII79Oo5/SuI0zxNqOtmNb+4WRYbuMRgRpGBujmzwoA52iqPi+9muLjT7SVyN9sJVjPABZ\niMfXAFc6ImXQb5WBVheW+QeCPkmrrqxbgr9WvzPLxU17tujX5nolzpaNp+uvGMeZZKpx/wBfEJ/p\nXHWWhh58rJIGB+lJoesanFo/iIfbZXEenIU8z59p+1QDvnsTWCdc1R+Ptki+8YCH8wBWOHpyjOav\n1X/pJ0Tqxlq0dJ4re3sNOtLFSryvJ5rjPIABAz9c/pVXR/Eq2EVyPPuY5JoyoYYcqSQcgnkHj36+\nwrmZNz7ndizdSWOSaYOldiirWZi5Xd0dO88+oXcV5Jq7yXSNlJGfJUDsAe3t0rtrLU7jVYEiubD7\nPKuTIw5RlweQD656c9PevJQ2K1dO8Rahpg2wzloj1jflT/h+GKpNohrW51WkXtxoFi620AneWVyu\n4EZA4HA+ma6TRtX1XV7O6l+z+U8K7iGPWsrw/q2nXlsBME2uOYzglGHGPp0NbCa3b6bIyQ2krpMm\n0GMDGK8uprNprU9eklypqWhFa3mqXbRCWSaF1UiSNbZWRjuODktn7uKk1LRra4uY70lw8YG5ERcs\nc8H7w/rUq63NaoskkUaBzjyiw3L/AI1HeaxJa27Xiwh3RgQgOM88c/r+FFOErtJtb/1scs8NGO05\nWvtdd7/ylqCCweaeK8nWa5KmMecg3pnBB784xyMd89aPGtlBZ+AiHlkitlniBeAeYxG1gAVJUY6c\n57D1rgbD7Xd6vPfXMrGRyXIAwM9hj0r1XS4LXXPB97pOruFe/ZY4ieoI+bcPoFz+GO9dlClqo8z/\nAA/yOWtFuV3OVvVf/InhUkukH5V1HUVPtYp/8erqLO0sbG4tLqS4u3e2dGZTbKCSpB5/eHH61R1f\n4banazSHSpV1OFMk7F2SAA4zsJ5z2AJNdD/Z08sMczRSRyso86GRSrI2OQQeRUYqjKCs27f15GdL\nCxqKUHN6ruv8hmjXz21p5Py+WeGB6GuanuWsNQnEIDDedqt2Hb9K6WLTZ3cKEIGcVleJbfT0iiME\nm++Q4fyzlQvP3vfPT8fas4z5pWPRcOWJi/YprsSXDCJGZskeag5/3c5qVY7tCkbbdg6YrOa5gUgF\n5D7FjxWtp19aRhm2FuOB6V1RRzyf3nTy6gYvB19Cke1XkRMsckLnIGfoorltKZWXVT5m0G1UEg9P\n30dddommQeK9Ov8ATLpzCZcPA4PCOM4J9Rzz9fpXFW2m3GlprcYkjmMdsF/dtkHE8Wf0zVV43pqX\np+aOKv8Aw2uzX4tC2viZrR5rW6txc2xclf4WUE5q3ba7o0MokT7RH7Mmf5GsDUPLkuEmiUqsigsp\nH3W6EVTbA4A5odKN20dUaskkjv7zxtDPZtDZ+YIkALFhjzD6fT/CuRe8aW8lu5yd7uWOal0uxY28\nt6y5htzgejSHoPfHU/Sp9M8PX+vXZtrGAuVUF5G4RMnqx+meOvXANVGNlZESqOWrNmz8XQ20f2fT\n4pSWGN8gC8/TJq34Z0bXfHOrJBDELKyaT95dvHkEqDwDxuI54GPfFdL4f+Hml6SkcuoH7ddLggEE\nRKfp/Fz68H0rq7u+msrC4mtSY5YIGMZX+EheAKfPZWRna5x0enxGyaGEN5aSOiA/ewGOCfcjB6Dr\n0FLaR7SUcln6Zr0fUdJt9etl1XRhEt6kaiW2Y4WUAcDPZgOA2MEcHtt5S30K51O8cWdvObhH2ywy\nJs8luOHPQdc8ZJHKhq4pwmpN73O+nODho7WKcdytmpd2ChRuLMcACup0JNb8T26tM76boUaYWZvl\nluFPUoD91SP4j26Z3EjKuPD1jY3yrqcn9oXMDCR4U4hjbqqY6se5LdtuFG41qnWLy4lE1xKTEDgI\nOFH4V1YeHK7yOavUvpE6DVYtD1LTF0eaEx2iR+XFIh2tGAMAj2+vXvXlXijwZq3h/Rguno1/psbN\nI1xAMkZ6l16rwOvI9+1dbcXRkcMGHXt6VoaTq09tKqpJgE9G6V3Ksk+V7GPM7K+yPGbG4kttEtVN\n6lvvuJiS+4j7sf8AdBPeu68I/EOfT5ktZb6C4jJxy5RR/wB9hQK6vXfAuj+Lbh0kT+zrpVEqSW6g\nKzsWDMy/xZCr3B4614l4n8M6h4R1s2N7jJAeORD8sinuPyNKinyycdVd6fM56VNTjKUXs3+Z9RaT\nrttqsKuh2lunOQ3+6eh/CtWvmjwh4ludJmVVlwmcspPDfUfnXvfhzxDb61bfI2JFAypNOUU1zRNq\nVd83JP7zcooorI6zy3UNSu0vprQ29vdTxN5bom4GWMgcKAx56g9Tz7GoYdXuodSG21trO6uphGqS\nlyyIzFiWBb1PHAzk4qWxk8NeINOuVtjGupXAZliuZMOsmDgKf4hnnjPv6Uy6s/DmjaKkWsyRR3yf\nO6wybpiecAAHpjj0+nWtpU5c3LbU8dKcXzN6epF49Mxs9LsZZEmvJbncjRxlAQBjGMnuw712t3xN\nnsa4bw5BdeK/Ev8Ab91E0en2vFqjHOSOmPXB5J9ePp3N395SKMQuWMY9V+p1UVo5d3f5bIr5wKrv\nLMJZVRckKpXPTnI/HGM/5zU2eKga5jWby8OWJ2napIHfnH1H5j3xys6YXvorikXPmFgYsbSoDZ5P\nODwf93j60scrmVkYrwBwD0OOf5j8x68V01K3lVdhfLEAAoR1OAc9P/1H0NCSGSFpUAVizY4zkZwO\n+OgHfFK/Y0cZW95FmQMchWCk9/SmKrIynflQuPqfX9P1qs09w0zgRBUDrgsDypJzj3HH600POGTf\nnlyDyORg8n06D8z7UXJ5XbVotKgjLFWbacnYSSMnvyai2uoAWQdefl68c9+ucHJ9KhmuZo5IUEG7\nzC2SG4XAyOcd/wClIZZyDiHHzd2Gcden6dR1o0Bc1rkiIYgoUjAUA8csR36/59ajkiDpjO3qeD3P\nP8+aQPMSCyIFYngHBUc4J9e35n0prufN2gNnAyScDvS0sLW+43AQHAAyScKMCoHbk013uCrEqgGA\nQuee+Rnp6D/PDWNBMlrqWbTC75ipYj5V46Hual1G4VYlUDJI5FVnvINO0/zbiQRoxySw49uKqG9t\nroB7K4WeI8nBP6Z5reKsjCWrsUbzIiYAkrn5ffg5H5VX09xMJPMIOCARnIJAxj86Wc+YkkROPmGw\n/gT/AEqnpDOLcCRdrgncPQ5IrOnvL1/RGNHefr+iLV/pkE42bF5OT79hXP6hoojhZrcbZkIkUj1B\n/wDrmupLDHBHSs29fDsF+8U4/MVqa7bDbjxBaQWKyzM28j/VKMnNcdrVtN4hnjuJIfIG0Iqbt2Fz\n1PqST6eldX/Z6XLxzXKIBEu58D7wXJyffkc1s6Dp8byvqtwBhDiJOxb1/DoPfPpWCpxpxfKtzdzd\nSV5dDM0nwlLZ6PGolW3C5KrMDIzZ9RkBO/HNc5r9ndRxPuEfByNittP6/wA/1ru9T1BVZ8vwAetc\ntqmvwtGE+zyurjhQANx9PX9K550479TRVJN6bHDa+ZRrmojcTm7kAH/AjVzw05gvJYWclzYXzuM8\nD/RZcD/PrW5c+HjeavfTXPy77h/L2noMnB+vSjSvCsNlrLTS3E5ge2uIGKIGI82J48gEjON+eo6V\nzujKWFsl9lfkjPDTSpQ9F+SPP4Wae5Vh2Neha5EZbiD5iHFjaYPp/o0dIngzRbYqqajqO7Oc/wBn\nof8A2tWpr8WkQ3kYuLjV4ytpbL+70zep/cIB8wkxnHUdjkZPWsqs37aFoy2l9l+R105x5WmzCtY7\nwQs8lydqjghefzrj9URze7mdmLZbcxyTXo8UGnX1s9rb3OrwpjmeXTkUEewaYE/XHFQT+CtHdlmO\npX4RMDH2BCMf9/q6IVLJ+7L/AMBZFSSbWpwVql3dXaNChbYMHHSvQZfDWpyeDbyKxRbh5bu3ZkVg\nGVAk2Tg9fvADBOa29N0DRLWVoYbq5JTqDaJ/8craSa1t7Jra3muHd5Ffc8QQAKGHZjn7womlU5Uo\nvddGtmQ52R86anbm11CWEqVKkDaRgjiqyFlYMpII6EV7B8SdDj1HRV1SFF+02uPMKj7yH/Dj9a8h\nz2rulGxlGVzvJbmabwdiSV3H/CN7uTnn+1gM/XAFcGK7fP8AxR//AHLX/uWriBXnYFL3/X/MuWlj\no/C+oLDNJbXCvLERlU34A9e3vXTW2raWrm3ubwQyjjbICPoc9MV55az/AGe6jl7Kefp3rR1yPcYb\nkDhwVOB3H/6/0rqqUlPRlU6rpvQ7/wC36JbLufUrcjt5bhj+Qya5XUNWn067mvrcKJZsopYfdX/O\nK5zT4/N1CBMZG8E/Qcn+VaPiFx5sEQxwpb8zj+lRChGL7lVK8pWtoSaPNJPpnimWVy8jachZieSf\ntdvSRafe3/hqz+yWVxc+XeXG7yYmfblIcZwOOhpNC/5A/ib/ALBif+ldvWFUKLlUny9Gv/STmqwl\nJRcXqnf8/wDM17nQdbmcP/Y2oZxg/wCiv/hUP/CO65/0BtR/8BX/AMKzwauaXo99rWoLZafbvNM3\nYdAPUnoBW373uvuf+ZKVbuvuf+Z2Wqp4h034g67cW2mag9rLqVwWCW7lXUyNgjjH0NdJNFqK2S3E\ncFwWchVTyjuHuVxkYHqK6A/D/SbXxJqmq38S3txc30s6LIPkjDOxA29+vOc9sAVR8cWk17pTSKST\nEBKFHouc/oTXNho1HQht8K6PsvMc/bczs19z/wAx3jHRTcuJ7RQ6yAKUj5KEDHQdsd+lea+ItEvJ\nNevpIuVkuZCM8dWNexW2u2w8KQXWoRm58+EL5Q6yNjn6D37ducVzUscWUdY8I/zKCd2B6ZxyelDj\nOD9pobUoSnUvUa0VtL979WVF0JrfWILlzumexsYI17bvs8SdfrXL+KvD89hqt/LbW7fYY7p0VkyR\nEM/KrehwRjPX8DXo+oarHousWGoz2TXUfkQLGivtIzCgLDjk4J44/Xh3hCVp7/WL0MxW7kEseeCF\n3MFP5AH8avLZShSU31X+Zdezaiuh4y+pSxRFJv3hI+Ut1H19ai8Qyl9RjJOSbS1P/kCOvatb8FaH\n4ihEj2q2ty4OJ7YbCDz1XoRk89+OteYeNvCd9pd0t3GDc2KwRRecin5diKnzj+HO3PfqOa3nyurF\nxVt/0OWTftYX7S/Q40nNbOiXSRRzRsGPIYYP1/8ArVjAVYs5PKuV9G4NW1obHo1nqOk6hbhvtaQy\nLxIszBCD+PH5VBeX9k839nWUkckh+aScMPKiX1LdO4/H8q4a6QGZhnG75qhJ2jA4FYKgk73NnXbW\nx38XiKw0HRbltCgWV1nijlnmUjzSwkOcA5wNnHTqfqYdE8Zy3+pTw6i20Tpth8scI2egGepGQCc8\n4965iwNtNo95aT3sNrI88MqGZXIYKsgP3Fbn5xS21jZw3UMp1zT8I6twk/Y5/wCeVQuROSlffs/L\nyOCnVVOcubv2fZeTPV7SdZrFVwHaLMTA89OB9eMVnRWVudREjWhOD90uSh/CmvqFppepG9urq3jt\nbpskBZSfYgbOe/cVN/wlvhoS7hqa4/64Sf8AxNYtpSdr29H/AJHZHGUnHV/hL/5E1dcliXSSkiZ8\nxl+VT6fN25xx29a8g164juNVuktLmaSxSVxbiR2ICZ4xnkcYr02DUf7Ws7vUhd2gghjPlgM5C7QT\nkhkBPI7DsBya8zOm2uf+Q3Yf98T/APxuuinOEY/8B/5GE8VCT6/c/wDI6+48T22myQxRQCTNrAwG\n7aEzGpAxg54wf/1VDZeKprvXbCJLdI0kuY1OSWOCwHtXJ6xNDLqAMEyzRpBBH5iggMViRTjIBxkH\nqKl0BmPiLSxn/l8h/wDQxS5V9Wd19l/kzlaawj/wv8pG7B4zn3CO7topImGCFznH4kg1zuvW1tZX\n0cumy/uLhPMCKfuckY/Ss0XLq3zckcVe0mNZ7wzyDIT7oI4zXTJRWqOxXW5NYR6s67kk8iNv43UZ\nP0GM1pQWSRSebIzTT95JOT+HpVgS5OTTlBfk1g5XJc2zX8MMUvL1wxMiWV1InGcYt5Rz7cj9K0bR\nbfVNoinjZm/hLYYfhWXoMf8AxNJfewvP/SaSo7ayRY1PU4Gc1ySipVZPyj/7cawrckTsXso9GsQo\naKKeRgiFuxPckeg/pWXJffYEms7eNlllULLIxBwOeBgdMH36e/GN5Si54xwMZGeP88VbRADz171q\noKMLPW5NSs5O60LCOFj2jtTHkhuFWK5ijmAOQsqBgPwNITjBB5qndkbfMXt6VKWpgWLrw/ptx4S1\nKKGMWrvfWzlo+hYJPj5emOT0xXLeDNInXxzaxS3TWf2OQTSTIASFUjG3dwdxIHPHzcgjiumtrgnw\nzqnzkhb+2xk9PknrKfxBNpFrfrbIpnuIlCSZIKEE8jHXALEe+D2p4aUk5rz/AEia3eiH/EvWdL1D\nxC39klXVQRcSoMB5MnOD/EBjr3yewFcP1GaTqCDSIflIPavQcm7JjsLjimFTup6/dFO7UhjVXing\nccUKPkrR0jQtV1uQx6Zp9xdMDg+VGSB9T0FAjMfkU2EYzmpp4ZIJWjlXa6nBHpTBwcUDEVtq49CR\nWnpt/JaXCOjYKMHH1HNZ6xFxIR0DL06857fhT5E8ifZnPTrx2o30BOzuPEuGJHGTjAqRXO519ORV\nVjtP45p8MmZfwxSGWThlyOahaJT2p+1kbK0EhvY0AQ+WQwCsVOcg56GvQvtlu+jRtOXe4YdZGLBf\nfB71wR5GG/A1v6VfrJB9jnAOBwCMhhXPiYtxujow8lGWp2ljpV41ov8AaGoFElj/AHMFxeLGZMZP\nyx7skjHcCp9K1C8WCS2vS4jUkKJMbtvvWdpM9vZPvaURhjlgvVvc1Y1HVLe4fbajeTwGrgfvaJHc\nnZXbMPxTqdtdW1ra2zqTEzeaFPRhhce3Qn8a5UXDQzKVOOqn8RVm9TZe3K5/5at/M1mTE7gD1zXp\n0oKEEkebVk5SbY/7esOjtaBcSM+WYHt9PXj9TWeZ2KBQx2joM9KS5GHBBzuGcelQ5xWhkSF8imxD\n9+npuFIO1GOaAOu0uVIv9GlcKWOUBPU+lbNnABdbtoJFcrrEYk06OdVxkq34Ef8A6q1/Dy31tp5n\nlvZPnGEhOMhRjnJ5HXoMVn7Fzd4lqqoxszVunun1K3ljjG22dXUNnBPXoOccdhXRyaHrc2rG8+x3\nl0lxp0LrcRQHYJPsaI2DjJyQPoRWIjbWz0BXkn+dN095LrUo2UkJ5cuAW25zGw5OfXFPFYRqHPBq\n6T3T627ehxV6vNTlfYYPCXiGTUQZtH1EoQw3fZpDjIIHarMPg3WL/TIXbStRt7qJdhDWrjOOBwR6\nY5qWDS5riOdR5KuoBj8udDtI5HQ8dua5p4dV0bURcWk1gUcYeOW9hAb83H6VzV5VJbTj90jWji6E\nev8AX3G5H4Y8V2rhP7FvJEB6rC2D+lX7PwjrU10J73S73aDuEf2d8Z/Kqtv4mRh/pNpaow/556pb\nN/NxTJ9XudWRraC0soYR99/7RhkJ9BhW4rnjKone8fuf+Z0SxmHtrP8Ar7iLxV4e1nUZY7tfDurP\nMXCAR2cvyoue2Pp/nNW9O8J6ndaXPa6noepA+dFsdbZwxAV8Hp2zjn1Fclrdne3cyRfaLJooF2IG\n1CAYPfq9b0GnTaX4WhmV4pTI0TMFdX4CyAjKnBAJGD2/CupVajaipReq6S7+pw161KpypPW629fQ\n0h4BvLDSdddEuI457JY1F1CY8H7RCwG7GCcKfSvPL/R73TXAurd4wejdVP0I4NdVJ4kk07TElEP2\nsySMipM2CgHPy8HPX14981oDXLTUbBQ9p5UEmBJHI2QozzuOB7YI/nxXoUqEW5OT1fa9tFbq2aOp\nJWuebkfK30qLoK6PVNAuLXzJoEMtmQHSRSD8p57fzrN0fTYtT1m3sZ7yGzilfa9xL91B1/PsOnJH\nI61nKLi7M2TTVzM7NzTQ1aOr6aNK1WezS6hu1iYATQHKtxn/AOsfeqAt2yckAUhl3Sb17XUI9gzv\nIQr65rvYgJ4xMRcSqVK+WhJCnHUDI5rG8O6DptzoL6gsjSajFcGNo3cKqL5bOrD6lSOT2PHetlPt\nFuvm2kuzeASCMhq469m7o68O3E2YraN1e4axS3IGB8oXaPQVTvWe10Ka5EYkYyqihuwwc/0qtZT3\nt5cBbtzsBztA4NbOqwGTRika5PmqcdzhWOPyyaulDqx1anM/Q4WPWrm3lJSGEH0OT/Wuk8K65NNr\nL3mpTqIbW2k8tcYAcjAxgdevX0rnriFWORiq6TmEtECAjfM/4Zx/Ouim1GV0cdS8la53kOuwRxsQ\nxAJ3MzMBz7msrUPiC0KeTaotwR/z2yUH07/lj61wt7ftcSHDHYPujNUmkO0nuelaSqOQlFLU0NQ1\n7UdQZlnu5HUnlAdqf98jitLT7VRoUkzkAMc5/wCBYrnUXC812digl8JMpGQYZMD3Bb/CsGlayKcm\n5Js5a4tfnOVIP0qW3i2DgHFVPtNwi7Q+VHQNzVvTJLm9vo4S2Ih8z7Rjgf5xU8stjX2kFqdNY3sm\nk2M12HaNxE20r1BIwP1IrnLCZzZ6yxYkmzGT9Z4v8at+I7rAitUyP42/p/X9Koabk2Gsf9ea/wDo\n+Kqqv91b0/NHFW+Bvu1+aGW14JLI28yK5TmMsM4HcUGztp42e2kKS4/1cnr7GqA+VqduK8g81rfu\nbWOyk8pdD0zRbYRFpZQZJFJPz9MHIH97nr257D0rTRbabpkdrbIEiTp6se5Pua8Y8PXCrrls8z42\nsdue5xxXq1vOZGRARjdz+VRJ9CUrP0N1GyhkYk44H1pz4MBVujdc96rRuJSP7o5AqeRsIFyRnnmk\nWiHR7m4tIIJUl/fxrsYg9WHBH5itjWfGGoParBbokU8nyLsYkliOvsOCfoOtc9p5EZuIsjMc7kn0\n3fP/AOz1FZubu6kvWB8vG2Eei/3vxwD9AKELbYuWlusEAjBLHkszHlmPJJ9zUjncdin5VpVOWAz0\npqfx4GeatPckRgfMVQeMflUc9yYI8xnLM4RSfU0Mf9IJ7Gq95dIJFREBcOFU9QCR/h/OjqM60alN\nBd2RDDe8CZI9if8AGqvxQ0aLxF4HOoqD9o05vNDKuTsPDD+R/Csya6Mt8nOPLAjI91VQf1zXXeFp\nU1S0v7G4G+GRSjLnqrAgj8q6cLU5W32b/PX8Dkwr5ZeV2vxPm6XNvLHIkiyQsqkSLnGccg+h/wA8\n16N8M9ZIuJpRIF8rDyn/AGOf68fVhXm+tQz+HtcvdOkO7ypGhlQ/dcKxH9ODV/wxqZt4NQMQMUbx\nqpPUt86kAn04z+FbSXs6zh0/Q1r0tLdmfWFpcLdWyTIeGFTVyHw+1M6hohUnJjOK6+sZx5ZNI6aF\nRzpqT3OAl8K2R1eHU4tMMFzDKJMQTgLIc91Ix2zxtPPc5FQ6h4a07UNXfUb+ylluCAPIa4AjIUfe\nGMHtjBP4V05e4zkGL/vk/wCNROZyS2ICcYyUP+NX7aW9znVOPYrpdC2s40is1jjVeEV1AReAMAdv\npSyyCW3EgH8RUc+h61PtlaNQVt8ED/lmcevrUM0ZS2wdoAJwFGBWM2mjQq5qCW1ilySoyxBPGc4I\nP64AqXNQyp5mO5HQdueP89D71DLi9R0k2wszsqxgcsT3yOvp/n0qD7WgZtzAfMQoIPbHX88/Q0sS\nAxo7FmbaDknv9OlKUQtu2LnO7OOp9anUpOC3DzdqKXIVj1+uMn+tQG8h2lkLP820gDp09eO4/OpX\njSRdrKGHXBFN2KpZlUAt94gdf85NDuJOPUrtdB0OxmyzcfKCU4xn35H649Kd58krBUQx45JkHbHH\nAP8AMjpUxPNNLY69KLMbkuwcgfMcn2FRPjIY9QMZpxaoXbJoII3aomNOYk5NQycDmkBW1aIXl6sJ\nlnChAERMAAYGcmsS58LvE3naTeNbXQOfmwEb6jH9K6meFY3a5d1UNGmCeg461jSXoL7IW2Ln/WyA\nlv8AgK9T9egrpMOpVlt9QNu4ntyJ1ZXHl5YNw3II7VDYSvI0g25feeAOa0ItSgRW2Fn2MAxc5Izk\n5Pp0P6etZ1xcz6jKVFxbRqTkRpGXPtkjofqawp7y9f0RnS3n6/oi205JK++Krwss+pyA9EXGB9aL\nS2EhPm30TMhxuVOT7dePzNJFps8E8sqXMZ3OCpViCK0ubdSS6uVYeShyzkdB25JH6AfjW3eEafps\nNuGyIl6rxknkn8yaxdKja41eZ5lbZbRkliernp9eBms/xNqzFjbx85GSM9frWNSfU1hHS3coXuqy\nvIZH2tGh6dAay9IY6lfQXUh4E5WOPsABuJqmYr3WH2jK24+8wHOPb1rU0REguJgOFjOyMD0IHP5A\nVlCF/eZo2lojsXj33EnHRzx61IIlwfl+uKhabN3IikFtzZ56c1KCSpVWwcdQK1ofwo+i/JHHQ/gw\n9F+SFaKJuCuBkdqn1W2ja+jLgFfs0OCQD/yzXqetcJLq2rx3EKNK0winO/YCgYgkeWSBg5Azx/er\na1/X7iLVYbf5ELQ2pySoC5gjZky2PUfMcdT6Con/AB4ekv8A203Xwm2kCjOAuB0pWDRgyIu5W4dR\n0rmo/F8DuSbeTbj5AGDMx44wOnJ4PetbStZi1WF3gDoyMA0bDBB9v1rpuSF0ojgF7bud0BGR3KdC\nD9K0XIZ1kUkAjk/WoJ40kEjDMbEEMM/KwPXPtWNaXrbY7UPhhuRge4HA/SgFqdGojuLSS3nTMcql\nHA7qcg/zr5+1G1ax1G4tXxvhkZDj1BxXudrcAnhsjoMGvKfH1mLXxTPIqFY7hVmXPfI+Y/8AfQal\nPVBHSRb8O+I9Qj8O61bYs5IrLTB5AnsYJSoN5CSpLISy5kY4JIBOcZAxmf8ACWaj/wA+2jf+CWz/\nAPjVR6D/AMgfxP8A9gxP/Su2rEzXm08PRdWpeK3X5epu27I6GPxRqk0ixx2mjszcADRbP/41W5/w\nkV8bP7Hdx6QJI8OGXSbVgFPbb5eP071yFjEGjnmLujRAFCpxzXVfYrK3sPNF1CbkZA/eDexxnJGc\n8nitZ4OnFJ8qs/67mU8VCmrNNu/yJLHWbj7aGibR5VCk/Lo1opHbtHnvWfqvizUP7QlVYNJKphRu\n0i0Y+/WP1p1vKmJXIHmKSCe+Kwb+NsR3DSFmnyx46f5zRTwdNxb5Vp/Xcbr+0aaXL5GnH4y1aJJE\njj0lElXZIq6PaAOuQ2D+65GVU49QPSoj4s1Ef8u2jf8Agls//jVYlIaPqtD+Rf18yuZm4PFmo5/4\n9tG/8Etn/wDGq9Q+F3iM39rd28kWnxXiyA5hsYYt6kHHCIASCD2714mK6PwVefZfEUSiXymlUoj/\nAN1xyvHfJGPxpPC0bfCv6+ZSkz6Ig1G5+2yWt55bSBTIkpgQFx9MdefpVPUrib7IxMNswBw6tbRk\nEHgDBX6U+2nS/gsbvGJFcZAOcbsqR+fFS3aZeaNxlFX5wP4iQTTWEoWsoL+vmS5M5/w3fm9+0WM8\nNoHtD2sYQDGSeQAuOenFa95Zy+W6RfYt5wVQ2MJwT1A+XpXKaUz2filofMct8yAEcHHzAeo6DNdL\nqesLpKfayPMm2v5add0nyhfwz+mal4Wi3blX9fMam0r3Ob8QT/2lrtpo3DXEe1ZSmFWNVA4G0AAn\naOgGOK6OxX7PJO425IRMAY6ZP8zWD4S00vcz6zcSeZLO7KJAc+YeNzfixP8A3zW/d4ieI93mwcnt\n/k1tGMYpRirJCu+pPE21NuOAHOKgnWM2BWUIwcHzAy5G3ofrnp+OKhvNUtNNgH2mbZywcAHgHuf8\n9qyrjxDYT3gD3UaxbRNknbvLDK9fY/rWcn+8j8/0MZa1Y+kv0PHfFujpo+tSRQqVt5B5kQPOAe34\nHNYWea9K8fRRXmkJdxsrmKTKsDnIPUfy/KvNK3TujUvT3H2gRsQAyoFJFVCxwRTxwoFRN940ICTO\neKcFpq9al70AdTfj7X4eWYvuYKkmR3PQ/wAz+Vcv7V2eg2b3/heYo8K7C8O0uAxLDIOPxP5Vx6Rm\nS5SJfvOwUfU0IS3OofNj4VASRkZ4gOOM7iCQfwJrlcV13iuNrTTrJDtKXH71CoPQcY5A7k+3HBrk\n8Zo6AiM9a0PDrn/hJNKz3vIv/QxVB+Mn2q14dP8AxU+kj/p8h/8AQxWdb+FL0f5MyxH8Gfo/yZnh\nFcZOc1s2QFuqR4xzk/Ws2xj8yRf9k7q0N22RCPUU5voas0Vb5ulXkJXAxkmszzzE6hQNxPX0FX4D\nj5z1PPPYVkyGjpvDMEUupTCQuCLG6xsGScwOP5E9+1ZrXEcDCPk84+YYx/8AXqx4fvIbHUkupYty\nPG8DfPs+V1KZJwcY3Z6HpViWbSZNfGnHRr43M8gUMNQjwWPTnyf1rC0lOT5W07bW6X813GldIrlB\nFbxXLNkzMwUZBxjj8P6/zWO5ib5Q+PqDzWhqt9o9nOIJNKuzJbDyT5eoR/Ng4yT5WCelZqeINB3A\nSaVqSAntfof/AGjTnUk9FCX4f/JCcfMcZldWVW5U4Psao3MhKsyn5h95T0Naz3+hNCHSwv3UDkjU\nE6e37mqlzeaAoy2m6lx3GoJ/8ZrNVX/I/wAP/khKPmUobhI/CesTN91b60wD6+XccVyN3cPKqsxO\n48/QVu6rrGmSaPcaZYWF3B5tzHO8k92sv+rWRQABGuP9YecnoK5y6IyoHpW+Gi/elJWu+vovXsaN\nIrZw1HRm9CKRqRj0PbFdgx6n5RT15qMcIK0NJt7O7vBDeXEsKvgIURCCxIGCXdQoxn5ieP1ouI6P\nwHptjPqUl9qsBntLZGZIBg+dJwFBB52jIJODjjIIzXY6DbW93qL2rwwEyKIwJlEcUavjcwUAEc54\nwo471p+Go7Cz0ueDRp7TyJ2Hnw3EhniXKkENsdtrEfgcDmq50CGPUopJpVvInwoEsf7tu/pgjjg+\n55rzZ1ZSqNPTs+v3HVBUlScpK77f0tDz/wAcaVcW95DqDqTHdIuH5OSEUgHPfayZPQnOK5NvlGfQ\n17jcXKaTNOt/NiGQ/KkzoSfl4PzOg4Ax37DBA48o8UXun3l4n2G0EHkoYpGVlIlYE/P8vHf1PSuu\nlUb922xjNW1vcyoAzSyKvOY8kEkZA5/pmi4bdd59VU9fYVEkrIVKtjcpUkeh60ciTJGMjityCScZ\nUNUEcgSTk96sk74iPSqLffNDBGj9oOcBQaAwPUYNVrfzZ5o4YYnklkYIiINxYngAAdTXp+g/B3V7\n63E+p3MVhnkRAea/44IA/M+4pbDPO93HXArQ0yzkeSOdgygk+UWHDYznn64H5+lezab8NdI0aQTL\nEbmcHKyz/MV6dB0HTrjPvU+s+GU1G3RMBZIjmMkcfQ+x4qZPTRDjueWMOCxjkKqeWUbhWlaELD5x\nt7kxJ96QRHYvuzdAK2ZdIFtIIrq3kRgwfAkZRuHAYYOPxFMuLGD7K1vBAfnP8UjuB7gEkA+9cLnS\n2aZ2KNXe6ON1FNJm1GeOd52Rmz9otmDBCTy2wgZ6E43D69qxNR02bTblVkkSWKRBJDNGcrKhyAw/\nEEEdiCO1exaLZeR4dvLK5QNHdJ9nhjlTcjSM2EYj/ZYg57cntWl4j+F3h6+gsLXTy+nGedkVkJlC\nkxu5OGOcYjxgEDvxzntpTUonJNWla586yHcxPvTf612njD4a654Sja6lRbrTw2BdQZIXJwN4PKnp\n7ZIGTXFkVoSC9ad3pq/epTwaBHW2MS3+jQJKQVIAYeyt0/IVeSR2chCQplC4X8qy9C3JpEjsMAv8\npz1zj/A1fgkEZiZ22IZQSQM9/wD9Vb042RhJ6mjdybUaP/nowTHfHVv5frUlrkbSG2k5Gfboa6/w\n94K0zVdNt9X1C5upIizEQxgIo2swO9jnIIUcjb1qDxjp+h6XqFhDoyxiNo2L7Z2kAJUAD5ie6t37\nj2rbnT0IS7FfRPMLzB8bSVwNuOfrXH+KtPhk0YXsEhIjnMbKyHggspw2NuMjpnNdtoVzJDcSNGkR\nTGCWjDDI/rXFR3H9o6Brtqdu6KSSdVaTAClwcKuOTnJ69M1lUSNKZw+2uxsrBNL8JvqMrOsrthF2\nAhmPQE5yOPY1g6Lpz6rrNpZRruMsgGOx56e3pXReKJHutQs9DgyzQYR8IcmQnkY6nBJ+vauc13dj\nkGy7ZPJ6mvU/B+y+8KwRMmRCXibPf5t3/s4/KuZv/Amo2NoLuT5YiSAzrheMdWBIXqPvEckVp/D2\n72Lf2jEcFJVU9uzH/wBBqqTvKxNVLlU4tNeTT/IzNStfs+oG0lHyo5MT54G4YII/Ac/5E4CrZMhH\nYcfiBW54htojcJKw5KgYx/tE5/WsC6cJLJGG3Y6nHJJwT+ua646MylrZl2wl8zSZLV3IDo8e4/Q/\n0ribiMQXMsZIYoxUkdMiuktrv5Gt1TcMvIWz07f0/WsLXl2XkdwoAWZecdMjg/0qKyvG5VJ2bRTL\nE85pMmoVYscjNSjJ61yGxpaBcx22uWrzDMRcK2Txz0P4HBr0a6it7GZftUwt4piRC0wZFYgc8sBj\nr3615Oa6+9mk1jwwrPK7uihsM3AZeDx9M/nUyipKzHGThK51tvBDjzhPF5Q58wuNuPr0rHvPFUVl\nrFn9ivozNbGSZHTDKHCHaSeh5xxzXmrZBoiieQuy/wACliaUIcr3NJ1eZWsa8GpyI2HO5fT/AApt\n/dI4UREcjLYFZEcjbsGpCc1ozLqKG60qfMwz0FRE4zT4249qQE57Cu08MsZtF8pvuLI0f4HB/rXE\ng5Oe1dT4UnP2a4iPRXDfmMf+y0mTI5duGweoOK6nw7Zx2+nyXkvHmZO7HARf8muevIS+sSwRjlpi\nqj/gXFdLrV0lppC2kRIyojA9h1P+fWmxy1djlbydrq7lmbgu2ceg7Crmm/8AHhq//Xov/o+Kn2Gh\nXupKHiUBWztyCWbHXCgEmrSaXcafYawZNrJ9lC7lPRvPi4IPIPsaxrTXLbzX5oWIpT9m5W7fdzLW\n172+Rz74yKaTlselNL/Nj8aM8e5rcocGKsGBwQcgivVPDl8NQslmyOUCkDse/wDKvKc8V1ngnUvs\n989o5wsg3Ln1HX9P5VMu5Mu56lBID8voOlTs+51XPSs62Ybwe3arKvukdufTikhlW4EjambcITDc\nqN5Hbbndn6jav4n0rQh5ViBjHbFVkiZtR87zDtWErs7ckHP6VPCQEc4ph1HgkdOKRmCFhn6VC0uO\nuKc/zRhu2McVcFcllWS4/wBLjAJ5PI9qzop0nu4woIKXT+axPUgf4YFWoxnUAcZwKpW0IgkuH6MZ\nZGI/3jgH8lppag9jXSYyo8zHBeVz+eK6/wADymK7cE/fAH0rjkQi3gQgcsc9/Sui0O4W3nUjIZWG\nCDjApYd3bXm/zOWPwNr+Z/med/HLSxZ+NxcRpxe26y8eoyp/9Bz+Nc5pNrHY+HDc3u9RPcARwrw0\nm0cnnovzdcHJ49celftAW4ksNFvEzktJGxHcfKQP515C/nfZ7aGKF2WNedq9zzXbVfwTfb8tDrq+\n9Z9z3P4Va1JdlosJHCrhUjQYAyD+JJ9Tk/lXrdeEfC+NdOht5LqdUlnuRtiRgzMB64Pyjr15Pp3r\n3bNRU1SZnhH8S8zH2H+8n5n/AAprJnIytOpO1YljWBAAULwAOSf8Kr3AYwNuAAzng5qyahnyYXAp\nPYDJzmmNhhgk/gSKD157U0mloMCeaaTQWpjHFAAW5phcU0k5NRlvekMkLU0k1HuwfajdnpSAD+tR\nv7U41n6tq9npFqZ7uYKMHav8Tn0AoC5ZzgmopCeawV1XVNVgMlpElnG33WcbnI/kP1ph07XTE0h1\nvDj7oMSkH9K53iKadrm6w9Rq9jT1q7jXT4VMgDg4K98euOuOlczJeSPF5VpcojOctKZAG/AZzVpL\nuU2Vzb6zFEZkjdopgPldgMjHo2cVzxDsqJMoMrDITndj6+vsfz6V205qcU0ck4OEmmbtvBDa6dIC\nJpAZFLFcZbhvUjimy65Z2imKGKVTjLZAz/OseBofsEqSPPEsjo6MkW4/KGGD8w9fXtUS6fHcgkXd\n4B/tWyc/+RK5vaqm5KSe/byRzw5oyknF79n2Rcs9atRFjc6szkkuOK0Yb6KSJ5vtsaqG45GMc5yc\n49K55NGikyDqVyq+n2VR/wC1Ksx+HNKIzJe3TsOQXQAfiAar2vk/uZfto9n9zNrTvFdjBbzQTRyf\nvJWcTquewA3DtwB+dYt6qalMJI5FImbGVPRe/I9qoay2nyPHYLe3cSqMBUtgwAPPUyfT8qxv7Q0u\nydVstTv4jGeXWyQ7vzlrmdS72f3M3WIjvyv7megQx4iwDgoPkIGdoHb6VzFvdFNQuQrAb5COB0A7\nCpNJ8U2k8gtZLqeRiPvtYqmfriU/yrS0bTtNt78uZpn3AsDLCoH1zuNaTxMYq1n9zCDcnez/APAW\nbMksqXk5CDYJWHTHc1OZTDFuY/Me1FxcQNNt3Z3HcPoTzUN/aw2mBeatZw/KjlSk7FQ6hlztjI6E\nd6wp4pRhGEtHbzeyXZM6KOG9lRg59l99kYt9AVEE322GBDNM5MqZDbsAgKeHxzn0zVzULiwfXlju\npFQx2Fn5MsRMf/LBScFcY4b6YArAuNNjumDTeJtOk2jC7o7s4X0H7n3re1PRYnukn/tO1C/Y7Rd4\nt7phgW8Y6iEjng+uCMgHipqYumqsHG7dpdJf3f7pUKDcv3nuoozabbWwMUMs00ckaunkxFmDg8Pk\ncevGcdenFb+ns8dgrzRpDcv802xRhm9Tjv0rB0+C0s7qNIPEunbWkBETxXOCTwefJ47fkM9BXXJa\nW7AkatZkf7sw/wDaddUMXSl3/wDAZf8AyJlVoum+5Xe+8hN08DFRlhJFyCB14z+nWuWvnjk1hDBK\nDCSZFZT6/wD1q6bUbZbexubmC/tJpYoJZhGGlXeI0LsOY8dFPU1wHh2dXZXLMpYclTWyrQndRe3k\n1+aRkou9zurMxLGq9PQH+tcl8TbTdb6feqhOC0Tv6dCo/wDQq623jXaD8p9wfzzXP+L9Vg1DRJNO\n0+3e/kLr+8gUukRHPUdTjj8TWjatqLW+hw+g/wDIH8T/APYMT/0rtqw66DRoZYNJ8TpLG8bf2YnD\nqQf+Pu2rn646X8Sp6r/0lGz2Rf05g3mwE4Mi8fWrKyp5cVsXZpFzuDDge1U7K3hmErSXsds6DMYc\nN859MgcfjV2S0S4dYvttj5m9l83ewGFHB5HQ54+nOK621KFnujCdO8hUcm1lWEje74yPw/8Ar1Vv\n5FZ0iU5WJdtWJbdreDENza4yynZJk8AHP0OcA+orMZSpwetHMow5V1Go63G96RqUUh71maCjpUtv\nPJbXMU8TFZInDqw7EHIqEU9I5JG2ojMT2ApAe86VqKrosV5EpKLJHcKAQcI2CwP0/pXTvdJd7mXK\n45cN1BOP6VwPgqK6i8IyWl3CBON6wRsQN6EZAOD1yW6+1b9hdiXcM4DsN3sdvP6/yqU7Da1MO4uf\nJ8XC5dZN/wBoCqQcjD8D2HQ/mah8TTrca19jdmdYIwipg4aQjqfpkHtTfFE5stahuFGUURSAbsZ2\nuevvVPTzPe6jvn+aTcWDhhjO4E8jtgk4pt9SUjvdOlg0zToIS3EaYBA6sef5k1z3ivxPHaSRQWhD\nziNW3Hov1HrWrquxbSQRIcJFkH3Xk144PtOrahLIHAZ3/i9zU7K5SV2TXrzX07z3EjyyyHO52yST\nWjc20Fw8cn9pWsZWCGNo3WXKlYlUg4QjqDVw+GL238uRCrlhgMOxp114UeOIv5hLnG7jqa5ZzjJp\n81i54SpNqSdrenX1Me4hWPT5bZ9csvJkGcFZuD68R1gjS7PP/Ie07/vi4/8AjVaus6ZcWluN8ZMf\nUNXMYw1bwjJq6n+CMJUakXZzf3I1zptpx/xPdP8A++Lj/wCNVGdMtN3/ACHdO/74uP8A41VHt0qJ\nvvVXJP8Am/BEezn/ADv7kaw0y0zn+3dP/wC+Lj/41Ug020/6Dmn/APfE/wD8arH3Y6U/J4o5J/zf\ngg9nU/nf3I9B8FrZ2r3kLapYzh1VwojkO3GQT86DnkdKyLLRLdvEywxapaSOkrbY1SbdkE4/5Z46\n471X8GXq2Xii2d1cqwIby8buPm4B4P3enfpxW3oME0XjLVL2NIzLbMcxyLuyzZOMHHoe4p+znb4/\nwRLhUT+N/cjRvdHg1TxZDYx3Wmp5SmPyGdwqMMsx3EbTwCeWwT+Aq7qvgrTpNNuGtb+3lkhBZt0k\nZKLgbSBGM9+Rg8elcH9pmn8QS3MBSQJMroW2r90/KcZ6YHIrbvb3W9SsVQabPMkOws0MTsGYbsNk\ncKp/ujjjin7G+rl+CMp/W4yioTfL1+H8VbX7znZtLtVDKdbsARxyk/8A8bpdJtrCx1ixvJddsWjg\nuI5WCxz5IVgTj9114rLmYsGZup5qpUSpSknFydn5I1lRnOLi5uz02RbsW2yj34q3LnBFZ8B2tket\naUuN2R0NXLc6GRrdNvDsAWUY59a27ORnjAc5yOc1zUpMcm7tnp61r2dzkdfeoktCWjVDyw39mylm\nUSAcn3/+vXRrZxjxXcrN5W1IFXGBtDP830HGf8OK5OWc/ZeDgnkGtQa3cySSTpKokuQA46kAAKuc\nk8jAPt2qovQV9CO+uFuZAURtrS7NyoFBUHg8e1VptPeSZ3kPljd8uB1rR2Ztlx26VLKu+2B7gVlz\na6E8xl2ULQiQyoudw25AyPxpt5IGUjjnjFSzcxE+lZd25x7EUbsa1Zks5LPnr0qOdsyY9BikDZkY\nn1yaY5ySfWt0WDdKYw+TPoae3KZphPyY96oY/wDhpUPNIelEZzQBoWWs6ppisthqV3aq5ywgnZAx\n98Grx8Ya6yKjagxYDHnFF83rn/WY3Zz3zmsM9aQdaTinugu1sWby6uLqUyXE8krkklpHLEk8k81W\nPMZp8nIFMQ8EUxDF+4p9DTtxMgz24pE/1ZX0Oaa3+sNAyzGwzz3qq/EhqVR3NPtbOS/1OGzhx5k8\nixpngZJwM+1AHq3wh8PW8Q/4SC+gDbnEVuxP+qBLBnx9QR7YPXNezCbybgRv908VzmhWEFvbpp6I\nRbm2WJQ3+yMDPqcVdMrfZNkhJlgJjck5PA4J+oxWV7u40bk0WO2VPQ1UltsngVQ8PaqGC2Ezd8Rk\nn9K6ExAZBwMVotQMaTT0nQLLEsgz0Y4/GobfRbWJznT5R6OzKR+jE/pWs1zEkuxQzgHDOiswX1BK\ng4PscU9byFVDTAxoRnftYoB7tt2j86mUIy3RSlJbM4zXFlt9X0wpFiNJWYnsvyFf5Mfy7da19LW6\nv9Vgnlt5Yre2idgZJEO92wBgKx6KH6/3h71t3NpBeoeFOOOnSnWNotrCygdT2qkkloS9WR3ixT20\nttPGksMqGORHGQykYIPtXyp4w8PP4a8RXWn8tCp3wOf4ozyvYc9j7g19U3Z4bmvK/iT4ftNatIdR\neUxXFupj3qM7lJyAR6Dn/vqm2orUa97Q8LXrTsFnCgZJOAKb0Yiremx+Zfx8Z2/N+X/16aV2S9Dp\nk8m20/a7HERXAA68YqOc/wCjjGRwKc4LW1yg+95e4Z9uag3iSyDjuM10vQ50T274kZGCkDoxFX7c\nATwkyNg7gF6diP54rK3FJzjvWxahZonfAJCZx75WmtgZ02g28kSea5OxwP5k5rzywvlW9vvMGVuY\nZOAP4h8w/ka9B00/ZdGurlsHy1LZBz2z/U15HKzDaw6jms6rLpLRnR+GRFbJdX06gxqhRSRkA4zj\n68cfQ03QtXmsvEkeq2wiW5jk8yIOoKg+mMj8KyxePFYm1Q/JKAX9+4ptjeS2M3mxLCzbSMTQpKvP\n+ywIz71jzWKaumu56/rPxElvtPmSO6mnnuIpISl1axFYlOAdw42gjODznrx0rifDF8jeM9qsiR3Q\naIs5wMYzn81H51lDxTqAbJh01sKVwdMtyMfTZg+3p2p2i3b3viuzmdIUbOCIoljXhCPuqAO39eta\nc93oYU8OqUJLmbv31PRPEFi0mnJckhowdm5cEdzxn8a4nUCBPGin5mhOcnOSSfT8K67Uv3umyLIR\ngAMGIzjtnj61yk8cTxR3Msi7CTHneN+ccYXOSOvIGBXQ9hooWT4M4/i8k/zNQ65EH0OGcA5Scr+B\nGf6frUhZW+2TooRWxGoxzjPNSXyb/DLqc5zuH4H/APXS3jYa0kctGwAqcHiqYPNTo2a4mdBNmuk8\nKyvM8ligBZiGUfoefyrmcjFWtNuTaahDMCRg4JBx1/zmgUr2E1Wyax1K4tmUqY3IweuO1Xba2WHQ\nZZ2HzOrEfyrU8cxGbULfUhGq/bIwxCkn5scjJA55x+FUpS76MtrESzmIEqo5Hc0MG1oznUXHNPoV\nlIxnn3pDQMjkOKN/AUfjRJTF60wLJYBK3PC85F1cx4ODGG/I4/rXPMegrQ0SUxagGyAGVl5+mR+o\nFIUtjVhgEvieVz92NfMP1I/xP6VV1q4Mt6I8nbGMAZzV6J/JW5uiygvgfUAf/Xrn5HMkjO3Ukk1W\nwLds9b8A6xpdhpdzZNpVtLcyS7g8k+xnXbkBAPm4weg7ntnFHxrqllqiai0Nk0ExtDJLKJg6TAzx\n7MYAGR83r1xXD2t7p0Kx4nv4JVGBJGquV9dp3KcHnirivp76NqYtbqdn+xoXie2EaqfOizhg5Len\nQfhRWmvZ/d+aPOqYWUajq82mmll3j13OSJJY1IOTUQ4epl4FI9Jh3qe0uHtLqKdPvIwNQ4paBHsW\nl3qXNskycgruzWla5Me4gc1xvgh5JNLmL52q3lrnv3I/UV29uNqDt7Go8hIS2gaOe5lYth8BRgDA\nA+vPX2qWI/u2BHepSenNRpwjdafQZSmBwccDPrVmE74mXgnqKguBjJHfvilt5NrAeoxVrSQnqiuM\nLd/Niq6kyahJGOMsAPcYB/mTWtPYIphnmv7WDzQXVHEhONxX+FCOoPeq9vp9u2pSMmr2ZPlg7Qk2\nR2/551hLFU03Z/hL/IfK7Fy1AlBcjKxswH5CrtmyrNlgNv0oWwjhhEI1C1UdWYiTBPt8v+c1LBDa\nWtwjvqlo2052ssmD/wCO1GHxNNX33fSXf/Cc9OLcZer/ADI/i9FI/wAP7S4VA8lpcxuwK7tqlWGT\nntnaK+f4nuNTvV82RnPqT90f0FfRd5oK67FrUVqbKe2vLV1WTzH8yMnDgbRHyPMUHrnHQZznxa30\n/TrRXhj17RDKxGXeO8JXHoPJx+ea76uJpuC1en92XX/t02inyWXTQ6fwfck63aLFkxJIsafnX0kO\nlfN+i32o6HrT6fNNDG1tcNFM1rGsQfaxUglVBZc5619HwsHhRgQQyg5q3JTpxlHVP/hzOhpUkn5f\nqZPzH/llJ/3zTctn/Vv/AN8mn96ZIzBGIJ6HvUFjSxPSOQ/8ANMk8zawET9DyV46VMCTGpyeQKM/\nMO/NAznnOHYe9MJp0vErDvmoiazWwxSRUbNg0pOeajJpgMZjTC3WnMQajbFIBCaTd3pjEg0oOFpD\nGzTLDE8r/cRSx+grxjU9SuNc1r7TMxKF8KmeFXPAFera4znRb5Y0Z2MDgKoJJOD2FcD4PsI7m6ku\npkDJAoKqRkFu39azqy5Ytl0Y81T0O3t+LG3HQlOlPeUqMYrlfEOryoSn2p7aNepRefx6n8qqaBe3\nk14YDdSzAjIWQDp1B6V5vsrLmPVhV5pWsdNdqk0Lqw4YVh28EX9i6m5bdeRP5JJ5O1sfN+IOPrmq\nmoa/epfPDbGDCcOrg9ver/ha0m1GPWbyUBfO8uIIDnkHJ/pXdglKMrHFjXGcbmzbaNbyaPbRMi7o\ngChZc8+hHpWTd2TWzsI9yP165XP411hXZbIgxkYGKybyVF45d/Ra9GUU1qedzvmujnYGn3MHQDAJ\nJzgVJIQ8IB+UEZZt2ABVm6T9w13c7Y0Tp8xBJ7AYPNcXrd550Mskcsy5GNoc7T26VD0Q92U767W7\nFxNBks7bV3cHbmsB4XDEcZ+tSeawHXGKiMhyTnrzXMlqb9Dp/BOkpqOtiNjyqFj9On9a6Lxb5dm0\nCWz4CArxXG+GtZ/sjUjOTw0ZQ/j/APXFXrvUJNQulIyyk5LHoKzlFueux0RnGMFbc39J1PzoI/M5\nZCcZrovEdhb39y0pglnn+wW8kaohKqBCmWJA9+hP05NcbbmMBUaDr0IbGa7G9dLW4kvAdQhZLG1R\nWiJ2E/Z4+CR9QP8A9dc017OtGXlL/wBtCpV9pC3Y5q18S3Vvo8sf9nslt5bW/UiEFjnOMcuM8En0\nr07TI0v9AgeO4s0DWUAWVbiPchECAlgTwQQRgjsK5LQ7u0fRnt2/s1Y2djIl1O8ZY+6iIjoAOCel\nMXw7FqFg8VpZ2pyWCzwRXO0HJ7+Rjj6/lW86Dc4zjKzV+l97enYznXbg4NeZt3umHVoN1ze24toZ\nAYiupw4bg4YkNgHk4/8Ar8ZcWj3qyNsvtN8sNhTJqEJJGeOj9enpTtP0aCO0WOaydSmVZTISoYHk\nYJ4OQQRVhoooAWt0giB/iJ5/CtYU6/Wa/wDAf/tjBuPRFC807UG1H7N9s0xl/s++3KNQgUkm1lUE\ngyZxk8noACTgDNee22ga1Ztut77SEPtrVnj/ANG1qxyed4zvZFkLbNNv1JOc5FpNXGzmNrhhGoVT\njgHIBxz+tc3LW9vP31tH7Pr5lxtyo6WfSPEF6oS51TTJUByEOuWhUfQebiur8HaFeWthMJJ9NOZc\n/Jqdu46DushryoccGuk8LX7wG4t85yA6/wAj/SitCu4fGv8AwH/7Y1o2UjovEdo9lF4hmuLnTwJt\nOjhiRL+GSR3+1QNgIrlvuqxzjHFeb1p61I91q8hAJPC8VbsLGK0dJbhVdj0HUL+FXSi6Sbk7t+Vu\nlvMlrmZh7SOoNGK68SJdDfcpAAxyo2DP59ajMFpDLlFiDDpvQMp/OtFVT0YnBozdYtfJ0zRJBcSS\nrLaM6qxyI/3rgqvoM5P1JPesiu1htIooixgtn8/K87JNvOeBzs/DFP1DwzFZ2wmYwOskTuAhGVIT\nIz+YrREN23OGHWpbVEku0SQZUnnnFRDrVixXdfRj3olomVH4keo6No+iJagx2EDSEZ3ON5H/AH1m\npBpdu2qecIlGzbwBxUGgbwDn7orasXMl5MgXdgqeBnHX/P4V51OT9pZs7qsUoXRrR2qtlgQpKsxx\n9a5fWJpbfVWgtnYK5MoOccnqfzBrqrm1u5rUxWlwkMjDCgJuLDPc9v6V5nY6pqJ8SrDdRtJOrlI9\njAggZ6HoR15HHOa7ktDgvqTeJXmvbzT4GmV3KiISBTjr+vWuy0TQ4rO1iRgQFO3JHPPeso3Lveqq\nWiGUNhkdwVx1ySOQQcYxjvVmaTWbl40klSKPHPlkjI7Ci9xG74qlto9JlTevmCGVEVRyTjgYFeR+\nGIDda3HFk+XHmRwO+On6murvIbhJvmYt7+9TW1tHC0FxhBNLGpbbxgYHGPqDWVefLG3c6MPTUpX7\nElz4nlsJDbw2DysDjOevf0xVu31zz9Kk1B7UhY+WHWrF2+mLaia4wzAYwD1NV7G806SCWMXEQ3HB\niJwT7VxJqysj0owkr3ZnXHiS213SLm2+xyRymNwhIBGQPb8K8uit5L2+EKFQ7ZxuOBwM/wBK9qlt\nrBNGmmssFihVVJyQfSvFpQttqUgUkqjEA124dp3sefiYtNXEaCVGKMvzDqAc1Xfhq6fTLD7UnmEf\nLgc1l63DHDMgRcHua0jUTlymUqbUeYyxT15NMHWnJ1rUzLdndGy1G2uVZl8qRWyvXAPOK63+0La1\nstYuDO32ieVjG2cmQEAYbjsT1+tcQ9aGoJPFa2izoyOy7yHGCQ2GB+hBBHqCDQS1cpA7gOakDMEI\nBNV88cdqmjORzQMaxzESetQAEnAq1EgdxG3IJxXa6RpVu8eyGBRJ6gc/nWc6ihuaQpuWxwkTBW+b\nIHfArTEsUkQVG3EVQvoGtb+4gbrHIyH8Dit/w9Zg2xlI5Y0qkko8wow5nYw7hTyanssuoAPJ4rU1\nO2i2ORGoI9Bis/S0AjMh98UlJSjcU48uhp7dwx26AVrTWLRpZSiJYop7cSRoGLE4ZkYnIHVkYjGQ\nAcZ4NZKXMRmkiXJKHBPY12Q0iUWmk3N47NFdxlbdgDu2qB8uAD8oPcD+LnNHK7GVjPjUG3A9qVcm\nIqcVo32nGwtkcMc7yjqc5XgEcEA4IPX2rNUjke1ZNNbkGdKCGK+orGuz8jKe3IrZuOCD6GsS9OMk\nVUdyomdawCd2y2MH0qKRRuYL0BNWLJgtww9eaqlsNxW63NWtEC9CpqP296l3NwSBUbHLg4x7VQhW\n6U6LpTG64p6fdoAXvRQKU8AUCHNygqNODUuPkqMfeoAtaXZx3uoPBJK0UYjklZ1TeQERnOBkZ+7j\nqKkkh0Tfn+0NQHHaxT/49UmijGqye9ldf+iJKynU7hWLTlNq7Vrfr5MwtKdRrmaSS2t1v5M3tIsN\nEudXsoWvb+VZbiNDG9oqBssBgkSkgH1Fdd4Mh0e71SW8s7Pa9sgAk8l02s+VA5nfORu4x+Prw3h0\nk+JdKBB/4/Ief+Biu9+HlukXhi7vEyJpbsIc9NqKCp/N2rGdLmnZyey6+b7Ixlh+eraU5bd7dX2S\nPTyskEqswdHH3WDAKB9NtVby/kW6M7w3LQyLslkwOo5HJA9wMA9etTQXFvfxrK0M6vwSI7gqM+wO\naZr11bQ6WbW4abdJIibY5mZ03MFBcgjHXv196X1WNvif3mn1KP8APL/wJlmwti9uksDoWBDcZ/T5\nSD+BIrUvPt8kBJLyuq8xq6sSDwMDYP8A64BFZlpqCoQowAOwpNUn+0xvGpG6eNUTJ4DqSQPqwJH4\ne9Cwy/mf3i+pR/ml/wCBMctjc3/728DDYwTLKyEDk4wFJ7gDt2APaW40yRCJbcvFJChbBkbeQMDC\nkIvPbqaytL1220/dazndiQk7SQEIGDn1554HB557aM/iK3g3W1tLulKup2rsVWz34GO/BBGW5PWq\n+qx/mf3sf1KP80v/AAJlnTbye6dzBNtCPh/3igZY4Uj5CMZHPqWB45rY8rUv+e3/AJFX/wCNVzGh\nSBUP2kFZJWRsH76qjbtxJ55IAA+pHFdKdQjzw+fahYVfzS+8Twcf55f+BMqXkN8F+eTOf+min/2m\nK5LWohPoeoxzDdGIXd0wTu2jdjAKk/d6ZH1FdH4g8QppWkyXhgE6oVDKH2kAsBkcH1rKjkR5GfIa\nPzDtHBDDNOWFil8UvvBYOP8APL/wJng/23wxn/jy/wDJSX/5LrRsY9AurGe4to5bd45Y4y8dswJD\nBzjDTsD93rxjHfPHM67pp0jXb3TyWYQTMiswwWXPB/EYNaugg/8ACPXx6f6XB/6BLR9XUXFqT3XU\nyrYVU0nGct118zaWLTop0f7Xef8AgKv/AMcrL1Gzh05oYYrh5YZkMyF49hUb2XGATn7tW4yJCFPJ\n/Kq/iYD7Lpki8MtuQfp5slds1KEo+83e+9u1+yGlKNSK5m733t29EVpi5+4jED+IDj86s6ZHJA00\n0hQ4GwjO7Oc/4VXW5QWgkYjoPxqxpGGgummJzI0ePYDdmtbHR0Op1SdLHwNMyt80vyL2znj+Vebb\nNx59K9R1OyB0GwhKo8buxQuFwSAM9f8AfFUF0FLmNYjpTSSEYBhhw34FRgnr1BrKo9bGlP4TlfFG\nkXGi642n3X2XzoYIAxtVIQ/ukIPIB3EEEnHJJrGAwxr0bS7KDUPGdsvim3nvGnlSO4+1yOkjA4VS\nxyDwCPToKy/F3hhNAvb+E2wRVBaFtxI27wAQc8+nf86g00exxtafh19mv2rHoCf5Gss1f0HB1+zV\niAC+CT9KcdyJ/Cz0rUlA0+YkcBQSRXIzsJEg2oco7EHOc9PUe1djdWsn2KfzW6ocgnr6VxkjiLbg\nAoGIyPxruWxzozvM2WxGDl2wB+JrSuYy1gsHcxkc+4rKtj9pvYoFTKg5Jz0rZuGDTMcjGSMUqeo5\naM4UDmpR8o+tI42zOp7MRSD5mxXC9zpJRwKXtSd6O1IDqnka/wDCUcnlnNqxYuOjNkZ4+hzn6VW0\nrVxYRTSm4CbolhZP76nkgjHT5R0xVHRblVeS3ldVjlXB3Z78HGKzZLl1BhliifaepXBB+owafSxn\nKHNHluRXMonuZJVjWMOxIRc4H51q2umG4gU9DjrWOi7nVfU4rsoQIbUH0FY1puKVjqowUnZnM3th\nNaHLYK+oNU0659K1NUuvMwPes1mBOB0q6bbjqTUSTsgUF2qxlowuw4YHINNiQKMmpAMtzVGZevZ2\nW2hgwQSoZv8AP1qhgkcda6HxlYNpuvfZpZJZJ/IhlmaYESeZJGrtuyTzljn3zWRFEFiLmTafbrRJ\n2Hy20GR6dcTEKsZBPQHqfwrVttJurWw1bzFQhrRQpRwf+W0R6ZyPxptrbXU6rHbAu+MEjsPrXQaf\naXE8F9ASoZbZURCOuJEJz+Q+maxqzjyfd+aMq8X7P5r84nnqxu0+xUJfP3QOa2LfQbyUZcLGP9o8\n/kK3JdIhS7RuVmIyFPX6fUVZQMi/Mc+hoq1JR2OunTjJ6nLXOmG3dYzOMk43Yp9vpbJKyyHcRzmr\nWoILmfYRkdTU2kWxSOWNRyXCgGqjJtakVIpbbHbeHLQWukQIAFLZc4PqeD+WK6SI8D6Vm2qLHGiK\nPlVQBV9H2jFMxWxLkF8nP4mhjheOlNDjdihmGCMVotge5DOd6+pqpGxU49DV3GQe9UJQY3B9aJLS\n4Iu66T9l0x8DBtj/AOjZKqaG9quoTS3l2lrAIQXmf+EA9s8Z+bvn6Vc1bD6LprddsBOfbzZK5c6S\n2vTLapgsgeUZ77ELEfkKxoa016v/ANKkU13/AK2Osm8XeErICa3tr3WI33RoRH8rOAC24PjAwydF\n7H8aL/FgaZbMbPwhb2wUZG64BH/fIQfzqxouk28OmRI8a9S+MeuP8BWjrPgmC+8LXl00ZULGzZXr\ngVOFqOcnFLq/zClSiou/80vzKXh/48Wd5qNrb3+iR229wr3KTHCg9wu0/wA68pnfS7zWrqfzbqOJ\n5mKbIVf5c8dXWtO90Oz029tnso9kU9qX3OSSCEIY/mDVnwX4C1DxFqMSxPB9mBBmljnRzGvuobI6\nd8V6t5qLg+v6GLqJRfZ2PTvD/guz13xPrl9ctcmCPUJhyioGfzCSBycj34NesBVUBQAABgVFbQJb\nw7YlRFYlyAOrMck/macfMz/rE/74P+NceGd8PTX92P8A6TE0jDkbb3f+ZnU1wSjeuDUgYjoF/wC+\nRSbsjlVP1UVsQRxgrBGD97aM0YpxYkjhcAf3RTRgucouApI+Udcgf1oHuzn735byUepOPzqsTxVz\nVABeE4ABxwPpVE1mhoRmqNm5pTTGpjEJqMtzTm71EakAJpN2aQ0w560mBT1EE2jHymmCsGMS9Xxz\njv3AP4VheHYoBd3flptBCsy9g2OR+ea2dTNwbGSO3GXkKp9ASMn8Ov4VWLJbKUTbuUBWYLjcR3rj\nrxdzuoTXIl1RFqljZSK/2jAXuQcGs7SrjR7JvMiaKNs4Xc2Wb3xUWpPLO+VUueyDvWNd3ErHZPps\nqkDr5i/y5rBQT0OtTtsize6Np+q6jLNDKpkJz8p/PHvXU+F447LTriJ2UKJeB/EflFcfYuZVDQwP\nC6tnLLjP4966LQgHubiZ2zjCkZ6HrXZhU/abnFiuX2eiNq5l2ozEEA9AKyXKRRSXV2xjt4+w6sew\nHvV26mVi2XCqOrHsK5/V7oyrbjJRXYrAp6oo+85/2j29K9GR5iKcv2vWLoXMy7IFO2KHso/xqtrN\njEljOm1N7r8oUdz2AH+f0zotdJFEiRr8gGBismWbz7iSUkk7iFz2H+RWbXVleSPPpjgle+eaiJzX\nWz6LHf3JlWI9fmIOAfrUF9DBZ2n2eNI/MdssV7Ae9ZcpqpFbQvDF7q4+07DHZqTmU/xY6gV1IsF0\n792ihlHQ+orW8HwyweGELj5ZXZ4+f4c4/mppL2EFjxxn8qLaXG3rYowwwy5EEixSH70UmNp+ma29\nZjEOoxNJZnP2O1+eJtpyIEHA6cVhNbbwVOPx4x+NbWqXN1a6hDGrGSEWVr8m8oQfs8fRh/XIrln/\nABoekv8A20roNhmtgS6xXRz97bHgn0yVIz+NWLR44keO3tL2ONzuIjcJg+xBB/Wqi6mo4kmu4P8A\nroBKP++hirEd9ERlNShz/wBc3z/KupEMspY2snLWEwI5O5sc/nSypHHCyxRBeMEIPmP1NRrdxSgB\npZpz2CKQPy4/WnTTN9nO2LylA+71P/1h+NaJks4LTIdnie+w4YtY6j0BGP8ARZvUfyrkJkMT7OOD\n1r0/RZ4v7TunQgk2V6QA7HpbydMAY/P+Yrg9WbcCDGoYH72ME/1rij/Hn6R/9uNF8KM04cbzjPQ1\nueGtJvby5a7tfsywQOkUz3F1FCoLhsAF2GSQrHA9KwoWCtg42twa6CyUx+DNVB6jVLL/ANF3VGIl\nJQtHdtLvuyou0rmnceFL7zJHWfSt7NuJ/ta1H4f6ypk8GavdRM8cunuB/c1O2b/2pXNvPuLYyXPP\n0FemaDb2cXh23M8cjM4yFiTczn8q46vt4Je8v/Af+CdNKCnc5yx8A63cSqxewxnGRqMDfyc11cnw\nviNo0guo2uAvH+kxhc/99Vp2uo6c7C3hlmikXgJIm3+lXZNQgtNvmTynP3VQbyx9gBWHPiW/i/8A\nJf8AgnSqMFE85i8O39peLFcT6YI9+G36nbZUeuPMBNP1qxnsJHicpJHLbySRSRyq4dPnXOVZh1Vu\nM9RSeOJoW1G3ZIJE3oS/moVbOeDg/WtO9utMt9H0bzlXc2k3ChmuUIyWuAPlxn75yDn26jNdlKdW\nMoqTunfpbpfuzgqxWvkeSjrV7SE3aintVHqxrY8NxebqQrrqu0WxUleaPR7GPybQHGCRzVzQ5GM9\ny5O1UI5Pc/5/pUBPl23J6CseHWJYJJooXHyPuOBznFefhk3O53Yl2hY9DimHkyb+A5xke46evP5+\ntVdX0fSdWKNdRgzKMIysQwxj0/yKxrHVWvbZQxX5cZI4/D/6wrWguBkKSRxwOBx/Su9HnsyV8KW+\nnsJNNvLiNt25objEqEd8AbSPrmtW2QwThn2MFAB2jbzj05x+dKXZT8p4HUKePzqk14wJLggZOSOB\n+ZoEWL2CVyDFah2K5yxAAJ7muSn0rUbW/u76WIQoyoEjRgRxw2ME45wfxNbMXiaC5votPspI7m5I\nOdrnaoHcnHPHYfpV6WJ5AfNIJK7TgYFYV6sYrle5tQpyb5lscVJBLqVzGFuNoGTx0zV6LR7QuXg2\nLccbWaYNz+CjP04qhfJcaPfNLCgKE52kcfhVm38SQuT5dpiY9wnU1laX2djrhOFve3KV/fTaVBdW\n4m3SM5IZcjBB6iuFnbfcsSep5NdVrUUSNOk10ou1AaZCQMZAIA9fwrkXILk9RmuulFJHFUm5S1PQ\nfDiB7PH4VzvimDyrpfqa3/CUvmQgdyMiqHjiLY8TeprlpO1Zo6aqvSTOOqRBnp1qOrtgAZeRnANd\n5xEBjfH3W/Kt3Vr21n8LaDZwxSJc2yzfaMDCFmkypx/e2gAk84CDooq3BpcDxFzqNorbd2wpJknG\ncfcxnt1x796lvLG2iVQtzaXI5GYUZcf99KPX9PpSuI5AI3I2mlhPNdjeaPYwW9tLDdLI0mN8W3BU\n9ce/p26Vxw+WYjtmgE09i3bAfakPqa9V8J2qxweaRyeleTWzbZlz2Ney+HBjS426cZrixmyOzC9T\nyrxfCIfFWoKMDMm7j3AP9a3vDyKdNjwecVz/AIsnW48S3jr/AH9p/Dj+lbPhyQi0QZ7VVa/sok0r\ne0ZBrH7qJwO9UraErBHCPvHA/E1e1s5ukT3zUUJ2MrjopBqqfwoxrv3rHXadoWu31nHc2mjRASjz\nY53ltNmG55UpuPDHgsSM+1elW+iNLZ2Edx5ZFouPLgEaqzEcHIAIxzwM5ODjjFc7oGp+HotC05J7\njSUkW1j8wSSwB9wUZyOufrzW0ms+GGwxv9HJGP8AltBx+ldkWkZNXOf8Z2s2nuPPTMU5Ty5Cd2SB\nzyD3OTjHHGK5I/eHPUVv+PdT0+6FhHp+oWkqqWZoLUowB/vEqeDzjH1rnI2Dxg56VhWtczkZ8zZU\njvWJenrWvdnEjD3zWTMnmy7SeKiO5UTOtTi6PuKiVWd9qIS3sM1tQW6KSqIufXGauXCCC22rtWQj\nqgxmr59Tbl0Ob2lD8ykH3prLkbh1rYZGuYAXAb3PUVWksttv5q5ZT19qtSTJaaMzqakHEdMwVYin\nfwgVQhyUp60oHcU0nmgRJ/BUY+9Ug+7UR4egDV0Yf8TBz/06XX/oiSsqTGQea1dFOb2b/rzuv/RE\nlZAJLZ9Kyj/El8v1MofxZei/U1PDpz4k0rP/AD+Q4/77Fel+CtR0hPBtlZy39nHdDezI8oU5LsRn\nJHbFeZ+Hx/xUulEf8/kOf++xVbR/+QpD6ZqZfG35fqxwV69vJfmz3OHxLpenMU+3Rrwf9WjSfkQC\nB+lc74l8ew3OmfYbOxmy7oTPKQpBVgeFGc9BySPpUbWqizDkDgZrn/sLanqsMCfdGXc+g6fzrGnX\n53ax6E6ChG53NvezXdpHKhMfmMgJzyNzAH+dSwyySFYXklaOWcICWODHtDZwT15HPTuAeKW3tGit\nPITBcFWGTgEgg9cH0q5a211ewPauq+YoBXN23GDnj93XQjlJLm1tXkj+0QtLKZWjWUPy0asFyxH3\njgjqB/jAUtRL5iwpHPM74mcmbBBzuwcDJAYjj+H8rdhdzrEdMu4rjfnEckbIxXgDGWZfT9aivoJr\neQiSykVFUqC0SNlc9OGPGOK0ArQanLFCp82MvIGdy8ZB+Xg7iXz7cA/gK1YrsyMqhtrEdPQ/Wshw\nobzQ7sWwGZo3TAyW5yNp6gcc8mq82p29mxkkuIkCkZy36fX29qL2Fa43W7uS/EWnuG8uaQbx0OFw\ncZ+uP1rTinCtDhS0cbBV5wDyMn/P9a47UfElk14JLebzQJHwFUggHHHOPSn2nim5uYpkht47d4sY\nYfOec88/TpUVKiSuXGnKTsjlPiZFKvji9lkjCJMEeMDptChePxUj8Kg0RP8Aim7n5sE3kB/8dlrH\n1rUr7VNTlmv7mSeUMVBc52jJ4A7D2HFbmkAHw1cjGQLiDj/gMtN/Z9V+Zy4v4V6r8y4WVcFiCeny\nrzVbxK4GmWHyjm3P1/1slW4JG8jClGA5+YkEfjVTXyJbXTcnObc8dv8AXSVvW+OHz/Ixl/Eh6v8A\nIw0V38mHJIUDI966rT7aPyY9xI3OAPoO/wDMVz1qpEpPrzW/LI9pawF0wVG4HnockfzrRaK5u9XY\n07q/hW2nglkYSR3DNGAMg/u4VGfQfK3T0981mLdyMCy5xjJ561kT3El1IyqcOz7iaEuSzSLErEgY\nBxwfwrhnJtnVGKSsbCXB3hwdko5BU8j3qLUtRutQW4iuJPNd4wgdzzwQev8AwHFU2SVoY2hYmbHz\nLj/PvUMcyscsv3eCPT8aUWxuK3MhwVJVhgjjFXNEAOsW4YZBJH6GoL3MkrTdicfjRpz+XqVs2ekq\n/wA63iYz2Z6xLIrW6CRvlGOvU1xeoKDp9zskwVYMRgjnOOPzNdZJ+8MZJ4XBNc3cR+bJdW+3KuGU\nKR/EQQD+fNdv2TniZ2lRrEHkB3NsxkGrMqH5icdTVTTU8qEYB78CrswYryRwO1VT2FLc467G28m/\n3z/Oo06E1Y1QbdRlHqQf0FQqjEAAE/SuKfxM6lsKKdSmGSMZeNlHuMU2oAIwTKgBwScU26UrcPu6\n55qe1YLdwsY1kAkB2MCQ3PQ45/KrHiGKKDWriO3LG1yHtgwIIicb04JOPlYdz9aARQtADdR56Zrt\nkjWSyI9RXEWzBLhCema7fTn8y2x3ArmxPRnVh3qzkdRQxXAU9qhjkQ8EAGtbXbfZIsmOhwayPlJ5\nFbU3eKMasbSaOz0DSVvtKhMMW+5knkyN235Aq45Jx13Vrnw7Lf6kiR2kXmO4DeWFdM59FyB9Bx6U\neEry1t/D8MZu4oJ2RgDIr8Au2cFVYg4C9u9X1srNnjNtfW/ms6gM9xIuCWAzzCOBnJ9gevSrVzHo\nUPE/hWz0i8ngtrnzRCg2GRRu6Dg8flSxaRqeo6jqLWNtpy21vPJHGjWcPzYYjAOzt710V3AniR3g\nu5rX7dICkE1teQssmTgDYzK4498+1VLTWn0pbwRpEWa7uG3SvtX/AFrj8a4sSm5LS+n6odOlTq4i\nMZq6s/zRsaDoV/BHPHcWsAIz5ciQoinPsoH41r2vh9Q8n7uMlsjIhUDBOecDn/61c/oXirU9almt\n4fLVo1LFkJC/rWfceIPEI1AxCW7Pl9VgjXJ+mRmuZYeMm1Jf1956E8HhZU2uRNfP/M9C1DwVaS6G\n+o2sNvLdQqzlJoFbJHoeCO9cAbpf7Fluv7PsGeLOB9nwuM4xjPHevR/AniC9umfTdQt58Y3LJIvt\n0PHNcXr2gzJqOr6dZyJBaB23SyZCICcjdgE+w4/Tmu+OCw8oX5fxf/yR5jy3DXVode8v/kjjLHWk\n1C/mt30fSgvkStuS32tkISOc8dKNOmga9CDTrVPm3Ehpew93rO0S3e31q7ilUrJHFOjqeoIjcEfn\nV7TCBdyMOwwK5oUKcarjFaadX5+ZCwlKEpxStt1l5/3jsbaRDH/qYx7At/jVkSL/AM81z+P+NULY\ngR8mrCydgK6fZQ/pv/MfsIef3v8AzLKum77ijH1/xqQuuOVX9f8AGqy4zk5qUHIPBq40Yf03/mJ0\nKfn97/zASICfkTP4/wCNQXTKAD5MR/76/wAakI754FNl+ZRz9OKv2EGv+C/8yfYQv1+9/wCY67lH\n9m2aGJG3WrMFJOB+9kGBzntn8fpXP2l6yX8KwWsKNIdu5Wkzg8H+L0JrY1FvKGkr2a1df/I0prH0\nWPdqjOeRCD+v+TXDThGNG/m+r/ml5m8cNCUra/fL/M65JQCsajjOBiu41GWOHwDcb5REssJiJIzy\nx29PxrgIsGZBxkmtP4kag9v4a0awTANxMZWx6IB/Vv0rry2CWh1VIxpaQ0Su/wCr3Oc0fT/7ftrT\nStO2vKrzQyT3VpGSq7Rjg7tuMk8Nzn6V7L4f8P2HhnR4dN0+MLEgyzH7zt3Zj3JrivhfYmK61u7l\njK5umWM/qf6V6OWFenWk27HBQSd5PvoCfcX6CmHqaEbCqMN0HY00tgkEN/3ya4sH/u9P/DH/ANJR\ntVd2USaaTmgx/wDTaT/vlaaIwP8AlrIfwFb2MxT78UgIG4c5K/1FRjLuyq5AXgnHNKIdpyZXJxjo\nKQGVqsLtKjLjGOeee9U/sMzf3R9TW7JbLIAJGDgc4aMGqV5pVlcW8iyxKEYfMYolV8ex9anld9B3\nsjiNR8U6bpt7LaOZ55om2sIIicEdRzitPTtV0rULGO5a8W13Z/dXPyOuDjkVi+GfCllrTag8k14I\nopykZcgEjGSCcEE9OldEvw+0WPG95pD3BND12GnpqNkvdDUc6zbZ9ua5rVPFEFnOi2kK3qFcsyzC\nPByRjkH0B/Gut/4QnQBn/QYyPeR/8a5jxBceAvDbGOeFZ7peDb20sjMp/wBr5gB06E59qTixNkmm\neJ9GurYNfLcWk5Yr5IxLkcYOR656e1dAILNow6i4Cns6hSfw6j8a4zwpcx6ncPqMdlHa28smLeHO\n8oAME7u/Oa7XUSkMKeWxb1yOlclSq43SOylQUrcxUa2hLcFufXn+VYWsaRd2rtKnzq3IXo35Vspe\nrChkDDf/AAn0qos7SdRn61y+0ctzsWHS2OHj1CJJyJgVI9f5U2eSwunEjsNw9DWh4w0oNbm8gADj\n/WL/AHh6/UV5+lpcSyYSP9a0UE9bmTnKL5WjprzVYYU+VwAo9elc/B4luINQeaIRqo45HLD0ODj/\nAArP1txaRLalgZm5cD+EdhWKkzKMZ49K6aS5VdHJUlzPU7qXxoJgRJCQcZ+Vgw/HpWbeeIFnZJi5\nJB+XPFcwwL8j8qjYtnkc1t7STMuSJ1h8VrCVTyy4GDzg/wBapL4mKnm3VgOnaueopczDlRr33iG7\nu1CKRFEP4F4rP+1zE5LZ/CoKKLsaSR7Zo2+Dw1pyhEkje3Vz5YwykjJyPqTWfeXCZbBOB1zxgVMk\n8Nr4Q02aSNRI9rHzjkgKB1rmbm/JQ+YOWOQOwHvWNWq0lGJpGCcm2Wri+CJ8vygDJJ7fWrPiG4ne\n+iMcrov2KzYsD0zbRnAFYO4yAMeFzkDGSx9TW5d6lp+pGBbjRtSMogihZob9I1fy41TIUwkgHbnr\nXn1JSVSMrN2v+NvNdjbk00Rlw+JLmCYJIBLEOMMob9a37XUbeZNy27R+8RVgfwyMfr9KzobbRJvl\nTRtVJHX/AImcf/xitCODSdNMSvpmqqJc4I1GNun/AGwHrXRHFNfYf4f/ACRm6TepoLc5xs2477lJ\nI/p+lJdSv9jdyhIIwWY4J+lTWl5oA4m0/UkcdBJcpg/lHV+efSJbYF7K8RSMLm7QH8AY66liHb+H\nL7l/8kc7j5nlXhm5uY/EF4jSsCun6hlc5AItZu34VgyXtxN/rH3fgK6u/wBW0DS9XvZLPS9Qa7e3\nntxJJqEZjBliaMsVEIJwHJ+8Oa4ysqfNKpKbi1e29ul/N9y+iFFdLaPv8B6k38S6lZZ+nl3OK5oV\n0el4PgnVlPRtSsh/5CuqeI2j/ij+Y0ZtrBLdXp8jpj5iegGcV7V/ZrPpsEUEzQrFGEwBycV5d4X1\nC0sZpoL1GMc+ArKMlZAfl/Dn+VerPqarEG6EryK5sS3zW7HdQUVC99zHt9HuDdJJcTM237x2hSTn\njgdKv6ppNzcXCSWUxh24yNobI9smqL3N1cGSeCcRMPlQFcj649asRXWpRQefPcK0iYwgXA68898i\nsk5OXMdNo8tjlfiJA9tDp7PLukO8E4x8vykd65XX+dI8M/8AYNf/ANK7mun+Jl/HcvYIP9YEZivo\nDj/A1zGvf8gfwx/2DH/9K7mulb0n5v8A9JZ5lX45GIOldB4RTdqDe1c/2rrvA1q0ktxNj5VwM1ti\nHamx4ZXqI6++kEdsc9AM5rzM6uy6hNOoJV26A447V1/i29a3050U8v8AIPx/+tXnlZYWFo3NMVK8\nrHXaT4rt7KYmVJQhGCFAzWx/wmmnMQ8kjt/dRVI2/XOK85o6108qOU9M/wCE7tGjO6KUqvQCQE/l\nmuU1rxNf6w7KzmK37QoePx9TWKgAFKSO4ppCLWmajNpWp297CcvE4bB7juPxGRXtdlqNnrWnJd2U\ngdSPmX+JD3BHY14QwwRVvT9SvdMn8+yuHhk6HaeGHoR0P41hWoKpZ9TanV5NOh67fWBvFK7Nx7VS\n0vQzBdGR4QCDnJFc5pvxKu7UD7ZYRXJAADRuYyfc8Eflik1X4lX17btDY2qWe4YaXeXf8DgAflms\nlSmtDV1IbnPeLnWTxNesjBsOASPUAA/rmsOpXySSTknrUVdiVlY5W7s7PwZLk7c/MrY/CrHjyLFt\nbyAfx/0rF8IT+Xqhj/vDP5V1PjW38zQfMA5Rgf1xXFL3cQjtXvUDzUda09JAN4FPIINZwGKvaZNH\nBdiSUZQDlc4z7ZrtOI7u5Omi3fyYmjZcEk3Ns4xnJwoCknGcD1rMa70V8jbqIPYhIzVW717TJJD5\nGk28MfZWnkdunruH8qj0ue1utRt7QBCbiRYgz5O0swGQARmlKSim30Jk1GLk9kLHue5BCkxhvlLD\nBx71zEnEgNdoupaHh9lzOu4Y4tl45B7y+1ZiaJpk2C2oXsfs9kgP/o2svbpbp/czNVV/K/uZjW8U\nksv7sZwefavTNO8TW1rpotRCxZcKZCygdPr9aoaVo9jPazG3nuRCoVGka2VVXJ/3+eOtb03w/EVm\nHW9kKodzIIgdw+ma5atenN2kn9zOmnVlFXUZf+As8t1KyuJrqa5GJN7FiF5Ira8OnFsAeorVSw0y\nMNGbq63pnn7OuRjqPv1PDaWRWWW3nkMiIHIaAID8yrzhjz83pRVrpx5Wn9wqdeMJ3kmr+T6nKeJJ\nWTUEKnBXmp7OeOdgrAAkdKr6oBPr+xhuAFOa224ZGww6V0R+BIVbWTL7WUIbPlIT7jNCxQpwYIf+\n+BS2l15y+XIdsg/WpJY9pz1+lF2Y6oYqxK5YRxqf9hQP5VatXAYqScVSDAHOeKkiYpOOeDSbuJml\nNoGs30S3FnpF/cQv92SG2d1PY4IGDyKzx4R8TtcADw9qv42cgH5kVp+JTnTNCPf7C3/pRNWL4fkK\n+IrdQOCTz+BrnhOtKDmrdej6Nrv5G9KK5ki/b+EvEq7mOgap34NnJ/hWhaeDfFOox7JtIvIl6DzY\nXU/qK6pdKhlxLHEiOepC4z71t6dD5JUKQoHFZe2rvbl+5/5noLDLqebHwX4gs5HhfRdQkQtw8Vq7\nY/IdKgl8I69BYtGuharKTn/lykyPTtXpniLRINW06RQiNPj5GGOo5xXm3h+3uH8e6PAEETQX8BkR\n2AwBIpPWrlOvGnKpeOib2fRN9/IwnT5XZI4KRdw3DqKZnFb3h7w5d67PhWENup+aVhn8AO5p3i/w\ny/hnUkjV2mtpk3wylcZ9QfcH9CK9Lnjzct9Tm5JcvNbQw1HFJTFfA5zSg5FUQTAcVG69xTgeOtNa\nXsBmgDQ0TI1CT0Nldf8AoiSs5VyM4rQ0Ms+oSggY+x3X/oiSuoi+H002hxXMdxtuSgYxuOOnTPau\nd1IwqPm8v1JpU5Tqz5ey/U5fQPl8UaSAeDeQ/wDoYqHRIfO1FQOqjcKsaPE8HjDTIZVKul9ErA9i\nHFHhgA6woPdD/MUVH70mv5f1Y6a/2mz7L82eoLGLjTNp+8U6iszwmE/tS7DsGkGEKd9o6/z/AErT\ni3JDwe1M0zS2vpn8u2EcsRLxzqRl+eh9+v4CuLDfEz08V8KNuSXbKNuQE4q8TOhjvINpHX5eQadZ\n3JwsF3ab8cZUc9PSpBc2Nq5MUjBX4eFlPHvnoK70cBPeQJqNqLu3wsqj5h3Bp1lqC6jCbO5IS5Qf\nIzHAb2pLSaGOQmCZWB6rVPWbRWje5tZPnVS2EJ3fhjnNDqxj8TsROpCHxyS9Wl+bR5z421+7W+uN\nPgl8uCH5Co4LN3Of6VxIll2+WzMcEkDtn/I/St1dF1i8vGkfTLxNzE/vIGwPxIroLH4e3tywa8uY\nkTYe+D64/OsZYmkt5L71/mWq1B7Tj96/zOLiDq6g/MzKSuOealtb2a1JeKTHPzdwa7lfhxcugJur\nWMgdBIOeen8qwLzwlqVlK8f2SWZMdYE3D9OtT9YoyWsl96/zH7ektqkf/Al/mcdqMTC6eXB2yMT+\nNbmjhhoN6pLIwuoPY/clqpqOj6xFmdtOvgkY3s7W7AKB3JxWzbX0s3hqabcy/wCkQ8qcYO2XP6iu\niM4yiuV31X5nHiKkZpODT1WzT6+VyBrxbZSJWSQlcYA5/GqutyyvbaPJ8nlfZjkJwM+dL+NX7vU7\nYDyyxkJHIVM4/Gs/XrmM2Gl+VF8rWxO5uo/fy8Y/Cuiq/eh8/wAiZL34er/IigdQWbqMVoa3cyRQ\nC3Yk+X+7yfaseNtsK+pFbV88FzfPDIxH7wrk89z+VXJ+7Y3j8VzBguGVgR1J6gV6z4SsEXT1kNsq\nmUBiGHNcDY6YNP1xFMiyxlDyB90kdD710UviXUbUrHDFCEAxt8o9vfNeZWTnZRPSpLkfvHY2ehQC\n9efyV3knqo44rnPFvhiG3tpruDCSE7tqirNxrGoR6IL2NE8wtt8stnaf8iq9pquoanast/JG8W0n\niHaFPQDNKkpbs1quNrdzz17dTbSdS3LHHSs2Jts8Z9HB/Wte905rTSDfNccuxj8vGNpBHH5ZrDjb\nM8YP94V2xPOqRa3PVgrTCID7pAzWPrLSW2pBVGOhHIxnH+NbWmSp9nTAzIyDOe1ZOvpHJPHNkHPy\n5PTjr/Ou5ao5ImLp6OBsAfAz05FWZnUNgsMDqarWx+eTHKhiOGHr6VJfSBYpMgY6UKfLFsrlvKxk\nvafbdQeWNdynGPwFbdmhtgocNtHQocYrd0rwXM1rDPHdpBO6BtjEHOR6Vr/8IZdzL891CkoHPyHB\n+teLWr88merTw0uW6MRbsSKy4VyRhtwySPeqU3hmyvpo5VU2wLgPt6Ed8DscVo3fhrVtPkMvlLKq\n9WgOePcU+0vARluBjDj09D/n/GnSVtYmdSLWkkcHNpd1bzHBt0ZTwGuYww57jdkH+VUfs0txfeS0\nkfmNyXaQEdM8tnFdb4jtbLUZFvhMIMDbI+0lT2BOBx6E/TiuYms7OLe0ep28jL0CiQFvplP5123u\nrnM1ZlCWNoJ2jJUlGxlTkH6EV1eh3G5F9xXIscnPP41p6Rd+TNsJ4zkVlWjzRNKUrSOk1u0EkD+u\nK4wAk7cdK6681iF4hFHy3diOKz0eNRldqk/7OAazoKUVqXXcZPQ3LOztUtYPtjXluPJTb5dsJAfl\nGerr3zU8kGlrHutrq/kf0e2VB+Ydv5VkWpjnyHiQ885P+FWry0vre2V9N1a8t4s4MRuX6nsCOMfW\nupK6uc3kWokSaZYzqPkoerzFsL/3yCfyFdpa2VixuheR/MtzK6v6AsTxnI715ZcSa9BAXe+uZI05\nO6UsOuM4P1rt5dYTUdRv7OMtHJbzSRNnud7YP07VwYqLclbt+qNcJKMcUnL+V/mjf0/xFoul3bqU\ndVOBlv4lB9fwou/EWmXU/nW9sbix/i86IBkOe3J46VysumzGWN7uNTCqgL5YBYj3DED+daqaX9qh\nAtrgxRYBdSiEn24UY/M1EbKNj1XN3uloeoeFL20lthJaBQQRwMdK0dTsLu5jkSGSNBK8rzA9HjZQ\nMHjr79sV5/4UlksNkMZOO4pfi9qF9a6Bp9zZ3ckHnNJA+xsFkO3I/HP6V30PehY5HP8Ae3ZwFncC\n98QaneqMLObqQfQo5o0cFmZh/Ex/wqPQo/KiZj/z7zH/AMhNVrRYtqoD1xXLGzqyt5fqclWTlVm+\n9n/6UdJEdqYFPVscnp61Gr4HcUdemce9dJBaUnqDUisRycHtis0s6nO5sVNEWOSGyRVREy50Yjd8\nvpTSeOuKiR8tgkbvQ09iRnGM/wAq1JIte+W10mTOAIDn6edJVLRU2m5kbqZMfgP/ANdaV5dafPaW\n8N3aXEjwxtGDDcqgILs3Qof73rVqxXTFsVkFleKHJbBu1PX/ALZ149ScoU+Rwe77d2+/mdlFXlcl\n05DNfRrjv1qr47J1HxVb20WXWyhSMhRkAn5j+POPwrqvC1pZ3d8GhtrhdvO6SYMP0QVyeualYWfx\nDu4n029lledt0gvEVPvdQvl5/Dca9LAyko39nL7l/wDJGOKleMrM9N8FMg0hkGN+7L4PfAH9K6Mk\n+tcd4Kmjle9MMc0SsQyrLIHBBJAIwox06c11pLeo/KuitVkptOnL7l/8kctBr2dkxVY7F57Ck3+9\nNLBVx6VWacbjz+tGFpuNGEZbpJfckhzneTsMZcjrTQuBzzUu1vRfzpCrZ6D86sCtGMyz56Bh/IVI\nRSrEQ5Ysoz15pZBt28jk4HPekMjIqrfP5VlM/dUJ/SrhRh6fnWT4il+zaHdSHoFx1pkyejKXgtEX\nw5DKo5lknc/99sAfyAraPWqXh+LyfDWloo4FsrMM92QE/rmqfizX08M+H7nUZFBkUbIUYg75D0HX\nkdz7A1Mdi6jUW/I4D4oePbrTLo6Jpc4ibYDcTRk7wT/AD24wSRzzjIwa8UnnZ35NSajeS3d2880j\nSSysXdmOSSTkk0/SrFNR1RIJXKRAFnZRk4Azge56D3NY1J9XsaUabbUVuz3fwvbxQ+HNMcYXNrGc\nKMZO0Z6e/NaV44EZO4n0ySf51l6WBGkFmAypBEiYc84CjrjvUeqaolnDIzlOOgdsCvKjCEqadtX/\nAME0w2GpuClOP9XfmP8AMkJ42kfQVZhY5AIx+GK40+KwCGVrRsdVVyD+Ga2hrGzS/tzJhR70o0YJ\n6o7fq9Br4V/XzNPUl3QN8q4I5DKGz+debw+KVbX7LT4YICsl1HFITbxjgsAR0rq4fEseoKyeUgbo\nAJwW/LFeX2KFPHtopOSupIM/9tRWsqFN05u2yf5M83H0acacnGKXuv8AJ+ZhyyvNI0kjFnY5JPem\njpSYpwr0ChynAp+Qwwaj7UgPSkAMu002pOCMGoyMHBpgFFFKBk4oA9FvXuJNK0uGQllhtIsKox/A\nCP0IqCysjf3sUBHzPzWtoVg/9iiOGNJ7hEKcA4HAOecHjDVe0mxkt9eVxy4j+YYxg159SerXY9CF\nJWjJdSyvh6G1UbI98nqRnH0FY16kytIkcEZXjc3mZP59K7m5gNxCYy7KXHOMZrl5fDs0bzK7TMpU\nbHYA7TnrkYrOCT3NJJrYtaNYRGFHjUg54RsEqfVvWoPFstuNORUniMkMijasoLDPBzwPWnj7Vo+i\nzTRb5ZQwUYHbucVheI7kSaREsy5k89S0hTaWXBJH8u9bU4p6mdZ2TRf0yFJV8zzJQT1KKfb+LB9f\nWtGS0CowCjDDBZmyT9c9a4xfGE8ESxW9pCEQc+axIP5YxVWfxNq16WWKfC9SkSAMOOoPXH416EU0\ntTzJWb0MXxJG0eu3AYYyQRj0wKya7Gx8NT6u6X2qXEiW5Hy5OZJB7Z6D3/8A11uDTdKswPstnEhU\n/eI3N9cnNZymk7IadlY87t7C7uxut7aWRQcFlQkD6ntXT2ui3sPgzVI5ECOdRs2A3bukdz6Z9RW3\ncTSMGLO+QOPmNPjLP4X1IZJH2+16n/pncVy4ibtH/FH8xxZmeFdAluNft5ZSnkxnzXTr06frXZXw\nH2hsfdJ4FZvgx1ENyxxvyB+FWtSnIlYqeV5wfSsKk3Ko0zvppRppowtZ02a8eN4p5EiXIZFJ4PY4\nHJqz4c0WSwE9/dzu24YRHbOF9T6dKha9spm2zzTRODyEOM1e1RVl8Ptb2sksZmG3dJ94+v4Y4/Gt\nI8z91lznCMOZLU898Q6odX1eW5BPlj5Ix6KOn+P41Pr3/IH8Mf8AYMf/ANK7is69sZ7GXZMhA7MB\nw30rR17/AJBHhj/sGP8A+ldxW9RJVKaXd/8ApJ56d7sxArOwRQSx4AA5Nem+G7FtL0hVddrv8zZH\nc/5xWL4esIbOGCWRf9IueQSOi9eK6uaZUtyoPOOtYYirzPlWx6GGo8i5nucb43ukkNrEn3uWbn8K\n5CtXxDMZtYlyeFAA/LNZVddJWgkcVWXNNsKVetJTl6VoZkgNDHKkd6QGkfpmgQ77xpwHNMQ5Oaf/\nABUAKaTpTqaelADSKiYVbt7aS6k8uIZOCSTwAB1Jpb3T7izx5qYDdD60rq9iuV2v0J/Dr7dbt+cA\nkg/lXp99bLqWjz22eXQheO9eTadI0Wo27KcESL/OvW9MlDRj8q4sXpJSR24Wzg4s8iurK6s323EE\nkZzxuUgH6etQ72FeveJDpsOmM2oRhkbgD+Intj3rymWA8sh+XPAPUV00ajqRvY5q1NU3a5XODzmt\nTw5t/wCEm0r/AK/If/QxWXyp5WtPw6G/4SbSTt4+2w84/wBsUV/4UvR/kzkxH8Gfo/yZniQxsCq4\nIOa1bKW4vboLGhdyOFHc1kqjscYNdt4L0pGnjui/zRyFGAHYqef8+tFW0Y8zOqlHmlZHf6AulaRA\n1rd3KCScLuR1IVsDHcYz+NdNaJpdlhxMiRkYBeThR6DJ4H0ri5/C7PN5pvZJIvRj05/yK3dY0JtQ\n0a2ggXZNEg6dW9f5frXnpJvc9GzS2Mnx34eF6o1jSJYZGT/WpGwO4evHeuM0+42WuoQzgpILcHcO\n372MY/Miu90fSdXsoh5rQC3wQ64wfw61wOrW0sV/r5SNjEsA2kDu08TY/wA+lbTV4W9PzR52MVoX\nXdf+lRMSCVX1u53LuyoAJq8/lAfMpWubuo7i0uQwZkYgcqcVah1e4jX99iQduMH8xXS4XV0ZTTb1\nNCaPIDx8FeRVqC6MsXK5YcEVRg1exfiaOSIn+IDI/T/CpwLV8S299bAH++4U/keahp9URYlYq3Re\nfSmE7eCOaqXGo20KjbIJn9IwcfiT/TNZz6rKzcAKvpTUGwUWdr4gYto2gE/8+LZ/8CJqxNEBOtwb\nWKt82G9DtI/rUmu6oRoXhxQpy+nM3PT/AI+rgf0rP0rUfIu4rlVBKHDKfQ1jQi/Y/wDgX/pUjWOk\n035HapFr1rcF1uZGjPUFTtH51ta5pGpXCRfZbmYIVBcBsZz+VUX1G5mIjZFRMcerH2rb/t37dEVW\nJ7PamPNn4AP0PB/Osk3e9j2FGPLa5N4e0i5s0SeeefKrjY5U5/I1Z0fRo08Yf2y7wqktzGm1wM9V\nAIPYl8AevSs3S9QvrgsrTI0QPDp0Yeo/KsDUNeu5PiZ4f0iK4kWzjv7QyRA4V3Mqtk+vBH5VNZSl\nRqL+7L8mZVJRhZmJ4Q1eMXKwA7B2HpXa+JdPtdb8N3EE08cckS+bDKxwFYD19D0rxKGeW3lWSJyr\njoRWi+q3t3HsnuZHUfwluPyrslQ9/mTscixC5LSVyhJGY2wecdxUfGaskZHINRNj0rpOUZu7dacq\n5PANIGxUiYJ64+poA2/DAjh1kSTAtGtvcMygdR5L5Fd3H4z0prfAmKYHR1xXnuisi38uG3N9juvp\n/qJKydzscniuWVKNSpK/l+oqFWVOrO3aP6mtp9wLvx1ZXC/dk1GNh9DIMVX8Oy+VrER9cipfD6Z8\nS6UcdLyH/wBDFULLfBqEDgdHFU0nJx/u/qyYy/2hy8l+bPW42PkcHIxW74WuUjtyHXDBjy3Q8+tc\n7Zyb7bnnIrY0OTy7Q9G5PBriw/xM9PE/Ajpry7tY8NPHNAAcrOi71/Tmq51zQ7s7J5QzDgyovDe+\nDzWJPP8AM3ll0PcLyDVCSW1mJ89YWb+8qYau7Q4Tpmj0v/WWV3hxz0wPoc1heJbxYdJvjGXKtC8L\n7ecMVI5H9c1SexQLuhYlR0UP/Q1h60ypq14pkZFeR1cDowJPXFZ71F6P80Y7V16P80UvA9uReSzs\nvyp8oJ9TXr9o7R26jcox2NeXaJaT2lnNDCQzOwkjfkBgQP14pYl1gahnzSAPvAPJj9eKwqLmk3c9\nal7sdj2OG63rjzUGOOvNcr4/01dS0lggxPGDIhH8R9KwfFOn6lbCFraeTY0YLYZhz6cUaZ/acdn5\nl3KrQgcDL5z/AMCqo6RuVK17WPNNGQy3tw2fmWzuhj1PkSCrWmbYfDd7DKwwLuEZHYlJf8K1Dpia\nQoVsi4ntLuV0OPlHkvjFc9pjf8SC/BzzeW+Of9iatVK82/OP6nh4qHLVafeH5yLsYllJWRkVewVO\nabr7lbXS7fy02PbElwOeJpOBViwtbi4PlxQO/wBATVrxXo19FpmnTPDgQQlZMEZGZHPTPuK6ak48\n8E31f5BKMnUhZdX/AOknMtJuHXHNadyfNd5ztAb5hn0Peslo5UHzxsv1XFWbt90CPzsMQGCe4GP5\nitmzWxv6f4itbiGGEh1ui21hj5WP97PuK6ISWZRJSgaYjoAMf5/GvKYJWgnjlX7yMGH4V6BG24+X\nI7KF5BjPX/61efWp8ruj08PV5tJHZRtYjRQoeCSVGMm0kHJbOcf4Vlz3FnaaZNIsJRcFn+gHpgVz\nFmYkvTmV1b1Mf8uf6VHrl1MugyrklMiMMepyc4/IUQjsiqkkkZXifWbTVZoobBGW2iXOWULvc9Tg\newArn1OGU9wab0pxwwz3rrSsjglJyd2el6RcmWKMIMZQc9adrMETWiD+KI5AJwfxrC8PXREEYJ//\nAF10l8I5dPkAQZcbmOe/+c1103ocjVmclHMkEsiuvVzwUPIz2wabfXUUto3kGQnI+V8cVoSJ+4yM\nbSMHI+n/ANaqB2Nq1hE+0JJOiMfYkZqaiai7l037yO4a/kjhijjjuHmiwgK5IyAM8f5+tdDPLqU3\nh6O7ER3Op3R4+bA6/wAqdeyaXFF55IBbkIWJUN9OlT2Pi3SDposPMc3AGVIhZhkZPOBwOa8OCU3s\nfQKMqa1Zl6XcXcUvlw2Uj/dbdGMocjJHbJHQgZqr4r0SSxs5dYgjMYIDSJ6Z71u2mq6TJL5gs0in\nB+b5ACDVvW7y31bSpdPVgElQrkV0UmjKdP8AmZ4XdXyy280TE7W5wO30/KsJxHxsZie+Rj+tdp4u\n8NpotvFJ5i5nkk8pV6+WOhPv0/OuJNdUXdHnVIOLsxKlgHz5qKtjQXEUepz+XC8kVoGTzYlkCkzR\nrnDAjOCR+NKpLli2YVJ8kea1/wDh0v1IY2wu0jnsatxEnGeAexPWuv0y0N9bRuYrTzCudq2EHP8A\n45Wxb6ekeC9raL6n7HGP/Za4pYprTl/H/gG0KGIkr8q/8C/+1OGsI5BcgiByvXggCu0Tw1e6vpzP\nZp5KquSp53Ec/hWmhiQYEcA/3YU/+JrqvDh+0QvGVcEH5QuUz69OOlXRxVScuXl/H/gBOhVguZxX\n/gX/ANqedaNZJLDIjDZJgoxYcE9Cpri9RvJrHxnqU0ILf6ZKGUdxvNeka88ujeL2tlEC21zHvCmB\nMlh6nGTx6msDxNpuo3TM0enSu4vJxuhtsEp8hUkqOercn3q6lXkqRVRJJprdd/kefOq6WJSqWjdf\nzLvfy7E+m+I7SOExajGHjAyhJ/z0q6viqxLLFZw8HsormNI0jVH8+C50u8VAQyF4GGfUcit2y0KS\nJ95tbhcc4MTf4VjzUFK/MvvX+Z6Kx1PlsqkfvX+Z1uhzRwqZZCCx4GDXR+KtJsvEvghYZWVXtpDP\nFkZyVRiRgEE5A7Ec45rgDcXcI+XT7vao+UCB8t+nFMn1rxJeYiTTrq3hUHAAcbgfXA9O1ddPFUou\n7kreq/zMvrNFa+0V/Vf5lCztMXa2pTYslvKFliO5DmNh0OCPofT8a0DpLaeVZJkmi/vJ/D9R2o0m\nS9heWOfSZCPLdgzKUBIU8biuOemSe/erOl6irI8ckSwTqBny1BTb7jPA7Z5pwdGpUm4babO/cxp1\nVUqyd09I7W8+1wjPA44qXgjGDz61YeCNAPtEPkbz8kkedjenBz75O4AelRXNpJbEEkNG3R16H/D1\nxWkqbWpvYjdAy/wjHrUURaOQZ6dKljHOM8f1pkqH1zQiSWb92RIB9aXzN6jFRRSgpsYe2aryM0W5\nf4abYJDpl81goHU4+lXbvUrSwRIZpdhXA2BSxHHHAFVLOJpSXdPkxhcj9azNSstQmvWkBkmDn7xG\n4qfccE/XNcrcZ1OVnUoTjTc0e3eELVY9OjuEZHWUAo6MCGGOoI615Drkxj8c6uBJt3XsoIXrjefT\nmvZPBOlnTPC1nEU2M+ZSp6jP4CvGvEWP+Ew1l0UFvtkuATx989h1r6HL0lzJdEvzPMr3dFuXVr9T\nvfANxu1eWLJ2Nahhnjo2P55r0FwuOS3/AH0a8u8BO0filUJOX00uw7D94B0/OvR5psZ5oxkb1r+S\nObCytBohuwhjZRJKpIPIkbj9azfKt+7zse5MhOfzqS5myG57etZ/n1zvQ23OpPHU03IPINI8qMpA\n3f8AfJqNpkXHOPWsDclqCYZ8s/8ATRf51KZY8feFQXEimNNjZPmJwOv3hQw6kpFcx46mMXhuZF6y\nHYBXTGWMZBYDHrXK+LZUlutGtiVaOW+i3c5GAwJ/TNJ7CtdpeaOlSNYokjQYVVCr7AV4P8ZfEbXu\ntppEMmbeyHzgHhpT1/IYHsd1e6XN9a2ttLPNPGscaF2O4dAMmvkXWL+XUtTuryZt0k8zSOQMZJOT\n/Opm7RKXvT/EzpTl1+lbPhO4WHxJb7sYclQD3P8ACPzArEkOSp9qIiwkDrwVOc+lc8480WjqpT9n\nNS7HvdsHs1dpmkMjmMfN0GIwDx9c06/tIpvvBcDjd3qja3Nxc6VZ3N1IXeWONm5HUxoScDpyT1ql\ne6mVV034PavNpq1OK/rqdOGmvZRb7f5jbnTLVHVGmMiscKhwcmtZobaXTzZSlNpGSoYZrgDq7uXM\n0bnPRm4FSwSixiW5ZUMR4KbuFz/OtFB3OlVIWsdjZaJaqwkRUYocglRuH44zXmNspHxBtw3X+00z\n/wB/RXa6frZk1GURlki2AspGOfbNcXZuLjx3aTLyr6kjA+xkFaf8up37P8mebmTTpSt/LL8mc7Rm\nn+W1NKkdq6zMRulN70pBHakpgOJobnmkpaAG1JbgG5iB6bxn86jp8IJnjx/eFAHeaJeXianBb2sr\nR/aXPmAdCvPr+FdZb3A0y+kTLO2epNZXgjR5hqEmpXaMscMYEe4YySM8fh/OodYu3fVXQZL85A71\n59a0qjO+lJxpxR1U2qYVpAR05J7VlXPieX7PvG3yV6hmwX/wFc1ey3s2mylcqgIz7jNQQR3mnwNc\nXltFeWyAM0crMhx7FSMfrShTujV1Wtkdlp/iiyurcpLEVzjcp5HX1xXN/EC5i+02Vnb8RiMyHHuc\nf0qobyG5uVuVt2srMopZWl39PQ4GegrnNX1I6jqrzHOzG1VJ6KK6KEOWWmxzYmpzRs9yPcr/ACZ+\nUc8d66jw5pdukZv5kBIP7sODgY6t/n0rndItTeXixKcAfM5HYV1eo3At7IxIoVduAB2AroqS6I4N\niaXUTOWcnAJwo9BVJ7rk88A1n/aQY0GfaoppfmYeuKxSCxqNN8hGSeMVo6bPY/2VeWV9NdQ+dPDM\njwQLL9xZAQQXXH+sHr0rAt3MsoPVc5+taWp6D4nXbFa+H9WJ/iZbKX+e2sq/s7ctSXL13S29Sop3\n0NOwvfDmiNK8mp6iUcdDYIOe3/LY+9MuPFPha5dsXerKSMf8eEZ/9rVyc3g/xZM2X8PayfrZSn/2\nWmjwd4oA48Naxn1+wy//ABNczjh27ut/5NE3jUnFWsdLbax4UjmDvdaxIQehsIx/7Wq/fav4Z1Mw\nJFqmoLgY2iwQnJPTmYfpXGjwb4oHXw3rGfaxl/8AiaVfBvilGDr4e1oPnIIsZeP0qv3F7+3/APJo\ng5SatY7H7N4euEMM19fyIRyraenb/ttWF4m0WG4OkxaQ001rZ23kEzoEckzSSZwGPH7zHXtVmz0f\nxFHA015oWpwrGpZ5JLSRVAHUkkccVahlVsDNa0lGUueM+a3mmtVboYNuL2KuES882RwBBF5cYBwC\nT1I9uwqJL6fbIUUPG55J/kKh13THknF3bOctwyDoDT7KO7IBuFSOMLtK+o9q0WHbt1O5YuLTexye\ntI66k7OMF1DD8qz66HxWsf2mCROpUqfw/wD11z1dVraHEnfVhT+1Mp4zxQMAaVgcVt6N4Yu9XDNC\nAFXqzcU+bwtqonaFbOaTHAKrway9rC9rl+zla9jBU7eDxUgPeteXwnrEYO7T5gVGTx2rGyyMUYEM\nDgirUk9iWmtx+aYTSbs0VRJqaBcQ22qQTTwtNFHIjvCrFfNVWBK5HqAa7HxjrVnrOmyNFZ2cSswM\nQjJ3RtkEgHcR0bkYx6V55CyJMjSBigYFgjbSR3wcHB/Cr1xf2TOGhtJx6+bOGyP+AqtNGNSlzyjK\n+xnRNsnRh2YGvTLC9MULAKxZRvAI/OvMndWkLKgQH+EE8fnXZW3iGKFIbkIHbDho88c4x/WuXEQc\nrWPRw81C9y34wuGvLbTJkTCneCP++cf1rlTEo5MZ/A1LLfTTIkbyMY0GFTPApokPrW1JOEFE560l\nObkhm2Pqy4rS0JYl1vS8EEm7iI/77FZ0iB1ODgmpfD6sPEulLz8t5Fn/AL7FFd/upej/ACZyYhfu\nZ+j/ACZWbC9OM1veFtXeyne1wHS5dBuJwUIPb8653nb83NKkhjcMnyspyCOxqppSjys6acnCXMj2\nOe6jksjDK04LjA8lWJ/NRxT9Ia2hmkVtXnWUj5IpCwzzkcOAfy/Gsa01W3Gl27agrhZolfzEzjJH\nT2q5b6poUVuTHfuznqkjb/yrzYpq6sep7RWvc37q8lO5VbC45Oa5W71bTb6xuYbFvNkMKNI4UjpK\ngxyMn738vwuXVzJf6TevCrrEIXAdhjcSDjA/rXA6HL9jtNSuAD5gtQSD0x50QrScPc89PzRw4ut7\nuvdf+lRI9egBjcoQTHgnFc6uSp5FdDb2N/q0lybe2klWU5Jx938aD4UukbyyAsn90muiE4wXK2Op\nFzfNFGAEyOtRsAOD1FaV5pt3p0vl3MJQkZBPQis+QYORWyaaujns07MVgC3A60hj54FRhmB4qaNs\nrzj8aYG3rsf/ABJ/DQ4/5Bj/APpXcVhITEcq2PWuh12YJpHhrj/mGv8A+ldxXPOwkO7tntXPhv4f\nzf8A6VIb3PR/Ds/9qaXDDOFZnUqoP8WDgfjxW5p3h6SO63TWyeX6MSc/UHisTwfBBNbW/k52QXQC\nknk4IOfxzSxWOoTTbX1STb/d7muOTl7Rxi7etzqVapzckUtr63fVro12OsukkEqpBDMWJwW2HH1z\nXHy6Tfr8XNMu1sro2cd9Zs1x5LbAqmPcS2MYGDk1O+u2Hh9jHAPtVyM7yWwFPuazdHvotb+I2jaj\nLDslk1C26NxlXUD+QoqRqRo1Nrcsu/Z+ZjVlXla7jf0f+Zyn/COa3/0BtQ/8Bn/wqaHw9rQ66RqA\n+ts/+FJJp0LKXh8zA68ZxVZ7V4AG+UgjOVNd3719V9z/AMzkard19z/zL58P6zjjSL//AMB3/wAK\nryeHtcyf+JRqH4Wr/wCFVCQUqo55p2q919z/AMwtW7r7n/mWLvTr6wKfbbO4tt+dnnRMm7HXGRz1\nH51Eq9Twfxq9P/yLFh/1+3P/AKBBWcp5FOnJyWvd/gVSlKUfe3Ta+5nUeENHlv8AUJJAD5QgmRiP\n9qJl/rWrB4AvJRhv3ZPQdau+Dw2maatw6ljMkjgZwAAjEZPbp+oroNK8XLNepbPZcu21XikDA/1/\nSuKVSftZcu2n6nXh6UHVlz9o/wDtxy+meD9Xstes5HtW8qK4jdnHTAYEmuf1LR73RbmL7ZbtEx5U\nkcH1r1288XfZdXTSfsBLySLGZHcDbnAyB1PWuX8Zyvr/AIfGprD5RtHAYbwwZW4yCPfHH1rSnUk6\nnvLovzZM6MVXkov7K/NkWlShrBG3dVB61oWU0tpH5uFe3ZiD82Cv+etcZYXbRwoA2PlFdNoWoFJz\nbzgSQT9QexrOEeSozpqvmpI6IhLmIPFGzjrwOv8An61mXC238e4Mf4SvP4Zq29nHa3O6NyFb7vXI\nqbzGYbZgSPU10HGc1dyQQoRGsu7/AGjgD8KyNaVm1y+2sAftEnX/AHjXVX9laPH8pCj26Vj6vZwr\nql5NJPHGpmcksvT5j71Kf7xej/NGL/jL0f5ol0K7lEcULoCsQJEgP3uen610Vxq8BCp5e1cZdgM8\nV5/ZaxLPrsNtbOzwhiCxXAPHp6V07QveDdFJIrofuqcD6Gsq8EpX7nqYao3Gx1Vx4nspbPOn7rma\nBfmTYeg68+vtVDUdegudKaRItm1C5wOoAzWZaaPeyKRKZoITy3kyOu8eh+Y8VRvgk9vdWcJBjjhf\nLdecEAfn/KnGKbSRtObitTl5NZuNb1ozTqieVptzEqoMAAQS/wCJqhom3+yrjcAR9utsg9P9XPVz\nRVMs80EoHmC1uU3beR+5cVFY6bcpodyI085mvrfasfJPyTDgfj0q5pRk1trH9TwK0+aq5PvD9ToL\n3xElhZrDb4DtggrxtrL1DUblLfS2EjHNsxOe/wC+lH8hUcPhjVtR1KNJbG7hjJwXkgYAD6kV2l34\nGt7qztIklmjaCLYCRnjezc/ixrCVahTlH3l16rt6m9XEQq1IWnGyb+0v5fU8+bUJTgnAHckUNPa3\nKCKcLtxwycEd/wDGuyl+Hcxyqzqyf7XBrF/4QHWDIwS1+7zkyKM/TJ5rqhiqMtpL71/mOVSlHecf\n/Ao/5nI3VoIHDRtvhY/Ke/0NdBDqyPDEkjCGWJQgbGVZR601vD2qlpYf7KvioBIYwNyR6cVUu9Kv\nra1E13Y3Nup+XdLEyAn0yRSlVpyaXMvvX+Z04WpRneKmm/Vf5mt/a9wWEUtxbJGOrLycfSqWt6ml\nzYC1iJMKtv56s3qf1/OsO2j8xmLHgVa8oPhcfKappRZ1UqbqQ5mtWZdFWpbYxsQRlezVC0TDkcit\n009UcE4Sg+WS1Nrw/NjKd1ORXcKyyQKNwCt1J6V5tpk3k3a+h616Po9tcalshtUMjjnqAFHqT2Fb\nU5Jas56iM+3SF9QFpMWAYlcg8g84PP8AKsHxDZSWOo2v2cmSTeSgVeSwx2r1a0+Hdotwt1f3szzB\ng4SDCKCOcEkEn68VpapbWOnaXL/Z0UMM5XYHVcsPq3U/ia5q2Kjze67mtOKS1Wpwcsd2YRcRLukj\n+YRScc+hBqpYajEtu1wJboXIJUxx2o4bj5Tk/WrMV4llviedpNhwS3Vs8/41fhglkcTW0Sur/NnA\n5+tcN0nqj1YNVErsrWEl7q19LdXFu0MQbYrkYMgHqPWujgiWCPJbJzn8KgvJZLOyjeYRKcgBFI4q\nsuqQzKSDkINzfQVrB3ZNR8ujZ5nqeqX2qTy3F3I0rJlFHZF5wBWGRg16NoOkC4uGuUee2kcEq8TY\n3joQR3HXP0rtLfwlouowLHqmkW8jou1ZbeMW7fUhCAT7mt/axi+U86c3KTZ4JWto4P2HWSBwLNRn\n/tvFXpup/BzTpEL6XqlxbsF4S6USBm/3lxtH4Gucs/COo6J/a+m6nBhJ7ZWinjOY3Cyr0P4jg4Pt\nU1px9m2vL80Y1VzRt5r/ANKRrW8YTTYgk5g3IMsOvTpUD3C2Eqsl08qnt52c/wDAc1csDB9m+yzI\nJFI7jNMl0qyWYN5UIJI52DNcCkk3c9xQfKrFtdYiTTzdpEAOhb3qpYXv228IOpSJI3IVZ+R6Y984\n4HNdBaaXDe6VNbhEPymQL64/+tn8qxLTT7WOXMaW4J4I8tT0PQ1rTaiubuKpCUlYk+IzyKuj6gZv\nOlaFg8mMEkEDJ9/8K5C5JbQ7Rup+0z/+gRV6f430xLzwXFOSgGnICCB94MQMfmR+VeXy86Faf9fU\n/wD6DFXZVd+T1/RnlYiNpU35r8pFiNIrBrIw7jI7gSenP+f0qzrJKusG7bkZOTSeTHPqOnW8qZjk\nuo1YZIyCcH+dZ8GrJqupRRPpVmzysFLF5s4/7+VlUk+ZWV9PIiNdxbgotu1913t1Ml4bqKVrgxsw\nzgMKQXM88gaV2GPur2Feq3Gk26WTpHp8LBVyFy//AMVXBXs6W05X+yLLj1ef/wCOVlTxLntH8Ua1\nI1Yq/I/viUDfvCFIjBB4YVsaF4iNpqUF3eFpIVIjl/vKh/n1P+TWYdRi6HRtPP0af/45TotTtk4b\nRrHaTgjfNz+cldEak19n8Uc7lUa+B/fE9X1W2K2MhtrhVVY1l2gZSRCMq654GR9PpWRoniGNont5\nl3R4O7cMgfh7/h6jBANS6Hq8V94fNuIo4zaoyQoQxURNjKnJJPPqePSuKS5+zTXKxfIrMVAyeOa6\nqdW99LWKo1HNNNWafl2v0PQ72xFpGl1EGktJBwyndsP91sfz7/pVMyxsp+b8G7VqeFbiG90y3gm2\ngNGYw+MlDnt+AWuZu/EL2l/PY3NvP5sTlG2kEZB7citKkElzLYq19S0SoPUYz3p0arcTKrHC55qr\nFM8+JPs0yqf4mAJ/IE0iTxJOkuW+QnKkEH8vxriq1Pd0NqFPmnY6UqI1+U5GOMDpXTeFLKC4lhaW\nHLFsgt7H/wCsa4aDVrd2+YsFHIyK6rw54htLVN0jyK4iknQHkfLk49s/zqMLFKV2dmITasjd+HOq\n3Or6cvnXG77JvhdM9W3Ag/ow/KvKNUnW68T6g6hsPdyEkn1Y1d+GfiWXRNcuRKN9tO2ZAT0yeo+l\nUdZs0tPEl01lKJraSTzYnZsZVuQDnnjOD7ivpMHON5Jdbfg9TxMS1ySj5/hrY73wnZyDxF9rh8sx\nx6YsZ3MRy0jEdj/dNddO8xzzB/38P+Fc/wCE5P8AiUFioD52kgdh/wDXJrRnm60sS/3jOTDL93ch\nuGm5z5WPUS//AFqzTJcA48uL/v8ACn3M3WqBn5NccmdKPS9x9TTWcgZ6ntTjUUmSp5xyP51BqSM7\n8AnnAzimFmPp+VOY7jkdwD+lNxQA3cd2SBkdMiuU1WQy+PNFgKgrGJJSMZ/gYZ/MiurNcpbMbj4j\n3G4ZFvYkD2LOmP0BpS2CPxr5/kJ49vxp/gjW7lkVm+zmIZA/j+T9N2a+VGbIzX1F8QtLm13w1PpV\nrcwwSTzxl2kJwFHPQAknpxXAWXwz8PaYEbUJpb487jJJ5URHTOFOeD6OenSuWvXhB2bN6FCc25JH\njO1n2hQSc4AHeuw8PeEdTE0l7qumTwWFpE08gnTYXIUlF2nBIZgBx2Jr0+2m07SZRb6VYRWpAVS0\nEOGYHkZYDcwPuasRXkd15trdgql3bu6pxlTnDAYPp82OOlccsU2+VI7Y4ay5r6nNtczSaLHM7l5Z\nBFKxbkkmFM5rjNc1FhcxsOgHT0rrLi2v7a8ksvsk8sKxxKskcbFSRGoODj2rD1Dw/eTqQLG5IPTE\nTZH6Vz0K1NRSk1968/M5IV6KoxtNbd15+Zd0a5hvrJVjZFmC4KtWhYafL55MpiZR2zXDDQdes5Mw\nafet6FYW/wAK07TTPFF0+17e5t0YYZ3jIwPYVd6a2mreq/zOiGYUbW51f/Ev8ybxJeyvqNwNOjaV\nokAdolJ/HjsM9a5fw+zf8JPpQP8Az+Q/+hiuu8O6bqthrYlFldqBuG94WUEYI6/57da7d9Ntrq9t\n7ifT45JRIJBMyYdWABGWIycH6D2pVMVSjTlG62fVdn5nJi5UpUJP2kb2l1XZ+Z4Nvb1pu9q9Vv8A\n4d6NeZeynmsn7p/rVHGfXP6mvPdb8P3uhXHl3Kbo2+5MnKP9DXdCrCezNJ05Q+JGUWJpKK0dK0O+\n1iQi1i/dqcNK3CL9T/QVqQZ1PRHkdUjVmdjgKoySa7vTvA+nxYOpXNw7ccRhUTPpuySfyWupg063\n0+Ix6dZrbjGCyrliPc9T+dK/YTZ5pD4R1iRUaS2+zoxxmZgpH1HUflXQ2Xg6wivraM3M88vmAnaA\nAw7gD1/Ht78bd2L6IZffMh9BzVK3nzqEPl3Tp83JYYdOO3+NJ3EmdfFfRjS2WLvI/J4ONxxn3xiv\nNtdnmt9QaZu/Sut+1LpcsdtOu1ZgSrZ4BBwQT+XesXXdPW6Ukg57EVx8vJUd9mdqlz01y9CbSbvz\n7D5kEgK4ZeuKsk2k8UsUjv5ZHKMvCgf7Wen4VyFlDq1pIfIgldR3QGrVzeajLphxAUgJy3zAlvr7\nVcaT5tC/rKjCzKGv6qby8cx/LCvyoo6YrEDZOeefSiYkynNKm/cAMnPTFdSSWhwtuWrOz8K2z/2Z\nNcRWzbA+HkPOSOg+gzUeou0u/vwcV2WnWi6XpFvZk5Ij/eE92PJ/WuT1S3aO5d0BZCe3auaNZSkz\narh3FXRgecVH0INN80yzlR0zyaW5ADEjv1qK0IQbs8k5rY5zZgxGhOADiqvjMl/HXiDcemo3A4/6\n6NUbXB2NyelTeLyv/Cc+IBycalc5/wC/rVja1df4X+aKWxjxohGWBNSwWz3BMUEDzSt90KM1Gzgk\nBRt/rXoHhnTH03SjdzJiSUbgD1A7VdSpyI1pU+d+Rg2PgXV7kBpzHboezHc35D/Grl18P9YhG63k\nhnT0B2t+R4/WtQajPeSSKt/JFLGpbZGh+Uev3SMe5NbGl+IL6PTZJrk+csR27ivJNYqpUTuzpVGm\n1Y5Pw/YzaT4oltZ8B1068L4/69pTVZLkrt5PNdG91PqHiAyzWcdtINOvsYOSQbWTGf8APeuJ8/5y\nc8Jxn3qqetWTfaP/ALccdVJOyOmtpRNA2eQcj8ajkZyhBJwPeoNHkDRAHualuOCec12Unujnluch\nrcxkvtmThBj8+azauark6lNn1H8qZDZyS/7P1pSeprHYrVLHksoAyc1eGnRjgkk+ualjVEdcRj5T\n6VLZSR6D4XkMGnwxHj5eR0rr7cGTaQOPevMotZuhH/oGxZAMHcAfyzWloWsa/LfJbvslDnqBjHHT\n0ry50ZXcmenTqx0SPQ7lh9mII4rxnxxaQQa40kGB5ihmA9en9K6ibxvfw3bQvp/mRKcHLEH+Vcj4\nune51RZWgeJSgwG9evX23CujD05RlqYYicZR0OfFLSUV3HCFNbrTqa1AEsEKyA5qZVCcL0pLb5Yy\naNyg4zmp6jY4c1IDTUSRvuxsfwqTy5V6xN+VF0HKxQav6HgeI9MOOt3F/wChiszzFBw3B96sWty9\npdwXcIVngkWVA3QlTkZpVU5U5RXVP8mY1ouVOUVu01+DKrMDUSnkH8K0xqdp/wBASw/77n/+O0g1\nK0yf+JHYf99z/wDx2o55/wAv4oXtJ/yP70djotysvh62R9rYUqQeeASKuadBpxkJit1znnK9D7Vz\nGja/aRyG2l06ygjc9Q8uM++ZDWlJ4gsdOYssFnK3VVilkfP/AI+QK5/fUnaL+9HQq82tYfjE63VZ\nJV0G4SztZZXZfLRYkLHJ78DsOa5Lw74cvp2vIbuzuIElhVMyxsvAlQkcj0U1W1HxAL7SrK5utOtJ\nGNxOiqWlwoCxej+/6fXO74PmE0srxafBB8u12jMh4zn+Jj6CuepKtyOytr+TXmc3PicTLljBJXtr\n5Nefl0OytrKGztkht4wkajpjGajfRoJ5A4Cq5PU1MssahUd1Unpk9a1LIxkcN8wrmSqX3X4nvqNd\nxteP3S/zMfWfDFnqWkPbSSxtKQTG2RlGrxW88N6ykzxppN86qSAy27kH9K+l1/e27L97ivn34kW0\nVt4qm8tQocBmA/vHqa9DD+0Wia/H/M87FU6+7cfuf+Zyc1tPaXDQXUMkMq43RyKVYZGRkH2pO+K0\nNc/5CEP/AF52v/oiOs8cmuunJyipM5aU3OCk+p008Gm6rouiK2v2FnNaWjwSw3MVwWDfaJnBykTA\nja69/WqZ0DTv+hs0f/v1ef8AxisYc0oAzWUaE46Rm0tei6tvt5ml/I9P8O29pot1HanW9NnEsy7F\nRbkMWJAwMw4yfcge9F9e6G9rKYPEWmrOVKozRXQC578Q1k2gWXUdLlA5F5A34FwP61haZY2+oalF\nb3AcRFJHby2AY7Y2bGSDjkelc06Fpubm9FfaPcz+sOE5N9Ir82a2n+Cn1Zj9l8QaZKDyWEV2B+Zh\nArvPCnw6h0rVbK/vNUtpPs0yTBYRJyVIPdB6Va0m5ht7ZI40ZUC7QCc4A7cAVqi4yAyg+1cdWrWq\nRcLuz02j10O6EUtXCX4f/JHQWGn2UVg6Q28UUajaFVQAa8h+I/hW20p/t9lFsglOXiHRGPp6A16n\naTXDxcOqr6Ef/Xql4j0n+2NIeGVt42lgqfKWPpk5/lXTQqtO9n/XzHWqNxt7OX3L/wCSPmpuGZSf\npUP8Hvmuz1fRNNsdLtNTexvnhuCw+W7AKEMygEmHHOxjwT05rBng06XR5ruzguoZIriKIia4WQMH\nWQ9kXBGwfnXeqy00fb+tTzpVHFpSi1e3br8yOf8A5Fiw/wCv25/9AgqrYqj3ao5wrBhn3wcfrirU\n/wDyLFh/1+3P/oEFZyOUdWHVTkU6esX6v8x0HZP1f5nsPhZoG0y3SQLgQc59NuD+lWUfR7a+ilVo\nUIf75AX8q5DwrftcQ+SCAVgmABPA/dt+lJHZ3epxolwxVDypRhtH4df0rz3S/eyu7bfqejQrL2kr\nK7tH/wBuPSJ5dEvL9LtTFNN5gVZU52t2B9Pak8Rx2c3hW9itwNphZhtHcDP9K5fR9NvrGa2htmP2\nYzI8jPIMNyMgDqTj1AqnqWpvp2jSQyyDPlYYAnBJHSqjD97ZPovzHKqvbttWfKvzZzdyqw30qAjC\nNtP1HBq/p155l3HCqnYFJcgdP88Vxz3krzNIWO5nLnnjJp8d3cCQSLKyMDkMpwRXY6Wt2YyqqzSP\nabKWK6tFhndWx0IJJx+VPk0ydF3QXKsnZZH/AK54ryaDxFq9uu2O9fH+0Ax/MitOy1zVpoy086NG\nwwFMY59/ahxaMTotauJ9PIFyU+Y/KAd2fxzXL+KZZhr1/G7fL57lcEYxuPpUd/K0sTgnqOPaptTh\nsNQ1S7uotYshHNO8gDJOCAWJGf3fXms21Gom+z6Puu1znqSUKqb2s+jfVdky/wCErOFkklKjegUr\nn3zk/p+tbN0k0bmW1lEcp/I1h6TJbafPGx1axKgbGASbkf8Afuuk8uC5hEiX0DIehAf/AOJoxEou\nfNHVej/yNMPi4Ri4u9/R/wCRmwy67IHjkuDHC33iG61f+zxWWkzc8spyfU9qiFqu/L6nCY15wVkA\nH/jtV794Z4wBqlqsK5PKSnJHTPydM4/OqpOLd+i1ej/yCrjaaVm3d7aP/Iw7Ef8AE6u2yMeRc555\n/wBS9aHh6YypBGuAI9StOgGT/rOp71Qj+xWLyznVbaST7PMiJGkuWZo2UdUA6kdTXReBreEae0uU\nJ+0w7gwB5AOD9ct+lc2NqKUalTvb/wBu7nBVjzwmo7WXR9FPukZfhONI2nvZeI4wATjqT6V2cuup\nbxWxk06ZopVJ3MB8vzEcjn0z+NZelaVB9jZLcmImV3UEnGN3GR9AKs3ml6i9pbwpcyhVQ7gzjaTu\nY5x9CPwrGq4zqJv+tD3nGcJ01Hu//SWdHb63p7wlmHl4XgFc/lgj+VWdN8VaHva3F4iyyfIA+Vwf\nxJrnk0G9n0WaWMxyXK4AXoMZHT3NZemWmoJNLbGKEyHAaJhtZvXOevt/Psd6Mbao661RpWaMrxvY\nC3126lRcLJl92OuRzXNQrjw1eY/5+oP/AECavSPEGkPfx2kd7MlpFFa7XcIZX+UluBx2968/ELW+\njalBJw8V7EjfULNWld6RXmvzMOS8VJ9ZR/Mw4bcpIVHzAgE547VZVSZDk/dGKZaMzoWYjPOO3Gak\njPH1Oaqbu2d2Fgo04koAzjFWNK06KXXdPyqmNrmMPG4yrDcMj/61VS4VwScCtfRD/wATzT/+vmP/\nANCFc9SUowk12f5M1xtKnVw1VSWqjK3l7rKdjLquoapDYwWlgbiVsL/xL4MY7knZ0Ayfwr23TdOj\n0fSkQbWlwPNljVYg7fRcD6Vw/gWwBgvNRZcsCIY8Hnj5mH/oHP1qbxN4vmtdJ+yxOA8y4V1b5lAJ\nBHseMc0SjGpZKJ8/XwFCFdwirpf13OlutctUEqSzxoQ3CtL1GeTgmqdzrmnNHGPtMDdSVyG7fpXk\nJvJZjgjJPtSMLnbuELY9cdaf1Wl/X/Dj+oUt+X+vvOr1vUiLeWWzNlKfMU/8e8TEjBz1WsiDxpqF\nsgG2FlHTbbxr+HC1gvOI5MvG6nvg4rV0ZILucq+MnGVPf3rVYeko6q5P1WknpFf18y5H4l1bU5hv\nWBl7B7WM/wA1rTvJJptNsraIRfaLq4eKR4oljwoCHHygcDOamk0m2sbZ5+QqjoO/tV3w5YyT28Ny\n2D5U0zewJWMD+VZyUI2lFWs/0ZjXowpODS1b/R+Zs2lmlvLH5a7VRAo9gP8AP61vw3QjA5/Ksld7\n8sRT2kCRuf61N7mmxvxX6ttB/iPHNV9Qij1C1eBmKpIeNvUY79PpWB9qZbhU6nYAqjrlif6A1Pc+\nIrDTxKpm+0XECgm3gwz8uE+g+ZgME5qpfD935owrt8mndfmjlZbWWzuJIW/1kbYz6+h/EYNVkNxN\nd8vtKcqPU1a1i+vZroXVykMTEYEUeWZR1+ZuhPOOB26ms+SBNQjzlif9lsVnJJS02PdhKTir7l62\n1DU4rkLGyAj+M9B68VXdby31VjuDGZ8sF4AJqKGO7ExXyXKkbTmGPH57ePrVqLT104POkRiggQyv\ngk8Dk9T1raKVrIipKSe5peNtclh0Sz0mJyBOm64BGdwUjb9OQa48J5uk6egz815MOB6rFWRrHiG5\n1LUpbk4AJARSAdqjoKlmu5JvDli74BN5cZ2jH8ENdM01yJ9/0Z5eJndwS7/pI6ia2+za/pAEokX7\nWnII4IdeD6HmsXwjaLNrolDIFgQsQxxUWh3Tz67paFiVW6iwMf7Yre+HunxXEV3JNgbyE/If/XrG\nu1Fv0X5sVCPNiLeS/NnQ3PiPBkWOe2YoANhBUsD3B7/hWNqdpDqtk935XlS8/d6EjvXQzaBHES+5\ndoHGBzj0FX4rOzl0kWcZiDcsVBGck8/WuaLi2lTPbjCbT59jzCKxE0kYJjT/AIFjP6VNq2jKtgtw\nWSOQA/LuHzD1FdPL4bWYNGiI8eeQTtIq7J4bLacUVDswcgHIB9s11RlqcrpNOzOU8HXxitryOZmw\nItwJ6ffjUfqRVTWkVNReSEERO27GfunuDVrSrMJbahE5KbYduSPSWP8AwrI1JpYbuSJixHUBu1bU\n370vl+R5lNWq1F5r8jrfDWpGGKNQTlXJrd8Yaat1Hb67CBlsRXGOzD7rfiOPwHrXnemXDLKEBIz6\nV6doDLf6bcafIc+fGUG48Bv4T+eD+Fd1P34OLNdmYdncDygGkHHrWbdX6W+oOHGF28MOc+tEOfMK\nswQqcEHrU9zYpcBHwXI6nbXluCUrM1hUcGMgurcWkZyh8xxuI5IydxH9K6eEo9tfKkJ2SWyxxS4H\nszY79Qe3eucttNUTAhMV6N4X0qO4XbNjGO/pXRShroTVxL7HnVvpt5pofUyphskdYXlZto3HJA9+\nFPT2q0l5FqWpxeW5eJYljXIIz3P6s1dv8axFZeDNPtIUVEe8DALx91G/+Kry/wALSEXqA9Aa9nB0\n1Bxl3uefjE1Tu+p6Z4T1GNLK4illRAj4XcwGeua1p763wf8ASIj/AMDFYHhh/LvNTXgruXGQD61r\nzzsTj5CB22DNVi/4rOegvcsUrm9gwT50f/fYrPN1ETnzV/76qe6cM3Krx04FUi/PQflXnyZ1JHsr\nIuTwfwYimsiFcFSR7sTUjdaaRVFjCEOTtbPruoO3sD+dKaQ0CIcqYmd+MdeemK5FStjfahqSzZa5\n2gvg7VVc4AIOf4sk/QAd6n1LVDI728ZBjLHaUYZPJ6jqe34A1zd7fGWMPCTvYho9qYZwvUEZ4z19\nc152JxL+GB6GHwqfvS/q5cmvvOuNjnZgAFfvEKe5PGByOCe4rJuLoIx8xBvVg5+dQQQdp6Z9qqS3\nazSRQjAhKYQkkKFJPHqSpP6VWgle6jJw7ZRgSIV64/xFefLXf+uqPRiv609HuPmYSlnMisQCBl2P\nKtx29BUN88kcyXEXWH5o8AAdc4yecYOPenN5iiEr5qhm2n5goOQBzx65qCYO9uZArMyxgHEe/PQ/\ne/AVL3uvy8vMe1k/xfZ26EWs2sms2SXNskQ8uESvNJhcDkBQe3RuPb8a5WG+iljCu2D71ttJbQS7\n5o52STKjbtUKHBLZ65+bH5ViSaVhBIuCD1A5xWrkopXKoUvaXX9eZat7SCR9zXEar71Yv9RtbSxe\nG1IZyMbh2rMjtgozxTY7V7+/js4lLFjlsED5R1x70Kq5OyNnhI04uUhdEtpkuI5o5DG/3ywIyBjJ\n/TtXYxTFHaMzblifC5lHQN759TWctsI55WBUleFZW2jAB9f93Hb8alllYNOx3kbicZV/4h71Mm2z\nnuv6v+hcivXRoiedyrkDYeh2+npSSzQ3VitvdwrJHKQHDIcMPmGfr8o5HNZkhIeNfLwMnloQP4s9\nqzX1OO1KKu1mAz8uRjnPP4Gmoty08/60MZSi46+XV9vMxdQ8Krba4scZJs5PmQE/N/un6evpXX2s\nSWtqkSBVRR0UYCis6yJnYXWBl12x5HT1/X+VWpXKjBOe59a9CCbiuY8ypZNpFnz92R1B4x1z+FV9\nOvpFaS2kyTGcqTnOO1JNIYow68AHP1FVZGEepR3KtxINjg+/Q/pWtjLqabahMB8jP781RvLhXUzv\nEkxU5+fr+Y5q0wjbnIz0Io+zLKhLcAj7tU7C8zPvrmzvrdfJgW32tuOGLAZ46kk+lOsJ1gIiuVLw\nn7rDkr/9asaEx2+sGAkjcTtroI4Y/L+6OfTipspKzLjJx1QviG+trPRZY7OUPLIuCydFH+NUNNIb\nTbbKgjylyfwqtr48qxKg8saji1a1s7KCPfllRQQvODipUVFWG5ud2yTUdBs75GaJfJn7OowD9R/W\nud0ixdPElpaXCEETLuX2BzW03iNEbAi47nODV3SbnT73WYbsuEliRgA38Wf8BmpqStBl0YvnR1mo\nMFiL9wMCuaeTJJOM1qXszTR5BOz+dc9czbFPavOir6HoydjL1yWEqSoAkJxkd6zrdPk3NwO1QXlx\n59wTnIXgVJGrOAznC16EI8sbHnTd3ce8oB4NdDq0VtcfE/XoZtoEmoXSqWPAbzGwa5iQgtwOBWl4\n2ynjvX2Bx/xMrggj/rq1ZSV6yX91/mh05KLTfc6lfClmMF/lkV/lcHII9fzrqomCqAxyvTFcvpdz\nILG3aeVnZow25j6ityKUHB7Vyzu9G7nqU+SLdti/PpNnLEbhMxSY52nGaig02K60y4swCRMp5buR\nyD+dLNexw2xWUnbjnFZdktkl95sd3IrP1j3Nlh+P9KSTdvI0vDWyKUVg+l63cRM+4LpV4VAJwoFr\nKMCuCV8Kqfifeu81S/WbxlcW6c7dLvsken2OYgfqa4K2jeRx8jH8K6qX8SV+0f8A248eovedu7Og\n0p8FRke9Xbs4zj1pmj6ZJMQW/dxjknFNvXGTXVSabdjGcWrNnN3EQ/tOaVgNoAxn1wKs2ltPe3KW\n8Cksep9KgnffKxz0Nd14CsYhbzXUigyEgLn0rGvPkTZ04enzySZVfwbdKiYIIxnOOTWnaeELYxje\nMuPyrrGlAiKDp2FR243MMGvNlWqPS56cMPT3sc7q3gdobJLrTgFuE5x/C/t7VW8O6xE9ypuyLeeH\nIKtxyOD16d69QjIaxEBAPFeY+KNFS08RLOscbLOpJR9wBcY9Pb+tbpOcbSMpR5JXiaVt/Zl3I7gI\n0i/xADkdR9a868bzRHWhbxsT5SfPxjDNzj8tororqS5ht5roRhJhHgIj557Ek+9cMukancebJ9ll\nYqfmJHJOM9+vHPFbYenrzHNiKitylEGlNJgiiuw4xaY3WndBTT1oAUudu0HArQ0yzNxIxC7tvb1r\nNrqfCJWS5kUjlQKzqtxjdG1GKlNJlu2tLmKYBQMcfIFJGO/WreqafMH2L8qYySq85/8A11uSlUQA\nL8v8TYqxLcQXR8y1JfaAG3Lgfma4faNu56PsYqNrnn99pUscZd1BUD7wGKwQzwvweK9R1NElsHOO\nNpry2ZsyMOwJrroTckcOIpqDsiZTuyRTvf1qKE8YqYcrWxzAqBzg9aUxLEMsxPoKZkhsipIY2nuF\nTBZm4AzjntSvYDsPDel2+qeGiLkRgR3E7LLI5XZ8kXTnHXHXNdT4St2g0ubkBnkIHHpxVDwpZxDQ\nxYzKyLLcSTIwYMCF8vjOP84NdJbBYBtTbgE5IGMn1ryZVOZySel2d2ChH2V+t5fmc5qen3Rk2x28\nJRj8zyO5f3PYfrWj4Xh1BJBFLMyITgZJb+da11fQqpwm5vT1NUk1i3tXVpiyv1KhDgfj0NPnk1ax\n3Rpxi73KN34n1uw12a0j89YonK5FuHBA79e9cV8QZJ7jVYLqYZ8+IOrhdoYdM4/CvYLO703U7iKR\n4lzIu9GYc4rzn4wyK2r6dEhG1ICQB7tj+ldlFrmskceJi+W7ZxWuf8f0XtZ2v/oiOs4d60Nc41CI\nf9Odr/6IjrPTla2o/wAOP9dzycP/AAo+n+Y5elKOp5pqHjFOzz0rQ1Oy0fPn6V84OZ7bI/7aJWT4\naSW41N9jcpaXBGfeJwP51p6KyvdaRgfN58H6OtQ+EYxEs07ZG+GfnPGBE3/165a32vT9Tjr7z/wr\n/wBKOs0bVohYLPMwUAfPu7HpV5fGUHmrCsMgB6O67c/TNcX4eLakJ4w+x/NMiZ5GTW+3h4wBp55M\nnqWZtxJ/p+Fc84QjJpn0NKc5RTRb16bUUuo453lMLLuWNZTGh+prq/CcdxJaiRysUZHEayMw/XjP\n0qSK5tLjQo5pxFJIq7drsBk9QBnvTtK1W2YFY0MZU4ZG6ihSdkkjaVPq2ct4v04Hwjr0ZmfyrK8j\nmgjLcKXI3AD0/e/oa8qi/wCRZv8A/r9tv/QJ69C+Ksbxva3EUz+VdE+agJ2lkAwSPof0rz6MAeGb\n/n/l9tv/AECeulK0F6r8zyMfJNxXZr8xs/8AyLFh/wBftz/6BBWZWpKpbwxYYH/L7c/+gQVSWED7\nxya1pbP1f5nPR2fq/wAzofCUHmLdSKSHSCfB9cwuKIr+OOMQ3wlUj+JGxkVpeEkCvOoHBt3P5xvU\nV3ZRvndwKw5l7aSfl+pvST9pJrtH/wBuLeiarbnU7G3s0mkLXEYLMSSBuGfwrO1TQb2fSUuvMLyb\n/mTOAQAT/StnQ7GKLDRkhgQQw4wa6e/ubqK3hWK5kEx3Hlz8wwQe45+YHr2o95VE4W17/MKlOrzO\norbW1v3v0v3PFGjeNyrqVYdiMVKo4FdTca5rR057iK9u2gWXyWlW5b5WxkA4ORnnGeu1sZwcUl8V\na1HatAt7cMZBzKZ5Gcc54y2B07Doa3/e9l97/wAjnvW7L73/AJGJ3Ga0Fk3LviyGXqvtXQSeI70+\nEtPj/tO4+2/bbgyETMJPL2Q7MnOcZ34/H1qjfahrthIiS6xdiRhv2C9LlP8AZYBjtPHIOCO4pNVW\nr2X3v/IOat2X3v8AyKCzebFyeQcZqARos4DcK4Iz6Grw8Qau7tjVb4ZHT7S/B/OnDxDq5WMnVL7r\ng4uH/wAan972X3v/ACHet2X3v/IpTBoVVWHIYkn1HHP6Vq2jyBcRSOhx1ViKcPEOoSI0bapfBh/d\nnbPsetdnoVnJf6ZbXp17VbsMQr29qGLhu4LFsLjjk4HNdeFqTi2pJfj/AJHLiVXaVkvvf+RyqRSX\nLASSysg5O48fzqlfs8kqhVZUJCLxwB1z+OM/hXpMksWiaStzeahOt27FvJmmM74SUjYqqQOQvLkk\ndcDnjltd165tZJFOrSfxTQPbE7ZFIUKOvGMHIPf1zmt61eo4csYq3q/8jGjSxHPzStf1f+RyVxDH\nG8ao2/Ayzds+ldl4AEbQ3SyEAedBtBPUjeR/L9K5L/hItYDyA6rf5LZx57YH05q3puqahea7o8Vz\nfXMyfbI22yyswzuHPJ9z+dePioVKlKSdrfPpfyOut7X2bckrJN7vtLyO0guWguQ+chiTmrN5eXUx\ngliZfLCFih6Md7Dn8qxtOmBsbcnGfLXr9KsarbzNbWlxb3DRbIyCoJCsCz9cEGuScVzxT8/yPd52\n502u7/8ASWaOm+KdShcwLZLMCf3oVtoIzzjuK1NC8RSHMN4MzqNu8nk+5ri4Rqkl2Gs9StIieitu\nbP8A31n+da1nGyXbNNIHm6MVGAfeupaWsbyk7PmR0GoeberLdGYpBbxSNMgP31K88fn9Oa8rllaX\nSdTmflpLuFz9Ss2a6rxB4gurDdYwCPyriA7mIyQDuU49OM/nXISts8OXp9bqD/0CalU1a9V+Yqqc\ncNGT/mi/xZkwHbaqc9SR+tW3IQZ7Coo7Z0tbcuU2yRmRQrhiBkjkD7p4PBwcYPQglxyUzuAHfIyK\n1lub0G1TTXYjlnjMnlnkkVs+H3zrGm56/aYh/wCPCucwEm5weymt3wrby6h4q0+JCRHDIJ3IHQKQ\nf1OB+NRiIL2Urdn+TOKvjGsPWlNfZkvwZ3E18/hnwnBEIyJdpLF+PnJJYH6Hj8K8ulu5by6J5LO3\nSuw8e3ZzHbLwCS3+f0rk9BVP7Zt/MIxvHWrpRSg5HNzNu/VnovhnwWj2iXF7jcwyEx0Fd3YaNpkc\nXlG2Q556VSsbtEgDSMqIR1JwKZHrttJceVbyLJKD8oU5zXBKTbuz1qcVy2L154J0zWo2tzbKjH7r\nKMEV43reh3XhHxF5MhPl7jsccZr0SXxrdw3oSMvGoba/lJvkxnnAyB+ZFTeIDJ468Ny2o0aVdRt4\nzLb3Akjy+37wbkY4PA5Ofxrtw7SVmcmIScrHKLeLqkNvCV3biMgZ6+vFbUbPoeirDaSW73b3H7yM\nguo4XcvBG0gFep/nXn3hvUWh1BbWYMGY7BjhgT6en/166C9vI/D2ixRrHvnkvJJFBbIGETOfzFVW\nik4xXf8AzPHxLcpwv3X5SOi1Jo7RmuL/AFSSK3X5liQ7N3t8vLfTmsuTxrpawOkMM+0KAgKAZ/Xg\nVwF5qE99cNcXUrSyOclif6dhVVpOODVqjHqaG9rfii61QqpCwRoNoWMnJHue9XPAAM+t3a4Lk2uQ\nMZ582M5rkgDnpk0qSSW8sc0Z2ujBlI7EU6tLnpuC0IqRco2Xl+DT/Q9X1bS5yw8u3nkY9SsZIrF/\ns3VYMtDZXYPoIW5/SpbXVLfV7BZosedjEid1P+FULtSqn2rzoxqRfK7fj/md7deS5k4/dL/MtW6e\nIXuVVrK6Rc8sYGH9K2vEUeoN4YvLW3tbmWWQKp2RMWb5hnoK4mEuJt/PBql4g1Rrkraq2UQ5Y+9d\ndONS+lvuf+ZyyliN3Jfc/wDMqnw7rhOf7G1H/wABX/wqzqFnd2Hh7T4ru2mt5Dd3DBJoyhI2Q84P\nbrWHSjrW7hNtOTWmvXz8/M5nCpJpyasnfRPz8/M2vDUx/wCEl0ten+lRD/x8V0vhbUxZjyiygZya\n5Pw6f+Ko0rH/AD+Q/wDoYp8FwYXDVlVhzTa8l+bNaMuXEN+S/NnrE2qJMp3yYTbyc/yrmYJ9t/5I\nuJiu7cCkhDHn1/pWJ9tuL8RxQylDjGRzUQ027hmDC52yA5I3VjTp8j3PXdadSNoo7yTUfsNyp3kp\nLySxzzXQWGuAWzK205ryxXubYhrmdZlYAY7CrEupuXVIiduO1aRi1K6InWduWR1sGwX1/OUzbsAS\nFAJ++uePSuN8V3sN3qQMO0oihVYLt4HTium0eNm067O/Dsgz7fMtVJPhnqt8ZJrbUtNlbGVjaV1d\n/blMZ+pA962pzSlK77fkeZT96rUa7r8jj9PkJu1x2ru9A1DyleVT/qyD+tcveeGdZ8NXapq1hLbF\nslHOGR+P4XUlW6joeKuaBOT9pQnh0P5120rxepTR0niO1WDxBLKqgxXIFwhxwd3J/XNNt1jKjaoB\n703Ubn7VY6YzHLwxNAT/ALrEj9GFJaP0/pXLilad0O9zSt4sSqTzzzXo+ixeRbQyBcqwPOOnFefw\nr0P616Z4XIudI2NglOmfpWmHMKvQ4P47XQbSdAUNnc8rHHsE/wAa888KkNewg8AsMn8a9H+Nlhu8\nLWNzn5rW8MQHs65/9krzjwoizNvx88ZB4r2KL1gvX8zHGO9JN9jvvDXEmoTgnDyBR6DH/wCutOeT\nk1R0m1SHTFY3U0TOzOVWJW7+pNE4AJxdSt9YlFZYqV6jMqC90hnkJbpVbJpZCuT87n3IFQ8H+Jvz\nribOhI9zOPX9KYwYsArqB6lf/r0+mmtQIwSxYB0O04PHQ9f8KytevJLWwZBgPJ8ow+Djvz2z0/Gt\nKM5eYc8P/QVyHizUvIm+ZchRtQFchu5P9KwxE+Sm2a0Ic9RI5e6kL3P2cyuBLIFkeRcAAD5vmHtj\n9axvtTy3zScKMbSsbZxj7pz2HtWhb5js7m6cFpDEQWVsks/U4PPQisGAFtS8gruZ2bcNm0kdBkdx\nwDXitWvJf5ntp3tF/Pp/XQu3OVngbIQMzx/KuQrYztUdOoHNQaU0csshkKFmdh87MTyPb3zUWpXK\n/ZhJuOVlQbicEEHgqPwIzVvQBiNGjLKskhcBHCgfvMdSfaqS2cdn/wAHqDfxRluvL02Q8/NbrsCN\ntw3ywkn7zD096sGOMOpbZteLne57HH8I44FQqQIXZh/APvybh9/0H+Iqw2ZEQx7mCGSP5IwO3HPP\nrUaN3fl5la6peeysZFzaCNHgkEXOVyilsFTnPJ9CRULYNpGjykzqCGjZSMLn5cHoeo/Ot58TKhZ3\nO7YzK84HUYbjjvisS4jKQFo3VWiyrIhJLDOTkn2J7jp7UOHNp+nYcarpy5vNde5h3AESsynitPQ7\nQWlhLfzIWaZc4I6p2CnsT3PpWdcW7STw26q7LIQQUUklO5A9gD+RrTndZPOjCxiOP5cLGcZyMnp7\nflgVnFNKzO3EVYytZ/iSEzOgkk3ksvQxg8bRj8MGmXMm0T5A3c43JjuvoaSUwr/zy4VsYDD2H8qq\nXcgJuMOANxHyyEd19a2tff8ALyPN5ktV+fmyne3W3aRsxhvukjufWsK4keW6VVJLYAxnP4Cr9+xb\nygGYkg9WDd6o2dzbQ6p9ouVdghG1EXkt2rppxVzkqSdlc6y0hMMUcRzkKFHHApsmBlcDIOOaI76C\nQbkSWNif+WqYz+XFLIucYJYEcmuuNraHFK99Rm5WhkifKuOVI6HPasuWTH2Udsnr3AIP+NW7t2W1\nklx88RGT7bgaxNSugbtI4iSFBJx2J7VRK3NuDUCzEJHhiPvNziraBnG6V2cntWJazDABKj8a1oJA\nRzn86VxtGNfxCHUrdsDIkBJ/Gt4zxwwNJIwCKMk1m6wgxHMMfKwzj60k0sb3ENtNGsyC3nmZCzAM\nUiZlzgg4yPWlOXJFyfQznPkg5PoYuqaq97J02xj7o/xrOtka4uFRFLn0q1JqtqXCDRLAk/7c/wD8\ndrr9CgtorcXH9lWkUh5ARpT/ADc1zVa8oK/L+KNaXtJO3I/viYEfha5nOZFKZ96gvfDV9pyefES6\nrzx2r060njcZ+zRg/wDAv8a0isMibXgQgjkc1yQxdVvb8jrlTklpTf3x/wAzy7TdeaeEW9wfnHGT\n3rL1e/JkaGM5PQn0rW8TrbaJqo8vRbJkk+YMzzA/pIB+lZN3JbXuiXF2mnwW00dzCgaFpDkMshII\nZiOqCumNk1Llsn5o5J4qpZRnFrW26MdQByamLl8KKgUE96l3CMe5rpKHNwMVveK7SW+8ea/DDGXc\n6lckAD/po3Nc2zE969U0g6Rf+M9cng8Q6bKmpXMtxCnlXKuoLswBzEB0PYnpXJiKnsqinbo+j7rs\nmaUoKb5WzHkheG0gjbho4wp+oGKZBqslsdr/AJ109/otqzn/AInVgn1Sf/41WRcaJYkEN4g00E+s\ndx/8armhiabWt/uf+R1yundEtrqKSncZgBjBzU0s1pBE863LugG5o2Py/XFYp8OxNIBF4l0sZP8A\ncuv/AIzUmseHY7CxhD+KdOjScMreZFc4JGOm2E9iOtaRnS5t390v/kSHVmouxg+Hrx77xXeXb/ek\nsNQbHp/ok3FWtI8uQruxUek2WmaNdXF5J4k024H2K7iWKCK63u8lvJGoG6ED7zDqRVDSZH84IM49\na1upznKO1l0a790jGm9UjvfMRLRgnA29q5S+lO04ro/lj0xpG6hc81zscP225WIdM8/StsI9JBil\nqkYxU+YhIIDHgkcGvRvCXmixJxiLoD61QvrbzYlt3DyAn90v8KjAwAMf5xU2ipf21ixsys6K2Ht2\n4Yf7rf0NY1Z+0j2OmnT9lK+6aOpkDqAT09qWGQr93tXJy+IGmdlh86Fo/vrJH909gc1ANeu4ZMts\nkU9WRcY/HNYKjI6VWieladffvArHmsXxvCZLaC4ClwtyOF6ng8fnXNXeuXUbxi2LbmVWZvLPy59Q\nfpUmo3OqSaHLcyzPJ5ZDK2SBkdAFwK6IRdkjKtONnYW1Urb3jMQ8h2q/pk9h7DI/GoN8q6hp1uvI\ngTv6YqxplpImgfOxaSU7ySOpPrSTRZ1hnXgGMKfYck/yX866YqyseZJ3ky7FZadPE4n0+0dpSQv7\npQSPX1HfmuK8Z+F49LEd5YRbLVgFkUMTsb155wfqa7KykRH6lnPXnP4VZ1KOK+0+a0lyySoVwFzg\n9j+BrSLvoQ1bVHiVS2kYluo1YgLnkn0olgaG6khf70bFT+BxRgKuR3pMtMbcxrHcSIjBkDEBh3FX\ndE1Aaffh2OI3G1vb3rPY5NNocbqzGnZ3R6nEj3y+ZFO6naAAmOasWlleMj+fNcImCMZH+FcRouq3\nFrBmJ/Yg1pnxJqJBQMnzfjXA6Uk7I9CNdcupd1vVBa2rQK26XbtH+JrzwnJJrZv5dkTM7FpX7msY\nDNddKKitDiqycnqSw4BFTcgnjg1FH1Axz2qcNngcj3rQyIieatacIm1GBZ2kSMuMtGcMPp+NVmUj\nmtXSPD2parFJdW8BW1hP7y4bhFPHGe55HA9RScW00CaTuzqWsIP7SsrS2dorVbqZsCQ8gRQNjJye\nW7V2JkCsw96a2nWpwllBiGGSSSWd1G4lVXoT93dhRgHovfFXb3S4beeSKXVLNJY2KsNspwRwekde\nLzqCSqPXXz2fkmdOCkuWdv5n+ZjXCXDENbNFu/6aAkfpSQ2t3I2dltKynlWO3H65rUjhsUHz6xZd\nf7k3/wAbqS3stAe5DvqNo7dcgzj/ANkxRGvBPW/3S/yPRUk0NjN1NPCtzZiBkHyukgYEfUV5p8R9\nUTUfEQt4UXZZR+SZB1ds5bP0Jxj2PrXshgsJpESDUrbavU4k4H12VvyWOn+IbeNrnTNMv4hGpRpU\nJYIyhl25jyOCOh/I8V34erScm5XXyl/8icGKqOySPl3Xh/p8X/Xna/8AoiOs1Ohr23xR8NrDWtTd\ndOuLexuMbI1kMwVlQYClTHxgADIbjb0zzXE658M77ww8a6vrOmWyyjKOYrpkb2DLCVJ9s5q6Vamq\ncUm/ul5/3TzsPf2Ub/1ucRRnjrW4dC07/obNH/79Xn/ximnQtO/6G3Rv+/V5/wDGKr6zT8/ul/8A\nIm/KzR8Ny5udIBAz9qjGf+2gp2nuLeOS3xg/YrgcdCfIcmrnh7TLJb7TEXxPpMmy7jIVYrrLfODg\nZhHX34pLHTNPkv5GHibSWP2W5G1Y7rvC4J5h7dfw4yeK5qteneW+3aXf/CcdeDvP/D+pg+H9Tayv\nUj42SyLu9cjIH4fMa72a9a4sWjAJVMEjPpXBnQtOz/yNujj/ALZXn/xiuu0aOGaLy4fEWmXNzHDJ\nJsjS4UyLGhdvvxKM7QT1FFepTfv/AKS/+RPWoVXBcpZ0hpRcmRQ688GWTC/T/JFdUthPBcxXqypN\nu4kVRwB6j/8AWa5CK8sJGBkcoRz8p/lW5D4mQw/ZbUF2IxmnFOTtY7J1Y8hF42sv7T8LzSkhTZSI\n4JHXcdmB/wB9A/hXmlvDGPD9+sjgYvbfI/4BNXpPia5ay8FXMLn99cMhb6Bgf6D8q4AAP4avHK5/\n0u33cZ/hlrf7NvNHj4zaL81+Y2aNf+EaswmMfap+n+5DWQVOSK03SM+G7IoSpF3cdDjnZDVRCCnz\n4Y88960pLR+r/Myo7S9X+Z0/huQC4hVSMtaSoQPaNiDVl4y/3qydFYLcusbsrC1uMHPI/dPzmpIP\nFLbBFqFvuIGPNiUBvxHr+VYSg3Vk15fqb4eaVWal/d/U6GwUxj5eAPSq3iHUwlnd7ZZFljjVIypB\nXJYbs8dcY9OlZcvihRCy2ELJkf6yQDI+g5rP1KQppJV85fqT3PU1rThJO7NqtSLXKjMtLucQNbI2\n2N85CqMt04J6kZAOOmRnrUaiUPtIwoPcVd8PQoZvPmZFQcDecAmuoOnQXURkjZCnqDkVNSvyStYq\nnQ543ucNNIWkwTnHaoHnkknLyuzk8Esck1vXuitI7PahiPdcCsCeGWCUpKhVh610qpGa0MJQcXqW\nA21kbORnFKOfMXPQ5FJLbTwxI0kbpvQOoYYyD0I9qajZk/3lqBFuALsByd4I/rXQW0N/pCrewxNL\nZyxq01q8+xpV28sVUhiuckH0HNczHNsevU9Onhl0a8S01G4vozZtCLeSLZHAdoyzSNhcLg4/D0zV\nRklq3YzqNlnUdXivNItY5b2xKyQJI6JF5VuYwCBuP32bI4UFRwcgV5XqBkMq702IRlOMZXoD+OM1\nYfWnS0ktLcKI3UKxf5sY3fdz93O45x6npk1ku7dTzWk5dBQjbUlklBOQKt+HjLceK9KUEnF3Ecew\nYE/oKzSQVzWn4Xuo7LxRYTysVRZRuYdh3rnqr93JLs/yYsQm6M0uz/JnYW/yWcDJjaEGPyrXF7bC\nztUuTjdETz/vuKo6XbwSQvbC/tnZSWARZBgE+6Dv/On6npsTQ2qtewLthIyVfn5254U+teXVlD2i\nT7vo+3odP1uClTkr9ekv5f8ACXrWXRoXLHajfwsDUT3cUlyzRnI9fWue/smHf8uqW2e3yS//ABFa\ncNgllbGWS/tyx4QssmM9sjbmt4zprr+D/wAjapjYSXX7pf8AyJk+NI3jgtL9CwYkxYPRl6gj8d4r\nH067jk0K8N5GZYhdwAoDtySk2OR74rU8SpZ3MdrAdUsYZUy0hKTfOTgZ4jPPBrFdbS00W4t49Rt7\nmWa5hkCwpIMKqyAkl0Xu4ronKE1FLe66P/I4KmKc0oxb3Wln39LEbvB9ozbrtjYcr6H29qV2AQ55\nHvWcHwflNO+0Ejb1rSVK7uj1aGN5I8skTOfMVRgAKc8Cu5+HtmIxLftw8kqwL67QQT+BJH/fNcHH\nycHvXqug24tLHTLbcyuuxiD13FgWH61zYl2pSXk/yZz5hNSw0muqf4JnFeNJvtF9uHReODXMWc3k\nXsMhJwrgnFdB4kYvIHP8fJ/wNcznbID6HNdVNe7Yzb2Z6xKl/fxxrbRukJQfPIgOCOoGTgfU0qaf\nLavFFLK8ihh+7LEqADk+3r09TTNC1WRbKN25O3n3qO61L7RformTB4RE4J9fpXnWlflPXhCDhzyZ\n0eoQ6XYxtdRJbMsp4j3pujP+ynUj+VXdC1+0tlmYEuZowobIG32GO3Xj3rnYNOeOFpFazhBH3ZJD\nJJz1wB9P1qtpemzYee42qSxwE4GPWqT5PeQXhJNWOB8RBrPxRdvD8uJjIhHbJyKNQuprvQrO4mbM\njXtyTxj+CCn+LGV9XJQg4GD65qpOSfDFhz/y+XP/AKBBXY9VB+f6M8TF6VV/i/RlAynFIH5plFdA\nyYS4oLbu9RYNG00ATwXM1pMJbeVkcd1Nao8TXDptnhjdv7y/LmsPFFRKEZboqM5R2Zfl1e4dSqBY\nwe46/nVDOTk9aKcqM5AUFiegA61SilsKUm9xlL3rbtfB/iC6JK6VcRoBkvOvlL+BbGfoK39P8Had\nBE7alcSXMwYBUtWxGPUFiMt+GMetDaW5yVcZRprWV/TU5fw5/wAjRpP/AF+Q/wDoYpZraaGTZNE8\nTjqjqQR+Br0SSSG3eNLK0tbTyxjfbxKjY9Cw+Y/UmsLVtR1n7Rut9SvACPurOw7fWuaUm53jbbr6\nnJRxkqta8EldW1fnfojBsblra4RuBzxW1cSxSzK+fvDJrITX9ZaT59Yv1A5I+0P/AI0r+J9XY4XV\nL0Y6f6Q3+NTKFRu9l+P+R6sateKtZfe/8ibULjcyxxjhTj6mnWUTSTDdnaB3quPE2rtg/wBqXvB/\n5+H/AMa29I1jVZp0V9Qu33nABnbv+NXCNRaWX3v/ACInUrvWy+9/5HTwWr6dpMl0sx5hDDDHnDru\nIHbGOfqKn07VQ4VkkIxz1rH1nxE8lmtiJGZgmyQlidx/rXN2Wpvp84V2/dE8H09qzqU3K8uv/AsR\ng6zhVnGpbW23pbqfQ2iXlrrujnTtSgjuraX5JI5BkEeo9COxHIrzLxb4In8F6jI1u5l0255t5T95\ncfwt7j17/mBs+CNfMG6bAkiPBU16NqsVp4v8MzacAVkkXdExG7y3HQ8c46j6E13YKvBrkk/67ndO\nGvkeK4E+lyspwEucqPTeOf8A0EVDp++PIkk3sWzn2rUsNHvtPutT0e9gKXSpuC9clcNkY65A/Wst\nfkm+hp4un1MO6OnthuiDf0ruPBF0C8kGfcVxtlH/AMSwykNg8ZFbPg648rWY1yMOcVFHSxjU1jcl\n+NUefBEx7Lcwv+jD+teP+BZ9usHHzYQll7HFezfGpox4ITzDxLdRJ9cK5/pXh3h3U7XSr95BA7sw\nCKVGeM//AFhXq0dORvuZ4tXpWSPV2bbCoAIXHFUZpB2OfcVKrvPbRSiNlR0DDcR0IqpKHHUfrXPW\nfvsin8KIXfmoyRmlcH2+lRhmxyQPbNczNT3p3VBlsjvTWcKfmyPwNPZQWUntk0hY9e/rXQIqqwRp\n5G+VC4wSMZ4/+tXmGruL/VZJkfIZ2CCNyp5OOh9hnpXo+t3DW+kyhMb3HloDxnPUZ+ma8xkZZrwj\nc3loNqtMvB/h+969T2rhxknpFep24OCbcn6akGosFghg4+eTewdSpUfUdeMdu1cvBLtvZZANoAKj\nL5HTHXtyRXRXrtJc7V3hVTAVW3qN3HfPYj8q5kMGuzuONzAEhcdTknHQ9q81rR/8Memnt2+9f1ZF\nS9utsE285JccA5PbAB+vNdFoZENpbBih+UMCQ2cF8j2rhr2UvCwLcNMpb3OfSu8tw9jAkZMo8uBV\nGZgn3WA6Y9jVtWtfv2Ii1aTXbuORGEMgYvjYRlAEAIIPWiKVMBpWjbEqMSzluoz6+1Rs8bzyhEiG\nWcYGX6j8RTomdbdivmKDGpxtCDIJHrWeqj8vTqWnFyu7b+b3Q/c8SoNpwY2XdFB0wSepx6USStJO\nWKyMC68GRV4Yc8c0yeYNNho1ALuMvLu6geg96gmmYbRCEJZU+5GWx09abtzfPv5CV+T5dktn5lGN\nRGZrhGTfEmxQ8hJXJHQ4/wA5pkYdB8pRgzjOJDxgc9V96msIJBaNh5BkMD+9Vc8k/wBKbETsBAkz\nmRjkB+309qjS1/08zV8ydtdPNdhjG4banOdg584d2z/Ws+6mcmTzBIdxJxw3ce/tWhMW8xQw4BjX\nBiA7Z7H2rFmkUFCQvAGcZGeTVxs/xMpXXfp0XmzMvpFMyjAG1T1XHc1seBdMjvLy7upUDLFtRMjP\nJ6n/AD61z8+MMTkHHrXongWy+zeG1mPWeRn/AKf0roqy5YadTmoxvO76GrNo9vPGVeNefasK/wBA\nvrFTJZkzwjkxE8j6GutDcAD8Kk3cZ7isKVRp6M1qUoyR5hqNxE2mSyFmXPyEN1DehHrXJqTJK0jZ\nyxzXReNrhLjXpI41CrGAGx/E3r+uPwrnYwRXp3ukeWo2bNW2xgcGtOKXC8msSKRx0q5E7njJoQNG\nhfSh7CRTk/L3rLt5d+ru+c/6Dckf9+JKlupD9nZQe3SqujRvc3sxUZ2WNyP/ACC4/rWdd2py/rsY\nV1elL0/yMewgku71I4xlmP5V6VDJFY2iRgeZKB07fia5PwnpySXz+eh6cZ6V2tz4aEq4VygJyBng\n/hXNiJxcuV7Hq0YSULx6maNc1GGQkRWuwdQjZIrVuPEl1ClubVIXLrkljkZz0rIm0aa1xbNIqRls\n/KP6Vr3mki4sLdlKh4BtUtnkf5/malOipK2xras4O5k+JdN1HxFDayPFbwyoeoYhWDEAfrj865OO\nEx+GdQSQEMLu34P+7NXbapplzbeHJLzdsKMuUUnGM/5/KuRA2eGdVMv3vtMAGfXbL/ia6Jzg4x5O\n6/M8zFQkn73eP5nP59KPrTAcGnVvYAYVJbXEtndR3EDFZY2DKfemA5z7UY4otfQadndHpumaxba9\nahlYJcAfvIieR9Pai40w3HCjBrzSN5YJRLC7RyL0ZTgiul07x7qVlHslt7a5/wBp1Ib9Dj9K5Xh3\nF3gdUaya947XSvD5j2s+Xbtmua+IGowS3Ntp0BDfZgxkYd2bHH4AfrVa9+IesXcBht0t7RTwWiUl\nsfUk4/CuWZmkYs5LMTkk9TV06TUuaRnUqJrliNx3q7pupPZTBtgkXup6/gapY4pG+Vga3lFSVmZR\nk4u6OvvdZjvrIRWxO5uqnqKteH7UqnmPgu/6VxkMjJIGViPpXf6KitbLKshVe2eh+lZxgoKyKnNz\nd2bEkaMI1IO4txxyO+M9qvKRFM/lgAEnpxWhJpDR+GrTVdu8y3BXP/PNBlc/Utx9MVlspUkVx4iK\nU9Op2UKjdNJ9BZrOGSb7QXAZkCsGUFcDP49z3rIkS3uJ5bZ3i8tRyypt3ew5NXLoSvbOsb7Wx1NZ\nlvaHBW4hAP8ACRlg3H5j/wCvURWm51KSfQ6L/RJrKSaGcJcxx7VVUB3AcgEEEH/69Vby6sbnwncX\nEhuJJV2DaXAAO8dhgdfarlrYvBCosjEWbiQvE+FUZ9SOenbv1rmNZC6dpQt9xMs8pkf0wM9vqR+V\ndFONmrGWIqLlldbmNYpcTXtxKl3Pbq5LYhkKD9DWnFI9vaK80sksjCRy0jFiR8oHJ+hrNtbxIbUq\nEy5BzzTL3UDKvyLt2oEUZz0rpvqeVqdHYhzGsjMvPOc1cvNQjsLNrmcOUQZOKzdKgElosglVsjnb\n2/Hmub8U35adbON22R8uDj734D/OaaBow9RnS61G4uUQoJXL7Sc4yapt6Z4NOY8VHntTAYamnSNZ\nmCHAB71EalmIaU+/IIoAkspRDN8zfI3Wtb7daRjcTvPoo5rA9qM+9S4JstTaJ7qc3EzSHp2HoKgB\nyaO3TrQBiqSsSyVRUgPYVEpzXr/gf4biGCHV9ftyTIN8Fs3G0Yzlxjrjt2788AJbsZPgfwFaap/p\n2uy7bdX2JZqSJJDgH5scqvPbk89Mc934rmWHwsLOzsGhtAqphI9igZBwB36GtTw5PBb3Gq2ccDoU\nuS5IXGQw4z/3z+tZ+vX9tOJbIBl+yyAvu4HzZK8/TNS5cs3psTbmSbMaxjnvdDneILE6QyRsXzwG\n6H1PMZH4dKuaw0NxrOpbLiP5buVWGGyCHOR0qtY61badot9ezqSZZBaQxLyZNgYggdMfvDk/zJAr\nNu5Wk8U65JC8avHqE8clux2lgJDhgTwT1yM56Yrgq0HKouV20b6dWi8NF05SXPZN32X6ktzZxOpI\nuolPuG/wqpb6KJZQRdwlc9g+f5VZ86K5j45FXLEwWymRsDHdjUxUlvL8EdboVH/y8f3RJr2EWOjT\nxLOkckkZVXYHjI68Amus0O7uNN0fS5DKWEjx2/mHaBIVJQIMkdcDqB0NchrBS3spb3Uy0SiM+TCw\nIeRjkfhxnr61S03U9Q13wRNo94rPJpl1HcW84HJBEmVY+oycHvn257qEJcrcpfgjhxEJ3UY1Hp5R\nO+15JcLPtMU0U5l2jDHazE9Rx04rQg1rSbjR3sNVWK4t8+XKkxDhmyTgqehHHGOK5qOW61jT4bk3\nkis0ZDoDyGDYYE59efxFL4XsYoYL55YFdWvZFkEgZsAgFTge1dFGCguVvYyS5IKK6aGLr3wW0zWo\nnvvCV75IIJFvcZMZIP8AC/Uceuee4rxfU9FvtF1KWx1K1ktp4jho3HP19x7jrX1R4Z1KUwSW15HJ\nDIshZcuCxj6KwPccYPXHr2rK+K2g23iPw2yxW6yarBGZ7Z0HzFFI3DOOhBPHqBVy323NYvTc+b7G\n9/s/UbW6CbxbypJszjdtIOM9s4rU0ebSGvpPLsb5WFrccteIRjyXyP8AVDtke3v0rnWJBYe9WLG6\nmsrgT25USAMvzIrghgVIIIIOQSOa5K1HnT72tvYyxFD2kXbe1t7eZO82id9P1D/wOT/4zW54Wl0l\n9QufKsb5dun3pO69Q8fZZc/8shzjjPbrz0rGbWrv/nlp/wD4Lrf/AOIroPBuq3M+szxNFZDOn3hG\nyxhXkW8von0471y4mE/Yy0e383p5EwhU5lo//A3/AJGdpdxotwwi+y36v2Vr1Tn/AMhiu40OPT4C\nWjtZ0YdfMlDY/wDHRXAXupXtpeEJFYDjcp/s+3z/AOgfWuh0rxyy6TN9siga8jwIysSgSZ9QBxjv\nTqUqu8U//Av+AXThN7p/+Bv/AORLvjPUbJlFvcQXMv8AGVjuFTHp1Q1zlvqWlpoF6RY3piF1ArKb\nxCTlZcHPldODxjnI6Y5u32q3r6TJdyC1eVlViz2cJ6keq81zE+p3V7bm2kFukJcOVhto4ssAQCSi\ngnAY/nThQnZJ919r/gGdSnOq1uldfbb2+RNfX9rLawWdlbzRRRSySlpZhIWLhB2VcDCD161WLnCg\neh/lUJAVgR7UCTC5POOK7IxUVZHRCCgrI2NCnJ1GQ5/5dLn/ANEyGqRlV2wQCOmfeq9neTWNytxA\nyiQBl+ZA4IYFSCCCCCCRzWjHrl2c5isMH/qHwf8AxFQ+dTbSve3W3fyM2qim5RV72622v5PuNt1D\nzxqR1PAq94gtBFJ9nBZiEDfMAD8wyOhI6Ed6s6LqF1faskZt7N0BAIWygBxkd9n1pl/4gu7ieaVR\navljtaSzhZsdskpzxii9T+X8f+AHNVv8K+//AIAzSwYNNjkjjBfDAknoRnj8TXQ6FbyXJYORtZMg\ngdGxkj8KpeHtebyJFuobRgG5AtY1H1wFFb1h4gWS5djbqIEyEEcK/j0GfWvOrSqXa5fx/wCAelRl\nWsnyr/wJf/ImS2nait4scbSj5myQoII7YPbvmm+LtFxYW42KZww3Njnnj/Ctu51u+tYheRRAW56o\nyIGHuMVj6v4knbTpLgC33DG0PAjd/cVMJ1uZWivv/wCAVP2lneK/8C/+1OV1eAWk0NuRhxCu8Hsf\nT8sVi/clA9DXSHVdV1W/RLe2tbu4lGQkenwyOxA542EnoT9KdZxeIdYvms9O0q2uLlAS8a6ZBlQO\nucpx6c9+K9GPtYqzivv/AOAedOrUbvyr/wAC/wCAc1Kdsmex5rYl1dH8Px2qlvOMh3/OcBQB26c/\n4+tT3txq9jBG95Y2duzjKrNpsCsRnGQCnTII/A1nf29dg/6nT8f9g63/APiKUlOVrx28/wDgC5qv\n8i/8C/4BRbB9j60ByODV8a7d9otP/wDBdb//ABFB128H/LHT/wDwXW//AMRV81T+X8f+ALmq/wAq\n+/8A4Bnr3FN3FHDKcMpyDWkNeuwf9Tp//gut/wD4ij+3Lsn/AFOn/wDgut//AIijmqfy/j/wA5qv\n8q+//gHSaZdBWtr5TtBxvx+o/nXb3NvBcWlvKCHjMeVdTwQST1/GuP8ACWryX1vc20lvppliIkDP\naKoCHg8IoGASDz6j6Vh3fiPWtO1CS3juljjiyixJCixhck8IFAzz1xmuarQnUkna1vP/AIBpRqVF\nOPPGyV+t+luyPSbXRonYEDIPtWNq0trLcKlvMssMOcuv3c9/riuKn8Va5qUa2TXjBJDt2xqqbs8Y\nJABI9q6XV/K0jwNFGjRtNcyGMZiG5FAA4bGeevX+Zq6WHlGXNJnXVrqS5Y9TiNQuTe3005zhm+Ue\nijgfpVXv+FLSMa6jmFY4XjrSou0ZpmfWgsf/AK1AFuzEcl3Akr7ImkUM3oM8169Gx/tG24ORIvbG\nef8A9deLqTkGvYfCVtqGpWkF7eOhhUKyMU+diPfjj9a48bpTb8n+TJqwnUpTjHXR/kzh/EUSrlo5\nAyMQ2O6nniuVcDJ9a9C8XWdgsnkLqVtA4OT5wlJ/8dQ1xx0y0zn+3dO/74uP/jVVRrwcb6/c/wDI\nqpVhDR/lL/5E1dA1oRSQ2sqnZtwp9etdvHLYXtuElC4/hb0NebQ2FpDOkg17T8qwP3Lj/wCNV2Vt\nBZ3kAlt9StWz1KrLz+GysK/JfmV/uf8AkdOHxsLcr/KX/wAidTZmztU/flTgYy3JPvWVrOuJBD5V\nqdzNyeOnpWa9hGeG1OAe373/AOIpj6bbhP8AkI22fdZP/iKyjKF7t/g/8jpnj6fLZJ/dL/5E4HUC\nz30xY5YtzVmf/kWLD/r9uf8A0CCprnTrR7iQtrunglumy4/+NVHqBtodGs7OG+hupEuJpXMKyAKG\nWID76rz8h6V3c6k4pd+z7PyPGq1Y1KkeW+/Z9n3SMqlApKXJrpOgcMilpgJp2aQgNNp1JjJwOtMC\n/o+j3Os3fkQABRy8jfdQe/8AhXp2g20HhmGSKzYmWQYkmZRub29h7Vm6VbDSNLS1A2yld8h7lj1/\nLpTje4hZzjKk8Y79q5pVG3ofPY3ESxDcF8P5lvUdQmuJhZpK5LjMrZ6L6VEhAuDGnCRgAAetUtPR\nt7zOcseSxqSBixdufmas22cjio+6uhNMww5H904H4VgTSuJFkGDsIIzyOK3HGI3JPbvWFOW5XHB6\n04nRh3Z3Rb1LQI76FNQ09VG9dxjAxj/6/wDhWDJoM8Y3bgW78Vv6JqRgLWkh+Rs7PY+laUkZc5Ud\ne9HPKGh9ZQ5K8FJb9Tio9InMnQEVc8xrFFhRv3gGM/3a1L68jgzFAd0nduw/+vWOsW98nknqa1jN\nvc5MRWhT92O4qZY8nNJPF5sbKRwRVtYuPQU7ysj2pX1PK9prc6nwKHi0n52yTIcZ9P8AOa9d8PbX\njUMuSvzDacHivJvC06wwxxlc7QWx68mu7tfEX2Oa3a1iA8vGQ3JPrz+NctOSjXcmfTUoueHjbsa/\njGwNl4r03WVH7iUCGVuoDDoT9VP/AI6a8w1G2NrqVxCf4JCM/jXsd3rem6zZ3Wm3SGF8DYzDcqt1\nU5A7EflXmHipFTXLjAwTgkfgK9mrJTpadDklFqWp0nhaGO+0SSFh8ynNQ6ah07XoWPADUvw9vLZb\niS2uCRvA2nPFdzqPh2KaRZYgo9c1lTjeKOdveJwHxou2GkeHWKGS2aSTzOOM4XGff736151a6TBq\nEavYTBX/AIouN2PavYviPp4n+G+pRyI0r2pS4jwc4IYBiP8AgLNXgWmvoVxMq3T3UUhOAXOU/HHI\n/I161GaUUnb+tTKunKEZLpoembRHawxhiQqAZLZPTufWq7HHA4qvY28dlZLbxABB8ykSbw2e+cCn\nu351x4mPLUaFR1ghC3B/nTM59abuyM89aTNcrNj6Cb6c0hrC/wCEz0Irj7ft78QyH/2WmJ4v0FlD\nNqR5/wCmUn/xNdF0LlZV8X3ptrY8NmJPMHGQScqBXCxDyLVhHkyy9GjOQf4Rwef7xrd8T3yahcwt\nbuWjYrIrIxViFGQMHtkjtXNahexIxijCNtBP7xdpwPlHI9yTXk4mSlUf9fierhoSVJWvr/W33lCa\nSN2mcvH1YjcpVsKOP5isW43j7oYooZjht4APA+natb52h2LvAIWPCurdeTxx7Vi3iiMl3GMKGOUI\n4C57Vgr2S/4PmbScVeWn4ryOaj/fXsEOMfvRx6AHmvQnxJLmMI2+N+FiyeCT6V51o1wPtzSsPm/h\nycV6Fav50FuSRtJZSzyZ6gdgPerq6f8ADipe8vv6X6FoffYfvMF0YhnVOoPbJ9aqwfIrIwi4DJyW\nJ4wc9hUhcKucxEmJcbULcgj1PtUvlyLcOCJgpkxkARjDA+1Yabade7NrvfXo+i8iu6uDE3zMPMTl\nYwOSB359KqSMHiUuysRGCPMkz0b0H+FTO8YjBYxkrtb5mLk4LD3pSpjVQu8YWRMqgQcZPXj1qk3v\n6eRMktVp17vzILctDIVSPeBJwwt938XqcVmzkB9+ANyEDK4wSeemfWtO0uXgmadX2MACWZ927JGR\nwp9arGNHaUAIdrdUJ6Ajv9PalG19fPqVUu07LouhUW5CzNh1GGY5V2XoD6/WsS5vFZ/LRm3Hrlgc\nCtW6gdFkOQWGQQG75HtzWLeR7T5mOpIPTtW1NJnNVuun4PsVp2by2GOvPSvXdJgFr4fsYsBSsK5H\nvjmvHDyR3JPpXtm5Vt1B4AUAUV+iHQ2bGCUZxUd/cm0spZypZUQtxTWRFbO81PHKCCpII6GuaL5W\nby95WPGrp2uJ5JpeXdix+pqEIK77xD4RExa705MMeWhH9K4iSB7eUpKjIw4IIwa9SFSM1dHkzg4O\nzHRLVyMY6HFU0fnrVqJge9aEMbdr+6POaTwzIBqc9u5wk1rOufT92x/pTrkEpjNVNGkVdQnDdRaX\nP/oiSscQr05L+uhlVly05Py/yOs06OKExtHtyABkHIxWjeazJCDjJCjsa5TQryR7XzZXJy5XJ9hW\npIfNkx/Ca4akbSsz2oT00GPdNcytcNfGOfHG05IFatpcTjT2W5vw0b46jG4e+RVa0MumqWs5mhYk\nHhVPOcjqD3Arcj1TVtR0ptPu72SW0YBSjpEAVHb5UB/Wrjytle/bQxDqMsnhy8tbgllRtmSeuCK4\n25n8/QtRcdDe23/oE1bPi+7SzRbC343MXcjt6D9T+lc5Gc+Gb7/r8tv/AECetowtFPu1+Z5WOqcz\nj6x/MzBTwex+tM6UHgA12CHr0p46VGp4qQHAoEPxxSbQetGaWgBAAKWk60UAKKGXIqU286xiRonC\nHoxU4rf8L+B9d8W3AXTrQi3GQ91NlIUOOhbHJ6cDJ56YoWoNNbnOxIXkVBgEkDJPFfRfhH4Vrplv\nA+sXi3RHWKBSF692PJH4D8q3fCXww0Dw1ZRFrW3v78YZ7ueMMdw5ygOQmPbn1Jrt1jAXaenpT5X0\nHYzdT02C601rQxqIim0KowAO2MdK8e1W3ewvJIJPvIevqPWvcChX5eq9vauR8XeFm1W2M1so+0oP\nl/2h6VjWoOS03NaVRQeux5XLz8wOQeKfGsewZOBVKf7RaTPFLE6sh2srDBU+lNieSV9sYb5veuJw\nezO2M+XU6jTkVbeVLYS3E7A7Y4lLM3GeAOfWvMvEWoNc3mOcgYr1v4fXkFl4tk0q4RRPLbhkfPPX\nkD9K7DVPh34V1bUnvtQ0lHupPvOkjxhj64VgM+prvoUly+ZyYmo5yTS0PmAOVWoZHZiAMknsK9g8\nb/CWDTtIuNW0OSciH5pbOT5iFz1RhyQPQ54yc8c8boHh5rWRb29QGYDMcJ52n1Pv7f1qpQ5TnUrl\nfT7eXR7NTdMA8xyVPHljH86w5tF1jV7mW7tdNvJ4nb5HSFiGHbHHNdHq1uNVvo7NmI8xuSD0Feka\ncVtreOJAFRVAAHYVy18R7KyW7N6NB1LtngF7pl7p8vlXtpPbydds0ZQ/kaoOMGvpm78i7tmguYUm\nibqkihgfwNeIePNEs9H1KNrH5YZ8nyyc7CMdD6c1VHEKppbUVSi6ZyeaU5wD+FIBk1NbuN+MA8hh\nn1FdJkRsGUDIIz0JFJGm9sZwO59K7bxnaabaWttaQQ7L1X3HZnaFI5DZ6EHAxknrnGBnjWAVQq/i\namEuaNxyTi7MaPmbjoKYTyak6L7mtiPw7D/Z9leXevaZZfbImmiinS4Z9gkePJ2RMB8yN3qZ1Y07\ncz39X+SYkrnWfCfw1balqLarfbWhtpAsMRP35Ox/DI/H6V7Lr93Hb2RtreRje3CskMSHGWx1HoBw\nfw+tc54K8NQad4UtrV7+1kZ90junmKDk8EBkBzgjqKLbSZrnWrm7bVLS5WAmGJP3u6MA8jOzr9Mj\nk1kq9Kct3p5S/wDkSZXW5LFotzpJj+yStGs6hZgx3ZIBIIBz7/Sub1jRZW1v55p5ZLpUUs2eG3hR\nx14GPzr0GC6srlGLahbmWP5GBDgKfXlQeTwT0rK1JLJtT0yV9StU+ztKzqFkJYYXHROxIPNN4yHM\n9Hf/AAy/+REqbtuc3Zae1wq2jQKqW7yqN/c+YcnHrjaPwqLVtOEXjrVYXfYtzM88ZHUEsc8fXJ/C\nugsoo7jUJxY31pLLJdMy71nUDex+U/u8HqO9ZXjW3u49fF95scUa3jx5UfNtJP8AQHv3rKnONTF8\nq/le6a6rukN6QM240ee0QzAENjL46E+o9v5fyfaJmESSSZkb5o1VhkDONxHvggfQnsM7Y0p54ohJ\nPLIzyCIh2yPmOOR161LaeHrG91S4jt4PJe1l8qRN+4EoMfKeu3AH+TiupUVzNy6D+sSUOVGPrNha\nyWltNd3QlmkuY0PmTFmKnOScn1rs7DTbSPQFhihTDsXcoOvyk8kfWuY1zQ4Bd6WnljJkkJGW/hCn\n/GulttHt7uxi2xupVSDtJ7YHetHFez3Mr+9sVNF2WN7facGWMMBcxE9ycqy89chAfrTNMvEtdS1i\n2b5nPlTxmR8bht29vQgVl2emxx+J7sYb/lkQSx/u8/zqHxJZPp3ilrtDMYntZPs/z5GVBcp+HH0B\nWmqd0tegc2r0O51e3gfw7b3UUrwXMHzQyDruI6Edwf5eoyDN4Pnmu7AX9+Va6kJLrHnaFUlQo9MD\nJ981jaCy6w9hc3DsLdIAlumciQqMFicdj+PvxzZ0rVoNK8Q6hp3mxLEJBMqBecOMkAcnAyo/GqpN\n6wCXRnh3xR8Mnw340u44oTHZ3X+kW/HGG6gfQ5GPpXGROBn617z8bLQ6r4btdTt4ZZBYSeXNIc4V\nW4yB0wTt568ivAc4FKfRs0jtoXCBtyOpra8HOsOvmVs4Sxv2IHp9klNc/A/3genat7wtzq9yP+ob\nf/8ApJLXNiv4EvT/ACKjuP1+3XyIbhNud2wkdwRx/n3rncboz7VvWtwb3w7Pbsw3wL6dQOR/LH4V\niRffK8cnFbIlHWa8kNpoixKJUkJSNldSAe5Iz7j9RXJqdj59a6XxTOr2VrEATuJcMBgEDjjge9cw\nBjrSWwIfJgqOeQOn4mos5B9M5pZMgg9qaGAjIpjDP5VLEcCq4PYCp1uCkRVAmc85UE/hnpQBvaJZ\n39xDOunwPPcTDZ5aR72C8fN7DPeuktPhvq+xTqEsFmufmXd5jgfReD/31T/C/wAUE0Pw+unf2FE0\n8f8Ay1gkEXmD1cbTlvf6dMVma18TtT1B2+x28dmp6fMXYfjwP0qW5bJDUe7GeKIbHw9cW9pasrMV\nHm/3j/tH0z6VX0yST7QhUo8D5+WRmCg+vykVyUssk8rSyuzyMcszHJJ9zWrYXRt1VSSEYAgjjB//\nAF1nUhpfqbU5pOz2Om1KzkupIJHeIIg+7EOPz5JPPcmuf1Vi6CEP8q5OPXHH9attetsKx+YSf4nb\npWVerlGYk4UAL9TWVO/MrmtaUbe6eo/COXRbHT1u5JIzqTTNEe7IpHHHXB9f/r17JJfWFjZT6pcO\nEhij8yaUjnA6D/Ae9fHaD58jI+laf9oXt35VvcXtzNCn3UllLBR3wCeOlbuF3e5yKxu+Ndfn8Ya/\nPflfLjUbIIj/AApkkAn15z9Sa4xuGI9K2LeQlifU5qtqNsAfPQYyfmx/Ot3Gy0Lkr6lEAHrTsHHq\nKQe4p4AqDMjZcDNIp5FSlD2PHoaixtagZq6Bf/2drMMxBZGJRgG25B469ucH8KveMLXydQimEqSi\nSPh0+6QCRx7cHHsOg6VL4N0Gy8QT3sF00qyRRLLD5coQyHzFUxDKnLuG2ryPmx16Vpaxp0VxpsL3\nvnQSPHH5bFRtjZijLvwuWHlsTwM5I7DFBPW5g+FNObUtciRSo2gkFjgZ7ZJ6ev4Vo+O73ztbNou4\nJajygrOGK44xkccAY4/M9Td0jTV0a0upJZIZchZFkG4I6Fdy/eAPIORxn2rcXwXoljr7HxJdyL++\nhd4Z5VtQqmWEOuCWLrskkwyMP9Q31C6gtzysnAphOatX8ccV/cRxLtjWRlVfOWXAz03r8rf7w4PU\nVVA5plC0UuKQ8UAdB4V0BtZ1JHmBWyicea56E9l+p/lXskt5BYosAHbChf8ACuB8JM0vh2K0EgxI\nWdc8YfJ/wFbt8jaho0Rt544LuOQEGTo3Yg+nXP4V5WIk51LPZHq4WnGML9WYfj7RS1qmrxZ7LIvs\neh/M/rXnO3BJ7V6t4d1T+1ItQ8MamYpZV3Krh8h+cYBPfNea6jZS2N/PaTKVeFyhB46d66sM2k6c\nun5HPiop++vT5lIgdq0dI1CSwuAQcxsfmWs/AB56VIoUcg10ySaszkg2ndHptqkN9Crqc5GafLYI\nMgA8VieFZ2kt8bj8hwK6TMjyBdteXNOMmj04WnG55rqtgyXc7oPlDnPtWUwwcV6LqFlbxWd3JMOS\nOAe5yP8AA1zmiaZFqN9JDKuBIh2gDkfT6V206y5bvocdWi+b1OcpVxU13bPaXUkD4yjEZHQ+4qCu\nhO+pztWdmPwBTetKpB6087R0FBIw8CtPw/bfatagBGUjPmNxxxz/ADwKyicmup8HxBTd3JzwoQfj\nyf5ClN2izHEz5KUmdDdTZ2uT907Wz+VVDyWGemDTrnhjuxskGNw6Z7Gq8cm6P5uHHysK5kjwVHS6\nNKIlLI8/e4qaFNkI7VWRwURcjip3cKozxUMwaexHMxKEDvWdOh5PerjXAOcDrUT5kB4qloaQvEyH\nTHQc1I19dNGIfMOwccdfzqWRRuxUZjHarO+FaUVo9yBY+eanjQZ9KesdSqmO1FzKU7ihAcU5kwKe\ni460S4AoMb6mpoMySTpGwAMYI47iuhgnB15rf7y+SGB44Of8/lXHaIS+pMc4BQjP5VanvTaalNKk\njCRU2BicADrXLUh77SPrcBW5sMnLpp9x6XqEa4FyHIWRI3OOvB2n8yKz/FNosl+8kpZGkRZF2Dfk\nEdiSM/41y1pq9zeaZbxyzZikmdGfPO1djDH4k1v+JIZtT8LWmqWMhiks38lgOQY+APwB/wDQq6aE\nJ1Iy95r/AIHyPIr4uHt40+d6uS0tZduhn6fPBaXCSRzzblORmID/ANmr2DQtcn1XTgyQQNtGGMk5\nU/XAU/zr53+1ajatumtS47tGf6Gu98CePtG0x5odVeeEOBtJjJA9Qf0rWlBp25n+H+RrKjK91J/h\n/kek+Jml/wCEW1hriC3WBrKYPIs5baNhGQCgBPtkZ9a+WFj0YS8X991/58k/+O19VLrfhrxhp9zp\nFtqUE/2qBkaNchsEdQDjkdfwr5GuojbX0kTAhkcqQfY12+ybpN870a7f5FyoPk+N/h/kejaSsMQa\nBJnmXy45AHG0gOgYHGTjg4PNXJAueFFYenz7dURMZ3Wlvg+n7lK2Xbnmk5udOEnu1/mcuFbcNRDg\n9aZtQdWYf5+tVLm8ZH8mECSdui54Ueregqv/AGbC/wA1wXllPLOWIyfoKyZ1bHTtHebCDp8pyMcM\nn/xVIReBcDTZeDx8yH/2auqKLTSi46DFFh3ZgPql/PK8s+mOu1MKT5ZCgdAOSRyc1gXepnYYmtZ1\nHHO8H3PB9z613UkSujLjqMVxOqQBSUIwRxxXDWoqOqZ6WGxClpON2vkzMudVVlO22O7LE7iV6/Q1\nzup6izxmNUKF+MBia1pLNjyZX2+maxdTtgm1h/Cecmogve1N604cloJ/eVra3KsrLxnnuK77RpW+\nwxkHPlyKeIwx79/yrmNNCYXHfpgnjnFdNbQrb28ykYUrnLgqfXoPUAfnSqNyugppQs3/AJGqUIEY\nYSKcOmHlCjjP09aSZ4llaT91uCxt91nz0HuPWqkF+hKsUGFkDZSMk4PXqPUVPK5lRFYSECMrhmC4\nIJPTk9qyd07+fpuUuXltps+72ZEN5WdAHAG4YACDgg/1o8kzMgVU5lBbexY4YA9gBU6xIJSq+X8z\nnbjcxw4/AelPCMbcx5KsFBPBBJU4xtX2Pes3a2n+ezNdU9b7+S3RQW3miwoDj5GOBGFPA9cH0ps2\n/wC1ygzOCQx+ebrkZ9qWS3X7akdyxAYYG1CDgk9iPcUl3bxpeTC281oedjeXglSpx1wa1S1f/A7G\nTasr22Xd7Mo3WWt22sgfCgKoBJJxz/4761hXYl4WVcLj0Hc1uSJKACHkA3J96XPY9hmsuUT/ACBU\nVm4OQuck1rHT8OxlNN3+ffuczKTDONwG3PBFeny61DcwRGOQMjDcCDXnN7J5p2SjY46Dpis8XUtu\npVHdT2weBWzp85jGr7O6PV01WHo2fzqzFqMDH749q8rg1uZRib5h6jrWra6jHOuUlIPoaxlh7Gir\npnpkd/E2AHFVtQ0iw1dcyqu/sw61xkd3Ihysgq3HrF0hABqFTlF3TKc4yVmJeeC54mJt5A46jisu\nXRr21OWjJHtXVWuvS5AYZxTrvxXptrEftZBcf8s0GW/+t+Naxq1NrXMZUae97HCXrvBC2UO4DuOl\nQeGYln1oKx3Bra4Dj2MLj+tRa7r8mrTYjhW3tx91F6n6nvUWgXT2urRyRyNG+1grKcEZFdE4uUGt\nmzgrU+aEoxe5saRcxrA9tKoXexKn39P0q4JZYWwASBVK71zVor0xHVb1Uk5VjcPx7da0bfX9SMGy\nS/uDIOM+c3P60q+GrTXtYJNdVd/5GtGvWtytRT9X/kWodctgAJlVSOuRW1Fr1gbJzGwJUbjg5A+t\ncy2qapu3vql0q9f+Phv8ahufEWpNGqwX92I1OWkEzZJ9M5rnoYSpVmlBL73/AJG88RiIwd1H73/k\nURJHqmoXv2kErJC7qSfukDK9/Ss23TPhm/wf+Xy3/wDQJ6ut4m1ONSE1a+kYg5JuHwOO3NZV1q2o\n30Yiu7+6uIw24LNMzgH1wT15NehioycoxVrK2zfT5HmuNabXNbdPr0foVmQgdKYw4FSq2KVkDj0N\nSdJACakU1F0NOBoGTg0uaiDcUu6gQ/PpV3S1LXyELGxB48w/Lnpk1ng1LBL5cmTyCMEe1Fk9GNNp\n3W57rbeCdLnuI7JdTju5XGZWgnLYAxu4PGOQOB36CvUdMs0sbaO1tSkdvGAqRxoNoFcH8P8AS203\nw3aSSxjzpkVnHIIXHyg5746+5Nd7aycijToiKcZpe/Jt+bb/ADNdAwAJTp3GP6U5kDj5WKt6j/Cm\nRTKFGc/lmrCsknKkE1rHY1tciBdlKHCyY4PY+9MtLhbpGG0LKh2umc4P+FTshI44I5BqpNGkV0tx\ntGH4bNWhPTcxfFHhKDWkNzBGq3qDrjAkHoff0P8AkcTaaAsMv7yIpIpwVIwQfevR7zSlM32mAbXx\nhh2PvWfdXJNu6XKEuikrxk5HOBWNaip6oqnV5NGeKX11LafEywug2zy7nycjpjOyvo1cTQIWGcqD\nXgN1YXN/q19dCBGt45ldbjYGCbsnkdCM+vrXr3gzVJdR0fy7ht09vhWY8FxzhvTtj6gnvSpdwbte\nLNtES4hlhdd0RBQhu4PBFeZeNfDNtoEC6haSyeSZAjRyfMFJyRj24I/LrmvUY12zuR0bmo76wttT\nsZbS7iEsEq7XU962snoyHG6utz5ntpBN4ljYDqpIHXFd/ZkPjjmuKuNKXRfHWqWSCRY7dykYl+9t\nJBB49Rj866q0lcRBo1HHrXh46LVW3Y9DCNezNK5Xama8Z+Ilz5uuRQhsiOLJHoSf/rCvUL7xFp9r\naObq7hikXgxlsv8A98jn9K8W12dtU1m5u1wEdvkB/ujgfyrXCQafMZYmXQyQcU5cghgcEHINDROv\nUVJZ28l5dw2sWzzJpFjXewVck4GSeAPc8V6ByehbvL6e5MZlfcQuc1UJFXdSRnvcFrfEcccWbdAq\nnagXPHUnGSe5yec5qt5aqPU+9SkkrIcpSm+aTuyuWzXd6dpA1Q+FmlTdBBpjyOCOCftlxgfn+gNc\nxouiTazqK28Rwg5kc/wivWryGHRND0i0tV+RLVgCTz/rpT/MmuHGVuWcIx31/wDSWbUad9Xsdjoc\n1qbFLcPF5q8qhILEMST+OT+VUfCgN0lxe4+e5neVkU4IycHH61T0GyOqaMHkQGVQRuAAJG48iq2h\n2M9lLdQW1xLa/Z7mSKOVG4yCfvDPuc4/Ku6jFJOz1OWo9dUaPi22hl0ZZLaFzNNiCJdvLFjgAkd+\npGeprO0q1kbV9QnuMbI4YoYy2PlO1Sw+uCvNaPiG7v00IrLPG6lkieYYAALAbg3r7Hms034juZXZ\nBu34PnKQCdq88dxjH+RWrvyX6md1e3Q3/D8ccurydWMd3twBnBV8Z/QVF41t/MsdSBhGRJvjyc5O\n4ZP5E1m+Gb+9tNdia7jAt7+8DRvGuBuZgCCOo56fTv1rqLhopNYuyMDy5W+VycMe546fWvOq3jmC\nv/J/7eaxt7N27mElwTaWbOxBnvLYBMYx86kj16ZotjHaaneXEXLLqUozvx8tZWrfaJtbtYUYwwC6\njkVUPOeM8j3zinjw95d7cJO0mLi6d4SsgIIJO0/p/nmu9RV5a7md1ZGt4jntEv7C+kuVhjhWR5Fd\njhgygccVNo+tWs00Zs7qSS3cFd4Py5yDgdwfY4/UVyer6e00LFrqUXFsMxNI2doJ5Hp6fpWdpxGo\n2U81gn2K+H7qRAAI39Dj+HB59OD15pOPuLmKT952OwlYR+LZ1zNPJIsfCqcpgAHPvke/UUnjAW8G\nlyzTGOOfO9VMwDhtuDgbucgkHGevtWPZw3WlW8dul1LJNMo8yaP5ZNx/hznPHIz9eOuX3GgW9hZh\nGUi5lmWZpGJO87skA/z/AM4JNJK72EtWaXhu+m1REktyLayhAVAo+cLjbgY6dhxjHvWhNpUNlrFh\nqaLuDP8AZ5txB4b7p/PJz7CqXhO3t7WG6tLd1ZJJeS5PyEgcAfXFdJqEE0ugzyxMXuQiyBsDAKMG\nOB/wE/jVRq8s1bqLlck7mjrllBqfhm+sZgHW5g+Y7d3lqeCwHcqPmA7kV8iXttLZXk1tPG0c0TlH\nRhgqwOCD75r660jxDa3FisyOoUgEqgzt6jH1Bz19K+aPiJZpZeN9QjihmijZg4877zkgbn/4E2T+\nNXK+tyoaHLq2GBrpPCbBtWuPX+zr7/0kmrmu9b3hFj/a9x7abf8A/pJNXHif4MvT/I0W5n2lybW7\nz/A42NUUJxOmPVaikYt1FSQEtPCR3YD8c1uI2vE1y82orDIMGNT1UDnvwOM5HasRs7s1PqszS6g7\nM24g4z69/wCtVg+QPpQgH7uMEZFROVwcCnM3H4UwAyMFUck4AoAaCR0p8C75MfjRNby277Jo3jYd\nmGKfbYySf1OKEND/APVSg9s81DINsrCppsY75pk3JR/VRTY2QVttDHbaQsdwha6YLKmD/q425Gfr\nkEf7wqhpdvFc3yLOSLdQZJiDg7FGWAJ7kDA9yK1NcuC/iAzTqiC6tYGlCDCjfCjZAHoTnHtUPV2B\nEunNHIqxl1Jx0zyar68sUItokPzHLN174/wNO0a2WK5uHnbYy4QZOOT1/LFUdWlE+oSFWLKmFBPt\n/wDXzURgotjlUctOxWQADNSQt8zn0Rv5VXViARUkTHcw7spH6VqiSe3mwRnP4VfcGWBgVIyO9UbB\nGIJx16NitHyJCvyylv8AZato7GkdUQ6LodzrUzRwmNNpwxkOMV29j8JZbjb52rRx5/uQl/8A2YVx\n2kXU2m6zuViocYINev6LrJmt13NhvrXm4mpUpvQ1pUYz3McfBa2ZcDxA6t72YP8A7PXnni/wjd+F\nb+OKaVJoZQTFKgIzjsQeh5HqPc177FqAZhlx0yOetc94102LWNKHnIriKRZSCSOAfm6f7O79Kyo4\nqUpJSNJ4aKi2jwiC/u7a2mt4LqaKCfHnRpIVWTByNwHBwemelb6TXUujx3st/dSvNOz+axOUmUgt\n82ck4ZGz6v7VQj022nMpM6xoikocj5/kdh1PXcoXHq35vmtDdausVlGGlluCiIjAKxZ8AAdB1Feo\n6btc872ibsdLoMX2uK5nd2J0+0lAk3EgMsErRZORwGjUDqM7VwQcVxmoaldanOsty6HYuxEjjWNE\nXJOFRQFUZJOABySepNaFhpkN5N5L+cUBVmeNdzLkZPygZYDgYHTr7UoTT4Ikd4o1kR9skZfJIOTk\nBhwRwOjfpy1Se7YvarZGGTxzSKpOSM1szXtk1k8SrbB1h2IUg5ZsrySfYHkAcn8a7z4TaRZ3FhdX\nl1bxSsZti+YgbAAHr9ayqtU1fc1ppzdrHlZRqaVxX07daXo7RY/smwJ7k2yH+lcT4k0HRWt5GTT7\nWJscGOMJ/KuVYyN7WOn6rO17nGeFtRt7Tw9crLKqyrL5kQzzkAcfiM1ka3rz31w4iMiITkjdgZ78\nVlXkKR3DJF0B4qsOvXFaxox5ufuS60lDkRPHIVIZXIYHIYHBqS4uZ7mZpriZ5pG+87sWY/Umq24/\n3xSh29Qa1sYXYPyKYD09KVuvTFN70wOk0CeW0ukZOI5AM5+v/wCuvQ4VZJPmrmfBulm+aG7LAJEN\nm0D+If8A1sV3osvm+Yjjgdjj/P8Anqa8rEyXOephXaN2Yes6K+pWTRxnEh+6ew4rk9KsLi11FrW9\nsZHw/RU3L6dTxXqsKICFJA9qLqGG0imvZyqRRqXdvQCsY1nFOJdSKk00eS+Pba2t7+3hhVVlWLdJ\nt7Z6D8v5iuOra1jUJNW1W5vZODK+QP7q9APwGKypY8HI6V61GLjBRe55lWSlNtEVKDSUVqZjtvvX\nb+GoDFoisMbpJGcA/l/Q1w4OK9H0qJDo9p9mYOREpZO4OMn9TWVZ+6efmMrUku7Ekjwnfyz/AAkd\nP8KyrhPKlVx06Z9q1LmYDcEyn95DzWVcuGRlzWMTzaV7lwXsMcZ2/O4FNVpJeXY89qyrTMny56tz\n+FbkahF96bVh1YKnohVjJwoouJRCmxeWpJLgIpC8tVMsA25uWqUjKMW3diYx8zHLGhRkZ4oUGQlz\n0HanJyc1RqxQtSBSBmjj0pw5oM2xBxniopG3DFPbI9qZgE8mga7jrGZbWcvIpZCu0gHB6/8A1qpX\n10bq4ZU+6Tk07UZhFbEqeSQo+pqjaq5XC5JPU1pBJandCc3Ss3omdBBI0ejWkcQ+ZriYf+OxV6Z4\nangudJ/snYcPCUMjdNx749j/ACrzi2iKaNbYzn7RKST/ALsddb4ZmME6NnAz71eFdm/V/meDi5W9\n+O6bf4kSw7GKSL8ynBB9qsJbW5YE28bH3Gc1e1eALq9yAc7mEn/fQDf1qtbgpMMjjPWpcbSaPsKV\nT2lOM+6udf4Lt7aaR7qK3ijntlYm3RNr5xwQT1BGR+NfNWo3D3OpXM8n+sklZm+pNfWHhtVGnSXQ\n+aRUbnOB0yP5V8pzWUslxI3qxr0oQvRdvL9RRaUG/P8AQ34pQuu23zYP2W26/wDXBK05L+a4ytqB\ns6GYjKj/AHfU/pWDq9qsFxHMWO/7LboB/wBsUBqgdQvAABdSgDsHIrii2qUE+3+ZjhV+7TX9bnZW\n0SwxnHUnLMeWY+pNSbj6VxA1C8xxdzD/ALaGj+0L3/n8n/7+Glc6OU9/yO5ppK81IVGM/wBKYQM+\ntWSNJGOlcv4hg2Sb1HD8jiuo21R1mCOXTJmbCmNdwP4gf1rOpHmjYunPklc4JiG69fSsnUofMhYY\nrYkXDkZxVS4jDocCuG9mek9UYWlyOu8Mx2jg8iuxs7qTyWjKyAhhhw3Xr1/AVzFjGkd+YWyGkYMv\ny7v89q6u2sYjEu4RgOc4cFSOw9P71E372xUfg3I3gYSMGQY3bBubccHkdMCkiuXt9juCvzZO6PIx\n36n6fnVrfsieQyoFwAojmVehJ+vQYo/cSyuVaLbvDkKu87W6+vqO9ZJbJr8Pu3NW9W7/AI/J7FmC\n9W5CeXId/lkEeYFGQc9BjsBVt5YIpGfI2Eh+PlUq3XnqeorFXThJhFJ3ncoy+PmHI4GT6iq0lpHH\nnzroRlcAqTg+4wcn9KVk7O/y/wCGG9LpLyv+T1NeSNZ1zBIvnQsVDIpwe+CSeh57dhURu4zEspI+\nTkMWVc8+mPcfrWPcalZxjybSOV3YhBPIPmJzxxn6fhUQto1WHLkbj8xPGz8s+1EYLTX8hubs9Pz+\nZqS6jZg4/dlcYwMHByehA/EVj3073UfyyNGqjjacdge56U28vbeyXylj33OOAoJ5B4JJPAx7dhWe\nVnvWDTFRjhVUbQO9dlKlfVbHFWrKOktzMvLcGBtm936jAz+tY0gmUjzEdf8AeGK7SOzCr2pJLYbP\nmUEemOtdappHDKo27nGAqeAeferEEgjjPPOeK0LzT4csVj8tiOMdPyrLWKSPjHI7VLjYalcuxXMy\n87zVldRmXvWYC2cbT+VSqHxk8D3pNJlcz6GhNrU4hKI20sMEisZ3LHrmiRiTUec8U1FLYTbluIaf\nay+TcxyZ+63P0qP1pvemB0mrW/2ix81R80fzfh3rKs71xKqyL5id/XFa2mXH2iwCnB2/KwP+fSqE\ndiYTOcjG7aCew9f1rownO6iUWQ7Lc1LmAt5OMskoB56jIGRUz2/lxvbhTsBIb6j1pxLGxgYDLLjH\nuO1PSVppLoEg5YtgejV9RSpQitFuc0pNnJTRGHcG4O7bj6VXrX1i2kUK4UhQMt+PesevlsRSdKo4\ns6oSurjwanSokFSgVgMhmGJPrUealn++PpUVAxwJoBpKUcUAOFbng/T31PxXp9qrFQZQzlSQdq8n\nkdMgEfjWF3r1b4R6ITBe6247/Z4SPbDMf1X8jSew13PXYt3lHBIIbpWjay7SM9azUkXCsOBID17E\nGrUW9MMoJpoR0drLuX5SPcVdARwDgZrFtp4pAMs8b/StWAyYwWWRf72ea0iNFgemahuIvOgdO/ap\nscUhOAdxqymtLENq/m2qMeoGDWdqViLh49o2hmwzDtVuwYYlUdAxxVh1y2OzfoaaM5K6ueZaNcWu\nj+KtV0C5hRbK6k8lcDiLP3O3AIKjnvg+uG+H7g6B4jZJrmMYmazkgQ/dXOA3/fWOfQHr2l+I2lrb\n7NWkt3kgdWjmMeQUO4lW4B6hiuT7DIBNcJBdSXEEkV6uZyhkMuRkSAc8nnLDaDyOfXFD0+ZCe3ke\n9WN0801xHLJEzRsBhQQVBH/6/wAjV5W3D2rzXwnq02q6tLdFyInUPKM9Xx09OpavR4X3qCCPpXPQ\nqc6d+534qkqc0k90np6Hl3xO0iG31iz1SGFxLdDy55P4Ts+7+OCfwUelY9kw8nHavTvGWiNrfh6S\nCIqJ4mE0W4kDI69M9ifxxXkNtchc45NcWYQ1U0VhXa8TifH1p9n15Z16Txhj9Rx/hXIu5PFei+Or\nKS60qG7CHdC3OOyn/Irzd/vVphpc1JeRhXVqjEY/LUavxn1p0h+Q1HGOK6DEmzRyxA7noBQB0rrP\nBnh6XUL9L6VCtvCcqSMbm9vpUVJqEeZlQi5OyOo8MaMNH0v58faJPnlPp6L+H880eLLwraaSE6G0\nY/8AkeUf0rYvUaNNkfC965vxg/lado6A4Bs269f9fLXjXc60G+rf5HdK0Y2R3fh5o4tFsHjdY5DD\nG7E9OYwTn8TUvh+8sm1nUEeW3jnNyZWjLqVIYZG0k8/SuV8PXGmx6ZCXlZ53SMgBwMjylBHXscjG\nKvaPOn9uahM2lO0chGwsSBwCOOOa9mlaLab6HnTTeyPQI/sJ8zfJZp+8YMpZRkEj36V51fwzy6Vp\nWo/alMTzfZZct6M5XH4Ag/h7121vdwbZf+JQX/e7hmUsBwOxWuE10QQeCbOCe1a3mW8O6N0wSD5h\nz6+natoJckiJJ3Wh3dncWyXmnQyLEk29Bsz0G4bSAOcZAA6dfaubuoLhvFWoSaXdKYzdyCaKY/KH\n3HOMcj8vzxWJ4OW2XXdPgikCxG7SZUJ+bdkDA9Rx+v41o+I7KW38Qahd2U32e5aeTO37rgsT8w/z\n6+9edHXHcqf/AC7/APby3ZQv5/oRyB7LX0LTCRllWXd97pjt25U1oyQSWms6lD5r747lLkBQoDbh\nu7f4VxMN9qF5q+TEiskT/vOdjBQeR/31XWW9te3fiHUjLcOpIhXMRwCdvqDivQjCV3fsZSaSLtzA\nLudZcs8GN7Ip5Pv9Bz+dVL3Tgl/BeWeFkyFni7PHxnP4dPpVNNSl0HVfslxduqSJlGlb7pz6+/v6\nVNqeowraSJHJwwIBH3wx9Kyg2nyvYuSvqhb2a8mVra2mFu1v+8SaNeQcFdrH3DH8sVw+u6/qsDxx\nSzSiaN1Yq7blbB6gGt+11V7OFrW8QiVfm81TlJO2c9jzxXJ+IkL7JWV9zPlt38I5wP8AH/61U7bS\nQ0mtnodxpOqtaav/AKIrzG7iEis3CeYB8x9+5/EV6BZwzPbbdTvFC8uqIRsIzyMcDIPpzzXkfhy5\nmnsILGUmE20olSQrlsc8c9uv5V2lvNF8ryFpZBgB5TuP+A/CsateNNWe5tSw8qjutjS0Z7bRdQu7\naxspLq0B82CQD5Tnqm5uOPpnrzzXmnxgukvNTspXszBPsIDgZ3rn+I5PzA/z616nBdb1wDXnfxWt\nhLpdtc4yYpsfgw5/UCop46VWaTRvPBRpQclueS4zx3rb8Jcaxcf9g2//APSSasPNdD4R2yatcA8M\nNNv/AMf9ElrTFfwZen+RyrcwM5XFKmQQPyoaMqfWpbMQm9hW6aRbcyKJGjXcwXPJUEjJx0GRW5I2\nUZdjnOeaixtNa2t6fNYaxc28tstswfcIkfeqqeV2tk7lwRg5ORg5NUCgK80AQk/LU9nHuuYvZgai\nC5OBzVzT8C69gKUtENbna6lLE2kMHVXAXgMM81xMESlmY4xnpWzqd0TbLED17VmW42D7uazw8bG9\nSzYMi7SFX8DVGQYjAIwVYitRyuMHP5VQlj864WJSBuIyT0Hufauma0M5IfC/2XSZsHEl2fL4P/LN\nSCcj0LbcH/YNP1dppjZ3Use1ZrSIR47rGPKz+cZqs5a8uRHBGzAYSJFGTjtwO56n3JrRu7C5i0ON\nri2nilt5ShMoK5RhlQoPYMHJP+2Kys9yLrYal7/xL/Ob5XUBAFUYYgAZ/ID6nNZ0CiedEJI3MAa6\nKD7Bd+HEtUUiRY8q0mOJM9ueASQOeMEntWBayCKclvkIBAPoaF2Fe2p0PiHwff6QLTzbdonmAAUp\njt/TI5OK19G8BWssYkvbmbf1xFhQPbkHP6UsGsXOpzW8l9PNcSpGP9a5baccnknrj/8AVXQW95sX\ndu4xwK1hFJHMp1Gveevy/Q4/WvBsmjo91bSm4tVJJRh86j146j34+lZSBQgaNSPXHUfhXeatrEcd\ns4Zwx2/d9a4Nm/dp5eEOAR6Z9DVo6qLbWo26bdD5oHzxnOa6zw5qXmCM7u361ykrCS3l6btpBHvi\noNEv2tpwmeM8VyYunzo6ac+WSPabOcMyk59quSOfKKupww6kcGuX0LV4pEClhv6c10r3DzRhYxuy\nOmM14tnCWp6N+ZaHhWpy3dhql3aJd3GyKVlXMh5APFW/CqLqvizSrHULqYWk91GkvznJUsOB7noK\n3vFOkXCeJluks5JEmCk7EyAwGO30BqfUdLlmupml0YRyAL/qYmQIRwGAH+6fY8969qFTmimjx6sO\nWbTRwl9N5t7OyySuhc7TI2WIzxk+tVq7CPSRc3CWqWxkkztVFjG4n8sk0ms6AmnWMjy27RSKOjAD\n6cYq+boTe5yFe5/DZBB4TtQRgvucn1yx/pivDSK9z8JMYtA05B1MCHH4VzYt2gjowy986a+uEVcE\nkZGPYVwXiK+It5cNzgjANdRqbyiE7umPSvPfEdxmNgDXnU1eZ6UnaNzjxFvd3PbjPvVCVdrkVrwP\nE1oyA/MSTmsiVj5rZ65xXtpWR4jd5MQJx0pwQd6ZktxTsjoooGK0Zxwc02MfvBT+e9CjDZoEd54G\n8T2WirLZ6krC3kfekyLnYcAHcOuMAdPyOa9JtrzTNXizYXsFxlSxVG+YD1K/eH4ivAFZh0NSJcSR\nuroxVgcgg4IPtXJVw0ajvfU6KddwVj3KG4KaoLSQZb7wb1Hb8eP/ANVcv8TfEKny9DtW4AElyQep\n/hX+p/D0rlLXxnqMbxvcymaSIYVj1b03Hqep56+uawLm9lubh5pGLySMWZj1JNY0cI41OaXQ1niE\n4WjuIevNNYZpA3qaC1d5xlV12tim1NLgioaoBQK7pLcx2lsyO0cixKMjjPArhK9BR/8AQ4jnOUHf\n2rKr0PPzBtKNipNcXTDErLKB3defzHNUZZ+T5kRGem1uB+dXJm9c/lVCUlflP3e3tWaOSkr9B+ny\nRpK+fXjNaMl4m3C1zayOtztHQ1eG5IyxpuJpVormuy20rseAc09I3Y/NkD3qpDcyL0Qn8Kvw3bMQ\nNhz6YpGM4yjsid12QADvSRIcdKW4yAin7x5NTpFhOpFTc527IiK7RyfzqMNT5W5x2qu7FVP6U0OK\nuSvyvBzUQJA65qNLjBwehpZSAMg07Gii1oZepyb5Yo8+rYqxZTSwrtUJg+o6VTu8NfR+y/1q3GMd\nq0OySXs1E6WKWRtGtyzAnz5fugf3Y629DmjW4jG5txPXHSuehc/2Pa4jyftE3f8A2Y62tImIk27V\nBPUmpw71fq/zPBxEfcl6s6/VUP8AaCOQMyRK2c+2P6VSUFWzxx2PetC9DNPakDP7rbg/U/41ErNE\nzcRqD1G4fyNdE4++z6LLp82Ep+h1mhzrHpF7IGVSIHY88cKa+cp7+IO2zAOex4r3g3i2fhbXGcqS\nLGYgjgZKED8yRXzsNOeYliBFnock13UpSVN8vdHR7vJ73f8AQ6XVLV7mSCU8qbaAj/v0lZbWYXqt\ndZZQTSkW80fyxW1uEkA4b9ymf1zRPpoycD9K4IRbhG/b/Myws17KK/rqcj9kTutM+yJ6V0M+nMvI\nWqv2NwfuGjlOnmPWoLu3uVDQ3EcgPTawNSFsemK8KW5vLNwyM6EfxIa3LHx1qMGEncTL/wBNBz+d\nTdmnLF7M9YLLjrWR4lkZfD9wVI+Zlj6+v/6qyNO8Z2F7tSZvIc+vK/nWhrMiSW2nKjq8c92gJBzk\nAj/E1UZESg1YzPEOnrbus8KYRuGwOFNc+cNXoNzCJ7aSNyvzLwW6Z7VxV5ZNa3LJkEA4ytclWOt0\nduHk2rMwdTiZVWaIYkQ5FWtAvr68IjiaV3X+Fecen8hV17cyo21C2B2qHSdTk0SCW3urMyRtJmMq\nPmGeoyOprG/NGy3OqK5ZeTNxrBZEzcylCT9zf8wzyOCB9ep60wWdpG4DRTsGCjExYKCTkY29q2rc\nNcWqyywPAgG5UlUNnI5wBnB6D6fSnKxDcojbjyysEIwOTtJxwDgDHrWd0nYtp2v/AJr+rIxZZg8U\ngKqkb7nZEwQegGR7ZPaqEsNhEpDSQISUX52K44yf1xXQ3Pkxw4eIISoZlI2NjsoI7nP6+1ee+J7i\nNIkjRVV2ctgcnHuTzn/63vTpw5ny7f1qTVq8q5t+v6IvzXtrcXUEdruuJfMLbUbI6+neumt9OzCG\nnkUSj7qxjbsOBxz16dQTzXn+l6bc6rbl5HVI844AXt/Lj6Vr3WnwaBAWkuzGxUMIQxBkBXpjg4J7\n4I96qcLy5U9RQmlC7Wn9blO+kgOoyRW58xIsp5g53nJOR9Sas2iDeQT+87+i+1Y+nxyzsx3EMx3O\n47Z9Pf8AlWwJ4rcCGEF2XgqvY+5r16cOWKR41STcrmhGoPGKc0YIOQDjrVWK4fblgqk9gelPe5AG\nMiiSIK9xbKc8AisW8seDtOPStaW6Vm696ikYPzxUblbHMSW80fOTj1zUY3ZJLE/Wte7QuDEkkfI5\nBPNY5UxgqeueaTVi07jGPNNPFLSMKRQ0Hmk70lL2FAzQ0m58mdoycK4/UVflkLQO68Ag9ayLWIuX\nYHBA4rYZF+xhtwChcY/xr08vp7zMqjLti7XMaKFJIwAfTkVHd4F2WhYluPwOOas6XMFVAcDCAkj8\nD/SmmDzr+RIjjjIP4CvoI60kYN2kU9WV5tPEzMM7QrD0x/8AqrmwOa6PVcx2bg9WPI/GueUV8/mn\n8VehtR2JF4p4NMHFPFeYakM33/wqOny/fplAxwWtzwkEGuM7wwy+VY3sypPEsib0tpWUlWBBwwB5\nHasHmt3wmT/bFx/2DNQ/9JJqwxX8GXp/kOO47/hK9RP/AC7aN/4JbP8A+NV7n8PLya58FWshSzW4\ncvKVitIolb5yv3UUDoMZxXzeOTX0FomnPZ+EbC2UtFNbRKTg8qWGW/8AHs1Lw1HT3V/XzGpOzOvk\nu5RGzBEUddr26fKfrjmrFjqEs0Y2tGp9PLUfyFYFpqcwHk3ABIHO4Zz9KtWE6i8Bj+RXP3RTWGof\nyr+vmF33Owtd7kFwhPqFptx4h020k8oSNPMvBEJyF+pzjqOQMkelcLqnio6jPJp9jKyWcRMcsiHB\nmI6gHsv8/p1qwOIwAoAAGBiueu6EHaMFf+vM6KVFy1kz0ODxB5wybbb7eeT/AErSjuI7iIuu9fZi\nf09a85ttSAcrnp71vQ6oYBatn70Zz9N7CuRVoqcY8kde6fa/RmtShpodPagDduOOfpVn93/f/wDH\n6q2U0csIeNgVarma9T6o3qlD7pf/ACRxc1tGZ2pwpcaXLCyLKWT7hAcZ+hyDXkV0niy11d7aHSPM\nVSQs0elRbCuOudnoeRmvYtRv7OzhP2uZIww4UnJP0A5NeceKbPRfEkUcLwyqyHKyqwVx6gdeD71M\n8MkveUPul/8AJEO97q4nhe61W206++2aLcpNHOhiVNOEAcMrZxtXkAjkn1Hc4rRGtXs0hSeVkwSD\nEMqB7Ef41zNj4R0ux0m4S0urtd11BIzSsr8qsoAwAv8AeP6Vq6jFLFq945G5Gncgrzj5jXmwlVhU\nnTVrXb0v/d7t9ztoyjJLm38zbiv5YRugco3oOh+o715myfYtYu7Uo8aI5Me85JTPynPfj9a6x74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KrJuhVstIeN7dzVYuIRtUdBXXhqPJG7OLFVueX9f1oSreXVhYfZorkxp95duAe/fqDzWBKy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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"threshold = 0.96\n",
"img = PIL.Image.open('test_vis4.jpg')\n",
"w, h = img.size\n",
"img = img.resize((int(w/orig_scale),int(h/orig_scale)), PIL.Image.NEAREST)\n",
"imbase = img\n",
"print \"adding overlay\"\n",
"for pair in found_pairs:\n",
" if pair[2]>threshold:\n",
" imbase = add_outline(np.array(imbase),pair[0],pair[1],pair[3], pair[2])\n",
"showarray(imbase)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-16T14:49:42.706000",
"start_time": "2016-11-16T14:49:42.476000"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4782\n"
]
}
],
"source": [
"img = PIL.Image.open('test_vis4.jpg').convert('LA')\n",
"arr = np.array(img)\n",
"w, h = img.size\n",
"print w"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-16T14:47:17.920000",
"start_time": "2016-11-16T14:47:17.914000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-16T16:28:29.064000",
"start_time": "2016-11-16T16:28:29.059000"
}
},
"source": [
"## Train dataset appender"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-16T17:17:47.673000",
"start_time": "2016-11-16T17:08:38.012000"
},
"code_folding": [
53
],
"collapsed": false
},
"outputs": [],
"source": [
"img = PIL.Image.open('neg1.jpg')\n",
"w, h = img.size\n",
"#img = img.resize((int(w/4),int(h/4)), PIL.Image.NEAREST)\n",
"imbase = img\n",
"img = img.convert('L')\n",
"w, h = img.size\n",
"scale = 1\n",
"width = 36\n",
"height = 36\n",
"import array\n",
"findex = 4327\n",
"with open('posneg.txt', 'a') as f:\n",
" print \"starting processing\"\n",
" while w >=36*2 and h >36*2:\n",
" img = img.resize((int(w/1.3),int(h/1.3)), PIL.Image.NEAREST)\n",
" w, h = img.size\n",
" print img.size\n",
" im = np.array(img)\n",
" scale*=1.3\n",
" found_pairs = []\n",
" i = 0\n",
" j = 0\n",
" last_result = 0\n",
" while i < int(h-36):\n",
" while j < int(w-36):\n",
" imtmp = np.array(im [i:i+36, j:j+36]/256.0)\n",
" face_prob = get_face_prob(imtmp)\n",
" if face_prob > 0.9:\n",
" showarray(imtmp*255)\n",
" buff=array.array('B')\n",
" for k in range(0, 36):\n",
" for l in range(0, 36):\n",
" buff.append(int(imtmp[k][l]*255))\n",
" findex += 1 \n",
" # open file for writing \n",
" filename = '0/lbe%i.pgm'%findex\n",
" try:\n",
" fout=open(\"train_images/\"+filename, 'wb')\n",
" except IOError, er:\n",
" print \"Cannot open file \", filename, \"Exiting … \\n\", er\n",
" # define PGM Header\n",
" pgmHeader = 'P5' + '\\n' + str(width) + ' ' + str(height) + ' ' + str(255) + '\\n'\n",
" # write the header to the file\n",
" fout.write(pgmHeader)\n",
" # write the data to the file \n",
" buff.tofile(fout)\n",
" # close the file\n",
" fout.close()\n",
" f.write(filename + \" 0\\n\")\n",
" j+=4\n",
" i+=4\n",
" j = 0\n",
" print \"adding overlay\"\n",
" for pair in found_pairs:\n",
" imbase = add_outline(np.array(imbase),pair[0],pair[1],scale, pair[2])\n",
"showarray(imbase)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-16T17:19:03.990000",
"start_time": "2016-11-16T17:19:03.984000"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4327\n"
]
}
],
"source": [
"print findex"
]
},
{
"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"
},
"nav_menu": {},
"toc": {
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 6,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_tf_dics.ipynb | 1 | 6648 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Time-frequency beamforming using DICS\n\n\nCompute DICS source power [1]_ in a grid of time-frequency windows and\ndisplay results.\n\nReferences\n----------\n.. [1] Dalal et al. Five-dimensional neuroimaging: Localization of the\n time-frequency dynamics of cortical activity.\n NeuroImage (2008) vol. 40 (4) pp. 1686-1700\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Roman Goj <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.event import make_fixed_length_events\nfrom mne.datasets import sample\nfrom mne.time_frequency import csd_fourier\nfrom mne.beamformer import tf_dics\nfrom mne.viz import plot_source_spectrogram\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'\nnoise_fname = data_path + '/MEG/sample/ernoise_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\nsubjects_dir = data_path + '/subjects'\nlabel_name = 'Aud-lh'\nfname_label = data_path + '/MEG/sample/labels/%s.label' % label_name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read raw data\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"raw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.info['bads'] = ['MEG 2443'] # 1 bad MEG channel\n\n# Pick a selection of magnetometer channels. A subset of all channels was used\n# to speed up the example. For a solution based on all MEG channels use\n# meg=True, selection=None and add mag=4e-12 to the reject dictionary.\nleft_temporal_channels = mne.read_selection('Left-temporal')\npicks = mne.pick_types(raw.info, meg='mag', eeg=False, eog=False,\n stim=False, exclude='bads',\n selection=left_temporal_channels)\nraw.pick_channels([raw.ch_names[pick] for pick in picks])\nreject = dict(mag=4e-12)\n# Re-normalize our empty-room projectors, which should be fine after\n# subselection\nraw.info.normalize_proj()\n\n# Setting time windows. Note that tmin and tmax are set so that time-frequency\n# beamforming will be performed for a wider range of time points than will\n# later be displayed on the final spectrogram. This ensures that all time bins\n# displayed represent an average of an equal number of time windows.\ntmin, tmax, tstep = -0.5, 0.75, 0.05 # s\ntmin_plot, tmax_plot = -0.3, 0.5 # s\n\n# Read epochs\nevent_id = 1\nevents = mne.read_events(event_fname)\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax,\n baseline=None, preload=True, proj=True, reject=reject)\n\n# Read empty room noise raw data\nraw_noise = mne.io.read_raw_fif(noise_fname, preload=True)\nraw_noise.info['bads'] = ['MEG 2443'] # 1 bad MEG channel\nraw_noise.pick_channels([raw_noise.ch_names[pick] for pick in picks])\nraw_noise.info.normalize_proj()\n\n# Create noise epochs and make sure the number of noise epochs corresponds to\n# the number of data epochs\nevents_noise = make_fixed_length_events(raw_noise, event_id)\nepochs_noise = mne.Epochs(raw_noise, events_noise, event_id, tmin_plot,\n tmax_plot, baseline=None, preload=True, proj=True,\n reject=reject)\nepochs_noise.info.normalize_proj()\nepochs_noise.apply_proj()\n# then make sure the number of epochs is the same\nepochs_noise = epochs_noise[:len(epochs.events)]\n\n# Read forward operator\nforward = mne.read_forward_solution(fname_fwd)\n\n# Read label\nlabel = mne.read_label(fname_label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Time-frequency beamforming based on DICS\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Setting frequency bins as in Dalal et al. 2008\nfreq_bins = [(4, 12), (12, 30), (30, 55), (65, 300)] # Hz\nwin_lengths = [0.3, 0.2, 0.15, 0.1] # s\n# Then set FFTs length for each frequency range.\n# Should be a power of 2 to be faster.\nn_ffts = [256, 128, 128, 128]\n\n# Subtract evoked response prior to computation?\nsubtract_evoked = False\n\n# Calculating noise cross-spectral density from empty room noise for each\n# frequency bin and the corresponding time window length. To calculate noise\n# from the baseline period in the data, change epochs_noise to epochs\nnoise_csds = []\nfor freq_bin, win_length, n_fft in zip(freq_bins, win_lengths, n_ffts):\n noise_csd = csd_fourier(epochs_noise, fmin=freq_bin[0], fmax=freq_bin[1],\n tmin=-win_length, tmax=0, n_fft=n_fft)\n noise_csds.append(noise_csd.sum())\n\n# Computing DICS solutions for time-frequency windows in a label in source\n# space for faster computation, use label=None for full solution\nstcs = tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths,\n freq_bins=freq_bins, subtract_evoked=subtract_evoked,\n n_ffts=n_ffts, reg=0.05, label=label, inversion='matrix')\n\n# Plotting source spectrogram for source with maximum activity\n# Note that tmin and tmax are set to display a time range that is smaller than\n# the one for which beamforming estimates were calculated. This ensures that\n# all time bins shown are a result of smoothing across an identical number of\n# time windows.\nplot_source_spectrogram(stcs, freq_bins, tmin=tmin_plot, tmax=tmax_plot,\n source_index=None, colorbar=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
catalystcomputing/DSIoT-Python-sessions | Session5/code/09 Pandas - Part 2.ipynb | 1 | 9867 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Air quality time series\n",
"\n",
"\n",
"## Let us first describe the data\n",
"\n",
"This dataset is made of the daily readings of the following air quality values for May 1, 1973 (a Tuesday) to September 30, 1973.\n",
"\n",
"- Ozone: Mean ozone in parts per billion from 1300 to 1500 hours at Roosevelt Island\n",
"- Solar.R: Solar radiation in Langleys in the frequency band 4000–7700 Angstroms from 0800 to 1200 hours at Central Park\n",
"- Wind: Average wind speed in miles per hour at 0700 and 1000 hours at LaGuardia Airport\n",
"- Temp: Maximum daily temperature in degrees Fahrenheit at La Guardia Airport.\n",
"\n",
"** Source: **\n",
"\n",
"The data was obtained from the New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df_temp = pd.read_csv('../data/airquality.csv', \n",
" usecols = [\"Ozone\", \"Solar.R\", \"Wind\", \"Temp\", \"Month\", \"Day\"])\n",
"\n",
"# We exclude the first column (= index) because we don't need it.\n",
"# To do that, just specify the columns of interest in usecols\n",
"\n",
"#Let's add a year column\n",
"df_temp[\"Year\"] = \"1973\"\n",
"\n",
"# Let's inspect the first few elements\n",
"df_temp.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# It would be useful to create a date column (better plotting)\n",
"df_temp[\"Date\"] = pd.to_datetime(df_temp[\"Year\"] \n",
" + df_temp[\"Month\"].astype(str) \n",
" + df_temp[\"Day\"].astype(str) , format = \"%Y%m%d\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Let's inspect the first few elements again\n",
"df_temp.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check each column's data type\n",
"print df_temp.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interpolation\n",
"\n",
"As you can see from the printed values, there are a few NaN in our array.\n",
"It probably means that the data was not recorded at this date.\n",
"\n",
"We have several ways of dealing with that :\n",
"\n",
"- We can ignore the relevant data line\n",
"- Or we can infer the missing data with, say, an average of a few previous points.\n",
"\n",
"We are going to infer the missing data through interpolation. \n",
"Say we have 1,NaN,2. \n",
"We infer the missing value with $(1+2)/2 = 1.5$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Let's see how to do that with pandas\n",
"\n",
"# Get the data to interpolate\n",
"ozone = df_temp[\"Ozone\"].values\n",
"solar = df_temp[\"Solar.R\"].values\n",
"# Get a series of timestamps\n",
"timestamps = pd.to_datetime(df_temp[\"Date\"].values)\n",
"\n",
"# Create a new Series with the timestamp as index \n",
"#(or we could have set the index as timestamp in our df_temp dataframe)\n",
"s_ozone = pd.Series(ozone, index=timestamps)\n",
"s_solar = pd.Series(solar, index=timestamps)\n",
"\n",
"oz_interp = s_ozone.interpolate(method = \"time\")\n",
"sol_interp = s_solar.interpolate(method = \"time\")\n",
"\n",
"df_temp[\"Ozone_interp\"] = oz_interp.values\n",
"df_temp[\"Solar.R_interp\"] = sol_interp.values\n",
"\n",
"df_temp.head(n=10) # use 10 rows to see how interpolation works"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Let's plot the data\n",
"import matplotlib.pylab as plt\n",
"from matplotlib.gridspec import GridSpec\n",
"\n",
"%matplotlib inline\n",
"fig = plt.figure(figsize=(15, 15))\n",
"gs = GridSpec(2, 2, bottom=0.18, left=0.18, right=0.88)\n",
"\n",
"axOz = fig.add_subplot(gs[0])\n",
"axSol = fig.add_subplot(gs[1])\n",
"axWind = fig.add_subplot(gs[2])\n",
"axTemp = fig.add_subplot(gs[3])\n",
"\n",
"# Get time axis\n",
"x_date = df_temp[\"Date\"].values\n",
"\n",
"# Get the y values \n",
"y_oz = df_temp[\"Ozone_interp\"].values\n",
"y_sol = df_temp[\"Solar.R_interp\"].values\n",
"y_temp = df_temp[\"Temp\"].values\n",
"y_wind = df_temp[\"Wind\"].values\n",
"\n",
"\n",
"# Plot\n",
"axOz.plot(x_date, y_oz, label = \"Ozone in ppm\", linewidth=1.5)\n",
"axSol.plot(x_date, y_sol, label = \"Solar Radiation\", linewidth=1.5)\n",
"axWind.plot(x_date, y_temp, label = \"Temperature in F\", linewidth=1.5)\n",
"axTemp.plot(x_date, y_wind, label = \"Wind\", linewidth=1.5)\n",
"\n",
"\n",
"#####################\n",
"# Figure cosmetics\n",
"#####################\n",
"\n",
"# Axis labels, legend and formatting\n",
"for ax in [axOz, axSol, axWind, axTemp]:\n",
" ax.set_xlabel(\"time\", fontsize=22)\n",
" ax.legend(loc=\"best\", fontsize=22)\n",
" \n",
"# improve plot layout\n",
"gs.tight_layout(fig, h_pad=3)\n",
"\n",
"#plt.savefig(\"visual.png\") #uncomment to save plot\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Correlations\n",
"\n",
"We will now look at an interesting way to see interactions between time-series data : running correlations. \n",
"\n",
"Below is the for the formula of the sample correlation coefficient $r$.\n",
"\n",
"\n",
"\n",
"The running correlation has an extra twist to it. We select a window of points (let's say 30) on which we compute the correlation coefficient. And then we slide this window in time.\n",
"\n",
"So the first value of $r$ is given by points $1, 2, 3, ... 30$. \n",
"The second value of $r$ is given by points $2, 3, 4, ... 31$.\n",
"And so on\n",
"\n",
"This is where the phrase \"running\" comes from. This running correlation gives us information of how the correlation of two time series evolves with time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Clear plotting\n",
"plt.clf()\n",
"plt.close()\n",
"\n",
"#Let's plot the data\n",
"fig = plt.figure(figsize=(15, 15))\n",
"gs = GridSpec(2, 1, bottom=0.18, left=0.18, right=0.88)\n",
"\n",
"ax7 = fig.add_subplot(gs[0])\n",
"ax30 = fig.add_subplot(gs[1])\n",
"\n",
"# Get time axis\n",
"x_date = df_temp[\"Date\"].values\n",
"\n",
"# Get the y values \n",
"\n",
"r_sol7 = df_temp[\"Ozone_interp\"].rolling(window=7).corr(df_temp[\"Solar.R_interp\"])\n",
"r_sol30 = df_temp[\"Ozone_interp\"].rolling(window=30).corr(df_temp[\"Solar.R_interp\"])\n",
"\n",
"r_wind7 = df_temp[\"Ozone_interp\"].rolling(window=7).corr(df_temp[\"Wind\"])\n",
"r_wind30 = df_temp[\"Ozone_interp\"].rolling(window=30).corr(df_temp[\"Wind\"])\n",
"\n",
"# On the first few points, no correlation can be computed (because there are not enough previous points)\n",
"# This gives a NaN value\n",
"# We arbitrarily set this to 0.\n",
"r_sol7 = r_sol7.fillna(0)\n",
"r_sol30 = r_sol30.fillna(0)\n",
"r_wind7 = r_wind7.fillna(0)\n",
"r_wind30 = r_wind30.fillna(0)\n",
"\n",
"# Plot\n",
"ax7.plot(x_date, r_sol7, label = \"7d corr Ozone/Solar\", linewidth=1.5, color = \"r\")\n",
"ax7.plot(x_date, r_wind7, label = \"7d corr Ozone/Wind\", linewidth=1.5, color = \"k\")\n",
"ax30.plot(x_date, r_sol30, label = \"30d corr Ozone/Solar\", linewidth=1.5, color = \"r\")\n",
"ax30.plot(x_date, r_wind30, label = \"30d corr Ozone/Wind\", linewidth=1.5, color = \"k\")\n",
"\n",
"#####################\n",
"# Figure cosmetics\n",
"#####################\n",
"\n",
"# Axis labels, legend and formatting\n",
"for ax in [ax7, ax30]:\n",
" ax.set_xlabel(\"time\", fontsize=22)\n",
" ax.set_ylabel(\"Correlation coefficient\", fontsize=22)\n",
" ax.legend(loc=\"best\", fontsize=22)\n",
"\n",
"# improve plot layout\n",
"gs.tight_layout(fig, h_pad=5)\n",
"\n",
"#plt.savefig(\"visual2.png\")\n",
"plt.show() #uncomment to plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What does this show?\n",
"\n",
"- On a 7 days window, the correlation coefficient changes wildly. \n",
"- There is a single period (july / august) where we see a trend (Solar radiation is correlated to Ozone, Wind is anti-correlated to Ozone)\n",
"- On a 30 days window, we see much stronger trends. Wind is strongly anti-correlated to Ozone and the anticorrelation increases up to September. Solar radiation is mildly correlated to Ozone"
]
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
guidj/xlab | lab/datio/_1.- Quijote.ipynb | 2 | 55257 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of lines in file: 5537\n"
]
}
],
"source": [
"import re\n",
"from operator import add\n",
"file_in = sc.textFile('/home/jjgarcia/clase/spark/quijote.txt')\n",
"print('number of lines in file: %s' % file_in.count())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of characters in file: 305678\n"
]
}
],
"source": [
"chars = file_in.map(lambda s: len(s)).reduce(add)\n",
"print('number of characters in file: %s' % chars)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(362, u'quijote'),\n",
" (343, u'como'),\n",
" (299, u'sancho'),\n",
" (272, u'dijo'),\n",
" (205, u'porque'),\n",
" (188, u'bien'),\n",
" (185, u'para'),\n",
" (176, u'caballero'),\n",
" (172, u'todo'),\n",
" (171, u'respondi'),\n",
" (166, u'vuestra'),\n",
" (150, u'merced'),\n",
" (136, u'esto'),\n",
" (133, u'este'),\n",
" (132, u'pero'),\n",
" (127, u'cual'),\n",
" (125, u'cuando'),\n",
" (123, u'pues'),\n",
" (119, u'donde'),\n",
" (118, u'todos')]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get words from the input file\n",
"words =file_in.flatMap(lambda line: re.split('\\W+', line.lower().strip()))\n",
"# words of more than 3 characters\n",
"words = words.filter(lambda x: len(x) > 3)\n",
"# set count 1 per word\n",
"words = words.map(lambda w: (w,1))\n",
"# reduce phase - sum count all the words\n",
"words = words.reduceByKey(add)\n",
"# create tuple (count, word) and sort in descending\n",
"words = words.map(lambda x: (x[1], x[0])).sortByKey(False)\n",
"# take top 20 words by frequency\n",
"words.take(20)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BQ1uzZJFWFRLig9FodHcZIiIizWaxWLBYLE5tVVVVLpm7XYfw8PBw7HY7X3/9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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff8e5f55bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create function for histogram of most frequent words\n",
"% matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.rcdefaults()\n",
"\n",
"def histogram(words):\n",
" count = map(lambda x: x[1], words)\n",
" word = map(lambda x: x[0], words)\n",
" plt.barh(range(len(count)), count,alpha=0.4)\n",
" plt.yticks(range(len(count)), word)\n",
"\n",
"\n",
"\n",
"# Change order of tuple (word, count) from (count, word)\n",
"words = words.map(lambda x:(x[1], x[0]))\n",
"words.take(25)\n",
"# display histogram\n",
"#plt.figure(figsize=(12, 14)) \n",
"histogram(words.take(25)) \n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
iABC2XYZ/abc | small/Untitled.ipynb | 1 | 2476 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'tf' 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-1-b694dc019916>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mb\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;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
]
}
],
"source": [
"a = tf.constant([1.0, 2.0])\n",
"b = tf.constant([3.0, 4.0])\n",
"c = a * b\n",
"\n",
"with tf.Session() as sess:\n",
" print sess.run(c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
SyrakuShaikh/python | learning/fluent_python_2015/Given a test score, return the corresponding letter grade.ipynb | 1 | 1472 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import bisect"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):\n",
" position = bisect.bisect(breakpoints, score)\n",
" return grades[position]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['F', 'A', 'C', 'C', 'B', 'A', 'A']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[grade(score) for score in [33, 99, 77, 70, 89, 90, 100]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# avoid the anoying multi-if codes."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
PMEAL/OpenPNM | examples/reference/uncategorized/overview_of_domain_syntax.ipynb | 1 | 64990 | {
"cells": [
{
"cell_type": "markdown",
"id": "c833e469",
"metadata": {},
"source": [
"# OpenPNM Version 3: The new @domain syntax "
]
},
{
"cell_type": "markdown",
"id": "58dbe0a2",
"metadata": {},
"source": [
"The latest version of OpenPNM includes a new syntax feature with several uses. This notebooks outlines benefits of this new feature, starting with the superficial or immediately visible aspects, then dives into the underlying impacts."
]
},
{
"cell_type": "markdown",
"id": "9db83958",
"metadata": {},
"source": [
"## Using the @ sytax to read and write data\n",
"At its core, the @ syntax uses the already existing *labels* feature of OpenPNM. Labels have been integral to the use of OpenPNM since its inception, but the new @ syntax moves the use of labels to forefront. "
]
},
{
"cell_type": "markdown",
"id": "7b7830b6",
"metadata": {},
"source": [
"Start by generating a simple 2D cubic network:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cf644968",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.network.Cubic : net_01\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.coords 8 / 8 \n",
"2 throat.conns 10 / 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.back 2 \n",
"2 pore.front 2 \n",
"3 pore.left 4 \n",
"4 pore.right 4 \n",
"5 pore.surface 8 \n",
"6 throat.surface 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"Parameters Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"import numpy as np\n",
"import openpnm as op\n",
"pn = op.network.Cubic([2, 4, 1])\n",
"print(pn)"
]
},
{
"cell_type": "markdown",
"id": "b307ddb5",
"metadata": {},
"source": [
"This network includes a few pre-defined labels (e.g. ``'pore.left'`` and ``'throat.surface'``), which are boolean masks of ``True|False`` values which indicate whether that label applies to a given pore/throat or not. As shown below, the label ``'pore.left'`` applies to pores ``[0, 1, 2, 3]``:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "efecdae8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ True True True True False False False False]\n"
]
}
],
"source": [
"print(pn['pore.left'])"
]
},
{
"cell_type": "markdown",
"id": "020e24dc",
"metadata": {},
"source": [
"We can use labels as masks to view only values for given locations, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "06c66042",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.5, 0.5, 0.5],\n",
" [0.5, 1.5, 0.5],\n",
" [0.5, 2.5, 0.5],\n",
" [0.5, 3.5, 0.5]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pn['pore.coords'][pn['pore.left']]"
]
},
{
"cell_type": "markdown",
"id": "68f2fb59",
"metadata": {},
"source": [
"or using the ``pores`` method:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9e4578ed",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.5, 0.5, 0.5],\n",
" [0.5, 1.5, 0.5],\n",
" [0.5, 2.5, 0.5],\n",
" [0.5, 3.5, 0.5]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pn['pore.coords'][pn.pores('left')]"
]
},
{
"cell_type": "markdown",
"id": "2557e12b",
"metadata": {},
"source": [
"In OpenPNM V3 there is a very handy new syntax, **the @ symbol**, used as follows:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a785a427",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.5, 0.5, 0.5],\n",
" [0.5, 1.5, 0.5],\n",
" [0.5, 2.5, 0.5],\n",
" [0.5, 3.5, 0.5]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pn['pore.coords@left']"
]
},
{
"cell_type": "markdown",
"id": "efb446f4",
"metadata": {},
"source": [
"This certainly saves some typing! It's also pretty intuitive since ``@`` is usually read as \"at\", inferring a location. \n",
"\n",
"The @ sytax can also be used to write data as well. Let's create an array of 1.0s, then use the @ syntax to change them:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c42f2b3c",
"metadata": {},
"outputs": [],
"source": [
"pn['pore.values'] = 1.0"
]
},
{
"cell_type": "markdown",
"id": "19382301",
"metadata": {},
"source": [
"If we supply a scalar, it is written to all locations belong to ``'left'``:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2705ff72",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2. 2. 2. 2. 1. 1. 1. 1.]\n"
]
}
],
"source": [
"pn['pore.values@left'] = 2.0\n",
"print(pn['pore.values'])"
]
},
{
"cell_type": "markdown",
"id": "3b8b3a4a",
"metadata": {},
"source": [
"We can of course pass in an array, which must have the correct number of elements:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "57f46949",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2. 2. 2. 2. 4. 5. 6. 7.]\n"
]
}
],
"source": [
"pn['pore.values@right'] = [4, 5, 6, 7]\n",
"print(pn['pore.values'])"
]
},
{
"cell_type": "markdown",
"id": "d0e0de0f",
"metadata": {},
"source": [
"One useful bonus is that you can create an array and assign values to certain locations at the same time:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "241107f8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2. 2. 2. 2. nan nan nan nan]\n"
]
}
],
"source": [
"pn['pore.new_array@left'] = 2.0\n",
"print(pn['pore.new_array'])"
]
},
{
"cell_type": "markdown",
"id": "e8ef1825",
"metadata": {},
"source": [
"The above line created an empty array of ``nans``, then added ``2.0`` to the pores labelled ``'left'``. This was not previously possible without first creating an empty array before adding ``2.0`` to specific locations."
]
},
{
"cell_type": "markdown",
"id": "7fc748d9",
"metadata": {},
"source": [
"You can use any label that is defined, and it will overwrite any values already present if that label happens to overlap the label used previously:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f2fca4dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 3. 2. 2. 2. 3. nan nan nan]\n"
]
}
],
"source": [
"pn['pore.new_array@front'] = 3.0\n",
"print(pn['pore.new_array'])"
]
},
{
"cell_type": "markdown",
"id": "9eba8d87",
"metadata": {},
"source": [
"which overwrote some locations that had ``2.0``, since some pores are both ``'front'`` and ``'left'``, as well as overwrote some of the ``nan`` values."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fd048997",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3. 2. 2. 2.]\n"
]
}
],
"source": [
"print(pn['pore.new_array@left'])"
]
},
{
"cell_type": "markdown",
"id": "a57dc7bd",
"metadata": {},
"source": [
"## Using the @ Syntax to Define Subdomains\n",
"\n",
"Using the @ symbol for data read/write as shown above is actually a side effect of a *major conceptual shift* made in V3. The ``Geometry`` and ``Physics`` objects are now *gone*. There was essentially only one use case for these, which was to model heterogeneous domains, like bimodal pore size distributions or layered structures. \n",
"\n",
"In V2 this was accomplished by using 2 (or more) ``Geometry`` objects to represent each class of pores, with unique pore-scale models attached to each. Without getting lost in the details, it is sufficient to say that having separate objects for managing each class of pores (and/or throats) created a *lot* of complications, both to the user and to the maintenence of the backend. \n",
"\n",
"In V3 we have developed what we think is a *much tidier approach* to managing heterogeneous domains. Instead of creating multiple ``Geometry`` objects (and consequently multiple ``Physics`` objects), you now add all the pore-scale models to the ``Network`` and ``Phase`` objects directly. The trick is that when adding models you specify one additional argument: the ``domain`` (i.e. pores or throats) to which the model applies, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0f8def0b",
"metadata": {},
"outputs": [],
"source": [
"pn.add_model(propname='pore.seed', \n",
" model=op.models.geometry.pore_seed.random,\n",
" domain='left',\n",
" seed=0,\n",
" num_range=[0.1, 0.5])"
]
},
{
"cell_type": "markdown",
"id": "2c493269",
"metadata": {},
"source": [
"where ``domain`` is given the label ``'left'`` which has already been defined on the network. \n",
"\n",
"This means that to create a heterogeneous model you only need to create labels marking the pores and/or throats of each domain, then pass those labels when adding models. You can also leave ``domain`` unspecified (``None``) which means the model is applied everywhere. For the above case, we can see that the ``'pore.seed'`` model was computed for 4 locations (corresponding the 4 pores labelled ``'left'``):"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "dee10e91",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.network.Cubic : net_01\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.coords 8 / 8 \n",
"2 pore.new_array 5 / 8 \n",
"3 pore.seed 4 / 8 \n",
"4 pore.values 8 / 8 \n",
"5 throat.conns 10 / 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.back 2 \n",
"2 pore.front 2 \n",
"3 pore.left 4 \n",
"4 pore.right 4 \n",
"5 pore.surface 8 \n",
"6 throat.surface 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"Parameters Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(pn)"
]
},
{
"cell_type": "markdown",
"id": "ac304b09",
"metadata": {},
"source": [
"The power of this new approach is really visible when we consider applying a model with different parameters to a different set of pores:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "4bb4285d",
"metadata": {},
"outputs": [],
"source": [
"pn.add_model(propname='pore.seed', \n",
" model=op.models.geometry.pore_seed.random,\n",
" domain='right',\n",
" seed=0,\n",
" num_range=[0.5, 0.9])"
]
},
{
"cell_type": "markdown",
"id": "3efc5427",
"metadata": {},
"source": [
"Now the ``pore.seed'`` values exist on 8 locations."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "dc791939",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.network.Cubic : net_01\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.coords 8 / 8 \n",
"2 pore.new_array 5 / 8 \n",
"3 pore.seed 8 / 8 \n",
"4 pore.values 8 / 8 \n",
"5 throat.conns 10 / 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.back 2 \n",
"2 pore.front 2 \n",
"3 pore.left 4 \n",
"4 pore.right 4 \n",
"5 pore.surface 8 \n",
"6 throat.surface 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"Parameters Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(pn)"
]
},
{
"cell_type": "markdown",
"id": "c11ae9f4",
"metadata": {},
"source": [
"The new approach was made possible by changing how pore-scale models are stored on objects. Each object has a ``models`` attribute, which is a ``dict`` where the ``key`` corresponds to the property being calculated. So the model to compute ``'pore.seed'`` is stored as ``pn.models['pore.seed']``. The new ``@`` notation makes it possible to store multiple models for ``'pore.seed'`` that apply to different location on the same object. This can be seen below by printing the models attribute:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e1d6a54a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Property Name Parameter Value\n",
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.coordination_number model: coordination_number\n",
" regeneration mode: deferred\n",
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"2 throat.spacing model: pore_to_pore_distance\n",
" regeneration mode: deferred\n",
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"3 pore.seed@left model: random\n",
" seed: 0\n",
" num_range: [0.1, 0.5]\n",
" regeneration mode: normal\n",
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"4 pore.seed@right model: random\n",
" seed: 0\n",
" num_range: [0.5, 0.9]\n",
" regeneration mode: normal\n",
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(pn.models)"
]
},
{
"cell_type": "markdown",
"id": "19860f2b",
"metadata": {},
"source": [
"Appending ``@`` to the model name creates a unique dictionary key. OpenPNM recoginizes that the models in ``'pore.seed@left'`` and ``'pore.seed@right'`` both compute values of ``'pore.seed'``, and directs the outputs of each function to the correct locations, which it can infer from the ``@right/left`` portion of the key."
]
},
{
"cell_type": "markdown",
"id": "7e72859f",
"metadata": {},
"source": [
"## Other Advantages of the @ Syntax\n",
"There are many upsides to this approach, as will be demonstrated in the following sections."
]
},
{
"cell_type": "markdown",
"id": "9c6ede7f",
"metadata": {},
"source": [
"### Defining and Changing Subdomain Locations\n",
"It becomes trivial to define and redefine the locations of a domain. This simply requires changing where ``pn['pore.left']`` is ``True``. This is demonstrated as follows:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "03e2168a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pn.pores('left')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "d260c555",
"metadata": {},
"outputs": [],
"source": [
"pn['pore.left'][[4, 5]] = True\n",
"del pn['pore.seed']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "cfb0fcbc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.network.Cubic : net_01\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.coords 8 / 8 \n",
"2 pore.new_array 5 / 8 \n",
"3 pore.seed 6 / 8 \n",
"4 pore.values 8 / 8 \n",
"5 throat.conns 10 / 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.back 2 \n",
"2 pore.front 2 \n",
"3 pore.left 6 \n",
"4 pore.right 4 \n",
"5 pore.surface 8 \n",
"6 throat.surface 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"Parameters Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"pn.run_model('pore.seed@left')\n",
"print(pn)"
]
},
{
"cell_type": "markdown",
"id": "07e76a49",
"metadata": {},
"source": [
"It can now been observed that ``'pore.seed'`` values are found in 6 locations because the domain labelled ``'left'`` was expanded by 2 pores. "
]
},
{
"cell_type": "markdown",
"id": "b97628ac",
"metadata": {},
"source": [
"### Mixing Full Domain and Subdomain Models\n",
"When defining two separate subdomains, the pore and throat sizes are often the only thing that is different. In V2, however, it was recommended practice to include ALL the additional models on each subdomain object as well, such as volume calculations. With the @ syntax, only models that actualy differ between the domains need to be specifically dealt with.\n",
"\n",
"This is demonstrated below by first deleting the individual ``'pore.seed'`` models applied above, and replacing them with a single model that applies uniform values on all locations, then applying two different normal distributions to the ``'left'`` and ``'right'`` domains."
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "e332fd2c",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'pore.seed@left'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_20732/767852880.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mpn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pore.seed@left'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mpn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pore.seed@right'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'pore.seed@left'"
]
}
],
"source": [
"del pn.models['pore.seed@left']\n",
"del pn.models['pore.seed@right']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "32853ff6",
"metadata": {},
"outputs": [],
"source": [
"pn.add_model(propname='pore.seed', \n",
" model=op.models.geometry.pore_seed.random)\n",
"pn.add_model(propname='pore.diameter', \n",
" model=op.models.geometry.pore_size.normal,\n",
" domain='left',\n",
" scale=0.1, \n",
" loc=1,\n",
" seeds='pore.seed')\n",
"pn.add_model(propname='pore.diameter', \n",
" model=op.models.geometry.pore_size.normal,\n",
" domain='right',\n",
" scale=2, \n",
" loc=10,\n",
" seeds='pore.seed')"
]
},
{
"cell_type": "markdown",
"id": "932940df",
"metadata": {},
"source": [
"As can be seen in the figures below, the ``'pore.seed'`` values are uniformly distributed on all locations, but ``'pore.diameter'`` differs due to the different parameter used in each model."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "015bbd36",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(1, 2, figsize=[12, 6])\n",
"\n",
"ax[0].plot(pn.Ps, pn['pore.seed'], 'o')\n",
"ax[0].set_ylabel('pore seed')\n",
"ax[0].set_xlabel('pore index')\n",
"\n",
"ax[1].plot(pn.pores('left'), pn['pore.diameter@left'], 'o', label='pore.left')\n",
"ax[1].plot(pn.pores('right'), pn['pore.diameter@right'], 'o', label='pore.right')\n",
"ax[1].set_ylabel('pore diameter')\n",
"ax[1].set_xlabel('pore index')\n",
"ax[1].legend();"
]
},
{
"cell_type": "markdown",
"id": "46e1f8e7",
"metadata": {},
"source": [
"And now we can apply a model to the full domain that computes the pore volume, using values of pore diameter that were computed uniquely for each domain:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "470abf20",
"metadata": {},
"outputs": [],
"source": [
"pn.add_model(propname='pore.volume', \n",
" model=op.models.geometry.pore_volume.sphere)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "69e2ad61",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(1, 1, figsize=[6, 6])\n",
"\n",
"ax.plot(pn.Ps, pn['pore.volume'], 'o')\n",
"ax.set_ylabel('pore volume')\n",
"ax.set_xlabel('pore index');"
]
},
{
"cell_type": "markdown",
"id": "0f9eba37",
"metadata": {},
"source": [
"> If an algorithm updates the labels then it effectively changes the domains! Re-running the models would automatically apply to the new locations! Not quite sure what this is useful for...maybe catalyst deactivation? Oooh, multiphase flow and percolation! The percolation algorithm could put True/False for occupancy, then a pore-scale model for hydraulic conductance that is applied to 'pore.invaded' domain, the it could update!"
]
},
{
"cell_type": "markdown",
"id": "51b42b89",
"metadata": {},
"source": [
"### Mixing Many Subdomains of Different Shape\n",
"Because subdomains are now very abstract (actuallyjust labels), it is possible to define multiple subdomains with different shape and apply models to each. So far we have added ``'pore.seed'`` and ``'pore.diameter'`` models to the ``'left'`` and ``'right'`` pores. We can now freely add another set of models to the ``'front'`` and ``'back'``, even though they partially overlap:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "7c9056f6",
"metadata": {},
"outputs": [],
"source": [
"Ps = pn.pores(['front', 'back'])\n",
"Ts = pn.find_neighbor_throats(Ps, asmask=True)\n",
"pn['throat.front'] = Ts\n",
"pn['throat.back'] = ~Ts"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "59396df4",
"metadata": {},
"outputs": [],
"source": [
"pn.add_model(propname='throat.diameter', \n",
" model=op.models.geometry.throat_size.from_neighbor_pores,\n",
" domain='front', \n",
" mode='min')\n",
"pn.add_model(propname='throat.diameter', \n",
" model=op.models.geometry.throat_size.from_neighbor_pores,\n",
" domain='back',\n",
" mode='max')"
]
},
{
"cell_type": "markdown",
"id": "330f6b45",
"metadata": {},
"source": [
"Now we can see that the throat diameters have beed added to the network:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "39b237c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.network.Cubic : net_01\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.coords 8 / 8 \n",
"2 pore.diameter 8 / 8 \n",
"3 pore.new_array 5 / 8 \n",
"4 pore.seed 8 / 8 \n",
"5 pore.values 8 / 8 \n",
"6 pore.volume 8 / 8 \n",
"7 throat.conns 10 / 10 \n",
"8 throat.diameter 10 / 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.back 2 \n",
"2 pore.front 2 \n",
"3 pore.left 6 \n",
"4 pore.net_01 8 \n",
"5 pore.right 4 \n",
"6 pore.surface 8 \n",
"7 throat.back 4 \n",
"8 throat.front 6 \n",
"9 throat.net_01 10 \n",
"10 throat.surface 10 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"Parameters Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(pn)"
]
},
{
"cell_type": "markdown",
"id": "fd6705d2",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| mit |
jasag/Phytoliths-recognition-system | code/notebooks/Prototypes/Classifiers_and_HoG/1_HoG.ipynb | 1 | 7568 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Características de HoG\n",
"\n",
"En este notebook creará un conjunto de imagenes con caras y no caras mediante las que obtendremos las características de HoG que nos servirán como conjunto de entrenamiento para nuestro clasificador.\n",
"\n",
"Además, estas características serán serializadas para que podamos acceder a ellas las veces que deseemos evitando su procesamiento."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histogram of Oriented Gradients (HoG)\n",
"\n",
"HoG es una técnica para la extracción de características, desarrollada en el contexto del procesamiento de imagenes, que involucra los siguientes pasos:\n",
"\n",
"1. Pre-normalizado de las imagenes. Aunque puede suponer una mayor dependencía de las características que varían segun la iluminación.\n",
"2. Aplicar a la imagen dos filtros sensibles al brillo tanto horizontal como vertical. Lo cual nos aporta información sobre bordes, contornos y texturas.\n",
"3. Subdividir la imagen en celdas de un tamaño concreto y calcular el histograma del gradiente para cada celda.\n",
"4. Normalizar los histogramas, previamente calculados, mediante la comparación con sus vecinos. Eliminando así el efecto de la iluminación en la imagen.\n",
"5. Construir un vector de caracteristicas unidimensional de la información de cada celda."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Crear un conjunto de entrenamiento de imagenes de caras que supongan positivos\n",
"Scikit nos proporciona un conjunto de imagenes variadas de caras que nos permitirán obtener un conjunto de entrenamiento de positivos para nuestro objetivo. Más de 13000 caras para ser concretos.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_lfw_people\n",
"faces = fetch_lfw_people()\n",
"positive_patches = faces.images\n",
"positive_patches.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Alternativa\n",
"\n",
"Vamos a proporcionar, de manera alternativa a la anterior, nuestro propio conjunto de imagenes."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# from skimage import io\n",
"# from skimage.color import rgb2gray\n",
"\n",
"# positive_patches = list()\n",
"\n",
"# path = \"../imgaug/imgs/\"\n",
"# for i in range(376):\n",
"# for j in range(63):\n",
"# image = io.imread(path+str(i)+str(j)+\".jpg\")\n",
"# positive_patches.append(rgb2gray(image))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Crear un conjunto de entrenamiento de imagenes de no-caras que supongan falsos-positivos\n",
"Una vez obtenido nuestro conjunto de positivos, necesitamos obtener un conjunto de imagenes que no tengan caras. Para ello, la técnica que se utiliza en el *notebook* en el que me estoy basando es subdividir imágenes de mayor tamaño que no contengan caras. Y, así, obtener múltiples imágenes. "
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from skimage import feature, color, data, transform\n",
"\n",
"imgs_to_use = ['camera', 'text', 'coins', 'moon',\n",
" 'page', 'clock', 'immunohistochemistry',\n",
" 'chelsea', 'coffee', 'hubble_deep_field']\n",
"images = [color.rgb2gray(getattr(data, name)())\n",
" for name in imgs_to_use]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(30000, 62, 47)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from sklearn.feature_extraction.image import PatchExtractor\n",
"\n",
"def extract_patches(img, N, scale=1.0, patch_size=positive_patches[0].shape):\n",
" \n",
" extracted_patch_size = tuple((scale * np.array(patch_size)).astype(int))\n",
" \n",
" extractor = PatchExtractor(patch_size=extracted_patch_size,\n",
" max_patches=N, random_state=0)\n",
" \n",
" patches = extractor.transform(img[np.newaxis])\n",
" \n",
" if scale != 1:\n",
" patches = np.array([transform.resize(patch, patch_size)\n",
" for patch in patches])\n",
" return patches\n",
"\n",
"negative_patches = np.vstack([extract_patches(im, 1000, scale)\n",
" for im in images for scale in [0.5, 1.0, 2.0]])\n",
"negative_patches.shape\n",
"\n",
"# Alternativa\n",
"# negative_patches = np.vstack([extract_patches(im, 1000, scale, patch_size=(62,47))\n",
"# for im in images for scale in [0.5, 1.0, 2.0]])\n",
"# negative_patches.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Extraer las características de HoG del conjunto de entrenamiento\n",
"Este tercer paso resulta de especial interes, puesto que vamos a obtener las características de HoG sobre las que previamente hemos hablado."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(30000, 62, 47) (23688, 62, 47)\n"
]
}
],
"source": [
"from itertools import chain\n",
"positive_patches = np.array(positive_patches)\n",
"print(negative_patches.shape, positive_patches.shape)\n",
"X_train = np.array([feature.hog(im)\n",
" for im in chain(positive_patches,\n",
" negative_patches)])\n",
"y_train = np.zeros(X_train.shape[0])\n",
"y_train[:positive_patches.shape[0]] = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Serializamos el conjunto de entrenamiento\n",
"Simplemente almacenamos los objetos *X_train* e *y_train* para, como explicabamos al principio, evitar el recalculo de estas características cada vez que deseemos utilizarlas."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pickle # Módulo para serializar\n",
"\n",
"path = '../../rsc/obj/'\n",
"\n",
"X_train_path = path + 'X_train.sav'\n",
"y_train_path = path + 'y_train.sav'\n",
"\n",
"pickle.dump(X_train, open(X_train_path, 'wb'))\n",
"pickle.dump(y_train, open(y_train_path, 'wb'))"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
amuesing1/spectrum | spectrum_display.ipynb | 1 | 4407 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pyaudio\n",
"import wave\n",
"from scipy.fftpack import fft\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as animation\n",
"\n",
"data_type = 16 # mapped to 2 ** 15 possible values\n",
"\n",
"FORMAT = pyaudio.paInt16\n",
"CHANNELS = 2\n",
"RATE = 44100\n",
"CHUNK = 1024\n",
"\n",
"audio = pyaudio.PyAudio()\n",
"\n",
"stream = audio.open(format=FORMAT, channels=CHANNELS,\n",
" rate=RATE, input=True,\n",
" frames_per_buffer=CHUNK)\n",
"\n",
"def init():\n",
" line.set_visible(False)\n",
" return line,\n",
"\n",
"fig, ax = plt.subplots()\n",
"line, = ax.plot(0, 0, color='DarkViolet')\n",
"\n",
"def update(frame, line):\n",
" if frame==1:\n",
" line.set_visible(True)\n",
" data = stream.read(CHUNK)\n",
" decoded = np.fromstring(data, dtype=np.int16)\n",
" sound = decoded #/ (2.0 ** (data_type - 1))\n",
" t=np.arange(sound.size)\n",
" line.set_data(t, sound)\n",
" line.axes.axis([0, sound.size, -7000, 7000])\n",
" return line,\n",
"\n",
"ani = animation.FuncAnimation(fig, update, init_func=init, fargs=[line],\n",
" interval=25, blit=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pyaudio\n",
"import wave\n",
"from scipy.fftpack import fft\n",
"from __future__ import division\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as animation\n",
"\n",
"data_type = 16 # mapped to 2 ** 15 possible values\n",
"\n",
"FORMAT = pyaudio.paInt16\n",
"CHANNELS = 2\n",
"RATE = 44100\n",
"CHUNK = 4410\n",
"\n",
"audio = pyaudio.PyAudio()\n",
"\n",
"stream = audio.open(format=FORMAT, channels=CHANNELS,\n",
" rate=RATE, input=True,\n",
" frames_per_buffer=CHUNK)\n",
"\n",
"def init():\n",
" line.set_visible(False)\n",
" return line,\n",
"\n",
"fig, ax = plt.subplots()\n",
"line, = ax.plot(0, 0, color='DarkViolet')\n",
"\n",
"def update(frame, line):\n",
" if frame==1:\n",
" line.set_visible(True)\n",
" data=stream.read(CHUNK)\n",
" decoded = np.fromstring(data, dtype=np.int16)\n",
"\n",
" signal = np.fft.rfft(decoded)\n",
" if signal.ndim>1:\n",
" signal=np.mean(signal,axis=1) # average both channels\n",
"\n",
" n_samples=signal.size\n",
"\n",
" magnitude = np.abs(signal)/CHUNK\n",
" magnitude = np.asarray(magnitude)\n",
" f = [(j*1.0/n_samples)*RATE \n",
" for j in range(n_samples)]\n",
" frequencies = np.asarray(f)\n",
" #print(np.amax(frequencies))\n",
" #print(np.amax(magnitude))\n",
" line.set_data(frequencies, magnitude)\n",
" line.axes.axis([0, frequencies.size, 0, 500])\n",
" return line,\n",
"\n",
"ani = animation.FuncAnimation(fig, update, init_func=init, fargs=[line],\n",
" interval=25, blit=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
ceos-seo/data_cube_notebooks | notebooks/training/ardc_training/Training_TaskB_Water.ipynb | 1 | 25295 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:32.377279Z",
"iopub.status.busy": "2020-09-29T01:00:32.376860Z",
"iopub.status.idle": "2020-09-29T01:00:32.379296Z",
"shell.execute_reply": "2020-09-29T01:00:32.378845Z"
}
},
"outputs": [],
"source": [
"# Enable importing of utilities.\n",
"import sys\n",
"import os\n",
"sys.path.append(os.environ.get('NOTEBOOK_ROOT'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ARDC Training: Python Notebooks\n",
"Task-B: Water Extent (WOFS) and Water Quality (TSM)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> ### Import the Datacube Configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:32.382561Z",
"iopub.status.busy": "2020-09-29T01:00:32.382143Z",
"iopub.status.idle": "2020-09-29T01:00:33.657251Z",
"shell.execute_reply": "2020-09-29T01:00:33.657746Z"
}
},
"outputs": [],
"source": [
"import datacube\n",
"import utils.data_cube_utilities.data_access_api as dc_api \n",
"\n",
"from datacube.utils.aws import configure_s3_access\n",
"configure_s3_access(requester_pays=True)\n",
"\n",
"api = dc_api.DataAccessApi()\n",
"dc = datacube.Datacube(app = 'ardc_task_b')\n",
"api.dc = dc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">### Browse the available Data Cubes "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:33.662142Z",
"iopub.status.busy": "2020-09-29T01:00:33.661408Z",
"iopub.status.idle": "2020-09-29T01:00:33.709380Z",
"shell.execute_reply": "2020-09-29T01:00:33.708971Z"
}
},
"outputs": [],
"source": [
"list_of_products = dc.list_products()\n",
"netCDF_products = list_of_products[list_of_products['format'] == 'NetCDF']\n",
"netCDF_products"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">### Pick a product \n",
">Use the platform and product names from the previous block to select a Data Cube. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:33.712553Z",
"iopub.status.busy": "2020-09-29T01:00:33.712131Z",
"iopub.status.idle": "2020-09-29T01:00:33.714423Z",
"shell.execute_reply": "2020-09-29T01:00:33.713961Z"
}
},
"outputs": [],
"source": [
"# Change the data platform and data cube here\n",
"\n",
"platform = 'LANDSAT_7'\n",
"product = 'ls7_usgs_sr_scene'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> #### Display Latitude-Longitude and Time Bounds of the Data Cube"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:33.719610Z",
"iopub.status.busy": "2020-09-29T01:00:33.718669Z",
"iopub.status.idle": "2020-09-29T01:00:35.861989Z",
"shell.execute_reply": "2020-09-29T01:00:35.862452Z"
},
"scrolled": false
},
"outputs": [],
"source": [
"from utils.data_cube_utilities.dc_time import _n64_to_datetime, dt_to_str\n",
"\n",
"extents = api.get_full_dataset_extent(platform = platform, product = product, measurements=[])\n",
"\n",
"latitude_extents = (min(extents['latitude'].values),max(extents['latitude'].values))\n",
"longitude_extents = (min(extents['longitude'].values),max(extents['longitude'].values))\n",
"time_extents = (min(extents['time'].values),max(extents['time'].values))\n",
"\n",
"print(\"Latitude Extents:\", latitude_extents)\n",
"print(\"Longitude Extents:\", longitude_extents)\n",
"print(\"Time Extents:\", list(map(dt_to_str, map(_n64_to_datetime, time_extents))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize Data Cube Region"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:35.865746Z",
"iopub.status.busy": "2020-09-29T01:00:35.865276Z",
"iopub.status.idle": "2020-09-29T01:00:36.038571Z",
"shell.execute_reply": "2020-09-29T01:00:36.038996Z"
}
},
"outputs": [],
"source": [
"## The code below renders a map that can be used to orient yourself with the region.\n",
"from utils.data_cube_utilities.dc_display_map import display_map\n",
"display_map(latitude = latitude_extents, longitude = longitude_extents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> ### Pick a smaller analysis region and display that region\n",
"Try to keep your region to less than 0.2-deg x 0.2-deg for rapid processing. You can click on the map above to find the Lat-Lon coordinates of any location. You will want to identify a region with an inland water body. Pick a time window of a few months so we can pick out some clear pixels and plot the water. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:36.042326Z",
"iopub.status.busy": "2020-09-29T01:00:36.041904Z",
"iopub.status.idle": "2020-09-29T01:00:36.043570Z",
"shell.execute_reply": "2020-09-29T01:00:36.043981Z"
}
},
"outputs": [],
"source": [
"## Vietnam - Central Lam Dong Province ##\n",
"longitude_extents = (107.0, 107.2)\n",
"latitude_extents = (11.7, 12.0)\n",
"\n",
"## Tanzania - Lake Sulunga\n",
"longitude_extents = (35.00, 35.37)\n",
"latitude_extents = (-6.28,-5.87)\n",
"\n",
"time_extents = ('2015-01-01', '2015-12-31')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:36.047832Z",
"iopub.status.busy": "2020-09-29T01:00:36.047171Z",
"iopub.status.idle": "2020-09-29T01:00:36.055239Z",
"shell.execute_reply": "2020-09-29T01:00:36.054802Z"
},
"scrolled": false
},
"outputs": [],
"source": [
"display_map(latitude = latitude_extents, longitude = longitude_extents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the dataset and the required spectral bands or other parameters\n",
"After loading, you will view the Xarray dataset. Notice the dimensions represent the number of pixels in your latitude and longitude dimension as well as the number of time slices (time) in your time series."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:36.060080Z",
"iopub.status.busy": "2020-09-29T01:00:36.059666Z",
"iopub.status.idle": "2020-09-29T01:00:40.483818Z",
"shell.execute_reply": "2020-09-29T01:00:40.482710Z"
}
},
"outputs": [],
"source": [
"landsat_dataset = dc.load(latitude = latitude_extents,\n",
" longitude = longitude_extents,\n",
" platform = platform,\n",
" time = time_extents,\n",
" product = product,\n",
" measurements = ['red', 'green', 'blue', 'nir', 'swir1', 'swir2', 'pixel_qa']) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:40.498207Z",
"iopub.status.busy": "2020-09-29T01:00:40.497104Z",
"iopub.status.idle": "2020-09-29T01:00:40.519704Z",
"shell.execute_reply": "2020-09-29T01:00:40.520094Z"
}
},
"outputs": [],
"source": [
"landsat_dataset\n",
"#view the dimensions and sample content from the cube"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Display Example Images \n",
"\n",
">#### Single band visualization \n",
"> For a quick inspection, let's look at one image. The code will allow the selection of any band (red, blue, green, nir, swir1, swir2) to produce a grey-scale image. Select the desired acquisition (time slice) in the block below. You can select from 1 to #, where the max value is the number of time slices noted in the block above. Change the comment statements below to select the bands for the first image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:40.523521Z",
"iopub.status.busy": "2020-09-29T01:00:40.522984Z",
"iopub.status.idle": "2020-09-29T01:00:40.525621Z",
"shell.execute_reply": "2020-09-29T01:00:40.525185Z"
}
},
"outputs": [],
"source": [
"acquisition_number = 2\n",
"# select an acquisition number from 1 to \"time\" using the array limits above"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:40.531815Z",
"iopub.status.busy": "2020-09-29T01:00:40.531381Z",
"iopub.status.idle": "2020-09-29T01:00:41.271791Z",
"shell.execute_reply": "2020-09-29T01:00:41.271336Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"#landsat_dataset.red.isel(time = acquisition_number).plot(cmap = \"Greys\")\n",
"landsat_dataset.green.isel(time = acquisition_number).plot(cmap = \"Greys\")\n",
"#landsat_dataset.blue.isel(time = acquisition_number).plot(cmap = \"Greys\")\n",
"#landsat_dataset.nir.isel(time = acquisition_number).plot(cmap = \"Greys\")\n",
"#landsat_dataset.swir1.isel(time = acquisition_number).plot(cmap = \"Greys\")\n",
"#landsat_dataset.swir2.isel(time = acquisition_number).plot(cmap = \"Greys\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">#### Define Cloud Masking Function \n",
"Removes clouds and cloud shadows based on the Landsat pixel QA information\n",
"This is only for reference ... nothing to modify here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:41.281407Z",
"iopub.status.busy": "2020-09-29T01:00:41.280959Z",
"iopub.status.idle": "2020-09-29T01:00:41.282615Z",
"shell.execute_reply": "2020-09-29T01:00:41.283032Z"
}
},
"outputs": [],
"source": [
"import numpy as np \n",
"\n",
"def generate_cloud_mask(dataset, include_shadows = False):\n",
" #Create boolean Masks for clear and water pixels\n",
" clear_pixels = dataset.pixel_qa.values == 2 + 64\n",
" water_pixels = dataset.pixel_qa.values == 4 + 64\n",
" shadow_pixels= dataset.pixel_qa.values == 8 + 64\n",
" \n",
" a_clean_mask = np.logical_or(clear_pixels, water_pixels)\n",
" \n",
" if include_shadows:\n",
" a_clean_mask = np.logical_or(a_clean_mask, shadow_pixels)\n",
" \n",
" return np.invert(a_clean_mask)\n",
"\n",
"def remove_clouds(dataset, include_shadows = False):\n",
" #Create boolean Masks for clear and water pixels\n",
" clear_pixels = dataset.pixel_qa.values == 2 + 64\n",
" water_pixels = dataset.pixel_qa.values == 4 + 64\n",
" shadow_pixels= dataset.pixel_qa.values == 8 + 64\n",
" \n",
" a_clean_mask = np.logical_or(clear_pixels, water_pixels)\n",
" \n",
" if include_shadows:\n",
" a_clean_mask = np.logical_or(a_clean_mask, shadow_pixels)\n",
" \n",
" return dataset.where(a_clean_mask)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:41.286117Z",
"iopub.status.busy": "2020-09-29T01:00:41.285695Z",
"iopub.status.idle": "2020-09-29T01:00:41.751012Z",
"shell.execute_reply": "2020-09-29T01:00:41.751479Z"
}
},
"outputs": [],
"source": [
"cloud_mask = generate_cloud_mask(landsat_dataset)\n",
"cloudless = remove_clouds(landsat_dataset) #landsat_dataset.where(image_is_clean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">Set up plotting function (to be used later)\n",
">Nothing to modify here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:41.754839Z",
"iopub.status.busy": "2020-09-29T01:00:41.754404Z",
"iopub.status.idle": "2020-09-29T01:00:41.756488Z",
"shell.execute_reply": "2020-09-29T01:00:41.756908Z"
}
},
"outputs": [],
"source": [
"from utils.data_cube_utilities.dc_rgb import rgb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">** Most Recent Pixel Mosaic ** \n",
">Masks clouds from imagery and uses the most recent cloud-free pixels. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:41.760234Z",
"iopub.status.busy": "2020-09-29T01:00:41.759821Z",
"iopub.status.idle": "2020-09-29T01:00:41.772488Z",
"shell.execute_reply": "2020-09-29T01:00:41.772055Z"
}
},
"outputs": [],
"source": [
"from utils.data_cube_utilities.dc_mosaic import create_mosaic\n",
"\n",
"def mrf_mosaic(dataset):\n",
" # The mask here is based on pixel_qa products. It comes bundled in with most Landsat Products.\n",
" cloud_free_boolean_mask = np.invert(generate_cloud_mask(dataset))\n",
" return create_mosaic(dataset, clean_mask = cloud_free_boolean_mask)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:41.775485Z",
"iopub.status.busy": "2020-09-29T01:00:41.775076Z",
"iopub.status.idle": "2020-09-29T01:00:42.644453Z",
"shell.execute_reply": "2020-09-29T01:00:42.644925Z"
}
},
"outputs": [],
"source": [
"recent_composite = mrf_mosaic(landsat_dataset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:42.648711Z",
"iopub.status.busy": "2020-09-29T01:00:42.648292Z",
"iopub.status.idle": "2020-09-29T01:00:43.217365Z",
"shell.execute_reply": "2020-09-29T01:00:43.217853Z"
}
},
"outputs": [],
"source": [
"recent_composite.nir.plot(cmap = \"Greys\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:43.221423Z",
"iopub.status.busy": "2020-09-29T01:00:43.220763Z",
"iopub.status.idle": "2020-09-29T01:00:43.839414Z",
"shell.execute_reply": "2020-09-29T01:00:43.839868Z"
}
},
"outputs": [],
"source": [
"rgb(recent_composite, width = 20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot WOFS water detection results\n",
"This example uses the Australian Water Detection from Space (WOFS) algorithm for water detection. The base image will use a most-recent pixel composite (from above). When reviewing the results, 1=water, 0=no water. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:43.843192Z",
"iopub.status.busy": "2020-09-29T01:00:43.842780Z",
"iopub.status.idle": "2020-09-29T01:00:43.845569Z",
"shell.execute_reply": "2020-09-29T01:00:43.845096Z"
}
},
"outputs": [],
"source": [
"from utils.data_cube_utilities.dc_water_classifier import wofs_classify"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:43.848871Z",
"iopub.status.busy": "2020-09-29T01:00:43.848465Z",
"iopub.status.idle": "2020-09-29T01:00:43.959418Z",
"shell.execute_reply": "2020-09-29T01:00:43.959862Z"
}
},
"outputs": [],
"source": [
"water_classification = wofs_classify(recent_composite, clean_mask = np.ones(recent_composite.pixel_qa.shape).astype(np.bool), mosaic = True) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:43.963800Z",
"iopub.status.busy": "2020-09-29T01:00:43.963384Z",
"iopub.status.idle": "2020-09-29T01:00:44.503600Z",
"shell.execute_reply": "2020-09-29T01:00:44.503952Z"
}
},
"outputs": [],
"source": [
"water_classification.wofs.plot(cmap='Blues')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot NDWI water detection results\n",
"This example uses the Normalized Difference Water Index (NDWI) which is a spectral \"index\" that correlates well with the existance of water. \n",
"<br>\n",
"$$ NDWI = \\frac{GREEN - NIR}{GREEN + NIR}$$ "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:44.507467Z",
"iopub.status.busy": "2020-09-29T01:00:44.506955Z",
"iopub.status.idle": "2020-09-29T01:00:44.509102Z",
"shell.execute_reply": "2020-09-29T01:00:44.508661Z"
}
},
"outputs": [],
"source": [
"def NDWI(dataset):\n",
" return (dataset.green - dataset.nir)/(dataset.green + dataset.nir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:44.512424Z",
"iopub.status.busy": "2020-09-29T01:00:44.512022Z",
"iopub.status.idle": "2020-09-29T01:00:44.518091Z",
"shell.execute_reply": "2020-09-29T01:00:44.517644Z"
}
},
"outputs": [],
"source": [
"ndwi = NDWI(recent_composite) # High Concentrations of Water - Blues "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:44.521289Z",
"iopub.status.busy": "2020-09-29T01:00:44.520881Z",
"iopub.status.idle": "2020-09-29T01:00:45.106253Z",
"shell.execute_reply": "2020-09-29T01:00:45.106692Z"
}
},
"outputs": [],
"source": [
"(ndwi).plot(cmap = \"Blues\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot TSM water quality results\n",
"This example uses the Australian Total Suspended Matter (TSM) algorithm. The TSM value is the mean over the entire time range. This parameter is a measure of the particulate matter in water and is often a proxy for water quality."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:45.110174Z",
"iopub.status.busy": "2020-09-29T01:00:45.109716Z",
"iopub.status.idle": "2020-09-29T01:00:45.112153Z",
"shell.execute_reply": "2020-09-29T01:00:45.111712Z"
}
},
"outputs": [],
"source": [
"from utils.data_cube_utilities.dc_water_quality import tsm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:45.115283Z",
"iopub.status.busy": "2020-09-29T01:00:45.114880Z",
"iopub.status.idle": "2020-09-29T01:00:45.176286Z",
"shell.execute_reply": "2020-09-29T01:00:45.176734Z"
}
},
"outputs": [],
"source": [
"mask_that_only_includes_water_pixels = water_classification.wofs == 1 \n",
"tsm_dataset = tsm(recent_composite, clean_mask = mask_that_only_includes_water_pixels )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:45.180461Z",
"iopub.status.busy": "2020-09-29T01:00:45.180046Z",
"iopub.status.idle": "2020-09-29T01:00:45.747519Z",
"shell.execute_reply": "2020-09-29T01:00:45.747971Z"
}
},
"outputs": [],
"source": [
"tsm_dataset.tsm.plot(cmap = \"jet\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time Series Water Detection Analysis\n",
"Time series output of the Australian Water Detection from Space (WOFS) results. The results show the percent of time that a pixel is classified as water over the entire time series. BLUE = frequent water, RED = infrequent water."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:45.751152Z",
"iopub.status.busy": "2020-09-29T01:00:45.750729Z",
"iopub.status.idle": "2020-09-29T01:00:46.350647Z",
"shell.execute_reply": "2020-09-29T01:00:46.351099Z"
}
},
"outputs": [],
"source": [
"ts_water_classification = wofs_classify(landsat_dataset,clean_mask = np.invert(cloud_mask)) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:46.354960Z",
"iopub.status.busy": "2020-09-29T01:00:46.354168Z",
"iopub.status.idle": "2020-09-29T01:00:46.494990Z",
"shell.execute_reply": "2020-09-29T01:00:46.494508Z"
}
},
"outputs": [],
"source": [
"# Apply nan to no_data values\n",
"ts_water_classification = ts_water_classification.where(ts_water_classification != -9999)\n",
"\n",
"##Time series aggregation that ignores nan values. \n",
"water_classification_percentages = (ts_water_classification.mean(dim = ['time']) * 100).wofs.rename('water_classification_percentages')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:46.498963Z",
"iopub.status.busy": "2020-09-29T01:00:46.498192Z",
"iopub.status.idle": "2020-09-29T01:00:47.046196Z",
"shell.execute_reply": "2020-09-29T01:00:47.045733Z"
}
},
"outputs": [],
"source": [
"## import color-scheme and set nans to black\n",
"from matplotlib.cm import jet_r as jet_r\n",
"jet_r.set_bad('black',1)\n",
"\n",
"## apply nan to percentage values that aren't greater than 0, then plot\n",
"water_classification_percentages\\\n",
" .where(water_classification_percentages > 0)\\\n",
" .plot(cmap = jet_r)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a WOFS plot for a single pixel\n",
"First select the Lat-Lon position. Then the code will find the closest pixel in the dataset using a \"nearest neighbor\" selection."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:47.049328Z",
"iopub.status.busy": "2020-09-29T01:00:47.048814Z",
"iopub.status.idle": "2020-09-29T01:00:47.050472Z",
"shell.execute_reply": "2020-09-29T01:00:47.050883Z"
}
},
"outputs": [],
"source": [
"pixel_lat = 11.84\n",
"pixel_lon = 107.09"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:47.055233Z",
"iopub.status.busy": "2020-09-29T01:00:47.054001Z",
"iopub.status.idle": "2020-09-29T01:00:47.057260Z",
"shell.execute_reply": "2020-09-29T01:00:47.056828Z"
}
},
"outputs": [],
"source": [
"pixel = ts_water_classification.sel( latitude = pixel_lat,\n",
" longitude = pixel_lon,\n",
" method = 'nearest') # nearest neighbor selection "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:47.069573Z",
"iopub.status.busy": "2020-09-29T01:00:47.066655Z",
"iopub.status.idle": "2020-09-29T01:00:47.312923Z",
"shell.execute_reply": "2020-09-29T01:00:47.313430Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt \n",
"plt.figure(figsize = (20,5)) \n",
"plt.scatter(pixel.time.values, pixel.wofs.values)"
]
}
],
"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": "3.6.9"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": []
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
tiagoantao/abjad-ipython | notebooks/Ligeti - Desordre.ipynb | 1 | 48953 | {
"metadata": {
"name": "",
"signature": "sha256:26395eb934ae63f36b097fd8eb91dae7f5397b03ac8b9f07771eb9053155c424"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from abjad import *\n",
"%load_ext abjad.ext.ipython"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from abjad.demos import desordre"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pitches = [1,2,3]\n",
"notes = scoretools.make_notes(pitches, [(1, 8)])\n",
"beam = Beam()\n",
"attach(beam, notes)\n",
"slur = Slur()\n",
"attach(slur, notes)\n",
"dynamic = Dynamic('f')\n",
"attach(dynamic, notes[0])\n",
"dynamic = Dynamic('p')\n",
"attach(dynamic, notes[1])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"voice_lower = Voice(notes)\n",
"voice_lower.name = 'rh_lower'\n",
"command = indicatortools.LilyPondCommand('voiceTwo')\n",
"attach(command, voice_lower)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"n = int(math.ceil(len(pitches) / 2.))\n",
"chord = Chord([pitches[0], pitches[0] + 12], (n, 8))\n",
"articulation = Articulation('>')\n",
"attach(articulation, chord)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"voice_higher = Voice([chord])\n",
"voice_higher.name = 'rh_higher'\n",
"command = indicatortools.LilyPondCommand('voiceOne')\n",
"attach(command, voice_higher)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"container = Container([voice_lower, voice_higher])\n",
"container.is_simultaneous = True"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cell = Staff([container])\n",
"show(cell)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAHsAAABnCAAAAAAFuGJDAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\ncHQgOS4xNJ2zXFgAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def make_desordre_cell(pitches):\n",
" '''The function constructs and returns a *D\u00e9sordre cell*.\n",
" `pitches` is a list of numbers or, more generally, pitch tokens.\n",
" '''\n",
"\n",
" notes = [scoretools.Note(pitch, (1, 8)) for pitch in pitches]\n",
" beam = spannertools.Beam()\n",
" attach(beam, notes)\n",
" slur = spannertools.Slur()\n",
" attach(slur, notes)\n",
" clef = indicatortools.Dynamic('f')\n",
" attach(clef, notes[0])\n",
" dynamic = indicatortools.Dynamic('p')\n",
" attach(dynamic, notes[1])\n",
"\n",
" # make the lower voice\n",
" lower_voice = scoretools.Voice(notes)\n",
" lower_voice.name = 'RH Lower Voice'\n",
" command = indicatortools.LilyPondCommand('voiceTwo')\n",
" attach(command, lower_voice)\n",
" n = int(math.ceil(len(pitches) / 2.))\n",
" chord = scoretools.Chord([pitches[0], pitches[0] + 12], (n, 8))\n",
" articulation = indicatortools.Articulation('>')\n",
" attach(articulation, chord)\n",
"\n",
" # make the upper voice\n",
" upper_voice = scoretools.Voice([chord])\n",
" upper_voice.name = 'RH Upper Voice'\n",
" command = indicatortools.LilyPondCommand('voiceOne')\n",
" attach(command, upper_voice)\n",
"\n",
" # combine them together\n",
" container = scoretools.Container([lower_voice, upper_voice])\n",
" container.is_simultaneous = True\n",
"\n",
" # make all 1/8 beats breakable\n",
" for leaf in lower_voice.select_leaves()[:-1]:\n",
" bar_line = indicatortools.BarLine('')\n",
" attach(bar_line, leaf)\n",
"\n",
" return container"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def make_desordre_measure(pitches):\n",
" '''Makes a measure composed of *D\u00e9sordre cells*.\n",
"\n",
" `pitches` is a list of lists of number (e.g., [[1, 2, 3], [2, 3, 4]])\n",
"\n",
" The function returns a measure.\n",
" '''\n",
"\n",
" for sequence in pitches:\n",
" container = make_desordre_cell(sequence)\n",
" time_signature = inspect_(container).get_duration()\n",
" time_signature = mathtools.NonreducedFraction(time_signature)\n",
" time_signature = time_signature.with_denominator(8)\n",
" measure = scoretools.Measure(time_signature, [container])\n",
"\n",
" return measure"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pitches = [[0, 4, 7], [0, 4, 7, 9], [4, 7, 9, 11]]\n",
"measure = make_desordre_measure(pitches)\n",
"staff = Staff([measure])\n",
"show(staff)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAIUAAABlCAAAAABG9X9iAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\ncmUAR1BMIEdob3N0c2NyaXB0IDkuMTSds1xYAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def make_desordre_staff(pitches):\n",
" r'''Makes D\u00e9sordre staff.\n",
" '''\n",
"\n",
" staff = scoretools.Staff()\n",
" for sequence in pitches:\n",
" measure = make_desordre_measure(sequence)\n",
" staff.append(measure)\n",
" return staff"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pitches = [[[-1, 4, 5], [-1, 4, 5, 7, 9]], [[0, 7, 9], [-1, 4, 5, 7, 9]]]\n",
"staff = make_desordre_staff(pitches)\n",
"show(staff)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAABnCAAAAADHgbOrAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\n"
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def make_desordre_score(pitches):\n",
" '''Returns a complete piano staff with Ligeti music.\n",
" '''\n",
"\n",
" assert len(pitches) == 2\n",
" piano = instrumenttools.Piano([])\n",
" staff_group = StaffGroup(context_name='PianoStaff', name='Piano')\n",
" attach(piano, staff_group)\n",
"\n",
" # build the music\n",
" for hand in pitches:\n",
" staff = make_desordre_staff(hand)\n",
" staff_group.append(staff)\n",
"\n",
" # set clef and key signature to left hand staff\n",
" clef = indicatortools.Clef('bass')\n",
" attach(clef, staff_group[1])\n",
" key_signature = KeySignature('b', 'major')\n",
" attach(key_signature, staff_group[1])\n",
"\n",
" # wrap the piano staff in a score\n",
" score = scoretools.Score([staff_group])\n",
"\n",
" return score"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"top = [\n",
" [[-1, 4, 5], [-1, 4, 5, 7, 9]],\n",
" [[0, 7, 9], [-1, 4, 5, 7, 9]],\n",
" [[2, 4, 5, 7, 9], [0, 5, 7]],\n",
" [[-3, -1, 0, 2, 4, 5, 7]],\n",
" [[-3, 2, 4], [-3, 2, 4, 5, 7]],\n",
" [[2, 5, 7], [-3, 9, 11, 12, 14]],\n",
" [[4, 5, 7, 9, 11], [2, 4, 5]],\n",
" [[-5, 4, 5, 7, 9, 11, 12]],\n",
" [[2, 9, 11], [2, 9, 11, 12, 14]],\n",
" ]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bottom = [\n",
" [[-9, -4, -2], [-9, -4, -2, 1, 3]],\n",
" [[-6, -2, 1], [-9, -4, -2, 1, 3]],\n",
" [[-4, -2, 1, 3, 6], [-4, -2, 1]],\n",
" [[-9, -6, -4, -2, 1, 3, 6, 1]],\n",
" [[-6, -2, 1], [-6, -2, 1, 3, -2]],\n",
" [[-4, 1, 3], [-6, 3, 6, -6, -4]],\n",
" [[-14, -11, -9, -6, -4], [-14, -11, -9]],\n",
" [[-11, -2, 1, -6, -4, -2, 1, 3]],\n",
" [[-6, 1, 3], [-6, -4, -2, 1, 3]],\n",
" ]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"score = make_desordre_score([top, bottom])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from abjad.tools import documentationtools\n",
"lilypond_file = documentationtools.make_ligeti_example_lilypond_file(score)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"show(lilypond_file)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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hRbqPljxHT0RtzSruAtmX5r15Fpw7ZdmhJllpcVGEMecL06WrCWoRzmagsOjt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\nc2NyaXB0IDkuMTSds1xYAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | cc0-1.0 |
metpy/MetPy | v1.1/_downloads/324acb7faa1ec1d6ac5849ea2223364d/Smoothing.ipynb | 1 | 3793 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Smoothing\n\nUsing MetPy's smoothing functions.\n\nThis example demonstrates the various ways that MetPy's smoothing function\ncan be utilized. While this example utilizes basic NumPy arrays, these\nfunctions all work equally well with Pint Quantities or xarray DataArrays.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from itertools import product\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport metpy.calc as mpcalc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start with a base pattern with random noise\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(61461542)\nsize = 128\nx, y = np.mgrid[:size, :size]\ndistance = np.sqrt((x - size / 2) ** 2 + (y - size / 2) ** 2)\nraw_data = np.random.random((size, size)) * 0.3 + distance / distance.max() * 0.7\n\nfig, ax = plt.subplots(1, 1, figsize=(4, 4))\nax.set_title('Raw Data')\nax.imshow(raw_data, vmin=0, vmax=1)\nax.axis('off')\nplt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, create a grid showing different smoothing options\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(3, 3, figsize=(12, 12))\nfor i, j in product(range(3), range(3)):\n ax[i, j].axis('off')\n\n# Gaussian Smoother\nax[0, 0].imshow(mpcalc.smooth_gaussian(raw_data, 3), vmin=0, vmax=1)\nax[0, 0].set_title('Gaussian - Low Degree')\n\nax[0, 1].imshow(mpcalc.smooth_gaussian(raw_data, 8), vmin=0, vmax=1)\nax[0, 1].set_title('Gaussian - High Degree')\n\n# Rectangular Smoother\nax[0, 2].imshow(mpcalc.smooth_rectangular(raw_data, (3, 7), 2), vmin=0, vmax=1)\nax[0, 2].set_title('Rectangular - 3x7 Window\\n2 Passes')\n\n# 5-point smoother\nax[1, 0].imshow(mpcalc.smooth_n_point(raw_data, 5, 1), vmin=0, vmax=1)\nax[1, 0].set_title('5-Point - 1 Pass')\n\nax[1, 1].imshow(mpcalc.smooth_n_point(raw_data, 5, 4), vmin=0, vmax=1)\nax[1, 1].set_title('5-Point - 4 Passes')\n\n# Circular Smoother\nax[1, 2].imshow(mpcalc.smooth_circular(raw_data, 2, 2), vmin=0, vmax=1)\nax[1, 2].set_title('Circular - Radius 2\\n2 Passes')\n\n# 9-point smoother\nax[2, 0].imshow(mpcalc.smooth_n_point(raw_data, 9, 1), vmin=0, vmax=1)\nax[2, 0].set_title('9-Point - 1 Pass')\n\nax[2, 1].imshow(mpcalc.smooth_n_point(raw_data, 9, 4), vmin=0, vmax=1)\nax[2, 1].set_title('9-Point - 4 Passes')\n\n# Arbitrary Window Smoother\nax[2, 2].imshow(mpcalc.smooth_window(raw_data, np.diag(np.ones(5)), 2), vmin=0, vmax=1)\nax[2, 2].set_title('Custom Window (Diagonal) \\n2 Passes')\n\nplt.show()"
]
}
],
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"display_name": "Python 3",
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"version": "3.9.6"
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"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
bsnacks000/IPySigma-Demo | DATA661_final.ipynb | 1 | 9743 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final - DATA 661\n",
" \n",
"## IPySigma Lives: Presenting an Extensible Prototype Network Visualization Frontend for Jupyter Notebooks\n",
"\n",
"### John DeBlase, Daina Bouquin\n",
" \n",
"#### Introduction\n",
"The potential uses of network analytics and visualizations are extensive, with applications ranging from social network analysis to environmental science to better understanding how political revolutions spread. However, many of the tools most commonly used for these types of analysis, particularly Python modules like NetworkX, are not designed to produce aesthetically pleasing, interactive visualizations that support the development of theories and inferences. Much of the time, data scientists using tools like Jupyter are left trying to work with network visualizations that look like hairballs or spending a great deal of time trying to use unfamiliar tools like JavaScript or Gephi to produce useful graphs. These people do not currently have access to simple interfaces that integrate with tools like Jupyter notebooks (formerly IPython) that the rest of their workflows rely upon. \n",
" \n",
"Throughout this semester, we thought through this problem and began prototyping a flexible architecture to help people create SigmaJS networks from NetworkX objects without abandoning their Jupyter Notebooks. The result is an extensible proof-of-concept for a side-by-side Jupyter network visualization GUI that will allow users to quickly create clear visualizations that can help drive research processes. \n",
" \n",
"The below sections aim to justify the use of Jupyter as a platform, the focus of the application, the components selected to create the application, and to outline steps that can be taken moving forward to improve and expand on the current infrastructure. \n",
"#### Why Jupyter?\n",
"Jupyter is becoming increasingly important to the data community for sharing and reproducibility, therefore tools that integrate with this environment are highly valuable. Reproducibility is also foundationally important to computational science endeavors more broadly, both in academia and in industry. \n",
"Why Focus on Network Visualization?\n",
"Visualization is integral to the data scientist’s ability to use network analytics to effectively derive theories and inferences. Research in social network analysis has shown that dynamic and interactive graph visualizations foster “theoretical insight” thus creating a real need for “dynamic network visualizations” [[1](http://www.journals.uchicago.edu.ezp-prod1.hul.harvard.edu/doi/full/10.1086/421509)]. Moreover, many scientific domains are “now convinced that network visualization is essential to improve their work since it allows them to see complex structures that statistics and modeling alone cannot reveal” [[2]( http://www.msr-inria.fr/projects/interactive-network-visualiation/)].\n",
"#### Why SigmaJS?\n",
"SigmaJS is a JavaScript library dedicated to graph drawing. Unlike libraries like D3, Sigma is optimized for our usecase.\n",
"\n",
"#### Evaluation\n",
"Over the 15 weeks of the CUNY SPS spring semester, an iterative, stepped approach was taken with the goal being to build a functional prototype to with the above described functionality. We were able to achive this goal, and also able to achieve our stretch goal by being accepted as speakers at [JupyterCon 2017](https://conferences.oreilly.com/jupyter/jup-ny), the Jupyter Project’s first international conference."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running the IPySigma prototype\n",
"\n",
"### Manual Install:\n",
"\n",
"git clone [this](https://github.com/bsnacks000/IPySigma-Demo) repo and install both the python and node components.\n",
"\n",
"#### Python\n",
"\n",
"The prototype python package is contained in the \"ipysig\" folder.\n",
"\n",
"1. From the root directory: Build and activate a clean python environment>=2.7.10 with requirements.txt using [virtualenv](https://virtualenv.pypa.io/en/stable/).\n",
"\n",
"2. `pip install -r requirements.txt` to get the required packages.\n",
"\n",
"#### Node.js\n",
"\n",
"1. The node-express application is contained in the app folder.\n",
"\n",
"*Make sure your node version is >= v6.9.4 and that both `npm` and `bower` are installed globally.*\n",
"\n",
"2. From the root directory: `cd ./app`\n",
"\n",
"3. type `npm install` to install the node modules locally in the app top-level folder\n",
"\n",
"4. From app: `cd ./browser`\n",
"\n",
"5. type `bower install` to install the bower_components folder (note: these steps might change in future versions with browserify)\n",
"\n",
"#### Run the Demo\n",
"\n",
"At the root directory launch a jupyter notebook server and run the notebook ipysig_test.ipynb and then follow the instructions for each cell."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overview of components\n",
"<img src=\"images/ipysig_diagram1.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overview of IPySigma Application System\n",
"\n",
"#### Jupyter Notebook Frontend/ Jupyter Notebook Server\n",
"\n",
"To boot up the app using a running notebook server, first the IPySig controller class is imported and instantiated within the user’s notebook session. This is the core Python class of the application and is implemented as a Singleton. An IPySig object does a few key things on instantiation:\n",
"If run for the first time, IPySig injects the API found in the ipysig.sigma_addon_methods into the nx.Graph class. This adds custom functionality to any existing or newly created graphs in the current session for exporting node and edge data in JSON format suitable for SigmaJS. \n",
"Next, using a system call it fetches and stores the url and token (if any) of the running notebook server. \n",
"Using the url and token, an express server process is spun up and stored in a class variable. This is run through several sets of protected method calls.\n",
"\n",
"Once the express server is running, a “session” can be created by calling the connect() method passing in the graph object and a key name. The graph object reference gets stored in IPySig under this key name. The key name is also emitted via a socket.io client to a listener on the express server which stores the reference in node.js and sends a callback response that tells IPySig to spin up a webbrowser tab. When the browser tab gets opened an event is emitted to fetch the key name to the graph reference, thus completing the connection between the new browser tab and the running graph object in the IPython kernel.\n",
"\n",
"A session has now been created and the user can use the frontend to display the graph object using the Load Graph button and giving the graph a Title. The title will serve as a means of saving a graph in BrowserDB via a LokiJS adaptor, allowing persistence of multiple graph objects in a single browser session. This functionality has not been implemented yet as of version 0.1.\n",
"\n",
"#### Jupyterlab Services/Express/SigmaJS \n",
"\n",
"Once a browser tab session is bound, pressing Load Graph and submitting a Title will emit to the ‘main-room’ socket.io listener on the express server. The jupyterlab services API connects to the running kernel and calls IPySig.export_graph_instance() with the correct graph_name as a promise. \n",
"\n",
"Once the promise is fulfilled, the JSON graph data is emitted back up to the browser via the correct socket.id. The graph data is then rendered in the browser main_room listener via a call to make_graph which returns the sigma graph.\n",
"\n",
"In future releases, each new graph will be saved to BrowserDB via a Loki.js adaptor. The user will be able to select from a list of past graphs rendered in that session for easy comparison. The option will also be given for the contents of the session to be exported to JSON.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The Future"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are a number of avenues that could be explored using the protocol and platform defined above. Over the next few months, Daina and John will define an applied usecase to be presented at JupyterCon along with a presentation on IPySigma's development and logic. Improvements will also be made to the code to address a number of issues currently [documented on GitHub](https://github.com/bsnacks000/IPySigma-Demo/issues). Additionally, documentation will be automated using [Sphinx](http://www.sphinx-doc.org/en/stable/) to improve the seamlessness of updates and allow us to create more professional web presence. We have also begun creating a [website](https://dbouquin.github.io/IPySigma-Demo/) that will act as a splash page for the tool."
]
}
],
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"nbformat": 4,
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}
| mit |
c22n/ion-channel-ABC | docs/examples/hl1/hl-1_extra-fullABC.ipynb | 1 | 2995697 | null | gpl-3.0 |
tensorflow/swift | docs/site/tutorials/Swift_autodiff_sharp_edges.ipynb | 1 | 17868 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Swift_autodiff_sharp_edges.ipynb",
"provenance": []
},
"kernelspec": {
"name": "swift",
"display_name": "Swift"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "JNwMxwcaa05q"
},
"source": [
"# Sharp edges in Differentiable Swift\n",
"Differentiable Swift has come a long way in terms of usability. Here is a heads-up about the parts that are still a little un-obvious. As progress continues, this guide will become smaller and smaller, and you'll be able to write differentiable code without needing special syntax.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_LTY5_lZbLMU"
},
"source": [
"##Loops\n",
"\n",
"Loops are differentiable, there's just one detail to know about. When you write the loop, wrap the bit where you specify what you're looping over in `withoutDerivative(at:)`\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "mvBQ2eCfTDrj"
},
"source": [
"var a: [Float] = [1,2,3]"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "to30MPP2TJqi"
},
"source": [
"for example:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9NRXNazqTMqV"
},
"source": [
"for _ in a.indices \n",
"{}"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "wPrrZ7qUTUMG"
},
"source": [
"becomes"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zzwe0d8JTVN7"
},
"source": [
"for _ in withoutDerivative(at: a.indices) \n",
"{}"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "UITuC4g9TYEp"
},
"source": [
"or:\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YRbV8eqATcnG"
},
"source": [
"for _ in 0..<a.count \n",
"{}"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "X5O0DPm6Tfxl"
},
"source": [
"becomes"
]
},
{
"cell_type": "code",
"metadata": {
"id": "n5c0Dsc3TllX"
},
"source": [
"for _ in 0..<withoutDerivative(at: a.count) \n",
"{}"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "BJpcKJbTTvTC"
},
"source": [
"This is necessary because the `Array.count` member doesn't contribute to the derivative with respect to the array. Only the actual elements in the array contribute to the derivative.\n",
"\n",
"If you've got a loop where you manually use an integer as the upper bound, there's no need to use `withoutDerivative(at:)`:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EDL4ykoZTwcl"
},
"source": [
"let iterations: Int = 10\n",
"for _ in 0..<iterations {} //this is fine as-is."
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "GBuKyDDVcb8M"
},
"source": [
"##Map and Reduce\n",
"`map` and `reduce` have special differentiable versions that work exactly like what you're used to:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vzZl_P6-W_nD",
"outputId": "0e058fb9-f6a9-4548-83cb-70475b09f1d4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"a = [1,2,3]\n",
"let aPlusOne = a.differentiableMap {$0 + 1}\n",
"let aSum = a.differentiableReduce(0, +)\n",
"print(\"aPlusOne\", aPlusOne)\n",
"print(\"aSum\", aSum)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"aPlusOne [2.0, 3.0, 4.0]\r\n",
"aSum 6.0\r\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qhHxI3xwck9j"
},
"source": [
"##Array subscript sets\n",
"Array subscript sets (`array[0] = 0`) aren't differentiable out of the box, but you can paste this extension:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vj5XDwl0XEGi"
},
"source": [
"extension Array where Element: Differentiable {\n",
" @differentiable(where Element: Differentiable)\n",
" mutating func updated(at index: Int, with newValue: Element) {\n",
" self[index] = newValue\n",
" }\n",
" \n",
" @derivative(of: updated)\n",
" mutating func vjpUpdated(at index: Int, with newValue: Element)\n",
" -> (value: Void, pullback: (inout TangentVector) -> (Element.TangentVector))\n",
" {\n",
" self.updated(at: index, with: newValue)\n",
" return ((), { v in\n",
" let dElement = v[index]\n",
" v.base[index] = .zero\n",
" return dElement\n",
" })\n",
" }\n",
"}"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "mCkdO-F8XLFo"
},
"source": [
"and then the workaround syntax is like this:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GxZnhZGdXMm0"
},
"source": [
"var b: [Float] = [1,2,3]"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qC5d-l3nXPxl"
},
"source": [
"instead of this:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5YNUPyS3XUQ-"
},
"source": [
"b[0] = 17"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "G1sCDA9zXWSA"
},
"source": [
"write this:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ze-zTQP-XbN8"
},
"source": [
"b.updated(at: 0, with: 17)"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bjDuPALzfKQC"
},
"source": [
"Let's make sure it works:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uKTN_ET6fNUc",
"outputId": "42c64291-0f91-45df-9b8e-1355a4a04a92",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"func plusOne(array: [Float]) -> Float{\n",
" var array = array\n",
" array.updated(at: 0, with: array[0] + 1)\n",
" return array[0]\n",
"}\n",
"\n",
"let plusOneValAndGrad = valueWithGradient(at: [2], in: plusOne)\n",
"print(plusOneValAndGrad)"
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"(value: 3.0, gradient: [1.0])\r\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6-xCTZXNXf5c"
},
"source": [
"The error you'll get without this workaround is `Differentiation of coroutine calls is not yet supported`.\n",
"Here is the link to see progress on making this workaround unnecessary: https://bugs.swift.org/browse/TF-1277 (it talks about Array.subscript._modify, which is what's called behind the scenes when you do an array subscript set)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nU5-Mheme8Aj"
},
"source": [
"##`Float` <-> `Double` conversions\n",
"If you're switching between `Float` and `Double`, their constructors aren't already differentiable. Here's a function that will let you go from a `Float` to a `Double` differentiably.\n",
"\n",
"(Switch `Float` and `Double` in the below code, and you've got a function that converts from `Double` to `Float`.)\n",
"\n",
"You can make similar converters for any other real Numeric types."
]
},
{
"cell_type": "code",
"metadata": {
"id": "vc0eakpEYE8B"
},
"source": [
"@differentiable\n",
"func convertToDouble(_ a: Float) -> Double {\n",
" return Double(a)\n",
"}\n",
" \n",
"@derivative(of: convertToDouble)\n",
"func convertToDoubleVJP(_ a: Float) -> (value: Double, pullback: (Double) -> Float) {\n",
" func pullback(_ v: Double) -> Float{\n",
" return Float(v)\n",
" }\n",
" return (value: Double(a), pullback: pullback)\n",
"}"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "W9Tup97yfu-x"
},
"source": [
"Here's an example usage:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pNeh1vHWfyOI",
"outputId": "a1ececc1-aa5a-4695-81c6-2183737bd40c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"@differentiable\n",
"func timesTwo(a: Float) -> Double {\n",
" return convertToDouble(a * 2)\n",
"}\n",
"let input: Float = 3\n",
"let valAndGrad = valueWithGradient(at: input, in: timesTwo)\n",
"print(\"grad\", valAndGrad.gradient)\n",
"print(\"type of input:\", type(of: input))\n",
"print(\"type of output:\", type(of: valAndGrad.value))\n",
"print(\"type of gradient:\", type(of: valAndGrad.gradient))"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"grad 2.0\r\n",
"type of input: Float\r\n",
"type of output: Double\r\n",
"type of gradient: Float\r\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hR-ED1-Jf1aH"
},
"source": [
"##Transcendental and other functions (sin, cos, abs, max)\n",
"A lot of transcendentals and other common built-in functions have already been made differentiable for `Float` and `Double`. There are fewer for `Double` than `Float`. Some aren't available for either. So here are a few manual derivative definitions to give you the idea of how to make what you need, in case it isn't already provided:\n",
"\n",
"pow (see [link](https://www.wolframalpha.com/input/?i=partial+derivatives+of+f%28x%2Cy%29+%3D+x%5Ey) for derivative explanation)\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JPXr_xtwYOV9"
},
"source": [
"import Foundation\n",
"\n",
"@usableFromInline\n",
"@derivative(of: pow) \n",
"func powVJP(_ base: Double, _ exponent: Double) -> (value: Double, pullback: (Double) -> (Double, Double)) {\n",
" let output: Double = pow(base, exponent)\n",
" func pullback(_ vector: Double) -> (Double, Double) {\n",
" let baseDerivative = vector * (exponent * pow(base, exponent - 1))\n",
" let exponentDerivative = vector * output * log(base)\n",
" return (baseDerivative, exponentDerivative)\n",
" }\n",
" \n",
" return (value: output, pullback: pullback)\n",
"}"
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "CxwAtO-vYPp0"
},
"source": [
"max"
]
},
{
"cell_type": "code",
"metadata": {
"id": "P_cqKUe1YWQz"
},
"source": [
"@usableFromInline\n",
"@derivative(of: max)\n",
"func maxVJP<T: Comparable & Differentiable>(_ x: T, _ y: T) -> (value: T, pullback: (T.TangentVector)\n",
" -> (T.TangentVector, T.TangentVector))\n",
"{\n",
" func pullback(_ v: T.TangentVector) -> (T.TangentVector, T.TangentVector) {\n",
" if x < y {\n",
" return (.zero, v)\n",
" } else {\n",
" return (v, .zero)\n",
" }\n",
" }\n",
" return (value: max(x, y), pullback: pullback)\n",
"}"
],
"execution_count": 16,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fwwzgOnNYXUR"
},
"source": [
"abs"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iNCladNcYaio"
},
"source": [
"@usableFromInline\n",
"@derivative(of: abs)\n",
"func absVJP<T: Comparable & SignedNumeric & Differentiable>(_ x: T)\n",
" -> (value: T, pullback: (T.TangentVector) -> T.TangentVector)\n",
"{\n",
" func pullback(_ v: T.TangentVector) -> T.TangentVector{\n",
" if x < 0 {\n",
" return .zero - v\n",
" }\n",
" else {\n",
" return v\n",
" }\n",
" }\n",
" return (value: abs(x), pullback: pullback)\n",
"}"
],
"execution_count": 17,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "OnLo4C7fYeK6"
},
"source": [
"sqrt (see [link](https://www.wolframalpha.com/input/?i=partial+derivative+of+f%28x%29+%3D+sqrt%28x%29) for derivative explanation)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bIO0M5ONYiRD"
},
"source": [
"@usableFromInline\n",
"@derivative(of: sqrt) \n",
"func sqrtVJP(_ x: Double) -> (value: Double, pullback: (Double) -> Double) {\n",
" let output = sqrt(x)\n",
" func pullback(_ v: Double) -> Double {\n",
" return v / (2 * output)\n",
" }\n",
" return (value: output, pullback: pullback)\n",
"}"
],
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "H45t3grVj2bx"
},
"source": [
"Let's check that these work:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "liU1ZR8_j5VN",
"outputId": "5ad8190f-a139-4540-9d40-95d0c2fb6b8d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"let powGrad = gradient(at: 2, 2, in: pow)\n",
"print(\"pow gradient: \", powGrad, \"which is\", powGrad == (4.0, 2.772588722239781) ? \"correct\" : \"incorrect\")\n",
"\n",
"let maxGrad = gradient(at: 1, 2, in: max)\n",
"print(\"max gradient: \", maxGrad, \"which is\", maxGrad == (0.0, 1.0) ? \"correct\" : \"incorrect\")\n",
"\n",
"let absGrad = gradient(at: 2, in: abs)\n",
"print(\"abs gradient: \", absGrad, \"which is\", absGrad == 1.0 ? \"correct\" : \"incorrect\")\n",
"\n",
"let sqrtGrad = gradient(at: 4, in: sqrt)\n",
"print(\"sqrt gradient: \", sqrtGrad, \"which is\", sqrtGrad == 0.25 ? \"correct\" : \"incorrect\")"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"pow gradient: (4.0, 2.772588722239781) which is correct\r\n",
"max gradient: (0.0, 1.0) which is correct\r\n",
"abs gradient: 1.0 which is correct\r\n",
"sqrt gradient: 0.25 which is correct\r\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bjXglKrIYpUy"
},
"source": [
"The compiler error that alerts you to the need for something like this is: `Expression is not differentiable. Cannot differentiate functions that have not been marked '@differentiable' and that are defined in other files`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZaBX70wGjlZ9"
},
"source": [
"##`KeyPath` subscripting\n",
"`KeyPath` subscripting (get or set) doesn't work out of the box, but once again, there are some extensions you can add, and then use a workaround syntax. Here it is:\n",
"\n",
"https://github.com/tensorflow/swift/issues/530#issuecomment-687400701\n",
"\n",
"This workaround is a little uglier than the others. It only works for custom objects, which must conform to Differentiable and AdditiveArithmetic. You have to add a `.tmp` member and a `.read()` function, and you use the `.tmp` member as intermediate storage when doing `KeyPath` subscript gets (there is an example in the linked code). `KeyPath` subscript sets work pretty simply with a `.write()` function.\n"
]
}
]
} | apache-2.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb | 1 | 45449 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2022 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "title:generic,gcp"
},
"source": [
"# E2E ML on GCP: MLOps stage 2 : experimentation: get started with Vertex Training for Scikit-Learn\n",
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
"Open in Vertex AI Workbench\n",
" </a>\n",
" </td>\n",
"</table>\n",
" </a>\n",
" </td>\n",
"</table>\n",
"<br/><br/><br/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "overview:mlops"
},
"source": [
"## Overview\n",
"\n",
"\n",
"This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation: get started with Vertex AI Training for scikit-Learn."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dataset:custom,newsaggr,tcn"
},
"source": [
"### Dataset\n",
"\n",
"The dataset used for this tutorial is the [News Aggregation](https://archive.ics.uci.edu/ml/datasets/News+Aggregator) from [ICS Machine Learning Datasets](https://archive.ics.uci.edu/ml/datasets.php). The trained model predicts the news category of the news article."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "objective:mlops,stage2,get_started_vertex_training_sklearn"
},
"source": [
"### Objective\n",
"\n",
"In this tutorial, you learn how to use `Vertex AI Training` for training a Scikit-Learn custom model.\n",
"\n",
"This tutorial uses the following Google Cloud ML services:\n",
"\n",
"- `Vertex AI Training`\n",
"- `Vertex AI Model` resource\n",
"\n",
"The steps performed include:\n",
"\n",
"- Training using a Python package.\n",
"- Report accuracy when hyperparameter tuning.\n",
"- Save the model artifacts to Cloud Storage using GCSFuse.\n",
"- Create a `Vertex AI Model` resource."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b132d4ef86d6"
},
"source": [
"### Costs \n",
"\n",
"This tutorial uses billable components of Google Cloud:\n",
"\n",
"* Vertex AI\n",
"* Cloud Storage\n",
"\n",
"Learn about [Vertex AI\n",
"pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
"pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
"Calculator](https://cloud.google.com/products/calculator/)\n",
"to generate a cost estimate based on your projected usage."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "94a148f11da5"
},
"source": [
"### Set up your local development environment\n",
"\n",
"**If you are using Colab or Google Cloud Notebooks**, your environment already meets\n",
"all the requirements to run this notebook. You can skip this step.\n",
"\n",
"**Otherwise**, make sure your environment meets this notebook's requirements.\n",
"You need the following:\n",
"\n",
"* The Google Cloud SDK\n",
"* Git\n",
"* Python 3\n",
"* virtualenv\n",
"* Jupyter notebook running in a virtual environment with Python 3\n",
"\n",
"The Google Cloud guide to [Setting up a Python development\n",
"environment](https://cloud.google.com/python/setup) and the [Jupyter\n",
"installation guide](https://jupyter.org/install) provide detailed instructions\n",
"for meeting these requirements. The following steps provide a condensed set of\n",
"instructions:\n",
"\n",
"1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n",
"\n",
"1. [Install Python 3.](https://cloud.google.com/python/setup#installing_python)\n",
"\n",
"1. [Install\n",
" virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n",
" and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
"\n",
"1. To install Jupyter, run `pip3 install jupyter` on the\n",
"command-line in a terminal shell.\n",
"\n",
"1. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
"\n",
"1. Open this notebook in the Jupyter Notebook Dashboard.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_mlops"
},
"source": [
"### Install additional packages\n",
"\n",
"Install the following packages for executing this notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "78168417490e"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# The Vertex AI Workbench Notebook product has specific requirements\n",
"IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
"IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
" \"/opt/deeplearning/metadata/env_version\"\n",
")\n",
"\n",
"# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
"USER_FLAG = \"\"\n",
"if IS_WORKBENCH_NOTEBOOK:\n",
" USER_FLAG = \"--user\"\n",
"\n",
"! pip3 install {USER_FLAG} --upgrade google-cloud-aiplatform -q"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "restart"
},
"source": [
"### Restart the kernel\n",
"\n",
"Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "restart"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if not os.getenv(\"IS_TESTING\"):\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
"\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2721ef0202d9"
},
"source": [
"## Before you begin\n",
"\n",
"### Set up your Google Cloud project\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
"\n",
"1. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). \n",
"\n",
"1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
"\n",
"1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
"Cloud SDK uses the right project for all the commands in this notebook.\n",
"\n",
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "project_id"
},
"source": [
"#### Set your project ID\n",
"\n",
"**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\n",
" shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT_ID = shell_output[0]\n",
" print(\"Project ID:\", PROJECT_ID)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "region"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
"\n",
"Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "region"
},
"outputs": [],
"source": [
"REGION = \"[your-region]\" # @param {type: \"string\"}\n",
"\n",
"if REGION == \"[your-region]\":\n",
" REGION = \"us-central1\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"source": [
"#### Timestamp\n",
"\n",
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "timestamp"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "77c385f0db59"
},
"source": [
"### Authenticate your Google Cloud account\n",
"\n",
"**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
"\n",
"**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
"\n",
"**Otherwise**, follow these steps:\n",
"\n",
"In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
"\n",
"1. **Click Create service account**.\n",
"\n",
"2. In the **Service account name** field, enter a name, and click **Create**.\n",
"\n",
"3. In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex AI\" into the filter box, and select **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
"\n",
"4. Click Create. A JSON file that contains your key downloads to your local environment.\n",
"\n",
"5. Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "535223fa4b84"
},
"outputs": [],
"source": [
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your GCP account. This provides access to your\n",
"# Cloud Storage bucket and lets you submit training jobs and prediction\n",
"# requests.\n",
"\n",
"import os\n",
"import sys\n",
"\n",
"# If on Vertex AI Workbench, then don't execute this code\n",
"IS_COLAB = False\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
" \"DL_ANACONDA_HOME\"\n",
"):\n",
" if \"google.colab\" in sys.modules:\n",
" IS_COLAB = True\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()\n",
"\n",
" # If you are running this notebook locally, replace the string below with the\n",
" # path to your service account key and run this cell to authenticate your GCP\n",
" # account.\n",
" elif not os.getenv(\"IS_TESTING\"):\n",
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bucket:mbsdk"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
"\n",
"Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bucket"
},
"outputs": [],
"source": [
"BUCKET_URI = \"gs://[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_bucket"
},
"outputs": [],
"source": [
"if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
" BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_bucket"
},
"source": [
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_bucket"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION $BUCKET_URI"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "validate_bucket"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "validate_bucket"
},
"outputs": [],
"source": [
"! gsutil ls -al $BUCKET_URI"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_vars"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_aip:mbsdk"
},
"outputs": [],
"source": [
"import google.cloud.aiplatform as aip"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "init_aip:mbsdk"
},
"source": [
"### Initialize Vertex AI SDK for Python\n",
"\n",
"Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "init_aip:mbsdk"
},
"outputs": [],
"source": [
"aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "accelerators:training,cpu,prediction,cpu,mbsdk"
},
"source": [
"#### Set hardware accelerators\n",
"\n",
"You can set hardware accelerators for training and prediction.\n",
"\n",
"Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
"\n",
" (aip.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
"\n",
"\n",
"Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
"\n",
"Learn more about [hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n",
"\n",
"*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3. This is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "accelerators:training,cpu,prediction,cpu,mbsdk"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if os.getenv(\"IS_TESTING_TRAIN_GPU\"):\n",
" TRAIN_GPU, TRAIN_NGPU = (\n",
" aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
" int(os.getenv(\"IS_TESTING_TRAIN_GPU\")),\n",
" )\n",
"else:\n",
" TRAIN_GPU, TRAIN_NGPU = (None, None)\n",
"\n",
"if os.getenv(\"IS_TESTING_DEPLOY_GPU\"):\n",
" DEPLOY_GPU, DEPLOY_NGPU = (\n",
" aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
" int(os.getenv(\"IS_TESTING_DEPLOY_GPU\")),\n",
" )\n",
"else:\n",
" DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "container:training,prediction,scilearn"
},
"source": [
"#### Set pre-built containers\n",
"\n",
"Set the pre-built Docker container image for training and prediction.\n",
"\n",
"\n",
"For the latest list, see [Pre-built containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
"\n",
"\n",
"For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "container:training,prediction,scilearn"
},
"outputs": [],
"source": [
"TRAIN_VERSION = \"scikit-learn-cpu.0-23\"\n",
"DEPLOY_VERSION = \"sklearn-cpu.0-23\"\n",
"\n",
"TRAIN_IMAGE = \"{}-docker.pkg.dev/vertex-ai/training/{}:latest\".format(\n",
" REGION.split(\"-\")[0], TRAIN_VERSION\n",
")\n",
"DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
" REGION.split(\"-\")[0], DEPLOY_VERSION\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "machine:training"
},
"source": [
"#### Set machine type\n",
"\n",
"Next, set the machine type to use for training.\n",
"\n",
"- Set the variable `TRAIN_COMPUTE` to configure the compute resources for the VMs you will use for for training.\n",
" - `machine type`\n",
" - `n1-standard`: 3.75GB of memory per vCPU.\n",
" - `n1-highmem`: 6.5GB of memory per vCPU\n",
" - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
" - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
"\n",
"*Note: The following is not supported for training:*\n",
"\n",
" - `standard`: 2 vCPUs\n",
" - `highcpu`: 2, 4 and 8 vCPUs\n",
"\n",
"*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "machine:training"
},
"outputs": [],
"source": [
"if os.getenv(\"IS_TESTING_TRAIN_MACHINE\"):\n",
" MACHINE_TYPE = os.getenv(\"IS_TESTING_TRAIN_MACHINE\")\n",
"else:\n",
" MACHINE_TYPE = \"n1-standard\"\n",
"\n",
"VCPU = \"4\"\n",
"TRAIN_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
"print(\"Train machine type\", TRAIN_COMPUTE)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sklearn_intro"
},
"source": [
"## Introduction to scikit-learn training\n",
"\n",
"Once you have trained a scikit-learn model, you will want to save it at a Cloud Storage location, so it can subsequently be uploaded to a `Vertex AI Model` resource. The Scikit-learn package does not have support to save the model to a Cloud Storage location. Instead, you will do the following steps to save to a Cloud Storage location.\n",
"\n",
"1. Save the in-memory model to the local filesystem in pickle format (e.g., model.pkl).\n",
"2. Create a Cloud Storage storage client.\n",
"3. Upload the pickle file as a blob to the specified Cloud Storage location using the Cloud Storage storage client.\n",
"\n",
"*Note*: You can do hyperparameter tuning with a Scikit-learn model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "examine_training_package:sklearn"
},
"source": [
"### Examine the training package\n",
"\n",
"#### Package layout\n",
"\n",
"Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
"\n",
"- PKG-INFO\n",
"- README.md\n",
"- setup.cfg\n",
"- setup.py\n",
"- trainer\n",
" - \\_\\_init\\_\\_.py\n",
" - task.py\n",
"\n",
"The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
"\n",
"The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
"\n",
"#### Package Assembly\n",
"\n",
"In the following cells, you will assemble the training package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "examine_training_package:sklearn"
},
"outputs": [],
"source": [
"# Make folder for Python training script\n",
"! rm -rf custom\n",
"! mkdir custom\n",
"\n",
"# Add package information\n",
"! touch custom/README.md\n",
"\n",
"setup_cfg = \"[egg_info]\\n\\ntag_build =\\n\\ntag_date = 0\"\n",
"! echo \"$setup_cfg\" > custom/setup.cfg\n",
"\n",
"setup_py = \"import setuptools\\n\\nsetuptools.setup(\\n\\n install_requires=[\\n\\n 'wget',\\n\\n 'cloudml-hypertune',\\n\\n ],\\n\\n packages=setuptools.find_packages())\"\n",
"! echo \"$setup_py\" > custom/setup.py\n",
"\n",
"pkg_info = \"Metadata-Version: 1.0\\n\\nName: News Aggregation text classification\\n\\nVersion: 0.0.0\\n\\nSummary: Demostration training script\\n\\nHome-page: www.google.com\\n\\nAuthor: Google\\n\\nAuthor-email: [email protected]\\n\\nLicense: Public\\n\\nDescription: Demo\\n\\nPlatform: Vertex\"\n",
"! echo \"$pkg_info\" > custom/PKG-INFO\n",
"\n",
"# Make the training subfolder\n",
"! mkdir custom/trainer\n",
"! touch custom/trainer/__init__.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "taskpy_contents:newsaggr,sklearn"
},
"source": [
"### Create the task script for the Python training package\n",
"\n",
"Next, you create the `task.py` script for driving the training package. Some noteable steps include:\n",
"\n",
"- Command-line arguments:\n",
" - `model-dir`: The location to save the trained model. When using Vertex AI custom training, the location will be specified in the environment variable: `AIP_MODEL_DIR`,\n",
" - `dataset_url`: The location of the dataset to download.\n",
" - `alpha`: Hyperparameter\n",
"- Data preprocessing (`get_data()`):\n",
" - Download the dataset and split into training and test.\n",
"- Model architecture (`get_model()`):\n",
" - Builds the corresponding model architecture.\n",
"- Training (`train_model()`):\n",
" - Trains the model\n",
"- Evaluation (`evaluate_model()`):\n",
" - Evaluates the model.\n",
" - If hyperparameter tuning, reports the metric for accuracy.\n",
"- Model artifact saving\n",
" - Saves the model artifacts and evaluation metrics where the Cloud Storage location specified by `model-dir`.\n",
" - *Note*: GCSFuse (`/gcs`) is used to do filesystem operations on Cloud Storage buckets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "taskpy_contents:newsaggr,sklearn"
},
"outputs": [],
"source": [
"%%writefile custom/trainer/task.py\n",
"import argparse\n",
"import logging\n",
"import os\n",
"import pickle\n",
"import zipfile\n",
"from typing import List, Tuple\n",
"\n",
"import pandas as pd\n",
"import wget\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.pipeline import Pipeline\n",
"import hypertune\n",
"\n",
"parser = argparse.ArgumentParser()\n",
"parser.add_argument('--model-dir', dest='model_dir',\n",
" default=os.getenv('AIP_MODEL_DIR'), type=str, help='Model dir.')\n",
"parser.add_argument(\"--dataset-url\", dest=\"dataset_url\",\n",
" type=str, help=\"Download url for the training data.\")\n",
"parser.add_argument('--alpha', dest='alpha',\n",
" default=1.0, type=float,\n",
" help='Alpha parameters for MultinomialNB')\n",
"args = parser.parse_args()\n",
"\n",
"logging.getLogger().setLevel(logging.INFO)\n",
"\n",
"def get_data(url: str, test_size: float = 0.2) -> Tuple[List, List, List, List]:\n",
" logging.info(\"Downloading training data from: {}\".format(args.dataset_url))\n",
"\n",
" zip_filepath = wget.download(url, out=\".\")\n",
"\n",
" with zipfile.ZipFile(zip_filepath, \"r\") as zf:\n",
" zf.extract(path=\".\", member=\"newsCorpora.csv\")\n",
"\n",
" COLUMN_NAMES = [\"id\", \"title\", \"url\", \"publisher\",\n",
" \"category\", \"story\", \"hostname\", \"timestamp\"]\n",
"\n",
" dataframe = pd.read_csv(\n",
" \"newsCorpora.csv\", delimiter=\"\t\", names=COLUMN_NAMES, index_col=0\n",
" )\n",
"\n",
" train, test = train_test_split(dataframe, test_size=test_size)\n",
"\n",
" x_train, y_train = train[\"title\"].values, train[\"category\"].values\n",
" x_test, y_test = test[\"title\"].values, test[\"category\"].values\n",
"\n",
" return x_train, y_train, x_test, y_test\n",
"\n",
"def get_model():\n",
" logging.info(\"Build model ...\")\n",
" model = Pipeline([\n",
" (\"vectorizer\", CountVectorizer()),\n",
" (\"tfidf\", TfidfTransformer()),\n",
" (\"naivebayes\", MultinomialNB(alpha=args.alpha)),\n",
" ])\n",
" return model\n",
"\n",
"def train_model(model: Pipeline, X_train: List, y_train: List, X_test: List, y_test: List\n",
") -> Pipeline:\n",
" logging.info(\"Training started ...\")\n",
" model.fit(X_train, y_train)\n",
" logging.info(\"Training completed\")\n",
" return model\n",
"\n",
"def evaluate_model(model: Pipeline, X_train: List, y_train: List, X_test: List, y_test: List\n",
") -> float:\n",
" score = model.score(X_test, y_test)\n",
" logging.info(f\"Evaluation completed with model score: {score}\")\n",
"\n",
" # report metric for hyperparameter tuning\n",
" hpt = hypertune.HyperTune()\n",
" hpt.report_hyperparameter_tuning_metric(\n",
" hyperparameter_metric_tag='accuracy',\n",
" metric_value=score\n",
" )\n",
" return score\n",
"\n",
"\n",
"def export_model_to_gcs(fitted_pipeline: Pipeline, gcs_uri: str) -> str:\n",
" \"\"\"Exports trained pipeline to GCS\n",
" Parameters:\n",
" fitted_pipeline (sklearn.pipelines.Pipeline): the Pipeline object\n",
" with data already fitted (trained pipeline object).\n",
" gcs_uri (str): GCS path to store the trained pipeline\n",
" i.e gs://example_bucket/training-job.\n",
" Returns:\n",
" export_path (str): Model GCS location\n",
" \"\"\"\n",
" # Upload model artifact to Cloud Storage\n",
" artifact_filename = 'model.pkl'\n",
" storage_path = os.path.join(gcs_uri, artifact_filename)\n",
"\n",
" # Save model artifact to local filesystem (doesn't persist)\n",
" with open(storage_path, 'wb') as model_file:\n",
" pickle.dump(fitted_pipeline, model_file)\n",
"\n",
"\n",
"def export_evaluation_report_to_gcs(report: str, gcs_uri: str) -> None:\n",
" \"\"\"\n",
" Exports training job report to GCS\n",
" Parameters:\n",
" report (str): Full report in text to sent to GCS\n",
" gcs_uri (str): GCS path to store the report\n",
" i.e gs://example_bucket/training-job\n",
" \"\"\"\n",
"\n",
" # Upload model artifact to Cloud Storage\n",
" artifact_filename = 'report.txt'\n",
" storage_path = os.path.join(gcs_uri, artifact_filename)\n",
"\n",
" # Save model artifact to local filesystem (doesn't persist)\n",
" with open(storage_path, 'w') as report_file:\n",
" report_file.write(report)\n",
"\n",
"\n",
"logging.info(\"Starting custom training job.\")\n",
"\n",
"data = get_data(args.dataset_url)\n",
"model = get_model()\n",
"model = train_model(model, *data)\n",
"score = evaluate_model(model, *data)\n",
"\n",
"# export model to gcs using GCSFuse\n",
"logging.info(\"Exporting model artifacts ...\")\n",
"gs_prefix = 'gs://'\n",
"gcsfuse_prefix = '/gcs/'\n",
"if args.model_dir.startswith(gs_prefix):\n",
" args.model_dir = args.model_dir.replace(gs_prefix, gcsfuse_prefix)\n",
" dirpath = os.path.split(args.model_dir)[0]\n",
" if not os.path.isdir(dirpath):\n",
" os.makedirs(dirpath)\n",
"\n",
"export_model_to_gcs(model, args.model_dir)\n",
"export_evaluation_report_to_gcs(str(score), args.model_dir)\n",
"logging.info(f\"Exported model artifacts to GCS bucket: {args.model_dir}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tarball_training_script"
},
"source": [
"#### Store training script on your Cloud Storage bucket\n",
"\n",
"Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tarball_training_script"
},
"outputs": [],
"source": [
"! rm -f custom.tar custom.tar.gz\n",
"! tar cvf custom.tar custom\n",
"! gzip custom.tar\n",
"! gsutil cp custom.tar.gz $BUCKET_URI/trainer_newsaggr.tar.gz"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_custom_pp_training_job:mbsdk"
},
"source": [
"### Create and run custom training job\n",
"\n",
"\n",
"To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
"\n",
"#### Create custom training job\n",
"\n",
"A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the custom training job.\n",
"- `container_uri`: The training container image.\n",
"\n",
"- `python_package_gcs_uri`: The location of the Python training package as a tarball.\n",
"- `python_module_name`: The relative path to the training script in the Python package.\n",
"- `model_serving_container_uri`: The container image for deploying the model.\n",
"\n",
"*Note:* There is no requirements parameter. You specify any requirements in the `setup.py` script in your Python package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_custom_pp_training_job:mbsdk"
},
"outputs": [],
"source": [
"DISPLAY_NAME = \"newsaggr_\" + TIMESTAMP\n",
"\n",
"job = aip.CustomPythonPackageTrainingJob(\n",
" display_name=DISPLAY_NAME,\n",
" python_package_gcs_uri=f\"{BUCKET_URI}/trainer_newsaggr.tar.gz\",\n",
" python_module_name=\"trainer.task\",\n",
" container_uri=TRAIN_IMAGE,\n",
" model_serving_container_image_uri=DEPLOY_IMAGE,\n",
" project=PROJECT_ID,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "prepare_custom_cmdargs:newsaggr,sklearn"
},
"source": [
"### Prepare your command-line arguments\n",
"\n",
"Now define the command-line arguments for your custom training container:\n",
"\n",
"- `args`: The command-line arguments to pass to the executable that is set as the entry point into the container.\n",
" - `--model-dir` : For our demonstrations, we use this command-line argument to specify where to store the model artifacts.\n",
" - direct: You pass the Cloud Storage location as a command line argument to your training script (set variable `DIRECT = True`), or\n",
" - indirect: The service passes the Cloud Storage location as the environment variable `AIP_MODEL_DIR` to your training script (set variable `DIRECT = False`). In this case, you tell the service the model artifact location in the job specification.\n",
" - `--dataset-url`: The location of the dataset to download.\n",
" - `--alpha`: Tunable hyperparameter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "prepare_custom_cmdargs:newsaggr,sklearn"
},
"outputs": [],
"source": [
"MODEL_DIR = \"{}/{}\".format(BUCKET_URI, TIMESTAMP)\n",
"DATASET_URL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00359/NewsAggregatorDataset.zip\"\n",
"\n",
"DIRECT = False\n",
"\n",
"if DIRECT:\n",
" CMDARGS = [\n",
" \"--alpha=\" + str(0.9),\n",
" \"--dataset-url=\" + DATASET_URL,\n",
" \"--model_dir=\" + MODEL_DIR,\n",
" ]\n",
"else:\n",
" CMDARGS = [\"--alpha=\" + str(0.9), \"--dataset-url=\" + DATASET_URL]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_custom_job:mbsdk"
},
"source": [
"#### Run the custom training job\n",
"\n",
"Next, you run the custom job to start the training job by invoking the method `run`, with the following parameters:\n",
"\n",
"- `model_display_name`: The human readable name for the `Model` resource.\n",
"- `args`: The command-line arguments to pass to the training script.\n",
"- `replica_count`: The number of compute instances for training (replica_count = 1 is single node training).\n",
"- `machine_type`: The machine type for the compute instances.\n",
"- `accelerator_type`: The hardware accelerator type.\n",
"- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
"- `base_output_dir`: The Cloud Storage location to write the model artifacts to.\n",
"- `sync`: Whether to block until completion of the job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_custom_job:mbsdk"
},
"outputs": [],
"source": [
"if TRAIN_GPU:\n",
" model = job.run(\n",
" model_display_name=\"newsaggr_\" + TIMESTAMP,\n",
" args=CMDARGS,\n",
" replica_count=1,\n",
" machine_type=TRAIN_COMPUTE,\n",
" accelerator_type=TRAIN_GPU.name,\n",
" accelerator_count=TRAIN_NGPU,\n",
" base_output_dir=MODEL_DIR,\n",
" sync=False,\n",
" )\n",
"else:\n",
" model = job.run(\n",
" model_display_name=\"newsaggr_\" + TIMESTAMP,\n",
" args=CMDARGS,\n",
" replica_count=1,\n",
" machine_type=TRAIN_COMPUTE,\n",
" base_output_dir=MODEL_DIR,\n",
" sync=False,\n",
" )\n",
"\n",
"model_path_to_deploy = MODEL_DIR"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "list_job"
},
"source": [
"### List a custom training job"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "list_job"
},
"outputs": [],
"source": [
"_job = job.list(filter=f\"display_name={DISPLAY_NAME}\")\n",
"print(_job)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "custom_job_wait:mbsdk"
},
"source": [
"### Wait for completion of custom training job\n",
"\n",
"Next, wait for the custom training job to complete. Alternatively, one can set the parameter `sync` to `True` in the `run()` method to block until the custom training job is completed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "custom_job_wait:mbsdk"
},
"outputs": [],
"source": [
"model.wait()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "delete_job"
},
"source": [
"### Delete a custom training job\n",
"\n",
"After a training job is completed, you can delete the training job with the method `delete()`. Prior to completion, a training job can be canceled with the method `cancel()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "delete_job"
},
"outputs": [],
"source": [
"job.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cleanup:mbsdk"
},
"source": [
"# Cleaning up\n",
"\n",
"To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
"project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:\n",
"\n",
"\n",
"- Model\n",
"- Cloud Storage Bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b413063dfdcf"
},
"outputs": [],
"source": [
"# Delete the model using the Vertex model object\n",
"model.delete()\n",
"\n",
"delete_bucket = False\n",
"if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
" ! gsutil rm -r $BUCKET_URI"
]
}
],
"metadata": {
"colab": {
"name": "get_started_vertex_training_sklearn.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
basnijholt/holoviews | examples/user_guide/04-Style_Mapping.ipynb | 1 | 24839 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Style Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"from holoviews import dim, opts\n",
"\n",
"hv.extension('bokeh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the major benefits of HoloViews is the fact that Elements are simple, declarative wrappers around your data, with clearly defined semantics describing how the dimensions of the data map to the screen. Usually the key dimensions (kdims) and value dimensions map to coordinates of the plot axes and/or the colormapped intensity. However there are a huge number of ways to augment the visual representation of an element by mapping dimensions to visual attributes. In this section we will explore how we can declare such mappings including complex transforms specified by so called ``dim`` objects.\n",
"\n",
"To illustrate this point let us create a set of three points with x/y-coordinates and alpha, color, marker and size values and then map each of those value dimensions to a visual attribute by name:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = {\n",
" 'x': [0, 1, 0.5],\n",
" 'y': [1, 0, 0.5],\n",
" 'alpha': [0.5, 1, 0.3],\n",
" 'color': ['red', 'blue', 'green'],\n",
" 'marker': ['circle', 'triangle', 'diamond'],\n",
" 'size': [15, 25, 40]\n",
"}\n",
"\n",
"opts.defaults(opts.Points(padding=0.1, size=8, line_color='black'))\n",
"\n",
"hv.Points(data, vdims=['alpha', 'color', 'marker', 'size']).opts(\n",
" alpha='alpha', color='color', marker='marker', size='size')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the simplest approach to style mapping, dimensions can be mapped to visual attributes directly by name. However often columns in the data will not directly map to a visual property, e.g. we might want to normalize values before mapping them to the alpha, or apply a scaling factor to some values before mapping them to the point size; this is where ``dim`` transforms come in. Below are a few examples of using ``dim`` transforms to map a dimension in the data to the visual style in the plot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"points = hv.Points(np.random.rand(400, 4))\n",
"\n",
"bins = [0, .25, 0.5, .75, 1]\n",
"labels = ['circle', 'triangle', 'diamond', 'square']\n",
"\n",
"layout = hv.Layout([\n",
" points.relabel('Alpha' ).opts(alpha =dim('x').norm()),\n",
" points.relabel('Angle' ).opts(angle =dim('x').norm()*360, marker='dash'),\n",
" points.relabel('Color' ).opts(color =dim('x')),\n",
" points.relabel('Marker').opts(marker=dim('x').bin(bins, labels)),\n",
" points.relabel('Size' ).opts(size =dim('x')*10)\n",
"])\n",
"\n",
"layout.opts(opts.Points(width=250, height=250, xaxis=None, yaxis=None)).cols(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What are dim transforms?\n",
"\n",
"In the above example we saw how to use an ``dim`` to define a transform from a dimension in your data to the visual property on screen. A ``dim`` therefore is a simple way to declare a deferred transform of your data. In the simplest case an ``dim`` simply returns the data for a dimension without transforming it, e.g. to look up the ``'alpha'`` dimension on the points object we can create an ``dim`` and use the ``apply`` method to evaluate the expression:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from holoviews import dim\n",
"\n",
"ds = hv.Dataset(np.random.rand(10, 4)*10, ['x', 'y'], ['alpha', 'size'])\n",
"\n",
"dim('alpha').apply(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Mathematical operators"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An ``dim`` declaration allow arbitrary mathematical operations to be performed, e.g. let us declare that we want to subtract 5 from the 'alpha' dimension and then compute the ``min``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"math_op = (dim('alpha')-5).min()\n",
"math_op"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing the repr of the ``math_op`` we can see that it builds up an nested expression. To see the transform in action we will once again ``apply`` it on the points:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"math_op.apply(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ``dim`` objects implement most of the NumPy API, supports all standard mathematical operators and also support NumPy ufuncs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Custom functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to standard mathematical operators it is also possible to declare custom functions which can be applied by name. By default HoloViews ships with three commonly useful functions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### **``norm``**\n",
"\n",
"Unity based normalization or features scaling normalizing the values to a range between 0-1 (optionally accepts ``min``/``max`` values as ``limits``, which are usually provided by the plotting system) using the expression:\n",
"\n",
" (values - min) / (max-min)\n",
" \n",
"for example we can rescale the alpha values into a 0-1 range:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dim('alpha').norm().apply(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### **``bin``**\n",
"\n",
"Bins values using the supplied bins specified as the edges of each bin:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bin_op = dim('alpha').bin([0, 5, 10])\n",
"\n",
"bin_op.apply(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is also possible to provide explicit labels for each bin which will replace the bin center value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dim('alpha').bin([0, 5, 10], ['Bin 1', 'Bin 2']).apply(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### **``categorize``**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maps a number of discrete values onto the supplied list of categories, e.g. having binned the data into 2 discrete bins we can map them to two discrete marker types 'circle' and 'triangle':"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dim(bin_op).categorize({2.5: 'circle', 7.5: 'square'}).apply(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This can be very useful to map discrete categories to markers or colors."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Style mapping with ``dim`` transforms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This allows a huge amount of flexibility to express how the data should be mapped to visual style without directly modifying the data. To demonstrate this we will use some of the more complex:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"points.opts(\n",
" alpha =(dim('x')+0.2).norm(),\n",
" angle =np.sin(dim('y'))*360,\n",
" color =dim('x')**2,\n",
" marker=dim('y').bin(bins, labels),\n",
" size =dim('x')**dim('y')*20, width=500, height=500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's summarize the style transforms we have applied:\n",
" \n",
"* **alpha**=``(dim('x')+0.2).norm()``: The alpha are mapped to the x-values offset by 0.2 and normalized.\n",
"* **angle**=``np.sin(dim('x'))*360``: The angle of each marker is the sine of the y-values, multiplied by 360\n",
"* **color**=``'x'``: The points are colormapped by square of their x-values.\n",
"* **marker**=``dim('y').bin(bins, labels)``: The y-values are binned and each bin is assignd a unique marker.\n",
"* **size**=``dim('x')**dim('y')*20``: The size of the points is mapped to the x-values exponentiated with the y-values and scaled by 20\n",
"\n",
"These are simply illustrative examples, transforms can be chained in arbitrarily complex ways to achieve almost any mapping from dimension values to visual style."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Colormapping\n",
"\n",
"Color cycles and styles are useful for categorical plots and when overlaying multiple subsets, but when we want to map data values to a color it is better to use HoloViews' facilities for color mapping. Certain image-like types will apply colormapping automatically; e.g. for ``Image``, ``QuadMesh`` or ``HeatMap`` types the first value dimension is automatically mapped to the color. In other cases the values to colormap can be declared by providing a ``color`` style option that specifies which dimension to map into the color value.\n",
"\n",
"#### Named colormaps\n",
"\n",
"HoloViews accepts colormaps specified either as an explicit list of hex or HTML colors, as a Matplotlib colormap object, or as the name of a bokeh, matplotlib, and colorcet palettes/colormap (which are available when the respective library is imported). The named colormaps available are listed here (suppressing the `_r` versions) and illustrated in detail in the separate [Colormaps](Colormaps.ipynb) user guide:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def format_list(l):\n",
" print(' '.join(sorted([k for k in l if not k.endswith('_r')])))\n",
"\n",
"format_list(hv.plotting.list_cmaps())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use one of these colormaps simply refer to it by name with the ``cmap`` style option:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ls = np.linspace(0, 10, 400)\n",
"xx, yy = np.meshgrid(ls, ls)\n",
"bounds=(-1,-1,1,1) # Coordinate system: (left, bottom, right, top)\n",
"img = hv.Image(np.sin(xx)*np.cos(yy), bounds=bounds).opts(colorbar=True, width=400)\n",
"\n",
"img.relabel('PiYG').opts(cmap='PiYG') + img.relabel('PiYG_r').opts(cmap='PiYG_r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Custom colormaps\n",
"\n",
"You can make your own custom colormaps by providing a list of hex colors:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img.relabel('Listed colors').opts(cmap=['#0000ff', '#8888ff', '#ffffff', '#ff8888', '#ff0000'], colorbar=True, width=400)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Discrete color levels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly, existing colormaps can be made discrete by defining an integer number of ``color_levels``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img.relabel('5 color levels').opts(cmap='PiYG', color_levels=5) + img.relabel('11 color levels').opts(cmap='PiYG', color_levels=11) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explicit color mapping\n",
"\n",
"Some elements work through implicit colormapping the prime example being the ``Image`` type, however other elements can be colormapped using style mapping instead, by setting the color to an existing dimension."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Continuous values\n",
"\n",
"If we provide a continuous value for the ``color`` style option along with a continuous colormap, we can also enable a ``colorbar``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"polygons = hv.Polygons([{('x', 'y'): hv.Ellipse(0, 0, (i, i)).array(), 'z': i} for i in range(1, 10)[::-1]], vdims='z')\n",
"\n",
"polygons.opts(color='z', colorbar=True, width=380)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Categorical values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conversely, when mapping a categorical value into a set of colors, we automatically get a legend (which can be disabled using the ``show_legend`` option):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"categorical_points = hv.Points((np.random.rand(100), \n",
" np.random.rand(100), \n",
" np.random.choice(list('ABCD'), 100)), vdims='Category')\n",
"\n",
"categorical_points.sort('Category').opts(\n",
" color='Category', cmap='Category20', size=8, legend_position='left', width=500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Explicit color mapping\n",
"\n",
"Instead of using a listed colormap, you can provide an explicit mapping from category to color. Here we will map the categories 'A', 'B', 'C' and 'D' to specific colors:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"explicit_mapping = {'A': 'blue', 'B': 'red', 'C': 'green', 'D': 'purple'}\n",
"\n",
"categorical_points.sort('Category').opts(color='Category', cmap=explicit_mapping, size=8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Custom color intervals\n",
"\n",
"In addition to a simple integer defining the number of discrete levels, the ``color_levels`` option also allows defining a set of custom intervals. This can be useful for defining a fixed scale, such as the Saffir-Simpson hurricane wind scale. Below we declare the color levels along with a list of colors, declaring the scale. Note that the levels define the intervals to map each color to, so if there are N colors we have to define N+1 levels.\n",
"\n",
"Having defined the scale we can generate a theoretical hurricane path with wind speed values and use the ``color_levels`` and ``cmap`` to supply the custom color scale:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"levels = [0, 38, 73, 95, 110, 130, 156, 999] \n",
"colors = ['#5ebaff', '#00faf4', '#ffffcc', '#ffe775', '#ffc140', '#ff8f20', '#ff6060']\n",
"\n",
"path = [\n",
" (-75.1, 23.1, 0), (-76.2, 23.8, 0), (-76.9, 25.4, 0), (-78.4, 26.1, 39), (-79.6, 26.2, 39),\n",
" (-80.3, 25.9, 39), (-82.0, 25.1, 74), (-83.3, 24.6, 74), (-84.7, 24.4, 96), (-85.9, 24.8, 111),\n",
" (-87.7, 25.7, 111), (-89.2, 27.2, 131), (-89.6, 29.3, 156), (-89.6, 30.2, 156), (-89.1, 32.6, 131),\n",
" (-88.0, 35.6, 111), (-85.3, 38.6, 96)\n",
"]\n",
"\n",
"hv.Path([path], vdims='Wind Speed').opts(\n",
" color='Wind Speed', color_levels=levels, cmap=colors, line_width=8, colorbar=True, width=450\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setting color ranges\n",
"\n",
"For an image-like element, color ranges are determined by the range of the `z` value dimension, and they can thus be controlled using the ``.redim.range`` method with `z`. As an example, let's set some values in the image array to NaN and then set the range to clip the data at 0 and 0.9. By declaring the ``clipping_colors`` option we can control what colors are used for NaN values and for values above and below the defined range:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clipping = {'min': 'red', 'max': 'green', 'NaN': 'gray'}\n",
"options = dict(cmap='Blues', colorbar=True, width=300, height=230, axiswise=True)\n",
"\n",
"arr = np.sin(xx)*np.cos(yy)\n",
"arr[:190, :127] = np.NaN\n",
"\n",
"original = hv.Image(arr, bounds=bounds).opts(**options)\n",
"colored = original.opts(clipping_colors=clipping, clone=True)\n",
"clipped = colored.redim.range(z=(0, 0.9))\n",
"\n",
"original + colored + clipped"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default (left plot above), the min and max values in the array map to the first color (white) and last color (dark blue) in the colormap, and NaNs are ``'transparent'`` (an RGBA tuple of (0, 0, 0, 0)), revealing the underlying plot background. When the specified `clipping_colors` are supplied (middle plot above), NaN values are now colored gray, but the plot is otherwise the same because the autoranging still ensures that no value is mapped outside the available color range. Finally, when the `z` range is reduced (right plot above), the color range is mapped from a different range of numerical `z` values, and some values now fall outside the range and are thus clipped to red or green as specified.\n",
" \n",
"#### Other options\n",
"\n",
"* ``logz``: Enable logarithmic color scale (e.g. ``logz=True``)\n",
"* ``symmetric``: Ensures that the color scale is centered on zero (e.g. ``symmetric=True``)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cycles and Palettes\n",
"\n",
"Frequently we want to plot multiple subsets of data, which is made easy by using ``Overlay`` and ``NdOverlay`` objects. When overlaying multiple elements of the same type they will need to be distinguished visually, and HoloViews provides two mechanisms for styling the different subsets automatically in those cases:\n",
"\n",
"* ``Cycle``: A Cycle defines a list of discrete styles\n",
"* ``Palette``: A Palette defines a continuous color space which will be sampled discretely"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cycle\n",
"\n",
"A ``Cycle`` can be applied to any of the style options on an element. By default, most elements define a ``Cycle`` on the color property. Here we will create an overlay of three ``Points`` objects using the default cycles, then display it using the default cycles along with a copy where we changed the dot color and size using a custom ``Cycle``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"points = (\n",
" hv.Points(np.random.randn(50, 2) ) *\n",
" hv.Points(np.random.randn(50, 2) + 1 ) *\n",
" hv.Points(np.random.randn(50, 2) * 0.5)\n",
")\n",
"\n",
"color_cycle = hv.Cycle(['red', 'green', 'blue'])\n",
"points + points.opts(opts.Points(color=color_cycle), clone=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here color has been changed to cycle over the three provided colors, while size has been specified as a constant (though a cycle like `hv.Cycle([2,5,10])` could just as easily have been used for the size as well).\n",
"\n",
"#### Defaults\n",
"\n",
"In addition to defining custom color cycles by explicitly defining a list of colors, ``Cycle`` also defines a list of default Cycles generated from bokeh Palettes and matplotlib colormaps:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"format_list(hv.Cycle.default_cycles.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Here some of these Cycles have a reversed variant ending in `_r` that is not shown.)\n",
"\n",
"To use one of these default Cycles simply construct the Cycle with the corresponding key:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xs = np.linspace(0, np.pi*2)\n",
"curves = hv.Overlay([hv.Curve(np.sin(xs+p)) for p in np.linspace(0, np.pi, 10)])\n",
"\n",
"curves.opts(opts.Curve(color=hv.Cycle('Category20'), width=600))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Markers and sizes\n",
"\n",
"The above examples focus on color Cycles, but Cycles may be used to define any style option. Here let's use them to cycle over a number of marker styles and sizes, which will be expanded by cycling over each item independently. In this case we are cycling over three Cycles, resulting in the following style combinations:\n",
"\n",
"1. ``{'color': '#30a2da', 'marker': 'x', 'size': 10}``\n",
"2. ``{'color': '#fc4f30', 'marker': '^', 'size': 5}``\n",
"3. ``{'color': '#e5ae38', 'marker': '+', 'size': 10}``"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"color = hv.Cycle(['#30a2da', '#fc4f30', '#e5ae38'])\n",
"markers = hv.Cycle(['x', '^', '+'])\n",
"sizes = hv.Cycle([10, 5])\n",
"points.opts(opts.Points(line_color=color, marker=markers, size=sizes))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Palettes\n",
"\n",
"Palettes are similar to cycles, but treat a set of colors as a continuous colorspace to be sampled at regularly spaced intervals. Again they are made automatically available from existing colormaps (with `_r` versions also available):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"format_list(hv.Palette.colormaps.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Here each colormap `X` has a corresponding version `X_r` with the values reversed; the `_r` variants are suppressed above.)\n",
"\n",
"As a simple example we will create a Palette from the Spectral colormap and apply it to an Overlay of 6 Ellipses. Comparing it to the Spectral ``Cycle`` we can immediately see that the Palette covers the entire color space spanned by the Spectral colormap, while the Cycle instead uses the first 6 colors of the Spectral colormap:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ellipses = hv.Overlay([hv.Ellipse(0, 0, s) for s in range(6)])\n",
"\n",
"ellipses.relabel('Palette').opts(opts.Ellipse(color=hv.Palette('Spectral'), line_width=5), clone=True) +\\\n",
"ellipses.relabel('Cycle' ).opts(opts.Ellipse(color=hv.Cycle( 'Spectral'), line_width=5), clone=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus if you want to have have a discrete set of distinguishable colors starting from a list of colors that vary slowly and continuously, you should usually supply it as a Palette, not a Cycle. Conversely, you should use a Cycle when you want to iterate through a specific list of colors, in order, without skipping around the list like a Palette will.\n"
]
}
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| bsd-3-clause |
yhat/ggplot | docs/how-to/Plotting two variables as lines on the same graph.ipynb | 1 | 118223 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from ggplot import *\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting two variables as lines on the same graph\n",
"So you've got 2 variables and you want to plot them on the same chart? How do you do it in ggplot? Well good news is it's super easy to do with ggplot!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're going to use a subset of the `meat` dataset for this example. We're going to use `pandas` to switch our data from \"wide\" to \"long\" format."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>variable</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <th>1</th>\n",
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" <td>1944-03-01</td>\n",
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" <td>1944-05-01</td>\n",
" <td>beef</td>\n",
" <td>681.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date variable value\n",
"0 1944-01-01 beef 751.0\n",
"1 1944-02-01 beef 713.0\n",
"2 1944-03-01 beef 741.0\n",
"3 1944-04-01 beef 650.0\n",
"4 1944-05-01 beef 681.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meat_subset = meat[['date', 'beef', 'pork']]\n",
"df = pd.melt(meat_subset, id_vars=['date'])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll setup our aesthetics so `date` is the x-axis value, `variable` is the color of each line and `value` is the y-axis value."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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fz/hgFgQijtf0i7ce/SGa+/9hbGMg48ff6AEQQgghZJMzShCvkWAo4hja97PIdkHwDocQ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aAAAg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fd7a5d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<ggplot: (274447253)>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df, aes(x='date', y='value', color='variable')) + geom_line()"
]
},
{
"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-2-clause |
CompPhysics/MachineLearning | doc/Programs/JupyterFiles/Examples/Youtube Tutorials/TensorFlow Tutorial Sentdex.ipynb | 1 | 2238 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Mul_11:0\", shape=(4,), dtype=int32)\n",
"[ 5 12 21 32]\n",
"[ 5 12 21 32]\n",
"30\n",
"30\n",
"30\n",
"30\n"
]
}
],
"source": [
"# Import `tensorflow`\n",
"import tensorflow as tf\n",
"import os\n",
"\n",
"# Initialize two constants\n",
"x1 = tf.constant([1,2,3,4])\n",
"x2 = tf.constant([5,6,7,8])\n",
"\n",
"# Multiply\n",
"result = tf.multiply(x1, x2)\n",
"\n",
"# Print the result\n",
"print(result)\n",
"\n",
"# Intialize the Session\n",
"sess = tf.Session()\n",
"\n",
"# Print the result\n",
"print(sess.run(result))\n",
"\n",
"# Close the session\n",
"sess.close()\n",
"\n",
"#Or you can run the session like so:\n",
"with tf.Session() as sess:\n",
" output = sess.run(result)\n",
" print(output)\n",
"\n",
" \n",
"y1=tf.constant(5)\n",
"y2=tf.constant(6)\n",
"result=tf.multiply(y1, y2)\n",
"sess=tf.Session()\n",
"print(sess.run(result))\n",
"sess.close()\n",
"\n",
"#or\n",
"\n",
"with tf.Session() as sess:\n",
" print (sess.run(result))\n",
" \n",
"#this closes the session automatically\n",
"\n",
"#try this:\n",
"with tf.Session() as sess:\n",
" output=sess.run(result)\n",
" print (output)\n",
" \n",
"print (output)\n",
"#you can't run sess.run(result) outside of the with action"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| cc0-1.0 |
ljubisap/ml-dojo-part-I | Do it yourself.ipynb | 1 | 10478 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Do it yourself..."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Python basics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Create one string, int, float and boolean variable and print them out"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Most basic Python string functions are:\n",
"1. **len()** - checks length of the string\n",
"usage example:\n",
"```python\n",
"len(string)\n",
"```\n",
"2. **lower()** - creates lower case string\n",
"usage example:\n",
"```python\n",
"string.lower()\n",
"```\n",
"3. **upper()** - creates upper case string\n",
"usage example:\n",
"```python\n",
"string.upper()\n",
"```\n",
"4. **str()** - creates string / implicit string conversion\n",
"usage example:\n",
"```python\n",
"str(string)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Check what above given functions will produce from following variables:\n",
"a = 'Some test string...'\n",
"b = 'WE ARE LEARNING...'\n",
"c = 123"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Concatenate all variables a, b and c into one and print it out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO String formatting, usefull for logging and debugging\n",
"print \"The %s who %s %s!\" % (\"Knights\", \"say\", \"Ni\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"string1 = ' embedded string '\n",
"string2 = ' This is one string {}'.format(string1)\n",
"string2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import random\n",
"# TODO print the biggest number from the three given bellow num1, num2 and num3\n",
"num1 = random.randint(1, 100) \n",
"num2 = random.randint(1, 100) \n",
"num3 = random.randint(1, 100) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO if number1 is bigger, print \"number1 is bigger\"\n",
"# if number2 is bigger, print \"number2 is bigger\"\n",
"# if they are equal, print \"Numbers are equal, you had 1% chance to get this...\"\n",
"number1 = random.randint(1, 100) \n",
"number2 = random.randint(1, 100) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO if you are German and n is greater than m \n",
"# print upper case lc variable otherwise\n",
"# print lower case up variable\n",
"n = random.randint(1, 100) \n",
"m = random.randint(1, 100) \n",
"german = ? (True/False)\n",
"lc = 'lower case string'\n",
"up = 'UPPER CASE STRING'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO remove false Beatle form the list\n",
"beatles = [\"john\",\"paul\",\"george\",\"ringo\",\"stuart\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO print out all the beatles with the loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO make John and Ringo switch their places in the list"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO So as a reminder the Beatles are John Lennon, Paul McCartney, George Harrison and Ringo Starr\n",
"# in that respect attach proper last name to every Beatle in the list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Now just execute this...\n",
"%run man.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Data - Titanic data set\n",
"From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship:\n",
"* **Survived**: Outcome of survival (0 = No; 1 = Yes)\n",
"* **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n",
"* **Name**: Name of passenger\n",
"* **Sex**: Sex of the passenger\n",
"* **Age**: Age of the passenger (Some entries contain NaN)\n",
"* **SibSp**: Number of siblings and spouses of the passenger aboard\n",
"* **Parch**: Number of parents and children of the passenger aboard\n",
"* **Ticket**: Ticket number of the passenger\n",
"* **Fare**: Fare paid by the passenger\n",
"* **Cabin**: Cabin number of the passenger (Some entries contain NaN)\n",
"* **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
"\n",
"More about this data set can be found on [Kaggle](https://www.kaggle.com/c/titanic) website."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO import proper Python libraries to examine and investigate Titanic data set\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO load Titanic training set. File name is titanic_train.csv\n",
"import pandas as pd\n",
"df = pd.read_csv('titanic_train.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Data investigation"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [],
"source": [
"# TODO Check does any column in Titanic data set contains NaN values"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Plot Pclass and Fare data distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Count how many passangers are over 40 years"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Count how many men among passangers are over 40 years"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Count how many men among passangers are over 40 years survived"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO Plot data distribution "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO if children are considered to be under the age of 16, how many children were in Titanic"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO How many men named Edward were among the passangers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# TODO experiment yourself a bit ;-)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"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
}
| apache-2.0 |
AssembleSoftware/IoTPy | examples/SlidingWindows.ipynb | 1 | 6886 | {
"cells": [
{
"cell_type": "markdown",
"id": "a5025450",
"metadata": {},
"source": [
"# Sliding Windows\n",
"\n",
"Many applications require computations on sliding windows of streams. A sliding window is specified by a window size and a step size, both of which are positive integers. Sliding windows generate a sequence of lists from a stream. For a stream x, the the n-th window is the list x[n*step_size: n*step_size + window_size]. For example if the step_size is 4 and the window size is 2, the sequence of windows is x[0:2], x[4:6], x[8:10], ...\n",
"\n",
"**map_window** applies a specified function to each window of a stream and the returned value is an element of the ouput stream\n",
"\n",
"**map_window(func, in_stream, out_stream, window_size, step_size, state=None, name=None, **kwargs)**\n",
"\n",
"In the next example, *y[n] = sum(x[3*n: 3*n + 2])*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "03b1bfb6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"recent values of stream y are\n",
"[1, 7, 13, 19]\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"sys.path.append(\"../\")\n",
"\n",
"from IoTPy.core.stream import Stream, run\n",
"from IoTPy.agent_types.op import map_window\n",
"from IoTPy.helper_functions.recent_values import recent_values\n",
"\n",
"def example():\n",
" x, y = Stream(), Stream()\n",
"\n",
" map_window(func=sum, in_stream=x, out_stream=y, window_size=2, step_size=3)\n",
"\n",
" x.extend([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
" \n",
" # Execute a step\n",
" run()\n",
" print ('recent values of stream y are')\n",
" print (recent_values(y))\n",
"\n",
"example()"
]
},
{
"cell_type": "markdown",
"id": "90ac779e",
"metadata": {},
"source": [
"## Sliding Windows with State and Keyword Arguments\n",
"\n",
"You can specify a state using the keyword **state** and giving the state an initial value. The function can also use keyword arguments as illustrated in the following example in which the keyword is *threshold*.\n",
"\n",
"This example computes *mean_of_this_window* which is the mean of the current window, and it computes *max_of_window_mean* which is the maxium of *mean_of_this_window* over all windows seen so far. It computes *deviation* which is the deviation of the mean of the current window from the max seen so far, and it sets the deviation to 0.0 if it is below a threshold."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1229e3a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"recent values of stream y are\n",
"[0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 18.0]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"def example():\n",
" \n",
" def deviation_from_max(window, max_of_window_mean, threshold):\n",
" # state is max_of_window_mean\n",
" mean_of_this_window = np.mean(window)\n",
" max_of_window_mean = max(max_of_window_mean, mean_of_this_window)\n",
" deviation = max_of_window_mean - mean_of_this_window\n",
" if deviation < threshold: deviation = 0.0\n",
" return deviation, max_of_window_mean\n",
" \n",
" \n",
" x, y = Stream(), Stream()\n",
" map_window(func=deviation_from_max, in_stream=x, out_stream=y, \n",
" window_size=2, step_size=1, state=0, threshold=4)\n",
"\n",
" \n",
" x.extend([0, 10, 2, 4, 0, 40, 20, 4])\n",
" \n",
" # Execute a step\n",
" run()\n",
" print ('recent values of stream y are')\n",
" print (recent_values(y))\n",
"\n",
"example()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9455add8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"recent values of stream y are\n",
"[(4, 350), (8, 800), (12, 250), (16, 350)]\n"
]
}
],
"source": [
"from IoTPy.agent_types.op import timed_window\n",
"def example():\n",
" def f(window):\n",
" return sum([v[1] for v in window])\n",
" \n",
" x, y = Stream(), Stream()\n",
" timed_window(func=f, in_stream=x, out_stream=y, window_duration=4, step_time=4)\n",
" \n",
" \n",
" x.extend([[1, 100], [3, 250], [5, 400], [5.5, 100], [7, 300], \n",
" [11.0, 250], [12.0, 150], [13.0, 200], [17.0, 100]])\n",
" \n",
" # Execute a step\n",
" run()\n",
" print ('recent values of stream y are')\n",
" print (recent_values(y))\n",
"\n",
"example()\n",
" "
]
},
{
"cell_type": "markdown",
"id": "849a1588",
"metadata": {},
"source": [
"## map_window_list\n",
"Same as map_window except that map_window_list returns a list rather than a single element.\n",
"\n",
"(You can also use map_window and output **_multivalue(output_list)** instead of calling map_window.)\n",
"\n",
"The next example subtracts the mean of each window from the elements of the window."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ae727127",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"recent values of stream y are\n",
"[-4.0, 6.0, -2.0, 0.0, -1.0, 1.0, -1.0, 0.0, 1.0]\n"
]
}
],
"source": [
"from IoTPy.agent_types.op import map_window_list\n",
"\n",
"def example():\n",
" \n",
" def f(window):\n",
" window_mean = np.mean(window)\n",
" return [v-window_mean for v in window]\n",
" \n",
" \n",
" x, y = Stream(), Stream()\n",
" map_window_list(func=f, in_stream=x, out_stream=y, \n",
" window_size=3, step_size=3)\n",
"\n",
" \n",
" x.extend([0, 10, 2, 4, 3, 5, 2, 3, 4])\n",
" \n",
" # Execute a step\n",
" run()\n",
" print ('recent values of stream y are')\n",
" print (recent_values(y))\n",
"\n",
"example()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| bsd-3-clause |
tpin3694/tpin3694.github.io | python/lambda_functions.ipynb | 1 | 1890 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Lambda Functions \n",
"Slug: lambda_functions \n",
"Summary: Lambda Functions \n",
"Date: 2016-05-01 12:00 \n",
"Category: Python \n",
"Tags: Basics \n",
"Authors: Chris Albon "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Python it is possible to string lambda functions together."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a series, called pipeline, that contains three mini functions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pipeline = [lambda x: x **2 - 1 + 5,\n",
" lambda x: x **20 - 2 + 3,\n",
" lambda x: x **200 - 1 + 4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### For each item in pipeline, run the lambda function with x = 3"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13\n",
"3486784402\n",
"265613988875874769338781322035779626829233452653394495974574961739092490901302182994384699044004\n"
]
}
],
"source": [
"for f in pipeline:\n",
" print(f(3))"
]
}
],
"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 |
catalyst-cooperative/pudl | notebooks/work-in-progress/make_master_unit_list_eia.ipynb | 1 | 13571 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recreating the Master Unit List"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### setup/imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pudl\n",
"import pudl.constants as pc\n",
"import sqlalchemy as sa\n",
"import logging\n",
"import sys\n",
"\n",
"from copy import deepcopy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"%matplotlib inline\n",
"mpl.style.use('dark_background')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pudl.analysis.plant_parts_eia import *\n",
"pd.options.display.max_columns = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"logger = logging.getLogger()\n",
"logger.setLevel(logging.INFO)\n",
"handler = logging.StreamHandler(stream=sys.stdout)\n",
"formatter = logging.Formatter('%(message)s')\n",
"handler.setFormatter(formatter)\n",
"logger.handlers = [handler]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make the Plant-Parts List"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pudl_settings = pudl.workspace.setup.get_defaults()\n",
"pudl_engine = sa.create_engine(pudl_settings['pudl_db'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pudl_out = pudl.output.pudltabl.PudlTabl(\n",
" pudl_engine,freq='AS',\n",
" roll_fuel_cost=True,\n",
" fill_fuel_cost=True,\n",
" fill_net_gen=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"# there is a warning in here that will scream if don't have utility ids\n",
"# for all of the generators. i've fixed this on the PUDL side by requiring\n",
"# utility id's to be present in the annual generators table\n",
"plant_parts_eia = pudl_out.plant_parts_eia(update=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# this is now integrated into the plant_parts_eia process so this is duplicative\n",
"test_merge = test_run_aggregations(plant_parts_eia=plant_parts_eia, gens_mega=pudl_out.gens_mega_eia())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the Plant-Parts List"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"compiled_plant_parts = {}\n",
"compiled_plant_parts_true = {}\n",
"true_parts_df = plant_parts_eia[plant_parts_eia.true_gran]\n",
"for part in PLANT_PARTS.keys():\n",
" part_df = plant_parts_eia[(plant_parts_df['plant_part'] == part)]\n",
" compiled_plant_parts_true[part] = true_parts_df[(true_parts_df['plant_part'] == part)]\n",
" compiled_plant_parts[part] = part_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_plant_vs_agg(compiled_plant_parts, field, xy_limits, scale):\n",
" \"\"\"\n",
" Make plots to compare FERC & EIA reported values for Coal & Gas plants.\n",
" \n",
" For each of the fields specified in fields_to_plot, create a pair of plots,\n",
" one for 'gas' and one for 'coal' in the same frame, showing the EIA quantity\n",
" vs. the FERC quantity in a scatter plot.\n",
" \"\"\"\n",
" gens = compiled_plant_parts['plant_gen']\n",
" negative_fields = gens[gens[field] < 0].plant_id_eia.unique()\n",
" for plant_gran, df in compiled_plant_parts.items():\n",
" if plant_gran != 'plant':\n",
" field_plant = field+'_plant'\n",
" field_gran = field+'_'+plant_gran\n",
" id_cols = ['plant_id_eia'] + IDX_TO_ADD + IDX_OWN_TO_ADD\n",
" try:\n",
" merge_df = (\n",
" compiled_plant_parts['plant'][id_cols + [field]]\n",
" .merge(\n",
" df[id_cols+ ['generator_id'] + [field]],\n",
" on=id_cols,\n",
" suffixes=('_plant',f'_{plant_gran}')\n",
" )\n",
" )\n",
" # this is for the try\n",
" if field in ['capacity_mw', 'net_generation_mwh', 'total_mmbtu']:\n",
" baddies = (\n",
" merge_df[\n",
" (merge_df[field_plant] < merge_df[field_gran])\n",
" & ~(merge_df.plant_id_eia.isin(negative_fields))\n",
" ]\n",
" .set_index(id_cols + ['generator_id'])\n",
" [[field_plant, field_gran]])\n",
" if not baddies.empty:\n",
" raise AssertionError(f\"{plant_gran}/{field} found some baddies {len(baddies)}\\n {baddies}\")\n",
" #merge_df = merge_df[merge_df['plant_id_eia'] == 3]\n",
" fig, (ax) = plt.subplots(ncols=1, nrows=1, figsize=(5, 5))\n",
" ax.scatter(merge_df[field_plant],\n",
" merge_df[field_gran],\n",
" color='aquamarine', alpha=0.1, label=field)\n",
" ax.set_ylim(xy_limits[field][0],xy_limits[field][1])\n",
" ax.set_xlim(xy_limits[field][0],xy_limits[field][1])\n",
" ax.set_xscale(scale)\n",
" ax.set_yscale(scale)\n",
" ax.set_ylabel(f'{plant_gran} {field}')\n",
" ax.set_xlabel(f'Plant {field}')\n",
" ax.set_title(f\"Plant vs {plant_gran}: {field}\")\n",
" except KeyError:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"fields_to_plot = [\n",
" # Simple Quantities\n",
" 'capacity_mw',\n",
" 'net_generation_mwh',\n",
" 'total_mmbtu',\n",
" # Derived values\n",
" #'capacity_factor',\n",
" 'heat_rate_mmbtu_mwh',\n",
" 'fuel_cost_per_mwh',\n",
" 'fuel_cost_per_mmbtu',\n",
" 'total_fuel_cost'\n",
"]\n",
"\n",
"xy_limits = {\n",
" # Simple Quantities\n",
" 'capacity_mw': (1e0, 1e4),\n",
" 'net_generation_mwh': (1e3,1e8),\n",
" 'total_mmbtu': (1e4,1e9),\n",
" # Derived values\n",
" 'capacity_factor': (0,1.0),\n",
" 'heat_rate_mmbtu_mwh': (6,16),\n",
" 'fuel_cost_per_mwh': (10,80),\n",
" 'fuel_cost_per_mmbtu': (1e0,1e1),\n",
" 'total_fuel_cost': (1e7,1e9)\n",
"}\n",
"\n",
"# with the allocate_net_gen=True, we got a small number of generators w/\n",
"# negative net generation within largers plants that have net postivie \n",
"# net gen. Thus we get some net_generation_mwh records above the diagonal line\n",
"for field in fields_to_plot:\n",
" plot_plant_vs_agg(compiled_plant_parts,field, xy_limits, scale=\"log\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_gens_vs(compiled_plant_parts,part_name, data_col, weight_col, x_range):\n",
" gen_df = compiled_plant_parts['plant_gen'][compiled_plant_parts['plant_gen'][data_col] != 0]\n",
" part_df = compiled_plant_parts[part_name][compiled_plant_parts[part_name][data_col] != 0]\n",
" if weight_col:\n",
" weights_gen = gen_df[weight_col]\n",
" weights_part = part_df[weight_col]\n",
" else:\n",
" weights_gen = None\n",
" weights_part = None\n",
"\n",
" plt.hist(gen_df[data_col], \n",
" weights=weights_gen,\n",
" range=x_range,\n",
" bins=100,\n",
" color=\"purple\", #alpha=test_alpha,\n",
" label=\"Generators\")\n",
"\n",
" plt.hist(part_df[data_col], \n",
" weights=weights_part,\n",
" range=x_range,\n",
" bins=100,\n",
" color=\"aquamarine\",\n",
" label=f'{part_name}')\n",
"\n",
" plt.title(f'Gens vs. {part_name}: {data_col}')\n",
" plt.xlabel(data_col)\n",
" plt.ylabel(None)\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_ranges = {\n",
" 'capacity_mw' : (0,400),\n",
" 'net_generation_mwh': (0, 2500000),\n",
" 'fuel_cost_per_mmbtu': (0, 5),\n",
" 'fuel_cost_per_mwh': (0, 100),\n",
" 'total_fuel_cost': (0,200000000)\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for part_name in compiled_plant_parts.keys():\n",
" data_col = 'net_generation_mwh'\n",
" weight_col = 'capacity_mw'\n",
" plot_gens_vs(compiled_plant_parts,\n",
" part_name=part_name,\n",
" data_col=data_col, \n",
" weight_col=weight_col,\n",
" x_range=x_ranges[data_col])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for part_name in compiled_plant_parts.keys():\n",
" data_col = 'total_fuel_cost'\n",
" weight_col = 'capacity_mw'\n",
" plot_gens_vs(compiled_plant_parts,\n",
" part_name=part_name,\n",
" data_col=data_col, \n",
" weight_col=weight_col,\n",
" x_range=x_ranges[data_col])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"for part_name in compiled_plant_parts.keys():\n",
" data_col = 'fuel_cost_per_mwh'\n",
" weight_col = 'capacity_mw'\n",
" plot_gens_vs(compiled_plant_parts,\n",
" part_name=part_name,\n",
" data_col=data_col, \n",
" weight_col=weight_col,\n",
" x_range=x_ranges[data_col])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"for part_name in compiled_plant_parts.keys():\n",
" data_col = 'fuel_cost_per_mmbtu'\n",
" weight_col = 'capacity_mw'\n",
" plot_gens_vs(compiled_plant_parts,\n",
" part_name=part_name,\n",
" data_col=data_col, \n",
" weight_col=weight_col,\n",
" x_range=x_ranges[data_col])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Playing with the compiled outputs "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"null_zeros = {0:np.NaN}\n",
"count_df = pd.DataFrame(index=list(compiled_plant_parts['plant'].columns))\n",
"for k,cpp_df in compiled_plant_parts.items():\n",
" cpp_df = cpp_df.replace({\n",
" 'net_generation_mwh':null_zeros,\n",
" 'capacity_factor' : null_zeros,\n",
" 'fuel_cost_per_mmbtu': null_zeros,\n",
" 'fuel_cost_per_mwh': null_zeros,\n",
" 'capacity_mw': null_zeros,\n",
" })\n",
" count_df = count_df.merge(\n",
" pd.DataFrame(cpp_df#[cpp_df['report_date'].dt.year == 2018]\n",
" .count(), columns=[k]),\n",
" right_index=True, left_index=True)\n",
"count_df = count_df.merge(pd.DataFrame(count_df.sum(axis=1),columns=['total']),\n",
" right_index=True, left_index=True)\n",
"count_df"
]
},
{
"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": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
ManyBodyPhysics/Course2ManyBodyMethods | doc/src/fci/fci.ipynb | 2 | 54643 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Slides for PHY981 -->\n",
"<!-- dom:TITLE: Nuclear Shell Model -->\n",
"# Nuclear Shell Model\n",
"<!-- dom:AUTHOR: [Morten Hjorth-Jensen](http://computationalphysics.no), National Superconducting Cyclotron Laboratory and Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA & Department of Physics, University of Oslo, Oslo, Norway -->\n",
"<!-- Author: --> **[Morten Hjorth-Jensen](http://computationalphysics.no), National Superconducting Cyclotron Laboratory and Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA & Department of Physics, University of Oslo, Oslo, Norway**\n",
"\n",
"Date: **July 6-24 2015**\n",
"\n",
"## Slater determinants as basis states\n",
"\n",
"The simplest possible choice for many-body wavefunctions are **product** wavefunctions.\n",
"That is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Psi(x_1, x_2, x_3, \\ldots, x_A) \\approx \\phi_1(x_1) \\phi_2(x_2) \\phi_3(x_3) \\ldots\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"because we are really only good at thinking about one particle at a time. Such \n",
"product wavefunctions, without correlations, are easy to \n",
"work with; for example, if the single-particle states $\\phi_i(x)$ are orthonormal, then \n",
"the product wavefunctions are easy to orthonormalize. \n",
"\n",
"Similarly, computing matrix elements of operators are relatively easy, because the \n",
"integrals factorize.\n",
"\n",
"\n",
"The price we pay is the lack of correlations, which we must build up by using many, many product \n",
"wavefunctions.\n",
"\n",
"\n",
"Because we have fermions, we are required to have antisymmetric wavefunctions, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Psi(x_1, x_2, x_3, \\ldots, x_A) = - \\Psi(x_2, x_1, x_3, \\ldots, x_A)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"etc. This is accomplished formally by using the determinantal formalism"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Psi(x_1, x_2, \\ldots, x_A) \n",
"= \\frac{1}{\\sqrt{A!}} \n",
"\\det \\left | \n",
"\\begin{array}{cccc}\n",
"\\phi_1(x_1) & \\phi_1(x_2) & \\ldots & \\phi_1(x_A) \\\\\n",
"\\phi_2(x_1) & \\phi_2(x_2) & \\ldots & \\phi_2(x_A) \\\\\n",
" \\vdots & & & \\\\\n",
"\\phi_A(x_1) & \\phi_A(x_2) & \\ldots & \\phi_A(x_A) \n",
"\\end{array}\n",
"\\right |\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Product wavefunction + antisymmetry (Pauli principle) = Slater determinant. \n",
"\n",
"\n",
"Properties of the determinant (interchange of any two rows or \n",
"any two columns yields a change in sign; thus no two rows and no \n",
"two columns can be the same) lead to the following consequence of the Pauli principle:\n",
"\n",
"* No two particles can be at the same place (two columns the same); and\n",
"\n",
"* No two particles can be in the same state (two rows the same).\n",
"\n",
"As a practical matter, however, Slater determinants beyond $N=4$ quickly become \n",
"unwieldy. Thus we turn to the **occupation representation** or **second quantization** to simplify calculations. \n",
"\n",
"The occupation representation, using fermion **creation** and **annihilation** \n",
"operators, is compact and efficient. It is also abstract and, at first encounter, not easy to \n",
"internalize. It is inspired by other operator formalism, such as the ladder operators for \n",
"the harmonic oscillator or for angular momentum, but unlike those cases, the operators **do not have coordinate space representations**.\n",
"\n",
"Instead, one can think of fermion creation/annihilation operators as a game of symbols that \n",
"compactly reproduces what one would do, albeit clumsily, with full coordinate-space Slater \n",
"determinants. \n",
"\n",
"\n",
"\n",
"We start with a set of orthonormal single-particle states $\\{ \\phi_i(x) \\}$. \n",
"(Note: this requirement, and others, can be relaxed, but leads to a \n",
"more involved formalism.) **Any** orthonormal set will do. \n",
"\n",
"To each single-particle state $\\phi_i(x)$ we associate a creation operator \n",
"$\\hat{a}^\\dagger_i$ and an annihilation operator $\\hat{a}_i$. \n",
"\n",
"When acting on the vacuum state $| 0 \\rangle$, the creation operator $\\hat{a}^\\dagger_i$ causes \n",
"a particle to occupy the single-particle state $\\phi_i(x)$:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\phi_i(x) \\rightarrow \\hat{a}^\\dagger_i |0 \\rangle\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But with multiple creation operators we can occupy multiple states:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\phi_i(x) \\phi_j(x^\\prime) \\phi_k(x^{\\prime \\prime}) \n",
"\\rightarrow \\hat{a}^\\dagger_i \\hat{a}^\\dagger_j \\hat{a}^\\dagger_k |0 \\rangle.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we impose antisymmetry, by having the fermion operators satisfy **anticommutation relations**:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{a}^\\dagger_i \\hat{a}^\\dagger_j + \\hat{a}^\\dagger_j \\hat{a}^\\dagger_i\n",
"= [ \\hat{a}^\\dagger_i ,\\hat{a}^\\dagger_j ]_+ \n",
"= \\{ \\hat{a}^\\dagger_i ,\\hat{a}^\\dagger_j \\} = 0\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"so that"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{a}^\\dagger_i \\hat{a}^\\dagger_j = - \\hat{a}^\\dagger_j \\hat{a}^\\dagger_i\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because of this property, automatically $\\hat{a}^\\dagger_i \\hat{a}^\\dagger_i = 0$, \n",
"enforcing the Pauli exclusion principle. Thus when writing a Slater determinant \n",
"using creation operators,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{a}^\\dagger_i \\hat{a}^\\dagger_j \\hat{a}^\\dagger_k \\ldots |0 \\rangle\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"each index $i,j,k, \\ldots$ must be unique.\n",
"\n",
"\n",
"\n",
"\n",
"## Full Configuration Interaction Theory\n",
"\n",
"We have defined the ansatz for the ground state as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Phi_0\\rangle = \\left(\\prod_{i\\le F}\\hat{a}_{i}^{\\dagger}\\right)|0\\rangle,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the index $i$ defines different single-particle states up to the Fermi level. We have assumed that we have $N$ fermions. \n",
"A given one-particle-one-hole ($1p1h$) state can be written as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Phi_i^a\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_i|\\Phi_0\\rangle,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while a $2p2h$ state can be written as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Phi_{ij}^{ab}\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_j\\hat{a}_i|\\Phi_0\\rangle,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and a general $ApAh$ state as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Phi_{ijk\\dots}^{abc\\dots}\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_{c}^{\\dagger}\\dots\\hat{a}_k\\hat{a}_j\\hat{a}_i|\\Phi_0\\rangle.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use letters $ijkl\\dots$ for states below the Fermi level and $abcd\\dots$ for states above the Fermi level. A general single-particle state is given by letters $pqrs\\dots$.\n",
"\n",
"We can then expand our exact state function for the ground state \n",
"as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Psi_0\\rangle=C_0|\\Phi_0\\rangle+\\sum_{ai}C_i^a|\\Phi_i^a\\rangle+\\sum_{abij}C_{ij}^{ab}|\\Phi_{ij}^{ab}\\rangle+\\dots\n",
"=(C_0+\\hat{C})|\\Phi_0\\rangle,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where we have introduced the so-called correlation operator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{C}=\\sum_{ai}C_i^a\\hat{a}_{a}^{\\dagger}\\hat{a}_i +\\sum_{abij}C_{ij}^{ab}\\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_j\\hat{a}_i+\\dots\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the normalization of $\\Psi_0$ is at our disposal and since $C_0$ is by hypothesis non-zero, we may arbitrarily set $C_0=1$ with \n",
"corresponding proportional changes in all other coefficients. Using this so-called intermediate normalization we have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle \\Psi_0 | \\Phi_0 \\rangle = \\langle \\Phi_0 | \\Phi_0 \\rangle = 1,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"resulting in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Psi_0\\rangle=(1+\\hat{C})|\\Phi_0\\rangle.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We rewrite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Psi_0\\rangle=C_0|\\Phi_0\\rangle+\\sum_{ai}C_i^a|\\Phi_i^a\\rangle+\\sum_{abij}C_{ij}^{ab}|\\Phi_{ij}^{ab}\\rangle+\\dots,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"in a more compact form as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"|\\Psi_0\\rangle=\\sum_{PH}C_H^P\\Phi_H^P=\\left(\\sum_{PH}C_H^P\\hat{A}_H^P\\right)|\\Phi_0\\rangle,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $H$ stands for $0,1,\\dots,n$ hole states and $P$ for $0,1,\\dots,n$ particle states. \n",
"Our requirement of unit normalization gives"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle \\Psi_0 | \\Psi_0 \\rangle = \\sum_{PH}|C_H^P|^2= 1,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the energy can be written as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E= \\langle \\Psi_0 | \\hat{H} |\\Psi_0 \\rangle= \\sum_{PP'HH'}C_H^{*P}\\langle \\Phi_H^P | \\hat{H} |\\Phi_{H'}^{P'} \\rangle C_{H'}^{P'}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normally"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E= \\langle \\Psi_0 | \\hat{H} |\\Psi_0 \\rangle= \\sum_{PP'HH'}C_H^{*P}\\langle \\Phi_H^P | \\hat{H} |\\Phi_{H'}^{P'} \\rangle C_{H'}^{P'},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"is solved by diagonalization setting up the Hamiltonian matrix defined by the basis of all possible Slater determinants. A diagonalization\n",
"is equivalent to finding the variational minimum of"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle \\Psi_0 | \\hat{H} |\\Psi_0 \\rangle-\\lambda \\langle \\Psi_0 |\\Psi_0 \\rangle,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\lambda$ is a variational multiplier to be identified with the energy of the system.\n",
"\n",
"The minimization process results in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2\n",
"2\n",
" \n",
"<\n",
"<\n",
"<\n",
"!\n",
"!\n",
"M\n",
"A\n",
"T\n",
"H\n",
"_\n",
"B\n",
"L\n",
"O\n",
"C\n",
"K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sum_{P'H'}\\left\\{\\delta[C_H^{*P}]\\langle \\Phi_H^P | \\hat{H} |\\Phi_{H'}^{P'} \\rangle C_{H'}^{P'}+\n",
"C_H^{*P}\\langle \\Phi_H^P | \\hat{H} |\\Phi_{H'}^{P'} \\rangle \\delta[C_{H'}^{P'}]-\n",
"\\lambda( \\delta[C_H^{*P}]C_{H'}^{P'}+C_H^{*P}\\delta[C_{H'}^{P'}]\\right\\} = 0.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the coefficients $\\delta[C_H^{*P}]$ and $\\delta[C_{H'}^{P'}]$ are complex conjugates it is necessary and sufficient to require the quantities that multiply with $\\delta[C_H^{*P}]$ to vanish. \n",
"\n",
"This leads to"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sum_{P'H'}\\langle \\Phi_H^P | \\hat{H} |\\Phi_{H'}^{P'} \\rangle C_{H'}^{P'}-\\lambda C_H^{P}=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for all sets of $P$ and $H$.\n",
"\n",
"If we then multiply by the corresponding $C_H^{*P}$ and sum over $PH$ we obtain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sum_{PP'HH'}C_H^{*P}\\langle \\Phi_H^P | \\hat{H} |\\Phi_{H'}^{P'} \\rangle C_{H'}^{P'}-\\lambda\\sum_{PH}|C_H^P|^2=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"leading to the identification $\\lambda = E$. This means that we have for all $PH$ sets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:fullci\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\sum_{P'H'}\\langle \\Phi_H^P | \\hat{H} -E|\\Phi_{H'}^{P'} \\rangle = 0. \\label{eq:fullci} \\tag{1}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An alternative way to derive the last equation is to start from"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"(\\hat{H} -E)|\\Psi_0\\rangle = (\\hat{H} -E)\\sum_{P'H'}C_{H'}^{P'}|\\Phi_{H'}^{P'} \\rangle=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and if this equation is successively projected against all $\\Phi_H^P$ in the expansion of $\\Psi$, we end up with Eq. [(1)](#eq:fullci).\n",
"\n",
"One solves this equation normally by diagonalization. If we are able to solve this equation exactly (that is\n",
"numerically exactly) in a large Hilbert space (it will be truncated in terms of the number of single-particle states included in the definition\n",
"of Slater determinants), it can then serve as a benchmark for other many-body methods which approximate the correlation operator\n",
"$\\hat{C}$. \n",
"\n",
"\n",
"## Example of a Hamiltonian matrix\n",
"\n",
"Suppose, as an example, that we have six fermions below the Fermi level.\n",
"This means that we can make at most $6p-6h$ excitations. If we have an infinity of single particle states above the Fermi level, we will obviously have an infinity of say $2p-2h$ excitations. Each such way to configure the particles is called a **configuration**. We will always have to truncate in the basis of single-particle states.\n",
"This gives us a finite number of possible Slater determinants. Our Hamiltonian matrix would then look like (where each block can have a large dimensionalities):\n",
"\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\"> </th> <th align=\"center\">$0p-0h$</th> <th align=\"center\">$1p-1h$</th> <th align=\"center\">$2p-2h$</th> <th align=\"center\">$3p-3h$</th> <th align=\"center\">$4p-4h$</th> <th align=\"center\">$5p-5h$</th> <th align=\"center\">$6p-6h$</th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> $0p-0h$ </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $1p-1h$ </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $2p-2h$ </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $3p-3h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $4p-4h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> </tr>\n",
"<tr><td align=\"center\"> $5p-5h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> </tr>\n",
"<tr><td align=\"center\"> $6p-6h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> <td align=\"center\"> x </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"with a two-body force. Why are there non-zero blocks of elements? \n",
"If we use a Hartree-Fock basis, this corresponds to a particular unitary transformation where matrix elements of the type $\\langle 0p-0h \\vert \\hat{H} \\vert 1p-1h\\rangle =\\langle \\Phi_0 | \\hat{H}|\\Phi_{i}^{a}\\rangle=0$ and our Hamiltonian matrix becomes \n",
"\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\"> </th> <th align=\"center\"> $0p-0h$ </th> <th align=\"center\"> $1p-1h$ </th> <th align=\"center\"> $2p-2h$ </th> <th align=\"center\"> $3p-3h$ </th> <th align=\"center\"> $4p-4h$ </th> <th align=\"center\"> $5p-5h$ </th> <th align=\"center\"> $6p-6h$ </th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> $0p-0h$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $1p-1h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $2p-2h$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $3p-3h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> $4p-4h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> </tr>\n",
"<tr><td align=\"center\"> $5p-5h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> </tr>\n",
"<tr><td align=\"center\"> $6p-6h$ </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> <td align=\"center\"> $\\tilde{x}$ </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"If we do not make any truncations in the possible sets of Slater determinants (many-body states) we can make by distributing $A$ nucleons among $n$ single-particle states, we call such a calculation for \n",
"* Full configuration interaction theory\n",
"\n",
"If we make truncations, we have different possibilities\n",
"\n",
"* The standard nuclear shell-model. Here we define an effective Hilbert space with respect to a given core. The calculations are normally then performed for all many-body states that can be constructed from the effective Hilbert spaces. This approach requires a properly defined effective Hamiltonian\n",
"\n",
"* We can truncate in the number of excitations. For example, we can limit the possible Slater determinants to only $1p-1h$ and $2p-2h$ excitations. This is called a configuration interaction calculation at the level of singles and doubles excitations, or just CISD. \n",
"\n",
"* We can limit the number of excitations in terms of the excitation energies. If we do not define a core, this defines normally what is called the no-core shell-model approach. \n",
"\n",
"What happens if we have a three-body interaction and a Hartree-Fock basis? \n",
"\n",
"Full configuration interaction theory calculations provide in principle, if we can diagonalize numerically, all states of interest. The dimensionality of the problem explodes however quickly.\n",
"\n",
"The total number of Slater determinants which can be built with say $N$ neutrons distributed among $n$ single particle states is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\left (\\begin{array}{c} n \\\\ N\\end{array} \\right) =\\frac{n!}{(n-N)!N!}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a model space which comprises the first for major shells only $0s$, $0p$, $1s0d$ and $1p0f$ we have $40$ single particle states for neutrons and protons. For the eight neutrons of oxygen-16 we would then have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\left (\\begin{array}{c} 40 \\\\ 8\\end{array} \\right) =\\frac{40!}{(32)!8!}\\sim 10^{9},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and multiplying this with the number of proton Slater determinants we end up with approximately witha dimensionality $d$ of $d\\sim 10^{18}$.\n",
"\n",
"\n",
"This number can be reduced if we look at specific symmetries only. However, the dimensionality explodes quickly!\n",
"\n",
"* For Hamiltonian matrices of dimensionalities which are smaller than $d\\sim 10^5$, we would use so-called direct methods for diagonalizing the Hamiltonian matrix\n",
"\n",
"* For larger dimensionalities iterative eigenvalue solvers like Lanczos' method are used. The most efficient codes at present can handle matrices of $d\\sim 10^{10}$. \n",
"\n",
"## A non-practical way of solving the eigenvalue problem\n",
"\n",
"For reasons to come (links with Coupled-Cluster theory and Many-Body perturbation theory), \n",
"we will rewrite Eq. [(1)](#eq:fullci) as a set of coupled non-linear equations in terms of the unknown coefficients $C_H^P$. \n",
"To obtain the eigenstates and eigenvalues in terms of non-linear equations is not a very practical approach. However, it serves the scope of linking FCI theory with approximative solutions to the many-body problem.\n",
"\n",
"To see this, we look at the contributions arising from"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle \\Phi_H^P | = \\langle \\Phi_0|\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"in Eq. [(1)](#eq:fullci), that is we multiply with $\\langle \\Phi_0 |$\n",
"from the left in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"(\\hat{H} -E)\\sum_{P'H'}C_{H'}^{P'}|\\Phi_{H'}^{P'} \\rangle=0.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we assume that we have a two-body operator at most, Slater's rule gives then an equation for the \n",
"correlation energy in terms of $C_i^a$ and $C_{ij}^{ab}$ only. We get then"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle \\Phi_0 | \\hat{H} -E| \\Phi_0\\rangle + \\sum_{ai}\\langle \\Phi_0 | \\hat{H} -E|\\Phi_{i}^{a} \\rangle C_{i}^{a}+\n",
"\\sum_{abij}\\langle \\Phi_0 | \\hat{H} -E|\\Phi_{ij}^{ab} \\rangle C_{ij}^{ab}=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E-E_0 =\\Delta E=\\sum_{ai}\\langle \\Phi_0 | \\hat{H}|\\Phi_{i}^{a} \\rangle C_{i}^{a}+\n",
"\\sum_{abij}\\langle \\Phi_0 | \\hat{H}|\\Phi_{ij}^{ab} \\rangle C_{ij}^{ab},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the energy $E_0$ is the reference energy and $\\Delta E$ defines the so-called correlation energy.\n",
"The single-particle basis functions could be the results of a Hartree-Fock calculation or just the eigenstates of the non-interacting part of the Hamiltonian. \n",
"\n",
"In our notes on Hartree-Fock calculations, \n",
"we have already computed the matrix $\\langle \\Phi_0 | \\hat{H}|\\Phi_{i}^{a}\\rangle $ and $\\langle \\Phi_0 | \\hat{H}|\\Phi_{ij}^{ab}\\rangle$. If we are using a Hartree-Fock basis, then the matrix elements\n",
"$\\langle \\Phi_0 | \\hat{H}|\\Phi_{i}^{a}\\rangle=0$ and we are left with a *correlation energy* given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E-E_0 =\\Delta E^{HF}=\\sum_{abij}\\langle \\Phi_0 | \\hat{H}|\\Phi_{ij}^{ab} \\rangle C_{ij}^{ab}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inserting the various matrix elements we can rewrite the previous equation as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Delta E=\\sum_{ai}\\langle i| \\hat{f}|a \\rangle C_{i}^{a}+\n",
"\\sum_{abij}\\langle ij | \\hat{v}| ab \\rangle C_{ij}^{ab}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This equation determines the correlation energy but not the coefficients $C$. \n",
"We need more equations. Our next step is to set up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle \\Phi_i^a | \\hat{H} -E| \\Phi_0\\rangle + \\sum_{bj}\\langle \\Phi_i^a | \\hat{H} -E|\\Phi_{j}^{b} \\rangle C_{j}^{b}+\n",
"\\sum_{bcjk}\\langle \\Phi_i^a | \\hat{H} -E|\\Phi_{jk}^{bc} \\rangle C_{jk}^{bc}+\n",
"\\sum_{bcdjkl}\\langle \\Phi_i^a | \\hat{H} -E|\\Phi_{jkl}^{bcd} \\rangle C_{jkl}^{bcd}=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"as this equation will allow us to find an expression for the coefficents $C_i^a$ since we can rewrite this equation as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle i | \\hat{f}| a\\rangle +\\langle \\Phi_i^a | \\hat{H}|\\Phi_{i}^{a} \\rangle C_{i}^{a}+ \\sum_{bj\\ne ai}\\langle \\Phi_i^a | \\hat{H}|\\Phi_{j}^{b} \\rangle C_{j}^{b}+\n",
"\\sum_{bcjk}\\langle \\Phi_i^a | \\hat{H}|\\Phi_{jk}^{bc} \\rangle C_{jk}^{bc}+\n",
"\\sum_{bcdjkl}\\langle \\Phi_i^a | \\hat{H}|\\Phi_{jkl}^{bcd} \\rangle C_{jkl}^{bcd}=EC_i^a.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that on the right-hand side we have the energy $E$. This leads to a non-linear equation in the unknown coefficients. \n",
"These equations are normally solved iteratively ( that is we can start with a guess for the coefficients $C_i^a$). A common choice is to use perturbation theory for the first guess, setting thereby"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C_{i}^{a}=\\frac{\\langle i | \\hat{f}| a\\rangle}{\\epsilon_i-\\epsilon_a}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The observant reader will however see that we need an equation for $C_{jk}^{bc}$ and $C_{jkl}^{bcd}$ as well.\n",
"To find equations for these coefficients we need then to continue our multiplications from the left with the various\n",
"$\\Phi_{H}^P$ terms. \n",
"\n",
"\n",
"For $C_{jk}^{bc}$ we need then"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3\n",
"9\n",
" \n",
"<\n",
"<\n",
"<\n",
"!\n",
"!\n",
"M\n",
"A\n",
"T\n",
"H\n",
"_\n",
"B\n",
"L\n",
"O\n",
"C\n",
"K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sum_{cdkl}\\langle \\Phi_{ij}^{ab} | \\hat{H} -E|\\Phi_{kl}^{cd} \\rangle C_{kl}^{cd}+\\sum_{cdeklm}\\langle \\Phi_{ij}^{ab} | \\hat{H} -E|\\Phi_{klm}^{cde} \\rangle C_{klm}^{cde}+\\sum_{cdefklmn}\\langle \\Phi_{ij}^{ab} | \\hat{H} -E|\\Phi_{klmn}^{cdef} \\rangle C_{klmn}^{cdef}=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and we can isolate the coefficients $C_{kl}^{cd}$ in a similar way as we did for the coefficients $C_{i}^{a}$. \n",
"A standard choice for the first iteration is to set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C_{ij}^{ab} =\\frac{\\langle ij \\vert \\hat{v} \\vert ab \\rangle}{\\epsilon_i+\\epsilon_j-\\epsilon_a-\\epsilon_b}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At the end we can rewrite our solution of the Schroedinger equation in terms of $n$ coupled equations for the coefficients $C_H^P$.\n",
"This is a very cumbersome way of solving the equation. However, by using this iterative scheme we can illustrate how we can compute the\n",
"various terms in the wave operator or correlation operator $\\hat{C}$. We will later identify the calculation of the various terms $C_H^P$\n",
"as parts of different many-body approximations to full CI. In particular, we can relate this non-linear scheme with Coupled Cluster theory and\n",
"many-body perturbation theory.\n",
"\n",
"\n",
"## Summarizing FCI and bringing in approximative methods\n",
"\n",
"\n",
"If we can diagonalize large matrices, FCI is the method of choice since:\n",
"* It gives all eigenvalues, ground state and excited states\n",
"\n",
"* The eigenvectors are obtained directly from the coefficients $C_H^P$ which result from the diagonalization\n",
"\n",
"* We can compute easily expectation values of other operators, as well as transition probabilities\n",
"\n",
"* Correlations are easy to understand in terms of contributions to a given operator beyond the Hartree-Fock contribution. This is the standard approach in many-body theory. \n",
"\n",
"The correlation energy is defined as, with a two-body Hamiltonian,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Delta E=\\sum_{ai}\\langle i| \\hat{f}|a \\rangle C_{i}^{a}+\n",
"\\sum_{abij}\\langle ij | \\hat{v}| ab \\rangle C_{ij}^{ab}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The coefficients $C$ result from the solution of the eigenvalue problem. \n",
"The energy of say the ground state is then"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E=E_{ref}+\\Delta E,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the so-called reference energy is the energy we obtain from a Hartree-Fock calculation, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E_{ref}=\\langle \\Phi_0 \\vert \\hat{H} \\vert \\Phi_0 \\rangle.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, as we have seen, even for a small case like the four first major shells and a nucleus like oxygen-16, the dimensionality becomes quickly intractable. If we wish to include single-particle states that reflect weakly bound systems, we need a much larger single-particle basis. We need thus approximative methods that sum specific correlations to infinite order. \n",
"\n",
"Popular methods are\n",
"* Many-body perturbation theory (in essence a Taylor expansion)\n",
"\n",
"* Coupled cluster theory (coupled non-linear equations)\n",
"\n",
"* Green's function approaches (matrix inversion)\n",
"\n",
"* Similarity group transformation methods (coupled ordinary differential equations\n",
"\n",
"All these methods start normally with a Hartree-Fock basis as the calculational basis. \n",
"\n",
"\n",
"## Building a many-body basis\n",
"\n",
"Here we will discuss how we can set up a single-particle basis which we can use in the various parts of our projects, from the simple pairing model to infinite nuclear matter. We will use here the simple pairing model to illustrate in particular how to set up a single-particle basis. We will also use this do discuss standard FCI approaches like:\n",
" 1. Standard shell-model basis in one or two major shells\n",
"\n",
" 2. Full CI in a given basis and no truncations\n",
"\n",
" 3. CISD and CISDT approximations\n",
"\n",
" 4. No-core shell model and truncation in excitation energy\n",
"\n",
"An important step in an FCI code is to construct the many-body basis. \n",
"\n",
"While the formalism is independent of the choice of basis, the **effectiveness** of a calculation \n",
"will certainly be basis dependent. \n",
"\n",
"Furthermore there are common conventions useful to know.\n",
"\n",
"First, the single-particle basis has angular momentum as a good quantum number. You can \n",
"imagine the single-particle wavefunctions being generated by a one-body Hamiltonian, \n",
"for example a harmonic oscillator. Modifications include harmonic oscillator plus \n",
"spin-orbit splitting, or self-consistent mean-field potentials, or the Woods-Saxon potential which mocks \n",
"up the self-consistent mean-field. \n",
"For nuclei, the harmonic oscillator, modified by spin-orbit splitting, provides a useful language \n",
"for describing single-particle states.\n",
"\n",
"\n",
"Each single-particle state is labeled by the following quantum numbers: \n",
"\n",
"* Orbital angular momentum $l$\n",
"\n",
"* Intrinsic spin $s$ = 1/2 for protons and neutrons\n",
"\n",
"* Angular momentum $j = l \\pm 1/2$\n",
"\n",
"* $z$-component $j_z$ (or $m$)\n",
"\n",
"* Some labeling of the radial wavefunction, typically $n$ the number of nodes in the radial wavefunction, but in the case of harmonic oscillator one can also use the principal quantum number $N$, where the harmonic oscillator energy is $(N+3/2)\\hbar \\omega$. For our nuclear matter projects, you will need to change the quantum numbers to those relevant for calculations\n",
"\n",
"in three-dimensional cartesian basis, see the relevante [lectures](https://github.com/NuclearTalent/Course2ManyBodyMethods/blob/master/doc/pub/cc/pdf/Lectures1-2_TALENT_NuclearMatter_GH.pdf).\n",
"\n",
"\n",
"In this format one labels states by $n(l)_j$, with $(l)$ replaced by a letter:\n",
"$s$ for $l=0$, $p$ for $l=1$, $d$ for $l=2$, $f$ for $l=3$, and thenceforth alphabetical.\n",
"\n",
"\n",
" In practice the single-particle space has to be severely truncated. This truncation is \n",
"typically based upon the single-particle energies, which is the effective energy \n",
"from a mean-field potential. \n",
"\n",
"Sometimes we freeze the core and only consider a valence space. For example, one \n",
"may assume a frozen ${}^{4}\\mbox{He}$ core, with two protons and two neutrons in the $0s_{1/2}$ \n",
"shell, and then only allow active particles in the $0p_{1/2}$ and $0p_{3/2}$ orbits. \n",
"\n",
"\n",
"Another example is a frozen ${}^{16}\\mbox{O}$ core, with eight protons and eight neutrons filling the \n",
"$0s_{1/2}$, $0p_{1/2}$ and $0p_{3/2}$ orbits, with valence particles in the \n",
"$0d_{5/2}, 1s_{1/2}$ and $0d_{3/2}$ orbits.\n",
"\n",
"\n",
"Sometimes we refer to nuclei by the valence space where their last nucleons go. \n",
"So, for example, we call ${}^{12}\\mbox{C}$ a $p$-shell nucleus, while ${}^{26}\\mbox{Al}$ is an \n",
"$sd$-shell nucleus and ${}^{56}\\mbox{Fe}$ is a $pf$-shell nucleus.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"There are different kinds of truncations.\n",
"\n",
"* For example, one can start with `filled' orbits (almost always the lowest), and then allow one, two, three... particles excited out of those filled orbits. These are called 1p-1h, 2p-2h, 3p-3h excitations. \n",
"\n",
"* Alternately, one can state a maximal orbit and allow all possible configurations with particles occupying states up to that maximum. This is called *full configuration*.\n",
"\n",
"* Finally, for particular use in nuclear physics, there is the *energy* truncation, also called the $N\\hbar\\Omega$ or $N_{max}$ truncation. \n",
"\n",
"Here one works in a harmonic oscillator basis, with each major oscillator shell assigned a principal quantum number $N=0,1,2,3,...$. \n",
"The $N\\hbar\\Omega$ or $N_{max}$ truncation: Any configuration is given an noninteracting energy, which is the sum \n",
"of the single-particle harmonic oscillator energies. (Thus this ignores \n",
"spin-orbit splitting.)\n",
"\n",
"Excited state are labeled relative to the lowest configuration by the \n",
"number of harmonic oscillator quanta.\n",
"\n",
"This truncation is useful because if one includes *all* configuration up to \n",
"some $N_{max}$, and has a translationally invariant interaction, then the intrinsic \n",
"motion and the center-of-mass motion factor. In other words, we can know exactly \n",
"the center-of-mass wavefunction. \n",
"\n",
"In almost all cases, the many-body Hamiltonian is rotationally invariant. This means \n",
"it commutes with the operators $\\hat{J}^2, \\hat{J}_z$ and so eigenstates will have \n",
"good $J,M$. Furthermore, the eigenenergies do not depend upon the orientation $M$. \n",
"\n",
"\n",
"Therefore we can choose to construct a many-body basis which has fixed $M$; this is \n",
"called an $M$-scheme basis. \n",
"\n",
"\n",
"Alternately, one can construct a many-body basis which has fixed $J$, or a $J$-scheme \n",
"basis. \n",
"\n",
"The Hamiltonian matrix will have smaller dimensions (a factor of 10 or more) in the $J$-scheme than in the $M$-scheme. \n",
"On the other hand, as we'll show in the next slide, the $M$-scheme is very easy to \n",
"construct with Slater determinants, while the $J$-scheme basis states, and thus the \n",
"matrix elements, are more complicated, almost always being linear combinations of \n",
"$M$-scheme states. $J$-scheme bases are important and useful, but we'll focus on the \n",
"simpler $M$-scheme.\n",
"\n",
"The quantum number $m$ is additive (because the underlying group is Abelian): \n",
"if a Slater determinant $\\hat{a}_i^\\dagger \\hat{a}^\\dagger_j \\hat{a}^\\dagger_k \\ldots | 0 \\rangle$ \n",
"is built from single-particle states all with good $m$, then the total"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"M = m_i + m_j + m_k + \\ldots\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is *not* true of $J$, because the angular momentum group SU(2) is not Abelian.\n",
"\n",
"The upshot is that \n",
"* It is easy to construct a Slater determinant with good total $M$;\n",
"\n",
"* It is trivial to calculate $M$ for each Slater determinant;\n",
"\n",
"* So it is easy to construct an $M$-scheme basis with fixed total $M$.\n",
"\n",
"Note that the individual $M$-scheme basis states will *not*, in general, \n",
"have good total $J$. \n",
"Because the Hamiltonian is rotationally invariant, however, the eigenstates will \n",
"have good $J$. (The situation is muddied when one has states of different $J$ that are \n",
"nonetheless degenerate.) \n",
"\n",
"\n",
"\n",
"\n",
"Example: two $j=1/2$ orbits\n",
"\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\">Index</th> <th align=\"center\">$n$</th> <th align=\"center\">$l$</th> <th align=\"center\">$j$</th> <th align=\"center\">$m_j$</th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> 1 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> -1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 2 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> 1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 3 </td> <td align=\"center\"> 1 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> -1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 4 </td> <td align=\"center\"> 1 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> 1/2 </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"Note that the order is arbitrary.\n",
"There are $\\left ( \\begin{array}{c} 4 \\\\ 2 \\end{array} \\right) = 6$ two-particle states, \n",
"which we list with the total $M$:\n",
"\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\">Occupied</th> <th align=\"center\">$M$</th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> 1,2 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> 1,3 </td> <td align=\"center\"> -1 </td> </tr>\n",
"<tr><td align=\"center\"> 1,4 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> 2,3 </td> <td align=\"center\"> 0 </td> </tr>\n",
"<tr><td align=\"center\"> 2,4 </td> <td align=\"center\"> 1 </td> </tr>\n",
"<tr><td align=\"center\"> 3,4 </td> <td align=\"center\"> 0 </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"and 1 each with $M = \\pm 1$.\n",
"\n",
"\n",
"\n",
"\n",
"As another example, consider using only single particle states from the $0d_{5/2}$ space. \n",
"They have the following quantum numbers\n",
"\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\">Index</th> <th align=\"center\">$n$</th> <th align=\"center\">$l$</th> <th align=\"center\">$j$</th> <th align=\"center\">$m_j$</th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> 1 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 5/2 </td> <td align=\"center\"> -5/2 </td> </tr>\n",
"<tr><td align=\"center\"> 2 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 5/2 </td> <td align=\"center\"> -3/2 </td> </tr>\n",
"<tr><td align=\"center\"> 3 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 5/2 </td> <td align=\"center\"> -1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 4 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 5/2 </td> <td align=\"center\"> 1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 5 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 5/2 </td> <td align=\"center\"> 3/2 </td> </tr>\n",
"<tr><td align=\"center\"> 6 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 5/2 </td> <td align=\"center\"> 5/2 </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"There are $\\left ( \\begin{array}{c} 6 \\\\ 2 \\end{array} \\right) = 15$ two-particle states, \n",
"which we list with the total $M$:\n",
"\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\">Occupied</th> <th align=\"center\">$M$</th> <th align=\"center\">Occupied</th> <th align=\"center\">$M$</th> <th align=\"center\">Occupied</th> <th align=\"center\">$M$</th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> 1,2 </td> <td align=\"center\"> -4 </td> <td align=\"center\"> 2,3 </td> <td align=\"center\"> -2 </td> <td align=\"center\"> 3,5 </td> <td align=\"center\"> 1 </td> </tr>\n",
"<tr><td align=\"center\"> 1,3 </td> <td align=\"center\"> -3 </td> <td align=\"center\"> 2,4 </td> <td align=\"center\"> -1 </td> <td align=\"center\"> 3,6 </td> <td align=\"center\"> 2 </td> </tr>\n",
"<tr><td align=\"center\"> 1,4 </td> <td align=\"center\"> -2 </td> <td align=\"center\"> 2,5 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 4,5 </td> <td align=\"center\"> 2 </td> </tr>\n",
"<tr><td align=\"center\"> 1,5 </td> <td align=\"center\"> -1 </td> <td align=\"center\"> 2,6 </td> <td align=\"center\"> 1 </td> <td align=\"center\"> 4,6 </td> <td align=\"center\"> 3 </td> </tr>\n",
"<tr><td align=\"center\"> 1,6 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 3,4 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 5,6 </td> <td align=\"center\"> 4 </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"\n",
"\n",
"\n",
"\n",
"## Example case: pairing Hamiltonian, the warm-up project\n",
"\n",
"\n",
"We consider a space with $2\\Omega$ single-particle states, with each \n",
"state labeled by \n",
"$k = 1, 2, 3, \\Omega$ and $m = \\pm 1/2$. The convention is that \n",
"the state with $k>0$ has $m = + 1/2$ while $-k$ has $m = -1/2$.\n",
"\n",
"\n",
"The Hamiltonian we consider is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{H} = -\\frac{g}{2} \\hat{P}_+ \\hat{P}_-,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{P}_+ = \\sum_{k > 0} \\hat{a}^\\dagger_k \\hat{a}^\\dagger_{-{k}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and $\\hat{P}_- = ( \\hat{P}_+)^\\dagger$.\n",
"\n",
"This problem can be solved using what is called the quasi-spin formalism to obtain the \n",
"exact results. Thereafter we will try again using the explicit Slater determinant formalism.\n",
"\n",
"\n",
"In the first part project we will consider four doubly degenerate single-particle states, resulting in eight single-particle states as shown here\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\">Index</th> <th align=\"center\">$n$</th> <th align=\"center\">$l$</th> <th align=\"center\">$s$</th> <th align=\"center\">$m_s$</th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> 1 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> -1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 2 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> 1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 3 </td> <td align=\"center\"> 1 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> -1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 4 </td> <td align=\"center\"> 1 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> 1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 5 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> -1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 6 </td> <td align=\"center\"> 2 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> 1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 7 </td> <td align=\"center\"> 3 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> -1/2 </td> </tr>\n",
"<tr><td align=\"center\"> 8 </td> <td align=\"center\"> 3 </td> <td align=\"center\"> 0 </td> <td align=\"center\"> 1/2 </td> <td align=\"center\"> 1/2 </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"\n",
"If we limit ourselves to four fermions only and states with no broken pairs, \n",
"total $M=0$ states, we end with sixSlater determinants\n",
"\n",
"* $| 1, 2 , 3 , 4 \\rangle , $\n",
"\n",
"* $| 1 , 2 , 5 , 6 \\rangle , $\n",
"\n",
"* $| 1 , 2 , 7 , 8 \\rangle , $\n",
"\n",
"* $| 3 , 4 , 5 , 6 \\rangle , $\n",
"\n",
"* $| 3 , 4 , 7 , 8 \\rangle , $\n",
"\n",
"* $| 5 , 6 , 7 , 8 \\rangle $\n",
"\n",
"For our example, the $ 6 \\times 6$ Hamiltonian matrix becomes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"H = \\left ( \n",
"\\begin{array}{cccccc}\n",
"2\\delta -g & -g/2 & -g/2 & -g/2 & -g/2 & 0 \\\\\n",
" -g/2 & 4\\delta -g & -g/2 & -g/2 & -0 & -g/2 \\\\\n",
"-g/2 & -g/2 & 6\\delta -g & 0 & -g/2 & -g/2 \\\\\n",
" -g/2 & -g/2 & 0 & 6\\delta-g & -g/2 & -g/2 \\\\\n",
" -g/2 & 0 & -g/2 & -g/2 & 8\\delta-g & -g/2 \\\\\n",
"0 & -g/2 & -g/2 & -g/2 & -g/2 & 10\\delta -g \n",
"\\end{array} \\right )\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(You should check by hand that this is correct.) \n",
"\n",
"For $\\delta = 0$ we have the closed form solution of the g.s. energy given by $-6G$."
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
hotfuzzy/go | Reading_csv_2_batches.ipynb | 1 | 3779 | {
"metadata": {
"name": "",
"signature": "sha256:cbcf9b2fef76537eccb9b989ad5a4b1a0a0e2b747b98818ab0a02d7c4d46ae56"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import tensorflow as tf\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def read_my_csv(filename_queue):\n",
" # Set up the reader\n",
" reader = tf.TextLineReader()\n",
" # Grab the values from the file(s)\n",
" key, value = reader.read(filename_queue)\n",
" # Perform the decoding\n",
" default_values = [[\"0\"],[\"0 0\"],[\"0 0\"]]\n",
" col1, col2, col3 = tf.decode_csv(value, record_defaults=default_values)\n",
" features = tf.stack([col1, col2])\n",
" # Perform preporcessing here\n",
" ##\n",
" return features, col3"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def input_pipeline(filenames, batch_size):\n",
" filename_queue = tf.train.string_input_producer(filenames, shuffle=True)\n",
" example, label = read_my_csv(filename_queue)\n",
" min_after_dequeue = 100\n",
" capacity = min_after_dequeue + 3 * batch_size\n",
" # Create the batches using shuffle_batch which performs random shuffling\n",
" example_batch, label_batch = tf.train.shuffle_batch([example, label], \n",
" batch_size=batch_size, \n",
" capacity=capacity, \n",
" min_after_dequeue=min_after_dequeue)\n",
" return example_batch, label_batch"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"example_batch, label_batch = input_pipeline([\"./Data/samplecsv.txt\"], 2)\n",
"\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" coordinator = tf.train.Coordinator()\n",
" threads = tf.train.start_queue_runners(coord=coordinator)\n",
" \n",
" for i in xrange(10):\n",
" print sess.run(example_batch)\n",
" \n",
" coordinator.request_stop()\n",
" coordinator.join(threads)\n",
" sess.close()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[['1' '0 1']\n",
" ['1' '1 0']]\n",
"[['1' '0 1']\n",
" ['-1' '0 -1']]\n",
"[['-1' '0 0']\n",
" ['-1' '0 -1']]\n",
"[['1' '0 1']\n",
" ['-1' '0 -1']]\n",
"[['1' '1 0']\n",
" ['-1' '0 0']]\n",
"[['-1' '0 -1']\n",
" ['-1' '0 -1']]\n",
"[['-1' '0 -1']\n",
" ['1' '1 0']]\n",
"[['-1' '0 -1']\n",
" ['1' '0 1']]\n",
"[['-1' '0 -1']\n",
" ['-1' '0 -1']]\n",
"[['-1' '0 -1']\n",
" ['-1' '0 -1']]\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
}
],
"metadata": {}
}
]
} | mit |
sebastien17/MAVlink_plug | example/Test_MessageType.ipynb | 1 | 3221 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Importing ardupilotmega\n"
]
}
],
"source": [
"import mavlinkplug.Message"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 4, 8, 16]\n",
"['\\x01', '\\x02', '\\x04', '\\x08', '\\x10']\n",
"[<class 'mavlinkplug.Message.MAVLinkData'>, <class 'mavlinkplug.Message.MavCommandData'>, <class 'mavlinkplug.Message.KillData'>, <class 'mavlinkplug.Message.RawData'>, <class 'mavlinkplug.Message.LogData'>]\n"
]
}
],
"source": [
"print(mavlinkplug.Message.TYPE._values)\n",
"print(mavlinkplug.Message.TYPE._p_values)\n",
"print(mavlinkplug.Message.TYPE._m_classes)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mavlinkplug.Message.RawData"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mavlinkplug.Message.TYPE.get_class_from_p_value('\\x08')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mavlinkplug.Message.MAVLinkData"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mavlinkplug.Message.TYPE.get_class_from_value(1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'_PACK_FORMAT': '!B',\n",
" '_names': ['ALL'],\n",
" '_p_values': ['\\xff'],\n",
" '_values': [255]}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mavlinkplug.Message.DESTINATION.__dict__"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DestinationItem(value=255, p_value='\\xff')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mavlinkplug.Message.DESTINATION.ALL"
]
},
{
"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 |
NoonienSoong/Data-Science-45min-Intros | Bokeh/04 - styling.ipynb | 7 | 1759762 | null | unlicense |
immersinn/ncga | notebooks/explore/Extract Content from Bill Text.ipynb | 1 | 80703 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'\\nSee \"bill_text_preproc.py\" file\\n\\n'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"See \"bill_text_preproc.py\" file\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pickle\n",
"from bs4 import BeautifulSoup as bs\n",
"import pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Bills"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bill_texts_file = \"data/bill_texts_filed.pkl\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(bill_texts_file, 'rb') as f1:\n",
" bill_texts_filed = pickle.load(f1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bill_texts_filed = pandas.DataFrame(bill_texts_filed)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2098, 4)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bill_texts_filed.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bill</th>\n",
" <th>house</th>\n",
" <th>session</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bill house session text\n",
"0 1 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"1 2 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"2 3 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"3 4 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"4 5 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con..."
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bill_texts_filed.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract Data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sample = bill_texts_filed.sample(25)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bill</th>\n",
" <th>house</th>\n",
" <th>session</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>952</th>\n",
" <td>908</td>\n",
" <td>H</td>\n",
" <td>2015</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1850</th>\n",
" <td>656</td>\n",
" <td>S</td>\n",
" <td>2015</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1027</th>\n",
" <td>983</td>\n",
" <td>H</td>\n",
" <td>2015</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>929</th>\n",
" <td>885</td>\n",
" <td>H</td>\n",
" <td>2015</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1676</th>\n",
" <td>482</td>\n",
" <td>S</td>\n",
" <td>2015</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bill house session text\n",
"952 908 H 2015 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"1850 656 S 2015 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"1027 983 H 2015 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"929 885 H 2015 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...\n",
"1676 482 S 2015 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con..."
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get some Examples"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ht = sample.text[952]\n",
"st = sample.text[1850]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hs = bs(ht, 'html.parser')\n",
"ss = bs(st, 'html.parser')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hs = hs.body\n",
"ss = ss.body"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<body lang=\"EN-US\">\n",
"<div class=\"WordSection1\">\n",
"<p align=\"center\" class=\"nonumber\" style=\"margin-top:24.0pt;text-align:center\"><b>GENERAL\n",
"ASSEMBLY OF NORTH CAROLINA</b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
"margin-bottom:6.0pt;margin-left:0in;text-align:center\"><b>SESSION 2015</b></p>\n",
"<p align=\"left\" class=\"nonumber\" style=\"text-align:left\"><b>H D</b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b>HOUSE\n",
"DRH10271-LRa-136A (04/02)</b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b> </b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b> </b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b> </b></p>\n",
"<table border=\"1\" cellpadding=\"0\" cellspacing=\"0\" class=\"MsoNormalTable\" style=\"width:6.55in;border-collapse:collapse;border:none\" width=\"629\">\n",
"<tr>\n",
"<td colspan=\"2\" style=\"width:408.25pt;border:none;\n",
" border-bottom:solid windowtext 1.0pt;padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"544\">\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-bottom:6.0pt;text-align:left\">Short\n",
" Title: DEM/Emp. Retention Funds/LRC Study.</p>\n",
"</td>\n",
"<td style=\"width:63.35pt;border:none;border-bottom:solid windowtext 1.0pt;\n",
" padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"84\">\n",
"<p align=\"right\" class=\"nonumber\" style=\"margin-bottom:6.0pt;text-align:right\">(Public)</p>\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td style=\"width:1.0in;border:none;border-bottom:solid windowtext 1.0pt;\n",
" padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"96\">\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Sponsors:</p>\n",
"</td>\n",
"<td colspan=\"2\" style=\"width:5.55in;border:none;\n",
" border-bottom:solid windowtext 1.0pt;padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"533\">\n",
"<p class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;margin-bottom:\n",
" 3.0pt;margin-left:0in\">Representative Whitmire.</p>\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td style=\"width:1.0in;border:none;border-bottom:solid windowtext 1.0pt;\n",
" padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"96\">\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Referred to:</p>\n",
"</td>\n",
"<td colspan=\"2\" style=\"width:5.55in;border:none;\n",
" border-bottom:solid windowtext 1.0pt;padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"533\">\n",
"<p class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;margin-bottom:\n",
" 3.0pt;margin-left:0in\"> </p>\n",
"</td>\n",
"</tr>\n",
"<tr height=\"0\">\n",
"<td style=\"border:none\" width=\"96\"></td>\n",
"<td style=\"border:none\" width=\"448\"></td>\n",
"<td style=\"border:none\" width=\"84\"></td>\n",
"</tr>\n",
"</table>\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-top:6.0pt;text-align:left\"> </p>\n",
"<p align=\"center\" class=\"aBase\" style=\"text-align:center\">A BILL TO BE ENTITLED</p>\n",
"<p class=\"aLongTitle\"><span style='font-family:\"Times New Roman\",\"serif\"'>AN ACT to\n",
"appropriate funds to the department of public safety for retention‑based\n",
"salary adjustments for division of emergency management employees and\n",
"authorizing the legislative research commission to review whether there should\n",
"be established an emergency management preparedness and response fee to support\n",
"the work of the division.</span></p>\n",
"<p class=\"aBase\">The General Assembly of North Carolina enacts:</p>\n",
"<p class=\"aBillSection\"><b>SECTION 1.(a)</b> There is appropriated from the\n",
"General Fund to the Department of Public Safety the sum of four hundred six\n",
"thousand four hundred fifty‑nine dollars ($406,459) for the 2015‑2016\n",
"fiscal year and the sum of four hundred six thousand four hundred fifty‑nine\n",
"dollars ($406,459) for the 2016‑2017 fiscal year to provide salary\n",
"adjustments for the retention of employees in the Division of Emergency\n",
"Management.</p>\n",
"<p class=\"aBillSection\"><b>SECTION 1.(b)</b> This section becomes effective July\n",
"1, 2015.</p>\n",
"<p class=\"aBillSection\"><b>SECTION 2.</b> The Legislative Research Commission\n",
"shall study whether an emergency management preparedness and response fee\n",
"should be assessed on each homeowners', mobile home owners', tenant homeowners',\n",
"and condominium unit owners' property insurance policy issued in this State for\n",
"the purpose of funding the operations of the Division of Emergency Management\n",
"of the Department of Public Safety. The Legislative Research Commission may\n",
"submit a report of its findings and recommendations to the 2015 General\n",
"Assembly, prior to the convening of the 2016 Regular Session, by filing the\n",
"report with the Speaker of the House of Representatives and the President Pro\n",
"Tempore of the Senate, with a copy of the report submitted to the respective\n",
"Chairs of the House of Representatives and Senate Appropriations Committees on\n",
"Justice and Public Safety.</p>\n",
"<p class=\"aBillSection\"><b>SECTION 3.</b> This act is effective when it becomes\n",
"law.</p>\n",
"</div>\n",
"</body>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hs"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<body lang=\"EN-US\">\n",
"<div class=\"WordSection1\">\n",
"<p align=\"center\" class=\"nonumber\" style=\"margin-top:24.0pt;text-align:center\"><b>GENERAL\n",
"ASSEMBLY OF NORTH CAROLINA</b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
"margin-bottom:6.0pt;margin-left:0in;text-align:center\"><b>SESSION 2015</b></p>\n",
"<p align=\"left\" class=\"nonumber\" style=\"text-align:left\"><b>S D</b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b>SENATE\n",
"DRS45321-LRf-105 (03/14)</b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b> </b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b> </b></p>\n",
"<p align=\"center\" class=\"nonumber\" style=\"text-align:center\"><b> </b></p>\n",
"<table border=\"1\" cellpadding=\"0\" cellspacing=\"0\" class=\"MsoNormalTable\" style=\"width:6.55in;border-collapse:collapse;border:none\" width=\"629\">\n",
"<tr>\n",
"<td colspan=\"2\" style=\"width:408.25pt;border:none;\n",
" border-bottom:solid windowtext 1.0pt;padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"544\">\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-bottom:6.0pt;text-align:left\">Short\n",
" Title: WC/2015 Omnibus Law Changes.</p>\n",
"</td>\n",
"<td style=\"width:63.35pt;border:none;border-bottom:solid windowtext 1.0pt;\n",
" padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"84\">\n",
"<p align=\"right\" class=\"nonumber\" style=\"margin-bottom:6.0pt;text-align:right\">(Public)</p>\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td style=\"width:1.0in;border:none;border-bottom:solid windowtext 1.0pt;\n",
" padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"96\">\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Sponsors:</p>\n",
"</td>\n",
"<td colspan=\"2\" style=\"width:5.55in;border:none;\n",
" border-bottom:solid windowtext 1.0pt;padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"533\">\n",
"<p class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;margin-bottom:\n",
" 3.0pt;margin-left:0in\">Senator Lee (Primary Sponsor).</p>\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td style=\"width:1.0in;border:none;border-bottom:solid windowtext 1.0pt;\n",
" padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"96\">\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Referred to:</p>\n",
"</td>\n",
"<td colspan=\"2\" style=\"width:5.55in;border:none;\n",
" border-bottom:solid windowtext 1.0pt;padding:0in .05in 0in 5.4pt\" valign=\"top\" width=\"533\">\n",
"<p class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;margin-bottom:\n",
" 3.0pt;margin-left:0in\"> </p>\n",
"</td>\n",
"</tr>\n",
"<tr height=\"0\">\n",
"<td style=\"border:none\" width=\"96\"></td>\n",
"<td style=\"border:none\" width=\"448\"></td>\n",
"<td style=\"border:none\" width=\"84\"></td>\n",
"</tr>\n",
"</table>\n",
"<p align=\"left\" class=\"nonumber\" style=\"margin-top:6.0pt;text-align:left\"> </p>\n",
"<p align=\"center\" class=\"aBase\" style=\"text-align:center\">A BILL TO BE ENTITLED</p>\n",
"<p class=\"aLongTitle\"><span style='font-family:\"Times New Roman\",\"serif\"'>AN ACT clarifying\n",
"the authority and duties of industrial commission fraud investigators and making\n",
"technical, conforming, and other changes to the workers' compensation laws of\n",
"north carolina.</span></p>\n",
"<p class=\"aBase\">The General Assembly of North Carolina enacts:</p>\n",
"<p class=\"aBillSection\"><b>SECTION 1.(a)</b> Article 1 of Chapter 97 of the\n",
"General Statutes is amended by adding a new section to read:</p>\n",
"<p class=\"aSection\"><span style=\"font-weight:normal\">\"</span><u>§ 97‑79.1. \n",
"Authority of Industrial Commission fraud investigators; inspection of records.</u></p>\n",
"<p class=\"aMargin1\"><u>(a)</u> <u>The Commission shall establish a Criminal\n",
"Investigation Unit to operate as a law enforcement agency for the enforcement\n",
"of this Chapter. Members of the unit shall serve as fraud investigators and\n",
"must be sworn law enforcement officers duly appointed and certified by the\n",
"North Carolina Criminal Justice Education and Training Standards Commission.</u></p>\n",
"<p class=\"aMargin1\"><u>(b)</u> <u>A fraud investigator employed by the\n",
"Commission, who has sworn the oath prescribed for a law enforcement officer, shall\n",
"have the following authority:</u></p>\n",
"<p class=\"aBlock1\"><u>(1)</u> <u>To make arrests and take other\n",
"investigatory and enforcement actions for both felonies and misdemeanors and to\n",
"charge for infractions for violations of the laws of the State, with the\n",
"primary responsibility of enforcing the Workers' Compensation Act.</u></p>\n",
"<p class=\"aBlock1\"><u>(2)</u> <u>To act as a State law enforcement officer\n",
"with jurisdiction throughout the State.</u></p>\n",
"<p class=\"aBlock1\"><u>(3)</u> <u>To serve and execute orders issued by the\n",
"Commission in connection with contempt proceedings. While serving and executing\n",
"such an order, a fraud investigator has the same authority and power possessed\n",
"by a local law officer or sheriff's deputy when executing an arrest warrant.</u></p>\n",
"<p class=\"aBlock1\"><u>(4)</u> <u>To inspect records of business kept under\n",
"G.S. 58‑2‑185 by insurance companies, agents, or brokers doing\n",
"any kind of business in this State involving workers' compensation.</u></p>\n",
"<p class=\"aMargin1\"><u>(c)</u> <u>Each insurance company, agent, or broker\n",
"keeping records under G.S. 58‑2‑185 shall furnish copies of these\n",
"records to the Commission's fraud investigators on demand, and the original\n",
"books of records shall be open to the inspection of the Commissioner when\n",
"demanded. Any person who refuses, on demand, to exhibit the records of business\n",
"as provided by this subsection or who knowingly makes a false statement in\n",
"regard to the records when demanded is guilty of a Class 1 misdemeanor.</u>\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 1.(b)</b> G.S. 143‑166.13 is amended\n",
"by adding a new subdivision to read:</p>\n",
"<p class=\"aBlock1\">\"<u>(20)</u> <u>Sworn State Law‑Enforcement\n",
"Officers with the power of arrest, Industrial Commission Fraud Investigators,\n",
"Department of Commerce.</u>\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 2.</b> G.S. 97‑88.2(b) reads as\n",
"rewritten:</p>\n",
"<p class=\"aMargin1\">\"(b) The Commission shall:</p>\n",
"<p class=\"aBlock1\">(1) Perform investigations regarding all cases of\n",
"suspected fraud and all violations related to workers' compensation claims, by\n",
"or against insurers or self‑funded employers, and refer possible criminal\n",
"violations to the <s>appropriate prosecutorial authorities;</s><u>Criminal Investigation\n",
"Unit.</u></p>\n",
"<p class=\"aBlock1\">(2) Conduct administrative violation proceedings; and</p>\n",
"<p class=\"aBlock1\">(3) Assess and collect civil penalties and restitution.</p>\n",
"<p class=\"aMargin1\">The Commission may employ sworn law enforcement officers <s>duly\n",
"appointed and certified through the North Carolina Criminal Justice Education\n",
"and Training Standards Commission </s><u>pursuant to G.S. 97‑79.1 </u>to\n",
"<u>enforce the laws and </u>conduct the investigations mandated by this <s>subsection.</s><u>section.</u>\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 3.</b> G.S. 97‑73(d) reads as\n",
"rewritten:</p>\n",
"<p class=\"aMargin1\">\"(d) Safety. – A fee in the amount set by the Industrial\n",
"Commission is imposed on an employer for whom the Industrial Commission\n",
"provides an educational training program on how to prevent or reduce accidents\n",
"or injuries that result in workers' compensation claims or a person for whom\n",
"the Industrial Commission provides other educational services. <u>The\n",
"Commission may set a reasonable fee imposed for a review of the safety rules. </u>The\n",
"fees are departmental receipts.\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 4.</b> G.S. 97‑87(c)(5) reads as\n",
"rewritten</p>\n",
"<p class=\"aBlock1\">\"(5) If any party disputes the decision of the\n",
"Commission entered under subdivision (c)(4) of this section, the party may\n",
"appeal to the full Commission within 10 days of the entry of the decision of\n",
"the Commission. The nonappealing party may file a response within 10 days of\n",
"receiving notice of appeal. The notice of appeal shall request one of the\n",
"following:</p>\n",
"<p class=\"aBlock2\">a. The Commission reconsider the decision entered\n",
"based on the record and any additional evidence that parties submit with the\n",
"notice and response.</p>\n",
"<p class=\"aBlock2\">b. A de novo evidentiary hearing before the <s>full </s>Commission.\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 5.</b> G.S. 97‑87(c)(7) reads as\n",
"rewritten:</p>\n",
"<p class=\"aMargin1\">\"(c) When an award or portion of an award provides\n",
"for periodic payments to be made on or after the date of the award, a judgment\n",
"may be docketed as provided in subsection (d) of this section, in an amount\n",
"equal to the sum stated in any Certificate of Accrued Arrearages that is issued\n",
"by the Commission under this subsection. If any payment that has accrued after\n",
"the date of the award, or after the date specified in the most recent\n",
"Certificate of Accrued Arrearages issued under this subsection, is not received\n",
"by the claimant when due, the following procedure is available for obtaining a\n",
"Certificate of Accrued Arrearages:</p>\n",
"<p class=\"aBlock1\">…</p>\n",
"<p class=\"aBlock1\">(7) If a notice of appeal is given under sub‑subdivision\n",
"(c)(5)a. of this section, the Commission shall issue its decision within 10\n",
"days of the filing of the response under subdivision (c)(5)b. of this section.\n",
"If a notice of appeal is given under sub‑subdivision (c)(5) of this\n",
"section, the Commission shall either <u>(i) </u>conduct an evidentiary hearing\n",
"and issue its decision on the appeal within 90 days of the filing of the <s>response\n",
"</s><u>response, or when a response is due if no response is filed, </u>under\n",
"subdivision (c)(5) of this section or <u>(ii) </u>deny the request for the\n",
"evidentiary hearing and issue its decision within 10 days of the filing of the\n",
"response under subdivision (c)(5) of this section. Further appeals are governed\n",
"by G.S. 97‑86.\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 6.</b> G.S. 97‑92(d) reads as\n",
"rewritten:</p>\n",
"<p class=\"aMargin1\">\"(d) The said report shall contain the name,\n",
"nature, and location of the business of the employer and name, age, sex, <s>and\n",
"wages </s><u>wages, if available, </u>and occupation of the injured employee,\n",
"and shall state the date and hour of the accident causing injury, the nature\n",
"and cause of the injury, and such other information as may be required by the\n",
"Commission.\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 7.</b> G.S. 97‑101 reads as\n",
"rewritten:</p>\n",
"<p class=\"aSection\"><span style=\"font-weight:normal\">\"</span>§ 97‑101. \n",
"Collection of fines and penalties.</p>\n",
"<p class=\"aMargin1\">The Industrial Commission shall have the power by civil\n",
"action brought in its own name to enforce the collection of any fines or\n",
"penalties provided by this <s>Article, and fines or penalties collected by the\n",
"Commission shall become a part of the maintenance fund referred to in\n",
"subsection (j) of G.S. 97‑100.</s><u>Article.</u>\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 8.</b> G.S. 97‑26.2 reads as\n",
"rewritten:</p>\n",
"<p class=\"aSection\"><span style=\"font-weight:normal\">\"</span>§ 97‑26.2. \n",
"Reimbursement for prescription <s>drugs </s><u>drugs, prescribed over‑the‑counter\n",
"drugs, </u>and professional pharmaceutical services.</p>\n",
"<p class=\"aMargin1\">(a) The reimbursement for prescription <s>drugs </s><u>drugs,\n",
"prescribed over‑the‑counter drugs, </u>and professional\n",
"pharmaceutical services shall be limited to <u>no greater than </u>ninety‑five\n",
"percent (95%) of the average wholesale price (AWP) of the product, calculated\n",
"on a per unit basis, as of the date of dispensing.</p>\n",
"<p class=\"aMargin1\">(b) All of the following shall apply to the\n",
"reimbursement for prescription drugs and professional pharmaceutical services:</p>\n",
"<p class=\"aBlock1\">(1) A health care provider seeking reimbursement for <s>drugs\n",
"dispensed by a physician </s><u>health care provider dispensed prescription\n",
"drugs, prescribed over‑the‑counter drugs, and pharmaceutical\n",
"services </u>shall include the original manufacturer's National Drug Code (NDC)\n",
"number, as assigned by the United States Food and Drug Administration, on <s>the\n",
"bills and reports required by this section.</s><u>any billing documents or\n",
"invoices issued.</u></p>\n",
"<p class=\"aBlock1\">(2) In no event may a <s>physician </s><u>health care\n",
"provider </u>receive reimbursement in excess of ninety‑five percent (95%)\n",
"of the AWP of the drugs dispensed by a <s>physician, </s><u>health care\n",
"provider, </u>as determined by reference to the original manufacturer's NDC\n",
"number.</p>\n",
"<p class=\"aBlock1\">(3) A repackaged NDC number may not be <u>individually </u>used\n",
"<u>on any billing documents or invoices issued </u>and will not be considered\n",
"the original manufacturer's NDC number. <u>A repackaged NDC number may only\n",
"appear in conjunction with the manufacturer's NDC number. </u>If a health care\n",
"provider seeking reimbursement for drugs dispensed by a <s>physician </s><u>health\n",
"care provider </u>does not include the original manufacturer's NDC number on <s>the\n",
"bills and reports required by this section, </s><u>any billing documents or\n",
"invoices issued, </u>reimbursement shall be limited to one hundred percent\n",
"(100%) of the AWP of the least expensive clinically equivalent drug, calculated\n",
"on a per unit basis.</p>\n",
"<p class=\"aBlock1\">(4) No outpatient provider, other than a licensed\n",
"pharmacy, may receive reimbursement for a Schedule II controlled substance, as\n",
"defined in G.S. 90‑90, or a Schedule III controlled substance, as\n",
"defined in G.S. 90‑91, <u>or a Schedule IV controlled substance, as\n",
"defined by G.S. 90‑92, </u>dispensed in excess of an initial five‑day\n",
"supply, commencing upon the employee's initial treatment following injury. <u>Only\n",
"the initial health care provider providing the employee's initial treatment\n",
"following injury may seek reimbursement for dispensing controlled substances as\n",
"described in this section, and any subsequent dispensing of controlled\n",
"substances by another health care provider will be ineligible for reimbursement.\n",
"</u>Reimbursement under this subdivision shall be made for the five‑day\n",
"supply at the rates provided in this section.</p>\n",
"<p class=\"aBlock1\">(5) For purposes of this section, the term \"clinically\n",
"equivalent\" means a drug has chemical equivalents which, when administered\n",
"in the same amounts, will provide essentially the same therapeutic effect as\n",
"measured by the control of a symptom or disease.\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 9.</b> G.S. 97‑200(a) reads as\n",
"rewritten:</p>\n",
"<p class=\"aMargin1\">\"(a) A self‑insurer shall not utilize any\n",
"claims adjuster unless the adjuster is licensed under <s>G.S. 58‑33‑25.</s><u>G.S. 58‑33‑26.</u>\"</p>\n",
"<p class=\"aBillSection\"><b>SECTION 10.</b> This act is effective when it becomes\n",
"law.</p>\n",
"</div>\n",
"</body>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Raw Text Extraction"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cleanRawText(text):\n",
" \"\"\"\n",
" vtype text: str\n",
" \n",
" rtype text: str / utf8\n",
" \"\"\"\n",
" text = text.encode('ascii', 'replace')\n",
" text = text.replace(b'?', b'')\n",
" text = text.replace(b'\\r\\n', b'\\n')\n",
" text = text.replace(b'\\n', b' ')\n",
" text = text.strip()\n",
" text = text.decode('utf8')\n",
" return(text)"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def extractRawText(body):\n",
" \"\"\"\n",
" vtype body: bs4.element.Tag\n",
" \n",
" rtype raw_text: str\n",
" \"\"\"\n",
" paras = body.find_all('p')\n",
" texts = [p.text for p in paras]\n",
" texts = [t.strip() for t in texts]\n",
" raw_text = '\\n\\n'.join(texts)\n",
" raw_text = cleanRawText(raw_text)\n",
" return(raw_text)"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10271-LRa-136A (04/02) Short Title: DEM/Emp. Retention Funds/LRC Study. (Public) Sponsors: Representative Whitmire. Referred to: A BILL TO BE ENTITLED AN ACT to appropriate funds to the department of public safety for retentionbased salary adjustments for division of emergency management employees and authorizing the legislative research commission to review whether there should be established an emergency managem'"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hrt = extractRawText(hs)\n",
"hrt[:500]"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'ided in this section. (5) For purposes of this section, the term \"clinically equivalent\" means a drug has chemical equivalents which, when administered in the same amounts, will provide essentially the same therapeutic effect as measured by the control of a symptom or disease.\" SECTION 9. G.S.97200(a) reads as rewritten: \"(a) A selfinsurer shall not utilize any claims adjuster unless the adjuster is licensed under G.S.583325.G.S.583326.\" SECTION 10. This act is effective when it becomes law.'"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"srt = extractRawText(ss)\n",
"srt[-500:]"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"text = hrt"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"b'GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10271-LRa-136A (04/02) Short Title: DEM/Emp. Retention Funds/LRC Study. (Public) Sponsors: Representative Whitmire. Referred to: A BILL TO BE ENTITLED AN ACT to appropriate funds to the department of public safety for retentionbased salary adjustments for division of emergency management employees and authorizing the legislative research commission to review whether there should be established an emergency managem'"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = text.encode('ascii', 'replace')\n",
"text[:500]"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"b'GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10271-LRa-136A (04/02) Short Title: DEM/Emp. Retention Funds/LRC Study. (Public) Sponsors: Representative Whitmire. Referred to: A BILL TO BE ENTITLED AN ACT to appropriate funds to the department of public safety for retentionbased salary adjustments for division of emergency management employees and authorizing the legislative research commission to review whether there should be established an emergency managem'"
]
},
"execution_count": 167,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = text.replace(b'?', b'')\n",
"text[:500]"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"b'GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10271-LRa-136A (04/02) Short Title: DEM/Emp. Retention Funds/LRC Study. (Public) Sponsors: Representative Whitmire. Referred to: A BILL TO BE ENTITLED AN ACT to appropriate funds to the department of public safety for retentionbased salary adjustments for division of emergency management employees and authorizing the legislative research commission to review whether there should be established an emergency managem'"
]
},
"execution_count": 168,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = text.replace(b'\\r\\n', b'\\n')\n",
"text[:500]"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"b'GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10271-LRa-136A (04/02) Short Title: DEM/Emp. Retention Funds/LRC Study. (Public) Sponsors: Representative Whitmire. Referred to: A BILL TO BE ENTITLED AN ACT to appropriate funds to the department of public safety for retentionbased salary adjustments for division of emergency management employees and authorizing the legislative research commission to review whether there should be established an emergency managem'"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = text.replace(b'\\n', b' ')\n",
"text[:500]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Test Code"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10271-LRa-136A (04/02) Short Title: DEM/Emp. Retention Funds/LRC Study. (Public) Sponsors: Representative Whitmire. Referred to: A BILL TO BE ENTITLED AN ACT to appropriate funds to the department of public safety for retentionbased salary adjustments for division of emergency management employees and authorizing the legislative research commission to review whether there should be established an emergency managem'"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cleanRawText(extractRawText(hs))[:500]"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE DRS45321-LRf-105 (03/14) Short Title: WC/2015 Omnibus Law Changes. (Public) Sponsors: Senator Lee (Primary Sponsor). Referred to: A BILL TO BE ENTITLED AN ACT clarifying the authority and duties of industrial commission fraud investigators and making technical, conforming, and other changes to the workers' compensation laws of north carolina. The General Assembly of North Carolina enacts: SECTION 1.(a) Article 1 o\""
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cleanRawText(extractRawText(ss))[:500]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (Select) Metadata Extraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### \"Long Title\" / Description"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def extractLongTitle(body):\n",
" return(cleanRawText(body.find('p', {'class':'aLongTitle'}).text))"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'AN ACT to appropriate funds to the department of public safety for retentionbased salary adjustments for division of emergency management employees and authorizing the legislative research commission to review whether there should be established an emergency management preparedness and response fee to support the work of the division.'"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extractLongTitle(hs)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"AN ACT clarifying the authority and duties of industrial commission fraud investigators and making technical, conforming, and other changes to the workers' compensation laws of north carolina.\""
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extractLongTitle(ss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Table Content"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<p align=\"left\" class=\"nonumber\" style=\"margin-bottom:6.0pt;text-align:left\">Short\n",
" Title: DEM/Emp. Retention Funds/LRC Study.</p>,\n",
" <p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Sponsors:</p>,\n",
" <p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Referred to:</p>]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[tr.find('p') for tr in hs.find('table').find_all('tr') if tr.find('p')]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<p align=\"left\" class=\"nonumber\" style=\"margin-bottom:6.0pt;text-align:left\">Short\n",
" Title: WC/2015 Omnibus Law Changes.</p>,\n",
" <p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Sponsors:</p>,\n",
" <p align=\"left\" class=\"nonumber\" style=\"margin-top:3.0pt;margin-right:0in;\n",
" margin-bottom:3.0pt;margin-left:0in;text-align:left\">Referred to:</p>]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[tr.find('p') for tr in ss.find('table').find_all('tr') if tr.find('p')]"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ATTR: b'Short Title'; VALUE: b'DEM/Emp. Retention Funds/LRC Study. (Public) '\n",
"ATTR: b'Sponsors'; VALUE: b' Representative Whitmire. '\n",
"ATTR: b'Referred to'; VALUE: b' '\n"
]
}
],
"source": [
"for row in c:\n",
" attr = cleanRawText(row.find('p').text)\n",
" value = cleanRawText(row.find_all('td')[-1].text)\n",
" if attr.lower().startswith(b'short'):\n",
" d = attr.split(b': ')\n",
" attr = d[0]\n",
" value = d[1].strip() + b' ' + value\n",
" attr = attr.strip().strip(b':')\n",
" print('ATTR: {}; VALUE: {}'.format(attr, value))"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def extractTableContent(body):\n",
" info = {}\n",
" contents = [tr for tr in body.find('table').find_all('tr') if tr.find('p')]\n",
" for row in contents:\n",
" attr = cleanRawText(row.find('p').text)\n",
" value = cleanRawText(row.find_all('td')[-1].text)\n",
" if attr.lower().startswith('short'):\n",
" d = attr.split(': ')\n",
" attr = d[0]\n",
" value = d[1].strip() + ' ' + value\n",
" attr = attr.strip().strip(':')\n",
" attr = ''.join(attr.split())\n",
" info[attr] = value\n",
" return(info)"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Referred to': '',\n",
" 'Short Title': 'DEM/Emp. Retention Funds/LRC Study. (Public)',\n",
" 'Sponsors': 'Representative Whitmire.'}"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extractTableContent(hs)"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Referred to': '',\n",
" 'Short Title': 'WC/2015 Omnibus Law Changes. (Public)',\n",
" 'Sponsors': 'Senator Lee (Primary Sponsor).'}"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extractTableContent(ss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test Bed"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soups = [bs(t, 'html.parser') for t in sample.text]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Text"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rts = [extractRawText(soup) for soup in soups]"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"crts = [cleanRawText(rt) for rt in rts]"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10271-LRa-136A (04/02) Short \n",
"ions Committees on Justice and Public Safety. SECTION 3. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE DRS45321-LRf-105 (03/14) Short \n",
"er is licensed under G.S.583325.G.S.583326.\" SECTION 10. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE BILL DRH10476-MGf-142 (03/14) Sh\n",
" (5) Any illicit spirituous liquor. (6) Mash.\" SECTION 6. This act becomes effective July 1, 2016.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10335-LR-125 (03/27) Short \n",
"shall be calculated to the nearest cent (1).\" SECTION 2. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE DRS15195-ML-136 (03/10) Short \n",
"(e), 57D941(b), (d), and (f), and 57D942(b).\" SECTION 2. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH20066-ST-18A (01/29) Short \n",
"ecomes effective May 1, 2015, and applies to zoning ordinance changes adopted on or after that date.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH20143-LL-147 (03/24) Short \n",
" act becomes effective July 1, 2015, and applies to eligible retirees who die on or after that date.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE DRS45308-ML-5C* (01/07) Short \n",
"t becomes effective December 1, 2015, and applies to any misconduct committed on or after that date.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH30090-LL-112 (3/4) Short Ti\n",
", 2015, and applies to persons placed on probation or postrelease supervision on or after that date.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH40369-LR-138 (04/02) Short \n",
"3. This act becomes effective January 1, 2016, and applies to sick leave used on or after that date.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE BILL DRS15327-MLf-265A (04/26) \n",
" this act become effective July 1, 2016. The remainder of this act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE BILL DRS25292-TAz-9A* (03/15) S\n",
"Human Services on or before December 1, 2016. SECTION 3. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH40397-MGfqq-4A* (11/10) Short\n",
"ately seek to become licensed under this act. SECTION 5. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH40096-ST-4A (10/30) Short T\n",
" among the permanent records of that office. SECTION 15. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH20091-MC-99A* (03/09) Short \n",
"e used for purposes consistent with this act. SECTION 3. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE BILL DRS15313-RW-18 (04/01) Sho\n",
"equal or greater funding from the applicant.\" SECTION 3. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE BILL DRH30495-MHa-162A (01/20) S\n",
"4 of this act is effective July 1, 2016. The remainder of the 0act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE DRS45161-MC-136 (03/17) Short \n",
"ON 2. This act is effective for investments for taxable years beginning on or after January 1, 2015.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH20101-MK-102 (03/10) Short \n",
"ON 2. This act is effective when it becomes law and applies beginning with the 20152016 school year.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH40076-LMx-21 (01/14) Short \n",
" corporate limits of the Town of Maggie Valley. SECTION 3. This act becomes effective July 1, 2015.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH20196-LL-149 (03/25) Short \n",
"h year and January 2 of each subsequent year. SECTION 2. This act is effective when it becomes law.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH10185-TD-13 (03/02) Short T\n",
"for all other nonliquid alternative fuels.\" SECTION 3. This section becomes effective July 1, 2015.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH30049-MH-27 (01/20) Short T\n",
"ission appointments made on or after that date. The remainder of this act is effective July 1, 2015.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 S D SENATE JOINT RESOLUTION DRSJR35301-LG-117 (06\n",
"esolution to the family of Harris Blake. SECTION 4. This resolution is effective upon ratification.\n",
"\n",
"\n",
"GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2015 H D HOUSE DRH30316-MLf-229 (04/06) Short \n",
"rules pursuant to Section 8 of this act. The remainder of this act is effective when it becomes law.\n",
"\n",
"\n"
]
}
],
"source": [
"for crt in crts:\n",
" print(crt[:100])\n",
" print(crt[-100:])\n",
" print('\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Long Titles"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lts = [extractLongTitle(soup) for soup in soups]"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AN ACT to appropriate funds to the department of public safety for ret\n",
"\n",
"\n",
"AN ACT clarifying the authority and duties of industrial commission fr\n",
"\n",
"\n",
"AN ACT ALLOWING PATIENTS WITH terminal or chronic ILLNESS TO LAWFULLY \n",
"\n",
"\n",
"AN ACT to allow a local government to set the minimum wage within its \n",
"\n",
"\n",
"AN ACT to make a technical and clarifying change to the limited liabil\n",
"\n",
"\n",
"AN ACT to amend the process by which the city councils receive citizen\n",
"\n",
"\n",
"AN ACT to increase the contributory death benefit payable on behalf of\n",
"\n",
"\n",
"AN ACT to (1) prohibit the use of discriminatory profiling by law enfo\n",
"\n",
"\n",
"AN ACT to amend provisions of the justice reinvestment act.\n",
"\n",
"\n",
"AN ACT to enact the eligible leave for employee caregiving time act.\n",
"\n",
"\n",
"AN ACT to include per transaction rates paid to license plate agency c\n",
"\n",
"\n",
"AN ACT to direct the division of child development and early education\n",
"\n",
"\n",
"AN ACT to establish licensure and education standards for the practice\n",
"\n",
"\n",
"AN ACT to provide FOR fouryear terms for members of the general assemb\n",
"\n",
"\n",
"AN ACT to enact the corner store initiative act to assist healthy food\n",
"\n",
"\n",
"AN ACT to CLARIFY that the freight rail and rail crossing safety impro\n",
"\n",
"\n",
"AN ACT to provide for a referendum to limit marine net fishing.\n",
"\n",
"\n",
"AN ACT to reenact the tax credit for qualified business investments.\n",
"\n",
"\n",
"AN ACT to allow local boards of education to set a school calendar tha\n",
"\n",
"\n",
"AN ACT to remove certain described property from the corporate limits \n",
"\n",
"\n",
"AN ACT TO provide that STATE WILDLIFE LAWS do not apply TO OPOSSUMS BE\n",
"\n",
"\n",
"AN ACT to equalize the taxation of LIQUEFIED propane gas when used as \n",
"\n",
"\n",
"AN ACT TO update the membership of the FIRE AND RESCUE COMMISSION to r\n",
"\n",
"\n",
"A JOINT RESOLUTION honoring the life and memory of harris blake, forme\n",
"\n",
"\n",
"AN ACT to authorize and regulate the sale of antique spirituous liquor\n",
"\n",
"\n"
]
}
],
"source": [
"for lt in lts:\n",
" print(lt[:70])\n",
" print('\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Info Tables"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tabs = [extractTableContent(soup) for soup in soups]"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Representative Whitmire.\n",
"Senator Lee (Primary Sponsor).\n",
"Representative Alexander.\n",
"Representative L. Hall.\n",
"Senators Barringer and Lee (Primary Sponsors).\n",
"Representatives Stam, Goodman, Jackson, and Fraley (Primary Sponsors).\n",
"Representative L. Bell.\n",
"Senators McKissick and Bryant (Primary Sponsors).\n",
"Representatives Faircloth, Daughtry, Boles, and Hurley (Primary Sponsors).\n",
"Representatives R. Turner and Meyer (Primary Sponsors).\n",
"Senator Tillman (Primary Sponsor).\n",
"Senator Pate (Primary Sponsor).\n",
"Representatives Collins and Fisher (Primary Sponsors).\n",
"Representatives Warren, Hardister, Malone, and Glazier (Primary Sponsors).\n",
"Representatives Holley, Whitmire, B. Brown, and Lambeth (Primary Sponsors).\n",
"Senators J. Davis and Rabon (Primary Sponsors).\n",
"Representative W. Richardson.\n",
"Senator Lowe (Primary Sponsor).\n",
"Representative Holloway.\n",
"Representative Presnell.\n",
"Representatives West, Hager, McElraft, and Lucas (Primary Sponsors).\n",
"Representative Collins.\n",
"Representatives Ross, Saine, Boles, and J. Bell (Primary Sponsors).\n",
"Senator Tillman (Primary Sponsor).\n",
"Representatives Hager and J. Bell (Primary Sponsors).\n"
]
}
],
"source": [
"for tab in tabs:\n",
" print(tab['Sponsors'])"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method apply in module pandas.core.frame:\n",
"\n",
"apply(func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds) method of pandas.core.frame.DataFrame instance\n",
" Applies function along input axis of DataFrame.\n",
" \n",
" Objects passed to functions are Series objects having index\n",
" either the DataFrame's index (axis=0) or the columns (axis=1).\n",
" Return type depends on whether passed function aggregates, or the\n",
" reduce argument if the DataFrame is empty.\n",
" \n",
" Parameters\n",
" ----------\n",
" func : function\n",
" Function to apply to each column/row\n",
" axis : {0 or 'index', 1 or 'columns'}, default 0\n",
" * 0 or 'index': apply function to each column\n",
" * 1 or 'columns': apply function to each row\n",
" broadcast : boolean, default False\n",
" For aggregation functions, return object of same size with values\n",
" propagated\n",
" raw : boolean, default False\n",
" If False, convert each row or column into a Series. If raw=True the\n",
" passed function will receive ndarray objects instead. If you are\n",
" just applying a NumPy reduction function this will achieve much\n",
" better performance\n",
" reduce : boolean or None, default None\n",
" Try to apply reduction procedures. If the DataFrame is empty,\n",
" apply will use reduce to determine whether the result should be a\n",
" Series or a DataFrame. If reduce is None (the default), apply's\n",
" return value will be guessed by calling func an empty Series (note:\n",
" while guessing, exceptions raised by func will be ignored). If\n",
" reduce is True a Series will always be returned, and if False a\n",
" DataFrame will always be returned.\n",
" args : tuple\n",
" Positional arguments to pass to function in addition to the\n",
" array/series\n",
" Additional keyword arguments will be passed as keywords to the function\n",
" \n",
" Notes\n",
" -----\n",
" In the current implementation apply calls func twice on the\n",
" first column/row to decide whether it can take a fast or slow\n",
" code path. This can lead to unexpected behavior if func has\n",
" side-effects, as they will take effect twice for the first\n",
" column/row.\n",
" \n",
" Examples\n",
" --------\n",
" >>> df.apply(numpy.sqrt) # returns DataFrame\n",
" >>> df.apply(numpy.sum, axis=0) # equiv to df.sum(0)\n",
" >>> df.apply(numpy.sum, axis=1) # equiv to df.sum(1)\n",
" \n",
" See also\n",
" --------\n",
" DataFrame.applymap: For elementwise operations\n",
" \n",
" Returns\n",
" -------\n",
" applied : Series or DataFrame\n",
"\n"
]
}
],
"source": [
"help(bill_texts_filed.apply)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Process"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bill_texts_filed['soup'] = bill_texts_filed.apply(lambda x: bs(x.text, 'html.parser'), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"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>bill</th>\n",
" <th>house</th>\n",
" <th>session</th>\n",
" <th>text</th>\n",
" <th>soup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bill house session text \\\n",
"0 1 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"1 2 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"2 3 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"3 4 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"4 5 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"\n",
" soup \n",
"0 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"1 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"2 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"3 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"4 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... "
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bill_texts_filed.head()"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bill_texts_filed['content'] = bill_texts_filed.apply(lambda x: extractRawText(x['soup'].body), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bill_texts_filed['long_title'] = bill_texts_filed.apply(lambda x: extractLongTitle(x['soup'].body), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bill_texts_filed['table_info'] = bill_texts_filed.apply(lambda x: extractTableContent(x['soup'].body), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 182,
"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>bill</th>\n",
" <th>house</th>\n",
" <th>session</th>\n",
" <th>text</th>\n",
" <th>soup</th>\n",
" <th>content</th>\n",
" <th>long_title</th>\n",
" <th>table_info</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>A HOUSE RESOLUTION adopting the permanent rule...</td>\n",
" <td>{'Referred to': '', 'Sponsors': 'Representativ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>A JOINT RESOLUTIOn providing for adjournment s...</td>\n",
" <td>{'Referred to': '', 'Sponsors': 'Representativ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>AN ACT to provide further REGULATORY RELIEF TO...</td>\n",
" <td>{'Referred to': '', 'Short Title': 'Regulato...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>AN ACT directing the department of transportat...</td>\n",
" <td>{'Referred to': '', 'Short Title': 'Terminat...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>H</td>\n",
" <td>2015E4</td>\n",
" <td>b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con...</td>\n",
" <td><html>\n",
"<head>\n",
"<meta content=\"text/html; charse...</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>AN ACT to clarify the service area for communi...</td>\n",
" <td>{'Referred to': '', 'Short Title': 'Municipa...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bill house session text \\\n",
"0 1 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"1 2 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"2 3 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"3 4 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"4 5 H 2015E4 b'<html>\\r\\n\\r\\n<head>\\r\\n<meta http-equiv=Con... \n",
"\n",
" soup \\\n",
"0 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"1 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"2 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"3 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"4 <html>\n",
"<head>\n",
"<meta content=\"text/html; charse... \n",
"\n",
" content \\\n",
"0 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"1 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"2 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"3 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"4 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"\n",
" long_title \\\n",
"0 A HOUSE RESOLUTION adopting the permanent rule... \n",
"1 A JOINT RESOLUTIOn providing for adjournment s... \n",
"2 AN ACT to provide further REGULATORY RELIEF TO... \n",
"3 AN ACT directing the department of transportat... \n",
"4 AN ACT to clarify the service area for communi... \n",
"\n",
" table_info \n",
"0 {'Referred to': '', 'Sponsors': 'Representativ... \n",
"1 {'Referred to': '', 'Sponsors': 'Representativ... \n",
"2 {'Referred to': '', 'Short Title': 'Regulato... \n",
"3 {'Referred to': '', 'Short Title': 'Terminat... \n",
"4 {'Referred to': '', 'Short Title': 'Municipa... "
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bill_texts_filed.head()"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bill_texts_filed_content = bill_texts_filed[['session', 'house', 'bill', 'content', 'long_title', 'table_info']]"
]
},
{
"cell_type": "code",
"execution_count": 184,
"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>session</th>\n",
" <th>house</th>\n",
" <th>bill</th>\n",
" <th>content</th>\n",
" <th>long_title</th>\n",
" <th>table_info</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015E4</td>\n",
" <td>H</td>\n",
" <td>1</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>A HOUSE RESOLUTION adopting the permanent rule...</td>\n",
" <td>{'Referred to': '', 'Sponsors': 'Representativ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015E4</td>\n",
" <td>H</td>\n",
" <td>2</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>A JOINT RESOLUTIOn providing for adjournment s...</td>\n",
" <td>{'Referred to': '', 'Sponsors': 'Representativ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015E4</td>\n",
" <td>H</td>\n",
" <td>3</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>AN ACT to provide further REGULATORY RELIEF TO...</td>\n",
" <td>{'Referred to': '', 'Short Title': 'Regulato...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015E4</td>\n",
" <td>H</td>\n",
" <td>4</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>AN ACT directing the department of transportat...</td>\n",
" <td>{'Referred to': '', 'Short Title': 'Terminat...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015E4</td>\n",
" <td>H</td>\n",
" <td>5</td>\n",
" <td>GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT...</td>\n",
" <td>AN ACT to clarify the service area for communi...</td>\n",
" <td>{'Referred to': '', 'Short Title': 'Municipa...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" session house bill content \\\n",
"0 2015E4 H 1 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"1 2015E4 H 2 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"2 2015E4 H 3 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"3 2015E4 H 4 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"4 2015E4 H 5 GENERAL ASSEMBLY OF NORTH CAROLINA FOURTH EXT... \n",
"\n",
" long_title \\\n",
"0 A HOUSE RESOLUTION adopting the permanent rule... \n",
"1 A JOINT RESOLUTIOn providing for adjournment s... \n",
"2 AN ACT to provide further REGULATORY RELIEF TO... \n",
"3 AN ACT directing the department of transportat... \n",
"4 AN ACT to clarify the service area for communi... \n",
"\n",
" table_info \n",
"0 {'Referred to': '', 'Sponsors': 'Representativ... \n",
"1 {'Referred to': '', 'Sponsors': 'Representativ... \n",
"2 {'Referred to': '', 'Short Title': 'Regulato... \n",
"3 {'Referred to': '', 'Short Title': 'Terminat... \n",
"4 {'Referred to': '', 'Short Title': 'Municipa... "
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bill_texts_filed_content.head()"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open('data/bill_texts_filed_content.pkl', 'wb') as f1:\n",
" pickle.dump(bill_texts_filed_content, f1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "ncga (P3)",
"language": "python",
"name": "ncga (p3)"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
m-weigand/ccd_tools | playground/Sensitivities/Debye Sensitivities.ipynb | 1 | 105344 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from NDimInv.plot_helper import *\n",
"import dd_res as DD_RES\n",
"dd_res = DD_RES.dd_resistivity()\n",
"import scipy.stats as stats"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named dd_res",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-780e071b2323>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mNDimInv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_helper\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdd_res\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mDD_RES\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdd_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDD_RES\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdd_resistivity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: No module named dd_res"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate multiple spectra exhibiting various high-frequency characteristics, e.g. an unfinished slope or a finished peak."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_spectrum(peak1, peak2, strength1, strength2, omega, s, rho0):\n",
" \"\"\"\n",
" Generate a two-peak spectrum and chargeability distribution for a Debye Composition\n",
" \n",
" peak1 and peak2 denote the mean of the norm-distributions for m values (in s coordinates).\n",
"\n",
" \"\"\"\n",
" m = stats.norm.pdf(s, peak1, 1.5) * (strength1) + \\\n",
" stats.norm.pdf(s, peak2, 1.5) * (strength2 * 2)\n",
"\n",
" # we need to norm the chargeabilities because they will be scaled by rho0. Arbitrarily set the norm factor so that it is 1 for rho0 = 100\n",
" m = m / rho0\n",
"\n",
" # we also want to normalize to the number of chargeabilities per frequency decade\n",
" f = 1 / (2 * np.pi * 10**s)\n",
" nr_per_decade = int(len(s) / (np.log10(np.max(f)) - np.log10(np.min(f))))\n",
" m = m / nr_per_decade\n",
"\n",
" # apply conversion\n",
" pars = np.hstack((rho0, m))\n",
" pars_converted = dd_res.convert_parameters(pars)\n",
" re,mim = dd_res.forward_re_mim(omega, pars_converted, s) \n",
" \n",
" return re, mim, rho0, m\n",
"\n",
"# input parameters\n",
"rho0 = 100\n",
"Nd = 20\n",
"f = np.logspace(-3,4,30)\n",
"omega = 2 * np.pi * f\n",
"tau_s, s_s, f_s = dd_res.get_tau_values_for_data(f, Nd)\n",
"\n",
"peaks = [\n",
" [0, -4.5, 'peak just inside'],\n",
" [0, -6, 'peak just outside'],\n",
" [0, -3, 'peak finished'],\n",
" [2, -1, 'peak really far']\n",
" ]\n",
"\n",
"re_list = []\n",
"mim_list = []\n",
"\n",
"for nr,options in enumerate(peaks):\n",
" re,mim,rho0,m = get_spectrum(options[0], options[1], 1, 1, omega, s_s, rho0) \n",
" re_list.append(re)\n",
" mim_list.append(mim)\n",
"\n",
" \n",
"# save mag/pha to file\n",
"m_re = np.array(re_list)\n",
"m_mim = np.array(mim_list)\n",
"\n",
"mag = np.abs(m_re - 1j * m_mim)\n",
"pha = np.arctan(-m_mim / m_re) * 1000\n",
"\n",
"magpha = np.hstack((mag,pha))\n",
"\n",
"np.savetxt('synthetic/data.dat', magpha)\n",
"np.savetxt('synthetic/frequencies.dat', f)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"# load data\n",
"frequencies = np.loadtxt('frequencies.dat')\n",
"omega = 2 * np.pi * frequencies\n",
"data = np.atleast_2d(np.loadtxt('data.dat'))\n",
"\n",
"mag = data[0, 0:data.shape[1]/2]\n",
"pha = data[0, data.shape[1]/2:]\n",
"\n",
"cmplx = mag * np.exp(1j*pha/1000)\n",
"re = np.real(cmplx)\n",
"mim = -np.imag(cmplx)\n",
"\n",
"odir = 'test_dd_orig'\n",
"# load final iteration\n",
"rho0 = np.atleast_2d(np.loadtxt(odir + '/rho0_results.dat'))[0,1]\n",
"#rho0 = 10**log10rho0\n",
"m = np.loadtxt(odir + '/m_results.dat')\n",
"\n",
"mtot = np.sum(10**m)\n",
"#m = 10**m\n",
"s = np.loadtxt(odir + '/s_results.dat')\n",
"\n",
"pars = np.hstack((rho0, m))\n",
"\n",
"fre,fmim = dd_res.forward_re_mim(omega, pars, s)\n",
"del_mim_del_m = dd_res.del_mim_del_chargeability(omega, pars, s)\n",
"nr_m = del_mim_del_m.shape[1]\n",
"\n",
"def plot_spectrum():\n",
" # plot spectrum, and results\n",
" fig = plt.figure(figsize=(15,6))\n",
" ax = fig.add_subplot(3,1,1)\n",
" ax.semilogx(frequencies, re, '.')\n",
" ax.semilogx(frequencies, fre, '-', color='r')\n",
" ax = fig.add_subplot(3,1,2)\n",
" ax.semilogx(frequencies, mim, '.')\n",
" ax.semilogx(frequencies, fmim, '-', color='r')\n",
"\n",
"#plot_spectrum()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imaginary part sensitivities for each chargeability"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_unnormalized():\n",
" fig, ax_list = plt.subplots(61, 1,figsize=(12,100))\n",
" for i in range(0,nr_m):\n",
" ax_list[i].semilogx(frequencies, del_mim_del_m[:,i])\n",
" fig.suptitle('Un-normalized')\n",
" fig.subplots_adjust(top=0.99,hspace=0.1)\n",
"\n",
"#plot_unnormalized()\n",
"\n",
"def plot_normalized():\n",
" max_sens = np.max(np.abs(del_mim_del_m))\n",
"\n",
" fig, ax_list = plt.subplots(nr_m, 1,figsize=(12,100))\n",
" for i in range(0,nr_m):\n",
" ax_list[i].semilogx(frequencies, del_mim_del_m[:,i] / max_sens)\n",
" ax_list[i].set_ylim([0,1])\n",
" ax_list[i].set_xlabel('Frequency (Hz)')\n",
" ax_list[i].set_title('s: {0}'.format(s[i]))\n",
" fig.suptitle('Normalized')\n",
" fig.subplots_adjust(top=0.97,hspace=0.6)\n",
" \n",
"#plot_normalized()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we try some comulative sensitivity values.\n",
"\n",
"For each frequency, sum up all chargeability sensitivities:\n",
"\n",
"$Covf(f) = \\sum_{i=1}^{N_m} \\left| \\frac{\\partial -Im(f)}{\\partial m_i} \\right|$\n",
"\n",
"For each chargeability, sum up all frequency sensitivities\n",
"\n",
"$Covm(m) = \\sum_{i=1}^{N_f} \\left| \\frac{\\partial -Im}{\\partial m} \\right|_f$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covf = np.abs(del_mim_del_m).sum(axis=1)\n",
"covf /= np.max(covf)\n",
"\n",
"covm = np.abs(del_mim_del_m).sum(axis=0)\n",
"covm /= np.max(covm)\n",
"\n",
"invcovm = np.abs(1 / del_mim_del_m).sum(axis=0)\n",
"invcovm /= np.max(invcovm)\n",
"\n",
"fig, (ax1,ax2,ax3,ax4) = plt.subplots(4,1,figsize=(15,12))\n",
"ax1.semilogx(frequencies, covf)\n",
"ax1.set_xlabel('Frequency (Hz)')\n",
"ax1.set_ylabel('Covf')\n",
"\n",
"ax2.plot(s, covm)\n",
"ax2.set_xlabel(r'$s = log_{10}(\\tau)$')\n",
"ax2.set_ylabel('Covm')\n",
"ax2.invert_xaxis()\n",
"\n",
"ax3.plot(s, np.log10(10**m / mtot))\n",
"ax3.set_xlabel(r'$s = log_{10}(\\tau)$')\n",
"ax3.set_ylabel(r'$log_{10}(m) / m_{tot}$')\n",
"ax3.invert_xaxis()\n",
"ax3.set_ylim([-2.4, -0.8])\n",
"\n",
"ax4.semilogx(frequencies, mim, '.-')\n",
"ax4.set_xlabel('Frequency (Hz)')\n",
"ax4.set_ylabel(r'$-Im(\\rho) (\\Omega m)$')\n",
"\n",
"fig.subplots_adjust(hspace=0.5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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N3aZ0AED6vfOOGaLg9UqvvWa2jR4tXXSR6ZJ21llMegYMVevWmeFG//M/ZjiS\nzWaWvLjhBumoo6yuLvN98on03e+a90fJdNW9/XYpL8/aunorI8Ys2mw2RaPRbmcm7e06i+lGWDQu\nu0y6+27zzVFtrWlVBACgO9u3SytXmrGNDz/c2SX1yCNNt7WvfU363OesrRFA76xZY0LiPfeY/8t7\n7CFdfrl0/fXm/zR2T2OjGb/Y3m4mALr7bumMM6yuatcyYsxifn5+j+shNjc393o2VKRfba301FPm\njYKgCADYmT32MDOkPvigaWFcuNBMfLNunTR3rrn+1a+avyt8Hwtkpn//2zQSHH20aU202cwkVq++\natYVJCj2TUWFGc51yinSm2+aHhcLF5ruvCNFn1sW6+vrtWjRIvn9fk2YMCGxPRKJyOl0yu12a968\neemqs1doWQQAoP+2bZP+9jfT2vjII50hceJE09p4xRVmsW4A1nrhBbMsms9nbo8ebZa+mD9f6vLx\nHP20bZsJiT/7mXk//NKXTCjP1PVrM6IbqiQtWrRINTU1Ki4ult1uV1tbm0KhkGprawc9KEqERQAA\n0m3DBjNu5/bbOyfA2XNPqbLSjG086STGNgKD7cUXTXi5/35ze8wYM5GNx5O5AWY4eOwxM9zrnXfM\nF2a3325mlc40GRMWJTM+MRgMKhwOq7CwUCUlJcrJyUlLcbuLsAgAwMDYulX6y19Ma6Pf37n9+ONN\naPzqV6VupjEAkEYvvSTdeGNnS+LYsWZ5NI+HdVMHy8aNpvX24YfN7TlzpF/8wkwqmSkyKixmEsIi\nAAADb/16Mw3/H/5gPjhJ0j77mDFT1dVSSYm19QHDzcsvm5DY2Gi6QcZDYk2NdMghVlc38sRi0m9/\na1Yb2LJFOvZYs0TdMcdYXZlBWOwBYREAgMHz3/9KK1aY1sYnnujcXlJixjZ+5SvSvvtaVx8w1K1Z\nI910kwkisZjpblpVZcYk0pJovZYWaeZMs4btnntKv/61CfFWd83PiNlQ0yUUCqmkpER2u13V1dU9\n7heNRuV0OmWz2VRaWqpIJDKIVQIAgB2NHWs+KDU1mZaP737XjONpbjYfmA44wIxtfOABEywB9M7a\ntWZs3DHHSPfeK40aZVrt16+XbruNoJgpJk8273dXX23WN6+uNutZDqf3O8vDosPh0Jw5cxSJRBQM\nBuWNr365g5KSEk2bNk3RaFSlpaVyu92DXCkAAOjJxInSr34lvfWW9Kc/SaefLn36qdTQYCaAOPBA\n84Fq1ao1y8BhAAAgAElEQVSRNe08sDvWrzdrm06caNb0s9nMFy/r1klLlrAkWibad18z0c0990j7\n7Wdagc85x6zNOBxY2g3V7/erurpa69evlyQFAgF5PJ6U9RtDoZBcLldiP0nasGFD0pIdEt1QAQDI\nJK+9Zj443Xuv9PzzndsPOEByuUw31VNOsb7LFmC11lbpJz+R7rpL2r7dtCReeaV0ww0sgTGUPP+8\ndPbZ0rvvSpMmSX//u3TQQYNfx7DphhoOh1VcXJy4XVJSonA4nLJfMBhUcXGxqqurVVRUJJfLpVwW\neAIAIKMdcYSZpXH1atNN9Yc/lIqKpPfeM13pTjtNys83k3Q8/3zneo7ASBGJSNdcI33hC9Kdd5pt\nV19tuqF6vQTFoeaLX5Seflo68kjzvnfaaaa1eCizNCy2t7fLbrcnbYtGoyn7RaNR+Xw+lZaWatWq\nVbLb7aqoqBisMgEAQD9NnGgm6li7VgoGpe9/34y7eu01qbbWfAt/7LGmdWWof7gCdmXtWjNRzec/\nL91xh9l25ZXSq6+aLo35+ZaWh37Iz5f++U+ptFQKh01gDIWsrqrvLO2G6vV6tWrVKjU0NEgyodBu\nt6ujoyNlP5/Pp0cffVSSCZm5ubkp+2VlZWnBggWJ22VlZSorKxvYFwEAAPqko0P6xz9MN9XGRmnT\nps77TjjBdFO95BLTQgkMdbGY9OSTZk2+hx82t202M5FNvNUdw8dHH0nTp0srV5pxjStWSFOnDsxz\nNTU1qampKXH7xhtvHB5LZ+w4RtHv96umpiZlzGIgEFBtba1WrlyZ2Gaz2boNi4xZBABg6Nm6VfL7\nTXBcscJ80IqbNEm68EJzmTSJMY4YWrZulXw+ExKbm822sWOlK66Q5s41rYsYnrZska66ykx+M3q0\nGZNaWTnwzzus1lm02+1qbGyUw+GQ0+nUtGnTNHfuXEmSz+eT0+lUdna27Ha7vF6vHA6HPB6PNmzY\nkGhpjCMsAgAw9H36qfTXv0r33Sc98khycDz8cOmCC0xwPPNM8wEMyETt7Wbc4W9+I73xhtn2uc9J\n3/iG9PWvm4meMPx1dEjXXmvWYMzKkm69VfrWtwb2OYdVWGxpaVFFRYXa2tpUWVmpJUuWJO6z2Wzy\n+/2aMmWKWlpaVFVVpVAoJKfTqcbGRo0bNy7pWIRFAACGl88+kx5/XHrwQekvf5Heeafzvuxs6dxz\nTXA85xxph48FgCU2bDCBYNmyzi86jjrKjNO97DJpr70sLQ8WiMWkRYvMZF6SdP31Znz2QPWSGFZh\nMZ0IiwAADF8dHWZynAcflB54wMywGjd6tHTWWSY4XnCBNH68dXViZPrXv0xXU5/PnKuSOSevvdZ8\nmWGzfHVzWO3OO6VZs8zyKNdcI9XVmWVS0o2w2APCIgAAI8f69SY4PvigmX2w61QGJSWd4xyPO45x\njhgY27ebFu9f/tJM1iSZD/+VlaYlscsKcYAkM7mRy2W6219wgRmnvffe6X0OwmIPCIsAAIxM779v\nxjk++KD06KPmg1jcQQeZWQidTvPzkEOsqxPDw8cfS3/4gxmH1tpqtmVnS263GY9GyzZ25plnpPPO\nkzZvNktrPPSQlM4l5AmLPSAsAgCATz81M6s++KAJkO++m3z/0Ueb4Oh0mkly9t3XmjoxtGzbJj32\nmJnZ8v77pQ8/NNsnTJC++13p6qul/faztEQMIS+/LE2bJr35pnTMMWYyr3R9yUBY7AFhEQAAdBWL\nmQ9lq1aZyxNPmFahuNGjpVNO6Wx1LC0dmDFEGJpiMem556S77zaz8/7nP533nXKK6Wp60UWcM+ib\nN96Qzj7bvEcddpjpFTFxYv+PS1jsAWERAADszJYt0v/+b2d4fO655LGO2dnSlCmd4bGoiPGOI9Ha\ntSYg3nOPGRsbV1QkffWr0qWXsj4i0qOtTfryl6Wnn5bsdtMb4uST+3dMwmIPCIsAAGB3bN5slubw\n+0147BoMJOmII0x4PO0005J01FHMajlcvf22aT28+26publz+0EHSTNnmoBYWsqXB0i/Tz4xkyI9\n/LBZWuX++02LY18RFntAWAQAAP2xYYMJjX6/FAhImzYl35+TY0LjqaeanyeeyDi1oay9XVq+3LQg\nPvaY6XYqmX/T6dNNQJwyRdpjD2vrxPC3bZs0e7aZOGn0aKmx0czm3BeExR4QFgEAQLp0dEgtLdJT\nT5kuYk8/Lb31VvI+Npt0/PGd4fHUU6X8fFqfMtknn5jJRO6+23T5++9/zfYxY6RzzzXdTM87z7Tw\nAIMpFpO+9z3p1lvNONi77zbLbOwuwmIPCIsAAGAgvfFGZ3B85hkTJrdtS97nwAM7g+Opp5o1H/fc\n05p6YWYt/ec/pSefNBMcPfectHWruS8rSyorMy2I06end/kCoC9iMWn+fKm21nwZ9cc/SpddtnvH\nICz2gLAIAAAG0yefSMFgZ3h8+mlp48bkfUaPlo49VjruuM7L8cebsXC0QKbf5s2mNTgeDkOh5EmM\nsrJMgK+sNGMRWRMRmSYWk266SVq40Jyv9fXSrFm9f/ywCouhUEhVVVWKRCJyuVyqq6vrdr+SkhK1\ntLQkbldUVOi+++5L2oewCAAArBSLmUlyuobHl17qHAvXVV5ecng87jiz3hrrPu6e994z4fCJJ0xA\nfOGF5N/3HnuYiWnOOMOsq3naaWbsKZDpbrnFtDJK0m23Sd/4Ru8eN6zCYm5urhYvXqyKigo5HA65\n3W5VVVWl7Ge32/XYY48p5///d9vtdo0bNy5pH8IiAADINB98YALjCy9IL77YeYlGu9+/oKAzPMYv\nRUWs5SeZEPjWW8nh8JVXkvcZM0Y66aTOcHjKKQRwDF2//rUZxyhJP/+5dO21u37MsAmLfr9f1dXV\nWv//81QHAgF5PB4Fg8GUfW02mzq69iHoBmERAAAMBbGY9OabyeHxhRekNWs6x9N1NXasWcbj8MPN\nZcfr48ebfYaDbduk11+XwmGptbXzEr/94YfJ+++1lwmEZ55pAuJJJzE5DYaXJUukr3/dXP/JT6Qb\nbtj5/unMRJZ+RxUOh1VcXJy4XVJSonA43O1+OTk5Ki8vTzzG6/UqOzt7MMsFAABIi6ws6bDDzOXc\nczu3b9liFoSPh8d4kHz9dbN97dqej3nQQTsPlLm5mTNG8qOPksNg1+uvvZY6aVBX2dnJ4bC01LQm\nAsPVnDlmkqxrrpF+8APps8/MmMbB+P9saVhsb2+X3W5P2hbtpk9Ge3u7CgsLVVNTo/z8fHk8HlVU\nVGjlypWDVSoAAMCAGzPGTIZz7LHSV77Suf3DD01gjF9eey35+ltvSe++ay7PPtv9sceONesH7ref\n6ZYZv+zObZvNTOrz6ad9+/nxx2ZG2f/8Z+e/h0MPNd1xCwuTLwUFZqxnpoReYLBcdZX5P3zFFaZ1\n8bPPpEWLBv7/gqXdUL1er1atWqWGhgZJJija7fZddjdtb29Xbm5uyn50QwUAACPRtm3S22/3HCZf\ne8205mWKMWPMepQ7BsHCQrOdbqRA95YvN7P4btsmffObZk1Gmy15n2HTDbWgoCCp22kwGEzqlhrX\n0tKiWCyWuG9n3U8XLlyYuF5WVqaysrK01QsAAJCJRo3q7HLanVjMtOx99FHq5cMPe78tFjNBbu+9\n+/Zzr71Mq+Ghh6Z+wAWwa9OnS/ffL82YYWZI/e9/pZkzm/Tkk00D8nyWz4Zqt9vV2Ngoh8Mhp9Op\nadOmae7cuZIkn88np9Op1tZWTZ06VYFAINENNRqNsnQGAAAAgBHn0Ueliy4y3VEvv1y6447OGZPT\nmYks/04nEAjI7XbLbrerqKgoERQlyeVyqbm5WcXFxaqtrVVFRYXsdrui0ai8Xq+FVQMAAACANaZN\nk/7+d2mffaS77pK++tXuZ1LuL8tbFtOJlkUAAAAAI8U//ymdc47pJn7hhdJ990l77jlM1llMN8Ii\nAAAAgJHkueek8nIpGjXB8e9/Jyx2i7AIAAAAYKRZvVpyOqWNGyVpGI1ZBAAAAAD03aRJUlOTdOCB\n6T0uLYsAAAAAMAysXSt94Qt0Q+0WYREAAADASDasls4AAAAAAGQewiIAAAAAIAVhEQAAAACQgrAI\nAAAAAEhBWAQAAAAApCAsAgAAAABSWB4WQ6GQSkpKZLfbVV1d3av97Xb7IFQGAAAAACOX5WHR4XBo\nzpw5ikQiCgaD8nq9O92/qqpK7e3tg1QdkD5NTU1WlwB0i3MTmYzzE5mKcxMjgaVh0e/3Ky8vT7Nm\nzVJ2drZqa2u1dOnSHvdftGiRCgsL07bIJDCY+KOCTMW5iUzG+YlMxbmJkcDSsBgOh1VcXJy4XVJS\nonA43OO+9fX1qq2tHazyLGXFG9BAPGd/j9nXx+/O43q77672Gyl/NKx6ncPl/LTi3Nzd5x3KeO/s\n++N39zHpOu84N4fWc6bjmEPlvZNzc+g9b6a/dw7Fv+uWhsX29vaU8YfRaLTbfV0ul7xer3Jzcwej\nNMvxR6V/j+ePysDhj0r/Hj/c/6hYjffOvj+esDiwODf7dwz+rg8c/q737/HD/e96VszCPp1er1er\nVq1SQ0ODJBMU7Xa7Ojo6kvarr69XKBRSXV2dNm/erLy8vJR9JKmoqEitra2DUjsAAAAAZJrCwkKt\nX78+LccalZaj9FFBQUFSt9NgMJjULTXO7/fL5/MlQqUk5eXlqbm5WRMmTEhsS9cvBQAAAABGOktb\nFiXJbrersbFRDodDTqdT06ZN09y5cyVJPp9PTqfTFJqVJUlqbW1VSUmJotGoxo0bZ1ndAAAAADCc\nWb50RiAQkNvtlt1uV1FRUSIoSmacYnNzs7KzszVu3DiNGzdO+fn5ysrK6lNQrKioUHl5uUpLS9XS\n0pLOlwH0m9vtVnl5uYqKirR8+XKrywGS1NfXa/HixVaXAcjj8ST+lkciEavLAVLwfolM1NfPmZaH\nxcmTJ2v9+vVqa2vTkiVLku7r6OjQlClTkrbl5ORo+/btu/089fX1Kioq0sqVK+X1euXxePpVN5BO\nfr9fNptNK1euVHNzs6qqqqwuCUhwOp2qrq5O9PAArBIKhdTS0pL4W+52u60uCUjC+yUyUX8+Z1oe\nFnvD6XSmfHsYCoVUUlIiu92u6urqXh1j9uzZkqRYLDZiZlXFwEvH+VlYWJj4AiM7OztllmCgL9Jx\nbkrSqlWrtHTpUta4xYDYnfPU7/ervLxckvmyORgMDmqtGHl2932U90sMlt05N/vzOTOjw6Lf75fb\n7VYgEEj5hsbhcGjOnDmKRCIKBoPyer07PVZ+fr7y8/PldrtVWlqq+fPnD2TpGAHSfX5OmDBB4XBY\npaWlqqmpGcjSMcyl89wEBkpfztO2tjbl5+dbUS5GGN5Hkan6cm7253OmpbOh7kpLS0u3zfh+v195\neXmaNWuWJKm2tlYej0dVVVVyuVwp+zudzkRz69KlS1VTUyOn08nsqeiXdJ2f5eXlmjVrlhYtWqTG\nxkYtW7ZMkyZNGvD6MXwNxHsnkG59OU/z8vKSZlHvaW1moL/6cn4Cg6Gv52ZfP2dmdFicN2+eJCUt\nmSFJ4XA4aYmNkpKSxB+PHfeNq6mpUWFhoaqqqpSbm6u2trYBqhojRTrPT5/PJ7/fr+eee26AqsVI\nks5zExgofTlPp06dKo/Ho3nz5ikUCiVmTAfSrS/nJzAY+nJu9udzZkaHxZ60t7en9LXd1beL8+fP\nV0VFhZYuXSrJ/NKAgdCX89Pv9ysYDKqoqEiSWSpm3bp1A1YjRqa+nJtdMWEDBsPOztPJkyeruLhY\n5eXlysrKSvxNBwZLb99Heb/EYNvZudmfz5lDMizm5OTsdstgdna2Vq5cOUAVAZ36cn7W1dWprq5u\ngCoCjL6cm3F0scJg2dV5essttwxiNUCy3ryP8n4JK+zs3OzP58yMnuCmJwUFBUlN/sFgMKnZFbAS\n5ycyFecmhgLOU2Qyzk9kqoE6N4dkWHQ4HAqHwwoEApLMAM6ZM2daXBVgcH4iU3FuYijgPEUm4/xE\nphqoc3NIhMXu+n0HAgG53W7Z7XYVFRVp7ty5FlQGcH4ic3FuYijgPEUm4/xEphqsczMrxqqhAAAA\nAIAdDImWRQAAAADA4CIsAgAAAABSEBYBAAAAACkIiwAAAACAFIRFAAAAAEAKwiIAYMiw2Wwpl7y8\nPKvLGjCLFi3S6tWrJZnX/sEHH6Tc73K5dnqM5cuXy+v1DliNAIDhi7AIABhSQqGQotFo4rJp0yar\nSxoQ0WhUDQ0NmjRpUo/7dLfO1o6mT5+u2tradJYGABghCIsAgCElJydH48aNS1wkKRwOy+l0yuPx\nqLS0VJLk9/tVWFgom82m8vJytbe3J45RX1+v3Nxc5eXlqb6+Xna7XZIJovHHx49RXl6edHvHY4bD\nYZWUlGjx4sWJhZBbWloSj/H5fCosLJTdbld1dbUkye12a/HixYl9PB6Pampqkl5nfX19Yv/e8Pl8\nKa2ujz32mCRpxowZWr58ea+PBQCARFgEAAwxsVis2+2BQEAffPCBfD6fotGoXC6Xli1bpmg0qoKC\nAlVVVUkygbCmpkaPP/64wuGwli5d2qsWup6OmZWVpZaWFmVlZamtrU1Tp06Vx+ORZELs7NmztXz5\ncjU3N8vv92v58uUqLy/XqlWrEsdevny5Zs6cmfR8DQ0Nmjp16k5fe9fbM2bMUEdHhzo6OtTQ0KDC\nwkJNmTJFkuR0OnXfffft8jUCANDVKKsLAABgdxQWFibd9vv9mjBhgiRpyZIlkkyr3NSpU3XWWWdJ\nkurq6hKth/fdd5/cbneie+eiRYtUUVGxy+eNh7fujpmTk6O5c+dKMq2G8WDq8/mSnquxsVFZWVma\nNGlS4jnD4bDa2tpSupuGQqHE64rLzc1NqWvH2qPRqGbPnq1QKJTYlp+fL7/fv8vXCABAV4RFAMCQ\n4vf7VVBQkLg9YcIEhcPhpG2tra3y+XyJMCd1ju+LRCJJXUvz8/N79bw7O2bXbV1b++JdVOMmT56c\nuD516lQFAgE1NzfL7XYnPVc0GlVOTk5KDaFQKGl7XV2dwuFw0j4Oh0OLFi1KCpoFBQWKRqO9ep0A\nAMQRFgEAQ0pBQUFKi9uOioqKNGPGDDU0NCS2xccR2u12rV+/PrF9x7DVVdeA1dMxe+oWK5kWx9bW\n1sTtUCikSCSi6dOny+l0qqGhQc3NzVq2bNlOX09cQUFBYpxm/LV0rd/j8ehzn/ucZs2a1avjAQCw\nM4xZBAAMOy6XS36/X4FAQNFoVG63OzGOsKKiQvX19WppaVE0GpXH40m0EObk5CQCXTQa1c0335w4\nZkVFRcoxa2pqdjre0e12J54rHA6roqJCmzdvlqRE8IxEIildUHNycna7JdDv98vr9SaF2bhwONxt\nSyUAADtDWAQADBk7C2Zd78vOzlZjY6Pcbrfsdrs2bNggn88nyXTTnD9/vhwOhwoLCzVz5sxE62BB\nQYFmz56twsJCnXDCCbr++uuTguSOx2xsbFQsFkupK347Pz9ftbW1cjgcKioqUnl5eaLVLz8/X3l5\neT2uk1hcXKxIJLLT156VlZXYXl9fr/b2duXm5iZmQ423WMZniwUAYHdkxXbWfwYAgGEuPrNpW1vb\noD93aWmpli1b1u1aiosXL1ZOTk5ispz+8Hg8Oumkk3TJJZf0+1gAgJGDsAgAGNGsCourVq3SzJkz\ntWnTpm7vb29vl8PhUDAY7PdzFRUVJY3TBACgN+iGCgAY8XqzzmI6+Xw+VVZWqrGxscd9srOzVVlZ\nmZiYp6+WL1+umpqafh0DADAy0bIIAAAAAEhByyIAAAAAIAVhEQAAAACQgrAIAAAAAEhBWAQAAAAA\npCAsAgAAAABSEBYBAAAAACkIiwAAAACAFIRFAAAAAEAKwiIAAAAAIAVhEQAAAACQgrAIAAAAAEhB\nWAQAAAAApCAsAgAAAABSEBYBAAAAACkIiwAAAACAFIRFAAAAAEAKwiIAAAAAIAVhEQAAAACQgrAI\nAAAAAEhBWAQAAAAApCAsAgAAAABSEBYBAAAAACkIiwAAAACAFBkRFp1OpyKRSI/3h0IhlZSUyG63\nq7q6ehArAwAAAICRydKw6Pf75Xa7FQgElJWV1eN+DodDc+bMUSQSUTAYlNfrHcQqAQAAAGDksTQs\ntrS07DQkSiZQ5uXladasWcrOzlZtba2WLl06SBUCAAAAwMg0ysonnzdvniSpoaGhx33C4bCKi4sT\nt0tKShQOhwe8NgAAAAAYyTJizOLOtLe3y263J22LRqMWVQMAAAAAI4OlLYu9kZOTo7a2tl7tW1RU\npNbW1gGuCAAAAAAyU2FhodavX5+WY2V8y2JBQUFSt9NgMJjULbWr1tZWxWIxLoN4WbBggeU1jLQL\nv3N+5yPhwu+c3/lIuPA753c+Ei78ztN3ufXWmKSYzjln5/uls/EsY8Oiz+dTe3u7HA6HwuGwAoGA\nJKm2tlYzZ860uDoAAAAAGDx/+IP5edVVg/ecGREWu5sR1eVyqbm5WZIUCATkdrtlt9tVVFSkuXPn\nDnaJAAAAAGCJ1avNxW6XLrhg8J43I8Ysbtq0KWVbR0dH4vrkyZPT1u8W6VVWVmZ1CSMOv/PBx+98\n8PE7H3z8zgcfv/PBx+988PE7T494q+Kll0pjxw7e82bFYrHY4D3dwMrKytIwejkAAAAARrgtW6RD\nDpE2bZKam6Uepm9JSGcmyohuqAAAAACAVA89ZILi8cdLkycP7nMTFgEAAAAgQ915p/l51VVSN1O9\nDCi6oQIAAABABnr3XWn8eBMS335b2n//XT+GbqgAAAAAMMzddZe0fbt0/vm9C4rpRlgEAAAAgAwT\ni1mztmJXdEMFAAAAgAzz7LPSySdLBxwgvfmmNHp07x5HN1QAAAAAGMbirYqXX977oJhutCwCAAAA\nQAb59FPp4IOl9nbppZekY47p/WNpWQQAAACAYeqBB0xQPOGE3QuK6UZYBAAAAIAMEu+CeuWVlpZB\nN1QAAAAAyBSvvy5NmCCNGSO9846Um7t7j6cbKgAAAAAMQ3/6k1k246KLdj8ophthEQAAAAAyQCwm\n3XmnuW7V2opdWR4WQ6GQSkpKZLfbVV1d3eN+9fX1ys3Nlc1mU3l5uSKRyCBWCQAAAAAD66mnpNZW\nafx4aepUq6vJgLDocDg0Z84cRSIRBYNBeb3elH3C4bCqq6t1//33KxqNqqCgQB6Px4JqAQAAAGBg\nxCe2ueIKaY89rK1Fsjgs+v1+5eXladasWcrOzlZtba2WLl2asp/dbpck5eTkaNy4cYrFYsrLyxvs\ncgEAAABgQHz0kdTYaK5bPQtq3CgrnzwcDqu4uDhxu6SkROFwOGW/nJwc1dXVqaSkRJKUm5urTZs2\nDVqdAAAAADCQfD7p44+l006TjjzS6moMS1sW29vbE62GcdFoNGW/cDismpoahUIhdXR0qKKiQi6X\na7DKBAAAAIABFe+CmgkT28RZ2rKYk5Ojtra2Xe7n8/lUWVmpSZMmSZLq6upks3WfcxcuXJi4XlZW\nprKysnSUCgAAAAADorVVevJJae+9pd1tE2tqalJTU9OA1GVpWCwoKEjqdhoMBpO6pcbtuLBkd62P\ncV3DIgAAAABkuvhyGTNmSPvtt3uP3bGB7MYbb0xbXZZ2Q3U4HAqHwwoEApKk2tpazZw5M3G/z+dT\ne3u7ZsyYoYaGBgUCAUWjUXk8HlVUVFhVNgAAAACkxfbt0h//aK5nUhdUKQOWzggEAnK73bLb7Soq\nKtLcuXMT97lcLjU3Nys/P19erzexXzQa7XaJDQAAAAAYSh57THrjDSk/XzrjDKurSZYV69q/c4jb\nsbsqAAAAAGSySy+V7r1XuvFG6Uc/6v/x0pmJCIsAAAAAYIFoVDr4YOmzz6RIRJowof/HTGcmsrwb\nKgAAAACMRPfdZ4LilCnpCYrpRlgEAAAAAAtk4tqKXdENFQAAAAAG2SuvSEcfLY0bJ73zjlljMR3o\nhgoAAAAAQ1i8VbGyMn1BMd1oWQQAAACAQbRtm3TYYdK770pPPy2dckr6jk3LIgAAAAAMUY88YoLi\nF74gnXyy1dX0jLAIAAAAAIMo3gX1yiulrCxLS9kpuqECAAAAwCDZuFE65BBp+3bpjTfM9XSiGyoA\nAAAADEH33CNt3SpNm5b+oJhuhEUAAAAAGCSZvrZiV3RDBQAAAIBBsHq1NHmyZLdLb78tjR2b/ueg\nGyoAAAAADDHxVsVLLx2YoJhutCwCAAAAwADbssWMUdy0SWpuloqLB+Z5hlXLYigUUklJiex2u6qr\nq3vcLxqNyul0ymazqbS0VJFIZBCrBAAAAIC+e+ghExSPO850RR0KLA+LDodDc+bMUSQSUTAYlNfr\n7Xa/kpISTZs2TdFoVKWlpXK73YNcKQAAAAD0zZ13mp9XXZXZayt2ZWk3VL/fr+rqaq1fv16SFAgE\n5PF4FAwGk/YLhUJyuVyJ/SRpw4YNmjBhQtJ+dEMFAAAAkGnefVcaP96ExLfflvbff+CeKyO7oX7w\nwQcpl10Jh8Mq7tJZt6SkROFwOGW/YDCo4uJiVVdXq6ioSC6XS7m5uekqHQAAAAAGzF13Sdu3S+ef\nP7BBMd36HRYXL14sm82mnJycpEtvwlx7e7vsdnvStmg0mrJfNBqVz+dTaWmpVq1aJbvdroqKiv6W\nDgAAAAADKhYbWmsrdjWqvwfweDxatWqVHA7Hbj82JydHbW1tu9wvNzdXTqdTs2bNkiTV1tbSsggA\nAAAg4/3rX9Irr0gHHCCdc47V1eyefofF/Px8nXDCCX16bEFBQVK303h30+7269rvNjs7u8djLly4\nMHG9rKxMZWVlfaoNAAAAAPrr9783Py+/XBo9Ov3Hb2pqUlNTU/oPrDRMcBMKhTR16lS53e6kLqVZ\nWVmaO3fuLh9vt9vV2Ngoh8Mhp9OpadOmJR7n8/nkdDqVnZ0tu90ur9crh8Mhj8ejDRs26NFHH01+\nMXWsDD4AACAASURBVExwAwAAACBDrFsnHXWUZLNJr74qFRQM/HNm1AQ3NTU1ys3N1ebNm7Vp06bE\nZePGjb16fCAQSATNoqKipIDpcrnU3Nyc2O/mm2+W3W7Xhg0b1NjY2N/SAQAAAGDA/OQnUkeH9LWv\nDU5QTLd+tyza7XZFIpGddg0dLLQsAgAAAMgEa9dKEyeaVsW1a6X8/MF53oxqWXS5XAoEAumoBQAA\nAACGhR//2LQqXnnl4AXFdOt3y2JJSYlaWlqUk5OTMmZx3bp1/S5wd9CyCAAAAMBqr74qHX20aVVc\nt06aMGHwnjudmajfs6H6fD4CGgAAAAD8v5tuMq2Ks2YNblBMt363LFZXV8vlcmnKlCnpqqnPaFkE\nAAAAYKVXXpGOOUYaNUpav146/PDBff6MGrOYnZ2t6667Tna7XXPmzNHq1avTURcAAAAADDk33STF\nYtI11wx+UEy3frcsxkWjUTU0NMjn86m5uVkul0tLlixJx6F7jZZFAAAAAFb597+l446TRo82rYqH\nHTb4NWRUy2JcTk6OCgsLNXnyZHV0dGjVqlXpOjQAAAAAZLwf/9i0Ks6aZU1QTLd+h0Wv1yuXyyWb\nzSaPx6PPfe5zCoVCWr9+fTrqAwAAAICM9+9/Sw0N0pgx0vz5VleTHv2eDbWhoUEul0ter1fZ2dnp\nqAkAAAAAhpQbbzStilVV0vjxVleTHv1uWYyPUczPz5fNZktMdPPBBx+koz4AAAAAyGgvvig1Nkpj\nxw6fVkUpDWGxqqpKbW1tam5uVkdHh8LhsDZu3Kiqqqp01AcAAAAAGe3GG83P2bOlQw+1tpZ06vds\nqDabTZs3b07qghqNRmW329XR0dHvAncHs6ECAAAAGEzPPy9NmiTtuafU2iodcoi19WTUbKj5+flq\nbm5O2tbc3KyCgoL+HhoAAAAAMlq8VdHttj4oplu/J7ipra3V1KlT5Xa7VVBQoNbWVtXX16uxsTEd\n9QEAAABARlq9WlqxwrQqejxWV5N+/W5ZnDFjhtavX6/8/Hxt2rRJhYWFWr9+vaZPn56O+gAAAAAg\nI8VbFaurpYMPtraWgdDvMYv9FQqFVFVVpUgkIpfLpbq6ul3uP3XqVLW1taXcx5hFAAAAAIOhpUUq\nLpb22ksKh6WDDrK6IiNjxiwuX75clZWVidter1dFRUVatmxZr4/hcDg0Z84cRSIRBYNBeb3ene5f\nVVWl9vb2PtcMAAAAAP21cKH5OWdO5gTFdOtzWPR6vaqqqtLUqVMT21wul6677jpdd911vQqMfr9f\neXl5mjVrlrKzs1VbW6ulS5f2uP+iRYtUWFhI6yEAAAAAyzQ3S3/5i2lVvO46q6sZOH0Oi7W1tWps\nbExaTzE7O1uzZ89WfX29amtrd3mMcDis4uLixO2SkhKFw+Ee9+3tcQEAAABgoMRbFb/xDenAAy0t\nZUD1OSyGw2GdcMIJ3d43depUtba27vIY7e3tstvtSdui0Wi3+7pcLnm9XuXm5u5+sQAAAACQBs89\nJz38sLT33tK8eVZXM7D6vHRGfn6+gsGgpkyZknJfb9dZzMnJ6Xaimh3V19ertLRUZ511ljZv3rzT\nfRfGY76ksrIylZWV7fL4AAAAANAb8bjxzW9KBxxgaSmSpKamJjU1NQ3Isfs8G2p9fb0WLVokv9+v\nCRMmJLZHIhE5nU653W7N20XUDgQC8ng8CgaDkswYxpqamsTtOJfLJZ/Pp5ycHEmm9TE3N1fNzc1J\nz81sqAAAAAD+j717j27rLvAE/pXyJEmth9P3g1g20BcktpI+GGhNZCnAvJY4kgrMMMtMYjs7O+wA\nae207JIygCNnYQZmdhzLA8vOmdMSWw6Hxw5LLAVThmlpZKktDC2NfRVa2qaPyFd5v+y7f/y4eliS\nLcVX99ry93PO71zdq2vp519vHX31e9xKefpp4O67gdWrgUQCuPpqo2uUT8tMNKdbZ/T09KCrqwtN\nTU2w2+1IJpOIxWIIBAKzBkWV3W7H4OAgXC4X3G43tmzZgl27dgEAQqEQ3G63qKjJBAAYHx+H0+mE\nLMuoqanJ/WUYFomIiIiIqEI+/GHghz8EOjuBvXuNrk1h8yYsAqKXLxqNQpIk1NfXw+l0pnsASxGP\nx+H1epFMJuH3+9Hb25t+zmw2IxwO5wx1lWUZtbW1mJyczP9lGBaJiIiIiKgCnnoKuPdeYM0a0au4\ndq3RNSpsXoXF+YRhkYiIiIiIKuGDHwR+9CNg927gy182ujbFMSwWwbBIRERERERae/JJ4L3vBa66\nSvQq1tYaXaPitMxEV3zrDCIiIiIiosXg858X2099an4HRa2xZ5GIiIiIiKiIn/0MeN/7RK/isWPA\ntNvEzzvsWSQiIiIiItKB2qv43/7b/A+KWmPPIhERERERUQE//Slw331ATY3oVbTZjK7R7NizSERE\nREREVGF79ojtX//1wgiKWmPPIhERERER0TRPPAHcfz9gsYhexTJuJW8o9iwSERERERFVkDpX8dOf\nXjhBUWvsWSQiIiIiIsry7W8DH/2oCInHjonexYWCPYtEREREREQV8PWvAx/7mHj8yCMLKyhqjWGR\niIiIiIgWvakp4DOfEbfIUBTgS18CPvtZo2tlrKVGV4CIiIiIiMhI588Df/qnQCgELFsGfPObwJ/8\nidG1Mh7DIhERERERLVonTgB//MfAz34m7qf4ne8AmzcbXav5gWGRiIiIiIgWJUkCPvQh4MUXgZtu\nAn74Q+DOO42u1fxh+JzFWCwGp9MJu92Ojo6OoueFw2E4nU6YzWZ4PB4kEgkda0lERERERNXkyBHg\n3ntFUHzPe4CnnmJQnM7wsOhyubBz504kEglEo1H09/fnnSPLMjweDx555BHIsoympiZ4vV4DaktE\nRERERAvdD34ANDcDb7wBuN3AT38K3Hij0bWafwy9z2I4HEZHRwfGxsYAAJFIBJ2dnYhGoznnhUIh\nBAIBHDlyBIAIj3a7HVNTUznn8T6LREREREQ0k/37gb/8S7H66X/+z0AwKBa1qRZVc59FSZLQ1NSU\n3nc6nZAkKe+8bdu2IRwOp/ej0Sjq6+t1qSMRERERES18U1NAVxewc6d4/PnPi1VPqykoas3QBW5S\nqRTsdnvOMVmWC55r+d3dMEOhENra2hAKhSpePyIiIiIiWvguXAA++Ung8ceBJUtEb+Kf/7nRtZr/\nDA2LVqsVyWSypHNlWYbX64Usyzh8+DA2bNhQ8Lw9e/akHzc3N6O5uVmDmhIRERER0UI0MQF85CPA\nT34CrFkj7qW4ZYvRtdLOyMgIRkZGKvLahs5ZnD5HMRwOo6urK2/OIiCGqHo8HnR3dxd9Pc5ZJKJy\nKQpw9ixw+jRw6lRmm/14+vb8eeDy5Uy5dOnK900mYOlS8S3n0qWZUu7+0qXAypXA6tXAqlWZkr1f\n7LFazIYveUZERKSt3/wG+PCHgV/9Crj+euBf/xUo0udUNbTMRIb2LLpcLkiShEgkApfLhUAggAce\neCD9fCgUgtvtxvDwMEwmE9ra2nLmNDocDiOqTUTzzKVLQDIJvPVWppw4kbs/MVE8API7JmHlykyQ\nrKkBLJbyS02NCLJERERGi8dFUDx+HLjjDhEUb7nF6FotLIb2LAJAPB6H1+tFMpmE3+9Hb29v+jmz\n2YxwOIxDhw6hp6cn5+dMJhMmJyfzjrFnkWjhO30aeO014PXXcwNfsSBYZKpzyd72NuCqq8TQlELb\n6cdWrhST4Zcty+/dK+eYGqrUXsbJydweyOn7M51z6ZLo8TxzRvSUqiV7f6bH587N/b+bas2aTHi0\nWgGbTRS7featzQYsX65dPYiIaPH6f/8P8HrFZ4rmZuA73xH/Ji0GWmYiw8OilhgWieYvRRG9f6+9\nll+OH8/dP326vNc2m0XgWLu2cKmtFc8XCoBr1rAnDBCrwqlh88wZ4ORJIJUqv5w6Nbee2tWrZw+W\n2UU9VlMjhvQSERF94xtAe7v4QvXjHxf7K1YYXSv9MCwWwbBIpD9FEb17r74KvPKKKIWC4PHjwMWL\npb3mypViXsG11wJXX108BKrFauV8u/liakoERjU8yrIYAjwxIb4syN4WOjZtwEjJliwpHiqLhUyr\nVZSVK7VtAyIiMsapU8DevcCXvyz2d+8GvvjFxfcZgWGxCIZFIm2dO5cJgNlhMHv/1VdLD4EWiwiB\n2eW66/KPWSzsJVqMFEX8Q18oRCaTxY8lk+X3RmdbsSIzXFYNkNPLTM9x6CwRkXEuXgR++EPgsceA\n731PjJIxm4F//EfRu7gYMSwWwbBIVJpLl8R8wNdeE2FPLdPD4MREaa9nswE33ADceKPY3nBD4UD4\ntrdV9veixeviRdGLmR0gC4XKiQkx51WWM72ely7N7b1XrBDDYNXhzeWU6T/HVWmJiGY3NQU88YQI\niKFQ7ueV++4DPvc5wO02rn5GY1gsgmGRFrtCIVB9nH3szTdLm1e2fHluCLzxxkzJPr5qVeV/N6JK\nUBTRg66Gx0JlYmLm56506Gwxy5eLL1YKlVWrij83vSxfnllMSV1kSX082/70xyZTprc/ezuXEQCK\nItpuclJ88Mvelnps+m1pZiszna++R/bjUvanH1NNb69Cx2bamkyi/VesEP8tS9kWe27lysx87Zqa\nxTV3i6qHogDPPCMC4uOPiy+2VevXAx/7GPDAA1ztFGBYLIphkarR5KToEXnjjfyizgssNwSaTGI+\nYHYPYKEguHYth4MSzUQNm+q9OaeXkyeLP1foHC1XpdVboRA5/ZiiZIIeGWfZspl7uGc6ZrFk5v5e\ndRX/jaDKGx8X4fCxx4Dnn88cX7dOBMSPfUzcFoMyGBaLYFikhUBRxGqThcJfofLmm+LDVSnUEHj9\n9bnDQac/vuYa0VtARPOLooj5NufOlVfOns0/pvakqWWm/dmeU/9pnb6dK7NZLE60ZEnmcTnHsm9L\nU+gWNcVK9rlLluRvs8v0Y7Ptq6F4ejsVa8Ni26kp0f4XLohh1uVspx87dy5zb9mTJ8V/Uy0sXZoJ\njuqq09mPix1bvZohk2b2xhvAgQMiID71VOb42rWA3y8C4r338joqhmGxCIZF0tOlS5kVHUstyaSY\nL1Vu74HdLgJedrn66kyvoBoGGQKJSG+Kkht0CoWf7H+a1aDHuZnGUBQRIkvtCZ9+LJXK/Ft25syV\n1WHZMhEaa2vFh3911eurr859nL3l0Nnqd+qUuBfiY48B4XBmBMLq1cBHPiICYkuLuH5oZgyLRTAs\n0kwmJ8W376dPZ+4lpz6evp1+TF2hMXslxiv9RxIQ84muvTY/ABYqa9fyDyMREc0/Fy5kFo1SA6S6\nmFShx+r27Nny3+uqq2YOldn31a2tFQuv8R6689PkJPDSS8DYGHD0qNj++tfA4cNiZAUgvvj+0IdE\nQPzDPxSBkUrHsFiEkWFxcjIzFOjsWREk1Mfnz+dP0r/SfUXJzP0wm+f+eKZjpTynbtVvjtU6Zj8u\n9Zi62MGlS2LYzJUUdciN+jg79Gk9F2jJksyS+tOLeh+3QqW2Viw0QEREtBidP58Jj2++Kcpbb+Vu\npx8rd+isyZT5Nze7F7PQ4+x93gpHG5cvi0CohsHsYChJxVehvu8+ERC3bRP/PejKMCwWoVXDPP44\n8NxzhYNfsf0LFzT4BajiVq8WZc2ambfTj111VX7o48R+IiKiylMUMfy1WKh8800RPE+cEMdPnCj9\n1k/TrVkj/o23WPKL1Vr4ePZza9YsjiHWiiI+Ax8/ngmB2cEwkZj5tkQ33AC84x1AQ0Nme9ddwM03\n6/c7VDOGxSK0apht24ChofJ/btUqUVavzjxetUosWT19gv70ifql7gOZXriZeuhme1zq86X0Dk5N\nXVnPZbHHy5dnirrs95WUFStyQ9/b3rY4/oATEREtdpcvZ4bITg+SMz2e60q9JlNm1ViLJfd2NytX\n5t/iptCxQsdXrsz/grrQbVlK2b98WYy6UqfZnDqVeTzTsenPzfaR+6abRAjMDoTveAfgcHBYaaUx\nLBahVcOEQsCLLxYPf4X2C/1PTEREREQLg9qDKctiO1Mpds7p00b/FvpZuVIM353eQ6gGQt6D2TgM\ni0VwgRsiIiIiMsrkpFhBVg2P6toVxW57U+y56cfVhV9UhW7LUuq+2Sym0qjTbNRtoWPFnluzhquv\nz2cMi0UwLBIRERER0WKmZSYyfAZXLBaD0+mE3W5HR0fHnM8jIiIiIiKiuTM8LLpcLuzcuROJRALR\naBT9/f1zOo+IiIiIiIjmztCwGA6HUVtbi+3bt8NisSAQCKCvr++KzyP9jYyMGF2FRYdtrj+2uf7Y\n5vpjm+uPba4/trn+2OYLm6FhUZIkNDU1pfedTickSbri80h//AOgP7a5/tjm+mOb649trj+2uf7Y\n5vpjmy9shobFVCoFu92ec0yW5Ss+j4iIiIiIiLRhaFi0Wq1IJpOanUdEREREREQaUQwUDocVp9OZ\n3h8eHs7ZL/e8+vp6BQALCwsLCwsLCwsLC8uiLPX19ZrlNcPvs2i32zE4OAiXywW3240tW7Zg165d\nAIBQKAS32w2LxTLjeURERERERKQtw2+dEYlE0N7eDrvdjoaGhpwA6PP5MDo6Out5REREREREpC3D\nexaJiIiIiIho/jG8Z7Fc4XAYTqcTZrMZHo8HiUSi4HmxWAxOpxN2ux0dHR0617L6uN3uom0NIP3f\nRC1+v1/H2lWn2dqc17i2Sm1PXuvaKbXNea1XFq994/DvvP74eUY//Myuv1LbvKzrXLPZjzqYmJhQ\nTCaTMjQ0pKRSKaWzs7PgQjeKoihWq1Xp7+9XZFlWnE6nEgwGda5tdRgeHlba2toUk8mkJBKJoufZ\nbDYlHo8riURCSSQSSiqV0q+SVabUNuc1rq1S25PXunZKbXNe65XFa19//DuvP36e0Rc/s+uvnDYv\n5zpfUGFxcHBQ2bhxY3pfbZTphoeHc1YBmr6aKpWup6dHaW9vn/WPa6H/DnRlSmlzXuPaKqc9ea1r\no9Q257VeWbz2jcG/8/rj5xl98TO7/kptc0Up7zpfUMNQt23bhnA4nN6PRqOor6/PO0+SJDQ1NaX3\nnU4nJEnSpY7V5sEHH8T+/fthtVqLniNJEqxWKzweDxoaGuDz+ZBKpXSsZXUptc15jWun1Pbkta6d\nctqc13rl8No3Bv/O64+fZ/TFz+z6K6fNy7nOF1RYBACLxQJA3FbD5/Ohr68v75xUKgW73Z5zTJZl\nXeq3GKVSKdTX16OrqwvDw8MAAK/Xa3CtqhuvcW2V2p681rVTTpvzWq8cXvvzF699/fE61xY/s+uv\n1DYv5zpfWpmqVo4sy/B6vZBlGYcPH8aGDRvyzrFarUgmkwbUbnFqbGzEkSNH0vv9/f2w2WwG1qj6\n8RrXVqntyWtdO6W2Oa/1ubPZbDCZTHnHfT4fnE4nr/0KKNbmfr8fvb29Jb0Gr/3yaNHmvM7LM1ub\n8zO79rRo83Kv8wUXFl0uFzweD7q7u4ue43A4crqwo9FoThc3aSsej0NRlHQbq99qUOXwGtdWqe3J\na107pbY5r/W5m5iYKPpcJBLhtV8BM7V5qXjtl0eLNud1Xp7Z2pyf2bWnRZuXe50vqGGooVAIJpMJ\nbW1tkCQpXbKfT6VScLlckCQJkUgEABAIBPDAAw8YVe2qpba3oihoaWlBPB6HLMtob2/nsI0K4TVe\nGbO1J6917ZXa5rzWK4vX/vzDa19/vM61x8/s+iu1zcu+zq9wwR1DdHZ2KiaTKaeYzeb08yaTSYlE\nIoqiKEosFlPq6+sVm82mdHR0GFXlqmG32/NWD8tu72AwqNTX1ysmk0nx+XxcaloDs7U5r3FtzdSe\nvNYro9Q257VeWbz2jcO/8/rj5xl98DO7/spp83Kuc5OiKIoOYZeIiIiIiIgWkAU1DJWIiIiIiIj0\nwbBIREREREREeRgWiYiIiIiIKA/DIhEREREREeVhWCQiIppFLBZDQ0ODru/Z09ODeDxe9PmhoSH0\n9/frWCMiIlpsGBaJiIjmGVmWMTAwgMbGxqLntLa2IhAI6FgrIiJabBgWiYioKrW3t8Nut8Nut2Pf\nvn2avnYoFEJ9fT3MZjN8Ph9SqVT6uWAwCLvdjoaGBgSDwSvqkQwGg+jo6Jj1vG3btmFoaKjs1yci\nIioFwyIREVWdUCiESCSCY8eOIRKJoKurCydPntTktSVJgs/nQ39/PyYmJgAAnZ2d6ee6urpw+PBh\njI6Ooq+vDyaTqez3GBgYQEtLy6znud1uHDhwoOzXJyIiKsVSoytARESkNTWgjY+Po7GxEclkEjU1\nNZq8digUQnt7OzZv3gwACAQCcDqd2L9/P/r6+tDe3o4NGzYAAB5++OF0kCxHLBbDunXrAACpVArd\n3d2QZRmSJMHhcMBqtWLv3r2oq6tDOBzW5PciIiKajmGRiIiqTmtrK5LJJLxeL5LJJHbv3o0HH3ww\n55yhoSHs2LEj55jJZEJ/fz+2bt1a9LWTySQcDkd6v66uDrIsAwASiQQ8Hk/Oc9mCwSBSqVROXTo7\nOxGPx5FMJjE4OAibzQar1Zp+XpIk7N27F4lEAolEIh1SAcDhcKTfm4iISGschkpERFVHkiS0tLRg\nbGwsPRx0+tw+NVBmlxMnTswYFAGgtrYW4+Pj6X1ZltPhzuFwYGxsLKceKrfbjY6OjpxhqbFYDPF4\nHIcOHUJ/fz/a29vzhq2qi9yEQqGcoEhERFRpDItERFR1QqEQvF4vUqkUFEUBgLwQpi5EM70cPHhw\nxtdubW1FMBhEJBKBLMvYsWMH/H4/AMDv9yMYDCIej0OWZXR3d6ffd3h4GH19fen6AEA4HE73RDY2\nNiIajcJisRTsLTxy5EjeMUmScnohiYiItMSwSEREVeehhx6C3W6HzWbDxo0b4fV683oM29ra8noW\nk8lk0Z5FNfQ5HA4MDg6mV1s1m83pW1g0NjYiEAjA5XJh06ZN6OjogMViKVrPZDKZN1QVAJqampBI\nJNL7qVQKtbW1eedJkgS32z17gxAREV0BzlkkIqKqdOjQIc1eq6mpCUePHk3vt7a2orW1Ne+8RCKB\njRs3IplMAhA9nHa7vejr1tbW5gxVVXsU/X4/wuFwek6lxWJBb29v3s8PDw+nezWJiIi0xp5FIiIi\njUxMTMDlcqXvu9jX1wefz1f0/JaWFgwPDwMQ8xfVXsK2tjb09fXN+n5DQ0OzzrEkIiK6UuxZJCIi\n0khTUxN2796dHlrq9/uxffv2nHOy5042NjaiqakJHo8HJpMpHRAtFgv8fj/i8Xh6gZvphoaG0NXV\nVaHfhIiICDAp2TPtiYiIiIiIiMBhqERERERERFQAwyIRERERERHlYVgkIiIiIiKiPAyLRERERERE\nlIdhkYiIiIiIiPIwLBIREREREVEehkUiIiIiIiLKw7BIREREREREeRgWiYiIiIiIKA/DIhERERER\nEeVhWCQiIiIiIqI8DItERERERESUh2GRiIiIiIiI8jAsEhERERERUR6GRSIiIiIiIsrDsEhERERE\nRER5GBaJiIiIiIgoD8MiERERERER5WFYJCIiIiIiojwMi0RERERERJSHYZGIiIiIiIjyMCwSERER\nERFRHoZFIiIiIiIiysOwSERERERERHnmRViMxWJwOp2w2+3o6Ogoel4wGITNZoPZbIbH40EikdCx\nlkRERERERIvHvAiLLpcLO3fuRCKRQDQaRX9/f945kiSho6MDBw8ehCzLcDgc6OzsNKC2RERERERE\n1c/wsBgOh1FbW4vt27fDYrEgEAigr68v7zy73Q4AsFqtqKmpgaIoqK2t1bu6REREREREi8JSoysg\nSRKamprS+06nE5Ik5Z1ntVqxf/9+OJ1OAIDNZsOJEyd0qycREREREdFiYnjPYiqVSvcaqmRZzjtP\nkiR0dXUhFothamoKXq8XPp9Pr2oSEREREREtKrr0LNpsNphMprzjPp8PTqcTyWRy1tcIhULw+/3Y\nsGEDAGD//v0wm3OzbkNDA8bHx7WpNBERERER0QJTX1+PsbExTV5Ll57FiYkJJJPJvLJ//344HI6c\nYafRaDRnWKrKZDJBUZT0fqHex/HxcSiKwqJj+fznP294HRZbYZuzzRdDYZuzzRdDYZuzzRdDYZvr\nX7TsPDN8GKrL5YIkSYhEIgCAQCCABx54IP18KBRCKpXCtm3bMDAwgEgkAlmW0dnZCa/Xa1S1iYiI\niIiIqprhYREAIpEI2tvbYbfb0dDQgF27dqWf8/l8GB0dRV1dHfr7+9PnybJc8BYbRERERERENHeG\nr4YKAI2NjUXH1U5NTaUft7a2orW1Va9qUQmam5uNrsKiwzbXH9tcf2xz/bHN9cc21x/bXH9s84XN\npCiKMvtpC8P0eY1ERERERESLiZaZaF4MQyUiIiIiIqL5hWGRiIiIiIiI8jAsEhERERERUR6GRSIi\nIiIiIsozr8Ki2+1GIpEo+nwsFoPT6YTdbkdHR4eONSMiIiIiIjLW5CRQ5CYSFTEvwmI4HEZ7ezsi\nkQhMJlPR81wuF3bu3IlEIoFoNMr7LBIRERER0aIwNQVs3w5s2gQ8/bQ+7zkvwmI8Hp8xJAIiUNbW\n1mL79u2wWCwIBALo6+vTqYZERERERETGUBRg507gW98CLl4ELlzQ532X6vM2M3vwwQcBAAMDA0XP\nkSQJTU1N6X2n0wlJkipeNyIiIiIiIqMoCvCpTwHBILByJfD97wPvf78+7z0vehZLkUqlYLfbc47J\nsmxQbYiIiIiIiCpLUYAHHwT+4R+A5cuB734X2LxZv/fXpWfRZrMVHGbq9/vR29tb0mtYrVYkk0mt\nq0ZERERERDTvKArwyCPAV74CLFsGHDwIeDz61kGXsDgxMTHn13A4HDnDTqPRaM6wVNWePXvSj5ub\nm9Hc3Dzn9yYiIiIiItLTF74AdHcDS5YABw4Av//7hc8bGRnByMhIRepgUhRFqcgrXwG73Y5YSiSB\ngwAAIABJREFULIZ169alj4VCIbjdblgsFtjtdgwODsLlcsHtdmPLli3YtWtX+lyTyYR59OsQERER\nERGVrbsbePhhwGwGHn8c8PlK/1ktM9G8mrNYaKiqz+fD6OgoACASiaC9vR12ux0NDQ05QZGIiIiI\niGih++pXRVA0mYB//ufygqLW5lXP4lyxZ5GIiIiIiBaqf/gH4K/+Sjz+xjeAP//z8l+jansWiYiI\niIiIFqNgMBMU9++/sqCoNYZFIiIiIiIiA33rW0B7u3j89a9nHhuNYZGIiIiIiMggjz2W6UXcty/T\nuzgfMCwSEREREREZYHAQ+MQnxD0Vv/QlYL6t38mwSEREREREpLPvfhf42MeAyUngf/wPsQLqfDOv\nwqLb7UYikSj6fDgchtPphNlshsfjmfFcIiIiIiKi+ehf/xXweoHLl4HOTmDPHqNrVNi8CIvhcBjt\n7e2IRCIF77UIALIsw+Px4JFHHoEsy2hqaoLX69W5pkRERERERFdueBjYuhW4dAn49KeB7m5xT8X5\nqOywePDgwRn3r0Q8Hi8aElVqr+LWrVtRU1ODrq4uxGKxOb83ERERERGRHkZGgD/6I+DCBeAv/xL4\nylfmb1AEAJNS4h0bh4aGcODAAYTDYbS0tKSPx2IxjI2NaVIZu92OWCyGdevWFXw+lUrBYrEAEOFx\n586dOHr0aPp5LW9ASUREREREpJV/+zfggx8EzpwBduwQ91I0V2Ccp5aZaGmpJ7a0tKCxsRGBQABd\nXV3pCtjtdk0qUgo1KIZCIbS1tSEUCun23kRERERERFfi5z8HPvxhERT/7M8qFxS1VnJYtFgssFgs\n6OvrK/tNbDZbwWGmfr8fvb29Jb+OLMvwer2QZRmHDx/Ghg0byq4LERERERGRXp55BtiyBTh1Cvjo\nR4FvfGNhBEWgjLCoikQi6YVlTCYTBgcHsXnz5hl/ZmJi4spqN43L5YLH40F3d3fRc/ZkLSXU3NyM\n5uZmTd6biIiIiIioHCdPAq2tQColtv/8z8CSJdq+x8jICEZGRrR90d8pec6iauPGjYhEIrBYLJAk\nCT6fD9FoVJPKFJqzGAqF4Ha7MTw8jL1792JwcDBnDK7D4Ug/5pxFIiIiIiKaDxQF+PjHgccfBxob\ngSefBFasqPz7GjJnUWW329NzBx0Oh6ZzFgsNVfX5fAiHw4hGo4jFYqivr885f3JyUrP3JyIiIiIi\n0sK3viWC4urVwLe/rU9Q1FrZPYterxd33303GhsbEYvFcOTIEQwMDFSqfmVhzyIRERERERnthRcA\npxM4exb4P/8H+MQn9HtvLTNR2VMrBwcHMTU1hcHBQQCYN0GRiIiIiIjIaOfPAw88IILin/yJvkFR\na2X3LB48eBBbt24tum8k9iwSEREREZGRPvUp4O//HmhoAGIx4Kqr9H1/LTNRyWFxaGgIBw4cQDgc\nRktLS/p4LBbD2NiYJpWZK4ZFIiIiIiIyyve+B/zxHwPLlokFbZxO/etgSFhMpVI4ceIEAoEAurq6\n0hWw2+2wWq2aVGauGBaJiIiIiMgIv/0tsH49kEwCX/kK8JnPGFMP3cPiyZMn048VRYHJZEJNTY0m\nFdASwyIREREREeltchLYvBl44gngQx8CfvADwFz26jDa0G2Bm3379sFsNsNqtaaLzWaDzWbT5M2J\niIiIiIgWui9+UQTF664Tt8wwKihqbcZfo7OzE8PDw5iamsoplbq3odvtRiKRmPW8WCym6f0diYiI\niIiIrsQTTwBf+AJgMgH/8i/ANdcYXSPtLJ3pybq6OmzatKnilQiHwxgcHEQkEoHJZJr1/B07diCV\nSlW8XkRERERERMWcOAF8/OPA1BSwezfgchldI23NGBYHBwexbt06tLe35/TkmUwm7Nq1S7NKxOPx\nkkIiAPT09KC+vh7xeFyz9yciIiIiIiqHogB/8RdiYZt77gEefdToGmlvxgVuPB4PxsfH4Xa781Y8\n3bt3r+aVsdvtiMViWLduXcHnJUmCx+PB8PAw6uvrMTU1lfM8F7ghIiIiIiI9/K//BfzX/wpYLMAz\nzwBFIozutMxEM/YsRqNRJBIJWCwWTd5srnw+H/r7+7nADhERERERGebZZ4HPflY8/qd/mj9BUWsz\nhkWfz4dIJIKtW7fO6U1sNlvBYaZ+vx+9vb0lvUYwGMTGjRvxgQ98ABMTE0XP27NnT/pxc3Mzmpub\ny60uERERERFRQWfOAA88AFy4ALS1Adu2GVufkZERjIyMVOS1ZxyG6nQ6EY/HYbVa8+YsHj16VPPK\nzDQM1efzIRQKpYfDyrIMm82G0dHR9PkchkpERERERJW0fTvwjW8Ad9wBPP00sGqV0TXKpWUmKhgW\n4/E4GhsbkUgkir6Rw+HQpALZCoXFUCgEt9stKvu73snx8XE4nU7Isoyampr0uVo1zOnTwMqVwNIZ\n+12JiIiIiGgx+fa3gY9+VGSFI0eAO+80ukb5Kh4We3p6EI1GYbfb4fP5sHnzZk3ebDa1tbU5PYUA\nYDabEQ6Hc+ogyzJqa2vz7veoVcP82Z+Je6Rcey1www3AjTeKbaHHdru4pwoREREREVUvSQI2bABO\nnQJ6e4GODqNrVFjFw6JKlmUMDAwgHA7DbrfD4/HMef5iJWnVMF4vMDQklsOdzYoVmfBYLFjedNP8\n654mIiIiIqLSXLoEvO99YthpayswODh/O4x0C4vThUIhhMNhJJNJDAwMaFIBLWnZMJcuAcePA6++\nKsorrxTeplKl1Auorwfe/e7c0tAALFmiSXWJiIiIiKhCOjuBnh7gllvEbTLm880ZDAuL850RC9yc\nOVM8UKqPX3oJmDZiFoAY63z77ZnweOedYnv99fP3mwqixUZRxJdHFy6IcvGiOLZsWaYsXy62/P+W\niIio+hw6BGzZIjp5fvIT4Pd+z+gazUy3sDg0NIQDBw4gHA5DlmUAgNVqhdvtht/vn3dDUufraqgX\nLgC//jXwi1/klpdfLnx+bW0mOGYHyauu0rfeRAvF5cuALAPJJHDihNhOLydP5gY+9fFs+xcvll6P\nJUtyw+P0MFlof80a8e2k1Sq2xR5brWI4OwMpERGRfl5/HVi/Xmz/5m+Az33O6BrNruJhsb+/H4FA\nANu2bYPb7UZdXV169VNJkiBJEoaHhzE0NISuri5s375dk8rM1XwNi8XIMvDLX+aHyGJDW9etE5Nq\nN27MlNpaXatMVHGTk8AbbwC//S3w2muFg9/0Uspw8LlYulTMT1YLIHobp5dKW7Zs5mB57bVirrRa\nrr+eqzoTERFdqakp4EMfEj2Lzc1AOLwwppBVPCwODQ2htbW1pBco59xKW2hhsRBFEUNXpwfI558v\n3MNRV5cJjps2AU1NgMWif72JSnH+vBie/dvfiuv8lVcyj9Xtq68WHrY9E5NJhKXaWrFCcaFSUyOG\nfi9fngl92Y+n76uPly8HzObZ66Aooofz0iXx/2p2iCy2f/GiuFXPxIT48mhiYubHFy6U3y7TA2Sh\nUlPDHksiIqLp9u0DHnpIfL549lnxb+ZCoPucxZMnT+Ydy76/oVbcbjeCwSDq6uoKPi/LMrxeLyKR\nCJqamjA4OJhzbjWExWIuXQKOHgXicXFPl2gUiMWAc+fyz33nOzPhceNGoLERWL1a/zrT4nLhAvCb\n3wDHjuUHQHX71lulvdbVV2dWE167tngAVIvFUlqgW+jOny8eKJNJ0ROrhvBXXhE9tKX8SVy9Ojc8\n3nQT4HCIhbkaGsT+YmhfIiIi1ZNPAvfdJ74I/v73gT/4A6NrVDrdwuK+ffvQ2dlZsALT73E4F+Fw\nGIODg+jv74ckSTn3WcxWX1+PnTt3oq2tDQ899BAkScKhQ4dy6lWtYbGQy5dFj2M0minPPJPfA2k2\ni4V0soevrl8velmISjU1JXr9EglRJCnzOJEQ4WS2//2WLhVDI2+6KRNKpm9vuCEz1JPmRl3VOTtA\nFipnz878OitW5IbHhobM47e/XQyPJSIiqhYvvQTcdZeYp/jXfw387d8aXaPy6BYWzWYzhoeH4XK5\nNHmzYvbt24fx8XEEg8GiYTEWi8Hn82FsbCx97NixYznnLrawWMjFi2IepBoejxwR+5cv5563dClw\nxx1iDqRa1q+f38sAU2Upiuilyg6A2YHw2LGZF3sxm4GbbxZDo2++uXAYvOaahTHWfzFRFDHnM3s1\n55dfFv/tx8ZEOX68+M8vWSLmU2cHSTVMOhz8UoqIiBaWM2fE/RSfeQZwuYAf/nDhfSmqW1isr69H\nPB6vyJDTQux2O2KxWMGwGAwGEQ6HYbfbEQ6H0dTUhP7+fliyJugxLBZ27hzw3HOZ8BiNih7Jqan8\nc9VFdLLLLbdwPlO1uHhRfFsmSZkyPp4JhbMtFHP11SIMOhxiqxaHQwTEhfbHlEpz+nRueBwbE9fN\n2JgIlsX+7JpM4ouC228X5Y47RLntNs6tJiKi+WdqCvB6gYMHxRefP/+5mO6y0OgWFmOxGFpaWtDe\n3g57VkuZTCbs2rVLkwpkmyks9vT0oKurC8FgEC6XC4FAYNEPQ52L06fFwjnPPJMpzz0n5kRNZ7Xm\nB8jbbhMLf9D8oihi7lp2GMwOhC+/XPhLAtXq1cXD4Lp14jYPRNnOnxdfNKjhMTtMJhLFFytSQ+Qd\nd+RuGSKJiMgo//2/A1/8ovi36KmngFtvNbpGV0bLTDTjoupdXV2w2WyYmJiY0xvabDaYCnRN+f1+\n9Pb2lvwabrc7fZuOQCAAW4Exk3v27Ek/bm5uRnNz8xXVudqtWQPce68oqsuXxSI62QEyHgfefBMY\nGRFFtWxZ7jDWd71LDDt7+9sZIivt1CkR+l5+OTNUNDsQFliPKs1kEj3FDkem1NdnAuHatexFpvKs\nXCm+PLrttvznLl0S1+jzzwP/8R/Ar34lti+8IBY9+u1vxXLk2W68MT9A3n67+NKKiIioUh5/XARF\nsxkYGFhYQXFkZAQj2R/UNTRjz6LdbkcikcgZ6llJM/UsRiIRBAKBnJ5Es9mMqaxuEvYsak9RxHyl\neDw3RB49Wvh8dd5afX1mzlL2Y37gm9mFC+ID9EsvZQKhWtRjsw0VXbMmt+2zg+Ett3DxGDLe5KQI\nkdkB8le/EqGy0OgGQCx8dOedwHveI+ZXv+c9IqBy6DMREc3V008D998v/g362teAT33K6BrNjW7D\nUDs6OuDxeLB161ZN3mw2hcJiKBSC2+2GxWKB3W5Hf38/XC4XOjs7cezYMfzoRz9Kn8uwqJ9TpzLD\nWJ99NjPsbLZhjnZ74RBZXy96FKp1eX5FET1+6u0NCoXAl18WtzqYzcqVIpDffHNmYZHsQFhby95B\nWpgmJ8VCSoVCZKHbBC1bJnod16/PBMj168XcWiIiolK88oq43dxrrwE7dgB9fQv/c5RuYdHpdCIe\nj8NqtebNWTxarGtpDmprazE6OpoTFs1mM8LhMDZv3ox4PI4dO3YgFovB7XZjcHAwZ/EdhkXjXbwo\n7rU3Pp4p2UMkZ1qif/lyEX7WrhU9kDab2BZ7rG5ravRbYVNRxHzPZBI4cUJssx/PdKyUu80sWSJC\n8y23ZAKhWtRjDIO02ExOir8rv/iF+HLq2WfFHOusxbFzXH99foB817vEKtBERESqs2fFvRRHR0XP\n4qFD1TGdSrewKElS0R90OByaVEBLDIvzm6KI+9UUCpLj46X1qhVjseSHyauuEu85OSl6O2fbzvTc\n+fOZ0Hfp0pXVcc0a0bN67bWFQ+DNNwPXXcdbSxCVSl2oSw2P6vb06fxzV6wQcyDVAMnbBRERLW6K\nAjzwgJif6HCIoai1tUbXShsVD4vxeByNjY0lvUA551Yaw+LCdvq06D2YmBBFljPbmR7PNodPa6tX\ni9Bnt4s/KtnbmR5XwzdVRPPd1JSYD5kdIJ99Vhwr5O1vz1/t+e1vZ+89EVG1e/RRYM8e0bnw5JPi\nC8VqUfGw2NPTg4GBAXR0dKClpSVvwZlEIoFwOIy+vj74/X48+OCDmlRmrhgWF6fJSTEfcHqIPHVK\nzIFcsuTKttmPV6wQwc9m403GiRaikydFeMwOkM89V3gupMWS6XlUA+Ttt3NxKCKiajE4CPh84jPe\n978PfPjDRtdIW7oMQ5VlGcFgEAMDA4jFYrD+bhlLWZbR1NQEv9+PtrY23VZKLQXDIhERlWpysvDt\nggoNiV+6VATG7B7I9esX5s2aiYgWs9FR4P3vF18WfuUrwGc+Y3SNtKfbnMVssizDZDLNq3A4HcMi\nERHN1fHjuQHymWeAF18U81umu+WWzDzId79bbN/xDi6mQ0Q0H732mlj59JVXgE9+EvjGN6pz2oEh\nYTGRSKC9vR3RaBSACI9utxt9fX0F74toBIZFIiKqhDNnMovpqAHyuecKr/CsLqaTHSDf8x7gmmv0\nrzcREQnnzokVT48cAd73PiAcrt7pBYaExY0bN6K/vz9nMZtYLIbdu3fn3OtwLtxuN4LBIOrq6go+\nHwwG0dnZiVQqhZaWFvT19eWcy7BIRER6UYex/uIXmfmQzz0n7hVZyLXX5gfI227jPGgiokpTFODj\nHwcef1wsYnbkSHXfk9eQsOjxeHDo0KGSj5cjHA5jcHAQ/f39kCSpYE+lJEloaGhAJBKB0+nEQw89\nhGQyiYGBgfQ5DItERGS0kyeBX/4yN0A+95xYdGu6JUuAd74zEx5vv10ESIcDWLZM/7oTEVWjL30J\n+NznxG3M/v3fxZd21UzLTFTyrIrGxkZ4PB54PB7U1dVBkiQMDw+jqalpzpWIx+MwzTJg2P67VQSs\nVitqamqgKApqq+VmKEREVDVqaoD3vlcUlaIAL72UHyBffBF4/nlRDhzInL9sGdDQIILjrbdmtrfe\nKj7sEBFRaQ4eFEHRZAIee6z6g6LWSu5ZBMSw03A4jGQyidraWrS0tGh6j0W73Y5YLFZ0DmQwGERH\nRwcAwGaz4cSJEznPs2eRiIgWknPnRFBUw+PzzwMvvFB8KCsA3HRTfoi87TYxzLUaF2ogIrpSzzwD\n/N7vifnlgQDw0ENG10gfhgxD1cNMYVGSJGzcuBGHDx/Ghg0b0NHRwWGoRERUlc6eze11fOEFsX3x\nReDixcI/Y7Hk9kDW1wN1daLYbAySRLS4HD8O3HUX8PLLwCc+AXzrW4vn76AhYXHfvn0F39hkMmHX\nrl0z/qzNZis4zNTv96O3tze9P1NY7OnpQSKRyDnfbDZjamoqpy4Mi0REVK0mJ4FEIhMe1e3zzwOy\nXPznamoywXF6WbcOWL1at1+BiKjizp8HPvAB4KmngHvvBQ4fXlyLiRkyZ9FisaCjowN9fX1lv8nE\nxETZPzPd9F9aLvKv4p49e9KPm5ub0dzcPOf3JiIimg+WLBFzGRsagD/4g8xxRQHeeCMTIF94QYRK\ntZw8KW778eyzhV/3mmuKh8lbbuFiO0S0cCgK0NYmguIttwDf+U71B8WRkRGMjIxU5LXLGobqdrsx\nPDxckYoAhXsWQ6EQ3G43kskknE4nBgcH4XQ60dnZCVmWcSBrRQD2LBIREeVSFODEidzwmF1+85vi\nQ1sBMWzr6quB664Drr8+dzv92Jo1i2eYFxHNT4EA0NUFrFolVj5dv97oGumvaucs1tbWYnR0NCcs\nms1mhMNhbN68GUNDQ+js7IQkSfB6vejv70dNTU36XIZFIiKi8kxNAa++CkhS4TD5yisicJZi1aqZ\nQ+U114j5kzabmGO5ZEllfzciWjwUBfi7vwM++1nx+OBB4CMfMbpWxqjasDhXDItERETaunxZDHE9\nfhx47bXc7fRj586V99o1NSI4Wq2ZEDnbY3Vb7cPKiKh0ly8Df/VXwP79Yv+rXwU+/Wlj62Qk3cJi\nQ0ND3puZTCZYLBbY7Xb4fD5s375dk4pogWGRiIjIGIoCnDpVPEgePy5CpywDExNAKjW391u+XCzM\ns2aNKLM9LuX51avF6xLRwpFKAT4fcOgQsGIF8L//N/DRjxpdK2PpFhaDwSD6+vrw8MMPo66uDpIk\nobu7Gx0dHbDZbOjr60NDQ0POCqVGYlgkIiJaGCYnxcI7ExOZAJn9eLZjly5Vpl5Ll+aGx+lhcrZ9\niyW3F5TDbYkq59gxsdjXf/wHsHYt8N3vAu99r9G1Mp6uPYujo6OwWCzpY7IsY9OmTTh69CgAsShN\nMpnUpDJzxbBIRERU/RQFuHABOHMGOH1alLk+VvcnJ7Wvb02NCI5qUYNkof3sobbXXsueTqJifv5z\n4I/+SIxYuPVW4P/+X8DhMLpW84Nut85IJpNIJBLYsGFD+lgikcCJEycAAKm5jiEhIiIiKpPJJOYs\nrlwJ1NZq97qKIlaGzQ6P6uNSjp0+LXpLs3tCUylx7ORJ4KWXyq/T2rXAjTcCN9xQfHv11YDZrF07\nEM13oRDwp38q7qfocol9q9XoWlWnGcPi3r17sXnzZrS3t6O+vh5jY2MIBoMIBAKIx+NwuVzYvXu3\nXnUlIiIiqhiTScx5WrECsNu1ec2pqUyAVIsaJGfaTyaB118H3npLlGL3yATE0Nnrry8eJm+6SfS4\n8H6ZtNApirg1hho//uIvgN5eXtuVNOtqqJIkoa+vD4lEAg6HA36/H42NjUgkEpiYmEBTU9OcKxEO\nh9HZ2Yl4PI6Wlhb09fWhrq4u77xYLIYdO3YgkUjA5/Nhv7rkkfrLcBgqERERVYnJSeDNN8XtS159\ntfj2dwO+ZrRkCVBfL4brqeVd7xJbrYIxUSVdvAjs3Al885tiv6cH2LWL93YtpKpunSHLMux2O0Kh\nEFpaWvDlL38Z4XAY0Wg071ybzYZ9+/bB6/XC5XKhvb0dO3bsSD/PsEhERESLzfnzYsXZYmHyN78R\npdhHpKuvLhwi163j4jw0P0xMAK2twI9/DLztbcC//AuwdavRtZq/dAuLqVQKnZ2dGBgYgCzLsFqt\n8Pv9CAQCqKmp0aQCoVAIgUAAR44cAZAJj1NTUznnhcNhdHR0YGxsDAAQiUTQ2dmZEyoZFomIiIjy\nnTsHHD0K/PrXwAsv5JazZwv/zIoVwDvekRsgb78duPNOLrxD+hkfB37/98W1e911wPe+B2zaZHSt\n5jfdwqLP5wMABAIB1NXVQZZl7NixA2azGQcOHNCkAoAIpeqKq+FwGDt37kyvtqoKBoMIh8MYGBgA\nIEKlw+HIWYmVYZGIiIiodFNTogeyUIh85ZXCP7NiBbB+vfjArpZbb+UiO6S9f/s34D/9JzHU+t3v\nBn7wA+CWW4yu1fynW1g0m82YmJjIu3VGoZ4/LYRCIbS1tSEUCmHz5s05z+3btw/j4+PpeYqF6sGw\nSERERKSNU6eAF1/MDZDPPSeOTbdmDbBxY26AfPvbOZ+MrtxjjwGf/KSYq/jBDwIHDojb0NDsdLt1\nRl1dHUZHR3OC2+joKBxl3sTEZrPBVOCvhd/vR29vL2RZhtfrhSzLOHz4cM6tOlRWq3Xe3M+RiIiI\nqNpddRXgdIqSTZaB0VHgyJFMefllYGREFNXatbnhcdMmce9IopkoCvCFLwB79oj9//JfgK99Taz6\nS/qbsdkDgQBaWlrQ3t4Oh8OB8fFxBINBDA4OlvUmExMTMz7vcrng8XjQ3d1d9ByHwwFJktL70Wi0\n4Eqse9QrC0BzczOam5vLqisRERERFWe1invbuVyZY8eP54bHI0fELT9++ENRVDffnAmO99wD3H23\nWLCECAAuXAC2bxcL2JhMwN/+LfCpT7GHejYjIyMYyf6mRkMl3TojFAohmUyitrYWra2tZfcsziQU\nCmHv3r0YHBzM6S5V3yMUCsHtdsNiscBut2NwcBAulwtutxtbtmzBrl27Mr8Mh6ESERERGU5RgGPH\ncsNjNAqcOZN73vLlwF13AfffL8q994ohrbT4vPUW8JGPiHmKq1cDjz8O/OEfGl2rhamqbp3R1dWF\nnp6enGMmkwmTk5MAxLzJcDiMzZs3Ix6Pw+v1IplMpoewTv85hkUiIiKi+WdyUsx7VMPjz34m5kBm\nf3RbulQMe73/fuC++4D3vQ/IWjqDqtSvfy1WPB0fB268Efj+94HGRqNrtXBVPCw2NDTMWoHpq5XO\nBwyLRERERAvHxIToSfrJT4AnngBiMREqVWYzsGGDCI733w+8//1Aba1x9SVtnTgB/M//Cfz934te\n58ZGERRvvNHomi1sFQ+LsVhs1h8sNF/QaAyLRERERAvXqVOix/GJJ0SAPHIEuHQp95w778wMW73v\nPi6asxAlk8BXvyoWrjl9WhzzeoFvfpPDkLVQVcNQtcSwSERERFQ9zp4Fnnoq0/P41FPA+fO557zr\nXUBzc6Zcd50BFaWSTEyIRWu+9jXg5ElxbMsWsfLpPfcYWrWqwrBYBMMiERERUfW6cEH0Nv7kJ6L8\n+7/nL5pz220iNH7gA6L38ZprDKkqZUmlgL/7OxEUUylxrKUFePRR4L3vNbZu1YhhsQiGRSIiIqLF\n49IlMc9xZAT48Y/F/Mfp4fGOO0RwbG4W4XHtWiNqujidPCl6Eb/6VXF/TgDYvFmExPe9z9i6VTOG\nxSIYFomIiIgWr0uXRM+jGh5/9jPg3Lncc97znkzP4333AXa7ETWtbqdOiUVrvvIVMT8REEH90UfF\nlipLy0xk1uRV5igcDsPpdMJsNsPj8SCRSMzpPCIiIiJafJYtE8MaH34YGB4Wc+R++lPgC18Q4XDF\nCnG7jq9/XdzTb+1asQLnZz4jVuFUe7/oypw+DezdC9TVAY88IoLi+98PHD4sAjyD4sJjeM+iLMuw\n2+0IhUJoaWnBl7/8ZYTDYUSj0bLPY88iERERERVz/jzw85+LXscf/1gsmHPxYuZ5s1mstnrvvWLB\nlXvuAd75TnGcijtzBvjHfwR6eoC33hLH3vte0ZPocgEmk7H1W2yqahhqKBRCIBDAkSNHAGRC4dTU\nVNnnMSwSERERUanOnQOefDITHp9+Ov9WHVYrcPfdmQB5112AzWZMfeebs2eB3l4REt+IBA1GAAAg\nAElEQVR4Qxy75x4REt1uhkSjVFVYBIBUKgWLxQJADDXduXMnjh49WvZ5DItEREREdKXOnhUL5jz1\nlChPPgm8+mr+ebfemul5vPdesYjOkiX619cIyaTonf3Zz4B/+ifg9dfF8bvuEiFxyxaGRKNVXVhU\nhUIhtLW1IRQKYfPmzWWfx7BIRERERFpRFOC3v82Ex6eeAkZHxS08sq1eLcKSGiDvuac6btkxOQn8\n8peZ4PzUU8Cvf517jtMpQuKHP8yQOF8suLBos9lgKnD1+P1+9Pb2QpZleL1eyLKM/v5+bNiwoeDr\nzHYewyIRERERVdLFi8Czz+b2PhZac7GuTvRA1tcDDkdm63AAq1bpX+9SvPlmbjB8+un8W5GsXCkC\n4r33inslejwMifPNgguLs3E6nfB4POju7p7TeSaTCZ///OfT+83NzWhubtayqkREREREOV5/XQzN\nVANkoZCV7brr8kOkur32Wn3C16VLYmXY7HA4Pp5/Xl1dZrjtPfcA69cDy5dXvn5UupGREYyMjKT3\nH3300eoJi6FQCHv37sXg4GDOL+VwONLPu91uDA8Pz3gewJ5FIiIiIjLe5ctiuObYGCBJIoSp20Qi\nfxGdbKtW5YfIm28WK7JOTYmhoVNT+Y9nei778euvi2AYjebfg3LVKmDTptzVYK+9trJtRdqrqp7F\nrq4u9PT05BwzmUyYnJwEAJjNZoTDYRw6dGjG89R9hkUiIiIimq8mJ4FXXskNkNlb9Sb2enjHO3KD\n4bvfDSxdqt/7U2VUVVjUEsMiERERES1kspwJj2qAfOUVMTTVbBZlyZLCj0t57qqrRO/h3XcDa9ca\n/dtSJTAsFsGwSEREREREi5mWmcisyasQERERERFRVWFYJCIiIiIiojwMi0RERERERJSHYZGIiIiI\niIjyMCwSERERERFRnnkRFsPhMJxOJ8xmMzweDxKJxIznx2Ix2O12nWpHRERERES0+BgeFmVZhsfj\nwSOPPAJZltHU1ASv1zvjz+zYsQOpVEqnGtJMRkZGjK7CosM21x/bXH9sc/2xzfXHNtcf21x/bPOF\nzfCwqPYqbt26FTU1Nejq6kIsFit6fk9PD+rr63k/xXmCfwD0xzbXH9tcf2xz/bHN9cc21x/bXH9s\n84XN8LC4bds2hMPh9H40GkV9fX3BcyVJQjAYRCAQ0Kt6REREREREi9JSoysAABaLBQAQCoXQ1taG\nUChU8Dyfz4f+/n7YbDY9q0dERERERLTomBQdxnPabDaYTKa8436/H729vZBlGV6vF7Iso7+/Hxs2\nbMg7NxgMIhaLYf/+/ZiYmEBtbS2mpqZyztmwYQOeffbZiv0eRERERERE89n69evxzDPPaPJauoTF\n2TidTng8HnR3dxc9x+fzIRQKwWq1AhAL49hsNoyOjmLdunU61ZSIiIiIiGhxMDwshkIh7N27F4OD\ngzmL1jgcjvTzbrcbANK9k+Pj43A6nZBlGTU1NfpXmoiIiIiIqMoZPmcxGo0iFovlLGpjMpkwOTkJ\nQPQohsNhbN68Of18XV0dTCYTgyIREREREVGFGL4a6t69ezE1NZVT1KAIAFNTUzlBMRwOw+VyQVEU\neDweJBKJgq8bi8XgdDpht9vR0dFR8d+j2rnd7qJtDYihxGazOV38fr+OtatOs7U5r3FtldqevNa1\nU2qb81qvLF77xuHfef3x84x+1Nvjmc1mfmbXSaltXtZ1riwgExMTislkUoaGhpRUKqV0dnYqTqez\n4LlWq1Xp7+9XZFlWnE6nEgwGda5tdRgeHlba2toUk8mkJBKJoufZbDYlHo8riURCSSQSSiqV0q+S\nVabUNuc1rq1S25PXunZKbXNe65XFa19//DuvP36e0Rc/s+uvnDYv5zpfUGFxcHBQ2bhxY3pfbZTp\nhoeHlfr6+vR+OBwu2lg0s56eHqW9vX3WP66F/jvQlSmlzXmNa6uc9uS1ro1S25zXemXx2jcG/87r\nj59n9MXP7Portc0Vpbzr3PBhqOXYtm0bwuFwej8ajebMdVRJkoSmpqb0vtPphCRJutSx2jz44IPY\nv39/ehXaQiRJgtVqhcfjQUNDA3w+H1KplI61rC6ltjmvce2U2p681rVTTpvzWq8cXvvG4N95/fHz\njL74mV1/5bR5Odf5ggqLAGCxWACIVVJ9Ph/6+vryzkmlUrDb7TnHZFnWpX6LUSqVQn19Pbq6ujA8\nPAwA8Hq9BtequvEa11ap7clrXTvltDmv9crhtT9/8drXH69zbfEzu/5KbfNyrnPDV0MtlyzL8Hq9\nkGUZhw8fxoYNG/LOsVqtSCaTBtRucWpsbMSRI0fS+/39/bDZbAbWqPrxGtdWqe3Ja107pbY5r/W5\ns9ls6VtPZfP5fHA6nbz2K6BYm/v9fvT29pb0Grz2y6NFm/M6L89sbc7P7NrTos3Lvc4XXFh0uVzw\neDzo7u4ueo7D4cjpwo5Gozld3KSteDwORVHSbax+q0GVw2tcW6W2J6917ZTa5rzW525iYqLoc5FI\nhNd+BczU5qXitV8eLdqc13l5ZmtzfmbXnhZtXu51vqCGoYZCIZhMJrS1tUGSpHTJfj6VSsHlckGS\nJEQiEQBAIBDAAw88YFS1q5ba3oqioKWlBfF4HLIso729ncM2KoTXeGXM1p681rVXapvzWq8sXvvz\nD699/fE61x4/s+uv1DYv+zq/wgV3DNHZ2amYTKacYjab08+bTCYlEokoiqIosVhMqa+vV2w2m9LR\n0WFUlauG3W7PWz0su72DwaBSX1+vmEwmxefzcalpDczW5rzGtTVTe/Jar4xS25zXemXx2jcO/87r\nj59n9MHP7Porp83Luc5NiqIoOoRdIiIiIiIiWkAW1DBUIiIiIiIi0gfDIhEREREREeVhWCQiIiIi\nIqI8DItERERERESUh2GRiIhoFrFYDA0NDbq+Z09PD+LxeNHnh4aG0N/fr2ONiIhosWFYJCIimmdk\nWcbAwAAaGxuLntPa2opAIKBjrYiIaLFhWCQioqrU3t4Ou90Ou92Offv2afraoVAI9fX1MJvN8Pl8\nSKVS6eeCwSDsdjsaGhoQDAavqEcyGAyio6Nj1vO2bduGoaGhsl+fiIioFAyLRERUdUKhECKRCI4d\nO4ZIJIKuri6cPHlSk9eWJAk+nw/9/f2YmJgAAHR2dqaf6+rqwuHDhzE6Ooq+vj6YTKay32NgYAAt\nLS2znud2u3HgwIGyX5+IiKgUS42uABERkdbUgDY+Po7GxkYkk0nU1NRo8tqhUAjt7e3YvHkzACAQ\nCMDpdGL//v3o6+tDe3s7NmzYAAB4+OGH00GyHLFYDOvWrQMApFIpdHd3Q5ZlSJIEh8MBq9WKvXv3\noq6uDuFwWJPfi4iIaDqGRSIiqjqtra1IJpPwer1IJpPYvXs3HnzwwZxzhoaGsGPHjpxjJpMJ/f39\n2Lp1a9HXTiaTcDgc6f26ujrIsgwASCQS8Hg8Oc9lCwaDSKVSOXXp7OxEPB5HMpnE4OAgbDYbrFZr\n+nlJkrB3714kEgkkEol0SAUAh8ORfm8iIiKtcRgqERFVHUmS0NLSgrGxsfRw0Olz+9RAmV1OnDgx\nY1AEgNraWoyPj6f3ZVlOhzuHw4GxsbGceqjcbjc6OjpyhqXGYjHE43EcOnQI/f39aG9vzxu2qi5y\nEwqFcoIiERFRpTEsEhFR1QmFQvB6vUilUlAUBQDyQpi6EM30cvDgwRlfu7W1FcFgEJFIBLIsY8eO\nHfD7/QAAv9+PYDCIeDwOWZbR3d2dft/h4WH09fWl6wMA4XA43RPZ2NiIaDQKi8VSsLfwyJEjecck\nScrphSQiItISwyIREVWdhx56CHa7HTabDRs3boTX683rMWxra8vrWUwmk0V7FtXQ53A4MDg4mF5t\n1Ww2p29h0djYiEAgAJfLhU2bNqGjowMWi6VoPZPJZN5QVQBoampCIpFI76dSKdTW1uadJ0kS3G73\n7A1CRER0BThnkYiIqtKhQ4c0e62mpiYcPXo0vd/a2orW1ta88xKJBDZu3IhkMglA9HDa7fair1tb\nW5szVFXtUfT7/QiHw+k5lRaLBb29vXk/Pzw8nO7VJCIi0hp7FomIiDQyMTEBl8uVvu9iX18ffD5f\n0fNbWlowPDwMQMxfVHsJ29ra0NfXN+v7DQ0NzTrHkoiI6EqxZ5GIiEgjTU1N2L17d3poqd/vx/bt\n23POyZ472djYiKamJng8HphMpnRAtFgs8P//9u49vun63h/4KwERUWmS4oUNtWnqpniDNCreS9PU\ny9mcA9Iy7yBtyn7TzYmk6M4BPU4I9bKbAxrOdKhT2nS6i2PSBHucZ6KWxNvACU1Ah4pI+aaoXITm\n98d7uTW9N+03TV/PxyMP8v3mk28+gS9JXt/PrbwcgUAgNsFNRw0NDaiurh6kd0JERARoIokj7YmI\niIiIiIjAbqhERERERETUCYZFIiIiIiIiSsGwSERERERERCkYFomIiIiIiCgFwyIRERERERGlYFgk\nIiIiIiKiFAyLRERERERElIJhkYiIiIiIiFIwLBIREREREVEKhkUiIiIiIiJKwbBIREREREREKRgW\niYiIiIiIKAXDIhEREREREaVgWCQiIiIiIqIUDItERERERESUgmGRiIiIiIiIUjAsEhERERERUQqG\nRSIiIiIiIkrBsEhEREREREQpGBaJiIiIiIgoBcMiERERERERpWBYJCIiIiIiohQMi0RERERERJSC\nYZGIiIiIiIhSMCwSERERERFRClXD4vLly6HValNub775ZkpZv9+PwsJCGAwGVFVVqVBbIiIiIiKi\nkUMTiUQiar14OBzG3r17Y9stLS2oqqrC1q1bU8rq9XrU1NTAbrfDarXC4XCgoqJiKKtLREREREQ0\nYqgaFjuy2+249957MWXKlKT9Xq8XVVVV2LZtGwDA5/PB6XSiublZjWoSERERERFlvYwZs+j3+6HV\nalOCIgAEg0GYzebYdmFhIYLB4FBWj4iIiIiIaEQZrXYFoiorK+HxeDp9LBwOw2AwJO1TFGUoqkVE\nRERERDQiZURY9Hq9yM3NRV5eXqeP63Q6tLa29nicgoICtLS0pLl2REREREREw4PJZIoN3xuojOiG\nWl9fD4fD0eXj+fn5Sd1Om5ubk7qlRrW0tCASiWTFbfHixVnxmgM9Zn+f35fn9bZsT+UG+vhwuan1\nPrLl/FTj3OypTLacm2q9l5F4bva2fDrKZMv5yXNzYMfg9/rg3fi9PrDnZ+L3ejobzzImLJaUlKTs\n93g8CIfDsFqtCAaD8Pl8AACXy4XZs2cPdTWHVFFRUVa85kCP2d/n9+V5vS3bUzk1/s3UoNb7zJbz\nU41zs6+vO5zxs7P/z+/rc9J13vHcHF6vmY5jDpfPTp6bw+91M/2zczh+r6s+G6rf74fNZsOePXtS\nHtNqtfB6vSguLkYgEIDdbkdrayvKy8uxYsWKlPIajQYqvx2iLi1ZsgRLlixRuxpEKXhuUibj+UmZ\niucmZap0ZiLVxyyazeZOgyIAtLe3x+5PnTo1bX1vidQwUq5Q0vDDc5MyGc9PylQ8N2kkUL1lMZ3Y\nskhERERERCNZOjNRRoxZJCIiIiIioszCsEhEREREREQpGBaJiIiIiIiywJYt6T0ewyIREREREdEw\nN3cuMHlyeo+pelhUFAU2mw1arRYWiwWhUKjTcoWFhdBqtbFbeXn5ENeUiIiIiIgo82zZAjz+ODB2\nbHqPq3pYLCwsxJVXXglFUWCxWOBwODotFwqF4Pf7EQwGEQwG4Xa7h7imREREREREmWfnTvnzwIH0\nHlfVpTP8fj/KysqS1k/cvn078vLyUspqtdqkdRc7w6UziGioRMcEnHmmuvUgIiKikW3rVsBqBT78\nMLonS5bOaG5uhtlsRlVVFQoKClBWVga9Xp9SLhgMQqfTobS0NFYuHA6rUGMioviYgMmT5T4RERGR\nGt59F7jsMgmKl1wCvPZaeo8/Or2H6xtFUeDxeFBbWwun0wmXywW73Y7169cnlQuHwzCZTKiurobR\naITT6ey0HBFlv3S16LW3S1eNvt527JAxAV/7GjBqlNyvrASmTRv4eyMiIiLqLb8fKC0F9uyRlsU/\n/AE49tj0voaq3VDdbjc8Hg9efPFFABIK9Xp9j91Nuyqn0WiwePHi2HZRURGKiorSXm8iGnr79wPf\n+558EALAlCnyAdnbkLd/f/L2V1+lt36nngqcd57Ua8oUuW80AlrVR4YTERFRtvn734Grrwba2oBp\n05pgtTZh9L+bAe+77760dUNVNSz6fD64XK6kFsLOxiYGAgFEIhGYzeZuy3HMItHwFokAH30E/POf\nwHvvyZ/R+zt2pP/1xo6V2zHHxO/35ubzAW+/Lcc44QTg888ljHZ0/PHAuecmB8izz5bXIyIiIuqP\nDRuAa68FvvgCmDULePppYMyY+OPpzESqhkUAMBgMcLvdsFqtcDqd2L59e6yl0ePxwGazoaWlBSUl\nJfD5fLFuqIqiYO3atUnHYlgkGh7275fB2NFAmBgMP/+88+dotdJ1VKcDjj4a2LULuPNOadFLDHK9\nDX5jxgAaTf/fQ2J32CNH5P289Rbw5pvxPz/+uPP38c1vxsNjNEiedFL/60JEREQjw1/+AsycKb2k\nbroJ+M1vEGtRjMqqsBgIBFBRUQG/3w+bzYb6+nqMHz8egLQeer1eFBcXw+12w+VyIRgMwm63w+12\nx8pFMSwSZY7uWgk/+EAe70xuroSpM86QP6P38/MBh0PGCALAnDnyAZnJPv00NUC+956Ey45OOik1\nQJ5+euoXABEREY1MDQ0yJOerr4CqKuCxxzof7pJVYTGdGBaJBldnk8vs3w+8/35yGOyplXD0aMBk\nSg6D0fsTJvS9DsPJgQPAP/6RHCDfekvGHHQ0dixwzjnxAHneedKtNXqdbLj/XRAREVHvPPUUcOut\ncsH5xz8GHnqo6x5SDItdYFgkGjy33AKsWSP3J08GTjllYK2ERx01dHXPdJEIsH17aoDcvr3z8iYT\ncPhwfBznDTfIlwgRERFln9paaUmMRID//E/gvvu6H0rDsNgFhkWi9Nm3T2ba+tvfgBdfBJqbOy+X\n2EqYGAp700pI3VMUCY2JAfLdd4FDh1LLWiwyhuHqq6X1cSDjMYmIiCgz/OxnMkcDACxbBjidPT+H\nYbELDItE/bd7twTD6C0QkAllEo0dCxx3HPDZZ8CvfgWUlLCVcKh99RWwbh3wne/IOMfPP5fZ0BJ9\n7WvAVVdJcLTZgJwcdepKRERE/ffTnwI/+Ync/8UvgNtv793zGBa7wLBI1Hs7dgAvvxwPh++9l/z4\n6NFAYSFw2WVyW7sW+N3v5LHhMLlMtps7Nz7Zz/XXA9ddJyHyr39NnoV11Cjg4oslOF59tYx7ZKsj\nERFR5opEJCQ++KB8Z69eLd/7vcWw2AWGRaLOtbfLZCiJLYcffphc5phjgIsuiofDadOAY49NLsMJ\nVTJLZ/8ekYh0V123Tm5//3vy7KsTJya3Oup0Q1tnIiIi6trmzdLd9Mkn5YLvk0/KDKh9kVVhUVEU\n2O12+Hw+mM1m1NfXw2g0ppTz+/2oqKhAKBRCWVkZVq5cmVKGYZFIHD4s3UijLYevvALs2ZNcRqeL\nB8PLLgPM5uQFXSk7KArg9cZbHT/6KP7YqFFygSDa6jhlClsdiYiI1DJnDvDEE3Jfq5WlMq67ru/H\nyaqwaDKZMH/+fFRWVmLhwoUIBoNYv359Sjm9Xo+amhrY7XZYrVY4HA5UVFQklWFYpJFq/37gtdfi\n4fDVVzsfx3b55fFweNZZna/NQ9krEgHefjve6vh//5fc6njyycmtjnq9enUlIiIaSQIBuXAPyIXb\nSERaGfvTmytrwqLf70dZWRm2bdsW27d9+3bk5eUllfN6vaiqqoqV8/l8cDqdaO4wPSPDImWzxC6H\niiI/9KPhsLlZJj5JdPrpyeHQaGSrESULh6XV8a9/lfC4c2f8sVGjpCtyYqsjLy4QERGl3+7dcpH2\nrbfku/Yb35C5JEZ8WKytrYXX64XBYIDX64XZbIbb7UZOh6n7ouXq6uoASNfV/Px8tLa2JpVjWKRs\nddNN8XX09HoJi4mnukYjE5dEw+Gll0orEVFvRSKyLEe01fGVV6Q7c9RJJ8VbHUtLk1sdOZaViIio\nf7Zule/WlhaZKyLaM2wgkwmmMxOpep1YURR4PB5YLBY0NjbCYDDAbrenlAuHwzAYDCnPJcpmR45I\nq8+11yYvuL53r8xUesklwKJFwF/+IvsCAeDnPwdmzWJQpL7TaIBzzgEWLgReeknGuP7+90BFBTBp\nErBrF/Db3wKzZ8v6mZdcAjzwgJyfkyfLrS8ztREREY10f/+7zB3Q0iJdULdtk9bEzZszZ9Z5VVsW\n3W43PB4PXnzxRQASCvV6Pdo7LO7mdrvR2NiY1LJoMBhSyrFlkbLB5s3AmjUSEBO7BR53nKyXt3On\nBMMpU9SrI40skQjwj38ktzomdnsePVpaGnfv7n+XGSIiopHE4wFuvBE4eBD4j/8Ann1WfuulQzoz\n0ei0HKWf8vPzk95Ix+6nieWCwWBsu7m5GeboCNAOlixZErtfVFSEoqKitNSVaDDt3i0fEmvWyPjD\nqPx84OabpXugxyMLsM+Zw6BIQ0ujAc4+W2533w3s2wf4fLLuZn29dFfdvVvKzp8PLF4MFBVxjCwR\nEVFHkQjw6KPAggVyv6oK+OUv5cJrfzU1NaGpqSltdUw04JbFhoYGrF27Fl6vN9Y1VKfTwWazoby8\nHDNmzOj2+QaDAW63G1arFU6nE9u3b4+1NHo8HthsNuTk5MBgMKC+vh5WqxU2mw1XXnklFixYkPxm\n2LJIw8jBg8ALL0hAfOGF+PiwnBygvFxC4sUXx39wc1wYZaLEab5HjYrPrjplCvDjH8u5zCVZiIiI\n5DvyRz8CfvUr2Xa55CJsui+uZsQEN263Gy6XC7NmzYLNZoPRaER+fj4AIBgMIhgMorGxEQ0NDaiu\nrsa8efM6PU4gEEBFRQX8fj9sNhvq6+sxfvx4AIBWq4XX60VxcTECgQDsdjtaW1tRXl6OFStWpL4Z\nhkXKcJEI8PrrMvbr2WdlrCEgP7KvukoC4re/DRxzjLr1JOqL6IWMCROAFSuAxx4DPv1U9k2cCNx+\nO+BwAB2GnhMREY0YX34JXH898Ic/yEXU6DwAgyEjwmJDQwNmzpyZ9rIDwbBImWrHDhmDuGYN8P77\n8f1TpkhAvP56mW2SKBscOCBdVB95RMY6AsC4cdIK+cMfyrIuREREI8Wnn0pjwOuvyxj/55+XGewH\nS0aExY7a2tpS9kVbCIcKwyJlkn37gIYGCYgvvRTff/LJwA03SEg891z16kc02CIRoLFRQuO/RxdA\no5EZVH/8Y1nmheMaiYgom/3zn7I0RigE5OXJRHFnnDG4r5lRYbGmpgZOpzP1wBoNjkQHrwwRhkVS\n25EjwIYNEhB//3vpcgAAY8cC110H3HILUFIysEHMRMPRu+8CP/sZ8OSTwKFDsq+wUEKj3Q4cdZS6\n9SMiIkq3V14BvvMdoLUVsFiAP/95aHqSZVRY1Gq1aGxshNVqTUuFBoJhkdSyebP0PX/qKeCjj+L7\nL79cWhBnzZKJa4hGul274uMaP/tM9n3968Add8iajnq9uvUjIiJKh7o6+Q148KB0QX3mGeDYY4fm\ntTMqLJpMJgQCgSHvctoZhkUaSrt3y3/8NWuATZvi+00m+XC48UZZ+oKIUu3fLxdXHn00PkHOsccC\nc+fKuEaTSd36ERER9dWWLTIE44UXgIULZd/3vw/84hcymeFQyaiw6Pf7UVJSAofDAUPCVHcajSZl\naYvBxrBIg+3gQelCsGYN8Je/pC53ccstwEUXcRwWUW+1t8t4xkceAbxe2afRSLftu+5KXj6GiIgo\nU82dCzz+ePK+mhr5Lhvq77F0ZiLtQA9QXV0NvV6PvXv3Ys+ePbHbZ9H+RT0oLCyEVquN3crLywdU\njijdIhFg40ZZbHziROlS+sc/yv5vfUu6GXzyCbBqFX/YEvWVVisD/xsbgbfeAm69Vcb0PvcccOml\nwLRpwNq18QszREREmWbLFgmKiUOOHn4YWLBg+P8uHHDLosFgQCgUQk4/B2QZDAZs2LABOp0utt1Z\nl9belGPLIqXTjh0yGceaNcDWrfH9U6dKN9PvfY/LXRANho8/Bn79axnbuGeP7Dv1VBnXOG8ex/8S\nEVFm+dOfZKZvQLqbHjki81mceaY69cmobqhVVVUoLS3FjBkz+vV8rVaL9vb2tJRjWKSB2rcP8Hgk\nIDY1xfeffLKMQbz5ZuCcc1SrHtGI8uWXcsHm0Udl6nEAOO44CYx33AEYjerWj4iIqKFBesV8/nl8\n35w5wG9+o1qVMissFhYWIhAIQKfTpYxZ3JrYHNOJYDAIi8UCi8WCYDAIs9kMt9ud0krZ23IMi9Qf\nR44APl98uYv9+2X/2LHAd78r4xCtVi53QaSW9nZZl+qRR2RpGkC6r86YIUtvXHRRfJIcta7iEhHR\nyHL4MHDvvcDy5bL9ve/J+MRx49T/LsqosBgKhWL3Ew+l0Whg7OGybyAQQGVlJVwuF4xGI5xOJxRF\nwfr16/tVjmGR+uIf/5CA2HG5iyuuiC93kQGT/BJRgjfflJbGZ54BvvpK9p1wgsxODKh/NZeIiLLf\nZ59JOPR6pdvpQw/JTN6ZMj4xY8JiIBDA0qVLoSgKCgsLsWjRotg4wvPPPx9vvPFGn44XDoeh1+t7\n7G7aVTmGReqoY2vDp5/Gl7vw++PlCgriy12waxtR5tu5U9ZqfOwxoK1N9o0ZAxw6JP+3p05Vt35E\nRJSdNm2Sni0ffACceKJMdHjFFWrXKlk6M1G/O9YFAgHY7XY4nU4YjUb4/X4UFxfj/PPPx8KFC9HS\n0tKrY0QiEZjNZgDocpKc3pYDgCVLlsTuFxUVoaioqPdvirJK4hTG06fLWKd16wiqWHEAACAASURB\nVOKzKup08eUupk3LnKtBRNSzr38dePBBYOZMwGIBjj5alrYBgJISWd9q/nz2DiAiovR54gmgqkq+\nby68UOa5mDRJ7VoBTU1NaEqcbCON+t2yWFpailWrVqV0NfX7/Vi6dCkCgQC2bdvW7TGiazT6fL6k\n7qVr164FAHg8HthsNrS0tHRbLvZm2LJI/7ZlCzB5skxM8+mnMuYJkK4CV18tAfFb35JxiUQ0vCVe\nGMrNjc+gqtMBt98uk+FMmKBe/YiIaHg7dAj40Y9klm4AcDiAn/9cLlRmoozohmqxWNDc3DzgCrjd\nbrhcLgSDQdjtdrjd7lhXVq1WC6/Xi+Li4m7Lxd4MwyJBxh8uWiRdTRNVVwN33ildBogou0S7nJ9x\nBrB+vbQ6vvyy7Bs3Tr7Y77pLWiSJiIh6a+dOmcdi40YJh489Btx2m9q16l7Gh8VwOIxNmzahuLh4\nQJXrK4bFke1f/wJcLsDtjndHi+KkF0QjzyuvSGhct062x4yR6c0XLgRMJlWrRkREw8DLLwNlZcCu\nXcApp8gyGeefr3atepYRYdHpdOKEE07AggULYvt8Ph9cLlfs/pEjR9JSyd5iWByZduwAli2TMHjo\nkOybNQv4yU/kxyGg/hTGRKSeQABYulTGlkQisuzG974nvQ3OPlvt2hERUaaJRIBf/lJ6pBw+DBQX\nA88+K7NvDwcZERYBwGazIRQKIT8/H83NzSgpKYktb2EwGNDa2pqWSvYWw+LIEgzKD8AnnpD/yBqN\nTFhz7738AUhEqf75T7mw9NRT8YmuvvMd4J57gAsuULduRESkruhwhtNOAyoqgN/9Trbvvlt6qQyn\n9bYzJiwCMklNa2srLBYLdDpdbH9tbS0qKysHXMG+YFgcGbZulf+0Tz4JHDkirQTXXy8/+NiCSEQ9\n2bFD1sRavRo4cED2lZTIZ0hREWdGJiIaaRInStPrgb17gWOPlX12u7p164+MCIuBQABTe7mQVV/K\nDgTDYnZ77z3gpz+VKz3t7TKz6Y03Skvi6aerXTsiGm527QIefRT49a+Bfftk37RpEhq/9S2GRiKi\nkSA6g/7ppwMtLfIb87TTgBdeAM46S+3a9U86M5G2v09sbGyExWLB6tWrsX379pTHQ6EQ3G43LBYL\nvF7vQOpII9w//iHjiyZPlu5jWi0wbx7w/vvSBZVBkYj646STpFvqjh3Af/+3LLuxcSNw7bXAlCky\nPmWIh94TEdEQ+/JL+XPr1vhSa3V1wzcoptuAuqEqioLa2lrU1dXB7/fHuqEqigKz2Yzy8nJUVlYi\nJycnbRXuDlsWs8vbb8sPOI9Hto86SqYqrq6WKz5EROn0+ecym/JDD8kSPABQUCCfOTfdFJ8wi4iI\nssNrr8nn+9at8X233hrvkjpcZUQ31M4oigIASWMXe1JYWIhAIBDbttvtWLt2bUo5v9+PiooKhEIh\nlJWVYeXKlSllGBazg98vIfH552X76KNloPHChTJtMRHRYDp4UNZpXbZMJtICgEmTgAULpFfDsceq\nWz8iIhqYr76S35oPPig9SM4+G7j/flmrNxvmv8jYsNgfBoMBGzZsiAVMg8GA8ePHp5TT6/WoqamB\n3W6H1WqFw+FARUVFUhmGxeHt9dflP+6f/yzbY8cCVVUyC9XXvqZu3Yho5Dl8WLoiPfigdIcHgAkT\ngDvvBL7/faAP10WJiChDvPeezHmxaZOMTb/rLvn9OXas2jVLn4wKiw0NDVi7di28Xm9Sy6LNZkN5\neTlmzJjR7fO1Wi3aox2Eu+D1elFVVYVt27YBkDUcnU4nmpubk8oxLA5Pr74qV3P++lfZHjdOfojd\ndRdw8snq1o2IqL1dLmL99KdyUQsAxo8H/t//A370I+DEE9WtHxER9ay9HXjsMempduAAcOqp0ovk\niivUrln6ZcQEN263GwUFBXjjjTfgcDjQ3NyM9vZ2tLe3o7m5GRUVFXjttddQUFCA1atXd3qMYDAI\nnU6H0tJSFBQUoKysDOFwuNNyZrM5tl1YWIhgtG8QDVt/+xtgswEXXyxB8bjjZGzQ9u1ATQ2DIhFl\nBq1WJr3ZuBHwemVx5rY2Wec1Lw/44Q+BDz+Uslu2xNfqIiKizPCvfwFXXgnccYcExVtukbkxsjEo\nplu/w6LBYMC2bduwbNkyWK1W5Ofnxx7Lz89HSUkJXC4Xtm3bBr1e3+kxwuEwTCYTqqur0djYCEDG\nLHZWzmAwJO2LtmLS8BKJAC+9BEyfDlx+ufzwGj8e+MlPJCQuXQqccILatSQiSqXRAFYr4PNJj4hv\nfxvYvx/4xS8Akwn4xjdk1ubJk2XNLiIiUt8zzwDnnCO/OSdMAH7/e5lNf4jm3xz20jZmsaGhAUuX\nLoXBYEB1dTWKi4v7fIxwOAy9Xp/SLdXtdqOxsRF1dXUAJCgaDIaUchqNBosXL45tFxUVoaioqO9v\nhtIuEpH/pPffD7zyiuzT6aQL1x13yAKoRETDzdtvy0Q4zz4rn3NAfEHnzZuzY6IEIqLhqLVVhgs8\n+6xs/8d/AKtXZ2fPtaamJjQ1NcW277vvvswZsxjV0NCAmTNnwu/3o66uDhdccEGP4xUDgQAikUhS\nF9POxjB2HKPo9XpRXV3NMYvDQCQiXUzvv1+6cAGAwQD8+MfAD37AqzpElB3WrQOuuSZ536WXAg88\nIL0oNBp16kVENBI1NsoSGB99JDNYP/KIzKw/Uj6LM2LMYketra0AALPZjGXLlvWqgpFIBCUlJQgE\nAlAUBQ6HI6kbqsfjQTgchtVqRTAYhM/nAwC4XC7Mnj07XVWnQRCJAH/6E3DBBfIDauNGafpftky6\nm957L4MiEWWPq68G5syJb48eLb0oioqASy6Rz8Me5nIjIqJ+io4X//JL6bFWWipB8aKLgLfeAior\nR05QTLe0hcXCwkKUlZVhw4YNAHq31qLZbIbL5YLdbofBYICiKHC73bHHy8rKsGnTJgDSuuhwOGAw\nGFBQUIAFCxakq+qURu3twHPPAWazTAjR3CwzBT70kIREpxM4/ni1a0lElH6/+Y10Pd28GfjkE2DJ\nEulJ8eqr8nl43nnAU0/JkhxERJQec+fGx4tPnAj88pdywe6nPwVeflnGlFP/pXWdRUVRUFtbGxtb\nOHv2bMyaNQt5eXnpeolusRuqetrbgYYGWafmnXdk38SJEg4rKmQ5DCKikebzzwG3G3j4YWDnTtmX\nlyfrx86ZAxxzjKrVIyIa1rZsiYfEjz+WfSaTrJGbMMptxMmodRa7oigKvF4vmpubsWzZssF4iRQM\ni0PvyBH5D/nAA3I1HQAmTZIlMG67LbsWOCUi6q9Dh6RV0eUC3n9f9p14okzy9f3vs1s+EVF/rF4t\njRKJ/H5g6lR16pMpMiIsBgIBTO3lv0Rfyg4Ew+LQOXxYpiJ+4IH4D59TTwXuuUcGFB99tKrVIyLK\nSEeOAM8/L8sE/XuUBcaPB+bPB+68EzjpJHXrR0Q0HHzyiUyW+MwzyfvnzJEhASNdRkxw09jYCIvF\ngtWrV2P79u0pj4dCIbjdblgsFni93oHUkTLIV18Bjz8OnHEGcPPNEhSNRrmys3Ur4HAwKBIRdWXU\nKGDmTOCNN4D164HiYqCtTVocTztNWhlDIbVrSUSUmdrbgZUr5XfoM89IV/5ly2QSm82bGRQHw4C6\noSaOUfT7/bFJbRRFgdlsRnl5OSorK5EzRP1r2LI4eA4dAn77W+DBB2WiGgAoKAB+8hPg+uuBo45S\ntXpERMPWa6/Jj53nn5ftUaOA2bNlzPc556hbNyKiTPHOO9Io8eqrsn3NNcCvfiWNFpQsI7qhdkZR\nFAC9mwl1MDAspt/Bg3KVZulS4MMPZd8ZZ0hILC+X2aaIiGjgtmyRFsann47PmPqtbwGLFgEXX6xu\n3YiI1PLFF7Je98MPS1f+iROBX/xCemlwOYzOZUQ31I7a2tqg1Wqh1WrR1taGtra2Pj3f7/fDYDB0\n+XhhYWHs+FqtFuXl5QOtMnVj/36Zethkkm5RH34InHUW8OyzwLvvAjfcwKBIRJROZ54JPPEEsG0b\ncPvt0r3qz3+WdRovvxxYt07WsCUiGileeEF+fy5fLl1Qf/ADubA2axaD4lAZcMtiTU0NnE5n6oE1\nGhw5cqTXxyksLMSbb77Z5XMMBgM2bNgQa7U0GAwYP358ymuyZXFgvvwSWLVK/lN+8onsO/dc4L/+\nC/judwFt2i4vEBFRd3bvlqvnv/oV8O+OOzjvPJlt2m6X7qpERNnoo4+AH/4Q8Hhke8oU+X16wQXq\n1mu4yKhuqFqtFo2NjbBarf0+xvLly9Hc3AyPx4P29vYuX6erx6IYFvvv88+BFSuAhx4CPv1U9pnN\nEhK//W2GRCIitbS1AbW1wCOPJK8jtnChTDTGJYqIKFscOQL8+tfAvfcC+/YBxx4ra3jffjt7tPVF\nRoVFk8mEQCCQ0srXW8FgEKWlpWhsbITJZOo0EAaDQVgsFlgsFgSDQZjNZrjd7pSJcxgW+66tDXjs\nMekHvmeP7LvgAgmJ11zDJn4iokxx4ACwZo30/GhpkX0TJ8qSG1VVwPHHq1s/IqL+2rJFZjNdtgxo\nbpZ93/mO9K449VR16zYcZVRY9Pv9KCkpgcPhSBpzqNFosGDBgh6fb7FYUFNTg6lTp8JgMHQaFgOB\nACorK+FyuWA0GuF0OqEoCtavX5/8ZhgWe01RZEzio48Ce/fKvosuAhYvBkpLGRKJiDLVkSPSNWvp\nUpkuHgB0OhnLc8cdwGefyb4zz1SvjkREvXXjjTKxV9SkSfIb9brr1KvTcJdRYbG0tBQtLS2w2Wwp\ns6AuW7as2+fW1tbC7/dj5cqV2Lt3L3Jzc3vsagoA4XAYer0+paxGo8HixYtj20VFRSgqKur9mxkB\nWluBn/9cbuGw7LvsMgmJxcUMiUREw0UkArz4ooTGl1+WfaNGSZgEuDg1EWW2gwelJ9vy5cn733gD\nsFjUqdNw1dTUhKamptj2fffdlzlh0WAwIBQK9WstxbKyMng8nqT1GfV6PTZt2oS8vLxYuUAggEgk\nArPZHNvX2RhGtix27bPPpBXxl7+UPuCAhMP/+i/giivUrRsREQ3M3/8O3HMP8L//m7z/f/5HQiMv\nBBJRpmhvB373O1mGbccO2Xf88cApp0hX1M2b2TNioDJq6YyysjL4fL5+PdftdkNRFGzfvj12jFAo\nFAuKHo8H4XAYkUgEJSUlCAQCUBQFDocDdrt9oFXPWlu2yA2QyWqcTiAvD3jwQQmKpaXA3/4G+HwM\nikRE2eDii2WSMgBIXIXqtttkbdyf/Sw+5ICISA3R3hBmM3DTTRIUzz4bsNnk9+nmzXJxi0Exswy4\nZbGwsBCBQAA6nS5lzOLWrVt7fRxFUZCbm5u0dIZWq4XX60VxcTHcbjdcLheCwSDsdjvcbjeXzujE\n3LnA44/L/bPOAoJBWTMRkAlr/vM/gWnT1KsfERENnsTvgKlT5YLhzp2yfcwxwOzZsnYuu3gR0VDa\ntEkaL6LtS5MmySynN90k3eejjRwMiumRUWMWg8Fg5wfWaGA0Ggdy6D4b6WFxyxZg8mTg5JOBXbvi\nizdfe62ERP44ICLKfok/ug4fBv78Z2l1TJwTzmIB5s+X8DhunDr1JKLsFwxKd9NnnpFtnU66zP/g\nB3IBiwZHRoTFhoYGaHoYBDFjxox+Vaq/GBYlLE6YEJ8Nz+MBZs5Ut15ERKS+rVtlUevf/CbeJVWn\nA269VZbe+OY3Va0eEWWR3buBBx6QC1VffQUcfbSslbhoUXJXeRocGREWTSZTj2Fx27Zt/apUf430\nsAgkd0HiTHhERNTR/v1AXZ38iHvttfh+q1VaG6+9FjjqKPXqR0TD1xdfyBhpl0vGIWo0wM03A/ff\nz/USh1JGhMVMxLAo2O+biIh6w++X0Pi73wFffin7Jk4EKiuBigrg619Xt35ElPm2bJEu7xs3ylJs\nH38s+6++Gli2DDj3XHXrNxJlbFicP38+VkSnY1MBwyIREVHfKQqwZo0Ex/fek32jRkkr4/z50uqo\nHfD86USUbebMAZ54Innf+edLy+L06apUiZDBYdFgMKC1tTVdh+szhkUiIqL+i0RkrcZf/xp47jlp\nLQCA00+XcY233srxRkQEHDwI1NTIBIqJHn4YuPNOru2qtoxaZzFd/H5/0tIbnT1eWFgIg8GAqqqq\nIawZERHRyKDRAEVFMqbxgw9knNGkSTI5zl13SbfUOXOA11+Pz7hNRCNHW5uExPz8eFA86qj4eMSr\nr2ZQzDYZExYrKioQDoe7fNxqtWL+/PkIhUJobm6G2+0ewtoRERGNLBMnyo/BUAh4/nmgtBQ4cEC6\nnF14oSy/sXq1TGhBRNnt449lJtNTTwUWLgQ++gg45xzg8stlttMPPpALSZwvI/tkRDfU5cuXo7m5\nGR6PB+3t7SmPe71eVFVVxWZX9fl8cDqdaG5uTirHbqhERESDZ9u2+PIb0a/7nBzglltkbOMZZ6hb\nPyJKr/ffBx56CPjtb4FDh2TfFVcATidw1VXSisiJFTNPVo1ZDAaDKC0tRWNjI0wmU6dhsba2Fl6v\nF3V1dQAARVGQn5+f8loMi0RERINv/36gvl4mxNm4Mb5/+nQJjdddJ8ES4A9IouHo9ddlkprnnpMu\n5xoN8N3vSqvihReqXTvqScaOWexPpcrKyuB2u6HX67ssEw6HU8YzKorS59ciIiKigTvmGFk77dVX\nZfmNigpg3DjgpZeAsjJpbZw8WW5z56pdWyLqypYt8ZbBSARYt07GLV94IfD738t4xIoKKdPQwKA4\nEo1O58H27t3bp/K1tbWwWCyYPn16t8/V6XS9brFcsmRJ7H5RURGKior6VCciIiLqvalTgdpamfTi\nySeBRx8FgsH4448/LuOc7riDM6kSZZK5c+X/JyBjD/fuBd55R7bHj5deAj/8oYxfpszW1NSEpqam\nQTl2Wruh9lVZWRk8Hg90Oh0AaS3U6/XYtGkT8vLyYuU6jlH0er2orq7mmEUiIqIMs3kzcNZZEgz3\n7o3PmnrUUTJT4g03AN/+trROEpE6tmyRlv9TTgE++UQmqQEkGN55J+BwSGCk4Sljxyz2VTgchubf\n8+u2tLSgsLAQiqJg/L/PTo/HA5vNhpycHBgMBtTX18NqtcJms+HKK6/EggULko7HsEhERKS+xBaL\nyy4Dxo4FfD4gOi3B8ccDM2ZIcCwuBkaNUq+uRCNJe7v8X3z4YeDFF5Mfu/9+GZN49NHq1I3SJ2vC\nYiJFUZCbm4sjR47E9mm1Wni9XhQXFyMQCMBut6O1tRXl5eVYsWJFyjEYFomIiDJDxxkSP/kEePZZ\n4OmngcSOQSefDMyeDdx4I2A2c402osGwc6dcwPmf/wG2b099/NZb4xd4aPjLyrCYDgyLREREme/9\n9yU0Pv000NIS3//Nb0pr4/XXAyYTp+QnGojDh2XCGrcbeOGFeMv+aacBt90m6yLu2yf7+H8suzAs\ndoFhkYiIaPiIRGSK/qefBtauBT79NP7YCScAu3fL/TlzZG1HIupZKCQtiI8/Dnz0kewbPVqWtKmo\nAEpKAG1a10OgTMOw2AWGRSIiouHp8GHA65Xg2NAgazkmuv12aQ0591x2VSUCklveDx0C/vAHaUX0\neuMTS33jG8C8ecAttwAnnqheXWloMSx2gWGRiIho+Nu0CbBYAJ0OCIfjP3wBWYbj2mvldsUVwJgx\n6tWTSC2Jk0iddRawaxfw2WeyffTRgN0uIfHyy3lxZSRiWOwCwyIREVF2SPwxXFICGI3An/4kE+VE\nHX88cNVVEhyvuYbrONLIsG6dnO/jxgFffhnff8450s30xhsBvV69+pH6GBa7wLBIRESUPTpOcNPe\nLjOp/vGPcosuIA7I8huXXhpvdSwo4AQ5lB0iEeDdd6V7dkOD3I/SauX/xTPPAOXlbEUkkVVh0el0\nwu12Q1EUlJSUYNWqVTAajSnlCgsLEQgEYtt2ux1r165NKsOwSERENHKEQtLa+Mc/Av/7vzLuMSon\nR7qwAjJe64knVKkiUb9EItIdOxoQt26NP5aTA0yYEJ9JmBNAUUdZExa9Xi+qqqrg9Xqh1+tRUVEB\nAKirq0spazAYsGHDBuh0utj2+PHjk8owLBIREY1MigL89a8SHP/0J+Dzz5Mfnz4dmDEDsNlk0g+2\nwJDaOms537gxHhB37IiXnTBBZjOdORMoLpaxumw5p65kTVgMhUIIh8OYMmUKFEXB0qVL0dbWhhUr\nVqSU1Wq1aI8uENMFhkUiIiJ6+23gvPOAk06SEHnwYPLjp5wi4yCjN84SSUMtcUzuVVfJuqLPPRdf\n6gIAJk6UCxwzZwKXXSbLXxD1RtaExSiPx4OysjLo9XoEg0Hk5OQkPR4MBmGxWGCxWBAMBmE2m+F2\nu1PKMSwSERERkPxj3G6XH+SNjbKsQHTWyKjzzpPQaLPJj/Jx4+KPsfWG0u3ll2Um3wkT5GJGYvfp\nU0+VcDhzJnDRRVwPkfon68JiVFVVFYLBINavX5+0PxAIoLKyEi6XC0ajEU6nE4qipJRjWCQiIqKo\nzoJee7u0PDY2yu1vfwMOHIg/PmYMcMklEhxffVW6tAIcF0b9t3s30NQEvPSS/Bk9LxPNmwc4HEBh\nIbtI08BlTVgMhUIwGAyxFsJwOAyj0YjW1tZunxcOh6HX61O6pTIsEhERUV8cOAD83/9Ji2NjI+D3\nJ6/rOGqULNGhKDJJzsyZwHHHqVZdGgZaW2XCpZdeklvi7KWAtFzrdPEup7feGm8FJ0qHrAmLNTU1\n2LNnD5YtWwYg3t20Y1gMBAKIRCIwm82xfZ2NYdRoNFi8eHFsu6ioCEVFRYP3BoiIiCir7NkDbNgA\n1NUBHk/q41otcPbZwIUXAtOmyZ9nnsnugiNNYqu1okjX0mg4fPvt5AsOY8dKa/X06XKzWDhBDaVX\nU1MTmpqaYtv33XdfdoTFQCAAq9UKn88Ho9GIiooKTJgwITbBjcfjgc1mQ0tLC0pKSmLlot1QuXQG\nERERDZbEcY9nnCEtQm+/nTzGDJCWx/PPj4fHCy+UyXWiGAqyR3u7rGcYvZCQmwvs3Sv7o8aMkfGG\n0XB44YXA0UerU18ambKmZREA3G43XC4XWltbYbPZ4Ha7Y0tiaLVaeL1eFBcXx8oFg0HY7fakclEM\ni0RERJROHYPe/v3SVXXjRuC11+T2wQepz8vLk5AQCgGvvy77OO5xeNmzB3jnHblA8M478fv79yeX\nGz1aLhREw+G0acAxx6hTZyIgy8JiOjEsEhER0VD7+ON4cNy4EXjjDeCLLzove8EFgNksaz1+85ty\nO+20npdFYOtk+nT8uzxwQPZ1DIYff9z1McaPB449Vso0N8vENESZgmGxCwyLREREpLYjR4DNm2Xd\nvMWLpZXpwIHkcWyJjjoKKCiIB8jEIDlhAnDbbfHusGyd7L8vvgBuuAH4wx9kOy9PxhNu3Sr/Zh2N\nGwecc07qbeFC/ntQZmNY7ALDIhEREWWSxHGPs2bJ9vvvA//8Z/zPf/2r6+cffzywb5+MjRszRlqy\nVqyQMXGTJgEGQ++WWsiWlsme3sf+/cC2bRIAo7foduKC94m0WuD004Fzz00OhUZj1xMXZcvfJ2Un\nhsUuMCwSERFRpukpWHzxhYSZxAAZve3b1/2xx46V0DhpEnDKKfH7iTenU5b9AAbWEpaOgDSQYyQG\n7+9+F7jlluRQuHVr98F79GiZnCgnR/7edu2SiWquuYZjDCm7MCx2gWGRiIiIskUkIoFm7lxg3TrZ\nd/rpQH6+hKIPPwTa2np3rDFjpLvrF18AV18tIXL8eGm5HD8++dZx3/HHAxUVA+96mRj2ysuBJUtk\n2YlwWP5MvN9x365d0kKo1SbPPNrR6NHSInj66am3U09Nz/sgynQMi11gWCQiIqJs1FWLXFsbsHOn\nhMfo7cMP4/d37Oh9oOzJ6NES1g4dAk48ERg1SgJte3v81tX2kSPAV1+lpx5Rl14KTJ0qQbCgQP7M\ny+NkQUQMi11gWCQiIiJKdvPNwJNPyv2SEmnha2tLvu3b1/12Omk0EiJPO01Cp04nt5yczv+M3n/g\nAaC+Xo7BVkGirmVVWHQ6nXC73VAUBSUlJVi1ahWMRmNKOb/fj4qKCoRCIZSVlWHlypUpZRgWiYiI\niFINpDWtvV3GBz71lGzb7cAjj0jo02rjt+62NRqgqgr47W/lGP0Ne2wVJOpZ1oRFr9eLqqoqeL1e\n6PV6VFRUAADq6upSyur1etTU1MBut8NqtcLhcMTKRzEsUiZrampCUVGR2tUgSsFzkzIZz8/MofYE\nN5mG5yZlqnRmoi4mBB4aJpMJHo8HeXl5iEQiMBqNyM3NTSnn9XqRm5uLefPmIScnBy6XC6tWrVKh\nxkT919TUpHYViDrFc5MyGc/PzHHmmQMPeek4RqbguUkjgaph0Wg0YsqUKfB4PDAYDFi9ejWWLVuW\nUi4YDMJsNse2CwsLEQwGh7KqQ06ND6DBeM2BHrO/z+/L83pbtqdyI+VLQ633mS3npxrnZl9fdzjj\nZ2f/n9/X56TrvOO5ObxeMx3HHC6fnTw3h9/rZvpn53D8Xlc1LEbNmjUL7e3tsNvtsNvtKY+Hw2EY\nDIakfYqiDFX1VMEvlYE9n18qg4dfKgN7frZ/qaiNn539fz7D4uDiuTmwY/B7ffDwe31gz8/273VV\nxyyGQiEYDAbk5OQAkFBoNBrR2tqaVM7tdqOxsTE2llFRFBgMBrR3WGinoKAALS0tQ1N5IiIiIiKi\nDGMymbBt27a0HKuHlWgGl8fjwZ49e2JdT/fs2dNpufz8/KRup83NzUndUqPS9ZdCREREREQ00qna\nDbWkpAS1tbUIBAJQFAVOpxPl5eWxxz0eD8LhMKxWK4LBIHw+HwDA5XJh9uzZalWbiIiIiIgo66ka\nFqdOnQqXywW73Y78/HxotVq4XK7Y42VlZdi0aRMAwOfzweFwwGAwoKCgwYJxFgAACORJREFUAAsW\nLOjz69ntdpSWlsJisSAQCKTtfRClg8PhQGlpKQoKCtDQ0KB2dYiS1NbWoqamRu1qEMHpdMa+y0Oh\nkNrVIUrBz0vKRP39nalqN1QAqKioSFkvMSpxTOLUqVMH1M20trYWBQUFWLp0KQKBAJxOJ9avX9/v\n4xGlk9frhVarxfr162Njd2fOnKl2tYgAADabDT6fD8uXL1e7KjTC+f1+BAIBrF+/HoFAAA6Hg9/l\nlFH4eUmZaCC/MzNiNtSe2Gy2lKuHfr8fhYWFMBgMqKqq6tUxKisrAQCRSAR6vX5Q6kojTzrOT5PJ\nBKfTCQDIyclJmf2XqD/ScW4CQGNjI1atWpW2BX6JEvXlPPV6vSgtLQUgF5Gbm5uHtK408vT1c5Sf\nlzRU+nJuDuR3ZkaHRa/XC4fDAZ/PB41Gk/SY1WrF/PnzEQqF0NzcDLfb3e2xjEYjjEYjHA4HLBYL\nFi1aNJhVpxEg3ednXl4egsEgLBYLqqurB7PqlOXSeW4SDZb+nKetra0wGo1qVJdGGH6OUqbqz7k5\nkN+ZqndD7U4gEEj5SwDkLyk3Nxfz5s0DIBPeOJ1OVFRUoKysLKW8zWaLdXVdtWoVqqurYbPZOHsq\nDUi6zs/S0lLMmzcPy5cvR319PVavXo0pU6YMev0pew3GZydRuvXnPM3NzU2aHT3b11wm9fTn/CQa\nCv09N/v7OzOjw+Ldd98NALH1FaOCwWDS0hmFhYWxL4+OZaOqq6thMplQUVEBvV6fspYjUV+l8/z0\neDzwer144403Bqm2NJKk89wkGiz9OU9LSkrgdDpx9913w+/3w2azDV2FaUTpz/lJNBT6c24O5Hdm\nRofFroTD4ZS+tj1dXVy0aBHsdjtWrVoFQP7SiAZDf85Pr9eL5uZmFBQUAAA0Gg22bt06aHWkkak/\n52aizq5kEqVbd+fp1KlTYTabUVpaCo1GE/tOJxoqvf0c5eclDbXuzs2B/M4clmFRp9P1uWUwJyeH\nM6bRkOjP+bly5UqsXLlykGpEJPpzbkaxixUNlZ7O02XLlg1hbYiS9eZzlJ+XpIbuzs2B/M7M6Alu\nupKfn5/U5N/c3JzU7EqkJp6flKl4btJwwPOUMhnPT8pUg3VuDsuwaLVaEQwG4fP5AMgAztmzZ6tc\nKyLB85MyFc9NGg54nlIm4/lJmWqwzs1hERY76/ft8/ngcDhgMBhQUFCABQsWqFAzIp6flLl4btJw\nwPOUMhnPT8pUQ3VuaiJcNZSIiIiIiIg6GBYti0RERERERDS0GBaJiIiIiIgoBcMiERERERERpWBY\nJCIiIiIiohQMi0RERERERJSCYZGIiIYNrVabcsvNzVW7WoNm+fLlePPNNwHIe29ra0t5vKysrNtj\nNDQ0wO12D1odiYgoezEsEhHRsOL3+6EoSuy2Z88etas0KBRFQV1dHaZMmdJlmc7W2epo5syZcLlc\n6awaERGNEAyLREQ0rOh0OowfPz52A4BgMAibzQan0wmLxQIA8Hq9MJlM0Gq1KC0tRTgcjh2jtrYW\ner0eubm5qK2thcFgACBBNPr86DFKS0uTtjseMxgMorCwEDU1NbGFkAOBQOw5Ho8HJpMJBoMBVVVV\nAACHw4GamppYGafTierq6qT3WVtbGyvfGx6PJ6XVdcOGDQCAWbNmoaGhodfHIiIiAhgWiYhomIlE\nIp3u9/l8aGtrg8fjgaIoKCsrw+rVq6EoCvLz81FRUQFAAmF1dTVeeuklBINBrFq1qlctdF0dU6PR\nIBAIQKPRoLW1FSUlJXA6nQAkxFZWVqKhoQGbNm2C1+tFQ0MDSktL0djYGDt2Q0MDZs+enfR6dXV1\nKCkp6fa9J27PmjUL7e3taG9vR11dHUwmE4qLiwEANpsNa9eu7fE9EhERJRqtdgWIiIj6wmQyJW17\nvV7k5eUBAFasWAFAWuVKSkowffp0AMDKlStjrYdr166Fw+GIde9cvnw57HZ7j68bDW+dHVOn02HB\nggUApNUwGkw9Hk/Sa9XX10Oj0WDKlCmx1wwGg2htbU3pbur3+2PvK0qv16fUq2PdFUVBZWUl/H5/\nbJ/RaITX6+3xPRIRESViWCQiomHF6/UiPz8/tp2Xl4dgMJi0r6WlBR6PJxbmgPj4vlAolNS11Gg0\n9up1uztm4r7E1r5oF9WoqVOnxu6XlJTA5/Nh06ZNcDgcSa+lKAp0Ol1KHfx+f9L+lStXIhgMJpWx\nWq1Yvnx5UtDMz8+Hoii9ep9ERERRDItERDSs5Ofnp7S4dVRQUIBZs2ahrq4uti86jtBgMGDbtm2x\n/R3DVqLEgNXVMbvqFgtIi2NLS0ts2+/3IxQKYebMmbDZbKirq8OmTZuwevXqbt9PVH5+fmycZvS9\nJNbf6XRiwoQJmDdvXq+OR0RE1B2OWSQioqxTVlYGr9cLn88HRVHgcDhi4wjtdjtqa2sRCASgKAqc\nTmeshVCn08UCnaIoWLp0aeyYdrs95ZjV1dXdjnd0OByx1woGg7Db7di7dy8AxIJnKBRK6YKq0+n6\n3BLo9XrhdruTwmxUMBjstKWSiIioOwyLREQ0bHQXzBIfy8nJQX19PRwOBwwGA7Zv3w6PxwNAumku\nWrQIVqsVJpMJs2fPjrUO5ufno7KyEiaTCeeffz7uueeepCDZ8Zj19fWIRCIp9YpuG41GuFwuWK1W\nFBQUoLS0NNbqZzQakZub2+U6iWazGaFQqNv3rtFoYvtra2sRDoeh1+tjs6FGWyyjs8USERH1hSbS\nXf8ZIiKiLBed2bS1tXXIX9tisWD16tWdrqVYU1MDnU4XmyxnIJxOJy688ELMmDFjwMciIqKRg2GR\niIhGNLXCYmNjI2bPno09e/Z0+ng4HIbVakVzc/OAX6ugoCBpnCYREVFvsBsqERGNeL1ZZzGdPB4P\nysvLUV9f32WZnJwclJeXxybm6a+GhgZUV1cP6BhERDQysWWRiIiIiIiIUrBlkYiIiIiIiFIwLBIR\nEREREVEKhkUiIiIiIiJKwbBIREREREREKRgWiYiIiIiIKAXDIhEREREREaX4/+XtAtMpfVfuAAAA\nAElFTkSuQmCC\n"
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute $\\left[A^T A\\right]^{-1}$ to get data (co-)variances"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"A = dd_res.del_mim_del_chargeability(omega, pars, s)\n",
"\n",
"B = A.T.dot(A)\n",
"#B = np.matrix(B).I.dot(B.T.dot(B))\n",
"B = np.matrix(B).I\n",
"\n",
"variances = diag(B)\n",
"vars_norm = variances / np.max(variances)\n",
"\n",
"fig, (ax1,ax2) = plt.subplots(2,1, figsize=(15,5))\n",
"ax1.plot(s, vars_norm)\n",
"ax1.invert_xaxis()\n",
"\n",
"a = ax2.hist(vars_norm, 100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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JkycPud2iRYt671dUVKiioiLZ4umhhyzkTZpkU+5jsHPOsZD0yCPSZZfZtM9X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\n+iXZrKDTp/d170x2+YqhOI6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}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
}
],
"metadata": {}
}
]
} | gpl-3.0 |
trangel/Data-Science | .ipynb_checkpoints/MNIST-dimensionality-reduction-checkpoint.ipynb | 1 | 430802 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"Let's use dimensionality reduction in a real example.\n",
"We will examine the MNIST database of digits, which has a large number of features 28x28. We reduce the dimensionality of our dataset to then use our preferred algorithm to predict the data.\n",
"\n",
"\n",
"We will see how different dimensionality reduction method work in the practice.\n",
"\n",
"\n",
"**Linear Discriminat Analysis** (LDA): supervised learning algorithm that takes into account the class labels and maximizes the class separation.\n",
"\n",
"**Principal Component Analysis** (PCA): unsupervised learning algorithm, i.e., it ignores the class labels and its goal is to find the ``principal components`` that maximize the variance.\n",
"\n",
"For more information on LDA and PCA see [Sebastian Raschka's article.](http://sebastianraschka.com/Articles/2014_python_lda.html#principal-component-analysis-vs-linear-discriminant-analysis).\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"import plotly.offline as py\n",
"py.init_notebook_mode(connected=True)\n",
"import plotly.graph_objs as go\n",
"import plotly.tools as tls\n",
"import seaborn as sns\n",
"import matplotlib.image as mpimg\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"%matplotlib inline\n",
"\n",
"# Import the 3 dimensionality reduction methods\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
"from sklearn.decomposition import PCA\n",
"#from sklearn.manifold import TSNE\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MNIST Dataset\n",
"We choose the popular MNIST (Mixed National Institute of Standards and Technology) computer vision digit dataset. This contains a series for images of handwriting letters, each of them of 28x28 pixels, see a few pick below.\n",
"\n",
"\n",
"The datasets are large, please download them from:\n",
"https://pjreddie.com/projects/mnist-in-csv/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of train dataset: (2000, 785)\n"
]
},
{
"data": {
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 785 columns</p>\n",
"</div>"
],
"text/plain": [
" label pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 \\\n",
"0 0 0 0 0 0 0 0 0 0 \n",
"1 4 0 0 0 0 0 0 0 0 \n",
"2 1 0 0 0 0 0 0 0 0 \n",
"3 9 0 0 0 0 0 0 0 0 \n",
"4 2 0 0 0 0 0 0 0 0 \n",
"\n",
" pixel9 ... pixel775 pixel776 pixel777 pixel778 pixel779 \\\n",
"0 0 ... 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 \n",
"2 0 ... 0 0 0 0 0 \n",
"3 0 ... 0 0 0 0 0 \n",
"4 0 ... 0 0 0 0 0 \n",
"\n",
" pixel780 pixel781 pixel782 pixel783 pixel784 \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
"[5 rows x 785 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv('./datasets/mnist_train.csv').head(2000) \n",
"#reduce the size to 3000 to make things fast\n",
"\n",
"columns=[]\n",
"columns.append(\"label\")\n",
"for ii in range(784):\n",
" columns.append(\"pixel\"+str(ii+1))\n",
"train.columns=columns\n",
" \n",
"print(\"Shape of train dataset: \"+str(train.shape))\n",
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The MNIST set consists of 59999 rows and 785 columns. There are 28 x 28 pixel images of digits ( contributing to 784 columns) as well as one extra label column which is essentially a class label to state whether the row-wise contribution to each digit gives a 1 or a 9. See a few pics here."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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rXFNGty4MJ3fb6TtFFm2b1+zf71NbGRo3V/KlJ+xVZBNOWdr1ddc4Lf55/Ln71yiybb7e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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109e52550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Copy the features and target columns to different arrays: \n",
"y_train= train['label']\n",
"# Drop the label feature\n",
"X_train = train.drop(\"label\",axis=1)\n",
"\n",
"# plot some of the numbers\n",
"plt.figure(figsize=(7,7))\n",
"for digit_num in range(0,6):\n",
" plt.subplot(2,3,digit_num+1)\n",
" grid_data = X_train.iloc[digit_num,:].as_matrix().reshape(28,28) # reshape from 1d to 2d pixel array\n",
" plt.imshow(grid_data, interpolation = \"none\", cmap = \"afmhot\")\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
"plt.tight_layout()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we proceed to reduce the dimensionality of our dataset.\n",
"\n",
"Several methods are proposed:\n",
"1. LDA\n",
"2. PCA\n",
"3. LDA followed by PCA\n",
"\n",
"I found that the third method is used in the practice, but in this case the results are comparable to doing just LDA.\n",
"In this example I found that PCA together with a polynomial regression yields the best results. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reducing dimensionality to 14 components\n",
"\n",
"(2000, 784)\n",
"(2000, 14)\n"
]
}
],
"source": [
"#Note than n_components for lda is < n_class (9)\n",
"\n",
"method=2 #PCA, read above.\n",
"\n",
"if ( method == 1 ):\n",
" dimensionality_reduction_method=\"lda\"\n",
" n_components=9\n",
" reduction_method = LDA(n_components=n_components)\n",
"elif ( method == 2 ):\n",
" dimensionality_reduction_method=\"pca\"\n",
" n_components=14\n",
" reduction_method = PCA(n_components=n_components)\n",
"elif ( method == 3 ):\n",
" dimensionality_reduction_method=\"lda\"\n",
" n_components=784\n",
" reduction_method = LDA()\n",
"\n",
"\n",
"print ( \"Reducing dimensionality to %d components\\n\" %(n_components))\n",
" \n",
"print(X_train.shape)\n",
"\n",
"#del X_train_red\n",
"# Taking in as second argument the Target as labels\n",
"reduction_method = reduction_method.fit(X_train.values, y_train.values )\n",
"X_train_red = reduction_method.transform(X_train.values)\n",
"print(X_train_red.shape)\n",
"\n",
"if ( method == 3 ):\n",
" dimensionality_reduction_method=\"pca\"\n",
" n_components=5\n",
" reduction_method2 = PCA(n_components=n_components)\n",
"\n",
"# Taking in as second argument the Target as labels\n",
" reduction_method2 = reduction_method2.fit(X_train_red, y_train.values )\n",
" X_train_red1 = reduction_method2.transform(X_train_red)\n",
" X_train_red = X_train_red1\n",
" del X_train_red1\n",
" print(X_train_red.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display, Math, Latex\n",
"\n",
"# Using the Plotly library again\n",
"traceDIM = go.Scatter(\n",
" x = X_train_red[:,0],\n",
" y = X_train_red[:,1],\n",
" name = y_train,\n",
" mode = 'markers',\n",
" text = y_train,\n",
" showlegend = True,\n",
" marker = dict(\n",
" size = 8,\n",
" color = y_train,\n",
" colorscale ='Jet',\n",
" showscale = False,\n",
" line = dict(\n",
" width = 2,\n",
" color = 'rgb(255, 255, 255)'\n",
" ),\n",
" opacity = 0.8\n",
" )\n",
")\n",
"data = [traceDIM]\n",
"\n",
"layout = go.Layout(\n",
"# title= title,\n",
" hovermode= 'closest',\n",
" xaxis= dict(\n",
" title= 'First Linear Discriminant',\n",
" ticklen= 5,\n",
" zeroline= False,\n",
" gridwidth= 2,\n",
" ),\n",
" yaxis=dict(\n",
" title= 'Second Linear Discriminant',\n",
" ticklen= 5,\n",
" gridwidth= 2,\n",
" ),\n",
" showlegend= False\n",
")\n",
"\n",
"if ( dimensionality_reduction_method == \"lda\"):\n",
" title = 'Linear Discriminant Analysis (LDA)'\n",
"elif ( dimensionality_reduction_method == \"pca\" ):\n",
" title = 'Principal Component Analysis (PCA)'\n",
"elif ( dimensionality_reduction_method == \"tsne\" ):\n",
" title = 'TSNE (T-Distributed Stochastic Neighbour Embedding)'\n",
"\n",
"layout.title=title\n",
"\n",
"fig = dict(data=data, layout=layout )\n",
"py.iplot(fig, filename='styled-scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above plot shows how the method results or not in separated classes. The LDA is made to maximize the class separation while the PCA maximizes the class variance. Here I chose PCA that results in not so clearly separated classes, to see this plot within LDA set method=1 above."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = pd.read_csv('./datasets/mnist_test.csv')\n",
"test.columns=columns\n",
" \n",
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save the labels to a Pandas series target\n",
"y_test = test['label']\n",
"# Drop the label feature\n",
"X_test = test.drop(\"label\",axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now predict in the test set.\n",
"Note that the LDA implementation in sklearn contains a predict feature that we test below, if not LDA then we skip this.\n",
"\n",
"Remember the precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Total of true positives:\n",
"from sklearn.metrics import precision_score\n",
"\n",
"#Only LDA has the prediction method:\n",
"if ( dimensionality_reduction_method == \"lda\"):\n",
" y_pred = reduction_method.predict(X_test.values) \n",
"\n",
"#Precision score for test dataset:\n",
" print(\"Precision score for test dataset: \\n\")\n",
" precision_score(y_test, y_pred, average='micro')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_test_red = reduction_method.transform(X_test) \n",
"\n",
"if ( method == 3 ):\n",
" X_test_red1 = reduction_method2.transform(X_test_red) \n",
" X_test_red=X_test_red1\n",
" del X_test_red1\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets compare this performance to that of svm methods: linear and polynomial"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision score for test dataset: \n",
"\n"
]
},
{
"data": {
"text/plain": [
"0.34233423342334235"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import svm\n",
"\n",
"from sklearn.metrics import classification_report\n",
"\n",
"estimator = svm.LinearSVC(C=1.0)\n",
"\n",
"estimator.fit(X_train_red, y_train) \n",
"\n",
"y_pred = estimator.predict(X_test_red)\n",
"\n",
"ntest=y_pred.size\n",
"\n",
"\n",
"#Precision score for test dataset:\n",
"print(\"Precision score for test dataset: \\n\")\n",
"precision_score(y_test, y_pred, average='micro')\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision score for test dataset: \n",
"\n"
]
},
{
"data": {
"text/plain": [
"0.87278727872787276"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Import\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"\n",
"poly = PolynomialFeatures(degree=3)\n",
"X_train_poly = poly.fit_transform(X_train_red)\n",
"X_test_poly=poly.fit_transform(X_test_red)\n",
"\n",
"estimator.fit(X_train_poly, y_train)\n",
"y_pred = estimator.predict(X_test_poly)\n",
"\n",
"#Precision score for test dataset:\n",
"print(\"Precision score for test dataset: \\n\")\n",
"precision_score(y_test, y_pred, average='micro')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Results:\n",
"\n",
"\n",
"For **LDA**: the linear regression performs best with a precision score of ~0.7.\n",
"\n",
"\n",
"For **PCA**: the linear regression performs poor with a precision score of ~0.5.\n",
"The accuracy is improved with a polynomial regression, for degree=5 the precision score is ~0.9. For degree higher than 5 the accuracy decreases due to overfitting.\n",
"\n",
"**PCA on LDA** performs as well as LDA.\n",
"\n",
"Note these results are particular to this example."
]
},
{
"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"
}
},
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| gpl-3.0 |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/TFLearn/AdversarialWorkshop/Adversarial Workshop - Dropout Classification.ipynb | 3 | 98408 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ipolosukhin/projects/tf_examples/.env/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": [
"%matplotlib inline\n",
"\n",
"import pandas\n",
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy\n",
"import scipy.stats\n",
"from sklearn import metrics\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow.contrib import layers\n",
"from tensorflow.contrib import learn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Sets logging to INFO to see all information from TensorFlow.\n",
"\n",
"tf.logging.set_verbosity(tf.logging.INFO)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"BASE_DIR = 'dropout_classification/'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
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"outputs": [
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" <td>0</td>\n",
" <td>3</td>\n",
" <td>Rice, Master. Eugene</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>382652</td>\n",
" <td>29.1250</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Williams, Mr. Charles Eugene</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>244373</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
" <td>female</td>\n",
" <td>31.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>345763</td>\n",
" <td>18.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Masselmani, Mrs. Fatima</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2649</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Fynney, Mr. Joseph J</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>239865</td>\n",
" <td>26.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Beesley, Mr. Lawrence</td>\n",
" <td>male</td>\n",
" <td>34.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>248698</td>\n",
" <td>13.0000</td>\n",
" <td>D56</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>McGowan, Miss. Anna \"Annie\"</td>\n",
" <td>female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330923</td>\n",
" <td>8.0292</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Sloper, Mr. William Thompson</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113788</td>\n",
" <td>35.5000</td>\n",
" <td>A6</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Miss. Torborg Danira</td>\n",
" <td>female</td>\n",
" <td>8.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>347077</td>\n",
" <td>31.3875</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Emir, Mr. Farred Chehab</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2631</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Fortune, Mr. Charles Alexander</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>19950</td>\n",
" <td>263.0000</td>\n",
" <td>C23 C25 C27</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330959</td>\n",
" <td>7.8792</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Todoroff, Mr. Lalio</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349216</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>861</th>\n",
" <td>862</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Giles, Mr. Frederick Edward</td>\n",
" <td>male</td>\n",
" <td>21.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>28134</td>\n",
" <td>11.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>862</th>\n",
" <td>863</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
" <td>female</td>\n",
" <td>48.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17466</td>\n",
" <td>25.9292</td>\n",
" <td>D17</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>864</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>CA. 2343</td>\n",
" <td>69.5500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>864</th>\n",
" <td>865</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Gill, Mr. John William</td>\n",
" <td>male</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>233866</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>865</th>\n",
" <td>866</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Bystrom, Mrs. (Karolina)</td>\n",
" <td>female</td>\n",
" <td>42.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>236852</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>866</th>\n",
" <td>867</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Duran y More, Miss. Asuncion</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>SC/PARIS 2149</td>\n",
" <td>13.8583</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867</th>\n",
" <td>868</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Roebling, Mr. Washington Augustus II</td>\n",
" <td>male</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17590</td>\n",
" <td>50.4958</td>\n",
" <td>A24</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>869</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>van Melkebeke, Mr. Philemon</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345777</td>\n",
" <td>9.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>869</th>\n",
" <td>870</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Master. Harold Theodor</td>\n",
" <td>male</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>871</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Balkic, Mr. Cerin</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349248</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>871</th>\n",
" <td>872</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>11751</td>\n",
" <td>52.5542</td>\n",
" <td>D35</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>872</th>\n",
" <td>873</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Carlsson, Mr. Frans Olof</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>695</td>\n",
" <td>5.0000</td>\n",
" <td>B51 B53 B55</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>873</th>\n",
" <td>874</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Vander Cruyssen, Mr. Victor</td>\n",
" <td>male</td>\n",
" <td>47.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345765</td>\n",
" <td>9.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>874</th>\n",
" <td>875</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
" <td>female</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>P/PP 3381</td>\n",
" <td>24.0000</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875</th>\n",
" <td>876</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
" <td>female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2667</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876</th>\n",
" <td>877</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Gustafsson, Mr. Alfred Ossian</td>\n",
" <td>male</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7534</td>\n",
" <td>9.8458</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>878</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Petroff, Mr. Nedelio</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349212</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>879</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Laleff, Mr. Kristo</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349217</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>880</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
" <td>female</td>\n",
" <td>56.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>11767</td>\n",
" <td>83.1583</td>\n",
" <td>C50</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>880</th>\n",
" <td>881</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
" <td>female</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>230433</td>\n",
" <td>26.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>882</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Markun, Mr. Johann</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349257</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>882</th>\n",
" <td>883</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7552</td>\n",
" <td>10.5167</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>884</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Banfield, Mr. Frederick James</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>C.A./SOTON 34068</td>\n",
" <td>10.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>884</th>\n",
" <td>885</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sutehall, Mr. Henry Jr</td>\n",
" <td>male</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/OQ 392076</td>\n",
" <td>7.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>885</th>\n",
" <td>886</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Rice, Mrs. William (Margaret Norton)</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>382652</td>\n",
" <td>29.1250</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>887</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>888</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.0000</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.4500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>890</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.0000</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>891</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"5 6 0 3 \n",
"6 7 0 1 \n",
"7 8 0 3 \n",
"8 9 1 3 \n",
"9 10 1 2 \n",
"10 11 1 3 \n",
"11 12 1 1 \n",
"12 13 0 3 \n",
"13 14 0 3 \n",
"14 15 0 3 \n",
"15 16 1 2 \n",
"16 17 0 3 \n",
"17 18 1 2 \n",
"18 19 0 3 \n",
"19 20 1 3 \n",
"20 21 0 2 \n",
"21 22 1 2 \n",
"22 23 1 3 \n",
"23 24 1 1 \n",
"24 25 0 3 \n",
"25 26 1 3 \n",
"26 27 0 3 \n",
"27 28 0 1 \n",
"28 29 1 3 \n",
"29 30 0 3 \n",
".. ... ... ... \n",
"861 862 0 2 \n",
"862 863 1 1 \n",
"863 864 0 3 \n",
"864 865 0 2 \n",
"865 866 1 2 \n",
"866 867 1 2 \n",
"867 868 0 1 \n",
"868 869 0 3 \n",
"869 870 1 3 \n",
"870 871 0 3 \n",
"871 872 1 1 \n",
"872 873 0 1 \n",
"873 874 0 3 \n",
"874 875 1 2 \n",
"875 876 1 3 \n",
"876 877 0 3 \n",
"877 878 0 3 \n",
"878 879 0 3 \n",
"879 880 1 1 \n",
"880 881 1 2 \n",
"881 882 0 3 \n",
"882 883 0 3 \n",
"883 884 0 2 \n",
"884 885 0 3 \n",
"885 886 0 3 \n",
"886 887 0 2 \n",
"887 888 1 1 \n",
"888 889 0 3 \n",
"889 890 1 1 \n",
"890 891 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",
"5 Moran, Mr. James male NaN 0 \n",
"6 McCarthy, Mr. Timothy J male 54.0 0 \n",
"7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
"10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n",
"11 Bonnell, Miss. Elizabeth female 58.0 0 \n",
"12 Saundercock, Mr. William Henry male 20.0 0 \n",
"13 Andersson, Mr. Anders Johan male 39.0 1 \n",
"14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 \n",
"15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n",
"16 Rice, Master. Eugene male 2.0 4 \n",
"17 Williams, Mr. Charles Eugene male NaN 0 \n",
"18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 \n",
"19 Masselmani, Mrs. Fatima female NaN 0 \n",
"20 Fynney, Mr. Joseph J male 35.0 0 \n",
"21 Beesley, Mr. Lawrence male 34.0 0 \n",
"22 McGowan, Miss. Anna \"Annie\" female 15.0 0 \n",
"23 Sloper, Mr. William Thompson male 28.0 0 \n",
"24 Palsson, Miss. Torborg Danira female 8.0 3 \n",
"25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 \n",
"26 Emir, Mr. Farred Chehab male NaN 0 \n",
"27 Fortune, Mr. Charles Alexander male 19.0 3 \n",
"28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n",
"29 Todoroff, Mr. Lalio male NaN 0 \n",
".. ... ... ... ... \n",
"861 Giles, Mr. Frederick Edward male 21.0 1 \n",
"862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 \n",
"863 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 \n",
"864 Gill, Mr. John William male 24.0 0 \n",
"865 Bystrom, Mrs. (Karolina) female 42.0 0 \n",
"866 Duran y More, Miss. Asuncion female 27.0 1 \n",
"867 Roebling, Mr. Washington Augustus II male 31.0 0 \n",
"868 van Melkebeke, Mr. Philemon male NaN 0 \n",
"869 Johnson, Master. Harold Theodor male 4.0 1 \n",
"870 Balkic, Mr. Cerin male 26.0 0 \n",
"871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 \n",
"872 Carlsson, Mr. Frans Olof male 33.0 0 \n",
"873 Vander Cruyssen, Mr. Victor male 47.0 0 \n",
"874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 \n",
"875 Najib, Miss. Adele Kiamie \"Jane\" female 15.0 0 \n",
"876 Gustafsson, Mr. Alfred Ossian male 20.0 0 \n",
"877 Petroff, Mr. Nedelio male 19.0 0 \n",
"878 Laleff, Mr. Kristo male NaN 0 \n",
"879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n",
"880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n",
"881 Markun, Mr. Johann male 33.0 0 \n",
"882 Dahlberg, Miss. Gerda Ulrika female 22.0 0 \n",
"883 Banfield, Mr. Frederick James male 28.0 0 \n",
"884 Sutehall, Mr. Henry Jr male 25.0 0 \n",
"885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n",
"886 Montvila, Rev. Juozas male 27.0 0 \n",
"887 Graham, Miss. Margaret Edith female 19.0 0 \n",
"888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n",
"889 Behr, Mr. Karl Howell male 26.0 0 \n",
"890 Dooley, Mr. Patrick male 32.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S \n",
"5 0 330877 8.4583 NaN Q \n",
"6 0 17463 51.8625 E46 S \n",
"7 1 349909 21.0750 NaN S \n",
"8 2 347742 11.1333 NaN S \n",
"9 0 237736 30.0708 NaN C \n",
"10 1 PP 9549 16.7000 G6 S \n",
"11 0 113783 26.5500 C103 S \n",
"12 0 A/5. 2151 8.0500 NaN S \n",
"13 5 347082 31.2750 NaN S \n",
"14 0 350406 7.8542 NaN S \n",
"15 0 248706 16.0000 NaN S \n",
"16 1 382652 29.1250 NaN Q \n",
"17 0 244373 13.0000 NaN S \n",
"18 0 345763 18.0000 NaN S \n",
"19 0 2649 7.2250 NaN C \n",
"20 0 239865 26.0000 NaN S \n",
"21 0 248698 13.0000 D56 S \n",
"22 0 330923 8.0292 NaN Q \n",
"23 0 113788 35.5000 A6 S \n",
"24 1 349909 21.0750 NaN S \n",
"25 5 347077 31.3875 NaN S \n",
"26 0 2631 7.2250 NaN C \n",
"27 2 19950 263.0000 C23 C25 C27 S \n",
"28 0 330959 7.8792 NaN Q \n",
"29 0 349216 7.8958 NaN S \n",
".. ... ... ... ... ... \n",
"861 0 28134 11.5000 NaN S \n",
"862 0 17466 25.9292 D17 S \n",
"863 2 CA. 2343 69.5500 NaN S \n",
"864 0 233866 13.0000 NaN S \n",
"865 0 236852 13.0000 NaN S \n",
"866 0 SC/PARIS 2149 13.8583 NaN C \n",
"867 0 PC 17590 50.4958 A24 S \n",
"868 0 345777 9.5000 NaN S \n",
"869 1 347742 11.1333 NaN S \n",
"870 0 349248 7.8958 NaN S \n",
"871 1 11751 52.5542 D35 S \n",
"872 0 695 5.0000 B51 B53 B55 S \n",
"873 0 345765 9.0000 NaN S \n",
"874 0 P/PP 3381 24.0000 NaN C \n",
"875 0 2667 7.2250 NaN C \n",
"876 0 7534 9.8458 NaN S \n",
"877 0 349212 7.8958 NaN S \n",
"878 0 349217 7.8958 NaN S \n",
"879 1 11767 83.1583 C50 C \n",
"880 1 230433 26.0000 NaN S \n",
"881 0 349257 7.8958 NaN S \n",
"882 0 7552 10.5167 NaN S \n",
"883 0 C.A./SOTON 34068 10.5000 NaN S \n",
"884 0 SOTON/OQ 392076 7.0500 NaN S \n",
"885 5 382652 29.1250 NaN Q \n",
"886 0 211536 13.0000 NaN S \n",
"887 0 112053 30.0000 B42 S \n",
"888 2 W./C. 6607 23.4500 NaN S \n",
"889 0 111369 30.0000 C148 C \n",
"890 0 370376 7.7500 NaN Q \n",
"\n",
"[891 rows x 12 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load data.\n",
"\n",
"train = pandas.read_csv('data/titanic_train.csv')\n",
"y, X = train['Survived'], train[['Age', 'SibSp', 'Fare']].fillna(0)\n",
"train_x, test_x, train_y, test_y = learn.estimators._sklearn.train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"train"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Setting feature info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(3)]), is_sparse=False)\n",
"WARNING:tensorflow:Setting targets info to TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False)\n",
"INFO:tensorflow:Transforming feature_column _RealValuedColumn(column_name='', dimension=3, default_value=None, dtype=tf.float32)\n",
"INFO:tensorflow:Create CheckpointSaver\n",
"INFO:tensorflow:Restored model from dropout_classification/linear/model.ckpt-400-?????-of-00001\n",
"INFO:tensorflow:Step 401: loss = 0.619374\n",
"INFO:tensorflow:Saving checkpoints for 401 into dropout_classification/linear/model.ckpt.\n",
"INFO:tensorflow:Saving checkpoints for 500 into dropout_classification/linear/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 0.619366.\n",
"WARNING:tensorflow:Given features: Tensor(\"input:0\", shape=(?, 3), dtype=float32), required signatures: TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(3)]), is_sparse=False).\n",
"WARNING:tensorflow:Given targets: Tensor(\"output:0\", shape=(?,), dtype=int64), required signatures: TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False).\n",
"INFO:tensorflow:Transforming feature_column _RealValuedColumn(column_name='', dimension=3, default_value=None, dtype=tf.float32)\n",
"INFO:tensorflow:Restored model from dropout_classification/linear/model.ckpt-500-?????-of-00001\n",
"INFO:tensorflow:Eval steps [0,inf) for training step 500.\n",
"INFO:tensorflow:Input iterator is exhausted.\n",
"INFO:tensorflow:Saving evaluation summary for 500 step: loss = 0.626126, auc = 0.685425, accuracy/threshold_0.500000_mean = 0.681564, labels/actual_target_mean = 0.363128, recall/positive_threshold_0.500000_mean = 0.246154, labels/prediction_mean = 0.378042, accuracy/baseline_target_mean = 0.363128, precision/positive_threshold_0.500000_mean = 0.666667, accuracy = 0.681564\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 0.62612557, 'auc': 0.68542516, 'global_step': 500, 'accuracy/threshold_0.500000_mean': 0.68156427, 'labels/actual_target_mean': 0.36312848, 'recall/positive_threshold_0.500000_mean': 0.24615385, 'labels/prediction_mean': 0.37804177, 'accuracy/baseline_target_mean': 0.36312848, 'precision/positive_threshold_0.500000_mean': 0.66666669, 'accuracy': 0.68156427}\n"
]
}
],
"source": [
"classifier = learn.LinearClassifier(\n",
" feature_columns=[layers.real_valued_column('', dimension=3)], model_dir=BASE_DIR + 'linear')\n",
"classifier.fit(x=train_x, y=train_y, steps=100)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Given features: Tensor(\"input:0\", shape=(?, 3), dtype=float32), required signatures: TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(3)]), is_sparse=False).\n",
"WARNING:tensorflow:Given targets: Tensor(\"output:0\", shape=(?,), dtype=int64), required signatures: TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False).\n",
"INFO:tensorflow:Transforming feature_column _RealValuedColumn(column_name='', dimension=3, default_value=None, dtype=tf.float32)\n",
"INFO:tensorflow:Restored model from dropout_classification/linear/model.ckpt-500-?????-of-00001\n",
"INFO:tensorflow:Eval steps [0,inf) for training step 500.\n",
"INFO:tensorflow:Input iterator is exhausted.\n",
"INFO:tensorflow:Saving evaluation summary for 500 step: loss = 0.626126, auc = 0.685425, accuracy/threshold_0.500000_mean = 0.681564, labels/actual_target_mean = 0.363128, recall/positive_threshold_0.500000_mean = 0.246154, labels/prediction_mean = 0.378042, accuracy/baseline_target_mean = 0.363128, precision/positive_threshold_0.500000_mean = 0.666667, accuracy = 0.681564\n"
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"data": {
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" <th>accuracy</th>\n",
" <th>accuracy/baseline_target_mean</th>\n",
" <th>accuracy/threshold_0.500000_mean</th>\n",
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" accuracy accuracy/baseline_target_mean accuracy/threshold_0.500000_mean \\\n",
"0 0.681564 0.363128 0.681564 \n",
"\n",
" auc global_step labels/actual_target_mean labels/prediction_mean \\\n",
"0 0.685425 500 0.363128 0.378042 \n",
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"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"pandas.DataFrame([classifier.evaluate(x=test_x, y=test_y)])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Setting feature info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(3)]), is_sparse=False)\n",
"WARNING:tensorflow:Setting targets info to TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False)\n",
"INFO:tensorflow:Transforming feature_column _RealValuedColumn(column_name='', dimension=3, default_value=None, dtype=tf.float32)\n",
"INFO:tensorflow:Create CheckpointSaver\n",
"INFO:tensorflow:Step 1: loss = 2.20242\n",
"INFO:tensorflow:Step 101: loss = 0.615088\n",
"INFO:tensorflow:Step 201: loss = 0.612931\n",
"INFO:tensorflow:Saving checkpoints for 300 into dropout_classification/dnn/model.ckpt.\n",
"INFO:tensorflow:Step 301: loss = 0.612602\n",
"INFO:tensorflow:Step 401: loss = 0.612483\n",
"INFO:tensorflow:Saving checkpoints for 500 into dropout_classification/dnn/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 0.61237.\n"
]
},
{
"data": {
"text/plain": [
"DNNClassifier(hidden_units=[5, 5], dropout=None, optimizer=None, feature_columns=[_RealValuedColumn(column_name='', dimension=3, default_value=None, dtype=tf.float32)])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dnn_classifier = learn.DNNClassifier(hidden_units=[5, 5],\n",
" feature_columns=[layers.real_valued_column('', dimension=3)], model_dir=BASE_DIR + 'dnn', enable_centered_bias=False)\n",
"dnn_classifier.fit(x=train_x, y=train_y, steps=500)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Given features: Tensor(\"input:0\", shape=(?, 3), dtype=float32), required signatures: TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(3)]), is_sparse=False).\n",
"WARNING:tensorflow:Given targets: Tensor(\"output:0\", shape=(?,), dtype=int64), required signatures: TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False).\n",
"INFO:tensorflow:Transforming feature_column _RealValuedColumn(column_name='', dimension=3, default_value=None, dtype=tf.float32)\n",
"INFO:tensorflow:Restored model from dropout_classification/dnn/model.ckpt-500-?????-of-00001\n",
"INFO:tensorflow:Eval steps [0,inf) for training step 500.\n",
"INFO:tensorflow:Input iterator is exhausted.\n",
"INFO:tensorflow:Saving evaluation summary for 500 step: loss = 0.604655, auc = 0.680634, accuracy/threshold_0.500000_mean = 0.692737, labels/actual_target_mean = 0.363128, recall/positive_threshold_0.500000_mean = 0.338462, labels/prediction_mean = 0.380738, accuracy/baseline_target_mean = 0.363128, precision/positive_threshold_0.500000_mean = 0.647059, accuracy = 0.692737\n"
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"text/plain": [
" accuracy accuracy/baseline_target_mean accuracy/threshold_0.500000_mean \\\n",
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"\n",
" auc global_step labels/actual_target_mean labels/prediction_mean \\\n",
"0 0.680634 500 0.363128 0.380738 \n",
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" recall/positive_threshold_0.500000_mean \n",
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"pandas.DataFrame([dnn_classifier.evaluate(x=test_x, y=test_y)])"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Setting feature info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(3)]), is_sparse=False)\n",
"WARNING:tensorflow:Setting targets info to TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False)\n",
"INFO:tensorflow:Create CheckpointSaver\n",
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"INFO:tensorflow:Step 201: loss = 0.602934\n",
"INFO:tensorflow:Saving checkpoints for 300 into dropout_classification/custom_dnn/model.ckpt.\n",
"INFO:tensorflow:Step 301: loss = 0.600027\n",
"INFO:tensorflow:Step 401: loss = 0.598391\n",
"INFO:tensorflow:Step 501: loss = 0.59656\n",
"INFO:tensorflow:Saving checkpoints for 600 into dropout_classification/custom_dnn/model.ckpt.\n",
"INFO:tensorflow:Step 601: loss = 0.595599\n",
"INFO:tensorflow:Step 701: loss = 0.594087\n",
"INFO:tensorflow:Step 801: loss = 0.593035\n",
"INFO:tensorflow:Saving checkpoints for 900 into dropout_classification/custom_dnn/model.ckpt.\n",
"INFO:tensorflow:Step 901: loss = 0.592026\n",
"INFO:tensorflow:Saving checkpoints for 1000 into dropout_classification/custom_dnn/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 0.591457.\n"
]
},
{
"data": {
"text/plain": [
"Estimator(params=None)"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def custom_dnn_model(feature, target):\n",
" target = tf.one_hot(target, 2, 1.0, 0.0)\n",
" feature = layers.fully_connected(feature, 10)\n",
" feature = layers.fully_connected(feature, 10)\n",
" logits = layers.fully_connected(feature, 2, activation_fn=None)\n",
" loss = tf.contrib.losses.softmax_cross_entropy(logits, target)\n",
" train_op = layers.optimize_loss(loss, tf.contrib.framework.get_global_step(), learning_rate=0.05, optimizer='Adagrad')\n",
" return tf.argmax(logits, dimension=1), loss, train_op\n",
"\n",
"custom_dnn_classifier = learn.Estimator(model_fn=custom_dnn_model, model_dir=BASE_DIR + 'custom_dnn')\n",
"custom_dnn_classifier.fit(x=train_x, y=train_y, steps=1000)"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'custom_dnn_classifier' 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-151-ce765d236d8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m pandas.DataFrame([custom_dnn_classifier.evaluate(x=test_x, y=test_y,\n\u001b[0m\u001b[1;32m 2\u001b[0m metrics={'accuracy': tf.contrib.metrics.streaming_accuracy})])\n",
"\u001b[0;31mNameError\u001b[0m: name 'custom_dnn_classifier' is not defined"
]
}
],
"source": [
"pandas.DataFrame([custom_dnn_classifier.evaluate(x=test_x, y=test_y,\n",
" metrics={'accuracy': tf.contrib.metrics.streaming_accuracy})])"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Setting feature info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(3)]), is_sparse=False)\n",
"WARNING:tensorflow:Setting targets info to TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False)\n",
"INFO:tensorflow:Create CheckpointSaver\n",
"INFO:tensorflow:Restored model from dropout_classification/dnn_dropout/model.ckpt-1000-?????-of-00001\n",
"INFO:tensorflow:Step 1001: loss = 0.608229\n",
"INFO:tensorflow:Saving checkpoints for 1001 into dropout_classification/dnn_dropout/model.ckpt.\n",
"INFO:tensorflow:Step 1101: loss = 0.610913\n",
"INFO:tensorflow:Step 1201: loss = 0.602962\n",
"INFO:tensorflow:Step 1301: loss = 0.59628\n",
"INFO:tensorflow:Saving checkpoints for 1301 into dropout_classification/dnn_dropout/model.ckpt.\n",
"INFO:tensorflow:Step 1401: loss = 0.60301\n",
"INFO:tensorflow:Step 1501: loss = 0.595864\n",
"INFO:tensorflow:Step 1601: loss = 0.59532\n",
"INFO:tensorflow:Saving checkpoints for 1601 into dropout_classification/dnn_dropout/model.ckpt.\n",
"INFO:tensorflow:Step 1701: loss = 0.601391\n",
"INFO:tensorflow:Step 1801: loss = 0.59406\n",
"INFO:tensorflow:Step 1901: loss = 0.594063\n",
"INFO:tensorflow:Saving checkpoints for 1901 into dropout_classification/dnn_dropout/model.ckpt.\n",
"INFO:tensorflow:Saving checkpoints for 2000 into dropout_classification/dnn_dropout/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 0.585586.\n"
]
},
{
"data": {
"text/plain": [
"Estimator(params=None)"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def uncertanty_dnn_model(feature, target, mode):\n",
" target = tf.one_hot(target, 2, 1.0, 0.0)\n",
" def get_logits(feature, is_training_or_sample):\n",
" feature = layers.fully_connected(feature, 30)\n",
" feature = layers.dropout(feature, 0.9, is_training=is_training_or_sample)\n",
" feature = layers.fully_connected(feature, 30)\n",
" feature = layers.dropout(feature, 0.9, is_training=is_training_or_sample)\n",
" return layers.fully_connected(feature, 2, activation_fn=None)\n",
" with tf.variable_scope('dnn'):\n",
" logits = get_logits(feature, mode == learn.ModeKeys.TRAIN)\n",
" with tf.variable_scope('dnn', reuse=True):\n",
" sampled_logits = get_logits(feature, True)\n",
" loss = tf.contrib.losses.softmax_cross_entropy(logits, target)\n",
" train_op = layers.optimize_loss(loss, tf.contrib.framework.get_global_step(), learning_rate=0.05, optimizer='Adagrad')\n",
" predictions = {'classes': tf.argmax(logits, dimension=1), \n",
" 'probabilities': tf.nn.softmax(logits),\n",
" 'sampled_probabilities': tf.nn.softmax(sampled_logits)}\n",
" return predictions, loss, train_op\n",
"\n",
"dropout_dnn_classifier = learn.Estimator(model_fn=uncertanty_dnn_model, model_dir=BASE_DIR + 'dnn_dropout')\n",
"dropout_dnn_classifier.fit(x=train_x, y=train_y, steps=1000)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Loading model from checkpoint: dropout_classification/dnn_dropout/model.ckpt-2000-?????-of-00001.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>0.709497</td>\n",
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"text/plain": [
" accuracy\n",
"0 0.709497"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred_y = dropout_dnn_classifier.predict(x=test_x, outputs=['classes'])['classes']\n",
"pandas.DataFrame([{'accuracy': metrics.accuracy_score(pred_y, test_y)}])"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Loading model from checkpoint: dropout_classification/dnn_dropout/model.ckpt-2000-?????-of-00001.\n"
]
}
],
"source": [
"# Sample predictions N times.\n",
"n_samples = 20\n",
"\n",
"samples = []\n",
"x_samples = np.concatenate([test_x for _ in range(n_samples)])\n",
"y_samples = dropout_dnn_classifier.predict(x=x_samples, outputs=['sampled_probabilities'])['sampled_probabilities']\n",
"samples = [y_samples[i * len(test_x):(i + 1) * len(test_x)] for i in range(n_samples)]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cross_entropy(a):\n",
" # Cross entropy is mean of -p*log(q).\n",
" return np.array([-x[1] * np.log(x[1]) for x in a]).mean()\n",
"\n",
"uncertanty = []\n",
"for i in range(test_x.shape[0]):\n",
" a = [samples[j][i] for j in range(n_samples)]\n",
" uncertanty.append(cross_entropy(a))"
]
},
{
"cell_type": "code",
"execution_count": 148,
"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>Range</th>\n",
" <th>Precision</th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.00-0.25</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0.25-0.30</td>\n",
" <td>0.750000</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.30-0.33</td>\n",
" <td>0.769231</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.33-0.34</td>\n",
" <td>0.810345</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.34-0.36</td>\n",
" <td>0.805556</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.36-0.37</td>\n",
" <td>0.750000</td>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.37-0.40</td>\n",
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" <td>179</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Range Precision Count\n",
"0 0.00-0.25 NaN 0\n",
"1 0.25-0.30 0.750000 16\n",
"2 0.30-0.33 0.769231 39\n",
"3 0.33-0.34 0.810345 58\n",
"4 0.34-0.36 0.805556 72\n",
"5 0.36-0.37 0.750000 112\n",
"6 0.37-0.40 0.709497 179"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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56aKL4ItfhI4d445OGnOHvn1hxowwcknSr7myzC6lDkbK3zvvhBbgAw+EGacXXgjLloWa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\nKbmLiKTQ/wekixDDltWMLgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ecd05d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"correctness = correctness.reset_index()['Survived']\n",
"correctness = test_y == pred_y\n",
"counts, ranges = np.histogram(uncertanty, bins=5)\n",
"ranges = np.array([0.0, 0.25, 0.3, 0.33, 0.34, 0.36, 0.37, 0.4])\n",
"precision, counts = [], []\n",
"\n",
"# Compute precision per range of uncertanty.\n",
"for i in range(len(ranges)):\n",
"# mask = np.logical_and(uncertanty >= ranges[i], uncertanty < ranges[i + 1])\n",
" mask = uncertanty < ranges[i]\n",
" precision.append(correctness[mask].mean())\n",
" counts.append(mask.sum())\n",
"\n",
"\n",
"str_ranges = ['%.2f-%.2f' % (ranges[i - 1], ranges[i]) for i in range(1, len(ranges))]\n",
"_ = plt.plot(ranges, precision)\n",
"pandas.DataFrame(zip(str_ranges, precision, counts), columns=['Range', 'Precision', 'Count'])"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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fhvffh8REKC17ujutR0If4d2UdynqU/R24s6cyP18/ShaqCjeXt5W1emlvHhm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\nhRDCjUmSF0IINyZJXggh3Nj/Axe0EY4jxTDKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11efdd610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Precision vs threshold plot.\n",
"th, precision, recall = metrics.precision_recall_curve(test_y, prob_y[:, 1])\n",
"_ = plt.plot(precision, th, c='g')\n",
"_ = plt.plot(recall, precision[:-1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 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": 1
}
| apache-2.0 |
ozanarkancan/navigation | inotes/nr.ipynb | 1 | 3626 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"StatsBase.WeightVec{Float64,Array{Float64,1}}([0.319088,0.344729,0.336182],1.0)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using StatsBase\n",
"\n",
"s = [874, 1293, 1070] ./ 3237\n",
"p = [224, 242, 236] ./ 702\n",
"\n",
"s = WeightVec(s)\n",
"p = WeightVec(p)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Single: [0.660183,0.722624,0.679128]\n",
"Paragraph: [0.267188,0.243607,0.2578]\n",
"Weighted Single: (0.6913868947041041,0.026501546828584657)\n",
"Weighted Paragraph: (0.2559030839067544,0.009693677779928798)\n",
"Ensemble Weighted Single: (0.717338923963562,0.023886226690260026)\n",
"Ensemble Weighted Paragraph: (0.29597361268810246,0.0317401082659962)\n"
]
}
],
"source": [
"model = \"new grid beam\"\n",
"rs_1 = [0.6590389016018307, 0.7001545595054096, 0.6766355140186916];\n",
"rp_1 = [0.2982456140350877, 0.20967741935483872, 0.2743362831858407];\n",
"\n",
"rs_2 = [0.6979405034324943, 0.7554179566563467, 0.6822429906542056];\n",
"rp_2 = [0.34234234234234234, 0.2773109243697479, 0.2682926829268293];\n",
"\n",
"rs_3 = [0.631578947368421, 0.6955177743431221, 0.6616822429906543];\n",
"rp_3 = [0.17543859649122806, 0.1693548387096774, 0.19469026548672566];\n",
"\n",
"rs_4 = [0.6430205949656751, 0.7383900928792569, 0.6822429906542056];\n",
"rp_4 = [0.25225225225225223, 0.2773109243697479, 0.2764227642276423];\n",
"\n",
"rs_5 = [0.6407322654462243, 0.714064914992272, 0.6841121495327103];\n",
"rp_5 = [0.21052631578947367, 0.23387096774193547, 0.25663716814159293];\n",
"\n",
"rs_6 = [0.6887871853546911, 0.7321981424148607, 0.6878504672897197];\n",
"rp_6 = [0.32432432432432434, 0.29411764705882354, 0.2764227642276423];\n",
"\n",
"rs_ens_1 = [0.6475972540045767, 0.7171561051004637, 0.7140186915887851];\n",
"rp_ens_1 = [0.21929824561403508, 0.23387096774193547, 0.336283185840708];\n",
"\n",
"rs_ens_2 = [0.7208237986270023, 0.7693498452012384, 0.7121495327102804];\n",
"rp_ens_2 = [0.35135135135135137, 0.29411764705882354, 0.34146341463414637];\n",
"\n",
"rs_m = (rs_1 + rs_2 + rs_3 + rs_4 + rs_5 + rs_6) / 6\n",
"rp_m = (rp_1 + rp_2 + rp_3 + rp_4 + rp_5 + rp_6) / 6\n",
"\n",
"rs_ens_m = (rs_ens_1 + rs_ens_2) / 2\n",
"rp_ens_m = (rp_ens_1 + rp_ens_2) / 2\n",
"\n",
"println(\"Single: $(rs_m)\")\n",
"println(\"Paragraph: $(rp_m)\")\n",
"println(\"Weighted Single: $(mean_and_std(rs_m, s))\")\n",
"println(\"Weighted Paragraph: $(mean_and_std(rp_m, p))\")\n",
"println(\"Ensemble Weighted Single: $(mean_and_std(rs_ens_m, s))\")\n",
"println(\"Ensemble Weighted Paragraph: $(mean_and_std(rp_ens_m, p))\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.5.0",
"language": "julia",
"name": "julia-0.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
par2/lamana | docs/package.ipynb | 1 | 6533 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"nbsphinx": "hidden"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last Run: 2016-07-26 14:03:06\n"
]
}
],
"source": [
"# Hidden TimeStamp\n",
"import time, datetime\n",
"st = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')\n",
"print('Last Run: {}'.format(st))"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. note ::\n",
"\n",
" This project is forked from legacy code: *Script - Laminate_Stress_Constant_Thickness_3a3.ipynb*."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Package Architecture"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"LamAna originally stems from a single legacy script (circa 2014). It has since grown into a package of modules and been abstracted to address more general problems related to laminate analysis. \n",
"\n",
"This repository is designed to analyze various geometries given a specified custom model based on Classical Laminate Theory (CLT). Package architecture is diagramed below: \n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As indicated in the diagram, each diamond shape represents a module. The diagram illustrates the traffic of exchanging data-contained `FeatureInput` and `LaminateModels` objects between modules. The user-related areas are highlighted blue. The package is most extensible in these blue areas. \n",
"\n",
"## Package Module Summary\n",
"\n",
"The following table summarizes the core + feature modules in this package, their intended functions and some key resulting objects. Objects that are exchanged between modules are italicized. The Auxillary/Utility modules house supporting code that will not be discussed.\n",
"\n",
"| Module | Classifier | Purpose | Product |\n",
"|:------ |:---------- |:----------- |:-------:|\n",
"| `input_` | Backend | Backend code processing user inputs for all feature modules. | User *Input object* i.e. `Geometry` |\n",
"| `distrubtions` | Feature | Analyze stress distributions for different geometries. | *FeatureInput object* | \n",
"| `ratios` | Feature | Thickness ratio analyses for optimizing stress-geomtry design. | |\n",
"| `predictions` | Feature | Failure predictions using experimental and laminate theory data. | |\n",
"| `constructs` | Backend | Build DataFrame representations of laminates. | *LaminateModel object* |\n",
"| `theories` | Backend | Handle custom model selection and handshaking. | |\n",
"| `<models>` | Extension | Directory of user-defined, custom LT models | *Model object* |\n",
"| `output_` | Backend | Plotting objects and export code. | Output object e.g. plots, .xlsx/.csv, figures |\n",
"\n",
"The components of the lamana project can thus be classified as three types:\n",
"\n",
"- **Frontend/Feature**: user-interacted, feature modules of particular interest that use models based on laminate theory\n",
"- **Extension**: plugin modules extending capabilities of the repository, e.g. `models` directory containing user defined laminate theories (`Classical_LT`, `Wilson_LT`).\n",
"- **Backend**: remaining Core modules, `input_`, `constructs_`, `theories_`, `output_`; workhorse factories of `LaminateModel` objects."
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. note::\n",
"\n",
" As of LamAna 0.4.10, only the ``distributions`` Feature module is implementated. ``ratios`` and ``predictions`` will be added in future releases."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Intramodular Products\n",
"\n",
"Intramodular products have information that is exchanged between package modules. These objects are illustrated as circles in the API Diagram.\n",
"\n",
"#### FeatureInput\n",
"\n",
"A `FeatureInput` is simply a Python dictionary that contains information from both a Feature module and user-information processed by the `input_` module. Here is a sample with the associated items tabulated:\n",
"\n",
"```python\n",
"\n",
"FeatureInput = {\n",
" 'Geometry': Geometry, # defined in Case \n",
" 'Parameters': load_params,\n",
" 'Properties': mat_props,\n",
" 'Materials': materials, # set material order\n",
" 'Model': model,\n",
" 'Globals': None, # defined in models\n",
"} \n",
"```\n",
"\n",
"| Key | Value | Description | \n",
"|:---|:-----|:-----------|\n",
"| `'Geometry'` | Geometry object | a namedtuple of geometry thicknesses |\n",
"|`'Parameters'`| load_params | loading parameters |\n",
"| `'Properties'` | mat_props | material properties, e.g. modulus, Poisson's ratio |\n",
"| `'Materials'` | materials index | ordered list of materials from DataFrame index |\n",
"| `'Model'` | model str | selected string of model name |\n",
"| `'Globals'` | None | a placeholder for future ubiquitous model variables |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### LaminateModel\n",
"\n",
"A `LaminateModel` is simply a `pandas` DataFrame that combines data processed by a `constructs` object and `theories` model. Details of this object will be discussed further in the [constructs](components.ipynb#Core-Module:-constructs) section. "
]
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python [py27]",
"language": "python",
"name": "Python [py27]"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython2",
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"toc_cell": false,
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| bsd-3-clause |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/sandbox-3/ocean.ipynb | 1 | 164419 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocean \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: CSIRO-BOM \n",
"**Source ID**: SANDBOX-3 \n",
"**Topic**: Ocean \n",
"**Sub-Topics**: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \n",
"**Properties**: 133 (101 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocean?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:53:56"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Document Setup \n",
"**IMPORTANT: to be executed each time you run the notebook** "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# DO NOT EDIT ! \n",
"from pyesdoc.ipython.model_topic import NotebookOutput \n",
"\n",
"# DO NOT EDIT ! \n",
"DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-3', 'ocean')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties](#1.-Key-Properties) \n",
"[2. Key Properties --> Seawater Properties](#2.-Key-Properties--->-Seawater-Properties) \n",
"[3. Key Properties --> Bathymetry](#3.-Key-Properties--->-Bathymetry) \n",
"[4. Key Properties --> Nonoceanic Waters](#4.-Key-Properties--->-Nonoceanic-Waters) \n",
"[5. Key Properties --> Software Properties](#5.-Key-Properties--->-Software-Properties) \n",
"[6. Key Properties --> Resolution](#6.-Key-Properties--->-Resolution) \n",
"[7. Key Properties --> Tuning Applied](#7.-Key-Properties--->-Tuning-Applied) \n",
"[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n",
"[9. Grid](#9.-Grid) \n",
"[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n",
"[11. Grid --> Discretisation --> Horizontal](#11.-Grid--->-Discretisation--->-Horizontal) \n",
"[12. Timestepping Framework](#12.-Timestepping-Framework) \n",
"[13. Timestepping Framework --> Tracers](#13.-Timestepping-Framework--->-Tracers) \n",
"[14. Timestepping Framework --> Baroclinic Dynamics](#14.-Timestepping-Framework--->-Baroclinic-Dynamics) \n",
"[15. Timestepping Framework --> Barotropic](#15.-Timestepping-Framework--->-Barotropic) \n",
"[16. Timestepping Framework --> Vertical Physics](#16.-Timestepping-Framework--->-Vertical-Physics) \n",
"[17. Advection](#17.-Advection) \n",
"[18. Advection --> Momentum](#18.-Advection--->-Momentum) \n",
"[19. Advection --> Lateral Tracers](#19.-Advection--->-Lateral-Tracers) \n",
"[20. Advection --> Vertical Tracers](#20.-Advection--->-Vertical-Tracers) \n",
"[21. Lateral Physics](#21.-Lateral-Physics) \n",
"[22. Lateral Physics --> Momentum --> Operator](#22.-Lateral-Physics--->-Momentum--->-Operator) \n",
"[23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff](#23.-Lateral-Physics--->-Momentum--->-Eddy-Viscosity-Coeff) \n",
"[24. Lateral Physics --> Tracers](#24.-Lateral-Physics--->-Tracers) \n",
"[25. Lateral Physics --> Tracers --> Operator](#25.-Lateral-Physics--->-Tracers--->-Operator) \n",
"[26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff](#26.-Lateral-Physics--->-Tracers--->-Eddy-Diffusity-Coeff) \n",
"[27. Lateral Physics --> Tracers --> Eddy Induced Velocity](#27.-Lateral-Physics--->-Tracers--->-Eddy-Induced-Velocity) \n",
"[28. Vertical Physics](#28.-Vertical-Physics) \n",
"[29. Vertical Physics --> Boundary Layer Mixing --> Details](#29.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Details) \n",
"[30. Vertical Physics --> Boundary Layer Mixing --> Tracers](#30.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Tracers) \n",
"[31. Vertical Physics --> Boundary Layer Mixing --> Momentum](#31.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Momentum) \n",
"[32. Vertical Physics --> Interior Mixing --> Details](#32.-Vertical-Physics--->-Interior-Mixing--->-Details) \n",
"[33. Vertical Physics --> Interior Mixing --> Tracers](#33.-Vertical-Physics--->-Interior-Mixing--->-Tracers) \n",
"[34. Vertical Physics --> Interior Mixing --> Momentum](#34.-Vertical-Physics--->-Interior-Mixing--->-Momentum) \n",
"[35. Uplow Boundaries --> Free Surface](#35.-Uplow-Boundaries--->-Free-Surface) \n",
"[36. Uplow Boundaries --> Bottom Boundary Layer](#36.-Uplow-Boundaries--->-Bottom-Boundary-Layer) \n",
"[37. Boundary Forcing](#37.-Boundary-Forcing) \n",
"[38. Boundary Forcing --> Momentum --> Bottom Friction](#38.-Boundary-Forcing--->-Momentum--->-Bottom-Friction) \n",
"[39. Boundary Forcing --> Momentum --> Lateral Friction](#39.-Boundary-Forcing--->-Momentum--->-Lateral-Friction) \n",
"[40. Boundary Forcing --> Tracers --> Sunlight Penetration](#40.-Boundary-Forcing--->-Tracers--->-Sunlight-Penetration) \n",
"[41. Boundary Forcing --> Tracers --> Fresh Water Forcing](#41.-Boundary-Forcing--->-Tracers--->-Fresh-Water-Forcing) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Ocean key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of ocean model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of ocean model code (NEMO 3.6, MOM 5.0,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Model Family\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of ocean model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.model_family') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OGCM\" \n",
"# \"slab ocean\" \n",
"# \"mixed layer ocean\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Basic approximations made in the ocean.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.basic_approximations') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Primitive equations\" \n",
"# \"Non-hydrostatic\" \n",
"# \"Boussinesq\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of prognostic variables in the ocean component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Potential temperature\" \n",
"# \"Conservative temperature\" \n",
"# \"Salinity\" \n",
"# \"U-velocity\" \n",
"# \"V-velocity\" \n",
"# \"W-velocity\" \n",
"# \"SSH\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Seawater Properties \n",
"*Physical properties of seawater in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Eos Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of EOS for sea water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Linear\" \n",
"# \"Wright, 1997\" \n",
"# \"Mc Dougall et al.\" \n",
"# \"Jackett et al. 2006\" \n",
"# \"TEOS 2010\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Eos Functional Temp\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Temperature used in EOS for sea water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_temp') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Potential temperature\" \n",
"# \"Conservative temperature\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Eos Functional Salt\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Salinity used in EOS for sea water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_salt') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Practical salinity Sp\" \n",
"# \"Absolute salinity Sa\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.4. Eos Functional Depth\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Depth or pressure used in EOS for sea water ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_depth') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Pressure (dbars)\" \n",
"# \"Depth (meters)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.5. Ocean Freezing Point\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_freezing_point') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"TEOS 2010\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.6. Ocean Specific Heat\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Specific heat in ocean (cpocean) in J/(kg K)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_specific_heat') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.7. Ocean Reference Density\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Boussinesq reference density (rhozero) in kg / m3*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_reference_density') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Bathymetry \n",
"*Properties of bathymetry in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Reference Dates\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Reference date of bathymetry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.reference_dates') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Present day\" \n",
"# \"21000 years BP\" \n",
"# \"6000 years BP\" \n",
"# \"LGM\" \n",
"# \"Pliocene\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Type\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the bathymetry fixed in time in the ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.3. Ocean Smoothing\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe any smoothing or hand editing of bathymetry in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.ocean_smoothing') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.4. Source\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe source of bathymetry in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.source') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Nonoceanic Waters \n",
"*Non oceanic waters treatement in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Isolated Seas\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how isolated seas is performed*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.isolated_seas') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. River Mouth\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how river mouth mixing or estuaries specific treatment is performed*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.river_mouth') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Key Properties --> Software Properties \n",
"*Software properties of ocean code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Location of code for this component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.software_properties.repository') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Code version identifier.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.software_properties.code_version') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *Code language(s).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.software_properties.code_languages') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Key Properties --> Resolution \n",
"*Resolution in the ocean grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. Canonical Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.canonical_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Range Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Range of horizontal resolution with spatial details, eg. 50(Equator)-100km or 0.1-0.5 degrees etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.range_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Number Of Horizontal Gridpoints\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_horizontal_gridpoints') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. Number Of Vertical Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of vertical levels resolved on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_vertical_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.6. Is Adaptive Grid\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Default is False. Set true if grid resolution changes during execution.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.is_adaptive_grid') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.7. Thickness Level 1\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Thickness of first surface ocean level (in meters)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.thickness_level_1') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Key Properties --> Tuning Applied \n",
"*Tuning methodology for ocean component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.description') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. Global Mean Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List set of metrics of the global mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.global_mean_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Regional Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List of regional metrics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.regional_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.4. Trend Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List observed trend metrics used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.trend_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Key Properties --> Conservation \n",
"*Conservation in the ocean component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Brief description of conservation methodology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.description') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Properties conserved in the ocean by the numerical schemes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.scheme') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Energy\" \n",
"# \"Enstrophy\" \n",
"# \"Salt\" \n",
"# \"Volume of ocean\" \n",
"# \"Momentum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Consistency Properties\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Any additional consistency properties (energy conversion, pressure gradient discretisation, ...)?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.consistency_properties') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Corrected Conserved Prognostic Variables\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Set of variables which are conserved by *more* than the numerical scheme alone.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.corrected_conserved_prognostic_variables') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Was Flux Correction Used\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Does conservation involve flux correction ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.was_flux_correction_used') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Grid \n",
"*Ocean grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of grid in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Grid --> Discretisation --> Vertical \n",
"*Properties of vertical discretisation in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Coordinates\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of vertical coordinates in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.vertical.coordinates') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Z-coordinate\" \n",
"# \"Z*-coordinate\" \n",
"# \"S-coordinate\" \n",
"# \"Isopycnic - sigma 0\" \n",
"# \"Isopycnic - sigma 2\" \n",
"# \"Isopycnic - sigma 4\" \n",
"# \"Isopycnic - other\" \n",
"# \"Hybrid / Z+S\" \n",
"# \"Hybrid / Z+isopycnic\" \n",
"# \"Hybrid / other\" \n",
"# \"Pressure referenced (P)\" \n",
"# \"P*\" \n",
"# \"Z**\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Partial Steps\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Using partial steps with Z or Z* vertical coordinate in ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.vertical.partial_steps') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Grid --> Discretisation --> Horizontal \n",
"*Type of horizontal discretisation scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal grid type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Lat-lon\" \n",
"# \"Rotated north pole\" \n",
"# \"Two north poles (ORCA-style)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Staggering\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Horizontal grid staggering type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.staggering') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Arakawa B-grid\" \n",
"# \"Arakawa C-grid\" \n",
"# \"Arakawa E-grid\" \n",
"# \"N/a\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.3. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Finite difference\" \n",
"# \"Finite volumes\" \n",
"# \"Finite elements\" \n",
"# \"Unstructured grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Timestepping Framework \n",
"*Ocean Timestepping Framework*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Diurnal Cycle\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Diurnal cycle type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.diurnal_cycle') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Via coupling\" \n",
"# \"Specific treatment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Timestepping Framework --> Tracers \n",
"*Properties of tracers time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Tracers time stepping scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.tracers.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Leap-frog + Asselin filter\" \n",
"# \"Leap-frog + Periodic Euler\" \n",
"# \"Predictor-corrector\" \n",
"# \"Runge-Kutta 2\" \n",
"# \"AM3-LF\" \n",
"# \"Forward-backward\" \n",
"# \"Forward operator\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Tracers time step (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.tracers.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Timestepping Framework --> Baroclinic Dynamics \n",
"*Baroclinic dynamics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Baroclinic dynamics type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Preconditioned conjugate gradient\" \n",
"# \"Sub cyling\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Baroclinic dynamics scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Leap-frog + Asselin filter\" \n",
"# \"Leap-frog + Periodic Euler\" \n",
"# \"Predictor-corrector\" \n",
"# \"Runge-Kutta 2\" \n",
"# \"AM3-LF\" \n",
"# \"Forward-backward\" \n",
"# \"Forward operator\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Time Step\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Baroclinic time step (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Timestepping Framework --> Barotropic \n",
"*Barotropic time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Splitting\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time splitting method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.splitting') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"split explicit\" \n",
"# \"implicit\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.2. Time Step\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Barotropic time step (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 16. Timestepping Framework --> Vertical Physics \n",
"*Vertical physics time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Details of vertical time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.vertical_physics.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Advection \n",
"*Ocean advection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of advection in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 18. Advection --> Momentum \n",
"*Properties of lateral momemtum advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 18.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of lateral momemtum advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.momentum.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Flux form\" \n",
"# \"Vector form\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.2. Scheme Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of ocean momemtum advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.momentum.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.3. ALE\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Using ALE for vertical advection ? (if vertical coordinates are sigma)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.momentum.ALE') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 19. Advection --> Lateral Tracers \n",
"*Properties of lateral tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 19.1. Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Order of lateral tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.2. Flux Limiter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Monotonic flux limiter for lateral tracer advection scheme in ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.flux_limiter') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.3. Effective Order\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Effective order of limited lateral tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.effective_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.4. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Descriptive text for lateral tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.5. Passive Tracers\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Passive tracers advected*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Ideal age\" \n",
"# \"CFC 11\" \n",
"# \"CFC 12\" \n",
"# \"SF6\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.6. Passive Tracers Advection\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Is advection of passive tracers different than active ? if so, describe.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers_advection') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 20. Advection --> Vertical Tracers \n",
"*Properties of vertical tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 20.1. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Descriptive text for vertical tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.vertical_tracers.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.2. Flux Limiter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Monotonic flux limiter for vertical tracer advection scheme in ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.vertical_tracers.flux_limiter') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 21. Lateral Physics \n",
"*Ocean lateral physics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 21.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of lateral physics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 21.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of transient eddy representation in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Eddy active\" \n",
"# \"Eddy admitting\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 22. Lateral Physics --> Momentum --> Operator \n",
"*Properties of lateral physics operator for momentum in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 22.1. Direction\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Direction of lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.direction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Horizontal\" \n",
"# \"Isopycnal\" \n",
"# \"Isoneutral\" \n",
"# \"Geopotential\" \n",
"# \"Iso-level\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.2. Order\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Order of lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Harmonic\" \n",
"# \"Bi-harmonic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.3. Discretisation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Discretisation of lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.discretisation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Second order\" \n",
"# \"Higher order\" \n",
"# \"Flux limiter\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff \n",
"*Properties of eddy viscosity coeff in lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 23.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Lateral physics momemtum eddy viscosity coeff type in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Space varying\" \n",
"# \"Time + space varying (Smagorinsky)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.2. Constant Coefficient\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant, value of eddy viscosity coeff in lateral physics momemtum scheme (in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.constant_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.3. Variable Coefficient\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If space-varying, describe variations of eddy viscosity coeff in lateral physics momemtum scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.variable_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.4. Coeff Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe background eddy viscosity coeff in lateral physics momemtum scheme (give values in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.5. Coeff Backscatter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there backscatter in eddy viscosity coeff in lateral physics momemtum scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_backscatter') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 24. Lateral Physics --> Tracers \n",
"*Properties of lateral physics for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 24.1. Mesoscale Closure\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there a mesoscale closure in the lateral physics tracers scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.mesoscale_closure') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 24.2. Submesoscale Mixing\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there a submesoscale mixing parameterisation (i.e Fox-Kemper) in the lateral physics tracers scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.submesoscale_mixing') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 25. Lateral Physics --> Tracers --> Operator \n",
"*Properties of lateral physics operator for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 25.1. Direction\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Direction of lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.direction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Horizontal\" \n",
"# \"Isopycnal\" \n",
"# \"Isoneutral\" \n",
"# \"Geopotential\" \n",
"# \"Iso-level\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.2. Order\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Order of lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Harmonic\" \n",
"# \"Bi-harmonic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.3. Discretisation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Discretisation of lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.discretisation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Second order\" \n",
"# \"Higher order\" \n",
"# \"Flux limiter\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff \n",
"*Properties of eddy diffusity coeff in lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 26.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Lateral physics tracers eddy diffusity coeff type in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Space varying\" \n",
"# \"Time + space varying (Smagorinsky)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.2. Constant Coefficient\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant, value of eddy diffusity coeff in lateral physics tracers scheme (in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.constant_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.3. Variable Coefficient\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If space-varying, describe variations of eddy diffusity coeff in lateral physics tracers scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.variable_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.4. Coeff Background\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Describe background eddy diffusity coeff in lateral physics tracers scheme (give values in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.5. Coeff Backscatter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there backscatter in eddy diffusity coeff in lateral physics tracers scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_backscatter') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 27. Lateral Physics --> Tracers --> Eddy Induced Velocity \n",
"*Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 27.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of EIV in lateral physics tracers in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"GM\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.2. Constant Val\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If EIV scheme for tracers is constant, specify coefficient value (M2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.constant_val') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.3. Flux Type\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Type of EIV flux (advective or skew)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.flux_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.4. Added Diffusivity\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Type of EIV added diffusivity (constant, flow dependent or none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.added_diffusivity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 28. Vertical Physics \n",
"*Ocean Vertical Physics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 28.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of vertical physics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 29. Vertical Physics --> Boundary Layer Mixing --> Details \n",
"*Properties of vertical physics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 29.1. Langmuir Cells Mixing\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there Langmuir cells mixing in upper ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.details.langmuir_cells_mixing') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 30. Vertical Physics --> Boundary Layer Mixing --> Tracers \n",
"*Properties of boundary layer (BL) mixing on tracers in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 30.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of boundary layer mixing for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure - TKE\" \n",
"# \"Turbulent closure - KPP\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Turbulent closure - Bulk Mixed Layer\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.2. Closure Order\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *If turbulent BL mixing of tracers, specific order of closure (0, 1, 2.5, 3)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.closure_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.3. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant BL mixing of tracers, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background BL mixing of tracers coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 31. Vertical Physics --> Boundary Layer Mixing --> Momentum \n",
"*Properties of boundary layer (BL) mixing on momentum in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 31.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of boundary layer mixing for momentum in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure - TKE\" \n",
"# \"Turbulent closure - KPP\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Turbulent closure - Bulk Mixed Layer\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.2. Closure Order\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *If turbulent BL mixing of momentum, specific order of closure (0, 1, 2.5, 3)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.closure_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.3. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant BL mixing of momentum, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background BL mixing of momentum coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 32. Vertical Physics --> Interior Mixing --> Details \n",
"*Properties of interior mixing in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 32.1. Convection Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of vertical convection in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.convection_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Non-penetrative convective adjustment\" \n",
"# \"Enhanced vertical diffusion\" \n",
"# \"Included in turbulence closure\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.2. Tide Induced Mixing\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe how tide induced mixing is modelled (barotropic, baroclinic, none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.tide_induced_mixing') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.3. Double Diffusion\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there double diffusion*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.double_diffusion') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.4. Shear Mixing\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there interior shear mixing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.shear_mixing') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 33. Vertical Physics --> Interior Mixing --> Tracers \n",
"*Properties of interior mixing on tracers in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 33.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of interior mixing for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure / TKE\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.2. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant interior mixing of tracers, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.3. Profile\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Is the background interior mixing using a vertical profile for tracers (i.e is NOT constant) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.profile') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background interior mixing of tracers coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 34. Vertical Physics --> Interior Mixing --> Momentum \n",
"*Properties of interior mixing on momentum in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 34.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of interior mixing for momentum in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure / TKE\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.2. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant interior mixing of momentum, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.3. Profile\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Is the background interior mixing using a vertical profile for momentum (i.e is NOT constant) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.profile') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background interior mixing of momentum coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 35. Uplow Boundaries --> Free Surface \n",
"*Properties of free surface in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 35.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of free surface in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 35.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Free surface scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Linear implicit\" \n",
"# \"Linear filtered\" \n",
"# \"Linear semi-explicit\" \n",
"# \"Non-linear implicit\" \n",
"# \"Non-linear filtered\" \n",
"# \"Non-linear semi-explicit\" \n",
"# \"Fully explicit\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 35.3. Embeded Seaice\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the sea-ice embeded in the ocean model (instead of levitating) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.embeded_seaice') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 36. Uplow Boundaries --> Bottom Boundary Layer \n",
"*Properties of bottom boundary layer in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 36.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of bottom boundary layer in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.2. Type Of Bbl\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of bottom boundary layer in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.type_of_bbl') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diffusive\" \n",
"# \"Acvective\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.3. Lateral Mixing Coef\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If bottom BL is diffusive, specify value of lateral mixing coefficient (in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.lateral_mixing_coef') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.4. Sill Overflow\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe any specific treatment of sill overflows*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.sill_overflow') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 37. Boundary Forcing \n",
"*Ocean boundary forcing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 37.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of boundary forcing in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.2. Surface Pressure\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe how surface pressure is transmitted to ocean (via sea-ice, nothing specific,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.surface_pressure') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.3. Momentum Flux Correction\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any type of ocean surface momentum flux correction and, if applicable, how it is applied and where.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.momentum_flux_correction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.4. Tracers Flux Correction\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any type of ocean surface tracers flux correction and, if applicable, how it is applied and where.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers_flux_correction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.5. Wave Effects\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe if/how wave effects are modelled at ocean surface.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.6. River Runoff Budget\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe how river runoff from land surface is routed to ocean and any global adjustment done.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.7. Geothermal Heating\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe if/how geothermal heating is present at ocean bottom.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 38. Boundary Forcing --> Momentum --> Bottom Friction \n",
"*Properties of momentum bottom friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 38.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of momentum bottom friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Linear\" \n",
"# \"Non-linear\" \n",
"# \"Non-linear (drag function of speed of tides)\" \n",
"# \"Constant drag coefficient\" \n",
"# \"None\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 39. Boundary Forcing --> Momentum --> Lateral Friction \n",
"*Properties of momentum lateral friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 39.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of momentum lateral friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Free-slip\" \n",
"# \"No-slip\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 40. Boundary Forcing --> Tracers --> Sunlight Penetration \n",
"*Properties of sunlight penetration scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 40.1. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of sunlight penetration scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"1 extinction depth\" \n",
"# \"2 extinction depth\" \n",
"# \"3 extinction depth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 40.2. Ocean Colour\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the ocean sunlight penetration scheme ocean colour dependent ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 40.3. Extinction Depth\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe and list extinctions depths for sunlight penetration scheme (if applicable).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 41. Boundary Forcing --> Tracers --> Fresh Water Forcing \n",
"*Properties of surface fresh water forcing in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 41.1. From Atmopshere\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of surface fresh water forcing from atmos in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Freshwater flux\" \n",
"# \"Virtual salt flux\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 41.2. From Sea Ice\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of surface fresh water forcing from sea-ice in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Freshwater flux\" \n",
"# \"Virtual salt flux\" \n",
"# \"Real salt flux\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 41.3. Forced Mode Restoring\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Type of surface salinity restoring in forced mode (OMIP)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
wkearn/RasterIO.jl | doc/GDALGuide.ipynb | 1 | 108711 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"This Quick Guide is based on the workshop in http://download.osgeo.org/gdal/workshop/foss4ge2015/workshop_gdal.pdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LICENSE and Copyrights\n",
"This document is authored by Even Rouault and (C) Copyright Spatialys 2015. It is licenced\n",
"under Creative Commons Attribution 3.0 (or any later version at the licensee choice) :\n",
"https://creativecommons.org/licenses/by/3.0/.\n",
"\n",
"Links to documentation in this tutorial used the versioned documentation for GDAL 1.11\n",
"(http://gdal.org/1.11/). For the documentation of the latest version (generally in\n",
"development), remove the « 1.11/ »\n",
"\n",
"Workshop data used and associated rights:\n",
"- `paris.tif` : extract of OpenStreetMap. (C) OpenStreetMap contributors :\n",
"http://www.openstreetmap.org/copyright\n",
"- `world.tif` : from OSGeo-Live sample data\n",
"- `ne_10m_admin_0_countries.*` and `ne_10m_admin_1_states_provinces_shp.*`: from\n",
"OSGeo -Live sample data (and originally from http://www.naturalearthdata.com,\n",
"public domain)\n",
"- `wellington_west` and `wellington_east.png` : derived from\n",
"https://data.linz.govt.nz/layer/1870-wellington-03m-rural-aerial-photos-2012-2013 ,\n",
"Licensed by Wellington Regional Council for reuse under the Creative Commons\n",
"Attribution 3.0 New Zealand licence (https://data.linz.govt.nz/license/attribution-3-0-\n",
"new-zealand/). For the purpose of this workshop, they have been post-processed to\n",
"decrease their resolution, reduce to 256 colors, add collars and convert to PNG.\n",
"- `MK_30m.tif` and `ML_30m.tif` : derived from https://data.linz.govt.nz/layer/1768-nz-\n",
"8m-digital-elevation-model-2012/ . License Creative Commons Attribution 3.0 New\n",
"Zealand (https://data.linz.govt.nz/license/attribution-3-0-new-zealand/). For the\n",
"purpose of this workshop, they have been post-processed to decrease their resolution.\n",
"- `geomatrix.tif`: from GDAL autotest suite. X/MIT License\n",
"- `m2frac10bit.l1b` : from GDAL extended data test suite, X/MIT License (http://download.osgeo.org/gdal/data/l1b/m2frac10bit.l1b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prerequisites\n",
"- GDAL 1.11.1 or later, with RasterIO.jl installed.\n",
"- Workshop test data. Available at http://download.osgeo.org/gdal/workshop/foss4ge2015/workshop_data.zip\n",
"- A JuPyTer notebook opened in the directory with the workshop test data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gdalworkshop/MK_30m.tif\n",
"gdalworkshop/ML_30m.tif\n",
"gdalworkshop/geomatrix.tif\n",
"gdalworkshop/m2frac10bit.l1b\n",
"gdalworkshop/ne_10m_admin_0_countries.README.html\n",
"gdalworkshop/ne_10m_admin_0_countries.VERSION.txt\n",
"gdalworkshop/ne_10m_admin_0_countries.dbf\n",
"gdalworkshop/ne_10m_admin_0_countries.prj\n",
"gdalworkshop/ne_10m_admin_0_countries.qix\n",
"gdalworkshop/ne_10m_admin_0_countries.shp\n",
"gdalworkshop/ne_10m_admin_0_countries.shx\n",
"gdalworkshop/ne_10m_admin_1_states_provinces_shp.README.html\n",
"gdalworkshop/ne_10m_admin_1_states_provinces_shp.VERSION.txt\n",
"gdalworkshop/ne_10m_admin_1_states_provinces_shp.dbf\n",
"gdalworkshop/ne_10m_admin_1_states_provinces_shp.prj\n",
"gdalworkshop/ne_10m_admin_1_states_provinces_shp.qix\n",
"gdalworkshop/ne_10m_admin_1_states_provinces_shp.shp\n",
"gdalworkshop/ne_10m_admin_1_states_provinces_shp.shx\n",
"gdalworkshop/paris.tif\n",
"gdalworkshop/wellington_east.png\n",
"gdalworkshop/wellington_west.png\n",
"gdalworkshop/wellington_west.wld\n",
"gdalworkshop/world.map\n",
"gdalworkshop/world.tif\n"
]
}
],
"source": [
"; ls gdalworkshop/*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Raster operations\n",
"\n",
"## Getting metadata about a raster / gdalinfo\n",
"\n",
"### Introduction\n",
"`gdalinfo` is the utility you will use all the time to discover metadata about a raster. This will also enable us to get a practical knowledge of most of the concepts of the [GDAL data model](http://www.gdal.org/gdal_datamodel.html).\n",
"\n",
"Documentation of the gdalinfo utility : http://gdal.org/1.11/gdalinfo.html"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: gdalworkshop/world.tif\n",
"Size is 2048, 1024\n",
"Coordinate System is:\n",
"GEOGCS[\"WGS 84\",\n",
" DATUM[\"WGS_1984\",\n",
" SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
" AUTHORITY[\"EPSG\",\"7030\"]],\n",
" AUTHORITY[\"EPSG\",\"6326\"]],\n",
" PRIMEM[\"Greenwich\",0],\n",
" UNIT[\"degree\",0.0174532925199433],\n",
" AUTHORITY[\"EPSG\",\"4326\"]]\n",
"Origin = (-180.000000000000000,90.000000000000000)\n",
"Pixel Size = (0.175781250000000,-0.175781250000000)\n",
"Metadata:\n",
" AREA_OR_POINT=Area\n",
"Image Structure Metadata:\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left (-180.0000000, 90.0000000) (180d 0' 0.00\"W, 90d 0' 0.00\"N)\n",
"Lower Left (-180.0000000, -90.0000000) (180d 0' 0.00\"W, 90d 0' 0.00\"S)\n",
"Upper Right ( 180.0000000, 90.0000000) (180d 0' 0.00\"E, 90d 0' 0.00\"N)\n",
"Lower Right ( 180.0000000, -90.0000000) (180d 0' 0.00\"E, 90d 0' 0.00\"S)\n",
"Center ( 0.0000000, 0.0000000) ( 0d 0' 0.01\"E, 0d 0' 0.01\"N)\n",
"Band 1 Block=256x256 Type=Byte, ColorInterp=Red\n",
" Overviews: 1024x512, 512x256, 256x128, 128x64, 64x32, 32x16, 16x8\n",
"Band 2 Block=256x256 Type=Byte, ColorInterp=Green\n",
" Overviews: 1024x512, 512x256, 256x128, 128x64, 64x32, 32x16, 16x8\n",
"Band 3 Block=256x256 Type=Byte, ColorInterp=Blue\n",
" Overviews: 1024x512, 512x256, 256x128, 128x64, 64x32, 32x16, 16x8\n"
]
}
],
"source": [
"; gdalinfo gdalworkshop/world.tif"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Description of output :\n",
"- Driver: Formats in GDAL are managed by different «drivers». Basically 1 driver is dedicated to 1 format. Lists of drivers available at http://gdal.org/1.11/formats_list.html\n",
"- Files: list of files. Main file + potential additional files (world files, etc...)\n",
"- Size is 2048, 1024. First figure is Width in pixels. Second one is Height in pixels.\n",
"- Coordinate System: Also called projection, SRS (Spatial Reference System), CRS (Coordinate Refrence System), … The string presented here is in WKT (Well Known Text) format. The one used here is one of the most simple one. Coordinates are expressed in longitude & latitude on the WGS84 (World Geodetic Survey 1984) datum (due to longitude & latitude being directly used, this is called a geographic coordinate system « GEOGCS »)\n",
"- Origin : This is the projected coordinate of the upper-left corner of the image (the upper-left corner of the upper-left pixel). Here -180 is the longitude and 90 the latitude.\n",
"- Pixel Size : The dimension of a pixel in the units of the coordinate system. The first value is the width of the pixel, the second one its height. Here 0.17578125 is in degrees (see UNIT[\"degree\"...] in the Coordinate System string). At the equator, this means roughly 0.17578125 * 40000 / 360 = 19.5 km (the circonference of the Earth is rougly 40 000 km, and covers 360 degrees). You can notice the negative value for the pixel height. This is to indicate that the geospatial coordinates are decreasing when you go from the top of the image to the bottom of the image. This is the case for most geospatial rasters, so they appear correctly in all viewers.\n",
"- Metadata : a list of KEY=VALUE pairs, depending on the format and data. Here AREA_OR_POINT=AREA is a GDAL specific metadata to indicate that the on-file convention for the geo-registration is to take the upper-left corner of pixels (to be opposed to AREA_OR_POINT=POINT where the center of pixel is considered). You generally don't have to care about this one. This is mostly informational. For more details (rather involved), see https://trac.osgeo.org/gdal/wiki/rfc33_gtiff_pixelispoint\n",
"- Image structure metadata : gives details about :\n",
" - the arrangement of pixels. INTERLEAVE=BAND here means that in the file you have first all the pixels for the Red band, then for the Green band and finally for the Blue band. The other formulation is INTERLEAVE=PIXEL which means that for each pixel you have the red value followed by the green and blue values, and then for the next pixel another R,G,B tuple, etc... This can be interesting to know for the efficiency of algorithms when processing big images. You might want to proceed closely with the natural organization of the data for best performance.\n",
" - Potentially, compression used (JPEG, LZW, DEFLATE, etc...). Here's none.\n",
" - Potentially, number of bits used when the data width is smaller than the data type holding it. For example 12-bit wide data (values between 0 and 4095) will be stored in a unsigned 16-bit integer, and NBITS=12 will be advertized\n",
"- Corner coordinates: the geospatial coordinates of the 4 corner of the images (including any\n",
"padding), as well as the center pixel, expressed in the coordinate system for the first tuple.\n",
"The second tuple gives their equivalents as longitude, latitude. Here since it is a geographic\n",
"coordinate system, both values are identical\n",
"- Band description :\n",
" - Block=256x256 : A block corresponds to a rectangular subpart of the raster. The first\n",
"value is the width of the block and the second value its height. Typical block shapes are lines or group of lines (in which case the block width is the raster width) or tiles (typically squares), such as here. Knowing the block size is important when efficient reading of a raster is needed. In case of tiles, this means reading rasters from the leftmost tile of the raster to the right-most of the upper lines and progressing that way downward to the bottom of the image.\n",
" - Type=Byte : This is the data type of a pixel. Byte (unsigned byte) can store integer\n",
"values between 0 and 255 and is the most common one. Other data types are possibles\n",
"such as Int16 ([-32768,32767], UInt16([0,65535],Int32,UInt32,Float32 (single-precision\n",
"floating point),Float64 (double-precision floating point). Int16 or Float32/Float64 can be\n",
"encountered for digital elevation models (DEMs). UInt16 for raw satellite imagery.\n",
"There are also data types that store complex numbers (with real and imaginary part), but\n",
"this is rather esoteric and only used in a few drivers, mainly in the field of SAR\n",
"(Synthetic Aperture Radar).\n",
" - ColorInterp=Red : The color interpretation of the band. Common values are Red, Green,\n",
"Blue, Alpha (for opacity channel. 0=fully transparent, 255=fully opaque) or Unknown.\n",
"Other values are possible for other color spaces such as Cyan, Magenta, Yellow, blacK,\n",
"but not often encountered. Note that there's no color interpretation fo Near InfraRed for\n",
"example. This is something that must be deduced from other metada or knowledge of the\n",
"product characteristics\n",
" - Overviews : this gives the list of overviews available for the band (this may be empty).\n",
"Overviews are also called pyramids in GIS. They are versions of reduced size of the full\n",
"resolution raster to enable fast zoom-out operations. The first overview level is typically\n",
"half the size (in both dimensions) as the full resolution one, the second overview level\n",
"half the size of the first one, and so on... So the extra « cost » in term of storage size to\n",
"add overviews is : 1 / (2*2) + 1 / (4*4) + 1 / (8*8) + etc.... which equals to 1 / 3. So\n",
"overviews are generally and worth building, especially for use that involves interactive\n",
"display of the raster. Here, looking at the file list and seing that only one file is\n",
"mentionned, you can deduce that the overviews are stored within that file. Only a few\n",
"formats, like TIFF/GeoTIFF, allow that. Otherwise files may be stored in external .ovr\n",
"files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting those information with RasterIO.jl"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"GDAL 2.1.0dev, released 2015/99/99\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using RasterIO\n",
"\n",
"RasterIO.versioninfo(\"--version\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RasterIO.Raster(Ptr{Void} @0x00007f8325dca4c0,2048,1024,3)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raster = RasterIO.openraster(\"gdalworkshop/world.tif\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1-element Array{ASCIIString,1}:\n",
" \"gdalworkshop/world.tif\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filelist = RasterIO.getfilelist(raster.dataset)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2048,1024,3)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raster.width, raster.height, raster.nband"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RasterIO.wktprojection(raster)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6-element Array{Float64,1}:\n",
" -180.0 \n",
" 0.175781\n",
" 0.0 \n",
" 90.0 \n",
" 0.0 \n",
" -0.175781"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gt = RasterIO.geotransform(raster)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"origin: (-180.0,90.0)"
]
}
],
"source": [
"print(\"origin: $((gt[1], gt[4]))\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pixel size: (0.17578125,-0.17578125)"
]
}
],
"source": [
"print(\"pixel size: $((gt[2], gt[6]))\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"upper left: [-180.0,90.0]"
]
}
],
"source": [
"print(\"upper left: $(RasterIO.applygeotransform(gt, 0.0, 0.0))\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"upper right: [180.0,90.0]"
]
}
],
"source": [
"print(\"upper right: $(RasterIO.applygeotransform(gt, Float64(raster.width), 0.0))\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lower left: [-180.0,-90.0]"
]
}
],
"source": [
"print(\"lower left: $(RasterIO.applygeotransform(gt, 0.0, Float64(raster.height)))\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lower right: [180.0,-90.0]"
]
}
],
"source": [
"print(\"lower right: $(RasterIO.applygeotransform(gt, Float64(raster.width), Float64(raster.height)))\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"center: [0.0,0.0]"
]
}
],
"source": [
"print(\"center: $(RasterIO.applygeotransform(gt, raster.width/2, raster.height/2))\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1-element Array{ASCIIString,1}:\n",
" \"AREA_OR_POINT=Area\""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RasterIO.getmetadata(raster.dataset)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1-element Array{ASCIIString,1}:\n",
" \"INTERLEAVE=BAND\""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RasterIO.getmetadata(raster.dataset, \"IMAGE_STRUCTURE\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Band 1, block size (256,256), color interp Red\n",
"Overview 1: 1024 x 512\n",
"Overview 2: 512 x 256\n",
"Overview 3: 256 x 128\n",
"Overview 4: 128 x 64\n",
"Overview 5: 64 x 32\n",
"Overview 6: 32 x 16\n",
"Overview 7: 16 x 8\n",
"Band 2, block size (256,256), color interp Green\n",
"Overview 1: 1024 x 512\n",
"Overview 2: 512 x 256\n",
"Overview 3: 256 x 128\n",
"Overview 4: 128 x 64\n",
"Overview 5: 64 x 32\n",
"Overview 6: 32 x 16\n",
"Overview 7: 16 x 8\n",
"Band 3, block size (256,256), color interp Blue\n",
"Overview 1: 1024 x 512\n",
"Overview 2: 512 x 256\n",
"Overview 3: 256 x 128\n",
"Overview 4: 128 x 64\n",
"Overview 5: 64 x 32\n",
"Overview 6: 32 x 16\n",
"Overview 7: 16 x 8\n"
]
}
],
"source": [
"for i in 1:raster.nband\n",
" band = RasterIO.getrasterband(raster.dataset, i)\n",
" interp = RasterIO.getcolorinterp(band)\n",
" interp_name = RasterIO.colorinterpname(interp)\n",
" (w,h) = RasterIO.getblocksize(band)\n",
" println(\"Band $i, block size $((w,h)), color interp $interp_name\")\n",
" for j in 1:RasterIO._overviewcount(band)\n",
" ovr_band = RasterIO.getoverview(band, j)\n",
" xsize = RasterIO._getrasterbandxsize(ovr_band)\n",
" ysize = RasterIO._getrasterbandysize(ovr_band)\n",
" println(\"Overview $j: $xsize x $ysize\")\n",
" end\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### General form of the geo-transformation matrix"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: gdalworkshop/geomatrix.tif\n",
"Size is 20, 20\n",
"Coordinate System is:\n",
"PROJCS[\"WGS 84 / UTM zone 11N\",\n",
" GEOGCS[\"WGS 84\",\n",
" DATUM[\"WGS_1984\",\n",
" SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
" AUTHORITY[\"EPSG\",\"7030\"]],\n",
" AUTHORITY[\"EPSG\",\"6326\"]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" AUTHORITY[\"EPSG\",\"8901\"]],\n",
" UNIT[\"degree\",0.0174532925199433,\n",
" AUTHORITY[\"EPSG\",\"9122\"]],\n",
" AUTHORITY[\"EPSG\",\"4326\"]],\n",
" PROJECTION[\"Transverse_Mercator\"],\n",
" PARAMETER[\"latitude_of_origin\",0],\n",
" PARAMETER[\"central_meridian\",-117],\n",
" PARAMETER[\"scale_factor\",0.9996],\n",
" PARAMETER[\"false_easting\",500000],\n",
" PARAMETER[\"false_northing\",0],\n",
" UNIT[\"metre\",1,\n",
" AUTHORITY[\"EPSG\",\"9001\"]],\n",
" AXIS[\"Easting\",EAST],\n",
" AXIS[\"Northing\",NORTH],\n",
" AUTHORITY[\"EPSG\",\"32611\"]]\n",
"GeoTransform =\n",
" 1841001.75, 1.5, -5\n",
" 1144003.25, -5, -1.5\n",
"Metadata:\n",
" AREA_OR_POINT=Point\n",
"Image Structure Metadata:\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left ( 1841001.750, 1144003.250) (104d50'47.45\"W, 10d 7'13.55\"N)\n",
"Lower Left ( 1840901.750, 1143973.250) (104d50'50.69\"W, 10d 7'12.72\"N)\n",
"Upper Right ( 1841031.750, 1143903.250) (104d50'46.60\"W, 10d 7'10.33\"N)\n",
"Lower Right ( 1840931.750, 1143873.250) (104d50'49.85\"W, 10d 7' 9.50\"N)\n",
"Center ( 1840966.750, 1143938.250) (104d50'48.65\"W, 10d 7'11.53\"N)\n",
"Band 1 Block=20x20 Type=Byte, ColorInterp=Gray\n"
]
}
],
"source": [
"; gdalinfo gdalworkshop/geomatrix.tif "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can notice that contrary to the first example we no longer have a Origin and Pixel Size reported, but instead a GeoTransform.\n",
"\n",
"The GeoTransform is the geo-transformation matrix, which is in mathematics described as an affine transformation from the coordinates in the pixel space (col,row) to the coordinates of the projected space (X,Y), with col and row starting from 0 for the upper-left pixel (in pixel space, not necessarily in projected space!). This is a series of 6 values gt[0], gt[1], … gt[5] such as :\n",
"\n",
" X = gt[0] + col * gt[1] + row * gt[2]\n",
" Y = gt[3] + col * gt[4] + row * gt[5]\n",
" \n",
"This is exactly the computation done by gdal.ApplyGeoTransform:\n",
"\n",
" [X,Y]=gdal.ApplyGeoTransform(gt,row,col)\n",
" \n",
"When the gt[2] and gt[4] terms are non zero, the image is no longer « north-up » with respect to the\n",
"projected space, and you may have rotation and/or shearing.\n",
"\n",
"To generate a more familiar image with those rotation/shearing being applied, you can use\n",
"`gdalwarp`.\n",
"\n",
"The related mathematics are detailed at http://en.wikipedia.org/wiki/Transformation_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ground Control Points (GCPs)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: L1B/NOAA Polar Orbiter Level 1b Data Set\n",
"Files: gdalworkshop/m2frac10bit.l1b\n",
"Size is 2048, 222\n",
"Coordinate System is `'\n",
"GCP Projection = \n",
"GEOGCS[\"WGS 72\",\n",
" DATUM[\"WGS_1972\",\n",
" SPHEROID[\"WGS 72\",6378135,298.26,\n",
" AUTHORITY[\"EPSG\",\"7043\"]],\n",
" TOWGS84[0,0,4.5,0,0,0.554,0.2263],\n",
" AUTHORITY[\"EPSG\",\"6322\"]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" AUTHORITY[\"EPSG\",\"8901\"]],\n",
" UNIT[\"degree\",0.0174532925199433,\n",
" AUTHORITY[\"EPSG\",\"9108\"]],\n",
" AUTHORITY[\"EPSG\",\"4322\"]]\n",
"GCP[ 0]: Id=, Info=\n",
" (2023.5,221.5) -> (34.5374,59.0067,0)\n",
"GCP[ 1]: Id=, Info=\n",
" (1983.5,221.5) -> (32.1151,59.0086,0)\n",
"GCP[ 2]: Id=, Info=\n",
" (1943.5,221.5) -> (30.1062,58.9758,0)\n",
"GCP[ 3]: Id=, Info=\n",
" (1903.5,221.5) -> (28.3943,58.9232,0)\n",
"GCP[ 4]: Id=, Info=\n",
" (1863.5,221.5) -> (26.9056,58.8588,0)\n",
"GCP[ 5]: Id=, Info=\n",
" (1823.5,221.5) -> (25.59,58.7875,0)\n",
"GCP[ 6]: Id=, Info=\n",
" (1783.5,221.5) -> (24.4121,58.712,0)\n",
"GCP[ 7]: Id=, Info=\n",
" (1743.5,221.5) -> (23.3459,58.634,0)\n",
"GCP[ 8]: Id=, Info=\n",
" (1703.5,221.5) -> (22.3716,58.5547,0)\n",
"GCP[ 9]: Id=, Info=\n",
" (1663.5,221.5) -> (21.4738,58.4748,0)\n",
"GCP[ 10]: Id=, Info=\n",
" (1623.5,221.5) -> (20.6405,58.3947,0)\n",
"GCP[ 11]: Id=, Info=\n",
" (1583.5,221.5) -> (19.8619,58.3145,0)\n",
"GCP[ 12]: Id=, Info=\n",
" (1543.5,221.5) -> (19.1299,58.2345,0)\n",
"GCP[ 13]: Id=, Info=\n",
" (1503.5,221.5) -> (18.4379,58.1547,0)\n",
"GCP[ 14]: Id=, Info=\n",
" (1463.5,221.5) -> (17.7805,58.0751,0)\n",
"GCP[ 15]: Id=, Info=\n",
" (1423.5,221.5) -> (17.1528,57.9955,0)\n",
"GCP[ 16]: Id=, Info=\n",
" (1383.5,221.5) -> (16.5508,57.916,0)\n"
]
}
],
"source": [
"; gdalinfo gdalworkshop/m2frac10bit.l1b | head -n 50"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lots of information here! The main novelty is the report of GCPs, which stand for Ground Control Points, also called Tiepoints or Control Points. Those are points of the raster for which the geolocation is known.\n",
"\n",
"Let's look at the first one :\n",
"\n",
" GCP[ 0]: Id=, Info=\n",
" (2023.5,221.5) -> (34.5374,59.0067,0)\n",
"\n",
"Id, Info are text fields identifying the ground control points. They are rarely used in practice and often let to empty.\n",
"\n",
"2023.5 and 221.5 are respectively the column and row in the raster for this GCP. As you can see, decimal values can be used. Here it means that the GCP is located at the center of the pixel (2023,221).\n",
"\n",
"34.5374, 59.0067 and 0 are respectively the longitude/easting, latitude/northing and altitude of the GCP in the coordinate space. Here the reported GCP coordinate system is a geographic coordinate system, so they are longitude and latitude. The altitude is in most of the case 0.\n",
"\n",
"You can also notice the lack of a geo-transform : the raster isn't projected yet. This can be done with gdalwarp\n",
"\n",
"If the GCPs annoy you in the gdalinfo output, you can suppress them with the -nogcp option:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: L1B/NOAA Polar Orbiter Level 1b Data Set\n",
"Files: gdalworkshop/m2frac10bit.l1b\n",
"Size is 2048, 222\n",
"Coordinate System is `'\n",
"Metadata:\n",
" DATASET_NAME=NSS.FRAC.M2.D08128.S1813.E1953.B0804243.SV\n",
" DATA_TYPE=AVHRR FRAC\n",
" LOCATION=Ascending\n",
" PROCESSING_CENTER=NOAA/NESDIS - Suitland, Maryland, USA\n",
" REVOLUTION=08042\n",
" SATELLITE=METOP-A(2)\n",
" SOURCE=Unknown receiving station\n",
" START=year: 2008, day: 128, millisecond: 71248670\n",
" STOP=year: 2008, day: 128, millisecond: 71285504\n",
"Subdatasets:\n",
" SUBDATASET_1_NAME=L1B_ANGLES:\"gdalworkshop/m2frac10bit.l1b\"\n",
" SUBDATASET_1_DESC=Solar zenith angles, satellite zenith angles and relative azimuth angles\n",
" SUBDATASET_2_NAME=L1B_CLOUDS:\"gdalworkshop/m2frac10bit.l1b\"\n",
" SUBDATASET_2_DESC=Clouds from AVHRR (CLAVR)\n",
"Geolocation:\n",
" LINE_OFFSET=0\n",
" LINE_STEP=1\n",
" PIXEL_OFFSET=0\n",
" PIXEL_STEP=1\n",
" SRS=GEOGCS[\"WGS 72\",DATUM[\"WGS_1972\",SPHEROID[\"WGS 72\",6378135,298.26,AUTHORITY[\"EPSG\",7043]],TOWGS84[0,0,4.5,0,0,0.554,0.2263],AUTHORITY[\"EPSG\",6322]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",8901]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",9108]],AUTHORITY[\"EPSG\",4322]]\n",
" X_BAND=1\n",
" X_DATASET=L1BGCPS_INTERPOL:\"gdalworkshop/m2frac10bit.l1b\"\n",
" Y_BAND=2\n",
" Y_DATASET=L1BGCPS_INTERPOL:\"gdalworkshop/m2frac10bit.l1b\"\n",
"Corner Coordinates:\n",
"Upper Left ( 0.0, 0.0)\n",
"Lower Left ( 0.0, 222.0)\n",
"Upper Right ( 2048.0, 0.0)\n",
"Lower Right ( 2048.0, 222.0)\n",
"Center ( 1024.0, 111.0)\n",
"Band 1 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 1: 0.58 micrometers -- 0.68 micrometers\n",
"Band 2 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 2: 0.725 micrometers -- 1.10 micrometers\n",
"Band 3 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 3A: 1.58 micrometers -- 1.64 micrometers\n",
"Band 4 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 4: 10.3 micrometers -- 11.3 micrometers\n",
"Band 5 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 5: 11.5 micrometers -- 12.5 micrometers\n"
]
}
],
"source": [
"; gdalinfo -nogcp gdalworkshop/m2frac10bit.l1b"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Statistics, histogram, checksum"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: L1B/NOAA Polar Orbiter Level 1b Data Set\n",
"Files: gdalworkshop/m2frac10bit.l1b\n",
"Size is 2048, 222\n",
"Coordinate System is `'\n",
"Corner Coordinates:\n",
"Upper Left ( 0.0, 0.0)\n",
"Lower Left ( 0.0, 222.0)\n",
"Upper Right ( 2048.0, 0.0)\n",
"Lower Right ( 2048.0, 222.0)\n",
"Center ( 1024.0, 111.0)\n",
"Band 1 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 1: 0.58 micrometers -- 0.68 micrometers\n",
" Minimum=39.000, Maximum=165.000, Mean=43.898, StdDev=7.642\n",
"Band 2 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 2: 0.725 micrometers -- 1.10 micrometers\n",
" Minimum=39.000, Maximum=226.000, Mean=43.882, StdDev=8.992\n",
"Band 3 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 3A: 1.58 micrometers -- 1.64 micrometers\n",
" Minimum=533.000, Maximum=983.000, Mean=906.890, StdDev=27.716\n",
"Band 4 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 4: 10.3 micrometers -- 11.3 micrometers\n",
" Minimum=454.000, Maximum=821.000, Mean=563.780, StdDev=61.628\n",
"Band 5 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 5: 11.5 micrometers -- 12.5 micrometers\n",
" Minimum=446.000, Maximum=803.000, Mean=550.119, StdDev=61.217\n"
]
}
],
"source": [
"; gdalinfo -stats -nogcp -nomd gdalworkshop/m2frac10bit.l1b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The -stats option asks gdalinfo to compute the minimum, maximum, mean and standard deviation for each band. The -nogcp and -nomd options are just to suppress GCP and metadata from the output.\n",
"\n",
"Now run"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: L1B/NOAA Polar Orbiter Level 1b Data Set\n",
"Files: gdalworkshop/m2frac10bit.l1b\n",
" gdalworkshop/m2frac10bit.l1b.aux.xml\n",
"Size is 2048, 222\n",
"Coordinate System is `'\n",
"Corner Coordinates:\n",
"Upper Left ( 0.0, 0.0)\n",
"Lower Left ( 0.0, 222.0)\n",
"Upper Right ( 2048.0, 0.0)\n",
"Lower Right ( 2048.0, 222.0)\n",
"Center ( 1024.0, 111.0)\n",
"Band 1 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 1: 0.58 micrometers -- 0.68 micrometers\n",
" Min=39.000 Max=165.000 \n",
" Minimum=39.000, Maximum=165.000, Mean=43.898, StdDev=7.642\n",
"Band 2 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 2: 0.725 micrometers -- 1.10 micrometers\n",
" Min=39.000 Max=226.000 \n",
" Minimum=39.000, Maximum=226.000, Mean=43.882, StdDev=8.992\n",
"Band 3 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 3A: 1.58 micrometers -- 1.64 micrometers\n",
" Min=533.000 Max=983.000 \n",
" Minimum=533.000, Maximum=983.000, Mean=906.890, StdDev=27.716\n",
"Band 4 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 4: 10.3 micrometers -- 11.3 micrometers\n",
" Min=454.000 Max=821.000 \n",
" Minimum=454.000, Maximum=821.000, Mean=563.780, StdDev=61.628\n",
"Band 5 Block=2048x1 Type=UInt16, ColorInterp=Undefined\n",
" Description = AVHRR Channel 5: 11.5 micrometers -- 12.5 micrometers\n",
" Min=446.000 Max=803.000 \n",
" Minimum=446.000, Maximum=803.000, Mean=550.119, StdDev=61.217\n"
]
}
],
"source": [
"; gdalinfo -nogcp -nomd gdalworkshop/m2frac10bit.l1b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"compare the output. You may notice that a m2frac10bit.l1b.aux.xml file is now mentioned, but the statistics are still reported. This file has been generated when computing the statistics. Let's display it"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<PAMDataset>\n",
" <Metadata>\n",
" <MDI key=\"DATASET_NAME\">NSS.FRAC.M2.D08128.S1813.E1953.B0804243.SV</MDI>\n",
" <MDI key=\"DATA_TYPE\">AVHRR FRAC</MDI>\n",
" <MDI key=\"LOCATION\">Ascending</MDI>\n",
" <MDI key=\"PROCESSING_CENTER\">NOAA/NESDIS - Suitland, Maryland, USA</MDI>\n",
" <MDI key=\"REVOLUTION\">08042</MDI>\n",
" <MDI key=\"SATELLITE\">METOP-A(2)</MDI>\n",
" <MDI key=\"SOURCE\">Unknown receiving station</MDI>\n",
" <MDI key=\"START\">year: 2008, day: 128, millisecond: 71248670</MDI>\n",
" <MDI key=\"STOP\">year: 2008, day: 128, millisecond: 71285504</MDI>\n",
" </Metadata>\n",
" <Metadata domain=\"GEOLOCATION\">\n",
" <MDI key=\"LINE_OFFSET\">0</MDI>\n",
" <MDI key=\"LINE_STEP\">1</MDI>\n",
" <MDI key=\"PIXEL_OFFSET\">0</MDI>\n",
" <MDI key=\"PIXEL_STEP\">1</MDI>\n",
" <MDI key=\"SRS\">GEOGCS[\"WGS 72\",DATUM[\"WGS_1972\",SPHEROID[\"WGS 72\",6378135,298.26,AUTHORITY[\"EPSG\",7043]],TOWGS84[0,0,4.5,0,0,0.554,0.2263],AUTHORITY[\"EPSG\",6322]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",8901]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",9108]],AUTHORITY[\"EPSG\",4322]]</MDI>\n",
" <MDI key=\"X_BAND\">1</MDI>\n",
" <MDI key=\"X_DATASET\">L1BGCPS_INTERPOL:\"gdalworkshop/m2frac10bit.l1b\"</MDI>\n",
" <MDI key=\"Y_BAND\">2</MDI>\n",
" <MDI key=\"Y_DATASET\">L1BGCPS_INTERPOL:\"gdalworkshop/m2frac10bit.l1b\"</MDI>\n",
" </Metadata>\n",
" <Metadata domain=\"SUBDATASETS\">\n",
" <MDI key=\"SUBDATASET_1_NAME\">L1B_ANGLES:\"gdalworkshop/m2frac10bit.l1b\"</MDI>\n",
" <MDI key=\"SUBDATASET_1_DESC\">Solar zenith angles, satellite zenith angles and relative azimuth angles</MDI>\n",
" <MDI key=\"SUBDATASET_2_NAME\">L1B_CLOUDS:\"gdalworkshop/m2frac10bit.l1b\"</MDI>\n",
" <MDI key=\"SUBDATASET_2_DESC\">Clouds from AVHRR (CLAVR)</MDI>\n",
" </Metadata>\n",
" <PAMRasterBand band=\"1\">\n",
" <Description>AVHRR Channel 1: 0.58 micrometers -- 0.68 micrometers</Description>\n",
" <Metadata>\n",
" <MDI key=\"STATISTICS_MAXIMUM\">165</MDI>\n",
" <MDI key=\"STATISTICS_MEAN\">43.898287936374</MDI>\n",
" <MDI key=\"STATISTICS_MINIMUM\">39</MDI>\n",
" <MDI key=\"STATISTICS_STDDEV\">7.6418424876281</MDI>\n",
" </Metadata>\n",
" </PAMRasterBand>\n",
" <PAMRasterBand band=\"2\">\n",
" <Description>AVHRR Channel 2: 0.725 micrometers -- 1.10 micrometers</Description>\n",
" <Metadata>\n",
" <MDI key=\"STATISTICS_MAXIMUM\">226</MDI>\n",
" <MDI key=\"STATISTICS_MEAN\">43.882075679195</MDI>\n",
" <MDI key=\"STATISTICS_MINIMUM\">39</MDI>\n",
" <MDI key=\"STATISTICS_STDDEV\">8.9918754785884</MDI>\n",
" </Metadata>\n",
" </PAMRasterBand>\n",
" <PAMRasterBand band=\"3\">\n",
" <Description>AVHRR Channel 3A: 1.58 micrometers -- 1.64 micrometers</Description>\n",
" <Metadata>\n",
" <MDI key=\"STATISTICS_MAXIMUM\">983</MDI>\n",
" <MDI key=\"STATISTICS_MEAN\">906.89034126901</MDI>\n",
" <MDI key=\"STATISTICS_MINIMUM\">533</MDI>\n",
" <MDI key=\"STATISTICS_STDDEV\">27.715692093585</MDI>\n",
" </Metadata>\n",
" </PAMRasterBand>\n",
" <PAMRasterBand band=\"4\">\n",
" <Description>AVHRR Channel 4: 10.3 micrometers -- 11.3 micrometers</Description>\n",
" <Metadata>\n",
" <MDI key=\"STATISTICS_MAXIMUM\">821</MDI>\n",
" <MDI key=\"STATISTICS_MEAN\">563.78016566372</MDI>\n",
" <MDI key=\"STATISTICS_MINIMUM\">454</MDI>\n",
" <MDI key=\"STATISTICS_STDDEV\">61.627940356415</MDI>\n",
" </Metadata>\n",
" </PAMRasterBand>\n",
" <PAMRasterBand band=\"5\">\n",
" <Description>AVHRR Channel 5: 11.5 micrometers -- 12.5 micrometers</Description>\n",
" <Metadata>\n",
" <MDI key=\"STATISTICS_MAXIMUM\">803</MDI>\n",
" <MDI key=\"STATISTICS_MEAN\">550.11860615498</MDI>\n",
" <MDI key=\"STATISTICS_MINIMUM\">446</MDI>\n",
" <MDI key=\"STATISTICS_STDDEV\">61.216671085772</MDI>\n",
" </Metadata>\n",
" </PAMRasterBand>\n",
"</PAMDataset>\n"
]
}
],
"source": [
"; cat gdalworkshop/m2frac10bit.l1b.aux.xml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see this file collects the statistics (as well as the other metadata). In case you are wondering what PAM in the above stands for, it is Persistant Auxiliary Metadata. The PAM .aux.xml format is something GDAL specific (and of course indirectly recognized by all software based on GDAL).\n",
"\n",
"**Caution**: when a .aux.xml file exists, even if you specify -stats, gdalinfo will not recompute the statistics from the raster file but will use the ones stored in the .aux.xml. So in case the raster file would have been updated with new values, you may need to manually delete the .aux.xml file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Raster conversion / gdal_translate\n",
"\n",
"### Introduction\n",
"This is the utility to use to do format conversions, subsetting, format optimization, adding georeferencing, ...\n",
"\n",
"Documentation of the gdal_translate utility: http://gdal.org/1.11/gdal_translate.html\n",
"\n",
"### Format conversion\n",
"Try:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning: The target file has a 'gif' extension, which is normally used by the GIF, BIGGIF drivers,\n",
"but the requested output driver is GTiff. Is it really what you want ?\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 1831, 1835\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"; gdal_translate gdalworkshop/wellington_west.png gdalworkshop/wellington_west.gif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You get a warning here mentionning that the conversion has been done to TIFF and not to GIF as it was perhaps intended. GDAL is generally not sensitive to extensions, so you have to be explicit about the format, otherwise the default output format, which is GeoTiff will be used.\n",
"\n",
"To get the list of supported formats, do:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Supported Formats:\n",
" VRT -raster- (rw+v): Virtual Raster\n",
" GTiff -raster- (rw+vs): GeoTIFF\n",
" NITF -raster- (rw+vs): National Imagery Transmission Format\n",
" RPFTOC -raster- (rovs): Raster Product Format TOC format\n",
" ECRGTOC -raster- (rovs): ECRG TOC format\n",
" HFA -raster- (rw+v): Erdas Imagine Images (.img)\n",
" SAR_CEOS -raster- (rov): CEOS SAR Image\n",
" CEOS -raster- (rov): CEOS Image\n",
" JAXAPALSAR -raster- (rov): JAXA PALSAR Product Reader (Level 1.1/1.5)\n",
" GFF -raster- (rov): Ground-based SAR Applications Testbed File Format (.gff)\n",
" ELAS -raster- (rw+v): ELAS\n",
" AIG -raster- (rov): Arc/Info Binary Grid\n",
" AAIGrid -raster- (rwv): Arc/Info ASCII Grid\n",
" GRASSASCIIGrid -raster- (rov): GRASS ASCII Grid\n",
" SDTS -raster- (rov): SDTS Raster\n",
" DTED -raster- (rwv): DTED Elevation Raster\n",
" PNG -raster- (rwv): Portable Network Graphics\n",
" JPEG -raster- (rwv): JPEG JFIF\n",
" MEM -raster- (rw+): In Memory Raster\n",
" JDEM -raster- (rov): Japanese DEM (.mem)\n",
" GIF -raster- (rwv): Graphics Interchange Format (.gif)\n",
" BIGGIF -raster- (rov): Graphics Interchange Format (.gif)\n",
" ESAT -raster- (rov): Envisat Image Format\n",
" BSB -raster- (rov): Maptech BSB Nautical Charts\n",
" XPM -raster- (rwv): X11 PixMap Format\n",
" BMP -raster- (rw+v): MS Windows Device Independent Bitmap\n",
" DIMAP -raster- (rov): SPOT DIMAP\n",
" AirSAR -raster- (rov): AirSAR Polarimetric Image\n",
" RS2 -raster- (ros): RadarSat 2 XML Product\n",
" SAFE -raster- (rov): Sentinel SAFE Product\n",
" PCIDSK -raster,vector- (rw+v): PCIDSK Database File\n",
" PCRaster -raster- (rw+): PCRaster Raster File\n",
" ILWIS -raster- (rw+v): ILWIS Raster Map\n",
" SGI -raster- (rw+): SGI Image File Format 1.0\n",
" SRTMHGT -raster- (rwv): SRTMHGT File Format\n",
" Leveller -raster- (rw+): Leveller heightfield\n",
" Terragen -raster- (rw+): Terragen heightfield\n",
" ISIS3 -raster- (rov): USGS Astrogeology ISIS cube (Version 3)\n",
" ISIS2 -raster- (rw+v): USGS Astrogeology ISIS cube (Version 2)\n",
" PDS -raster- (rov): NASA Planetary Data System\n",
" VICAR -raster- (rov): MIPL VICAR file\n",
" TIL -raster- (rov): EarthWatch .TIL\n",
" ERS -raster- (rw+v): ERMapper .ers Labelled\n",
" L1B -raster- (rovs): NOAA Polar Orbiter Level 1b Data Set\n",
" FIT -raster- (rwv): FIT Image\n",
" GRIB -raster- (rov): GRIdded Binary (.grb)\n",
" RMF -raster- (rw+v): Raster Matrix Format\n",
" WCS -raster- (rovs): OGC Web Coverage Service\n",
" WMS -raster- (rwvs): OGC Web Map Service\n",
" MSGN -raster- (ro): EUMETSAT Archive native (.nat)\n",
" RST -raster- (rw+v): Idrisi Raster A.1\n",
" INGR -raster- (rw+v): Intergraph Raster\n",
" GSAG -raster- (rwv): Golden Software ASCII Grid (.grd)\n",
" GSBG -raster- (rw+v): Golden Software Binary Grid (.grd)\n",
" GS7BG -raster- (rw+v): Golden Software 7 Binary Grid (.grd)\n",
" COSAR -raster- (rov): COSAR Annotated Binary Matrix (TerraSAR-X)\n",
" TSX -raster- (rov): TerraSAR-X Product\n",
" COASP -raster- (ro): DRDC COASP SAR Processor Raster\n",
" R -raster- (rwv): R Object Data Store\n",
" MAP -raster- (rov): OziExplorer .MAP\n",
" PNM -raster- (rw+v): Portable Pixmap Format (netpbm)\n",
" DOQ1 -raster- (rov): USGS DOQ (Old Style)\n",
" DOQ2 -raster- (rov): USGS DOQ (New Style)\n",
" ENVI -raster- (rw+v): ENVI .hdr Labelled\n",
" EHdr -raster- (rw+v): ESRI .hdr Labelled\n",
" GenBin -raster- (rov): Generic Binary (.hdr Labelled)\n",
" PAux -raster- (rw+): PCI .aux Labelled\n",
" MFF -raster- (rw+v): Vexcel MFF Raster\n",
" MFF2 -raster- (rw+): Vexcel MFF2 (HKV) Raster\n",
" FujiBAS -raster- (ro): Fuji BAS Scanner Image\n",
" GSC -raster- (rov): GSC Geogrid\n",
" FAST -raster- (rov): EOSAT FAST Format\n",
" BT -raster- (rw+v): VTP .bt (Binary Terrain) 1.3 Format\n",
" LAN -raster- (rw+v): Erdas .LAN/.GIS\n",
" CPG -raster- (ro): Convair PolGASP\n",
" IDA -raster- (rw+v): Image Data and Analysis\n",
" NDF -raster- (rov): NLAPS Data Format\n",
" EIR -raster- (rov): Erdas Imagine Raw\n",
" DIPEx -raster- (rov): DIPEx\n",
" LCP -raster- (rwv): FARSITE v.4 Landscape File (.lcp)\n",
" GTX -raster- (rw+v): NOAA Vertical Datum .GTX\n",
" LOSLAS -raster- (rov): NADCON .los/.las Datum Grid Shift\n",
" NTv2 -raster- (rw+vs): NTv2 Datum Grid Shift\n",
" CTable2 -raster- (rw+v): CTable2 Datum Grid Shift\n",
" ACE2 -raster- (rov): ACE2\n",
" SNODAS -raster- (rov): Snow Data Assimilation System\n",
" KRO -raster- (rw+v): KOLOR Raw\n",
" ROI_PAC -raster- (rw+v): ROI_PAC raster\n",
" ISCE -raster- (rw+v): ISCE raster\n",
" ARG -raster- (rwv): Azavea Raster Grid format\n",
" RIK -raster- (rov): Swedish Grid RIK (.rik)\n",
" USGSDEM -raster- (rwv): USGS Optional ASCII DEM (and CDED)\n",
" GXF -raster- (ro): GeoSoft Grid Exchange Format\n",
" NWT_GRD -raster- (rov): Northwood Numeric Grid Format .grd/.tab\n",
" NWT_GRC -raster- (rov): Northwood Classified Grid Format .grc/.tab\n",
" ADRG -raster- (rw+vs): ARC Digitized Raster Graphics\n",
" SRP -raster- (rovs): Standard Raster Product (ASRP/USRP)\n",
" BLX -raster- (rw): Magellan topo (.blx)\n",
" Rasterlite -raster- (rws): Rasterlite\n",
" SAGA -raster- (rw+v): SAGA GIS Binary Grid (.sdat)\n",
" KMLSUPEROVERLAY -raster- (rwv): Kml Super Overlay\n",
" XYZ -raster- (rwv): ASCII Gridded XYZ\n",
" HF2 -raster- (rwv): HF2/HFZ heightfield raster\n",
" PDF -raster,vector- (w+): Geospatial PDF\n",
" OZI -raster- (rov): OziExplorer Image File\n",
" CTG -raster- (rov): USGS LULC Composite Theme Grid\n",
" E00GRID -raster- (rov): Arc/Info Export E00 GRID\n",
" ZMap -raster- (rwv): ZMap Plus Grid\n",
" NGSGEOID -raster- (rov): NOAA NGS Geoid Height Grids\n",
" MBTiles -raster- (rov): MBTiles\n",
" IRIS -raster- (rov): IRIS data (.PPI, .CAPPi etc)\n",
" PLMOSAIC -raster- (ro): Planet Labs Mosaics API\n",
" CALS -raster- (rw): CALS (Type 1)\n",
" WMTS -raster- (rwv): OGC Web Mab Tile Service\n",
" GPKG -raster,vector- (rw+vs): GeoPackage\n",
" PLSCENES -raster,vector- (ro): Planet Labs Scenes API\n",
" HTTP -raster,vector- (ro): HTTP Fetching Wrapper\n"
]
}
],
"source": [
"; gdal_translate --formats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The letters between parenthesits after the format short name indicate the capabilities :\n",
"- `ro` means read-only\n",
"- `rw` means read and one-time creation only\n",
"- `rw+` means read, create and update\n",
"- The `v` signs means that it support virtual files, we will see that later.\n",
"\n",
"So the GIF driver support read and one-time creation, which is enough for gdal_translate. So let's\n",
"correct our last attempt to specify the output format with -of :"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 1831, 1835\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"; gdal_translate -of GIF gdalworkshop/wellington_west.png gdalworkshop/wellington_west.gif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adding georeferencing\n",
"Let's look at wellington_west.png with gdalinfo. A long list of quadruplets is displayed. It is a color table. This image has a single band with Byte values, that point to 256 coded colors. Use -noct to disable the display of this color table."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: PNG/Portable Network Graphics\n",
"Files: gdalworkshop/wellington_west.png\n",
" gdalworkshop/wellington_west.wld\n",
"Size is 1831, 1835\n",
"Coordinate System is `'\n",
"Origin = (1731543.836827248800546,5461586.738620690070093)\n",
"Pixel Size = (28.001501693600002,-28.001034482800002)\n",
"Corner Coordinates:\n",
"Upper Left ( 1731543.837, 5461586.739) \n",
"Lower Left ( 1731543.837, 5410204.840) \n",
"Upper Right ( 1782814.586, 5461586.739) \n",
"Lower Right ( 1782814.586, 5410204.840) \n",
"Center ( 1757179.212, 5435895.789) \n",
"Band 1 Block=1831x1 Type=Byte, ColorInterp=Palette\n",
" Color Table (RGB with 256 entries)\n"
]
}
],
"source": [
"; gdalinfo -noct gdalworkshop/wellington_west.png"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the presence of georeferenced coordinates (Origin and Pixel Size), that comes from wellington_west.wld, a World File. This is a 6 line text file that gives the information needed to build the 6-value geo-transformation matrix. Note that the order of values and their semantics are not exactly the same (World File use center of pixel convention), but GDAL will make the needed conversions.\n",
"\n",
"But here we lack the Coordinate System. We must use external knowedge here to fill the gap. This dataset being from New Zealand and the coordinates being obviously not longitudes/latitudes, we could assume that this dataset is in the New Zealand Transverse Mercator (NTZM) map projection (for example by searching New Zealand in http://epsg.io/?q=new+zealand), and this is indeed the case. This coordinate system is codified by the EPSG (European Petroleum Survey Group) as being « EPSG:2193 »\n",
"\n",
"We can see its definition with:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"PROJ.4 : '+proj=tmerc +lat_0=0 +lon_0=173 +k=0.9996 +x_0=1600000 +y_0=10000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs '\n",
"\n",
"OGC WKT :\n",
"PROJCS[\"NZGD2000 / New Zealand Transverse Mercator 2000\",\n",
" GEOGCS[\"NZGD2000\",\n",
" DATUM[\"New_Zealand_Geodetic_Datum_2000\",\n",
" SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
" AUTHORITY[\"EPSG\",\"7019\"]],\n",
" TOWGS84[0,0,0,0,0,0,0],\n",
" AUTHORITY[\"EPSG\",\"6167\"]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" AUTHORITY[\"EPSG\",\"8901\"]],\n",
" UNIT[\"degree\",0.0174532925199433,\n",
" AUTHORITY[\"EPSG\",\"9122\"]],\n",
" AUTHORITY[\"EPSG\",\"4167\"]],\n",
" PROJECTION[\"Transverse_Mercator\"],\n",
" PARAMETER[\"latitude_of_origin\",0],\n",
" PARAMETER[\"central_meridian\",173],\n",
" PARAMETER[\"scale_factor\",0.9996],\n",
" PARAMETER[\"false_easting\",1600000],\n",
" PARAMETER[\"false_northing\",10000000],\n",
" UNIT[\"metre\",1,\n",
" AUTHORITY[\"EPSG\",\"9001\"]],\n",
" AUTHORITY[\"EPSG\",\"2193\"]]\n",
"\n"
]
}
],
"source": [
"; gdalsrsinfo EPSG:2193"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us create a GeoTIFF from that:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 1831, 1835\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"; gdal_translate -a_srs EPSG:2193 gdalworkshop/wellington_west.png gdalworkshop/wellington_west.tif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the result:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: gdalworkshop/wellington_west.tif\n",
"Size is 1831, 1835\n",
"Coordinate System is:\n",
"PROJCS[\"NZGD2000 / New Zealand Transverse Mercator 2000\",\n",
" GEOGCS[\"NZGD2000\",\n",
" DATUM[\"New_Zealand_Geodetic_Datum_2000\",\n",
" SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
" AUTHORITY[\"EPSG\",\"7019\"]],\n",
" TOWGS84[0,0,0,0,0,0,0],\n",
" AUTHORITY[\"EPSG\",\"6167\"]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" AUTHORITY[\"EPSG\",\"8901\"]],\n",
" UNIT[\"degree\",0.0174532925199433,\n",
" AUTHORITY[\"EPSG\",\"9122\"]],\n",
" AUTHORITY[\"EPSG\",\"4167\"]],\n",
" PROJECTION[\"Transverse_Mercator\"],\n",
" PARAMETER[\"latitude_of_origin\",0],\n",
" PARAMETER[\"central_meridian\",173],\n",
" PARAMETER[\"scale_factor\",0.9996],\n",
" PARAMETER[\"false_easting\",1600000],\n",
" PARAMETER[\"false_northing\",10000000],\n",
" UNIT[\"metre\",1,\n",
" AUTHORITY[\"EPSG\",\"9001\"]],\n",
" AUTHORITY[\"EPSG\",\"2193\"]]\n",
"Origin = (1731543.836827248800546,5461586.738620690070093)\n",
"Pixel Size = (28.001501693600002,-28.001034482800002)\n",
"Metadata:\n",
" AREA_OR_POINT=Area\n",
"Image Structure Metadata:\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left ( 1731543.837, 5461586.739) (174d33'49.52\"E, 40d59'10.66\"S)\n",
"Lower Left ( 1731543.837, 5410204.840) (174d34'29.34\"E, 41d26'56.27\"S)\n",
"Upper Right ( 1782814.586, 5461586.739) (175d10'22.40\"E, 40d58'35.11\"S)\n",
"Lower Right ( 1782814.586, 5410204.840) (175d11'17.70\"E, 41d26'20.14\"S)\n",
"Center ( 1757179.212, 5435895.789) (174d52'29.74\"E, 41d12'47.03\"S)\n",
"Band 1 Block=1831x4 Type=Byte, ColorInterp=Palette\n",
" Color Table (RGB with 256 entries)\n"
]
}
],
"source": [
"; gdalinfo -noct gdalworkshop/wellington_west.tif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And let's check that the coordinate system we select is consistant by using the OGR geocoding API:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Layer name: SELECT\n",
"OGRFeature(SELECT):0\n",
" POINT (174.7772239 -41.2887639)\n",
"\n"
]
}
],
"source": [
"; ogrinfo :memory: -q -sql \"SELECT ogr_geocode('Wellington, New Zealand')\" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Subsetting (extracting a sub-window)\n",
"Let's suppose we are only interested in a 4 km x 4 km area centered around Wellington city center.\n",
"\n",
"We will convert first the above longitude, latitude in NZGD2000 with :\n",
"\n",
" $ gdaltransform -s_srs WGS84 -t_srs EPSG:2193\n",
" 174.7772239 -41.2887639\n",
"\n",
"The command expects tuple of longitude/easting latitude/northing to be entered on the console and\n",
"validated by Enter. The application may be terminated with Ctrl+Z.\n",
"\n",
"Output :\n",
"\n",
" 1748816.1501341 5427663.38112408 0\n",
"\n",
"So if we substract/add 2000 from this coordinate, we can do:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 1831, 1835\n",
"Computed -srcwin 545 1140 143 143 from projected window.\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"; gdal_translate -projwin 1746816 5429663 1750816 5425663 gdalworkshop/wellington_west.tif gdalworkshop/wellington_city.tif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conventions are « -projwin ulx uly lrx lry » where :\n",
"- `ulx`: X of upper-left corner\n",
"- `uly`: Y of upper-left corner\n",
"- `lrx`: X of lower-right corner\n",
"- `lry`: Y of lower-right corner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data type conversion\n",
"Let's look at a digital elevation model file:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: gdalworkshop/MK_30m.tif\n",
"Size is 2185, 2185\n",
"Coordinate System is:\n",
"PROJCS[\"NZGD2000 / New Zealand Transverse Mercator 2000\",\n",
" GEOGCS[\"NZGD2000\",\n",
" DATUM[\"New_Zealand_Geodetic_Datum_2000\",\n",
" SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
" AUTHORITY[\"EPSG\",\"7019\"]],\n",
" TOWGS84[0,0,0,0,0,0,0],\n",
" AUTHORITY[\"EPSG\",\"6167\"]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" AUTHORITY[\"EPSG\",\"8901\"]],\n",
" UNIT[\"degree\",0.0174532925199433,\n",
" AUTHORITY[\"EPSG\",\"9122\"]],\n",
" AUTHORITY[\"EPSG\",\"4167\"]],\n",
" PROJECTION[\"Transverse_Mercator\"],\n",
" PARAMETER[\"latitude_of_origin\",0],\n",
" PARAMETER[\"central_meridian\",173],\n",
" PARAMETER[\"scale_factor\",0.9996],\n",
" PARAMETER[\"false_easting\",1600000],\n",
" PARAMETER[\"false_northing\",10000000],\n",
" UNIT[\"metre\",1,\n",
" AUTHORITY[\"EPSG\",\"9001\"]],\n",
" AUTHORITY[\"EPSG\",\"2193\"]]\n",
"Origin = (1703936.000000000000000,5439488.000000000000000)\n",
"Pixel Size = (30.000000000000000,-30.000000000000000)\n",
"Metadata:\n",
" AREA_OR_POINT=Point\n",
"Image Structure Metadata:\n",
" COMPRESSION=DEFLATE\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left ( 1703936.000, 5439488.000) (174d14'21.77\"E, 41d11'21.48\"S)\n",
"Lower Left ( 1703936.000, 5373938.000) (174d15' 2.45\"E, 41d46'46.57\"S)\n",
"Upper Right ( 1769486.000, 5439488.000) (175d 1'14.35\"E, 41d10'41.67\"S)\n",
"Lower Right ( 1769486.000, 5373938.000) (175d 2'20.64\"E, 41d46' 5.92\"S)\n",
"Center ( 1736711.000, 5406713.000) (174d38'14.80\"E, 41d28'46.35\"S)\n",
"Band 1 Block=2185x1 Type=Float32, ColorInterp=Gray\n",
" Computed Min/Max=0.000,928.151\n",
" NoData Value=-32767\n"
]
}
],
"source": [
"; gdalinfo -mm gdalworkshop/MK_30m.tif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The -mm option computes only the min/max values. We can also see a NoData value being advertized. The NoData value is a special value which means that pixel that have that value have no valid information. The NoData value is ignored in min/max, statistics or histogram computations.\n",
"\n",
"We might want to generate a more convenient visual dataset from the DEM with:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 2185, 2185\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"; gdal_translate -outsize 50% 50% -ot Byte -of PNG gdalworkshop/MK_30m.tif gdalworkshop/MK_vis.png"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- `-outsize 50% 50%` is just to get a half-size reduction. Note that gdal_translate uses nearest neighbour sampling, which is not always appropriate. gdalwarp can be used for other resampling methods.\n",
"- `-ot Byte`: asks for data type conversion"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using Images"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"Gray Images.Image with:\n",
" data: 1092x1092 Array{ColorTypes.Gray{FixedPointNumbers.UfixedBase{UInt8,8}},2}\n",
" properties:\n",
" imagedescription: <suppressed>\n",
" spatialorder: x y\n",
" pixelspacing: 1 1"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = imread(\"gdalworkshop/MK_vis.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can notice large zones saturated to white. Why so ? Because -ot does only data type conversion and clip values that would become out-of-range. So any elevation above 255 got clipped to 255. We also want to rescale the range [0,928.151] to [0,255]. We can do this by adding -scale"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 2185, 2185\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"; gdal_translate -outsize 50% 50% -ot Byte -scale -of PNG gdalworkshop/MK_30m.tif gdalworkshop/MK_vis.png"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can control more precisely the rescaling with:\n",
"\n",
" -scale src_min src_max\n",
"\n",
"or\n",
"\n",
" -scale src_min src_max dst_min dst_max\n",
"\n",
"When not specifying dst_min and dst_max, 0 and 255 are selected. When not specifying src_min and src_max, the minimum/maximum values of the dataset are used.\n",
"\n",
"You can also experiment with non-linear scaling with the -exponent value. For example try with -exponent 0.9. This will give an image slightly brighter. For reference, the destination value (dst_val) is computed from the (src_val) according to the following formula :\n",
"\n",
" dst_val = dst_min + (dst_max – dst_min) * ((src_val-src_min)/(src_max – src_min)) ^ exponent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.0-pre",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
SteveDiamond/cvxpy | examples/notebooks/dqcp/concave_fractional_function.ipynb | 3 | 18945 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "concave_fractional_function.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "hj5vNlJzqoQr",
"colab_type": "text"
},
"source": [
"# Fractional optimization\n",
"\n",
"This notebook shows how to solve a simple *concave fractional problem*, in which the objective is to maximize the ratio of a nonnegative concave function and a positive\n",
"convex function. Concave fractional problems are quasiconvex programs (QCPs). They can be specified using disciplined quasiconvex programming ([DQCP](https://www.cvxpy.org/tutorial/dqcp/index.html)), and hence can be solved using CVXPY."
]
},
{
"cell_type": "code",
"metadata": {
"id": "310PJvINl2JU",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
},
"outputId": "bdd451f6-2c9f-4185-8f3f-d8756dd97c95"
},
"source": [
"!pip install --upgrade cvxpy"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already up-to-date: cvxpy in /usr/local/lib/python3.6/dist-packages (1.0.23)\n",
"Requirement already satisfied, skipping upgrade: scs>=1.1.3 in /usr/local/lib/python3.6/dist-packages (from cvxpy) (2.1.0)\n",
"Requirement already satisfied, skipping upgrade: multiprocess in /usr/local/lib/python3.6/dist-packages (from cvxpy) (0.70.7)\n",
"Requirement already satisfied, skipping upgrade: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from cvxpy) (1.16.3)\n",
"Requirement already satisfied, skipping upgrade: scipy>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from cvxpy) (1.3.0)\n",
"Requirement already satisfied, skipping upgrade: ecos>=2 in /usr/local/lib/python3.6/dist-packages (from cvxpy) (2.0.7.post1)\n",
"Requirement already satisfied, skipping upgrade: osqp>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from cvxpy) (0.5.0)\n",
"Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from cvxpy) (1.12.0)\n",
"Requirement already satisfied, skipping upgrade: dill>=0.2.9 in /usr/local/lib/python3.6/dist-packages (from multiprocess->cvxpy) (0.2.9)\n",
"Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.6/dist-packages (from osqp>=0.4.1->cvxpy) (0.16.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ehIgvu6rmlRr",
"colab_type": "code",
"colab": {}
},
"source": [
"import cvxpy as cp\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "j8E5ouimrBoV",
"colab_type": "text"
},
"source": [
"Our goal is to minimize the function\n",
"\n",
"$$\\frac{\\sqrt{x}}{\\exp(x)}.$$\n",
"\n",
"This function is not concave, but it is quasiconcave, as can be seen by inspecting its graph."
]
},
{
"cell_type": "code",
"metadata": {
"id": "AgPgroRVm5Mz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "65bd8263-698c-49a5-d7d1-290718e7a43e"
},
"source": [
"plt.plot([np.sqrt(y) / np.exp(y) for y in np.linspace(0, 10)])\n",
"plt.show()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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Zi4icKt+He6wrRXlJcMId4BMXN7P7cCdPLd3udSki4lM5hbuZzTazNWa2zszu7eXxL5jZ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\nRUQCSOEuIhJACncRkQD6/7ioX0aYNsOtAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-jgzBqQ6rZPM",
"colab_type": "text"
},
"source": [
"The below code specifies and solves the QCP, using DQCP. The concave fraction function is DQCP-compliant, because the ratio atom is quasiconcave (actually, quasilinear), increasing in the numerator when the denominator is positive, and decreasing in the denominator when the numerator is nonnegative."
]
},
{
"cell_type": "code",
"metadata": {
"id": "uAbu67IemoDK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "a1e231df-a97d-43ca-a427-5290bbb8bff5"
},
"source": [
"x = cp.Variable()\n",
"concave_fractional_fn = cp.sqrt(x) / cp.exp(x)\n",
"problem = cp.Problem(cp.Maximize(concave_fractional_fn))\n",
"assert problem.is_dqcp()\n",
"problem.solve(qcp=True)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.4288821220397949"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9auLCvH3m366",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "8511813a-562a-4cae-deaf-504d6c825aa6"
},
"source": [
"x.value"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(0.50000165)"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
}
]
} | gpl-3.0 |
quantopian/pyfolio | pyfolio/examples/pyfolio_talk_slides.ipynb | 2 | 2413820 | null | apache-2.0 |
resbazaz/Weekly_Projects | notebooks/2017-08-03-hackyhour.ipynb | 1 | 2403 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook name: \n",
"\n",
"year-month-day-event\n",
"\n",
"(Example: 2017-08-01-hackyhour)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attendance:\n",
"\n",
"* **Megan Willi** - @mkittles : speech & hearing sciences, speech tech, natural language processing\n",
"* **Heather Lent** - @hclent : natural language processing, machine learning, flask \n",
"* **Blake Joyce** - @bjoyce3 : cyber infrastructure, biology, ecology, python, plant metabolism\n",
"* **Grant H** - @??? : web development, software, python\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary:\n",
"\n",
"Projects worked on:\n",
"\n",
"* **HLT Wiki Project**\n",
"* **Planning Software Carpentry for HLT**\n",
"* **Cyverse Read The Docs Update**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### HLT Wiki Project\n",
" \n",
" * Heather and Megan contributed to the wiki for HLT program \n",
"\n",
"https://github.com/arizona-hlt/hlt-resources/wiki"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Planning Software Carpentry for HLT\n",
"\n",
"* Editing RSVP for workshop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cyverse Read The Docs Update\n",
"\n",
"* Cyverse documentation is becoming \"Read The Docs\" \n",
"* Updating \"ez\" documentation for Atmosphere installer to \"Read The Docs\"\n",
"* Documenting the idiosyncracies of restructured text, \"Read The Docs\", & Cyverse \"Read The Docs\" templates. \n",
" * How to document the new documentation "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notes:\n",
"\n",
"* It rained\n",
"* Beer\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Shinichi-Nakagawa/pitchpx-example-ichiro-2016 | analyze_votto_2016.ipynb | 1 | 168978 | {
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Joey Votto analytics(2015-2016)\n",
"\n",
"# ライブラリのインポートと設定\n",
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pd.options.display.max_columns = 100\n",
"pd.options.display.max_rows = 300"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 変化球の正式名称(あとで使う)\n",
"\n",
"PITCH_TYPE_NAME = {\n",
" 'CH': 'Change-up',\n",
" 'CU': 'Curveball',\n",
" 'EP': 'Ephuus',\n",
" 'FA': 'Fastball',\n",
" 'FC': 'Cut Fastball',\n",
" 'FF': 'four-seam Fastball',\n",
" 'FO': 'Forkball',\n",
" 'FS': 'Split-finger Fastball',\n",
" 'FT': 'two-seam Fastball',\n",
" 'KC': 'Knuckle Curve',\n",
" 'KN': 'Knuckleball',\n",
" 'SC': 'Screwball',\n",
" 'SI': 'Sinker',\n",
" 'SL': 'Slider',\n",
" 'UN': 'Unknown'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 2015年の一年間および、2016年途中(開幕〜8/31まで)のデータセットを取る\n",
"votto_atbat_2015 = pd.read_csv('./output/player_stats/joey_votto_2015_atbat.csv')\n",
"votto_pitchfx_2015 = pd.read_csv('./output/player_stats/joey_votto_2015_pitch.csv')\n",
"votto_atbat_2016 = pd.read_csv('./output/player_stats/joey_votto_2016_atbat.csv')\n",
"votto_pitchfx_2016 = pd.read_csv('./output/player_stats/joey_votto_2016_pitch.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 時系列分析用に試合の日(game_day)および、変化球の正式名称を加える\n",
"for df in (votto_atbat_2015, votto_atbat_2016, votto_pitchfx_2015, votto_pitchfx_2016):\n",
" df['game_day'] = pd.to_datetime(df[['year', 'month', 'day']])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# pitchfxデータから、ヒットとなった投球を抽出\n",
"\n",
"# 条件式(共通)\n",
"QUERY_TEMPLATE = \"pitch_res == 'X' and pa_event_cd in [{}]\"\n",
"\n",
"# ヒットの条件(コード値はRETROSHEETの仕様に従う)\n",
"query_hits = QUERY_TEMPLATE.format(','.join(['20', '21', '22', '23']))\n",
"votto_pitchfx_2015_hits = votto_pitchfx_2015.query(query_hits)\n",
"votto_pitchfx_2016_hits = votto_pitchfx_2016.query(query_hits)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start_speed</th>\n",
" <th>end_speed</th>\n",
" <th>spin_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>168.000000</td>\n",
" <td>168.000000</td>\n",
" <td>168.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>88.904762</td>\n",
" <td>82.303571</td>\n",
" <td>1707.477292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.484369</td>\n",
" <td>4.844280</td>\n",
" <td>583.200493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>73.400000</td>\n",
" <td>68.300000</td>\n",
" <td>160.046000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>85.275000</td>\n",
" <td>79.300000</td>\n",
" <td>1362.092250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>90.450000</td>\n",
" <td>83.400000</td>\n",
" <td>1762.140000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>93.025000</td>\n",
" <td>85.925000</td>\n",
" <td>2086.731000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>97.000000</td>\n",
" <td>89.600000</td>\n",
" <td>2935.263000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start_speed end_speed spin_rate\n",
"count 168.000000 168.000000 168.000000\n",
"mean 88.904762 82.303571 1707.477292\n",
"std 5.484369 4.844280 583.200493\n",
"min 73.400000 68.300000 160.046000\n",
"25% 85.275000 79.300000 1362.092250\n",
"50% 90.450000 83.400000 1762.140000\n",
"75% 93.025000 85.925000 2086.731000\n",
"max 97.000000 89.600000 2935.263000"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# まずは軽くDataframeを眺める\n",
"\n",
"# 初速・終速・回転数の統計を見てみる(2015)\n",
"votto_pitchfx_2015_hits[['start_speed', 'end_speed', 'spin_rate']].describe()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start_speed</th>\n",
" <th>end_speed</th>\n",
" <th>spin_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>137.000000</td>\n",
" <td>137.000000</td>\n",
" <td>137.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>89.026277</td>\n",
" <td>82.568613</td>\n",
" <td>1702.062401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.925583</td>\n",
" <td>5.268214</td>\n",
" <td>518.460390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>70.800000</td>\n",
" <td>65.700000</td>\n",
" <td>232.591000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>85.100000</td>\n",
" <td>79.200000</td>\n",
" <td>1388.268000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>91.100000</td>\n",
" <td>84.300000</td>\n",
" <td>1726.547000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>92.900000</td>\n",
" <td>86.200000</td>\n",
" <td>2124.984000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>99.700000</td>\n",
" <td>91.700000</td>\n",
" <td>2794.557000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start_speed end_speed spin_rate\n",
"count 137.000000 137.000000 137.000000\n",
"mean 89.026277 82.568613 1702.062401\n",
"std 5.925583 5.268214 518.460390\n",
"min 70.800000 65.700000 232.591000\n",
"25% 85.100000 79.200000 1388.268000\n",
"50% 91.100000 84.300000 1726.547000\n",
"75% 92.900000 86.200000 2124.984000\n",
"max 99.700000 91.700000 2794.557000"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 初速・終速・回転数の統計を見てみる(2016)\n",
"votto_pitchfx_2016_hits[['start_speed', 'end_speed', 'spin_rate']].describe()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# ちょっとイマイチわかりにくいので軽く可視化してみる"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# まずはヒットの傾向を探ってみる. ヒットに仕留めた変化球ごとに集計してみる\n",
"pitch_types_2015 = votto_pitchfx_2015_hits.pitch_type.groupby(votto_pitchfx_2015_hits.pitch_type).count()\n",
"pitch_types_2016 = votto_pitchfx_2016_hits.pitch_type.groupby(votto_pitchfx_2016_hits.pitch_type).count()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10af35400>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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+Tj82kHWsUBLHcXjCOmDPu2HJtBdgJMrSMuxufZwZVQtvy61oul1HknVy6CuW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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c7ad7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ヒットにした変化球の割合(2015)\n",
"pitch_types_2015.plot.pie(autopct='%.2f')\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x105310668>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RNPz7iFXBW6nKL6+O295yhakMIe/JP1uCIBDyDaJr51Mg+RKIlKYRj2FZBrZD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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b2ab2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 速球系を中心に打ち込んでいる、2016年は?\n",
"pitch_types_2016.plot.pie(autopct='%.2f')\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 多少変化球を打ってる以外変わらないかも...次はアウトになった時の変化球をみる\n",
"query_outs = QUERY_TEMPLATE.format(','.join(['2']))\n",
"votto_pitchfx_2015_outs = votto_pitchfx_2015.query(query_outs)\n",
"votto_pitchfx_2016_outs = votto_pitchfx_2016.query(query_outs)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# アウトの傾向を探ってみる. ヒットに仕留めた変化球ごとに集計してみる\n",
"pitch_types_2015_outs = votto_pitchfx_2015_outs.pitch_type.groupby(votto_pitchfx_2015_outs.pitch_type).count()\n",
"pitch_types_2016_outs = votto_pitchfx_2016_outs.pitch_type.groupby(votto_pitchfx_2016_outs.pitch_type).count()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10a1ee208>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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222QTEbdr90vd308OZE5krY5tPV2DRx6uSBLLJjZZqmtfm43vXqQ2ZlZEflUq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\nEP8Gas9Kk7HoG7EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a3d3940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# アウトになった変化球の割合(2015)、速球系に苦戦してる\n",
"pitch_types_2015_outs.plot.pie(autopct='%.2f')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x105322e48>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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GVdU3QSZRo7bpozHnC/hCFOWuwFkLv4P5pRdh255nwHIscMwikkAgRjCBF5Gm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105390240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# アウトになった変化球の割合(2016)、変化球系にやられてる(ちょっと違いが出てきた！)\n",
"pitch_types_2016_outs.plot.pie(autopct='%.2f')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10c299438>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Reaj6tZCMUIBdzsgywwLB0WRT+56oz41UJ6UvRFoySklJja8lI0VRLMDjwBTA\nC1wHtANPE9ga3aaq6k9jNZ6/qADfgQqspQeCbXpSYogsrpSQjeHr1s+RUib1yRlUeOq5r+JFdrvL\nmWLP5/r8Cxhny47NGxhluHUv7zV/GdY+3RHbymwCQX9IMyfRFKHEa08m2nIGXOFvKJaMhiLs9HLA\nparqccCPgSeB+4AbVFVdBsiKopwbywHdS+bhKxyHYTKhZabjPHlpMNpIb92H0bov5Hxz/tf61P+q\nypfZ7Q7I1O5xV3BX+b9iYvdoRELC1ENUMEG28qOxZw6RRYKRgtzQhG3jFiy7SsAfWeZ+oPwoJ/Rz\nGGmHziHbODtjcdjnuK9ELpU5uAmgQ7FkNAN4A0BV1d2KouQDU1VV7XICbwCnAa/EbESbDdcpx0d8\nSWtWw9qMPuwhAOx2lYcc7/dUs62j78kqnzR6eavOi12G83PtKElxvaIXEZts4esZS3ih4UMg4CCu\nyv06KebBKXQiiA9MVTU43vwgGA5tKS3DueLkmI8zJ3EitxVeyhuNn2OVzZyeNp9P2nayzbkfjx4I\nO12ePn/A0vc5OblhqqYAr7zy5oD6PRJDccfZDJwNvKIoymKg59pKG3DU4jZNGTMJTJS6I41MWbP7\n1MdEew673RUhbf9t+qxPDmFbq4/7SrvDRre1tfOXWSmkW+I6dzAil4xdzuzEiexzVzM7cSKTh3Cj\nXjAysO7YE3QGAObqWuSGJvTM2BdWmpM4kTmHVESckVgU8zGGiqFwCE8C0xRFWQd8DOwGsg55PRlo\njnThoaSnOzCb+1+AIkj2NGqLTsK57z0AJJONjPzJ2LKiL6n5U9M5XLc1dKMnyW4nOzv6PrbUhVYW\n8+hQqps5I3t0xvgvz5471CYIRhA+hy0sZSw9Kxm5D99jwdA4hIXA+6qqXq8oynxgEbBbUZRlqqqu\nBc4E3j9SJ01NziOd0o3fH9gzMIU7EMPbhnP/B93Hmofa9auxL/pD1N1PJJ9FScV81r4LALtk5fTE\nBdTVtUXdR6oeHqGU6PVSV9dbCrJAIOhCnjyRxD37kTrL5voKx9FqmKEP38HRwuEeVIfCIajA84qi\n3AC4CGwyy8BjnRFIO4EXYjKSrmP/dCOW3aUgS/gmFeJeujBEvsJfsQZ6hIsa7r5rEf1m/HfY0K7S\n4GtlUXIxWZa+rXqdmmVlU4uPr1r9yMDpY6wUJ4++PQSBoD/o2Zm0n78Cc1klRpIjYq1vwZGJa/lr\ny559JHxdW3xxAAAgAElEQVQYKn/tT0/Bec7p0Lnc5Nl0F/7yd0POMReuwDbn/0VtS5O/nbeaNuDS\nvZySNpcC25ior+1JtVvDKktkWEff3oFAIBh8Rq38tdzYFNZmbmrFsv8gvskTAJCSxoWdY5l4fsT+\nfP42ZNmGSe7WB3LrXn6x75GgnsnrjZ/x54lX9Nsp5NhjsC8yxPj2vYy/7C2wpmBVLsGUMT3kdU33\nouseLGaxviuIMZoWWCK22Y7qsB5dw6NrJJssvYSLjgzi2iFoeTmwfXdYu+Tpzkq2FJ2HVr8ZvX4z\nSGYsU76DnByaBOXXXGzdcwd1TZ9iNjmYUnAZ43O+DsCGNjVE3Mpr+Hi3eRM/HHvGIL2r4Y2/Yg3e\nrf8bPHY37cJx6t+QrIGbf1nVS+w5+BSa5mJM+nHMmvIbTCb7UJkriCMsu/di/3wzkteHPz8X50lL\nwDo41dO6qHB38Fz1fnZ2NOPWdfJtCSxLz+Hs7PAHzb7w5ZcbefnlF7n11jsAWLPmXZ5++nHuuecB\n1q//hLfffgNJktA0jcsvv5K5c+fH4u3Et0Pwj8/DfcwMbJu3B9M5DFnGd8j6omRJJOG4e9Cd1Uhm\nB5I1XM3wQOUL1DUFCuD4NSc79/2FrPRFJNjGYpfDP3AJEdpGC/7qz3o0ONEatmDOXYrTXcmu/Q/R\nlW1Z2/QxB6r/w8T87x59QwVxheR0Yf/4i2DoqbmiCtuWXXiO7VsI+ZE46Knl7aaNmCUTS5Pmsqp8\nHwfd3QEuJa529rlK8OgaF4wdWHZ910zjnXfe5Pnnn+OBB1azcePnfPHF56xatRpZlqmqquTqq3/M\nU0/9nZSUgUfrx7VDAPDOmwUmE/aNWwCQdJ2E9Ztwnv61kPNkR06vfbQ5S3u06LQ7D5BgG8u8pMlM\nTyhkhysgjZFlTuH0tAWxfAsjCjm5gJ7xUnJyoBhLu3M/PVPv2zt6/m0Fgr4jt7SG5CEAyE1HjF7v\nE5XeBq4vfQS34QXg9bo6/Fp+2Hka8H5jNV/PHo9F7v9eoGEYvPXW//HCC8/zwAMPkZiYxCuv/Idr\nrrkeubPf3Nw8nnrqOVJSwh9k+0PcOwQAc3ll6HFFNVJbe7BAvFb/Fb7y9zCPXYw597iw67PSjqW2\n8cPgscnkIC05sC5ukkzcMeFSNraX4NI9LEhScJiO7vrlcMIycSV6w1a0ui9AtmJRLkJOGg9AevJM\nTLIdTXcHz88cJOfp1w02t/rRDIO5qRas8shd1xUcGS0rA8NqRfJ6g23+/N4f8vrD2pavgs4AwKvZ\ne9X+qfS62NrexLyU/pfr3bJlMw0N9bS1teHvlOKor68jPz/UCcXKGcAocQiGxRJ6LElgDrx1z7aH\n8Zf+BwCt7A38ucdjX/C7kPPzx6zA62uisu4drJYMphT8EMsh2vwmycTCZGWQ38XIQDInYF9yJ7qr\nHsmcgGTpTqyzWFKYW3w7JQefxOtrIW/McvLHnB7Wxy5nGe+1bCZZTuCsjEURi5IcDq9ucNOudvY6\nA3OVPLvMncVJJJpF5FbcYrHgXH4iti++QnK68E8sxDdtSkyHSJB77HVJxmG15iwD1DLKysriz3/+\nX1599SVuvfUm/vSnVeTk5FFTU0NRUXem9Oefr2fy5ClkZAy8VvyocAjeY2Zgrq5D6vSy3hkKRkLg\nn+vfFyqZpFV9FCZ9LUkSE8ddxMRxFx09o0c4ckJWxPaM1DksTH2g1+t2Osv49f4ngqUK17Zu4eFJ\n12KVLb1e05PPm3xBZwBQ6db5oMHHWWNH78xtNKCNycK54pRB6//UtLm82bSBCm8gTynTYtDkjXxu\nkT2R6UlpAxovP388FouFCy74Fhs2rOevf32Cs846h6effoybb74Nk8lEWdkB7rrrNp544m8DGquL\nwzoERVHWcBgfqKpq7NWjBgHDYsEzbyaGrqPljkXPDnhSwzDCqqUBoPuA6OWvBbHjveYvQ+rW1vqa\n2dxR2qcZmFMP/8i6tJGZbyMYPiSZElg18So2tu/BIpmZ5pjAbaXb2OVsDTkvQTaxIisfUwzDT3/9\n65v54Q+/x003/Z4ZM2Zz1VWXYbFY0HWdm2++jbS0gTmfLo40Q/hF589rgVbgCcAPfBeIvWrUIGDZ\nvRf7RxuCUUae4sl4Oh2CJEnImbPRG74Kni8lFUSMNBIcHZIihKBGajsci9Ms/LPCTas/4AQSZDgh\nM/oZhkDQG1bZwpKU7ryaG4tm8XzNfna0t+DWNfLtDk7LyGVBauQZcrTMnTs/JJQ0LS2N//znv8HX\nvvWt7wyo/96IKlNZUZQNqqou6NH2haqqxw6KVVEQTaYyQNI/X0F2ukLanMuW4J8UCAkzDB3PlgfR\n6jZiypiBdfbPkM1Ds7RQ7dZINEskj+K17npfC/+z7zHqO8sULk6exo3j+x6WWufRebvOg2bAKdlW\n8uMg4U8giAWxyFS2K4oyTVXVnQCKohzTh2uHFMnnC2uz7ikNOgStej1a5Qfga0fHAGclpBxdOds2\nv86dezrY3aFhluDbeXZW5o7OZK0sSyqrJ1/Hlx0lJJsczOhnpbRsm8z3xollP4GgL0R7U78eeE9R\nlEoCQnTZwLcHzaoY4p1ShG1HaB1Uwx6YARi6hnfL/dBZPtNwVuPd/gj2JX/s11hrW7bwadsO8qyZ\nrMxcSrIpuqIur1Z72N0R2AT1G/BchZvjMiyMtY3Op1qbbGFx8rShNkMgGHVE5RBUVX1HUZQJwCwC\nm8xbVFUdnBp1McazaB5yWweWg4FcBN1mxTNnRuBFfweGJ1TvSO+o6NlFVLxQ/yF/rX07eLypvYT7\nJ14Z1bWV7tCNbQOocuv9cghShw4eAyNjdDoTgWCg+DUXLncliQmFyHLkW6Rm6JR5asm2pJJkip+Z\naFQOQVGUdOBuYBLwLeBRRVF+rqpquHrccEOScJ12Ip7GZuT2Dvy5Y6AzL0GypiBnzEBv3B48XU7t\nX+zyv+vXhhzvdVdS4akn33bkzaWF6RY+a+5e2koxS0zrR/lM6ycuLJvcSAZoOSZcX08C2+jdjxAI\n+kpNw0ds23s3mubEZs1ibvFtpCRODjmn0tPALWXPUOVrxCpZuDL3bE5NmzdEFseWaO8WjwEbgEwC\nJS4rgdgEvh4l9Iw0/AX5QWfQhe3Ym5EzZwWPtaoP8e56ps/9O3VPWFu0Tw7LMq1cVpDAlEQTC9Ms\n3Dw1CZupbyFrcoOGdWPAGQCYqjUsm8NtEggEkdENjZ37HkDTAtpEHm896v7VYec9U/cOVb5GICBm\n+Wj1f2n2u2n16YzUcgJdRPsYWqSq6qOKolypqqoH+K2iKF8d8aoRgGzPwPCGxhH79v4by5QLkUzR\ni9QV2MZQ5qkNHmebU0k1R1/+cmqSibUNsKXVh1mCnxQm9CmzVmoJr7gmtwzPamt7Dz7LwZpXkGU7\nk8dfTF728qE2SSBA01x4faGLHi53Zdh51d5DztHS8XmO4/ptLnyGmzybzImZVs4aO/CgkL/97Wm+\n+OJz/H4/JpOJq666jhdffJ5TTz2dhQsXD7j/SETrEPyKoqTSmaSmKMoUCCthOnLRe2yHGBqHzUmP\nwC/zv8U9Ff/mgKeGAusYfjP+wuiHNwz+tNdJjSfwJ/2kyUeCSeLKCdFtSgNo4ywYNgnJ0223f9Lw\ni72vbljH3vK/Bo+3ldxDSqJCUj+jiQSCWGExJ5GecgxNrZuDbWMyjw87b0nyNPa6K0FPwOI9B9nI\npuuRcq9TZ7/TjUczOD+v/3sL+/fv4+OP1/Hww08CUFKyh9tu+x1Tpw6uRE60DuFm4AOgQFGUl4El\nwA8Hy6hYY95/EMuefRh2G97Z09FTQwuzWCZegHfrqu7zC89C6qNAXaF9LH+ZdDUe3YetDzILAA1e\nI+gMutjZ1sc9e6uEa2US1g1uJLeBb5oVbdLwk+Fubt3So8WguW2bcAiCYcGcqTdRcvAp2jr2kpl2\nbERp9m9knYhJknmj2kKLkR32ugZ80ODjnBw7ln6KKiYlJVFTU8Prr7/C4sXHMXnyFB5//Bnuvvv2\nfvUXLdFGGb2lKMpGYBFgAn6iqmrNoFoWI8wHK3G8/3H3cXkV7d88OyhuB2ApOgc5aRxa/ZfIqZMx\n5Z7QpzF2u8r536pXKfPUcmzSVK7NOy/qkFOADKtEpkWiwdf9dD85qe9RQnq2GfeKpCOfOISkJk0D\nXo7QJhAMPVZLKtMn/uyw55gkmW9kncjOhna+ckd+cKv06Gxt8zEvtX8PZVlZ2dx113288MLzPPXU\nYyQkJHD55dFFLQ6EaKOMrMCPgWICMhbXKoryR1VVe5F2Gj6YSw+EHMsuN+aqWvzj80LaTdlzMWXP\n7XP/uqFzV/nzwapp69t2klyTwLV5K6PuwyRJ/L9Jiaze76TCrTMnxcwlcZpUlZN1Mq0dJRyseRWT\nbGPS+EtITpx45AsFgmHGkR7+rQPQMqqoKMfhSOQ3v7kZAFXdxc9/fg2zZsW24E9Pol0y+l+gDpgH\n+IDJBHSNvj9IdsUMIzH8SV2P0BZVX4YRVi+13t8aUkITYIezrM99FyeZuX9mCrphII/gmqxHQpIk\nlAk/YWrh5UgDlAcWCIaSqUkmvmyNPEOYkCAzLbn/Yg4lJXt49dWXuOuu+zCbzYwbN46kpGRkWR7U\nSKZoLZ6vquo8RVHOVFW1Q1GUS4Ctg2ZVDPHOUDCXVWJqDmjjeKZPQc/omzJgW0cp2/beQ1tHCekp\nc5g1+VfYbYG1w0xzCtmWVOp8LcHzpzkK+m1vPDuDQxHOQDDSOWesnc2tftT20Ai/BBlWjLUNSO10\n2bKTKCvbz2WXXUxCQgJgcPXV17Fu3Qc88MC9JCYGIhgLCgq56aY/DORthBCtuN1GAhvJ6zsdQzbw\nvqqqs45w6aARrbgdAIaBqa4R3W7FSEk+8vk9+HjzZXS49gePs9OXMLe4+5+w21XOXypfCewhJE/l\n2tyVpJj7NwsRCAQjh3a/zr8r3exo8+PRId8uc0q2jWPThl+EXxexELe7H3gXyFUU5X5gJXBrDGw7\nOkgS2pj+VRPya84QZwDQ3LYz5HhqwjhWTfppf60TCAaE013J9pJ7aW7fTlrSDGZM/gUOe96RLxQM\nmCSzzKUF8fPwF9W8XVXVZ4ErgNuAvcA5qqo+OZiGDRfMJgfJjkkhbekpM/vcz0F3B1vamvDp8ZO+\nIRgebC+5l6a2LRiGRlPbFraX3DvUJglGKNFGGVmA5cApBDaV3YqibFVVdWTnaUfJ7Kk3sn3vfbR2\n7CEj5RimFV3bp+sfKd/Nmw2BjMcxVju3TzqGLOvolLcWxJ7m9u2HPRYIoiXaJaPHCdSUfJTArOJi\nYAZw+IDdEYShB8TlpAhJZYkJBSyceX+/+j3o7gg6A4Bar5uX6w5yWX6oiN5btR7WNXhJs8h8M8/O\nBIdQKxVER1rSDJratoQcCwT9IVqHsEhV1eKuA0VRXgO2DY5JRx/vzifwlb4EgGXiSqzTfhSzvpt8\n4akazT3a1jV4eaysq6qbxs52Pw/PSumzwJ1gdDJj8i/C9hAEgv4QrUMoVxRloqqqpZ3H2QQUT0c8\n/toN+Pb8M3js2/NP5MzZmMcsOMxV0TMtMZWxVjs1XnewbVnG2JBzPm8OrerW6jfY2e7nmNThG6kw\nUGoa1tHUuo3U5GnkZH4tLL9DED0Oex4LZt431GaMGrZ27OOztl3k27I4NXUuFtmM5gddA7MVRvJH\nOVqHYABfKYryLgGpjpOACkVR/g9AVdUV0Q6oKIpEYAlK6ezrcsABvA7s7jztYVVV/x1tnwNBb94d\nua2HQ9B1H7ruxdwHBVMAiyxz26RjeKXuIE0+L8syxrIgJbRGQk6PmgVShLZ4YsvuO6lueC9wUA1t\nHSVMLbx8aI0SCKJgXcsW7qnovjXtqK/i7JZzaW2Q0TVISDLIGmeQN3Hg26ulpXtZvfpB3G43LpeL\nJUuWcuaZZ3PLLTfyyCNPBc97+eUXaWpq5NJLB/4ditYh3Nbj+MEBjLkcSFRV9XhFUU4F7gDeAP6k\nquqfB9BvvzBlzcXH02Fth3Kw5nX2HHgMv+ZkTMbxzJr8K0ym6DeFs6x2fpTfe+Gdc3NsbG/zs6ez\npvI3c+3kxGlR+HbngW5n0ElZ1UtMKbgsprMEn27wWZOPKo/O4jQz4x0jogS4YJjzeuNnwd8d/kQm\nlR1Hg7f7u9reLNHeYqD7dcZN7b9TaG9v59Zbb+SOO+4lP38chmFw002/4rPPPh3U2XS035JPgWJV\nVbcoivJdYC5wn6qqVf0Y0w2kds4UUgEvMB9QFEU5D9gDXKeqakc/+u4zpozpWOf8P3wl/wLAMvlb\nmDKmB193eWrYWbqKLrXv2sYPOVA9JaIKYn9JNsvcOS2ZSrdGslkiuQ91EEYa9c2fhTdKUkw/5G1+\nnV9sbwuKBT5fCRfm2fjGAOSIBQIgRMl4QfPxjPHmhJ9kSNQelMmbpCH387nuww8/YP78BeTnjwMC\nki+//e3vqaur5c03/9u/TqMg2jvP34BvKIqykEBCWivw18Nf0isfEYhY2gU8AqwCPgN+oarqMqAU\nuKWfffcLS+EKHKc8jeOUp7EUhq5+tTv30bP0Q03DupjbsKfdz+NlLm5R23m12n3kC0YoiQnhsh45\nmV+L6Rjv1HlDlGMBXqjy4NRGRZS0YBD5ZtaJWKTAc/RYT+/Jf+4Oieb6/o9TX19PXl5+SJvdbsdi\nibyvGKsHqr5UTPuWoih3A4+rqnqXoigb+jnmL4GPVVW9UVGUfGANcLyqql3lxl4i4CQOS3q6A7N5\n8JdVUlIXsmW3HU3vvkm3dZTg8n1OQd4pMRmjw6dz++ZK2v2BG9Yz5W7y0h2cOa5v+xUjgaysU2lz\nbaDkwKuAQd6YpSxb9DtkKXb/S71BIzAR7cZvQFJ6Ipm2+FyKExwdTsmew4ycAtY37SKhrQC3q/dz\nMzIcZGf37/M2dWoR27dvJzu7W2qnvLwct7sFw9BC2k0mnYyMlJC2/hKtQzAripIFnAecryhKDoGN\n4P6QBHQpwTUDFuA1RVGuVlV1A4Hkt41H6qSpydnP4fuKTGHeNyktfzakde+BtSRYFsZkhC9bfEFn\n0MUH5W0ca4vPrOaJedeQn3URoGOzZtJQH9v/5bEJ8C9C53VzUszorU7qYjqSYDRiwsxS80wOpkkc\n7KUqjCNFxzA7qevnB27WrGN56KGHOf30r5OfPw6/38+tt97GggWLaGlpY8OGLUyYUISmaaxZs47v\nf/8H1NW1RdX34RxHtA7hHgLLOq+qqrpNURSVQBW1/nAP8JSiKB92jv9rYCfwkKIoXqCaQO2FYUNO\n5rIwh9CfCl+NPg8JsokEU+ifPd8uIxN6Axtvj999BACbNX3Q+p7gMHHHtCT+Xu6iyWdwbJqZb+SK\n/QNBbMmbZNBcq9PWFPpdNZkNcouMAYWfOhyJ3HjjLdx99+0YhoHT6eT4409k5cpvMGnSZO688/fI\nsozf7+eEE5Yxd+78Ab6bAFGpnfZEURRZVVW98/dHVVU96jfwPqmdxoDS8r9TWv53dMNLVvpi5kz5\nbdSRRh2an3v2b+er9iZsssz3coo4J3t8yDmvVbt5rsKNz4BpSSZ+MyUJR4TENL8XmmolrDZIyRrY\nhy6eKenwU+7SmZViJtMa385VMHT4vFC+W6KlQUL3SyQkGYwt1MmIsNc8XIiF2mkIXc6gk2P708dI\nQtO9yLKN7IylpCfPpCD33D5d/0rdQb5qbwLAo+s8XbmXRanZjDlEz6hDM+jaBy3p0Cjp8DM7JXQD\nydkG2z4y4fcF/p+ZeTrKsfG5rDQQ/lHh4sUqDwAWCX4zJTHsbykQxAKLFYpmGgRStUY+Ijg7CraV\n3EVNw1oAahrWoOkuivIvjPr6g+7QCFodqHA7gw7BqRm8XO0Jvu4z4N+V7rCbWFWpHHQGAA2VMh0t\nOompfX1H8UuHX+eVHn/LFyL8LQUCQThiLn0EfP72sDDT8pq+xQHPTw6txZBoMlOcmNI9hm7QMyLS\nFVqECQAtQrW+SG2jGZ8RiCg6FLeYRAkEUSEcwhGQZSsmOXSvwOOtx+ePbkcf4NTMXC7JnUihPZE5\nSencPHF2yMZyqkVmUXroE+zpY6xh/YwtNEDqvts5UgySM6I2Y1SQZpFZ1KNa1fLs8L+lQCAIJxZL\nRnG9rWmSrWSnL6a6YU2wTTd8VNW9S0Huyqj7OW9MAeeN6b3W8nVFDpJNLtY3+TCASreOXzcwy91/\n3tQsg1nHa9RXyFhsBjmFYlM5Ej+b6OC9ei8HXRrz0yzMi2ORQIEglkQ1Q1AUJUxgXVGUxZ2/vhNT\ni4YhiY4JYW2a7gk/cQA8U+7inXovbZpBu2bwWo2H12rCx0hOh6KZOuOmGJjFg29ELLLEGWNsnJdj\np86js6nFh96PaDrB6EPT3FTVvUdF7dv4tT7kx/gMcOkwwj9nh50hKIqyFDABjyuK8iO6ZwNmYDUw\nVVXVXw6uiUNPh/NAWFtWWmyS0iAQIvlGbXjdhK1tflbmxmyYUYXa7ufW3e14O/cPTsiwcN3E+Mv8\nFsQOv+bis60/pcNVBkBp+TMsnvUQFktKr9dITRrW9S5MlX4kv4GeZsKnWPEfM7CKiNXVVVxyyYUo\nyjQMw0CSJObNO5YVK87hL3+5n+bmJjweD4pSzLXX/hyzOTbxQUfq5TRgGZAL/P6Qdj8BHaJRwaHV\nqLowiLDr20+qetn1nCiqpvWbV6o9QWcA8GGjj2/naXGrIisYODUNa4POAMDlqaay/l0Kc8+PfEGH\nhv2NdkwN3R80U62GXOdC8hv4jh1YMmRR0SRWrVodPNZ1ncsuu5hf/vIGiosDApyrVv2JJ554hJ/8\n5KcDGquLwzoEVVVvAVAU5WJVVZ+JyYgjEL+/Pawt0T4+wpn9Y3aKGbscGg0zNdHEBbmi7nJ/8UeY\nuveMPhIIDkXXfVG1dWHZ7AlxBl1IBph3evHNtcMAqh72TBresmUzY8fmBJ0BwJVXXouuxy6MLtp5\nxq+AUesQ7LZcOlz7gscWcyqybItZ/6kWmZunJvFClRunX+ekLBunZMeu/9HImWNsbG7xB+VAZqeY\nGZcgZgeC3hmbeSKl5X/D42sAwGJOITerdwFLU13vqwSmZh253Ide2P+Nvv37S7n22iuCS0bnnHNe\nmAJqb+qn/SVah7BXUZQnCegZBfX9RsusQZnwYzart6LrbiTJTPGEq2JepGJqkpkbpiTFtM/RzNxU\nC3dMS+KzJh9jbDLLMsUOvODwWC2pLJ79MJV1b2EYGnnZy7Hbsno93zhMSI4BA5odQPiS0bZtW1i7\ndk3IOa2tLWzduoWlS08Y0FhdROsQGghsKC8+pM1glMwastIWsGzec7S07yI5cRI2a+aRLxIMOZMT\nzUxOFMn4guixWTMoyv9OVOfqOWY4EDkzVM+S0fMG9tnruWQ0Y8Ysqqur2LVrB8XF0zEMgyeffBSb\nzX50HYKqqpf2bFMUZVTJRza0bqay9i2sljSK8r9DYsK4oTZJ0Avtfp0XqzwccGnMTTGzYqwNk0jY\nEMQY31w7pjIf5qrQpSPDCr45dpAH9pnruQohSRJ/+MMfue++u3C73bjdLmbMmMXll185oHFCxohG\n7VRRlAsIyF0nEZgpmAC7qqpjY2ZJHzmaaqe1jZ+yWb0peGy1ZHDC3Gf6VFdZcPS4RW1nW1v3k9s3\nc218O39UPb8IjhZuHevnbkyVPvCBni7jn2FDKxq+S5SxUDu9G7gM+DlwO3A6EL12wwinpuGDkGOv\nr5HG1q/ITl80NAYJeqXFp4c4A4CPG33CIQgGB7uM98T+1gobfkSrZdSkquoaYD2Q2hmOGr1uwwjH\nbs0Ob7ONiekYmmHwSrWb36ntPHbASYtPKLL1B4dJIrHHZl6WqIcgEERFtN8Ul6IoUwlUNvuaoihW\nYBiXgIgthXnfIClhQueRREHu+SQ7imI6xguVbp4td7O9zc9bdV7uKuk48kUjmHbnfppat2EYsXV8\nFlniB+PtmDt9QopZ4nvjxNKeQBAN0S4Z/Ra4Dfg+gZKXPwEeHyyjhhtWSxpL5jxKa8duLOZUHPbY\n6kn0rIcAsLtDo8Grx2W1r60ld1FVF5DASkqYwLEz/oTVEruiDidl2ZiXaqHKrTMx0YR1gJt7AsFo\nIVqHUK+q6rc6f1+gKEo6oAySTcMSSZJJTSoelL7fq/MEq6V1YZMhyRx/N7Lmtu1BZwDQ7trPwepX\nmTT++zEdJ9Uik2qJP2cqEAwmAxa3G1zzRgeNPb0BsDTdgi0On2zd3oawNo+3fggsEQgio+temlq3\nYrVm9HlpuNrj4sGDu1A7WlESU7hmfDE5tpET0NAXcbtbCTgEg1EmbjfYLM2w8N8aT1BmIUGG740b\nOR+ivpCVdiwWcxo+f3Nni0RO1slDapNA0IXLU8OG7dfj9tQAMG7s2Uyf+LOor3/w4C52dLQAsKOj\nhQcP7uL2yXMHxdbB4LBzalVVb1FV9SQCYaevA2cQcAbzgHBN6GGI+UA5jv97H8cbazBVVA21ORGZ\nnGjm5qmJTEuSSTNDrk1mV3t81sY0mxwsnHk/+WNWMDbzROZNu4OM1DlDbZZAAMCByheCzgCgvOZ1\n2p37o75e7Wg97PFwJ9pF1rOAL4DzAScwl4Dg3bBGrm8k4f2PMVfXYq6qwfH2OuSW4fcP8ugGr9Z4\n2Nmu0+yHUpfOvXud7IlTp5CYMI4Zk65nztSbyUpbMNTmCARBPL7GCG1NUV+vJKYc9ni4E61DkFVV\nXQecDbyoqupBYlN+c1CxlFUgHZKJLRkG5oOVQ2hRZP6vxsOmltCbvwF80dK79K5AIIg9edmnhRwn\n2HJIT54V9fXXjC9memIqJiSmJ6ZyzfjBCUQZLKK9qTsVRfk5cDJwtaIo1zECMpW11OSwNj0lvG2o\nKXVGltHNtQm5ZoHgaJKdvpi5xbdTVfcuNmsGhbnfQJajf/bNsSWMqD2DnkQ7Q/gekAhcoKpqE4Gk\ntFIsw94AAA7lSURBVO8OmlUxwl9UgK8oUNjeALxTivCPzxtaoyIwKyX8Azc/1czxGaI4vEBwtMlO\nX8TsqTeiTLgSuy1cpSCeiUrcbjjSF3E7qcMJkoThGJ6RO7ph8K9KNx80eEk0SZyXY+cEod8vEAgG\ngcOJ240KhyAQCASCAIdzCCKVUyAQCATAEEQKKYoiEdBBUgANuLzz59OADmxTVfWnR9sugUAgGO0M\nxQxhOZCoqurxwB+AO4D7gBtUVV0GyIqinDsEdgkEAsGoZigcghtI7ZwppAI+YJ6qqh92vv4GcOoQ\n2CUQCASjmqFILvsISAB2AZnAOcChFaLbCDgKgUAgEBxFhsIh/BL4WFXVGxVFyQc+AA6NsUwGmiNd\neCjp6Q7MZpG4Jegf9VUaOzb4cLtg/CQTxfPNYUXNBYLRxlA4hCSgpfP35k4bvlQUZZmqqmuBM4H3\nj9RJU5Nz8CwUxDV+H2x8x4TmDziA3V/58ekecotEJLMg/snO7l2tYSgcwj3AU4qifNg5/q+BjQRq\nLlgIlOl8YQjsEowS2pukoDPooqVOEg5BMOo56g5BVdVmYGWEl752lE0RjFIcKQaSZGAY3U4hMVU4\nA0EAn78Nj7eRxISCUbeMOOwVSwWCWGO1w8Q5Oge2y/h9kJFjkDdJOAQBlFW9xO4Dj6IbPpISJjBv\n2p2jSs9ISFcIRi26DoYOJvFYJAC8vmbWbrwQw+iWoh835iymT/p/Q2hV7DmcdIX4KghGLbKMEG8R\nBHF5akKcAUCHu2KIrBkaxNdBIBAIgJTEySTYckLaxmYcP0TWDA1iyWiE0N4CVXtldB1yJhikZo2q\nty8QHBWcrgpKDj6Ny1NDTtYyCnLOj7uNZSF/PcLxumHTeyZ0LfB/lCSDWSdqJIl8boFA0Ef+f3v3\nHiRXWeZx/Hu6J5OZJDPJTGZyD8EA88SgFcRIKSWg6KqoW2vhFUW0RNAqtLDKdYV1V+EPy9oVNSUU\n6kpZXpBCXRaXpfCOF9Qqr0GMmEcgGAKTO5NMkrnPOfvHeybTQ7pnJsOkz5zu3+efTJ8+p/udNz3n\n1+97znmOyl/n3NN7ouNhAJAkEQe79V8nIrNLe5UcaCxzo7fD+6H/aPXbIiK1S4GQA23LEtpXxBOW\nHT1UYNuvioyOVNhIROQkKRByIIpgw3kxrR0TQ2F4MKL3YG0d8KqWJIb9T0bs8oijU5ZSFKkPug4h\nJ4aH4NihE3f+85vr5tj6rHpka4EDT4XvQ7s8YcOLYtpXqi+lvmmEkBMHu08syNbSHrOgNaMG5djQ\nABx4qrQvI7p36E9BRH8FObZ0lb7RzppIfSmiQMiJjtUJTQvGd1qNzQmda7QTm4nGJlh22njfRVHC\nahW3E9GFaXkyMhymjpIEOlYlNDROvY2UlyTQszei/yi0LU9YUPmeISKZiGPoPRDRMD+Z1YtQVdyu\nRjTMg+Xr6i4HT4koCmWvReaioQH48y+LDPaFfXfnmpizzo2n2OrZ05SRiMgcs3tH4XgYAOx/ssCR\nnlP/vgoEEZE5Zniw3LJTf82RAkFEZI7pXJsAJSeRNCUs7jz1U5w6hpATg/2w8+ECx3oj2pYlnLYh\nplDMulUicios7kjY+JKY/bsiGhph1fqYYhX+3hUIObH9t0WOHQ5Dxv4j4Uyj5zzv1B9kEpFsLOlM\nWFKFUUEpTRnlwNAAx8NgTM8e1TASkdmlQMiBhkaY1zjxm0Jzi06ZFJHZpUDIgUIBzjgnpiENheZF\nCaefrekiEZldOoaQEy1tCcvXJQwcC/82L8q6RSJSaxQIOZAksO3XRfqPhOMGB7sTnvvimLZlmjYS\nqUWjI6HkfdOC6r6vAiEHjjzN8TAIIvY9ESkQRGrQnr9H/P0vBeLRiEVtCRvOG6VxfnXeW8cQcqBh\n3vSWiUi+DQ/C49tCGAAc7Yl48m/V200rEHJgQSt0rh0/iDyvMWHVGTqoLFJrBo5BEk88pbz/SPXe\nv+pTRmb2LuDdhOuym4FNwPnAvcDf0tW+4O7fqXbb5rKzXhCz4vSYoYGIJZ0JRU32idSchUtCmYqh\ngfFQaKtiVd5M74dgZrcADxLCodXdPzfdbevxfggiUvv6euGJ7QUG+iI6VsesPjMhmsXrUCe7H0Jm\ngWBmm4H/dPeLzexWoAuYBzwCXOvuxybbXoEgInLyJguELI8hXA/ckP78G+Aj7n4RsKNkuYiIVEkm\ngWBmi4Eud/9Fuui77r41/flu4Jws2iUiUs+yOjR5IfCTksffN7MPuvvvgVcAf5jqBdraFtDQoPrP\nUtm+/hF6h2PObNXNp0WmI6tAMMLU0Jj3Abea2RCwB7h6qhfo6ek7RU2TWvD1Xf3cu3eQGFi/oMi/\ndy2kpUFnWYt0drZUfC7Ts4yeDR1Ulkp29o3y4Yc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"text/plain": [
"<matplotlib.figure.Figure at 0x10537df60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# さらに詳しく、ヒットの傾向をみてみる.\n",
"\n",
"# X軸:球種 Y軸: 球速(初速)でヒット/アウトのゾーンを把握してみる\n",
"\n",
"# pa_event_cd(打席でのイベントID):は、 20:単打, 21:二塁打, 22:三塁打, 23:本塁打\n",
"\n",
"sns.stripplot(x='pa_event_cd', y='start_speed', data=votto_pitchfx_2015_hits, hue='pitch_type', split=True, jitter=True)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'ichiro_pitchfx_2016_hits' 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-39-3e23932a4401>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 同じ条件で2016年も見る\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstripplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pitch_type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'start_speed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0michiro_pitchfx_2016_hits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pa_event_cd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'ichiro_pitchfx_2016_hits' is not defined"
]
}
],
"source": [
"# 同じ条件で2016年も見る\n",
"sns.stripplot(x='pitch_type', y='start_speed', data=ichiro_pitchfx_2016_hits, hue='pa_event_cd', split=True, jitter=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Unnamed: 0.1</th>\n",
" <th>retro_game_id</th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>st_fl</th>\n",
" <th>regseason_fl</th>\n",
" <th>playoff_fl</th>\n",
" <th>game_type</th>\n",
" <th>game_type_des</th>\n",
" <th>local_game_time</th>\n",
" <th>game_id</th>\n",
" <th>home_team_id</th>\n",
" <th>away_team_id</th>\n",
" <th>home_team_lg</th>\n",
" <th>away_team_lg</th>\n",
" <th>interleague_fl</th>\n",
" <th>park_id</th>\n",
" <th>park_name</th>\n",
" <th>park_location</th>\n",
" <th>inning_number</th>\n",
" <th>bat_home_id</th>\n",
" <th>outs_ct</th>\n",
" <th>pit_mlbid</th>\n",
" <th>pit_first_name</th>\n",
" <th>pit_last_name</th>\n",
" <th>pit_box_name</th>\n",
" <th>pit_hand_cd</th>\n",
" <th>bat_mlbid</th>\n",
" <th>bat_first_name</th>\n",
" <th>bat_last_name</th>\n",
" <th>bat_box_name</th>\n",
" <th>bat_hand_cd</th>\n",
" <th>ab_number</th>\n",
" <th>start_bases</th>\n",
" <th>end_bases</th>\n",
" <th>event_outs_ct</th>\n",
" <th>pa_ball_ct</th>\n",
" <th>pa_strike_ct</th>\n",
" <th>pitch_seq</th>\n",
" <th>pa_terminal_fl</th>\n",
" <th>pa_event_cd</th>\n",
" <th>pitch_res</th>\n",
" <th>pitch_des</th>\n",
" <th>pitch_id</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>start_speed</th>\n",
" <th>end_speed</th>\n",
" <th>sz_top</th>\n",
" <th>sz_bot</th>\n",
" <th>pfx_x</th>\n",
" <th>pfx_z</th>\n",
" <th>px</th>\n",
" <th>pz</th>\n",
" <th>x0</th>\n",
" <th>y0</th>\n",
" <th>z0</th>\n",
" <th>vx0</th>\n",
" <th>vy0</th>\n",
" <th>vz0</th>\n",
" <th>ax</th>\n",
" <th>ay</th>\n",
" <th>az</th>\n",
" <th>break_y</th>\n",
" <th>break_angle</th>\n",
" <th>break_length</th>\n",
" <th>pitch_type</th>\n",
" <th>pitch_type_seq</th>\n",
" <th>type_confidence</th>\n",
" <th>zone</th>\n",
" <th>spin_dir</th>\n",
" <th>spin_rate</th>\n",
" <th>sv_id</th>\n",
" <th>game_day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>723</th>\n",
" <td>362869</td>\n",
" <td>2904</td>\n",
" <td>ATL201607020</td>\n",
" <td>2016</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>F</td>\n",
" <td>T</td>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>Regular Season</td>\n",
" <td>16:10</td>\n",
" <td>448081</td>\n",
" <td>atl</td>\n",
" <td>mia</td>\n",
" <td>NL</td>\n",
" <td>NL</td>\n",
" <td>F</td>\n",
" <td>16</td>\n",
" <td>Turner Field</td>\n",
" <td>Atlanta, GA</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>449173</td>\n",
" <td>Lucas</td>\n",
" <td>Harrell</td>\n",
" <td>Harrell, L</td>\n",
" <td>R</td>\n",
" <td>400085</td>\n",
" <td>Ichiro</td>\n",
" <td>Suzuki</td>\n",
" <td>Suzuki, I</td>\n",
" <td>L</td>\n",
" <td>19</td>\n",
" <td>_2_</td>\n",
" <td>__3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>SSBBX</td>\n",
" <td>T</td>\n",
" <td>22</td>\n",
" <td>X</td>\n",
" <td>In play, run(s)</td>\n",
" <td>144</td>\n",
" <td>140.98</td>\n",
" <td>179.89</td>\n",
" <td>82.8</td>\n",
" <td>77.4</td>\n",
" <td>3.39</td>\n",
" <td>1.46</td>\n",
" <td>-8.00</td>\n",
" <td>6.65</td>\n",
" <td>-0.629</td>\n",
" <td>2.181</td>\n",
" <td>-2.433</td>\n",
" <td>50.0</td>\n",
" <td>6.030</td>\n",
" <td>6.846</td>\n",
" <td>-121.087</td>\n",
" <td>-4.670</td>\n",
" <td>-12.064</td>\n",
" <td>20.291</td>\n",
" <td>-22.077</td>\n",
" <td>23.9</td>\n",
" <td>25.7</td>\n",
" <td>6.8</td>\n",
" <td>CH</td>\n",
" <td>FT|SL|CU|FT|CH</td>\n",
" <td>0.905</td>\n",
" <td>4.0</td>\n",
" <td>230.071</td>\n",
" <td>1884.877</td>\n",
" <td>160702_164701</td>\n",
" <td>2016-07-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>946</th>\n",
" <td>493517</td>\n",
" <td>2256</td>\n",
" <td>COL201608070</td>\n",
" <td>2016</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>F</td>\n",
" <td>T</td>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>Regular Season</td>\n",
" <td>14:10</td>\n",
" <td>448525</td>\n",
" <td>col</td>\n",
" <td>mia</td>\n",
" <td>NL</td>\n",
" <td>NL</td>\n",
" <td>F</td>\n",
" <td>19</td>\n",
" <td>Coors Field</td>\n",
" <td>Denver, CO</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>543734</td>\n",
" <td>Chris</td>\n",
" <td>Rusin</td>\n",
" <td>Rusin</td>\n",
" <td>L</td>\n",
" <td>400085</td>\n",
" <td>Ichiro</td>\n",
" <td>Suzuki</td>\n",
" <td>Suzuki, I</td>\n",
" <td>L</td>\n",
" <td>60</td>\n",
" <td>___</td>\n",
" <td>__3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>BBX</td>\n",
" <td>T</td>\n",
" <td>22</td>\n",
" <td>X</td>\n",
" <td>In play, no out</td>\n",
" <td>499</td>\n",
" <td>116.73</td>\n",
" <td>175.49</td>\n",
" <td>86.1</td>\n",
" <td>80.8</td>\n",
" <td>3.39</td>\n",
" <td>1.46</td>\n",
" <td>2.86</td>\n",
" <td>2.38</td>\n",
" <td>0.007</td>\n",
" <td>2.344</td>\n",
" <td>2.015</td>\n",
" <td>50.0</td>\n",
" <td>5.919</td>\n",
" <td>-5.971</td>\n",
" <td>-126.155</td>\n",
" <td>-3.343</td>\n",
" <td>4.691</td>\n",
" <td>21.671</td>\n",
" <td>-28.199</td>\n",
" <td>23.9</td>\n",
" <td>-7.4</td>\n",
" <td>7.1</td>\n",
" <td>FC</td>\n",
" <td>FC|SI|FC</td>\n",
" <td>0.894</td>\n",
" <td>5.0</td>\n",
" <td>130.278</td>\n",
" <td>707.097</td>\n",
" <td>160807_163801</td>\n",
" <td>2016-08-07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 0.1 retro_game_id year month day st_fl \\\n",
"723 362869 2904 ATL201607020 2016 7 2 F \n",
"946 493517 2256 COL201608070 2016 8 7 F \n",
"\n",
" regseason_fl playoff_fl game_type game_type_des local_game_time \\\n",
"723 T F R Regular Season 16:10 \n",
"946 T F R Regular Season 14:10 \n",
"\n",
" game_id home_team_id away_team_id home_team_lg away_team_lg \\\n",
"723 448081 atl mia NL NL \n",
"946 448525 col mia NL NL \n",
"\n",
" interleague_fl park_id park_name park_location inning_number \\\n",
"723 F 16 Turner Field Atlanta, GA 3 \n",
"946 F 19 Coors Field Denver, CO 7 \n",
"\n",
" bat_home_id outs_ct pit_mlbid pit_first_name pit_last_name \\\n",
"723 0 2 449173 Lucas Harrell \n",
"946 0 1 543734 Chris Rusin \n",
"\n",
" pit_box_name pit_hand_cd bat_mlbid bat_first_name bat_last_name \\\n",
"723 Harrell, L R 400085 Ichiro Suzuki \n",
"946 Rusin L 400085 Ichiro Suzuki \n",
"\n",
" bat_box_name bat_hand_cd ab_number start_bases end_bases event_outs_ct \\\n",
"723 Suzuki, I L 19 _2_ __3 2 \n",
"946 Suzuki, I L 60 ___ __3 1 \n",
"\n",
" pa_ball_ct pa_strike_ct pitch_seq pa_terminal_fl pa_event_cd pitch_res \\\n",
"723 2 2 SSBBX T 22 X \n",
"946 2 0 BBX T 22 X \n",
"\n",
" pitch_des pitch_id x y start_speed end_speed \\\n",
"723 In play, run(s) 144 140.98 179.89 82.8 77.4 \n",
"946 In play, no out 499 116.73 175.49 86.1 80.8 \n",
"\n",
" sz_top sz_bot pfx_x pfx_z px pz x0 y0 z0 vx0 \\\n",
"723 3.39 1.46 -8.00 6.65 -0.629 2.181 -2.433 50.0 6.030 6.846 \n",
"946 3.39 1.46 2.86 2.38 0.007 2.344 2.015 50.0 5.919 -5.971 \n",
"\n",
" vy0 vz0 ax ay az break_y break_angle \\\n",
"723 -121.087 -4.670 -12.064 20.291 -22.077 23.9 25.7 \n",
"946 -126.155 -3.343 4.691 21.671 -28.199 23.9 -7.4 \n",
"\n",
" break_length pitch_type pitch_type_seq type_confidence zone spin_dir \\\n",
"723 6.8 CH FT|SL|CU|FT|CH 0.905 4.0 230.071 \n",
"946 7.1 FC FC|SI|FC 0.894 5.0 130.278 \n",
"\n",
" spin_rate sv_id game_day \n",
"723 1884.877 160702_164701 2016-07-02 \n",
"946 707.097 160807_163801 2016-08-07 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2016年の三塁打はCH(チェンジアップ)とFC(Cut Fastball)を打ち込んでるっぽい、興味深いので見てみる\n",
"ichiro_pitchfx_2016_hits.query('pa_event_cd == 22')"
]
},
{
"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": 1
}
| mit |
ElReySol/UniPrime | PrimeInvestigation00.ipynb | 1 | 20801 | {
"metadata": {
"name": "",
"signature": "sha256:02508117e2889dfd5b1b5c7dc668498b0d0343fc934de63206bb0abe7e78423c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Investigations into the frequency and occurence of prime numbers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following program requires a text file named primes_list.txt to be in the same directory as the .ipynb file which in this case is named PrimeInvestigation.ipynb. The file primes_list.txt must be a list of consecutive prime numbers with no space or punctuation separated by a carriage return after each number. It is fine to start with 2 as the first line and nothing else.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Creates a list of integers from numbers in file primes_list.txt \n",
"with open(\"./primes_list.txt\", \"r\") as f:\n",
" lines = f.readlines()\n",
"\n",
"prime = [int(e.strip()) for e in lines]\n",
"\n",
"def test_prime(candidate):\n",
" \"\"\"\n",
" Takes an argument which must be a positive integer and returns True \n",
" if it is prime False if it isn't. Will only work on numbers not more than\n",
" twice the size of the largest prime in primes_list.txt \n",
" \"\"\" \n",
" for i in prime:\n",
" #The number is not prime if it is evenly divided.\n",
" if candidate % i == 0:\n",
" return False\n",
" break\n",
" #If the number is not divisible by any prime number less than half it's size it is prime.\n",
" #Add it to the list of prime numbers.\n",
" elif i > candidate / 2:\n",
" write_prime(candidate) \n",
" return True\n",
"\n",
" \n",
"def write_prime(candidate):\n",
" \"\"\"\n",
" Appends the value of the variable candidate to the list called prime.\n",
" \"\"\"\n",
" prime.append(candidate) \n",
"\n",
"def find_prime(hmny_more):\n",
" \"\"\"\n",
" Takes an argument which should be an int. It takes the last element of the list prime and adds 1 to\n",
" it and passes it to the function test_prime(candidate). It then adds one and repeats as many times as \n",
" the integer argument.\n",
" \"\"\"\n",
" print 'Welcome to Prime Finder! The largest prime catalogued so far is ', prime[-1]\n",
" candidate = prime[-1]\n",
" for i in range(hmny_more):\n",
" candidate += 1\n",
" test_prime(candidate)\n",
" return \"Done. The largest prime catalogued on this run was\", prime[-1]\n",
" \n",
"def frequencyPrime(frequency):\n",
" count = 0\n",
" hundreds = frequency\n",
"\n",
" for i in prime:\n",
" compare = int(i)\n",
" if compare < hundreds:\n",
" count += 1\n",
" #print i\n",
" else:\n",
" print hundreds - frequency, \"-\", hundreds, \":\", count\n",
" hundreds += frequency\n",
" count = 0\n",
"\n",
" \n",
"#number = int(raw_input('How many more numbers would you like to check for primacy?: '))\n",
"#number = 100000\n",
"#find_prime(number)\n",
"#print prime\n",
"\n",
"with open(\"./primes_list.txt\", 'w') as f:\n",
" f.write(\"\\n\".join(map(str,prime)))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"find_prime(1000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Welcome to Prime Finder! The largest prime catalogued so far is 2\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"('Done. The largest prime catalogued on this run was ', 997)"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"prime"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"[2,\n",
" 3,\n",
" 5,\n",
" 7,\n",
" 11,\n",
" 13,\n",
" 17,\n",
" 19,\n",
" 23,\n",
" 29,\n",
" 31,\n",
" 37,\n",
" 41,\n",
" 43,\n",
" 47,\n",
" 53,\n",
" 59,\n",
" 61,\n",
" 67,\n",
" 71,\n",
" 73,\n",
" 79,\n",
" 83,\n",
" 89,\n",
" 97,\n",
" 101,\n",
" 103,\n",
" 107,\n",
" 109,\n",
" 113,\n",
" 127,\n",
" 131,\n",
" 137,\n",
" 139,\n",
" 149,\n",
" 151,\n",
" 157,\n",
" 163,\n",
" 167,\n",
" 173,\n",
" 179,\n",
" 181,\n",
" 191,\n",
" 193,\n",
" 197,\n",
" 199,\n",
" 211,\n",
" 223,\n",
" 227,\n",
" 229,\n",
" 233,\n",
" 239,\n",
" 241,\n",
" 251,\n",
" 257,\n",
" 263,\n",
" 269,\n",
" 271,\n",
" 277,\n",
" 281,\n",
" 283,\n",
" 293,\n",
" 307,\n",
" 311,\n",
" 313,\n",
" 317,\n",
" 331,\n",
" 337,\n",
" 347,\n",
" 349,\n",
" 353,\n",
" 359,\n",
" 367,\n",
" 373,\n",
" 379,\n",
" 383,\n",
" 389,\n",
" 397,\n",
" 401,\n",
" 409,\n",
" 419,\n",
" 421,\n",
" 431,\n",
" 433,\n",
" 439,\n",
" 443,\n",
" 449,\n",
" 457,\n",
" 461,\n",
" 463,\n",
" 467,\n",
" 479,\n",
" 487,\n",
" 491,\n",
" 499,\n",
" 503,\n",
" 509,\n",
" 521,\n",
" 523,\n",
" 541,\n",
" 547,\n",
" 557,\n",
" 563,\n",
" 569,\n",
" 571,\n",
" 577,\n",
" 587,\n",
" 593,\n",
" 599,\n",
" 601,\n",
" 607,\n",
" 613,\n",
" 617,\n",
" 619,\n",
" 631,\n",
" 641,\n",
" 643,\n",
" 647,\n",
" 653,\n",
" 659,\n",
" 661,\n",
" 673,\n",
" 677,\n",
" 683,\n",
" 691,\n",
" 701,\n",
" 709,\n",
" 719,\n",
" 727,\n",
" 733,\n",
" 739,\n",
" 743,\n",
" 751,\n",
" 757,\n",
" 761,\n",
" 769,\n",
" 773,\n",
" 787,\n",
" 797,\n",
" 809,\n",
" 811,\n",
" 821,\n",
" 823,\n",
" 827,\n",
" 829,\n",
" 839,\n",
" 853,\n",
" 857,\n",
" 859,\n",
" 863,\n",
" 877,\n",
" 881,\n",
" 883,\n",
" 887,\n",
" 907,\n",
" 911,\n",
" 919,\n",
" 929,\n",
" 937,\n",
" 941,\n",
" 947,\n",
" 953,\n",
" 967,\n",
" 971,\n",
" 977,\n",
" 983,\n",
" 991,\n",
" 997]"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"count = 0\n",
"hundreds = 1000\n",
"\n",
"for i in prime:\n",
" compare = int(i)\n",
" if compare < hundreds:\n",
" count += 1\n",
" #print i\n",
" else:\n",
" print hundreds - 1000, \"-\", hundreds, \":\", count\n",
" hundreds += 1000\n",
" count = 0"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 - 1000 : 168\n",
"1000 - 2000 : 134\n",
"2000 - 3000 : 126\n",
"3000 - 4000 : 119\n",
"4000 - 5000 : 118\n",
"5000 - 6000 : 113\n",
"6000 - 7000 : 116\n",
"7000 - 8000 : 106\n",
"8000 - 9000 : 109\n",
"9000 - 10000 : 111\n",
"10000 - 11000 : 105\n",
"11000 - 12000 : 102\n",
"12000 - 13000 : 108\n",
"13000 - 14000 : 104\n",
"14000 - 15000 : 101\n",
"15000 - 16000 : 107\n",
"16000 - 17000 : 97\n",
"17000 - 18000 : 103\n",
"18000 - 19000 : 93\n",
"19000 - 20000 : 103\n",
"20000 - 21000 : 97\n",
"21000 - 22000 : 103\n",
"22000 - 23000 : 99\n",
"23000 - 24000 : 103\n",
"24000 - 25000 : 93\n",
"25000 - 26000 : 97\n",
"26000 - 27000 : 100\n",
"27000 - 28000 : 93\n",
"28000 - 29000 : 97\n",
"29000 - 30000 : 91\n",
"30000 - 31000 : 94\n",
"31000 - 32000 : 91\n",
"32000 - 33000 : 105\n",
"33000 - 34000 : 99\n",
"34000 - 35000 : 93\n",
"35000 - 36000 : 91\n",
"36000 - 37000 : 98\n",
"37000 - 38000 : 93\n",
"38000 - 39000 : 89\n",
"39000 - 40000 : 95\n",
"40000 - 41000 : 87\n",
"41000 - 42000 : 100\n",
"42000 - 43000 : 101\n",
"43000 - 44000 : 84\n",
"44000 - 45000 : 95\n",
"45000 - 46000 : 85\n",
"46000 - 47000 : 89\n",
"47000 - 48000 : 94\n",
"48000 - 49000 : 88\n",
"49000 - 50000 : 97\n",
"50000 - 51000 : 88\n",
"51000 - 52000 : 96\n",
"52000 - 53000 : 88\n",
"53000 - 54000 : 91\n",
"54000 - 55000 : 89\n",
"55000 - 56000 : 92\n",
"56000 - 57000 : 98\n",
"57000 - 58000 : 90\n",
"58000 - 59000 : 89\n",
"59000 - 60000 : 93\n",
"60000 - 61000 : 87\n",
"61000 - 62000 : 86\n",
"62000 - 63000 : 87\n",
"63000 - 64000 : 92\n",
"64000 - 65000 : 79\n",
"65000 - 66000 : 97\n",
"66000 - 67000 : 83\n",
"67000 - 68000 : 98\n",
"68000 - 69000 : 79\n",
"69000 - 70000 : 80\n",
"70000 - 71000 : 97\n",
"71000 - 72000 : 94\n",
"72000 - 73000 : 89\n",
"73000 - 74000 : 82\n",
"74000 - 75000 : 91\n",
"75000 - 76000 : 90\n",
"76000 - 77000 : 82\n",
"77000 - 78000 : 94\n",
"78000 - 79000 : 83\n",
"79000 - 80000 : 90\n",
"80000 - 81000 : 87\n",
"81000 - 82000 : 91\n",
"82000 - 83000 : 88\n",
"83000 - 84000 : 83\n",
"84000 - 85000 : 86\n",
"85000 - 86000 : 84\n",
"86000 - 87000 : 87\n",
"87000 - 88000 : 92\n",
"88000 - 89000 : 75\n",
"89000 - 90000 : 93\n",
"90000 - 91000 : 88\n",
"91000 - 92000 : 84\n",
"92000 - 93000 : 96\n",
"93000 - 94000 : 85\n",
"94000 - 95000 : 86\n",
"95000 - 96000 : 94\n",
"96000 - 97000 : 83\n",
"97000 - 98000 : 81\n",
"98000 - 99000 : 86\n",
"99000 - 100000 : 86\n",
"100000 - 101000 : 80\n",
"101000 - 102000 : 92\n",
"102000 - 103000 : 86\n",
"103000 - 104000 : 79\n",
"104000 - 105000 : 90\n",
"105000 - 106000 : 81\n",
"106000 - 107000 : 91\n",
"107000 - 108000 : 75\n",
"108000 - 109000 : 90\n",
"109000 - 110000 : 87\n",
"110000 - 111000 : 82\n",
"111000 - 112000 : 83\n",
"112000 - 113000 : 80\n",
"113000 - 114000 : 87\n",
"114000 - 115000 : 81\n",
"115000 - 116000 : 92\n",
"116000 - 117000 : 80\n",
"117000 - 118000 : 89\n",
"118000 - 119000 : 78\n",
"119000 - 120000 : 86\n",
"120000 - 121000 : 87\n",
"121000 - 122000 : 85\n",
"122000 - 123000 : 87\n",
"123000 - 124000 : 87\n",
"124000 - 125000 : 82\n",
"125000 - 126000 : 83\n",
"126000 - 127000 : 82\n",
"127000 - 128000 : 85\n",
"128000 - 129000 : 88\n",
"129000 - 130000 : 82\n",
"130000 - 131000 : 84\n",
"131000 - 132000 : 82\n",
"132000 - 133000 : 86\n",
"133000 - 134000 : 81\n",
"134000 - 135000 : 79\n",
"135000 - 136000 : 88\n",
"136000 - 137000 : 95\n",
"137000 - 138000 : 79\n",
"138000 - 139000 : 84\n",
"139000 - 140000 : 83\n",
"140000 - 141000 : 86\n",
"141000 - 142000 : 86\n",
"142000 - 143000 : 81\n",
"143000 - 144000 : 76\n",
"144000 - 145000 : 78\n",
"145000 - 146000 : 84\n",
"146000 - 147000 : 83\n",
"147000 - 148000 : 82\n",
"148000 - 149000 : 82\n",
"149000 - 150000 : 90\n",
"150000 - 151000 : 84\n",
"151000 - 152000 : 89\n",
"152000 - 153000 : 87\n",
"153000 - 154000 : 76\n",
"154000 - 155000 : 83\n",
"155000 - 156000 : 84\n",
"156000 - 157000 : 75\n",
"157000 - 158000 : 87\n",
"158000 - 159000 : 76\n",
"159000 - 160000 : 84\n",
"160000 - 161000 : 84\n",
"161000 - 162000 : 83\n",
"162000 - 163000 : 80\n",
"163000 - 164000 : 82\n",
"164000 - 165000 : 76\n",
"165000 - 166000 : 79\n",
"166000 - 167000 : 80\n",
"167000 - 168000 : 82\n",
"168000 - 169000 : 72\n",
"169000 - 170000 : 86\n",
"170000 - 171000 : 86\n",
"171000 - 172000 : 80\n",
"172000 - 173000 : 88\n",
"173000 - 174000 : 78\n",
"174000 - 175000 : 82\n",
"175000 - 176000 : 74\n",
"176000 - 177000 : 94\n",
"177000 - 178000 : 72\n",
"178000 - 179000 : 88\n",
"179000 - 180000 : 93\n",
"180000 - 181000 : 70\n",
"181000 - 182000 : 78\n",
"182000 - 183000 : 90\n",
"183000 - 184000 : 78\n",
"184000 - 185000 : 82\n",
"185000 - 186000 : 90\n",
"186000 - 187000 : 78\n",
"187000 - 188000 : 86\n",
"188000 - 189000 : 79\n",
"189000 - 190000 : 87\n",
"190000 - 191000 : 74\n",
"191000 - 192000 : 80\n",
"192000 - 193000 : 88\n",
"193000 - 194000 : 83\n",
"194000 - 195000 : 73\n",
"195000 - 196000 : 84\n",
"196000 - 197000 : 75\n",
"197000 - 198000 : 86\n",
"198000 - 199000 : 85\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"count = 0\n",
"hundreds = 10000\n",
"\n",
"for i in prime:\n",
" compare = int(i)\n",
" if compare < hundreds:\n",
" count += 1\n",
" #print i\n",
" else:\n",
" print hundreds - 10000, \"-\", hundreds, \":\", count\n",
" hundreds += 10000\n",
" count = 0"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 - 10000 : 1229\n",
"10000 - 20000 : 1032\n",
"20000 - 30000 : 982\n",
"30000 - 40000 : 957\n",
"40000 - 50000 : 929\n",
"50000 - 60000 : 923\n",
"60000 - 70000 : 877\n",
"70000 - 80000 : 901\n",
"80000 - 90000 : 875\n",
"90000 - 100000 : 878\n",
"100000 - 110000 : 860\n",
"110000 - 120000 : 847\n",
"120000 - 130000 : 857\n",
"130000 - 140000 : 850\n",
"140000 - 150000 : 837\n",
"150000 - 160000 : 834\n",
"160000 - 170000 : 813\n",
"170000 - 180000 : 844\n",
"180000 - 190000 : 827\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"frequencyPrime(2000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 - 2000 : 303\n",
"2000 - 4000 : 246\n",
"4000 - 6000 : 232\n",
"6000 - 8000 : 223\n",
"8000 - 10000 : 221\n",
"10000 - 12000 : 208\n",
"12000 - 14000 : 213\n",
"14000 - 16000 : 209\n",
"16000 - 18000 : 201\n",
"18000 - 20000 : 197\n",
"20000 - 22000 : 201\n",
"22000 - 24000 : 203\n",
"24000 - 26000 : 191\n",
"26000 - 28000 : 194\n",
"28000 - 30000 : 189\n",
"30000 - 32000 : 186\n",
"32000 - 34000 : 205\n",
"34000 - 36000 : 185\n",
"36000 - 38000 : 192\n",
"38000 - 40000 : 185\n",
"40000 - 42000 : 188\n",
"42000 - 44000 : 186\n",
"44000 - 46000 : 181\n",
"46000 - 48000 : 184\n",
"48000 - 50000 : 186\n",
"50000 - 52000 : 185\n",
"52000 - 54000 : 180\n",
"54000 - 56000 : 182\n",
"56000 - 58000 : 189\n",
"58000 - 60000 : 183\n",
"60000 - 62000 : 174\n",
"62000 - 64000 : 180\n",
"64000 - 66000 : 177\n",
"66000 - 68000 : 182\n",
"68000 - 70000 : 160\n",
"70000 - 72000 : 192\n",
"72000 - 74000 : 172\n",
"74000 - 76000 : 182\n",
"76000 - 78000 : 177\n",
"78000 - 80000 : 174\n",
"80000 - 82000 : 179\n",
"82000 - 84000 : 172\n",
"84000 - 86000 : 171\n",
"86000 - 88000 : 180\n",
"88000 - 90000 : 169\n",
"90000 - 92000 : 173\n",
"92000 - 94000 : 182\n",
"94000 - 96000 : 181\n",
"96000 - 98000 : 165\n",
"98000 - 100000 : 173\n",
"100000 - 102000 : 173\n",
"102000 - 104000 : 166\n",
"104000 - 106000 : 172\n",
"106000 - 108000 : 167\n",
"108000 - 110000 : 178\n",
"110000 - 112000 : 166\n",
"112000 - 114000 : 168\n",
"114000 - 116000 : 174\n",
"116000 - 118000 : 170\n",
"118000 - 120000 : 165\n",
"120000 - 122000 : 173\n",
"122000 - 124000 : 175\n",
"124000 - 126000 : 166\n",
"126000 - 128000 : 168\n",
"128000 - 130000 : 171\n",
"130000 - 132000 : 167\n",
"132000 - 134000 : 168\n",
"134000 - 136000 : 168\n",
"136000 - 138000 : 175\n",
"138000 - 140000 : 168\n",
"140000 - 142000 : 173\n",
"142000 - 144000 : 158\n",
"144000 - 146000 : 163\n",
"146000 - 148000 : 166\n",
"148000 - 150000 : 173\n",
"150000 - 152000 : 174\n",
"152000 - 154000 : 164\n",
"154000 - 156000 : 168\n",
"156000 - 158000 : 163\n",
"158000 - 160000 : 161\n",
"160000 - 162000 : 168\n",
"162000 - 164000 : 163\n",
"164000 - 166000 : 156\n",
"166000 - 168000 : 163\n",
"168000 - 170000 : 159\n",
"170000 - 172000 : 167\n",
"172000 - 174000 : 167\n",
"174000 - 176000 : 157\n",
"176000 - 178000 : 167\n",
"178000 - 180000 : 182\n",
"180000 - 182000 : 149\n",
"182000 - 184000 : 169\n",
"184000 - 186000 : 173\n",
"186000 - 188000 : 165\n",
"188000 - 190000 : 167\n",
"190000 - 192000 : 155\n",
"192000 - 194000 : 172\n",
"194000 - 196000 : 158\n",
"196000 - 198000 : 162\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
coolharsh55/advent-of-code | 2016/python3/Day06.ipynb | 1 | 6781 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 6: Signals and Noise\n",
"\n",
"author: Harshvardhan Pandit\n",
"\n",
"license: [MIT](https://opensource.org/licenses/MIT)\n",
"\n",
"[link to problem statement](http://adventofcode.com/2016/day/6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Something is jamming your communications with Santa. Fortunately, your signal is only partially jammed, and protocol in situations like this is to switch to a [simple repetition](https://en.wikipedia.org/wiki/Repetition_code) code to get the message through.\n",
"\n",
"In this model, the same message is sent repeatedly. You've recorded the repeating message signal (your puzzle input), but the data seems quite corrupted - almost too badly to recover. Almost.\n",
"\n",
"All you need to do is figure out which character is most frequent for each position. For example, suppose you had recorded the following messages:\n",
"\n",
" eedadn\n",
" drvtee\n",
" eandsr\n",
" raavrd\n",
" atevrs\n",
" tsrnev\n",
" sdttsa\n",
" rasrtv\n",
" nssdts\n",
" ntnada\n",
" svetve\n",
" tesnvt\n",
" vntsnd\n",
" vrdear\n",
" dvrsen\n",
" enarar\n",
" \n",
"The most common character in the first column is `e`; in the second, `a`; in the third, `s`, and so on. Combining these characters returns the error-corrected message, `easter`.\n",
"\n",
"Given the recording in your puzzle input, what is the error-corrected version of the message being sent?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution logic\n",
"So all we need to do here is to keep track of which characters are the most frequent at the given position? Hah! Easy-peasy. We'll simply use the `Counter` from `collections`, which I've described in a previous puzzle. We need counters for each position in the input. We're making a few assumptions here:\n",
"\n",
" - all input lines are of the same length\n",
" - what happens in case of ties is not specified, so we will not deal with it\n",
"\n",
"To create the appropriate number of counters, we inspect the first line and then create as many counters as its length, and store them in a list. This way, we can match the specific index of the counter with the index in the string. To get the most common element from the counter, we use the `most_common(n)` method, which returns `n` most common elements (we want only 1).\n",
"\n",
"#### Algorithm\n",
" - Read input\n",
" - Create a list of counters of length as the first line of input\n",
" - For each line in input:\n",
" - For each character in input, add character to counter\n",
" - For each counter, get the most common character"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('../inputs/day06.txt', 'r') as f:\n",
" data = [line.strip() for line in f.readlines()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating counters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import Counter\n",
"counters = [Counter() for i in range(0, len(data[0]))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading characters from input, and counting them. To add an item to the counter, simply increment its value."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for line in data:\n",
" for index, char in enumerate(line):\n",
" counters[index][char] += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the most common element from each counter. Since the `most_common` method returns a list of tuples (key-value), we need to access the first element from each of them respectively. And finally join them together into a string to get the string."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"answer = ''.join([counter.most_common(1)[0][0] for counter in counters])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part Two\n",
"Of course, that would be the message - if you hadn't agreed to use a modified repetition code instead.\n",
"\n",
"In this modified code, the sender instead transmits what looks like random data, but for each character, the character they actually want to send is slightly less likely than the others. Even after signal-jamming noise, you can look at the letter distributions in each column and choose the least common letter to reconstruct the original message.\n",
"\n",
"In the above example, the least common character in the first column is `a`; in the second, `d`, and so on. Repeating this process for the remaining characters produces the original message, `advent`.\n",
"\n",
"Given the recording in your puzzle input and this new decoding methodology, what is the original message that Santa is trying to send?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution logic\n",
"Small change - instead of most common, we now need the least common characters from the counters. Thankfully, since the counter returns a nice sorted list in `most_common`, we can use that to get the least common element - it's the one that is last!!!\n",
"\n",
" characters = counter.most_common()[-1]\n",
"\n",
"That being done, we combine the characters back into a string to get the answer."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"answer = ''.join([counter.most_common()[-1][0] for counter in counters])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"== END =="
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
kevroy314/msl-iposition-pipeline | examples/archived_tests/MSL-Virtual Morris Water Maze TCP Client.ipynb | 1 | 5357 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
" Set up some parsing functions first.\n",
"'''\n",
"\n",
"import io\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"\n",
"# Helper function for decoding state information\n",
"def decode_state(state_message):\n",
" raise NotImplementedError\n",
" return None\n",
"\n",
"# Helper function for parsing messages\n",
"def parse_message(message):\n",
" # For image messages\n",
" if message[0:len(\"Image\")] == bytes(\"Image\", 'utf8'):\n",
" # There may be a cleaner way to do this conversion, but this works fast enough for comfort\n",
" return {'type':\"Image\", \n",
" 'value': cv2.cvtColor(plt.imread(io.BytesIO(message[len(\"Image\"):]), format='JPG'), cv2.COLOR_BGR2RGB)}\n",
" elif message[0:len(\"State\")] == bytes(\"State\", 'utf8'):\n",
" # If we receive state information, decode it\n",
" return {'type':\"State\", \n",
" 'value': decode_state(message[len(\"State\"):-1])}\n",
" else:\n",
" # Default messages are returned as-is (with type as None and value as message)\n",
" return {'type': None, \n",
" 'value': message}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"'''\n",
" Set up the communication client and begin. Keyboard Interrupt closes.\n",
"'''\n",
"\n",
"import socket\n",
"import select\n",
"import numpy as np\n",
"import time\n",
"import cv2\n",
"\n",
"# Plot inline\n",
"%matplotlib inline\n",
"# Set up OpenCV Window\n",
"cv2.startWindowThread()\n",
"\n",
"# TCP Socket Parameters\n",
"TCP_IP = '10.0.75.1'\n",
"TCP_PORT = 5005\n",
"# Data Buffer\n",
"BUFFER_SIZE = 1024\n",
"data = b''\n",
"\n",
"# End Message Token\n",
"END_TOKEN = \"<EOF>\"\n",
"\n",
"# Set up Socket\n",
"s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
"s.connect((TCP_IP, TCP_PORT))\n",
"s.setblocking(0)\n",
"\n",
"# Set Start Trial Option\n",
"s.send(bytes(\"PlayerPrefsStrial,Practice - Hills\" + END_TOKEN, 'utf8'))\n",
"\n",
"# Start Scene\n",
"s.send(bytes(\"Scene1\" + END_TOKEN, 'utf8'))\n",
"\n",
"# Request First Image Frame\n",
"# s.send(bytes(\"ImageRequest\" + END_TOKEN, 'utf8'))\n",
"\n",
"# Loop until keyboard interrupt, reading/requesting data\n",
"while True:\n",
" try:\n",
" # Wait for data and store in buffer\n",
" ready = select.select([s], [], [], 0)\n",
" if ready[0]:\n",
" try:\n",
" data += s.recv(BUFFER_SIZE)\n",
" except ConnectionResetError:\n",
" print(\"The connection was closed.\")\n",
" break\n",
" \n",
" # Check if buffer has a full message\n",
" if(bytes(END_TOKEN, 'utf8') in data):\n",
" # Decode the message and clear it from the buffer\n",
" idx = data.index(bytes(END_TOKEN, 'utf8'))\n",
" while idx != -1:\n",
" parsed_message = parse_message(data[0:idx])\n",
" \n",
" # If the message was an image, display it using OpenCV (faster than matplotlib)\n",
" if parsed_message['type'] == 'Image':\n",
" cv2.imshow('frame', parsed_message['value'])\n",
" cv2.waitKey(1)\n",
"\n",
" # Send a request for a new frame\n",
" s.send(bytes(\"KeyUP\" + END_TOKEN, 'utf8'))\n",
" # s.send(bytes(\"ImageRequest\" + END_TOKEN, 'utf8'))\n",
"\n",
" data = data[idx + len(END_TOKEN):-1]\n",
" if(data != ''):\n",
" try:\n",
" idx = str(data).index(END_TOKEN)\n",
" except ValueError:\n",
" break\n",
"\n",
" # Wait for a keyboard interrupt to stop the loop\n",
" except KeyboardInterrupt:\n",
" break\n",
"\n",
"# Close the socket and destroy the display window\n",
"s.close()\n",
"cv2.destroyAllWindows()\n",
"\n",
"# Confirm exit\n",
"print('Done')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
jamessdixon/Kaggle.HomeDepot | ProjectSearchRelevance.Python/Home Depot Product Search Relevance Polynomial.ipynb | 2 | 51472 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Home Depot Product Search Relevance\n",
"The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LabGraph Create\n",
"This notebook uses the LabGraph create machine learning iPython module. You need a personal licence to run this code."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import graphlab as gl\n",
"from nltk.stem import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load data from CSV files"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] This non-commercial license of GraphLab Create is assigned to [email protected] and will expire on October 12, 2016. For commercial licensing options, visit https://dato.com/buy/.\n",
"\n",
"[INFO] Start server at: ipc:///tmp/graphlab_server-34069 - Server binary: /Users/tjaskula/.graphlab/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1455056183.log\n",
"[INFO] GraphLab Server Version: 1.8.1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Finished parsing file /Users/tjaskula/Documents/GitHub/Kaggle.HomeDepot/data/train.csv\n",
"PROGRESS: Parsing completed. Parsed 100 lines in 0.123565 secs.\n",
"------------------------------------------------------\n",
"Inferred types from first line of file as \n",
"column_type_hints=[int,int,str,str,float]\n",
"If parsing fails due to incorrect types, you can correct\n",
"the inferred type list above and pass it to read_csv in\n",
"the column_type_hints argument\n",
"------------------------------------------------------\n",
"PROGRESS: Finished parsing file /Users/tjaskula/Documents/GitHub/Kaggle.HomeDepot/data/train.csv\n",
"PROGRESS: Parsing completed. Parsed 74067 lines in 0.1662 secs.\n"
]
}
],
"source": [
"train = gl.SFrame.read_csv(\"../data/train.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Finished parsing file /Users/tjaskula/Documents/GitHub/Kaggle.HomeDepot/data/test.csv\n",
"PROGRESS: Parsing completed. Parsed 100 lines in 0.210436 secs.\n",
"------------------------------------------------------\n",
"Inferred types from first line of file as \n",
"column_type_hints=[int,int,str,str]\n",
"If parsing fails due to incorrect types, you can correct\n",
"the inferred type list above and pass it to read_csv in\n",
"the column_type_hints argument\n",
"------------------------------------------------------\n",
"PROGRESS: Finished parsing file /Users/tjaskula/Documents/GitHub/Kaggle.HomeDepot/data/test.csv\n",
"PROGRESS: Parsing completed. Parsed 166693 lines in 0.321425 secs.\n"
]
}
],
"source": [
"test = gl.SFrame.read_csv(\"../data/test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Finished parsing file /Users/tjaskula/Documents/GitHub/Kaggle.HomeDepot/data/product_descriptions.csv\n",
"PROGRESS: Parsing completed. Parsed 100 lines in 0.512102 secs.\n",
"------------------------------------------------------\n",
"Inferred types from first line of file as \n",
"column_type_hints=[int,str]\n",
"If parsing fails due to incorrect types, you can correct\n",
"the inferred type list above and pass it to read_csv in\n",
"the column_type_hints argument\n",
"------------------------------------------------------\n",
"PROGRESS: Read 61134 lines. Lines per second: 61129.8\n",
"PROGRESS: Finished parsing file /Users/tjaskula/Documents/GitHub/Kaggle.HomeDepot/data/product_descriptions.csv\n",
"PROGRESS: Parsing completed. Parsed 124428 lines in 1.5747 secs.\n"
]
}
],
"source": [
"desc = gl.SFrame.read_csv(\"../data/product_descriptions.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data merging"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# merge train with description\n",
"train = train.join(desc, on = 'product_uid', how = 'left')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# merge test with description\n",
"test = test.join(desc, on = 'product_uid', how = 'left')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Let's explore some data"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Let's examine 3 different queries and products:\n",
"* first from the training set\n",
"* somewhere in the moddle in the training set\n",
"* the last one from the training set"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 2,\n",
" 'product_description': 'Not only do angles make joints stronger, they also provide more consistent, straight corners. Simpson Strong-Tie offers a wide variety of angles in various sizes and thicknesses to handle light-duty jobs or projects where a structural connection is needed. Some can be bent (skewed) to match the project. For outdoor projects or those where moisture is present, use our ZMAX zinc-coated connectors, which provide extra resistance against corrosion (look for a \"Z\" at the end of the model number).Versatile connector for various 90 connections and home repair projectsStronger than angled nailing or screw fastening aloneHelp ensure joints are consistently straight and strongDimensions: 3 in. x 3 in. x 1-1/2 in.Made from 12-Gauge steelGalvanized for extra corrosion resistanceInstall with 10d common nails or #9 x 1-1/2 in. Strong-Drive SD screws',\n",
" 'product_title': 'Simpson Strong-Tie 12-Gauge Angle',\n",
" 'product_uid': 100001,\n",
" 'relevance': 3.0,\n",
" 'search_term': 'angle bracket'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first_doc = train[0]\n",
"first_doc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**'angle bracket'** search term is not contained in the body. **'angle'** would be after stemming however **'bracket'** is not."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 113228,\n",
" 'product_description': 'PureBond Plywood Project Panels are a convenient and cost-effective way to build cabinets, furniture and other woodworking projects. It provides a beautiful wood veneer face bonded to a strong and flat wood core. These PureBond Project Panels are made with no added formaldehyde, eliminating the concern about off-gassing dangerous fumes during fabrication or when installed in your home. Their smaller size makes them easy to handle and allows you to order just the amount of wood you need. PureBond plywood, in Project Panels sizes or in full sheet sizes, are a Home Depot exclusive.California residents: see Proposition 65 informationDecorative mahogany veneer applied to both sides of this panelB-2 plain sliced mahogany - 7-ply constructionLight weight, all-wood veneer constructionPrecision-cut hardwood plywood panels in convenient small sizesCommon: 3/4 in. x 2 ft. x 4 ft.; Actual: 0.703 in. x 24 in. x 48 in.Grade: B-2',\n",
" 'product_title': '3/4 in. x 2 ft. x 4 ft. PureBond Mahogany Plywood Project Panel',\n",
" 'product_uid': 137334,\n",
" 'relevance': 3.0,\n",
" 'search_term': 'table top wood'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"middle_doc = train[37033]\n",
"middle_doc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"only **'wood'** is present from search term"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 221473,\n",
" 'product_description': 'No. 918 Millennial Ryan heathered texture semi-sheer curtain is a casual solid that adds freshness and a finishing touch to any decor setting. Enhances privacy while allowing light to gently filter through. Clean, simple one-pocket pole top design can be used with a standard or decorative curtain rod. Mix and match with other solids and prints for a look that is all your own.Sheer panel, gently filters lightNo header pole top panelMachine washableWide array of colors to choose from100% polyesterContains 1-curtain panel',\n",
" 'product_title': 'LICHTENBERG Pool Blue No. 918 Millennial Ryan Heathered Texture Sheer Curtain Panel, 40 in. W x 63 in. L',\n",
" 'product_uid': 206650,\n",
" 'relevance': 2.33,\n",
" 'search_term': 'fine sheer curtain 63 inches'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"last_doc = train[-1]\n",
"last_doc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**'sheer'** and **'courtain'** are present and that's all"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How many search terms are not present in description and title for ranked 3 documents\n",
"Ranked 3 documents are the most relevents searches, but how many search queries doesn't include the searched term in the description and the title"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-----+-------------+-------------------------------+\n",
"| id | product_uid | product_title |\n",
"+-----+-------------+-------------------------------+\n",
"| 2 | 100001 | Simpson Strong-Tie 12-Gaug... |\n",
"| 9 | 100002 | BEHR Premium Textured Deck... |\n",
"| 18 | 100006 | Whirlpool 1.9 cu. ft. Over... |\n",
"| 21 | 100006 | Whirlpool 1.9 cu. ft. Over... |\n",
"| 27 | 100009 | House of Fara 3/4 in. x 3 ... |\n",
"| 35 | 100011 | Toro Personal Pace Recycle... |\n",
"| 37 | 100011 | Toro Personal Pace Recycle... |\n",
"| 65 | 100016 | Sunjoy Calais 8 ft. x 5 ft... |\n",
"| 123 | 100023 | Quikrete 80 lb. Crack-Resi... |\n",
"| 162 | 100029 | DecoArt Americana Decor 16... |\n",
"+-----+-------------+-------------------------------+\n",
"+--------------------------------+-----------+-------------------------------+\n",
"| search_term | relevance | product_description |\n",
"+--------------------------------+-----------+-------------------------------+\n",
"| angle bracket | 3.0 | Not only do angles make jo... |\n",
"| deck over | 3.0 | BEHR Premium Textured DECK... |\n",
"| convection otr | 3.0 | Achieving delicious result... |\n",
"| microwaves | 3.0 | Achieving delicious result... |\n",
"| mdf 3/4 | 3.0 | Get the House of Fara 3/4 ... |\n",
"| briggs and stratton lawn mower | 3.0 | Recycler 22 in. Personal P... |\n",
"| gas mowe | 3.0 | Recycler 22 in. Personal P... |\n",
"| grill gazebo | 3.0 | Make grilling great with t... |\n",
"| CONCRETE & MASONRY CLEANER... | 3.0 | Quikrete 80 lb. Crack-Resi... |\n",
"| chalk paint | 3.0 | Achieving a vintage, time-... |\n",
"+--------------------------------+-----------+-------------------------------+\n",
"+-------------------------------+\n",
"| search_term_word_count |\n",
"+-------------------------------+\n",
"| {'bracket': 1, 'angle': 1} |\n",
"| {'over': 1, 'deck': 1} |\n",
"| {'otr': 1, 'convection': 1} |\n",
"| {'microwaves': 1} |\n",
"| {'mdf': 1, '3/4': 1} |\n",
"| {'and': 1, 'stratton': 1, ... |\n",
"| {'gas': 1, 'mowe': 1} |\n",
"| {'grill': 1, 'gazebo': 1} |\n",
"| {'etcher': 1, 'cleaner': 1... |\n",
"| {'chalk': 1, 'paint': 1} |\n",
"+-------------------------------+\n",
"[10 rows x 7 columns]\n",
"\n"
]
},
{
"data": {
"text/plain": [
"19125"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['search_term_word_count'] = gl.text_analytics.count_words(train['search_term'])\n",
"ranked3doc = train[train['relevance'] == 3]\n",
"print ranked3doc.head()\n",
"len(ranked3doc)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"words_search = gl.text_analytics.tokenize(ranked3doc['search_term'], to_lower = True)\n",
"words_description = gl.text_analytics.tokenize(ranked3doc['product_description'], to_lower = True)\n",
"words_title = gl.text_analytics.tokenize(ranked3doc['product_title'], to_lower = True)\n",
"wordsdiff_desc = []\n",
"wordsdiff_title = []\n",
"puid = []\n",
"search_term = []\n",
"ws_count = []\n",
"ws_count_used_desc = []\n",
"ws_count_used_title = []\n",
"for item in xrange(len(ranked3doc)):\n",
" ws = words_search[item]\n",
" pd = words_description[item]\n",
" pt = words_title[item]\n",
" diff = set(ws) - set(pd)\n",
" if diff is None:\n",
" diff = 0\n",
" wordsdiff_desc.append(diff)\n",
" \n",
" diff2 = set(ws) - set(pt)\n",
" if diff2 is None:\n",
" diff2 = 0\n",
" wordsdiff_title.append(diff2)\n",
" \n",
" puid.append(ranked3doc[item]['product_uid'])\n",
" search_term.append(ranked3doc[item]['search_term'])\n",
" ws_count.append(len(ws))\n",
" ws_count_used_desc.append(len(ws) - len(diff))\n",
" ws_count_used_title.append(len(ws) - len(diff2))\n",
" \n",
"differences = gl.SFrame({\"puid\" : puid,\n",
" \"search term\": search_term,\n",
" \"diff desc\" : wordsdiff_desc,\n",
" \"diff title\" : wordsdiff_title,\n",
" \"ws count\" : ws_count, \n",
" \"ws count used desc\" : ws_count_used_desc,\n",
" \"ws count used title\" : ws_count_used_title})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">diff desc</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">diff title</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">puid</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">search term</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">ws count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">ws count used desc</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[recycling, bins]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[recycling, bins]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">145727</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">recycling bins</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[over, deck]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[over, deck]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">100002</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">deck over</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[hammer, electric, drill]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[hammer, electric, drill]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">120061</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">electric hammer drill</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[microwaves]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[microwaves]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">100006</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">microwaves</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[plywoods]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[plywoods]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">119996</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">plywoods</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[coca, cola]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[coca, cola]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">120276</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">coca cola</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[greenhouses]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[greenhouses]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">120318</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">greenhouses</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[pipe, cutters]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[pipe, cutters]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">119840</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">pipe cutters</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[buit, themostat, in]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[buit, themostat, in]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">206359</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">buit in themostat</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[mowers, ridding]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[mowers, ridding]</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">120366</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">ridding mowers</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">ws count used title</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
"</table>\n",
"[19125 rows x 7 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tdiff desc\tlist\n",
"\tdiff title\tlist\n",
"\tpuid\tint\n",
"\tsearch term\tstr\n",
"\tws count\tint\n",
"\tws count used desc\tint\n",
"\tws count used title\tint\n",
"\n",
"Rows: 19125\n",
"\n",
"Data:\n",
"+---------------------------+---------------------------+--------+\n",
"| diff desc | diff title | puid |\n",
"+---------------------------+---------------------------+--------+\n",
"| [recycling, bins] | [recycling, bins] | 145727 |\n",
"| [over, deck] | [over, deck] | 100002 |\n",
"| [hammer, electric, drill] | [hammer, electric, drill] | 120061 |\n",
"| [microwaves] | [microwaves] | 100006 |\n",
"| [plywoods] | [plywoods] | 119996 |\n",
"| [coca, cola] | [coca, cola] | 120276 |\n",
"| [greenhouses] | [greenhouses] | 120318 |\n",
"| [pipe, cutters] | [pipe, cutters] | 119840 |\n",
"| [buit, themostat, in] | [buit, themostat, in] | 206359 |\n",
"| [mowers, ridding] | [mowers, ridding] | 120366 |\n",
"+---------------------------+---------------------------+--------+\n",
"+-----------------------+----------+--------------------+---------------------+\n",
"| search term | ws count | ws count used desc | ws count used title |\n",
"+-----------------------+----------+--------------------+---------------------+\n",
"| recycling bins | 2 | 0 | 0 |\n",
"| deck over | 2 | 0 | 0 |\n",
"| electric hammer drill | 3 | 0 | 0 |\n",
"| microwaves | 1 | 0 | 0 |\n",
"| plywoods | 1 | 0 | 0 |\n",
"| coca cola | 2 | 0 | 0 |\n",
"| greenhouses | 1 | 0 | 0 |\n",
"| pipe cutters | 2 | 0 | 0 |\n",
"| buit in themostat | 3 | 0 | 0 |\n",
"| ridding mowers | 2 | 0 | 0 |\n",
"+-----------------------+----------+--------------------+---------------------+\n",
"[19125 rows x 7 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"differences.sort(['ws count used desc', 'ws count used title'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No terms used in description : 2666\n",
"No terms used in title : 2152\n",
"No terms used in description and title : 1206\n"
]
}
],
"source": [
"print \"No terms used in description : \" + str(len(differences[differences['ws count used desc'] == 0]))\n",
"print \"No terms used in title : \" + str(len(differences[differences['ws count used title'] == 0]))\n",
"print \"No terms used in description and title : \" + str(len(differences[(differences['ws count used desc'] == 0) & \n",
" (differences['ws count used title'] == 0)]))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stemming"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#stemmer = SnowballStemmer(\"english\")\n",
"stemmer = PorterStemmer()\n",
"def stem(word):\n",
" singles = [stemmer.stem(plural) for plural in unicode(word, errors='replace').split()]\n",
" text = ' '.join(singles)\n",
" return text"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting stemming train search term...\n",
"Starting stemming train product description...\n",
"Starting stemming train product title...\n",
"Starting stemming test search term...\n",
"Starting stemming test product description...\n",
"Starting stemming test product title...\n"
]
}
],
"source": [
"print \"Starting stemming train search term...\"\n",
"stemmed = train['search_term'].apply(stem)\n",
"train['stem_search_term'] = stemmed\n",
"\n",
"print \"Starting stemming train product description...\"\n",
"stemmed = train['product_description'].apply(stem)\n",
"train['stem_product_description'] = stemmed\n",
"\n",
"print \"Starting stemming train product title...\"\n",
"stemmed = train['product_title'].apply(stem)\n",
"train['stem_product_title'] = stemmed\n",
"\n",
"print \"Starting stemming test search term...\"\n",
"stemmed = test['search_term'].apply(stem)\n",
"test['stem_search_term'] = stemmed\n",
"\n",
"print \"Starting stemming test product description...\"\n",
"stemmed = test['product_description'].apply(stem)\n",
"test['stem_product_description'] = stemmed\n",
"\n",
"print \"Starting stemming test product title...\"\n",
"stemmed = test['product_title'].apply(stem)\n",
"test['stem_product_title'] = stemmed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TF-IDF with linear regression"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train['search_term_word_count'] = gl.text_analytics.count_words(train['stem_search_term'])\n",
"train_search_tfidf = gl.text_analytics.tf_idf(train['search_term_word_count'])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train['search_tfidf'] = train_search_tfidf"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train['product_desc_word_count'] = gl.text_analytics.count_words(train['stem_product_description'])\n",
"train_desc_tfidf = gl.text_analytics.tf_idf(train['product_desc_word_count'])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train['desc_tfidf'] = train_desc_tfidf"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train['product_title_word_count'] = gl.text_analytics.count_words(train['stem_product_title'])\n",
"train_title_tfidf = gl.text_analytics.tf_idf(train['product_title_word_count'])\n",
"train['title_tfidf'] = train_title_tfidf"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train['distance_desc'] = train.apply(lambda x: gl.distances.cosine(x['search_tfidf'],x['desc_tfidf']))\n",
"#train['distance_desc_sqrt'] = train['distance_desc'] ** 2\n",
"train['distance_title'] = train.apply(lambda x: gl.distances.cosine(x['search_tfidf'],x['title_tfidf']))\n",
"#train['distance_title_sqrt'] = train['distance_title'] ** 3"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Linear regression:\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: Number of examples : 74067\n",
"PROGRESS: Number of features : 2\n",
"PROGRESS: Number of unpacked features : 2\n",
"PROGRESS: Number of coefficients : 3\n",
"PROGRESS: Starting Newton Method\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: | Iteration | Passes | Elapsed Time | Training-max_error | Training-rmse |\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: | 1 | 2 | 0.054827 | 1.934252 | 0.502806 |\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: SUCCESS: Optimal solution found.\n",
"PROGRESS:\n"
]
}
],
"source": [
"model1 = gl.linear_regression.create(train, target = 'relevance', \n",
" features = ['distance_desc', 'distance_title'], \n",
" validation_set = None)\n",
"# model1 = gl.linear_regression.create(train, target = 'relevance', \n",
"# features = ['distance_desc', 'distance_desc_sqrt', 'distance_title', 'distance_title_sqrt'], \n",
"# validation_set = None)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.36098120617</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0130126162735</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">distance_desc</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.471115683671</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0175727545026</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">distance_title</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.792854603251</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0120369738892</td>\n",
" </tr>\n",
"</table>\n",
"[3 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\tindex\tstr\n",
"\tvalue\tfloat\n",
"\tstderr\tfloat\n",
"\n",
"Rows: 3\n",
"\n",
"Data:\n",
"+----------------+-------+-----------------+-----------------+\n",
"| name | index | value | stderr |\n",
"+----------------+-------+-----------------+-----------------+\n",
"| (intercept) | None | 3.36098120617 | 0.0130126162735 |\n",
"| distance_desc | None | -0.471115683671 | 0.0175727545026 |\n",
"| distance_title | None | -0.792854603251 | 0.0120369738892 |\n",
"+----------------+-------+-----------------+-----------------+\n",
"[3 rows x 4 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#let's take a look at the weights before we plot\n",
"model1.get(\"coefficients\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test['search_term_word_count'] = gl.text_analytics.count_words(test['stem_search_term'])\n",
"test_search_tfidf = gl.text_analytics.tf_idf(test['search_term_word_count'])\n",
"test['search_tfidf'] = test_search_tfidf\n",
"test['product_desc_word_count'] = gl.text_analytics.count_words(test['stem_product_description'])\n",
"test_desc_tfidf = gl.text_analytics.tf_idf(test['product_desc_word_count'])\n",
"test['desc_tfidf'] = test_desc_tfidf\n",
"test['product_title_word_count'] = gl.text_analytics.count_words(test['stem_product_title'])\n",
"test_title_tfidf = gl.text_analytics.tf_idf(test['product_title_word_count'])\n",
"test['title_tfidf'] = test_title_tfidf\n",
"\n",
"test['distance_desc'] = test.apply(lambda x: gl.distances.cosine(x['search_tfidf'],x['desc_tfidf']))\n",
"#test['distance_desc_sqrt'] = test['distance_desc'] ** 2\n",
"test['distance_title'] = test.apply(lambda x: gl.distances.cosine(x['search_tfidf'],x['title_tfidf']))\n",
"#test['distance_title_sqrt'] = test['distance_title'] ** 3"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"\\npredictions_test = model1.predict(test)\\ntest_errors = predictions_test - test['relevance']\\nRSS_test = sum(test_errors * test_errors)\\nprint RSS_test\\n\""
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"predictions_test = model1.predict(test)\n",
"test_errors = predictions_test - test['relevance']\n",
"RSS_test = sum(test_errors * test_errors)\n",
"print RSS_test\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: float\n",
"Rows: 166693\n",
"[2.1194586905641986, 2.097010919250315, 2.327318656459769, 2.3423416569379105, 2.291363750454904, 2.1292410028129387, 2.3700891659576886, 2.380158719246286, 2.1461522961232458, 2.6725514683935634, 2.4735444612741126, 2.3577916980297187, 2.527370985769263, 2.5464611241149497, 2.2433784781304618, 2.3610473142083634, 2.097010919250315, 2.615135319749975, 2.1428328384749085, 2.1832873644759365, 2.7538729574608336, 2.7335476206465064, 2.1493438560051157, 2.3267645430460764, 2.2512091489393167, 2.503199755290125, 2.097010919250315, 2.2981432458210644, 2.3803635622873522, 2.322596343031413, 2.519767096157715, 2.362660486577712, 2.1974497974380167, 2.309948689847278, 2.313598821940017, 2.341687147636327, 2.4205333515242975, 2.3366063448390766, 2.8853671419333744, 2.8633757709368384, 2.2552865952704444, 2.297532949563152, 2.165997301067405, 2.097010919250315, 2.4552670270468595, 2.3625494876731397, 2.5106462498135387, 2.6188007396757573, 2.61900376832135, 2.2454680169370245, 2.1036340833149754, 2.102527092843549, 2.122092869812211, 2.097010919250315, 2.097010919250315, 2.097010919250315, 2.097010919250315, 2.097010919250315, 2.103358463836085, 2.097010919250315, 2.097010919250315, 2.1267995136846127, 2.1269757879601405, 2.097010919250315, 2.4744936795651946, 2.4089294758879447, 2.360521564894908, 2.097010919250315, 2.097010919250315, 2.097010919250315, 2.097010919250315, 2.097010919250315, 2.097010919250315, 2.224736363810293, 2.097010919250315, 2.557408344984125, 2.243072745659523, 2.097010919250315, 2.097010919250315, 2.5501178083366147, 2.097010919250315, 2.480335363483051, 2.283461192815867, 2.283461192815867, 2.1872823298107584, 2.1683105172043, 2.3396260222904397, 2.387438457989694, 2.505582843823445, 2.418305094666935, 2.5793821608777128, 2.3349132621118036, 2.097010919250315, 2.5821088945080928, 2.3188142743790774, 2.270976568556923, 2.242832290300672, 2.4621407613916673, 2.338149998696164, 2.3557175413828784, ... ]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions_test = model1.predict(test)\n",
"predictions_test"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"submission = gl.SFrame(test['id'])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">relevance</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.11945869056</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.09701091925</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.32731865646</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.34234165694</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.29136375045</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.12924100281</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.37008916596</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.38015871925</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.14615229612</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.67255146839</td>\n",
" </tr>\n",
"</table>\n",
"[166693 rows x 2 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tid\tint\n",
"\trelevance\tfloat\n",
"\n",
"Rows: 166693\n",
"\n",
"Data:\n",
"+----+---------------+\n",
"| id | relevance |\n",
"+----+---------------+\n",
"| 1 | 2.11945869056 |\n",
"| 4 | 2.09701091925 |\n",
"| 5 | 2.32731865646 |\n",
"| 6 | 2.34234165694 |\n",
"| 7 | 2.29136375045 |\n",
"| 8 | 2.12924100281 |\n",
"| 10 | 2.37008916596 |\n",
"| 11 | 2.38015871925 |\n",
"| 12 | 2.14615229612 |\n",
"| 13 | 2.67255146839 |\n",
"+----+---------------+\n",
"[166693 rows x 2 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submission.add_column(predictions_test)\n",
"submission.rename({'X1': 'id', 'X2':'relevance'})"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"submission['relevance'] = submission.apply(lambda x: 3.0 if x['relevance'] > 3.0 else x['relevance'])\n",
"submission['relevance'] = submission.apply(lambda x: 1.0 if x['relevance'] < 1.0 else x['relevance'])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"submission['relevance'] = submission.apply(lambda x: str(x['relevance']))"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"submission.export_csv('../data/submission2.csv', quote_level = 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#gl.canvas.set_target('ipynb')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
masterfish2015/my_project | python/Untitled.ipynb | 3 | 581 | {
"cells": [
{
"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",
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| mit |
Jiri-Kremser/bitcoin-insights | graphx-notebook/blockchain.snb.ipynb | 1 | 26761 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "1BD94847B63C4394AF1B3AAFABA54F4C"
},
"source": "# Analyse Blockchain with GraphX"
},
{
"cell_type": "markdown",
"metadata": {
"id": "1C6EE6059BED491E873A4D067054036E"
},
"source": "_Trying identify interesting addresses in the blockchain transaction graph_"
},
{
"cell_type": "markdown",
"metadata": {
"id": "9D487F6FAEDE4C6B8976FAABA268FCAD"
},
"source": "## Basic setup\n\nHere we will create spark session that is necessary for further dataframe processing.\n"
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "2C7CCB3C43D2425BAD7017C0F0780AF7",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"val spark = SparkSession.builder\n",
" .master(\"local[4]\")\n",
" .getOrCreate()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6BAD062F226548978710E3B28A975836"
},
"source": "## Check the data on disk\nGraph data is stored on the dist as two Parquet files. One with vertices and the second one with the edges."
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "DABBD04745134831822739BFF782EF9F",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
":sh du -h /tmp/nodes.parquet"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "0605F16CF90642DE8A71D7A676CE5CA5",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
":sh du -h /tmp/edges.parquet"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2DA4D8DC9A01453ABF0832C1A461AC71"
},
"source": "## Load the data"
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "58F5BF50D84345C193F523A533DEC6E9",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"val rawNodes = spark.read.load(\"/tmp/nodes.parquet\")\n",
"rawNodes.show(5, false)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5C9D921E0A8744A18015E46326C4EB34"
},
"source": "#### Number of vertices"
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "14CFF48EA8F94EC48BA1E896BA7B0DD3",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"rawNodes.count"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4C715D1FA97341C98A3A7A3E87747855"
},
"source": "### Clean the data"
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "71BC35B9AF0244C38005D16168B622E3",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"import org.apache.spark.sql.functions.regexp_replace\n",
"\n",
"val nodes = rawNodes.na.drop()\n",
" .withColumnRenamed(\"_1\", \"id\")\n",
" .withColumnRenamed(\"_2\", \"address\")\n",
" .withColumn(\"address\", regexp_replace($\"address\", \"bitcoinaddress_\", \"\"))\n",
"nodes.show(5, false)"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "E6EE8C40BD2645378DA7868C9B91A1DA",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"val edges = spark.read.load(\"/tmp/edges.parquet\")\n",
" .drop($\"attr\")\n",
"edges.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5C9D921E0A8744A18015E46326C4EB34"
},
"source": "#### Number of edges"
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "DB8F7E1290F94FD8A101CC98F8007FE1",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"edges.count()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D07A81FBF86A4D7FAA6C14469730D4C4"
},
"source": "# Creating the Graph\nGraphX library expects RDDs, so we need to do the conversion from the dataframes here"
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "0060C72E46054322B4C6F8AB8AF21F9E",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"// todo: ugly\n",
"import org.apache.spark.graphx._\n",
"val nodesRdd: RDD[(VertexId, String)] = nodes.rdd.map(row => (row(0).asInstanceOf[Long], row(1).asInstanceOf[String]))\n",
"val edgesRdd: RDD[Edge[Option[String]]] = edges.rdd.map(row => Edge(row(0).asInstanceOf[Long], row(1).asInstanceOf[Long]))\n"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "064059438BE447369AAA0E9268B9DC5F",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"val graph = Graph(nodesRdd, edgesRdd)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "56D05A8F5C364252B90D311FB6545A70"
},
"source": "## Calculate the Page Rank\n\nThis may take couple of minutes depending on the size of the data. The implementation of the algorithm is described [here](https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.graphx.lib.PageRank$)."
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "F447A11E0445478C86437D6DE93DEA92",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"val ranks = graph.pageRank(0.001)\n",
" .vertices\n",
" .toDF(\"id\", \"rank\")\n",
"\n",
"ranks.show"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FC227B64660044C28631DE9832169B6D"
},
"source": "Now we can sort the vertices by their calculated page ranks."
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "306648DE2A9742788B669AB3E366CF41",
"input_collapsed": false,
"trusted": true
},
"outputs": [],
"source": [
"val sortedRanks = ranks.join(nodes, \"id\")\n",
" .sort(desc(\"rank\"))\n",
"\n",
"sortedRanks.show(5, false)"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "A98EF9E40EE34BB88EF64CD145BB2612",
"input_collapsed": false,
"presentation": {
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"tabs_state": "{\n \"tab_id\": \"#tab1059669048-0\"\n}"
},
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "top10: Array[String] = Array(C825A1ECF2A6830C4401620C3A16F1995057C2AB, DE21D51F82F065DF011CFB3CDCE09C6F71FC716B, D63066643AFA128CE4BEBB2523242ADF5F07A0A9, AA3750AA18B8A0F3F0590731E1FAB934856680CF, 4FA170CFDE2372AC91D479F989DC4DB5AA8D47E0, 9A4E5250E56CA29765635022FB11624116B226BE, 200413B74F3B34198333778C79AF1728AC9A912A, 7773B5B0576CCC2FC79E94098B7D879CCE8BB377, 7C154ED1DC59609E3D26ABB2DF2EA3D587CD8C41, 9B71CA50A249F283DCE5848A6259EFDD2E47FA4B)\nres299: Array[String] = Array(C825A1ECF2A6830C4401620C3A16F1995057C2AB, DE21D51F82F065DF011CFB3CDCE09C6F71FC716B, D63066643AFA128CE4BEBB2523242ADF5F07A0A9, AA3750AA18B8A0F3F0590731E1FAB934856680CF, 4FA170CFDE2372AC91D479F989DC4DB5AA8D47E0, 9A4E5250E56CA29765635022FB11624116B226BE, 200413B74F3B34198333778C79AF1728AC9A912A, 7773B5B0576CCC2FC79E9409..."
},
{
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},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result",
"time": "Took: 4.559s, at 2017-09-30 14:45"
}
],
"source": [
"val top10 = sortedRanks.take(10).map(_(2).toString)\n",
"\n",
"top10"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B4A4BDBC889444F39BEF8D9A2DB1A7F8"
},
"source": "### Helper functions\n\nBitcoin address is essentially a hash or fingerprint of the public key. In the blockchain for the addresses Bitcoin uses internally `hash160` with zero redundancy. However, humans tend to make mistakes and in order to mittigate the risk of sending money to wrong address by making a typo in the address, there is also address that uses a checksum. It's possible to convert between the two forms of the address.\n\nWe will be using `blockchain.info` API for fetching some useful information about the top ten addresses in our Page Rank calculation. To do that we need to define couple of helper functions."
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "F158F501F8544DBD8453FF6C1738157D",
"input_collapsed": false,
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"tabs_state": "{\n \"tab_id\": \"#tab744741656-0\"\n}"
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"outputs": [],
"source": [
"import scala.io.Source.fromURL\n",
"\n",
"def makeFunc(path: String)(param: String) = \n",
" fromURL(s\"https://blockchain.info/q/$path/$param\").mkString\n",
"\n",
"def hashToAddress = makeFunc(\"hashtoaddress\") _\n",
"def balance = makeFunc(\"addressbalance\") _\n",
"def totalReceived = makeFunc(\"getreceivedbyaddress\") _\n",
"def totalSent = makeFunc(\"getsentbyaddress\") _\n",
"def firstSeen = makeFunc(\"addressfirstseen\") _\n",
"val rawJson = (addr: String) => fromURL(s\"https://blockchain.info/rawaddr/$addr?limit=0\").mkString\n",
"\n",
"val parseJson = (jsonStr: String) => {\n",
" val result = scala.util.parsing.json.JSON.parseFull(jsonStr)\n",
" result match {\n",
" case Some(hash: Map[String, Any]) => List(\"address\", \"total_received\", \"total_sent\", \"final_balance\", \"n_tx\")\n",
" .map(x => hash(x))\n",
" case _ => Nil\n",
" }\n",
"}\n",
"\n",
"val getInfo = rawJson.andThen(parseJson)\n",
"val satoshi2BTC = (input: Double) => input / 1.0E8\n",
"\n",
"// https://blockchain.info/ticker\n",
"val btcInUsd = 4279.92\n",
"val BTC2USD = (input: Double) => input * btcInUsd\n",
"val toUSD = satoshi2BTC.andThen(BTC2USD)\n",
"val formatter = java.text.NumberFormat.getCurrencyInstance\n",
"val toReadable = satoshi2BTC.andThen(BTC2USD).andThen(formatter.format(_))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2593C099C1694CE8B6C98F072E0B0F63"
},
"source": "Now, let's apply the `getInfo` function to our top 10 addresses."
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "2967D4FE1DA14F0996DE30AAE7871CB4",
"input_collapsed": false,
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"tabs_state": "{\n \"tab_id\": \"#tab360202904-0\"\n}"
},
"trusted": true
},
"outputs": [],
"source": [
"val top10detailed = top10.map(getInfo)\n",
"top10detailed"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6251CD095E48489E88ABE9F331CBDF67"
},
"source": "And present the results in an HTML table."
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "12C1B66003C147128E2BC40B169826E0",
"input_collapsed": false,
"presentation": {
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"tabs_state": "{\n \"tab_id\": \"#tab434507380-0\"\n}"
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"outputs": [],
"source": [
"<table>\n",
" <tr><td><b>Address</b></td><td><b>Received Ttl</b></td>\n",
" <td><b>Sent Ttl</b></td><td><b>Balance</b></td><td><b>Transactions</b></td></tr>\n",
"{\n",
"top10detailed.map(record => {\n",
" val address = record(0)\n",
" val totalRcv = toReadable(record(1).toString.toDouble)\n",
" val totalSnt = toReadable(record(2).toString.toDouble)\n",
" val balance = toReadable(record(3).toString.toDouble)\n",
" val txNumber = record(4)\n",
" <tr><td><a href={\"https://blockchain.info/address/\" + address}>{address}</a></td>\n",
" <td>{totalRcv}</td>\n",
" <td>{totalSnt}</td>\n",
" <td>{balance}</td>\n",
" <td>{txNumber}</td>\n",
" </tr>\n",
"})\n",
"}\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D57F8A582AE04F9D91EA083A30FD4838"
},
"source": "We can also display the detailed information about any given Bitcoin address."
},
{
"cell_type": "code",
"metadata": {
"collapsed": false,
"id": "9DB5325B8EA5496088E5C0D3D37A16CD",
"input_collapsed": false,
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "displayAddress: (address: String)scala.xml.Elem\nres318: scala.xml.Elem = <iframe width=\"1024\" frameborder=\"0\" height=\"630\" src=\"http://bitcoinwhoswho.com/address/1MFXYK1XucKFfhPhW9HDHD3vsM9BKey4qm\"></iframe>\n"
},
{
"data": {
"text/html": "<iframe width=\"1024\" frameborder=\"0\" height=\"630\" src=\"http://bitcoinwhoswho.com/address/1MFXYK1XucKFfhPhW9HDHD3vsM9BKey4qm\"></iframe>"
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result",
"time": "Took: 2.202s, at 2017-09-30 14:58"
}
],
"source": [
"def displayAddress(address: String) = <iframe \n",
" width=\"1024\" frameborder=\"0\" height=\"630\" \n",
" src={\"http://bitcoinwhoswho.com/address/\" + address}></iframe>\n",
"\n",
"displayAddress(\"1MFXYK1XucKFfhPhW9HDHD3vsM9BKey4qm\")"
]
}
],
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"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": "scala",
"name": "scala"
},
"name": "Blockchain",
"sparkNotebook": null,
"trusted": true,
"user_save_timestamp": "1970-01-01T01:00:00.000Z"
},
"nbformat": 4
}
| apache-2.0 |
geoneill12/phys202-2015-work | assignments/assignment05/InteractEx01.ipynb | 1 | 5244 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Interact Exercise 01"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
}
],
"source": [
"from IPython.html.widgets import interact, interactive, fixed\n",
"from IPython.display import display\n",
"from IPython.html import widgets"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Interact basics"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a `print_sum` function that `prints` the sum of its arguments `a` and `b`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "4d7fa34d285413499aa7359dda2a2dcc",
"solution": true
}
},
"outputs": [],
"source": [
"def print_sum(a, b):\n",
" c = a + b\n",
" print c"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use the `interact` function to interact with the `print_sum` function.\n",
"\n",
"* `a` should be a floating point slider over the interval `[-10., 10.]` with step sizes of `0.1`\n",
"* `b` should be an integer slider the interval [-8, 8] with step sizes of `2`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.1\n"
]
},
{
"data": {
"text/plain": [
"<function __main__.print_sum>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(print_sum, a = (-10., 10., 0.1), b = (-8, 8, 2))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "42c776e2480b70e6a45ee325285f2977",
"grade": true,
"grade_id": "interactex01a",
"points": 5
}
},
"outputs": [],
"source": [
"assert True # leave this for grading the print_sum exercise"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function named `print_string` that prints a string and additionally prints the length of that string if a boolean parameter is `True`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "0a454725f1214af3f65e36c5bc4123e9",
"solution": true
}
},
"outputs": [],
"source": [
"def print_string(s, length=False):\n",
" print 's'\n",
" if length == True:\n",
" print len(s)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use the `interact` function to interact with the `print_string` function.\n",
"\n",
"* `s` should be a textbox with the initial value `\"Hello World!\"`.\n",
"* `length` should be a checkbox with an initial value of `True`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"s\n",
"12\n"
]
}
],
"source": [
"interact(print_string, s = 'Hello World!', length = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "414350009853ea9cb00917ef3bec7b10",
"grade": true,
"grade_id": "interactex01b",
"points": 5
}
},
"outputs": [],
"source": [
"assert True # leave this for grading the print_string exercise"
]
}
],
"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 |
fggp/ctcsound | cookbook/07-icsound.ipynb | 1 | 183716 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using ICsound\n",
"\n",
"*csoundmagics* includes an *ICsound* class which is adapted from Andrés Cabrera's [icsound](https://github.com/csound/csound/wiki/icsound) module. *ICsound* is bound to the *%%csound* and *%csound* magics command.\n",
"\n",
"This notebook is an adaptation of Andrés' [icsound test notebook](https://github.com/csound/csound/blob/develop/frontends/icsound/icsound%20test%20notebook.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Starting the Csound engine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use *ICsound* create an *ICsound* instance:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext csoundmagics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating an *ICsound* object automatically starts the engine:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Csound engine started at slot#: 1.\n",
"Listening to port 12894\n"
]
}
],
"source": [
"cs = ICsound(port=12894)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can set the properties of the Csound engine with parameters to the startEngine() function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method startEngine in module csoundmagics:\n",
"\n",
"startEngine(sr=44100, ksmps=32, nchnls=2, zerodbfs=1.0, dac='dac', adc='', port=0, bufferSize=0) method of csoundmagics.ICsound instance\n",
" Start an ICsound engine.\n",
" \n",
" The user can specify values for sr, ksmps, nchnls, zerodbfs, dac, adc,\n",
" a port number, and the messages buffer size. If a port number is given,\n",
" this engine will listen to that port for csound code and events.\n",
"\n"
]
}
],
"source": [
"help(cs.startEngine)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The engine runs in a separate thread, so it doesn't block execution of python."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CsoundMagics: Csound already running\n"
]
}
],
"source": [
"cs.startEngine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the *%%csound* magic command to directly type csound language code in the cell and send it to the engine. The number after the magic command is optional; it references the slot where the engine is running. If omitted, slot#1 is assumed."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"%%csound 1\n",
"gkinstr init 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"%%csound\n",
"print i(gkinstr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So where did it print?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading options from $HOME/.csound6rc\n",
"rtaudio: ALSA module enabled\n",
"rtmidi: ALSA Raw MIDI module enabled\n",
"UDP server started on port 12894\n",
"displays suppressed\n",
"0dBFS level = 1.0\n",
"orch now loaded\n",
"audio buffered in 1024 sample-frame blocks\n",
"ALSA: -b 512 not allowed on this device; using 341 instead\n",
"writing 2048 sample blks of 64-bit floats to dac\n",
"SECTION 1:\n",
"instr 0: #i0 = 1.000\n",
"\n"
]
}
],
"source": [
"cs.printLog()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, messages from Csound are not shown, but they are stored in an internal buffer. You can view them with the printLog() function. If the log is getting too long and confusing, use the clearLog() function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Function tables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can create csound f-tables directly from python lists or numpy arrays:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"cs.fillTable(1, np.array([8, 7, 9, 1, 1, 1]))\n",
"cs.fillTable(2, [4, 5, 7, 0, 8, 7, 9, 6])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tables can be plotted in the usual matplotlib way, but *ICsound* provides a plotTable function which styles the graphs."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cs.plotTable(1)\n",
"cs.plotTable(2, reuse=True)\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can get the function table values from the csound instance:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4., 5., 7., 0., 8., 7., 9., 6.])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cs.table(2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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6yMyXvdu4cSNnzpzBz8+PatWq6Y5jEwcOHKB27dqUL1+ebdu2kSFDBgYPHkxERAQhISFPLLwAsmbNStOmTVFKmfdDSwmcnZ1p3rw5e/bsYcuWLQQEBHD79m0GDx5M7ty5+eabb564VYYQQojUS4ovjaZOnQpA+/btcXR01JzGui5dukRwcDAlSpRgzZo1eHp68vnnnxMREcHw4cPx9fV97j2SNp+dMWMG0dHR1o5sUYZhUKlSJTZv3szmzZspX748N2/eZMCAARQqVIiFCxdiz7PQQgghLEeKL02uXLlCaGgojo6OtGnTRnccq4mNjWXUqFEULFiQOXPm4OzsTK9evQgPD2fYsGEvtOxWqlQpypUrx507d/jpp5+smNq6AgIC+PXXXwkLC6NYsWJERETQtGlT3n//fVLzMrsQQohEUnxpMmvWLOLj46lduzbZsmXTHccqNmzYQLFixejfvz/R0dHUr1+fY8eO8cMPP5A5c+aXumevXr0ACAkJeeLu8ymFYRh88MEH7Nu3jylTppA5c2Z27dpF6dKl6dmzp0WORxJCCGGfpPjSwGQyMW3aNAA6deqkOY3l3bx5k9atW1O9enVOnjxJwYIFCQsLY/ny5a/cKN+gQQOyZ8/OiRMn2LBhg4US6+Po6EjHjh35+++/+eSTT3BwcCAkJITChQuzbNkyWYoUQohUSIovDTZt2kR4eDg5c+bkgw8+0B3HopYsWULhwoWZPXs2bm5ujBw5koMHD1rsv9PJyYlu3boBMHbsWIvc0x54e3vz7bffsnfvXsqUKcOlS5do1KgRDRs2JDIyUnc8IYQQFiTFlwapsdH+2rVrNGrUiKCgIK5evUr58uU5cOAA/fv3x8XFxaJjdejQATc3N9avX8+JEycsem/dihcvzs6dO5kwYQLe3t6sWLGCokWLsmzZMt3RhBBCWIgUXzZ248YNQkNDcXBwSDWN9kmN48uWLcPLy4uJEyeydetWChQoYJXxMmXKRMuWLYHE3q/UxtHRka5du3L48GGqVKnC9evXadSoEcHBwdy+fVt3PCGEEK9Iii8bW7BgAXFxcVSrVo3s2bPrjvNKHjx4QO/evalRowaRkZFUqFCBw4cP06VLFxwcrPu/VtK2Ez/99BNRUVFWHUuXHDlysGHDBkJCQnB3d2fOnDkUK1aM7du3644mhBDiFUjxZWNJWyR89NFHeoO8oqNHj1KmTBl++OEHnJyc+PLLL9m8eTN+fn42Gb9YsWKUL1+e6Oho5s2bZ5MxdXBwcKB79+7s37+fMmXKcOHCBQICAvj6669T9NOeQgiRlknxZUNHjhxhz549pE+fnnr16umO89IWLFjAO++8w8GDB8mXLx87d+5k0KBBNu9f69KlCwCTJk1K9U8FFihQgB07djBgwAASEhIYNGgQNWvW5Nq1a7qjCSGEeEFSfNlQ0qxX48aNcXd315zmxcXGxtKjRw+aN29OTEwMLVq0YN++fZQuXVpLnoYNG+Lr68vBgwf57bfftGSwJScnJ77++mvWrl1LpkyZCAsL4+2335ZlSCGESGGk+LKR+Ph45s6dC6TMJccLFy5QqVIlxo8fj7OzMxMnTmTOnDl4eXlpy+Tq6krbtm2BxNmvtOLDDz9k//79vP/++1y6dInKlSunidk/IYRILaT4spGNGzdy+fJl8ufPz7vvvqs7zgvZsWMHpUqVYvfu3WTPnp3t27fTpUsXDCNZh7dbVceOHTEMg0WLFnHjxg3dcWwme/bsbNmyhb59+xIfH0/Xrl3p1KkTsbGxuqMJIYR4Dim+bGTWrFkAtG7d2i6KluSaPXs2VapU4erVq1SpUoW//vqLMmXK6I5llidPHqpXr87Dhw/NX+O0wsnJidGjRzN37lzc3NyYNm0aAQEBXLlyRXc0IYQQzyDFlw3cvn2bFStWYBgGrVq10h0nWUwmEwMHDqR169bExsbSvXt31q9fj6+vr+5o/5HUeD958uQ0+QRgixYt2LFjB9mzZ2fXrl34+/uzf/9+3bGEEEI8hRRfNrBo0SIePnxI5cqVyZkzp+44z3Xv3j0CAwMZOXIkjo6OTJgwgZCQEJycnHRHe6JatWqRI0cOTp06xebNm3XH0aJUqVLs2bOH999/n4sXL1K+fHnWr1+vO5YQQognkOLLBmbPng0kLjnau8uXL1O+fHmWL19O+vTpWbduHV27dtUd65kcHR3p0KEDkLYa7/8tS5YsbNy4kebNmxMdHU3t2rXNB7gLIYSwH4Y9PyHl7++v9uzZozvGKzl79iy5c+fGw8ODyMhIrU8HPs+JEyeoXr06ERER5M2bl9WrV1OoUCHdsZLl8uXL5MyZE6UUERERZMuWTXckbZRSDB48mC+//BKAgQMHMmLECKufOiCEEIJkNXXLd2Mr+/nnnwGoW7euXRdev//+O+XKlSMiIoLSpUuze/fuFFN4Abz++uvUr1+fhISENNd4/2+GYTBixAimTp2Ko6MjX3/9NS1btuThw4e6owkhhECKL6tbsGABAM2bN9ec5OnWrVtH5cqVuXHjBh9++CGbN2+2y8b652nfvj0AM2bMSJON9//WoUMHVq9ejZeXFwsWLKBu3brcu3dPdywhhEjzpPiyoiNHjnDw4EEyZsxI9erVdcd5otmzZ1OnTh1iYmJo3bo1oaGheHp66o71UqpWrUrOnDk5c+YMW7du1R3HLtSoUYNt27bh6+vLhg0bqFatGrdu3dIdSwgh0jQpvqwoadarUaNGuLi4aE7zXyEhIbRu3ZqEhAQGDBjAjz/+iLOzs+5YL83R0ZE2bdoAMH36dM1p7EeJEiXYsWMHOXLkYPfu3VSsWFH2AhNCCI2k+LISpZRdLzmOGjWKnj17AvDdd9/x9ddfp6jNX5+mTZs2GIbBsmXLuHnzpu44dqNAgQLs3LmTggULcujQId5//33OnDmjO5YQQqRJUnxZyR9//EF4eDivv/46FSpU0B3HTCnFsGHD6N+/P4ZhMGXKFHr37q07lsX4+flRrVo1Hj58yLx583THsSs5cuRg+/btlCxZktOnT/P+++9z4sQJ3bGEECLNkeLLSpJmvZo0aYKjo6PmNImUUgwcOJChQ4fi4ODArFmz6Nixo+5YFpfUeD99+nQ5bPpffH192bJlCxUqVODSpUsEBARw/Phx3bGEECJNkeLLChISEli4cCFgP0uOSik+/vhjvvnmG5ycnFiwYAHBwcG6Y1lF3bp1yZQpEwcPHmTv3r2649gdb29v1q5dS0BAAJcvXyYgIIBjx47pjiWEEGmGFF9WsHXrVq5cuULevHnx9/fXHQelFN26dWPcuHG4uLiwZMkSGjdurDuW1bi6upoLS2m8fzJPT09Wr15N5cqVuXLlCgEBARw9elR3LPGSEhISOH/+PEePHmX//v1ERERw//593bGEEE8hxZcVJC05NmvWTHsTe9KM16RJk3B1dWXlypXUq1dPayZbaNeuHQDz58+Xva2ewsPDg1WrVlG1alUiIyMJCAjgyJEjumOJZIiNjWX16tX07NmT4sWL4+npSc6cOXnzzTcpUaIEuXLlwsPDgwIFCtCiRQvmzp3LnTt3dMcWQjwixwtZ2MOHD8mSJQt37tzh6NGjFC5cWFsWpRT9+vVjzJgxuLi4EBoaSo0aNbTlsbV3332X3377jVmzZqWIczV1uX//PvXq1eOXX37B19eXzZs3U7RoUd2xxBOcO3eOsWPHMnv2bK5fv/6Pz2XNmpUMGTLg7OzMrVu3iIyMJC4uzvx5FxcXmjRpQu/evSlRooStowuRViRvxkUpZbdvpUqVUilNaGioAlTx4sW15jCZTGrgwIEKUE5OTmrlypVa8+gwffp0Baj3339fdxS7FxMTo6pXr64AlSVLFnXy5EndkcRjrly5ojp37qycnZ0VoABVtGhR9fnnn6tt27apO3fu/Oc1sbGxat++fWrcuHGqUqVKysHBwfzaBg0aqFOnTmn4LxEi1UtWfaO9wHrWW0osvlq1aqUA9eWXX2rNMXToUAUoR0dHtXTpUq1ZdLl7967y9PRUgDp+/LjuOHbv/v37qkqVKgpQOXLkUGfPntUdKc1LSEhQEydOVBkyZFCAMgxDNWvWTP3xxx/KZDK90L3Cw8NV7969lYeHhwKUs7Oz+uKLL1RcXJyV0guRJknxZWsPHjxQ3t7eClAnTpzQluOrr75SgHJwcFA///yzthz2oF27dgpQ/fr10x0lRYiOjlblypVTgMqbN6+6dOmS7khp1uXLl1W1atXMs1U1atRQR48efeX7XrhwQbVu3dp839KlS6vTp09bILEQQknxZXurV69WgHrrrbe0ZZgwYYL5N+Q5c+Zoy2Evdu3apQCVOXNmFRsbqztOinD79m1VsmRJBagiRYqoa9eu6Y6U5vzyyy8qc+bMClA+Pj5qyZIlLzzT9TwbN25U2bNnV4DKmDGj2rBhg0XvL0Qalaz6Rp52tKAlS5YAEBQUpGX8BQsW0L17dwCmTJlCy5YtteSwJ2XLlqVgwYJcvXqVsLAw3XFShPTp0xMWFkaRIkU4evQo1atXlyflbGjKlCnUqFGDq1evUrlyZQ4cOECjRo0s/uR0lSpVOHToELVr1+bWrVvUqFGDqVOnWnQMIcSTSfFlIbGxsaxYsQKAwMBAm4+/bt06goODUUoxcuRIOnToYPMM9sgwDD766CMAZs2apTVLSuLj48PGjRvJmzcvf/31FzVr1iQmJkZ3rFTNZDLRr18/OnfuTEJCAgMHDmTDhg288cYbVhszQ4YMhIaG8tlnn2EymejUqRPffPON1cYTQjyS3CkyHW8padlx3bp1ClBvvvmmzcfeuXOncnd3V4D65JNPLL48kdJduHBBOTg4KBcXF3X9+nXdcVKUs2fPqhw5cihA1a5dW5qzrSQhIUG1b9/e/HTyzJkzbZ5h4sSJyjAMBahBgwbJ9xEhXo4sO9rS4sWLAdsvOR48eJBatWpx//592rZty6hRo7Rv7GpvsmXLRrVq1YiNjeXnn3/WHSdF8fPzIywsjNdee43Vq1fTqVOnxGZRYTEJCQm0b9+e6dOn4+7uztq1a2nTpo3Nc3Tp0oW5c+fi6OjIV199JTNgQlhTcqs0HW8pZeYrNjZWvfbaawpQhw8fttm4p0+fVlmzZlWAql+/vsxKPMOCBQsUoPz9/XVHSZF2795tnl0dNGiQ7jiphslkUm3btlWA8vDwUJs3b9YdSc2fP988AzZlyhTdcYRIaZJV32gvsJ71llKKr7CwMAWowoUL22zMyMhIlTdvXgWogIAAdf/+fZuNnRLFxMSo9OnTK0AdOnRId5wUafXq1crR0VEBaty4cbrjpAoDBgwwF15bt27VHcds4sSJ5qemV6xYoTuOECmJLDvaStJTjrZqtI+JiaFOnTqcPn2akiVLsmLFCtzc3Gwydkrl7u5O06ZNAfjpp580p0mZatWqZT6ovFevXixatEhzopRt3LhxjBw5EkdHR5YsWULFihV1RzLr0qULw4YNQylFy5YtOXz4sO5IQqQuya3SdLylhJmvuLg45ePjowB14MABq48XHx+v6tWrpwDl5+enLl++bPUxU4ukPb+yZMkiS7Sv4Ouvv1aAcnFxUZs2bdIdJ0UKDQ01L+3NmjVLd5wnMplMqlmzZgpQefLkkYdVhEgemfmyhV9//ZXr169ToEABihUrZtWxlFL06tWL0NBQMmbMyLp168iaNatVx0xNypYtS4ECBYiMjJQ9v15B//796dmzJ7GxsdSvX59Dhw7pjpSiHD16lBYtWqCUYsSIEXZ76LthGEyfPp1SpUoRHh5O06ZNSUhI0B1LiFTBIsWXYRg1DMM4YRjGKcMwBjzh8x8ZhnHNMIz9j97aW2Jce/D4kqO1nzL87rvvmDBhAi4uLoSGhlK4cGGrjpfayJ5flmEYBt9//z2NGzcmKiqKWrVqcfnyZd2xUoRbt25Rr149oqOjadKkCYMGDdId6Zk8PDxYsWIFmTNnZuPGjfIEpBCWktwpsqe9AY7AaSAP4AIcAIr865qPgPEvem97X3aMj483HwGyb98+q461cOFC81lsaf28xldx/vx5ZRiG7PllATExMerdd99VgCpVqpSKjo7WHcmuxcfHq+rVqytAvf2si7+RAAAgAElEQVT22ynq67V+/XoFKEdHR7Vjxw7dcYSwZzZbdiwNnFJKhSulYoGfgXoWuK/d27FjB1evXiVv3rwUL17cauNs376dVq1aATBq1CiaNGlitbFSu+zZs8ueXxbi7u5OaGgoefLkYe/evbRo0UKWpZ7hq6++IiwsDB8fH1asWIGnp6fuSMlWvXp1/ve//5GQkECzZs24efOm7khCpGiWKL6yAecfe//Co4/9WyPDMA4ahrHEMIwcFhhXu6TjhBo2bGi1JccTJ05Qr149YmNj6datG5988olVxklLkpYe5anHV+fr68vatWvJmDEjoaGh9OvXT3cku7R9+3aGDh0KwPz58/Hz89Mb6CWMGDGCMmXKcP78eXr16qU7jhApmq0a7lcBuZRSbwG/AE/9qWcYRkfDMPYYhrHn2rVrNor34pRSLF++HID69etbZYybN2+aD72tW7cuY8eOld3rLaB+/fp4e3vz559/cuTIEd1xUryCBQuyfPlynJ2d+f7775kwYYLuSHblxo0bNG/eHJPJxIABA6hWrZruSC/F2dmZOXPm4O7uzty5cwkNDdUdSYgUyxLF10Xg8Zms7I8+ZqaUuqGUevjo3elAqafdTCk1VSnlr5Ty9/X1tUA86zhw4AARERFkyZKFsmXLWvz+cXFxBAYGcurUKd5++23mz5+Po6OjxcdJi2TPL8urWLEiM2bMAKBnz56sWbNGcyL7oJSibdu2XLhwgbJlyzJ8+HDdkV5J/vz5+frrrwHo1KkTN27c0JxIiJTJEsXXn0B+wzByG4bhAjQFVj5+gWEYrz/2bl3gmAXG1SppybFevXo4OFh2AlEpRffu3dmyZQtZs2Zl5cqVKao/JCVIWnqcM2cO8fHxesOkEq1atWLIkCGYTCaaNGnC/v37dUfSbsaMGaxcuZL06dOzYMECnJ2ddUd6ZT169KB8+fJERkbK8qMQLyu5nfnPegNqAidJfOrx00cfGw7UffTnr4EjJD4JuQUolJz72vPTjm+99ZYC1Nq1ay1+7x9++EEBys3NTf3+++8Wv79I3ECyQIECClBr1qzRHSfVMJlMqmXLlgpQOXPmVJGRkbojaRMREaHSpUunADVv3jzdcSzq77//Vm5ubgpQGzdu1B1HCHuSvLopuRfqeLPX4uv06dMKUOnSpVMPHjyw6L3Xrl2rHBwcZEsJG/jqq68UoIKCgnRHSVXu37+vypYtqwBVrlw5i/8bSQlMJpOqWrWqAlSDBg2UyWTSHcniRowYoQBVoECBNPl3LMRTyA731pLUaFqzZk1cXV0tdt8jR47QpEkTTCYTQ4YMkS0lrKxVq1YYhkFoaKg8Om9Bbm5uLF++nOzZs7Nz5066du2aNEOeZkydOpWNGzeSKVMmJk2alCoflPnkk08oWLAgJ0+eZPTo0brjCJGiSPH1EpL6vSz5lOP169epU6cOUVFRNG7cmM8//9xi9xZPlj17dqpWrSp7fllBUq+iu7s7M2fOZOzYsboj2czZs2fNW8JMnDiRLFmyaE5kHa6urkycOBFI3IYiPDxccyIhUg4pvl7QtWvX2LFjB87OztSsWdMi94yNjaVhw4acOXMGf39/fvzxR4s38Ysnkz2/rKdEiRLmr2vfvn3TxHmaSik6depEdHQ0gYGBNG7cWHckq6pcuTLNmzfnwYMHfPzxx7rjCJFiyE/4F7Rq1SpMJhNVqlTB29vbIvfs0aMH27dv54033iA0NBQPDw+L3Fc8X/369UmXLh1//PEHx46l+Idw7U5QUBCff/65+QnIEydO6I5kVQsXLmTDhg1kzJgxzex3NmbMGLy8vFi1ahWbNm3SHUeIFEGKrxdk6SXHKVOmMHXqVFxdXQkNDeWNN96wyH1F8nh4eJh762T2yzqGDBlCo0aNuHPnDnXq1OHWrVu6I1nF7du36d27N5B4DFjmzJk1J7KNrFmzMnDgQCBxhlOOmBLi+aT4egHR0dFs2LABwzCoV+/Vj6/cuXMnPXr0ABIbdP39/V/5nuLFPb7nl/zgsDwHBwd++uknihcvzt9//02TJk1S5d5qn376KVeuXKFcuXK0bdtWdxyb6t27Nzly5ODAgQPMnj1bdxwh7J4UXy8gLCyMhw8fUrZsWbJmzfpK97p48SKNGjUiLi6OXr16ERwcbKGU4kW999575MuXj0uXLrFx40bdcVIlT09PQkND8fX15ZdffmHAgAG6I1nUH3/8waRJk3BycmLSpElprmfT3d3dvPP9p59+yr179zQnEsK+pa3vEK/IUkuODx48oGHDhkRGRhIQEMC3335riXjiJRmGQevWrQGYNWuW3jCpmJ+fH8uWLcPJyYkxY8akmidM4+Pj6dy5M0op+vTpQ7FixXRH0qJZs2b4+/tz+fJl+Z4mxHMY9rz/jr+/v9qzZ4/uGEDiWYuZM2fm9u3bnDx5kvz587/UfZRStG/fnpkzZ5IzZ0727NmDPZ9hmVZERESQK1cu3NzcuHz5MhkyZNAdKdUaP348PXr0wMPDg927d/PWW2/pjvRKJk+eTJcuXfDz8+PIkSNp+iiw7du3U6FCBby8vAgPD5fvbSItStamfjLzlUy//vort2/fpkiRIi9deAFMmjSJmTNn4u7uzooVK+Sbk53w8/OjcuXKPHjwgEWLFumOk6p169aN4OBgYmJiaNCgQYpuwL916xafffYZkPjUX1ouvADKly/Phx9+SHR0NKNGjdIdRwi7JcVXMiUtOTZo0OCl77Ft2zbzQbTTp0+nRIkSFskmLCNp6VGeerQuwzCYPHkyJUuWJDw8nBYtWqTYBx2GDRvGjRs3qFSpEg0bNtQdxy588cUXQOIM56VLlzSnEcI+SfGVDEqpV+73On/+PIGBgcTHx9O3b1+aN29uyYjCAho1aoSXlxe7du3i5MmTuuOkau7u7ixbtoxMmTKxbt06hg4dqjvSCzt69Cjjx4/HwcGBH374IVUeIfQySpUqRcOGDXnw4AFfffWV7jhC2CUpvpJh7969XLx4kWzZslGqVKkXfn1Sg/21a9eoUqUKI0eOtEJK8ao8PT0JDAwEkMflbcDPz4+ff/4ZBwcHRowYYf4FJyVQStG7d28SEhLo2LEjxYsX1x3JrgwbNgzDMJg6dSoRERG64whhd6T4SoZVq1YBULdu3Zf67bZ79+7s2bOHXLlysXDhQpycnCwdUVhI0p5fs2fPxmQy6Q2TBlStWtX8y0hwcDDHjx/XnCh51qxZw4YNG8iQIQPDhw/XHcfuFC1alGbNmhEXFydfHyGeQIqvZEgqvurUqfPCr50xYwYzZszAzc3NvMwi7Ff58uXJlSsX58+fZ8uWLbrjpAmffPIJjRs3Jioqivr163P37l3dkZ4pNjaWPn36ADB06FB5aOYphg4diqOjIz/99BN///237jhC2BUpvp7jwoUL7Nu3D09PTwICAl7otfv27aNbt24ATJw4URrsUwAHBwfZ88vGDMNgxowZFC1alBMnTtC6dWu7nnWcMmUKf//9NwULFqRr166649it/PnzExwcTEJCAt98843uOELYFSm+nmP16tUAVKtWDTc3t2S/7tatWzRq1IiHDx/SoUMH2rRpY62IwsKSThtYunSp3c/CpBZeXl4sX76c9OnTs2LFCrvdpuDu3bvmZbRvvvkGZ2dnzYns24ABA3BwcGD27NmcP39edxwh7IYUX8/xMkuOJpOJ4OBgzpw5Q8mSJRk3bpy14gkryJMnDxUqVOD+/fssWbJEd5w0I1++fMydOxdIPKJm69ategM9wbfffsv169cpV64cdevW1R3H7hUoUICgoCDi4uJk13shHiPF1zPcu3ePTZs2YRgGtWrVSvbrRo4cyerVq8mYMSNLlix5oRkzYR9kzy89ateuzcCBAzGZTDRt2pTLly/rjmR26dIlxowZA8CoUaNka4lkGjRoEADTpk0jMjJScxoh7IMUX8/wyy+/8PDhQ0qXLk2WLFmS9ZqNGzcyePBgAObOnUvu3LmtGVFYSVBQEB4eHmzbto3Tp0/rjpOmDB8+nICAACIjI2nSpAnx8fG6IwGJDeT379+nYcOGvPfee7rjpBhvvfUWderU4cGDB/zwww+64whhF6T4eoYXXXI8f/48zZo1w2QyMXjwYGrWrGnNeMKK0qVLR6NGjQDZ88vWnJycWLBgAa+//jrbt283z5zodOzYMWbMmIGjo6NsHPoSPv30UwAmTJiQoo+TEsJSpPh6CpPJxJo1a4DkFV+xsbEEBQVx/fp1qlWrxpAhQ6wdUVhZ0tKj7Plle1myZGHhwoU4Ojry7bffat+ANWkptEOHDhQsWFBrlpSoTJkyVKlShaioKMaPH687jhDaSfH1FH/++SeRkZH4+flRrFix517ft29ffv/9d3LkyMH8+fNxdHS0QUphTQEBAeTIkYOzZ8+ybds23XHSnPLly5u3KPjoo4+0Lf/u2LGD0NBQPD095ZeqV5A0gxkSEsL9+/c1pxFCLym+nuLxJcfnNdbOnz+f8ePH4+zszOLFi/Hx8bFFRGFlDg4O5m0npPFejz59+tCgQQPu3LlDo0aNbP5DWyllXjLr27cvWbNmten4qUlAQAAlSpTg2rVrzJs3T3ccIbSS4uspktvvdeTIETp06ADA2LFjKVOmjNWzCdtJWnpcvHgx0dHRmtOkPYZh8OOPP5I3b14OHDhAjx49bDr+li1b2LZtGxkzZqRv3742HTu1MQzD/DX87rvvZClfpGlSfD1BREQEBw8exMvLi4oVKz71uujoaAIDA4mJiaFly5Z07tzZhimFLeTPn5/33nuPe/fusWzZMt1x0qT06dOzdOlS3NzcmDFjBj/++KNNxlVKmZcZ+/bti7e3t03GTc0aN25MtmzZOHbsGOvXr9cdRwhtpPh6gqRd7atXr46rq+sTr1FK0aVLF44fP06RIkWYPHmy7PuTSiUdti3HDelTvHhxJk6cCEDXrl05cOCA1cfctGkTO3bs4LXXXrP5jFtq5ezsTM+ePQHMe6YJkRZJ8fUEyVlynDlzJnPnzsXDw4PFixfj6elpq3jCxho3boybmxtbtmwhIiJCd5w0q02bNrRt25YHDx4QGBjInTt3rDbW47Ne/fr1k1kvC+rYsSNeXl5s3ryZ/fv3644jhBZSfP1LVFQUW7ZswTCMp+7TdejQIbp37w7ApEmTKFKkiC0jChtLnz49DRo0AGTPL93Gjx/P22+/zalTp2jbti1KKauMs2HDBnbt2oWPj4/537qwjAwZMtCuXTsgsfdLiLRIiq9/2bBhA7Gxsbz77rv4+vr+5/PR0dEEBQXx4MED2rRpY34aTqRujx83ZK0f+OL53N3dWbJkCd7e3ixbtswqe0b9e9bLy8vL4mOkdb169cLBwYEFCxZw8eJF3XGEsDkpvv7lWUuOSX1eJ06c4M0335TNAtOQqlWr8sYbb3D69Gl27typO06aljdvXmbMmAEkNsLv2bPHovdfv349v//+O76+vnTr1s2i9xaJcufOTcOGDYmPjyckJER3HCFsToqvxyQkJDxzV/sZM2aY+7wWLVqEh4eHrSMKTRwdHWnVqhUge37Zg8DAQLp3705cXByNGzfm9u3bFrnv47Ne//vf/6SX04r69OkDwPTp02XTVZHmSPH1mN9//53r16+TO3fu//RxHTx40PzEk/R5pU1JS48LFy4kJiZGcxoxevRoSpYsyZkzZ2jXrp1FloPXrl3Ln3/+SZYsWejatasFUoqnKVu2LKVKleLGjRssXLhQdxwhbEqKr8c8bVf7qKgoc59X27Ztpc8rjSpcuDClS5cmKipK+1mDAlxdXVm0aJHF+r8en/Xq37+/zGxbmWEY5ocZQkJCpJdSpClSfD3mSf1eSik6d+7MyZMnefPNN6U/IY2TPb/sS968eZk+fTrw6v1fq1atYu/evWTNmlU2TLaRJk2akClTJv766y9+++033XGEsBkpvh45c+YMR44cwdvbmwoVKpg/PmPGDObPn2/ez0t+G07bmjZtiouLCxs3buTChQu64wggKCiIbt26vVL/l1KKoUOHAjBgwADc3d0tnFI8ibu7u/l4NnmASaQlUnw9kjTrVb16dVxcXIB/9nlNnjyZwoULa8sn7EPGjBmpV68eSinZ88uOPN7/1b59+xdewgoNDWXfvn28/vrrdOzY0UopxZN07twZBwcHFi9ezJUrV3THEcImpPh65N9Ljo/3ebVr1878pJsQSUuPM2fOlD4VO+Hm5mbu/1q6dCkTJkxI9mtNJpN51mvQoEEy62Vjfn5+1K1bl7i4OKZNm6Y7jhA2YdjzDw9/f39l6T18nuTu3bv4+PiQkJDA1atXee2112jRogULFiygaNGi/P7777LcKMwSEhLw8/Pj4sWLbNmyhUqVKumOJB5ZvHgxjRs3xsXFhZ07d+Lv7//c1yxdupTAwECyZcvGqVOncHNzs0FS8bhNmzaZ99I7e/Yszs7OuiMJ8bKSdcizzHwBYWFhxMXFUa5cOTJlysT06dNZsGABnp6e0ucl/sPR0ZG2bdsCmJu9hX1I6v+KjY1NVv/Xv2e9pPDSo3LlyhQuXJhLly6xfPly3XGEsDopvoCVK1cCiUuOBw4c+EefV6FChXRGE3aqbdu2GIbBkiVLuHXrlu444jEv0v+1dOlSDh8+TI4cOcznDQrbe3zbiUmTJmlOI4T1pfniKz4+nrVr1wKJv301btyYhw8f0q5dO1q2bKk5nbBXuXLlolq1ajx8+JC5c+fqjiMek9z+L5PJxLBhw4DEWS9XV1dbxhT/0rJlSzw8PNi6dSsnT57UHUcIq0rzxdfu3bu5efMm+fLlY/To0Zw8eZJixYrJfl7iudq3bw/AtGnTpPHeziRn/6/Fixdz5MgRcubMaV5GFvp4e3vTrFkzAKZOnao5jRDWleaLr6SnHHPlysXPP/+Mp6cnixYtkieexHPVrVsXHx8fDh06ZPHDncWre1b/V0JCgrnX67PPPjNvLyP06tSpE5C4ifGDBw80pxHCeqT4elR8/frrrwBMmTJF+rxEsri6upqPmpLGe/s0evRoSpQo8Z/+r4ULF3L8+HFy5cpl3jpE6Ofv70+JEiW4ceMGy5Yt0x1HCKuxSPFlGEYNwzBOGIZxyjCMAU/4vKthGAsfff53wzByWWLcV3Xq1CmOHz+Og4MDcXFxtG/fnhYtWuiOJVKQpCbt+fPnEx0drTmN+Lek/q906dKZ+78SEhIYPnw4kDjrJdsa2A/DMMyb3MrSo0jNXrn4MgzDEZgAfAgUAZoZhlHkX5e1A24ppfIB3wPfvOq4lpD0lKPJZKJYsWKMGzdOcyKR0hQpUoT33nuP6OhoFi9erDuOeIJ8+fL9o//ryy+/5MSJE+TJk8c8cynsR/PmzfH09OTXX3/l+PHjuuMIYRWWmPkqDZxSSoUrpWKBn4F6/7qmHvDToz8vAaoYhpGsjcisKek3K1dXVxYvXix9XuKlPN54L+xT48aN6dq1K7GxsXzxxReAzHrZK29vb5o3bw7I7JewDHt8IOqVd7g3DCMQqKGUav/o/VZAGaVU98euOfzomguP3j/96Jrrz7q3NXe437lzJ++//z6QuDQhj5mLl6WU4u7duwB4eXnh6OioOZF4EqUUUVFR5m/E6dKlw8Ehzbe92qX4+Hju3buHYRikS5cOO/hdXaRQSinu3bvHypUrqVmzpi2GTJk73BuG0dEwjD2GYey5du2a1cZ57bXXyJUrF4ZhSOElXolhGOan5WJjYzWnEckVFxenO4J4CicnJxwcHFBKyd+TeCUxMTEkJCTQrl074uPjdccxc7LAPS4COR57P/ujjz3pmguGYTgB6YEbT7qZUmoqMBUSZ74skO+JChcuTHh4OAkJCTg5WeLLINKyP//8k9KlS5MuXTouXrwoBb0d+vHHH2nbti2vv/46ly9fxmQysXHjxmSd/yhsb+rUqXTq1Al/f3+2bdumO45IgebMmWPu68yTJ49d/ay3xMzXn0B+wzByG4bhAjQFVv7rmpVA60d/DgQ2KztYhDUMw67+MkTK5e/vT/Hixblx4wZLlizRHUf8S1xcnLnXa9SoUeb+r+Sc/yj0aNasGV5eXmzfvp1jx47pjiNSmGPHjtG5c2fz+02bNtWY5r9eufhSSsUD3YEw4BiwSCl1xDCM4YZh1H102Qwgk2EYp4A+wH+2oxAiJTMMgy5dugByNp09mj17NmfOnKFgwYI0a9aMMWPGPHH/L2E/0qVLZ268l4dZxIuIiYkhKCiImJgY80M1tWvX1pzqn1654d6arNlwL4SlRUdH88YbbxAVFcXBgwcpVqyY7kiCxD68AgUKEBERwfz5881H2Jw+fZqSJUty9+5dQkJCzAc7C/uxZ88e3nnnHXx8fLh48aKcRCCSpV27dsycOZPs2bNz4cIF3nzzTQ4fPmyr4VNmw70QKZWXlxetWrUCZPbLnsyaNYuIiAgKFy5M48aNzR9PzvmPQq9SpUpRrFgxrl+/zurVq3XHESnA7NmzmTlzJm5ublSoUAGAOnXqaE71X1J8CWFBSUuPc+bMISoqSnMaERsby5dffgnAkCFD/rMNyLPOfxT6GYZhPvR8xowZmtMIe3fs2DHz9+CQkBB2794NSPElRKpXtGhRypcvT3R0NHPnztUdJ82bOXMm586d48033yQoKOiJ14wePZqSJUtK/5edatGiBc7Ozqxfv56LF//9IL0QiR7v82rZsiXvvvsuZ86cwcfHhzJlyuiO9x9SfAlhYY833ssPcn0ePnz4j1mvp22omnT+o7e3t/n8R2E/fH19qVu3LiaTidmzZ+uOI+xUz549OXLkCAULFmTSpEmsWbMGgJo1a9rlxtdSfAlhYQ0bNsTX15dDhw6xa9cu3XHSrOnTp3PhwgWKFStGo0aNnnmt9H/Zt6Slx5kzZ8ovNOI/5syZw4wZM3Bzc2Px4sV4eXmxatUqwD6XHEGKLyEsztXVlXbt2gHSeK/LgwcP+PrrrwEYOnRoso4Rkv4v+1W9enWyZcvGqVOn2LFjh+44wo48vp9XSEgIxYoV48aNG+zatQtnZ2c++OADzQmfTIovIaygU6dOGIbB4sWLseYxWeLJpkyZwsWLF3n77bepX79+sl8n/V/2ydHRkdatE/fpnjlzpuY0wl7ExMTQuHFjYmJiaNGihfmX3nXr1mEymahYsSLe3t6aUz6ZFF9CWEGuXLmoWbMmsbGx8pSWjcXExJhnvYYPH/5Ch2dL/5f9atOmDQCLFi2SJ4kFkNjndfjwYQoWLMjkyZPNB7AnLTna28aqj5PiSwgr6datGwATJ060qwNdU7sJEyYQGRnJO++881LffKX/yz7ly5ePChUqEBMTw6JFi3THEZo93ue1aNEivLy8gMSjxNavXw/Yb78XSPElhNVUr16dAgUKcP78eZYvX647TpoQFRXFN998AyTOeiX9JvyipP/LPsmeXwL+2+f11ltvmT+3fft27t69S5EiRciTJ4+uiM8lxZcQVuLg4EDPnj0B+OGHHzSnSRtCQkK4ceMG7733HtWrV3+le0n/l/0JDAzEy8uL3bt3y2HbadTT+rySJJ2EYM9LjiDFlxBW1bp1a9KnT8+uXbtk+crK7ty5w+jRowH44osvXnrWK4n0f9kfT09PmjZtCsCPP/6oOY3QIanPq0CBAkyaNOkf/86VUna/xUQSKb6EsCIvLy/at28PwNixYzWnSd2+//57bt26RaVKlahcubJF7in9X/YnaaZj9uzZxMXFaU4jbOnf+3mlS5fuH58/ceIEp06d4rXXXqNs2bKaUiaPFF9CWFn37t1xcHBg4cKFXL58WXecVOnmzZt8//33QGKvlyVJ/5d9KVOmDIULFyYyMpK1a9fqjiNs5Pjx4+bTQ8aNG/ePPq8kSUuONWvWxMnJyab5XpQUX0JYWa5cuahXrx5xcXFMnjxZd5xUacyYMdy9e5dq1apRvnx5i99f+r/sh2EY5m0nZs2apTeMsIl79+4RGBjIvXv3aN68uXk14d9SypIjgGHP30T8/f2VTPOL1GDbtm1UrFgRX19fzp07h5ubm+5Iqca1a9fInTs39+7d47fffrPaIbqnT5+mZMmS3L17l5CQELp3726VccTzXb58mezZs+Po6MilS5fw8fHRHUlYiVKK4OBg5s6dS8GCBfnzzz//s9wIibPfmTNnxjAMrl+/Tvr06TWkBSBZzaYy8yWEDZQvX563336ba9eusWDBAt1xUpVRo0Zx7949atWqZbXCC6T/y568/vrrVK9enbi4OPn3lMpNnTqVuXPn4uHhwdKlS59YeAGsX7+ehIQEKlSooLPwSjYpvoSwAcMw6N27NwDffvstJpNJc6LU4cKFC4wfPx6wfK/Xk/y7/+vWrVtWH1M8WdJxQz/99JPmJMJa9uzZY96uZ+rUqbz55ptPvTYlLTmCFF9C2EzTpk3Jnj07x44dY82aNbrjpArDhg3jwYMHBAUFUbJkSZuMOWbMGEqVKsWZM2cIDg6WQlqTevXqkT59evbu3cuRI0d0xxEWdvPmTQIDA4mNjaVLly60aNHiqdfGxcWxbt06wP7390oixZcQNuLi4kKfPn0AzLuwi5d3/PhxZs6ciaOjIyNGjLDZuK6urixZsoSMGTOyevVqRo4cabOxxf9zc3OjSZMmgMx+pTYmk4ng4GAiIiLw9/c3P8n8NNu2bePOnTsUKVKEfPny2Sjlq5HiSwgb6tChAxkzZmTnzp3s3LlTd5wU7dNPP8VkMtG+fXsKFChg07Fz5crFvHnzMAyDwYMHs3HjRpuOLxIlLT3OnTtXzk9NRUaOHMmaNWvImDEjixcvxtXV9ZnXr1y5EkicDU0ppPgSwoa8vLzMB27L7NfL+/3331m2bBnu7u58/vnnWjJ8+OGHDB48GJPJRLNmzbhw4YKWHGnZu+++S/78+bl8+TK//PKL7jjCAjZv3omlmb0AACAASURBVMzgwYOBxKI6V65cz7xeKUVoaCgAdevWtXY8i5HiSwgb69GjB25ubqxatUp6VV6CUooBAwYA8PHHH/PGG29oy/L555/zwQcfcP36dYKCgoiNjdWWJS0yDIPg4GBAlh5Tg4sXL9KsWTNMJhOffvopNWvWfO5rDh06REREBFmyZKF06dI2SGkZUnwJYWOZM2embdu2QOKTj+LFbNiwga1bt5IxY0b+97//ac3i6OjIvHnzyJkzJ7/99ht9+/bVmictatWqFQArVqyQ0wdSsLi4OJo2bcrVq1epUqUKw4YNS9brkma96tSpg4NDyilpUk5SIVKRvn374uDgwLx582S56gWYTCbzrNfAgQPJkCGD5kTg4+PDkiVLcHFxYfz48cyfP193pDTFz8+PypUr8/DhQxYtWqQ7jnhJAwcOZMeOHbzxxhvMnz8fR0fHZL0uJfZ7gRRfQmiRJ08egoKCiI+PZ9SoUbrjpBjz589n//79ZM+e3a52mH/nnXfMB6d36NBBlpNtTPb8StmWLVvGmDFjcHJyYtGiRWTOnDlZr7t48SJ79uzBw8ODKlWqWDmlZUnxJYQmn376KZC4eeClS5c0p7F/MTExDBw4EIAvvvgCd3d3zYn+qVOnTrRq1YqYmBgaNmzI3bt3dUdKMxo2bIinpye7du3i77//1h1HvIATJ06Yz+ocNWoU5cqVS/ZrkzZW/eCDD+zu+8HzSPElhCbFihUjMDCQhw8fypOPyTBmzBguXLhAyZIlzU3W9sQwDCZPnkyxYsU4efIk7dq1kwO4bcTLy4vAwEAAZs+erTmNSK6oqCgaNGjA3bt3CQwM5OOPP36h16fEpxyTyMHaQmh06NAh3nrrLVxdXQkPD9f65J49u3TpEvnz5ycmJoYtW7ZQqVIl3ZGe6u+//8bf35+7d+/y3XffmY+VEta1ZcsWKleuTM6cOTlz5kyKar5Oi5RSBAUFsXTpUooUKcJvv/321HMbnyQqKgofHx/i4uKIjIzE19fXimlfiBysLYS9k9mv5Pnss8+IiYmhfv36dl14AeTPn59Zs2YB0K9fP7Zt26Y3UBpRsWJF/Pz8OHfuHL/++qvuOOI5vvnmG5YuXYq3tzfLly9/ocILICwsjNjYWN577z17KrySTYovITRL2iR0ypQp0vv1BH/99RezZs3C2dk5xTyc0KBBA/r160dCQgKBgYGcP39ed6RUz8HBwbzthDTe27ewsDAGDRoEwLx5817qhIqU+pRjEim+hNBMZr+eTqn/a+/O46Ku9j+Ovw6bqKjgjuKCphaKuSApWi6FSy6Jpam451pmLrmVZerPsrI07y1zF7wqueOSkhtariGFuWGJmRrkgqgIyXZ+fzBwvfdaoszMd2A+z8eDBzNfZr7njQPOh3PO9xzNuHHj0FozcuRIatWqZXSkPHv//fcJDAzk6tWrBAUFkZqaanSkQi9nLuC6detITk42OI24n7i4OHr16oXWmqlTpz7SRtgZGRls27YNKJjzvUCKLyFsQk7v15dffsmFCxcMTmM7NmzYQGRkJKVLl87dcqSgcHJyIiwsDG9vb44dO8bw4cNlAr6F1apVi4CAAO7cucP69euNjiP+S86VwDdu3KBTp06PvDXYgQMHSExMpE6dOtSpU8fMKa1Dii8hbICvry+9e/cmLS2NqVOnGh3HJty5cyd3svqMGTPw8PAwONHDK126NJs2baJYsWKEhoYyb948oyMVerLml23SWjNkyBBiYmKoVasWK1aseOSLInKGHAtqrxdI8SWEzZgxYwbOzs6Ehoby008/GR3HcDNnzuTixYs0atSIYcOGGR3nkdWvX59ly5YB2Tsb7N271+BEhVuPHj1wdXVl79690otsQz777DNWrVpF8eLF2bhx4yPvTnHvRtoFdb4XSPElhM2oUaMGI0aMQGudu5iovYqNjWX27NkAfPHFF3neasRW9ejRg4kTJ5KZmUmPHj2kKLAgd3d3unbtCsCKFSsMTiMAIiMjefPNNwFYvnw5devWfeRznT59mnPnzlG2bFmaNm1qrohWJ8WXEDZkypQplChRgm3bttnt5fJaa15//XXS09MZPHgwTz31lNGRzGLmzJm0a9eOa9euERQUREpKitGRCq2cocfQ0FCZZ2ewCxcu0KNHDzIzM5k4cWLuYriPKqfXq1OnTgX6jzIpvoSwIeXKlWP8+PEATJgwgaysLIMTWd/69evZuXMnHh4efPDBB0bHMRtHR0dWr15NzZo1+eGHHxgyZIgUBhYSGBiIp6cnP//8M4cOHTI6jt1KTk6mS5cuXL16lbZt2zJz5sx8n7OgLzGRQ4ovIWzMmDFj8PT05OjRo3Y3bJKUlMSoUaOA7KUaypYta3Ai8/Lw8GDTpk0UL16cVatWMWfOHKMjFUqOjo706dMHkIn3RsnKyqJfv34cP36c2rVrExYWlu+eqoSEBI4cOYKrqyuBgYFmSmoMKb6EsDFubm65i4lOmjTJrjZoHj9+PPHx8QQEBDBkyBCj41hEvXr1cguC8ePHExERYXCiwiln6PGrr76SNdYM8N5777Fx40ZKlSrF5s2bzXK1cnh4OFprnnvuOYoXL26GlMaR4ksIGxQcHEyzZs1ISEjg//7v/4yOYxV79uxh8eLFuLi4sHjx4gI9n+NBXnzxRaZMmUJWVhYvv/wyp0+fNjpSoVO3bl38/Py4efNm7jwhYR1r1qxhxowZODg48NVXX5ltLa6NGzcC0K1bN7Ocz0hSfAlhg5RSzJs3D6UUc+fO5ezZs0ZHsqiUlJTcnq53332XJ554wuBEljdt2jS6devGzZs36dy5M9evXzc6UqEzYMAAgNy9NoXlRUdH5/67z549m3bt2pnlvElJSezevRtHR0c6d+5slnMaSYovIWyUn58fr7zyCunp6YwaNapQT85+9913iYuLo379+kyYMMHoOFbh4OBAaGgojRo14ty5c7z44oukpaUZHatQ6dmzJy4uLuzcuZPLly8bHafQS0hI4IUXXiA1NZWBAwcyevRos51769atZGRk0LJly0IxF1SKLyFs2MyZM3F3dyciIoKwsDCj41jEoUOHmDNnDg4ODixZsgRnZ2ejI1lN8eLFCQ8Px9PTk3379vHqq68W6iLb2sqUKUOXLl3Iysqyu4tXrO3PP/8kKCiIS5cuERAQwPz581FKme38OUOOQUFBZjunkaT4EsKGlS9fnk8++QSAUaNGce3aNYMTmdft27fp06cPWVlZjB8/Hj8/P6MjWZ2Xlxfh4eG4urqyZMkS5s6da3SkQuXeoUcpbC1Da82wYcM4fPgwVapUYcOGDRQpUsRs509JSWH79u0AuQvoFnT5Kr6UUqWVUjuVUj+bPt/3cgalVKZS6kfTx+b8tCmEvRk4cCBt2rTh2rVrjB071ug4ZjV69Gji4uJo0KAB06dPNzqOYZo0aZJ7BeS4cePYtm2bwYkKj3bt2lGhQgViY2M5evSo0XEKpZkzZxIaGkqxYsXYvHkzFSpUMOv5IyIiSE1Nxd/fHy8vL7Oe2yj57fmaBOzWWtcCdpvu30+q1rqB6aPg7oQphAGUUixYsABXV1dWrFhRaJYm2LBhA0uXLsXV1ZWVK1fi4uJidCRD9ejRg/feew+tNT179uTEiRNGRyoUnJycctf8kon35rdq1SreeecdlFKsXr2aBg0amL2NwnSVYy6t9SN/ALGAp+m2JxD7F49LfpTzN27cWAshsn344Yca0FWqVNGJiYlGx8mXixcv6jJlymhA/+Mf/zA6js3IysrSL7/8sgZ0tWrVdEJCgtGRCoXjx49rQJcqVUqnpqYaHafQ2L9/v3ZxcdGAnjt3rkXauHv3rnZ3d9eAjo2NtUgbZpan+ia/PV8VtNbxptsJwF/1NboqpaKUUoeVUoVjwFYIKxs7diz+/v5cvHixQE/MTktLo3v37ly/fp0OHTrw2muvGR3JZiilWLZsGf7+/ly4cIFOnTpx584do2MVeL6+vjRu3FjW/DKjn3/+ma5du5KWlsbrr7/OG2+8YZF2IiMjSUpKom7dutSuXdsibRjhgcWXUmqXUurEfT7+Y2Mlnf1O8FfvBtW01n5Ab2CuUqrm37Q31FSoRV29evVhvhchCjUnJydWrlxJ8eLFCQsLY+XKlUZHeiTjx4/PnZgbGhpq1iuiCoOiRYuyefNmvL29iYqKomfPnmRkZBgdq8CTNb/M5/r16zz//PMkJibSqVMni26TVSiHHME6w47/9ZzlwEt5Ob8MOwrxvxYvXqwBXbJkSX3u3Dmj4zyUsLAwDWhnZ2d95MgRo+PYtDNnzujSpUtrQI8YMUJnZWUZHalAu3btmnZ2dtYODg760qVLRscpsFJTU3WLFi00oBs2bKhv375tsbYyMjJ0hQoVNKCjo6Mt1o6ZWWXYcTPQ33S7P/A//blKKQ+lVBHT7bJAc+BUPtsVwm4NGjSIF198kVu3btGtWzdSUlKMjpQnx48fZ/DgwQDMmTMHf39/gxPZtjp16hAeHk6RIkWYP38+H3/8sdGRCrR71/z617/+ZXScAklrzaBBg/juu+/w8vJi69atuLm5Way9w4cP88cff1C9enWLTOQ3Un6Lr1lAoFLqZ+A5032UUn5KqcWmxzwBRCmlYoC9wCyttRRfQjwipRRLlizhscceIyYmhhEjRtj8/K/4+Hg6depEcnIywcHBvPrqq0ZHKhBatGiRuzjoxIkTWb16tcGJCjZZ8yt/Jk+ezOrVq3Fzc2Pr1q1UqlTJou3du7BqoZuekNcuMiM+ZNhRiL92/PhxXaxYMQ3of/7zn0bH+Ut37tzRTZo00YAOCAiQq80ewezZszWgXVxcdGRkpNFxCqy0tDRdvnx5Dciw90OaO3euBrSTk5Pevn27xdvLysrS3t7eGtDffvutxdszI6sMOwohDOLr68vixdkdzKNGjbLJhTnT09Pp2bMn33//PdWrV2fjxo24uroaHavAGTt2LK+//jppaWl07dqVU6dk8OBRODs7y5pfjyAsLCx3n8alS5fSvn17i7f5448/cv78ecqXL0+zZs0s3p61SfElRAHWq1cvpkyZQlZWFj169CAqKsroSLkyMzMZMGAAW7ZswcPDg23btlG+fHmjYxVISinmzJlD165dSUpKol27dvz2229GxyqQ+vfPnqa8evVq/vzzT4PT2L6dO3fSr18/AD766CP69u1rlXbXrFkDwIsvvoijo6NV2rQmKb6EKOCmT59Ov379SElJoWPHjpw9e9boSGRlZfHqq6+yatUq3Nzc2LFjBz4+PkbHKtAcHR1ZuXIlLVq04NKlS7Rt2xZZjufh1a9fn0aNGpGUlMTmzbLb3d85duwY3bp1Iz09nbFjx/Lmm29apV2tNWvXrgWyd34ojKT4EqKAU0qxaNEiAgMDuXLlCq1atSI2NtawPBkZGQwaNIiFCxfi6urKli1b5MpGMylWrBhbtmyhfv36xMbG0qFDB27dumV0rAJH1vx6sF9++YUOHTrkXiTz8ccfW23S+w8//MC5c+eoUKECTz/9tFXatDYpvoQoBFxcXNi4cSOtW7cmPj6eVq1acebMGavnuHv3Li+//DIhISG5m+y2atXK6jkKM3d3dyIiIqhZsybHjh2ja9euMnz2kHr16oWzszMRERH8/vvvRsexOfHx8bRr146rV6/Stm1bli5dioOD9cqFnF6vwjrkCFJ8CVFoFC9enK1bt9KmTRsSEhJo3rw5+/bts1r7CQkJtGnThg0bNuDu7s6uXbsIDAy0Wvv2pGLFiuzcuRNPT0/27t1Lr169ZBX8h1C2bFk6d+4sa37dx7Vr1wgMDCQuLo7GjRuzbt06q256r7XOne/VvXt3q7VrbVJ8CVGI5AxLde7cmcTERAIDA1m6dKnF2/3+++/x8/Pj4MGDeHl5ERkZWSivULIl3t7eRERE4O7uzqZNmxg6dKisXfUQcoYeQ0JC5N/N5ObNm7Rv356TJ0/i4+PDjh07KFGihFUz/PDDD8TFxRXqIUeQ4kuIQqdYsWJs3LiRMWPGkJ6eziuvvMKAAQO4ffu22dvKzMxk1qxZNG/enMuXL9OiRQuioqJ48sknzd6W+F++vr58/fXXFCtWjGXLljF27FgpJPKoffv2lC9fnlOnTtnUVcJGuXPnDh07duTYsWPUrFmTnTt3UrZsWavnKOxXOeaQ4kuIQsjR0ZFPP/2URYsW4erqSkhICA0aNGD79u1mayMqKormzZszefJk0tPTGTlyJLt376ZChQpma0M8WLNmzdiwYQPOzs7MnTuXSZMmSQGWB87OzgQHBwMy8f7PP/8kKCiIAwcO4OXlxa5duyy+ev392MNVjrnyuhqrER+ywr0Q+Xfy5EndoEEDDWhAd+7cWUdFRT3y+U6fPq379u2rlVIa0JUqVdI7duwwY2LxKDZt2qSdnJw0oN955x2j4xQIMTExGtDu7u46JSXF6DiGSEtL0126dNGArlChgo6NjTUsS1RUVG6OjIwMw3Lkk6xwL4QAHx8fjh49yuzZs3Fzc2PLli34+fnx7LPPEhoamqfhyDt37vDVV1/RqVMnfHx8WLFiBU5OTowfP57Tp0/Trl07K3wn4u+88MILhIWF4ejoyIwZM5gxY4bRkWxe/fr18fPzIykpiQ0bNhgdx+oyMjLo27cvmzdvxsPDg507d1K7dm3D8uT0er300kuFesgRQGkb7p728/PTMhYvhPkkJCTwySefMH/+fO7cuQOAk5MTjRo1ws/Pj2rVqlG6dGm01iQlJXHhwgWio6OJiooiPT0dgCJFijBw4EDGjx9PjRo1jPx2xH2sXr2aPn36kJWVxaxZs5g4caLRkWzaggULGD58OK1atWLv3r1Gx7Ga9PR0goODWbt2LSVKlGDXrl2GrsentaZmzZqcP3+eyMhIWrZsaViWfMrTYmhSfAlhh27cuMGaNWtYuXIlBw4cICsr628fr5TC39+f3r178/LLL8u8LhsXEhLCwIED0VrzySefMHbsWKMj2axbt27h6elJSkoKZ8+epVatWkZHsrj09HR69+7NunXrKFmyJBERETRt2tTQTFFRUTRp0oSKFSty6dKlgtzzlafiy8nSKYQQtsfDw4Nhw4YxbNgwbt++zaFDhzh58iQXL17k5s2bKKVwc3OjevXq1KlTh4CAAEqVKmV0bJFH/fv3Jz09nSFDhjBu3DgyMjKYMGGC0bFsUsmSJenRowfLly9n6dKlfPDBB0ZHsqicze43bNhAqVKl+Oabb2xiBwp7ucoxh/R8CSFEIbVw4UKGDx+O1pqpU6cydepUq20RU5AcOHCAFi1aULFiRX777TecnZ2NjmQRaWlp9OzZk40bN+Lu7s7OnTvx8/MzOhZZWVlUr16dixcvsn///oK+vleefsFkwr0QQhRSQ4cOZfny5Tg4ODBt2jRZhuIvBAQE8Pjjj5OQkMDXX39tdByLuHv3Lj169GDjxo14eHiwa9cumyi8AL777jsuXrxI1apVad68udFxrEKKLyGEKMT69etHWFgYTk5OfPTRR7zxxhsPnONnb5RSDB48GIDFixcbnMb8kpOT6dy5M+Hh4bmFV+PGjY2OlWvVqlVA9p6b1txD0kgy7CiEEHZg8+bNdO/enbS0NIYMGcKXX35pN290eXHlyhW8vLzIzMzkt99+o3LlykZHMovExEQ6duzI4cOHqVChAhERETa1A0VaWhqenp4kJiYSExND/fr1jY6UXzLsKIQQIluXLl3YsmULRYsWZdGiRfTq1Yu7d+8aHctmlC9fnhdeeIGsrCxCQkKMjmMW8fHxtGzZksOHD1OtWjW+/fZbmyq8ACIiIkhMTKRevXqFofDKMym+hBDCTrRt25bt27dTsmRJ1qxZw/PPP8+tW7eMjmUzcoYelyxZUuCHZuPi4mjRogUnTpzgiSee4MCBAza5jEbOkGPv3r0NTmJdUnwJIYQdadmyJfv27aNixYrs2bOHli1bkpCQYHQsm/Dcc89RtWpV4uLiiIyMNDrOI/vxxx9p0aIFcXFx+Pn5sX//fpscRk1OTiY8PBzInu9lT6T4EkIIO9OgQQMOHjxIrVq1+PHHHwkICODnn382OpbhHB0dGTRoEFBwJ95v376dp59+mvj4eFq1asXu3bspW7as0bHua9OmTaSmptK8eXOqV69udByrkuJLCCHskLe3NwcOHKBJkyacP3+egIAADh8+bHQsww0cOBClFOvXr+f69etGx3koCxcupHPnziQnJxMcHMyOHTsoWbKk0bH+kr0OOYIUX0IIYbfKlSvHnj17aN++PdeuXaNVq1a5b4j2qmrVqrRr1460tDRCQ0ONjpMnWVlZTJ48mWHDhpGZmcmUKVNYsWIFRYoUMTraX7p69SrffPMNjo6OdO/e3eg4VifFlxBC2DE3Nzc2b97MiBEjuHv3LsHBwbz77rsFfsJ5fgwfPhyA+fPn2/y/Q2pqKsHBwcyaNQtHR0cWL17MjBkzbH4ngzVr1pCZmUm7du0oV66c0XGsToovIYSwc87Oznz++efMmzcPBwcHZsyYQc+ePUlJSTE6miE6duyIl5cXP//8M3v27DE6zl/67bffaNGiBWFhYZQoUYKvv/6aV155xehYeZLTqxgcHGxwEmNI8SWEEAKlFK+//jrbtm2jZMmSrF27lpYtW3Lp0iWjo1mdk5MTQ4cOBbJ7v2zRt99+S5MmTYiOjqZGjRocPHiQtm3bGh0rT06dOsXRo0cpWbIkXbt2NTqOIaT4EkIIkat9+/YcOnQIb29voqKiaNSokU33/ljK4MGDcXJyIjw8nMuXLxsd5z98+eWXtGnThitXrvDcc8/x/fffU69ePaNj5VnOIrY9evSgWLFiBqcxhhRfQggh/oOPjw9Hjx4lMDCQq1evEhgYyKxZs+xqU25PT0+6du1KZmamzSw7kZKSwiuvvMKIESPIyMhgzJgxbN++ndKlSxsdLc8yMjJYsWIFAAMGDDA2jIGk+BJCCPE/ypYty/bt25kyZUru1XRBQUHcvHnT6GhWM2LECCB7CYf09HRDs5w+fRp/f3+WLl2Kq6srISEhfPrppzg5ORma62Ht3LmT+Ph4HnvsMQICAoyOYxgpvoQQQtyXo6MjM2bMYOvWrbi7uxMeHk7Dhg05dOiQ0dGsonXr1tSpU4fff/+dLVu2GJYjNDQUPz8/Tp48SZ06dThy5Aj9+vUzLE9+5Aw5DhgwwOavyLQkKb6EEEL8rY4dO3Ls2DEaNWrE+fPnefrpp5k+fToZGRlGR7MopVRu75cRE+9v3brFwIED6d+/PykpKfTp04eoqKgCuwH1jRs32LRpE0op+vbta3QcQ0nxJYQQ4oFq1KjBoUOHGD9+PJmZmUydOpVWrVrx66+/Gh3Novr370/RokXZtWuXVbdg2rdvH08++STLly/H1dWVJUuWEBoaipubm9UymNtXX33F3bt3efbZZ6latarRcQwlxZcQQog8cXFx4aOPPmLXrl1UqlSJAwcO8OSTT7J48eJCOxnf3d09d9Nna/R+/fnnn4wbN47WrVvz66+/0qhRI44dO8agQYMK/DDd8uXLAfueaJ9D2fIvjJ+fn46KijI6hhBCiP9y/fp1Bg8ezKZNmwB49tlnWbhwITVq1DA4mfkdO3YMPz8/SpUqxaVLlyzW+3TkyBEGDRrEqVOncHR05O2332bKlCk4OztbpD1rOnHiBL6+vpQoUYKEhITCvMREnipk6fkSQgjx0MqUKcOGDRtYtWoVZcuWZffu3fj6+jJ37lwyMzONjmdWjRs3JiAggJs3b1pkv8ebN28ycuRImjVrxqlTp6hTpw4HDx5k2rRphaLwAli0aBEAffr0KcyFV55J8SWEEOKRKKXo1asXp06dyt2OaMyYMTRt2pQjR44YHc+sRo8eDcBnn31mtv0etdasW7eOJ554gs8//xxHR0cmTpxIdHQ0/v7+ZmnDFqSmpuYWrTk7B9g7Kb6EEELkS7ly5Vi9ejXh4eFUrlyZqKgomjZtysCBA/njjz+MjmcWQUFBVKlShbNnzxIREZHv8x0/fpzAwEC6d+9OfHw8TZs2JTo6mlmzZhW6nqF169aRlJREkyZNaNCggdFxbIIUX0IIIcyiS5cunDlzhsmTJ+Pi4sLy5cupXbs2H374YYHfpNvJyYmRI0cCMHfu3Ec+zx9//MHQoUNp2LAhu3fvxsPDgy+++IIDBw7g6+trrrg2ZcGCBYD0et1LJtwLIYQwu19++YUxY8awdetWIHu7nqlTpzJo0KACO48pMTERLy8vUlNTOXnyJD4+Pnl+7o0bN5gzZw5z587l9u3bODk58eqrrzJ16tQCtT3Qwzp58iT16tXDzc2N+Pj4Ar1URh7JhHshhBDGeOyxx9iyZQsRERE0btyY+Ph4hg8fjo+PDyEhIYZv1/MoSpcuTf/+/QGYN29enp5z8+ZNpk2bhre3NzNmzOD27dt06tSJEydO8NlnnxXqwgv+PdE+ODjYHgqvPJOeLyGEEBaltWb9+vW8/fbbnD17FoAqVaowbtw4Bg8eTPHixQ1OmHenT5/Gx8eHokWLcunSpb8snn7//Xf++c9/Mn/+fJKSkgBo06YN06ZNo0WLFtaMbJjU1FQqV67MjRs3iI6OpmHDhkZHsgbp+RJCCGE8pRQvvfQSJ0+eZPny5fj4+HDx4kVGjx5N1apVmTx5MnFxcUbHzJMnnniCtm3bkpqamturc68ffviBfv36Ub16dT744AOSkpJ45plniIyMZPfu3XZTeEH2RPsbN27g5+dnL4VXnknPlxBCCKvKyspi69atfPDBBxw+fBjILtDatm3L8OHD6dixo03PC9u+fTvPP/88lSpVIi4ujpSUFMLCwli6dCk571kODg5069aNsWPH0qxZM4MTG+Opp57i6NGjLFq0iMGDBxsdx1ry1PMlxZcQQghDaK05dOgQCxYsyN33O7O2MwAAEkBJREFUD7IXcO3WrRs9e/akZcuWODo6Gpz0P2mtqVevHqdOncLf35+YmJjc7KVKlWLgwIGMGjUKb29vg5Ma58iRIzRt2hQPDw8uXbpU6JbP+BuWH3ZUSnVXSp1USmUppfz+5nHtlVKxSqlflFKT8tOmEEKIwkEpRUBAACEhIVy+fJlPP/0UHx8frl+/zqJFi3j22WepXLkygwYNYs2aNSQmJhqa9+LFiyxfvpygoKDcTbaPHj3K3bt3CQwMZNWqVcTHxzNnzhy7LrwA/vGPfwAwZMgQeyq88ixfPV9KqSeALGAB8KbW+n+6qZRSjsBZIBC4BHwP9NJan3rQ+aXnSwgh7IvWmhMnTvDVV18RFhbGuXPncr/m4OBAkyZNaN68OU899RT+/v5Uq1bNIhtOZ2ZmcvbsWY4dO8a+ffvYu3fvf2SB7I3G09LSWLhwIUOGDDF7hoIqISGBqlWrkpmZSVxcHNWqVTM6kjXl6YfRKT8taK1PAw/6wfcHftFax5keGwa8ADyw+BJCCGFflFL4+vri6+vLjBkziImJISIigoiICL777juOHDnyH1sXlStXDh8fH+rUqUOdOnXw9vbG09OTihUrUr58eYoWLXrf96isrCxu3rzJtWvXuHr1KhcuXODcuXOcO3eOM2fOcPz48f9ZGLZEiRI888wzdOjQga5du7Ju3TpGjx7NsmXLGDx4sEWKwIJowYIFpKenExQUZG+FV56ZZc6XUiqSv+75eglor7UebLrfF3hKaz3yQeeVni8hhBA5kpOT+fbbbzl69ChHjhzh6NGjXL9+/W+fo5SiaNGiuUVYeno6aWlp3L1794F7NFatWpWGDRvSrFkzWrduTaNGjXBy+nefxZ07d6hatSqJiYns37+fp59+2izfZ0GWlpZGtWrVSEhIYM+ePbRu3droSNZmnp4vpdQuoOJ9vvS21jr8YVPlob2hwFDI/sEXQgghANzc3OjQoQMdOnQAsocoL1y4QGxsLLGxsZw5c4ZLly4RHx9PQkICV69e5e7du6SkpNx3e6OSJUtStmxZypYtS5UqVahRowY1a9bkscceo0GDBpQpU+Zv8xQvXpyRI0cyffp0PvzwQym+gLVr15KQkEC9evVo1aqV0XFsljV6vpoB72mt25nuTwbQWn/woPNKz5cQQoj8yMzMJDU1ldTUVLTWODs74+zsTJEiRcyynMW1a9eoWrUqqampxMTEUL9+fTOkLpi01jRs2JCYmBh7W17iXjazyOr3QC2llLdSygXoCWy2QrtCCCHsnKOjI25ubpQrV47y5cvj4eGBm5ub2dYRK1u2bO6G0dOnTzfLOQuqnTt3EhMTQ8WKFenTp4/RcWxafpeaCFJKXQKaAduUUhGm45WUUl8DaK0zgJFABHAaWKO1Ppm/2EIIIYRtmDhxIq6urqxfv57jx48bHccwH330EQBvvPEGrq6uBqexbbLIqhBCCJFPb7zxBvPmzeOll15i7dq1RsexumPHjuHn54ebmxsXL17E3d3d6EhGsZlhRyGEEKJQmzhxIkWKFGHdunX89NNPRsexuo8//hiAYcOG2XPhlWdSfAkhhBD5VKlSJYYNGwbY39yvuLg41q5di7OzM6NHjzY6ToEgxZcQQghhBvba+zVr1iyysrLo3bs3Xl5eRscpEKT4EkIIIczg3t6vd9991+A01vHrr7+ybNkyHBwceOutt4yOU2BI8SWEEEKYyaRJkyhWrBibNm3i0KFDRsexuJkzZ5KRkUFwcDC1a9c2Ok6BIcWXEEIIYSaenp6MGTMGgAkTJmDLKwrk1/nz51m+fDkODg5MmTLF6DgFihRfQgghhBlNmDCBMmXK8N1337F161aj41jM+++/T0ZGBn369JFer4ckxZcQQghhRiVLluSdd94BsochMzIyDE5kftLrlT9SfAkhhBBmNnz4cKpXr86pU6cICQkxOo7ZvfPOO7m9XrVq1TI6ToEjK9wLIYQQFrBq1SqCg4MpX748Z8+epVSpUkZHMovo6GgaN26Mi4sLsbGxVK9e3ehItkRWuBdCCCGM0qtXLwICArhy5QrTpk0zOo5ZaK0ZP348AK+//roUXo9Ier6EEEIIC4mOjsbPzw9HR0diYmLw8fExOlK+7Nixgw4dOuDu7s65c+coXbq00ZFsjfR8CSGEEEZq1KgRQ4cOJSMjg1GjRhXopSfS09Nze73efvttKbzyQYovIYQQwoJmzpyJh4cHu3fvZv369UbHeWSff/45J06cwNvbm5EjRxodp0CT4ksIIYSwoDJlyjBz5kwge57UjRs3DE708OLj43O3TJo3bx6urq4GJyrYpPgSQgghLGzYsGEEBASQkJDAuHHjjI7z0MaPH8/t27fp3LkznTp1MjpOgSfFlxBCCGFhDg4OLFmyhCJFirBs2TK++eYboyPl2d69e1m5ciWurq589tlnRscpFKT4EkIIIazg8ccf57333gNg6NChJCcnGxsoD5KTk3nllVcAeOutt/D29jY4UeEgxZcQQghhJW+++SaNGjXiwoULjB071ug4DzRp0iTOnz9PgwYNmDRpktFxCg0pvoQQQggrcXJyYvny5RQpUoRFixaxbt06oyP9pcjISD7//HOcnJxYtmwZzs7ORkcqNKT4EkIIIazI19eX2bNnAzBkyBAuXLhgcKL/lZiYyIABA4DsNb0aNGhgbKBCRoovIYQQwspee+01unTpQlJSEr169SItLc3oSLm01gwcOJALFy7g5+fHW2+9ZXSkQkeKLyGEEMLKlFIsXboULy8vDh06xMiRI21m9fvPPvuMzZs3U6pUKdasWYOLi4vRkQodKb6EEEIIA5QpU4YNGzbkzv/64osvjI7EgQMHmDBhAgDLli2TqxstRIovIYQQwiBNmjRhyZIlALzxxhvs3LnTsCxxcXF07dqV9PR0Ro8eTVBQkGFZCjspvoQQQggDBQcHM3HiRDIzMwkKCuLw4cNWz3Djxg06derEtWvXaN++PR9//LHVM9gTKb6EEEIIg73//vv07duXO3fu0KFDB44fP261tm/dukX79u05ffo0devWJSwsDCcnJ6u1b4+k+BJCCCEM5uDgwNKlS+natStJSUk899xzREdHW7zd5ORkOnfuzNGjR6levTrbt2+nVKlSFm/X3knxJYQQQtgAJycnwsLCaN++PVevXqVVq1ZERkZarL0rV67QunVr9u/fT+XKldm9ezdVqlSxWHvi36T4EkIIIWxEkSJFCA8P5+WXX+b27du0bduW+fPnm30ZipMnT9K8eXOioqKoUaMGkZGR1KhRw6xtiL8mxZcQQghhQ1xcXFi5ciVjxowhPT2dV199lX79+nHjxo18n1trTWhoKP7+/vzyyy80bNiQAwcO8Nhjj5khucgrKb6EEEIIG+Po6Minn37KypUrKVq0KP/617+oW7cu69ate+ResF9//ZXOnTvTv39/UlJS6Nu3L99++y0VK1Y0c3rxIFJ8CSGEEDaqd+/eREdHExAQQHx8PN27d8ff35/w8HDS09PzdI7z58/z2muv8fjjj7Nt2zZKlizJ4sWLCQkJoXjx4hb+DsT9KFvZzuB+/Pz8dFRUlNExhBBCCENlZmaycOFCpk+fTkJCAgAVKlQgKCiIZ555Bl9fXzw9PXF0dCQ5OZnY2FiOHTtGeHg4Bw8ezD1Pr169+OSTT/D09DTqWynsVJ4eJMWXEEIIUTCkpKSwYMECFi5cyJkzZ/L0HFdXV7p3787EiROpW7euhRPaPSm+hBBCiMJIa01UVBS7du3iu+++Iy4ujvj4eLTWFC9enGrVqtGwYUOeeeYZOnbsSIkSJYyObC+k+BJCCCGEsKI8FV8y4V4IIYQQwoqk+BJCCCGEsCIpvoQQQgghrEiKLyGEEEIIK5LiSwghhBDCiqT4EkIIIYSwIim+hBBCCCGsSIovIYQQQggrylfxpZTqrpQ6qZTKUkr5/c3jflVK/aSU+lEpJaumCiGEEMJuOeXz+SeAbsCCPDy2tdb6Wj7bE0IIIYQo0PJVfGmtTwMolafV9IUQQggh7J615nxp4Bul1DGl1FArtSmEEEIIYXMe2POllNoFVLzPl97WWofnsZ0WWuvLSqnywE6l1Bmt9f6/aG8oMBSgatWqeTy9EEIIIUTBoLTW+T+JUpHAm1rrB06mV0q9ByRrrWfn4bE7tNbt8x3w79sYqrVeaMk2hG2S195+yWtvn+R1t1+29tpbfNhRKVVcKVUi5zbQluyJ+g9k6cLLRIZB7Ze89vZLXnv7JK+7/bKp1z6/S00EKaUuAc2AbUqpCNPxSkqpr00PqwB8p5SKAY4C27TWO/LTrhBCCCFEQZXfqx03Ahvvc/x34HnT7Tjgyfy0I4QQQghRWMgK92AzY8DC6uS1t1/y2tsned3tl0299maZcC+EEEIIIfJGer6EEEIIIazIbosvpVR7pVSsUuoXpdQko/MI81JKVVFK7VVKnTLtP/qG6fh7SqnLpn1Gf1RKPX/Pc+orpQ6ZHv+TUsrVuO9A5Mf99pP9q71olVKBpgWgfzJ9bmNccpEfSqk69/xu/6iUuqWUGm362utKqTOmn4GPTMeclVIhptf+tFJqsrHfgXgYSqmlSqkrSqkT9xwrrZTaqZT62fTZw3Q8WCl13PRaH1RKPflf53JUSv2glNpqlez2OOyolHIEzgKBwCXge6CX1vqUocGE2SilPAFPrXW0aamTY0BXoAf3WWdOKeUERAN9tdYxSqkyQJLWOtPa2UX+KaV+Bfzu3U9WKfUEkEX2XrS56xIqpRoCf2itf1dK1QMitNaVDYgtzMj0//xl4CmgBvA20FFrfVcpVV5rfUUp1RvoorXuqZQqBpwCWmmtfzUsuMgzpdQzQDIQqrWuZzr2EZCotZ5l6ljx0FpPVEoFAKe11jeUUh2A97TWT91zrrGAH1BSa93J0tnttefLH/hFax2ntU4DwoAXDM4kzEhrHa+1jjbdvg2cBv7uDbUtcFxrHWN6znUpvAoXrfVprXXsfY7/YLpCG+AkUFQpVcS66YQFPAuc01pfAEYAs7TWdwG01ldMj9FAcdMfX0WBNOCWEWHFwzPtlJP4X4dfAEJMt0PI/qMbrfVBrfUN0/HDgFfOE5RSXkBHYLFFA9/DXouvysDFe+5f4u/fmEUBppSqDjQEjpgOjTR1Py/N6ZIGagNaKRWhlIpWSk0wIKown0fdT/ZFIDrnTVoUaD2B1abbtYGnlVJHlFL7lFJNTMfXAXeAeOA3YLbW+r/fzEXBUkFrHW+6nUD2WqP/7RVg+z335wITyO4Ztwp7Lb6EnVBKuQHrgdFa61vAfKAm0IDs/3A/MT3UCWgBBJs+BymlnrV+YmEmLbTWjYAOwGum4Ym/pZSqC3wIDLN0OGFZSikXoAuw1nTICSgNNAXGA2uUUorsUZBMoBLgDYxTStWwfmJhCTp7XtV/zK1SSrUmu/iaaLrfCbiitT5mzWz2WnxdBqrcc9/LdEwUIkopZ7ILr5Va6w0AWus/tNaZWussYBHZ//lCdu/nfq31Na11CvA10MiI3CL/tNaXTZ+vkL0QtP/fPd407LAR6Ke1Pmf5hMLCOpDdg/mH6f4lYIPOdpTsHo6yQG9gh9Y63fSzcoDseT+i4PrDNOc3Z+5vzhAzSqn6ZA8tvqC1vm463BzoYponGga0UUr9y9Ih7bX4+h6opZTyNv2F1BPYbHAmYUamv2qXkD3B8tN7jnve87Ag/r3PaATgq5QqZpr/0ZLsybeigFEPuZ+sUsod2AZM0lofsE5KYWG9+PeQI8AmoDWAUqo24AJcI3uosY3peHGye8bOWDWpMLfNQH/T7f5AOIBSqiqwgeyLqs7mPFhrPVlr7aW1rk52LbBHa93H0iHt8mpHANMSA3MBR2Cp1nqmwZGEGSmlWgDfAj/x73H8t8j+T7kB2V3RvwLDcuYHKKX6AJNNX/taay3zvgog07BRzrZnTsAqrfVMpVQQ8A+gHJAE/Ki1bqeUmkL26/7zPadpe8+kbFGAmIqo34AaWuubpmMuwFKyf/fTyL7adY9pWsIywAdQwDKt9cfGJBcPSym1GmhFdi/mH8BUsgvtNUBV4ALQQ2udqJRaTPaczgump2dorf3+63ytyP7ZsPjVjnZbfAkhhBBCGMFehx2FEEIIIQwhxZcQQgghhBVJ8SWEEEIIYUVSfAkhhBBCWJEUX0IIIYQQViTFlxBCCCGEFUnxJYQQQghhRVJ8CSGEEEJY0f8Ds4ghtLylS64AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cs.makeTable(2, 1024, 10, 1)\n",
"cs.makeTable(3, 1024, -10, 0.5, 1)\n",
"cs.plotTable(2)\n",
"cs.plotTable(3, reuse=True)\n",
"#ylim((-1.1,1.1))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.57580819, 0.58081396, 0.58579786, 0.5907597 , 0.5956993 ])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cs.table(2)[100: 105]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tables can also be passed by their variable name in Csound:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"%%csound 1\n",
"giHalfSine ftgen 0, 0, 1024, 9, .5, 1, 0"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cs.plotTable('giHalfSine')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following will create 320 tables with 720 points each:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 .. 1 .. 2 .. 3 .. 4 .. 5 .. 6 .. 7 .. 8 .. 9 .. 10 .. 11 .. 12 .. 13 .. 14 .. 15 .. 16 .. 17 .. 18 .. 19 .. 20 .. 21 .. 22 .. 23 .. 24 .. 25 .. 26 .. 27 .. 28 .. 29 .. 30 .. 31 .. 32 .. 33 .. 34 .. 35 .. 36 .. 37 .. 38 .. 39 .. 40 .. 41 .. 42 .. 43 .. 44 .. 45 .. 46 .. 47 .. 48 .. 49 .. 50 .. 51 .. 52 .. 53 .. 54 .. 55 .. 56 .. 57 .. 58 .. 59 .. 60 .. 61 .. 62 .. 63 .. 64 .. 65 .. 66 .. 67 .. 68 .. 69 .. 70 .. 71 .. 72 .. 73 .. 74 .. 75 .. 76 .. 77 .. 78 .. 79 .. 80 .. 81 .. 82 .. 83 .. 84 .. 85 .. 86 .. 87 .. 88 .. 89 .. 90 .. 91 .. 92 .. 93 .. 94 .. 95 .. 96 .. 97 .. 98 .. 99 .. 100 .. 101 .. 102 .. 103 .. 104 .. 105 .. 106 .. 107 .. 108 .. 109 .. 110 .. 111 .. 112 .. 113 .. 114 .. 115 .. 116 .. 117 .. 118 .. 119 .. 120 .. 121 .. 122 .. 123 .. 124 .. 125 .. 126 .. 127 .. 128 .. 129 .. 130 .. 131 .. 132 .. 133 .. 134 .. 135 .. 136 .. 137 .. 138 .. 139 .. 140 .. 141 .. 142 .. 143 .. 144 .. 145 .. 146 .. 147 .. 148 .. 149 .. 150 .. 151 .. 152 .. 153 .. 154 .. 155 .. 156 .. 157 .. 158 .. 159 .. 160 .. 161 .. 162 .. 163 .. 164 .. 165 .. 166 .. 167 .. 168 .. 169 .. 170 .. 171 .. 172 .. 173 .. 174 .. 175 .. 176 .. 177 .. 178 .. 179 .. 180 .. 181 .. 182 .. 183 .. 184 .. 185 .. 186 .. 187 .. 188 .. 189 .. 190 .. 191 .. 192 .. 193 .. 194 .. 195 .. 196 .. 197 .. 198 .. 199 .. 200 .. 201 .. 202 .. 203 .. 204 .. 205 .. 206 .. 207 .. 208 .. 209 .. 210 .. 211 .. 212 .. 213 .. 214 .. 215 .. 216 .. 217 .. 218 .. 219 .. 220 .. 221 .. 222 .. 223 .. 224 .. 225 .. 226 .. 227 .. 228 .. 229 .. 230 .. 231 .. 232 .. 233 .. 234 .. 235 .. 236 .. 237 .. 238 .. 239 .. 240 .. 241 .. 242 .. 243 .. 244 .. 245 .. 246 .. 247 .. 248 .. 249 .. 250 .. 251 .. 252 .. 253 .. 254 .. 255 .. 256 .. 257 .. 258 .. 259 .. 260 .. 261 .. 262 .. 263 .. 264 .. 265 .. 266 .. 267 .. 268 .. 269 .. 270 .. 271 .. 272 .. 273 .. 274 .. 275 .. 276 .. 277 .. 278 .. 279 .. 280 .. 281 .. 282 .. 283 .. 284 .. 285 .. 286 .. 287 .. 288 .. 289 .. 290 .. 291 .. 292 .. 293 .. 294 .. 295 .. 296 .. 297 .. 298 .. 299 .. 300 .. 301 .. 302 .. 303 .. 304 .. 305 .. 306 .. 307 .. 308 .. 309 .. 310 .. 311 .. 312 .. 313 .. 314 .. 315 .. 316 .. 317 .. 318 .. 319 .. "
]
}
],
"source": [
"randsig = np.random.random((320, 720))\n",
"i = 0\n",
"for i, row in enumerate(randsig):\n",
" cs.fillTable(50 + i, row)\n",
" print(i, '..', end=' ')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cs.plotTable(104)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sending instruments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can send instruments to a running csound engine with the *%%csound* magic. Any syntax errors will be displayed inline."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"error: syntax error, unexpected T_IDENT (token \"asds\") line 2:\n",
">>>asig asds <<<\n",
"Unexpected untyped word asig when expecting a variable\n",
"Parsing failed due to invalid input!\n",
"Stopping on parser failure\n",
"\n"
]
}
],
"source": [
"%%csound 1\n",
"instr 1\n",
"asig asds"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"error: syntax error, unexpected $end (token \"\") line 3:\n",
">>>outs asig, asig <<<\n",
"Parsing failed due to invalid input!\n",
"Stopping on parser failure\n",
"\n"
]
}
],
"source": [
"%%csound 1\n",
"instr 1\n",
"asig oscil 0.5, 440\n",
"outs asig, asig"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"%%csound 1\n",
"instr 1\n",
"asig oscil 0.5, 440\n",
"outs asig, asig\n",
"endin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Channels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Csound channels can be used to send values to Csound. They can affect running instances of instruments by using the *invalue/chnget* opcodes:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"cs.setChannel(\"val\", 20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also read the channels from Csound. These channels can be set from *ICsound* or within instruments with the *outvalue/chnset* opcodes:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(20.0, c_int(0))"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cs.channel(\"val\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recording the output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can record the realtime output from csound:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"cs.startRecord(\"out.wav\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"cs.sendScore(\"i 1 0 1\")\n",
"import time\n",
"time.sleep(1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"cs.stopRecord()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lecture WAVE 'out.wav' : Signed 16 bit Little Endian, Fréquence 44100 Hz, Stéréo\r\n"
]
}
],
"source": [
"!aplay out.wav"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Remote engines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also interact with engines through UDP. Note that not all operations are available, notably reading f-tables, but you can send instruments and note events to the remote engine."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Csound engine started at slot#: 2.\n"
]
}
],
"source": [
"cs_client = ICsound()\n",
"cs_client.startClient()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"cs.clearLog()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now send notes and instruments from the client:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"cs_client.sendScore(\"i 1 0 1\")\n",
"cs_client.sendCode(\"print i(gkinstr)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And show the log in the server:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"instr 0: #i0 = 1.000\n",
"\n"
]
}
],
"source": [
"cs.printLog()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stopping the engine"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"cs.stopEngine()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<csoundmagics.ICsound at 0x7fca3a1ae668>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we don't need *cs_client* anymore, we can delete its slot with the *%csound* line magic (note the single % sign and the negative slot#). The python instance *cs_client* can then be deleted:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Erasing slot#: 2\n"
]
}
],
"source": [
"%csound -2\n",
"del cs_client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Audification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading Earthquake data through a web API (might take a few minutes):"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"prefix = 'http://service.iris.edu/irisws/timeseries/1/query?'\n",
"SCNL_parameters = 'net=IU&sta=ANMO&loc=00&cha=BHZ&'\n",
"times = 'starttime=2005-01-01T00:00:00&endtime=2005-01-02T00:00:00&'\n",
"output = 'output=ascii'\n",
"import urllib\n",
"f = urllib.request.urlopen(prefix + SCNL_parameters + times + output)\n",
"timeseries = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fca00beb048>]"
]
},
"execution_count": 35,
"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": [
"import ctcsound\n",
"data = ctcsound.pstring(timeseries).split('\\n')\n",
"dates = []\n",
"values = []\n",
"\n",
"for line in data[1:-1]:\n",
" date, val = line.split()\n",
" dates.append(date)\n",
" values.append(float(val))\n",
"\n",
"plt.plot(values)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Csound engine started at slot#: 1.\n"
]
}
],
"source": [
"cs.startEngine()\n",
"cs.fillTable(1, values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instrument to play back the earthquake data stored in a table:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"%%csound 1\n",
"instr 1\n",
"idur = p3\n",
"itable = p4\n",
"asig poscil 1/8000, 1/p3, p4\n",
"outs asig, asig\n",
"endin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Listen:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"cs.sendScore('i 1 0 3 1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Slower:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"cs.sendScore('i 1 0 7 1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quicker:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"cs.sendScore('i 1 0 1 1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other tests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another engine:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Csound engine started at slot#: 2.\n"
]
}
],
"source": [
"ics = ICsound(bufferSize=64)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ics.listInterfaces()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"%%csound 2\n",
"\n",
"instr 1\n",
"asig oscil 0.5, 440\n",
"outs asig, asig\n",
"endin"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"ics.sendScore(\"i 1 0 0.5\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Erasing slot#: 2\n"
]
}
],
"source": [
"%csound -2"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"del ics"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"cs.stopEngine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Authors: Andrés Cabrera, November 2014, François Pinot, June 2016"
]
}
],
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| lgpl-2.1 |
gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | Texture_and_Dealiasing.ipynb | 2 | 775926 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"##Calculating Texture and Dealiasing\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
"## library for working with weather radar data.\n",
"##\n",
"## If you use this software to prepare a publication, please cite:\n",
"##\n",
"## JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.4/json/encoder.py:192: DeprecationWarning: Interpreting naive datetime as local 2017-06-18 17:46:25.330017. Please add timezone info to timestamps.\n",
" chunks = self.iterencode(o, _one_shot=True)\n"
]
}
],
"source": [
"import pyart\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"from scipy import ndimage, signal\n",
"import time\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"radar = pyart.io.read('./data/csapr_test_case.nc')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Data available by FigShare Here: http://figshare.com/articles/Data_for_AMS_Short_Course_on_Open_Source_Radar_Software/1537461\n",
"Download and unpack into the data subdirectory of this repository "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"nyq = radar.instrument_parameters['nyquist_velocity']['data'][0]\n",
"\n",
"display = pyart.graph.RadarMapDisplay(radar)\n",
"fig = plt.figure(figsize = [10,8])\n",
"display.plot_ppi_map('velocity', sweep = 2, resolution = 'i',\n",
" vmin = -nyq, vmax = nyq, mask_outside = False,\n",
" cmap = pyart.graph.cm.NWSVel)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"266.757094145\n"
]
}
],
"source": [
"start_time = time.time()\n",
"data = ndimage.filters.generic_filter(radar.fields['velocity']['data'],\n",
" pyart.util.interval_std, size = (4,4),\n",
" extra_arguments = (-nyq, nyq))\n",
"total_time = time.time() - start_time\n",
"print(total_time)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'ndimage' 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-1-c3dda5c466f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfiltered_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mndimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtexture_field\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpyart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'velocity'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtexture_field\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mradar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'velocity_texture'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtexture_field\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace_existing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'ndimage' is not defined"
]
}
],
"source": [
"filtered_data = ndimage.filters.median_filter(data, size = (4,4))\n",
"texture_field = pyart.config.get_metadata('velocity')\n",
"texture_field['data'] = filtered_data\n",
"radar.add_field('velocity_texture', texture_field, replace_existing = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"display = pyart.graph.RadarMapDisplay(radar)\n",
"fig = plt.figure(figsize = [10,8])\n",
"display.plot_ppi_map('velocity_texture', sweep = 2, resolution = 'i',\n",
" mask_outside = False,\n",
" cmap = pyart.graph.cm.NWSRef,\n",
" vmin = 0, vmax = 14)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"n, bins = np.histogram(filtered_data.flatten(), bins = 150)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"peaks = signal.find_peaks_cwt(n, np.array([10]))\n",
"centers = bins[0:-1] + (bins[1] - bins[0])\n",
"search_data = n[peaks[0]:peaks[1]]\n",
"search_centers = centers[peaks[0]:peaks[1]]\n",
"locs = search_data.argsort()\n",
"location_of_minima = locs[0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.59590638749\n"
]
},
{
"data": {
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TcX83YEpK6eWU0svAVHJwrNuSdvIGDYKhQ5fkFSVJkpZeLWfX7kkOeH8qHm8WEVfUcvCI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\n1Lz4Ythuu7KrUU9ZY4384f7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9LSOl9A/gfPIH3fY5Pb8or6LmFhEXAzcB/xERT0bEZ4GT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d34cc90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = [10,6])\n",
"plt.plot(centers, n)\n",
"zmax = n.max()\n",
"plt.xlabel('Radial velocity texture')\n",
"plt.ylabel('Number of Gates')\n",
"\n",
"plt.plot([centers[peaks[0]], centers[peaks[0]]], [0, zmax])\n",
"plt.plot([centers[peaks[1]], centers[peaks[1]]], [0, zmax])\n",
"plt.plot([search_centers[location_of_minima], search_centers[location_of_minima]], [0, zmax])\n",
"noise_threshold = search_centers[locs[0]]\n",
"print(noise_threshold)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"likely_noise = filtered_data > noise_threshold\n",
"likely_signal = np.logical_not(likely_noise)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"z_masked = np.ma.masked_where(likely_noise, radar.fields['reflectivity']['data'])\n",
"radar.add_field_like('reflectivity', \n",
" 'reflectivity_masked', \n",
" z_masked, replace_existing = True)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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iL7kTf9YJeWEeALpuRrBDjw2xZdJI15+ethzmDLfEUTDEtsmNwPRJaKKbk7fb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O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\nI9VAQ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Xo9E8oTwWCjnlsqte/mxVBw6tf4+J/i4xeXoIEiXW6Y62PQsYMkcqBIoR9swb/f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YtxDBWROHmXEZaLSt83wXZYp8YO9yJOiLOezEFPaXBi6NYH7uJvIT81CDiOcxeezoDHwr\nBP+W0zEl3OfZ7YGqMsxvXD1ZyELUiowNTQv3QhbbO6vAopM6ms0+OTLWeLW9Lynp5DofJqbllXku\noGxbzCOXR9fMmqPz+9v+13qdjb9q0VrH5jjmNDQffv1JZUZnXA8nrMSOTcO0KVCax/xXWKRvAnDG\n/SoW2oEm6HeBrRs0kXVYAC17dH8tUKc6tDd3wR3vwFULhIXZdVAyAIKVcKeD5uZ1YDojR+NmBdcS\nVa7+qdn1Dq1VHXOoCULrtW1xQblIrWs3N8hhZIkCj019hI8NR8Du44Ql7bYpkxVqlNLDv0GGVtmQ\nM7e6GzxPKMCZvR78/wEs0L5HdAZzqTt/ryBpesjJGMudgqJcnzPtcNnXIpJYPwsYDg1P6r1yfpG/\n0hMgsUMYmR+tz1E1RtgW7gQlPUWOsvUQqhNJenEnTHO4loqKtO4dIIxs7l+00M80Q6BMWJtvltlK\nYJjwu3mN/F+SvR3JyulxENkq1LmXLSleMs1pBZxtTt+Jf5/OWXN/J89c60h1iUxPwnUqPQCKMv42\nddcHtghtQUHN3e/BXtSWJgNafwxAaxJ36ZV8558bIz/L8Y/i42GCdoiMpqamT9wHbb9sWFet3m3n\nYhDAtJFyeF6QV6vtnlWT65UHkMKKPMxLSHN/blygVp2HXQ2w929aYNQC06+D6jxmSkgkLmmKxdQn\nLlQBdA8k46hDgug6tKKuQZq1BLAPPL+Dm5bD5H4wu0lglK7XZFrSS899+F0os+rrEqiQLW1sk1z/\n0lEIORNK64HkOcV+LYEYRJ8SAYhXO3miH3ZPhn29RZL8ZZpETU7ywtIjoMNyRfJKXtDEmj9GIBFZ\nWTTnKNkMWSf/8yUlN6Qn7BgMe4+CzFQk0ZuNtPRno7RPo7T7qaiIZOZYKDlNIJIpUfTRH9fxBCtE\nlnwRqD5eE1aguwhsPqeJfuYunbtsXEXh+WbYExUAlXRzUdW+kkC+161o1e9zxdmeiJwdjU/vZbzg\nd5HHdEMxM8dsV2iNAM7koGwhsMU1CX1Gl8exnWBCTyeX7OrIsYsOBl1Lg0K0MO/VoiDUgFoLzADK\nde7rU7qECvLGKrTmTACNi93r2167jtx5nnD/L+Bfchhjuhtj5htj3jXGrDLGfPOAx280xuSNMR8n\nAHo7yts2AFhrlyGx8eHxWYztXWHCOkzNjyRfnH4DdvL5cObTsKhNlmzqbfu/roPLbs0dd9DdmooL\nsXevgMgYTMWFmK+BuQXYCfZ7BvotF0koi4FR6toWGtK+5bZt+x0mHSlKVGP+3UkVLwB7k1FmrgIZ\nkYAyaW2PZQjYKWB+HlET54e8cBaqb9v498/lyTgkDGazX4RvFdhL/K39N+3ElF63z4PZ61U9WoVq\nvTgi7xoe56CsUc/bbESQnvBI8rjZJ0n+Sg9mYRBbnhdJSxo1pK71QtII61zWzEYttnMO80YIOzkl\nY5Kvh9SjbbXkkvbXntZjBFplmqYvUpasa/PY0DxmSRPm3ImaBF8di3koLyL5TgobzcMxOewX0nBG\nvihndEEycu62CRG3HCIuCVhxgYKVgyvh8t6a85/aAGVnwHTn5jfyr5q/q06QvDHdIAyJHwHmZGFA\n+VZhn/VAdKfUDCVrhAftHtMcHusCpe8VMTITEW76yzTfB2JQvVolASYHhKDsWc3r+Z7CNG/GSRSH\nAafJldiTh7pfA9+VJHJFGPKudxdNqBJogXvNb5WZqz0SwnvccXUV4aod7ByafcLscB14yyE02mWj\nosI4jw9+6xOpLYx1iJzN3KUWBbZFio/rR+i8VYyGyEB4b4jKBjK9JKGsWiszLYD4ECVzS3sKr2On\n6D2TLhDsb5GE1HhlXpYq0/dQNhdiI3Xuu5WqLHRtFhgAwRORJN8t0wrOmRn3e23p4ExN6pEKZ476\nnvKeCILPncIoWnZtRWSsINitpIih+cIfDhtLDmR2/wLjnwkfDxO0Q2R80gyaZ6ZUxgf2P7MDywVa\nQP6UKPl/kyuE7ZwToeufUd+YHkVjEltVJbljSAXXrQ2s1/lEvvpnML97Aia9AeuHY3/igc9QxgAA\nIABJREFUxd6eEokLOfnG8Rn46+mK7mxAEcImZI9bixqh7kIg1A2tuPvCXcNh+jJNXlMXw1IjQNqe\n0e2hcdpWDFfUKbVTk2TJLi3wozuVlSqb62q6BrWRB54hl8VsSICTDSma2GGOIm+5lLJImRLJPVq2\nK4rWPFqSxgaXXQMVS3tWILIVkq7dvC0QqVojjX3pYOgYg2SBKByDlM4rnHuj6y/jSwkIfRFNnMFK\n5yaJ62Gzx9nouqB7Ni4CVTZABLa0p87XVwbr3O30gKdM4LC6Xm6Y/rKinND41AAzOQbSXZwLlmM4\nmd3F9ygk+GOb9J2l90J4LdROdbV0jmSV1Lri74L9fgi8gWL9GTj7/qMlrTQ5kelIraQy4XqoeNv1\n6fFJvtI6nBLMOQzTieJkmECA49WlQzAInXsCX1Kj0pZrIbkBaudA8HzJND/t8RmbhGSAG6y1R6Iq\nja8bYwaBwAk4FeHwxxkZa23jAfcdxAz68PiHx4SXP/AhM9etvu5zNWrbu8KZT2Nv2IuZbmH1QEyP\nu2XHPvU2uSseu0R1vhOuwv6x9gP3feAxmLOq4QK/SF9ZTJmunYh0XZxuJVz2mQNemwCWxkXU/htl\n1x5BK7xVYKca7IP7MzB7jwd7fxz7QzSXz01j/wvM9VJhtD5vbFJOjyfnMbeAGaPfPJs8mN8FsQln\nGvFbjxpYfyOAmRNUDXUFMMJCfRV2ehy6W+HNOXkZefTOYh6MYCelsUelMdt8qkfb5FPgsGAYUp2j\n1aK/exayYPtlFJQEeNhV7NR69flnxoWDKwOqx3bPs99yKZGoa3y9NKjPNzgjh8oLZmJWvgCbPDIk\neTyo9++ckwHJHR5NXO+j2qp5wHrouh0RtJUIJ48EesCwvXDhIpG0K46Exz8Pk/uC/z/hCi9EnpdR\nyJXzIFOrzgq7B6kOy8Yg85YaJje4OqlkZ+FVwNWZZUOw+6sHx0iTFzEy3mKfyaYxIk+1R0PzBGC4\nyAmIXHicIUXLcXqv+qtVapAvmEZ1g2GdYd/lKJPTiPC0IK2/jFbb/FxAEv2K4WoiXerkgtYr/Gjq\nJYmhzSpg6YvA6IikjDcOhxM6wftJPadbqbJpXxksZUhhhOp03kCqk9Eu25iNqXyhfqjWBclaKVNs\nTmuGrCtJzNeJrKWjwnNPBvx1Os7yLTLrKmBkshxiq/V3c1q12DYHu+/Qd2LyMgarXO/6qm1xGOnu\nA4q6fkc+LdDhgIdAMfFyIDhA2/Jh4Pkm1F0KddMhOR2aFolY/zNj5EHGPw0+HiZoh8j4WC6OB4xC\nvZlZGoT1HjyTE3huT2Dq6kTcaryYDQ2YDQ14XnWr9O5ZRRw3O5lI76ws+dtk4QqjIO/In1Qm4hYT\nGDO3v1yrnowL8Lq1uUz3eeB4N3N1QiEd0AxSDdEAyscnEEmrBrop6/KSgZNa+jKrD5w0W+5VU16V\n82MOZdH2hUQ6wr0huxDSJyjC19hLb5MdIfe/+n7Ack2ILZ1gz/ma3BNVuq98q4qWS2ohsLPYFyQT\ngfAOFe0WTEOCzSIXea8DL5c1y4ah8pfQdA5yoXIB8pb3YOfJjmy9h2bHV9STxbdM5yWyDio2QLux\nTs7QokxVpFaRwcRAgWDWyS8CnV2mLyig8JcVbXjvXq5sY3MbJ6gTElC/TLb3Nisg8EWkt4/00X3B\nz0F0vEDceiGzUWQuvhbiG0UGPd103pJDVOi8r6PIFUDJfEUI6SnnrUR7EecB2+HUKsi8CLzh+rW0\nQQ5fQsYpQKvUAtw5vAxYAJn3tN5rh/q1JtxpzLhbazVmIbvXCPEe+rMgAY0M5DMbn2UNmrV2l7V2\nuft7H7AGCb4AfsH/zOTjXWPMxYDPGNPPGPNL4I1/6MMfHgcfU78PT14Nt96JHXUVvDZO901+Qpb6\nPUNw9ELY3sZZ99tqMk1Z7AN2Ctz4ICwok+Rx13Lsq2BvT2L/AzgJOGIhZvxw7O3OsfDRg9ezmf/O\nauX916TqiEMLoeNG7FxHmGYf5DVfA2YkMdPQj3B3H7gor/8P7DO4A7glILOSkZLR21W0qjTMzRl4\nxaPjrkAEZxUwPoe5zIoQ/TQJm/0ihZ2tMnQhwJcGT6OT3Oclacy6PmYrA9Avj/lpANPgVTuC3hlh\nUUj2/mZBSFmv8pywb50fvhlU4LHWq7qzmNG2Ig8PJ1t7o9mhacwvlF1jm7J15vkIlNqi4QjoeKpz\n6hd6SRx7SVwSy4UOo7tnVcP2Paum1ZXIybEejqmGHQZFnQLuthbVpFUDLz/EhXk4aQP490DV02hi\nHAA/HKT5/5EW4cibu2BImTDC31FZqGS5CEWut9z/PHmn+FgufKzYeHCMLOBjcEURI31r9PrKla5E\noLfIRNo11k5P0DbYKMJS+YTwpXqey3xlYOUOiPweqR46oXKHcgX8mvs7N8YSYWT9m9C0Dn76vvYZ\n3SnCFOsijG3ZDsm9wrRIN2HYUxsgVS/8W10vjJzyKiyO6fECyapfJmMVb1BZx7KAcNjXH6L9hHsV\nn2vze/ApmFpoFxOsVl14fqXKEFrcz7kgazRZKNkD9NS5LanVewEcZcH/O0i87dofdNJaI+9X0LJA\negstBsx2RGTPZn/cdNtyRBicUXLx55mEgpFydpC2fkdQ8xvAXMtnMg4FfDxM0A6REY1G8fs/uv2C\nZ/IBzajfBu736td4ZU5Sizlh7MQ45mdBFTm7yF8rCcu4vjMLQpifR1r19GZH/X5EzSwJSu5RV1es\nVWujvTfzwpABMzkk4Nrmg4QR8ASQbXC9u/UEvBCrQsTs6A7QG6i5CDqEOelvwB1vQNcNrb1hZuyE\nWc3QtRSG/h5+MEi2tLmUm1Cj0uGXHgHlEyUbTEfBzFKmp/ksRbCq1ohgpcoEJGXbimACmvzCOyB4\nqmtw6SnKIQsjtEZSycA612OmvR5v/op7Xi9gAXimQof/hOpq18SzES0whju3x17AlqL5SNMCKD9a\nYFr3tt43EIOSd6Tx92YEuqvr5eyUbpZFfTYOf9wg2ecVg2HaSfA5p2VI1kLSZcTMBtWuFWrL7LsC\nvIIkNJ9yPcwGQWiw5KPeMdpaJ+XIe3UM/hYBs39UcYKPPgX5J4DFkk1WbNJrAPZ1FaCWr9FniixR\nHzi2yN3K/xwitVuUhWzNxDWCv6e08wVgKVx1HqDzMVByJ+ybD7tfgj1/hd3PwBcisGwMtBsticvP\ndvAvP4wxvYARwGJjzNnAdmvtyv/BLr6BYvEp4DF0Sq//lA/z8HDDnuEmloLN/uOXfvCTO4SwA8+H\nfUYE68UH4dafiIgdMw3eHI1Z+CAc5+SPpS7MXX6QOrSdYJ71q6fa9b+C4c8rmzZlAlwakf0/wPQb\n9nuZGaZ92tlgJoC5OYK9Ma3lTpfq4iqv4Bj5a9uqmLCzURRlfETbwjmozqmx9biEZI/3+VTXDCJu\noxDZ65yDi7LwusOfmyPCqmd1LIRdTfS7wLoQtCtTjfMTXjWhHpfAjk5h+2d0TF+02M5ZOUG+4cfe\n5cU8FYQGZfDMrAjM8MvWf7MPc60zAokZeMGr90uC/bYPMyWEPdNbNA3pA+YHQfVNA0kioZh9A7UG\nKChJCudiaBqejIvEbfPB2DD2t0bXRU+kKvHC4g4ID9s2EB7pHl85Fq69Uuf4j9OhQ1girGoF3u7I\nwLC/wvPDJWkf6ymSluzOovqj3UYoWyQiUDdSc3j9+crMNHUX1hyIkdmgCFEuoDYswVMlPwzXCfd8\nSWj3iCszWAYlTzvr+lIF7bIhoFwNp3HZuabucOQS8AzXfa3mWacJI31J8G8XaUmXSV5Y2hNuPcbV\nZfdTzVvBXMPbrCbdvhIRqAV59Rh7xl27E2fDV9sr+zi0RcSteQ20bNXz49slW0yuhs5LhcO0mWHj\n29Xapt1gKVeStToXvojWIdH3aTX2KPSbA6dO2SWJZ2EEYgqEvuOkhXsHiLhlSqDdfKl+Iu8Dy0XC\nQ/OBRjCr3HlaDMyGvU2QckHOgmqxnmIPtPJhUPGoSkB2/zeEH4DEHPBcAdfsgug58PYEuKf9wXuS\n/quM/9/4eJigHSKjqamJXO5/Zq5me2clV+mCti+4ZewKFWLb25Jys5oVabXoNwtCmCyt8kc2IvnG\npEgruJhHIyJ51bnWLJq5OYTtVSFQBen7xyUEjrclMZt8mMcCcqraiVIcPVGErAfq9dIBEbcJwNgt\ncMYbmLce1P4aL4LAcHjkGgHVCGAc0A52lMOaKAz/M2zdB81GxcO5MmWBUg1gZkB2mNyYMhdrcgzX\nS7Oe/7qIRaCl2AemubukCA1HuD5pnURigrFio+R4tSZNk5e7VO2VwC6RCf9rigparwhgU2+IXQnJ\nn0Lt12BbSrVwnEYrCSEkYlc7UUCV9xYjgL4SKNsiYunNCIAyJY6sdRP5ClTKFMS3rVijl6gRafVk\ndfwNrwqY8ep1md3KylW/I8mGOVLkLDROpCufLUos8+6SKAtImugvg/rlIqwl7wtIyrY54A+5xt0D\nwTMMNe5GQOV/Whr5inVFY5W8HxIjEWHd4qKBbdwi7Uh3rrZAcxPs3ap+LgXASSHdQQtARzlxeiKS\npwAYpw7uVqrP89/b4Y41YM6EoU992K/okw2P79O7fdAwxpQCTwHfch//+0Db4qWDdAbef1hrW6y1\n37fWjnS3W6y1H20ZeHh8omFeiEumOO17cOYzcMyCVnLDTpQpGx9Rps0N+1/uD1d/ZtYfYNU/f6Iy\nM57l0DIcc/oYzG+DMgVZtxHTt41Dx0n7v9ROboSh0zCdpsDDPoz/HthiVSP31MjWRteFHpp2J5g5\nAcx/Z0WuzsliZoVETIa02XGjwYwBEwZzZw4zCrk17lpefM7gFtWxVQCvx7FHtJHTXyETKlb6RDY2\nGzgjL9v6LijwGDOqDEkAvRPgbZJr4vk5WG0wD0Yw88KYxUFl0Srykjf+MgK9rWrfhuYlVyyMy/KY\nK10G7jWD/UoC9nmwl6YlVTw7AY+kpRQ5CewJKbljXhLH3ugkmzFP0Ya/8L0vDf4dOTOPRkQAXU2b\nWRnA3JnHXGxVw12HcHIokvtvv0iftRT42la4YyocHYcvvaACo75xeONq8CQ0d/aE4DxUStAJJi6C\nby9SVsfm1C+UcplledOQGQPxcSJmkU3CNZMXRnryrmb7AzAyXi18Sc5zbWDiui/YCPTS8zJDdBzh\nl4vBzbKVMsCqmyDDDZOHipdh5ekQ64Ns80+D+ktpDVzu6wQtg1zdmVeY3rwGGt4WPpbsck2oHVYH\nYq5MoVbOwad3gYodkoGWe2DfORDsBv3WKbCZ3KDsWDYm9Uh8F/ie0n7yAbliZnq54+8v+d+0vcLc\nVIPMRkABTONTlo2hMihpt8V994VLoxGVeCwA8zL439b7nPhHCP8Jqp93ctIWnSe2SAmSPcW9fjet\nrXlYAQyDzFYRgw3uIS+KDWTQ/Y0AA4rfgcfFRpJOxjm5nzKKAHesEz7+M2PkB41/Bnz8kMM7PP4v\njY+bQSv0oQFgrwc7MQmNHkzCQMgP6z3YL2TUCPpzwPAAfD8Pf/Zi7o+IHEWtsl+NBjsjoSjlY0kB\nTzSPnZzA3BuGwRSLq4eA6ZnAbq1Sli3kpCUrfbI5Xukh/7Mwnj/EMfdm1DOrDoFPjzhsjgh4zgDz\nk7jS9EcOV3S0cgfmKyHsnWeosH7GTarn8C5Tg+UdfaHbDtb8JUHvmWpqXRaABtcrzRtQjZNvBXRc\no8k93kk1VqEekPoj+CNyI2xJQcW7On0tnTThm5yIVrqkGG1Mdlbmp+U4mYHYUmWHGC4p376OkjZ0\nfE1koXJ9kXR1qISmI5ysbxCE1kL+NLkZ+veq1qzQLyVTIvnIvgjEuysb6HtOk2tJDbALEmOg+VkZ\niXRKw9slMCwJJ7vLxTpyFqiQfCPSXoQvvt01I612GS+vrIW7ThCgBSp1LDYrWUfLeogHJOlo2S6p\nYyClcxHdKNlL6W71PfPnZcRSyHpZryKrLdWKuPpqxMUz3STV8GQc8XWtCViLCsO3Ah3B7KLVN78e\nJV8LFZntEOh4cIrGBVBVDomfayFRVi3i+tIq6P0SHJNVtJQsB28O8ymMf8T+d+Eb8Oqij9i/MX5g\nFvCotfYZY8zn0BlaYYwBLen+ZowZba3dc5DX//lDdm+ttWd9wsM/PD5obO9KujZCYMJFstEv1KQ5\n0w7CbnvVDfCje/T3m6OhC6r1CvfBrP4RNvyYek0OeUx1Wac8D5Mm6vkHygq9fdQUOgyc64WlSbhx\nECyaqDq35ujfHaaZEsbWA9082CwwdDn28eHa9xDVppnRMgkx46z+7+rBhMH+3sBdEfgq2PvjmGsi\nylSdBuYMC75h2O8Y+EsaJoL5NpBAksBNiKx9K4N52C/ydRIinw3uBxWSS6S5NaeAYx/gRAsDLLzl\nxFtLvTDYYrtnMUkj847n/NgvZPT/0Dzs9Yhw9chibgjAzyPYX4C5xQMVYKqN+r1t0hLLzAyIjC5x\n+rP7Atifp0USl2i9Zrb5VLNd64VoVmoSF7BslTkWxjYfDM7JSXJlQPLGfR4da0UevjcFvnurJrrZ\n10BzmcxFzgbaxWWUsnIK5hYnPR2MUkbHu7YMHcKwLyFy2xs1Cq+GGSUwYxZsPgvabVXmaR/qbblv\nk3DAPx78z6q5s79ZGMlKYZ+nShgZqlGwMlXuiFBdESNBmJmtgFwMGsZDxSqZZ2UHCdPCr8HuqxSk\ny4aAWtnWpzvo+cOaIdEPsik1k67YpFY2viR0WKLndbwHmi6H0h06NhODzKkQma2AX2gRsAv2XQUN\nS6REMV54Y6+WPvlmaA5BtEL1e1U9RM4C3YV9gZgCoaYR6gbq3IS7KrMWclLFZgPNSbj9aK01yvpL\nCpl3NWiJ58CERIjDLqvX0kl4V3EfMr5yCprMJGX/yrahNgY9ihgH7v5ecsakEbGvLcgS/7faB42C\ntcLV5kNBzATCxxacrf5W4XY4K6Ke7gRnLIJplSoVaU5LeWP/CoPHw+ovW8ylH8ln/kfjk2LkvxI+\nHiZoh8j4uDVotnMOz+0J8reHJdWYAGwE+90E5gE//JvFPOOXzOMtL2ZzXrLDM3LYL2YwfUIKK0Fr\nY2vzQFC9ZTb7seMSKpK+MK2IYMjKdng0MMmHqYurt1pdHTaax1QAC3ywCjwv7cP2z2FqfDAsDudM\ngz1rFRG9NI65Lg+LPTApAzP8smteBQwPqQj+HOCa+2HQO1pYfGGZ3re3kz2OAJqgaiYMCsPKySIh\nqQbwhVR71DJGssZ2G5XCP7IeMo6geHbKvj3uJt+SXSoANjK2JNSkSKM/DmWLJZkMNkKimwAptEYm\nH/4WJ/sI0epOmCwvFlfvfBfSa8B3lACAkyUXyZRA0K9J3HoFgE29BH6eMkW6Sv4D6r8mEhRZB+yC\n8B8g202f1RdRS4IV9TCoBtqNALLO/jdVdJfK59QgOudIXKyLHqvyFw1JEjWSe6wD+nuh8hRIbFFv\nHbNP0cQsKnjORETOUlGdJ08O8gPA8x6tdr8mr/o/sxe5Vn7Jvf8AiP7UXcBvIsa1FUWBT6PVEIRG\nYI7sgSuQ1iCkr5wQAp8SIN8EqfHF85FuADrK4exY1wz0ws3AUcglbftH/qz+V8eY43UrjDvv2f9x\nI4R5CFhtrZ0OYK19h2ITAYwxm4GjrbUHLtkLYxqKIFr+PpJo//7ph8enPjZcCL0uhCunYbZPwb5L\nkbRdOU2ZtsFrJadKsp9M0JyNiNd9V0FZM0yeCBesgdUDsY+HoIuBbRuVkbPNwBhZ6wPsmYh5KI99\n/7fw83tg+nXYyY/BnAsxZ1hs81+AifsfawWqIXMeIPbffZhvy22xtbqji7ug3q7FnluiDFgXiqRx\nhYGjrVwhjwtgHqpurcu1fwRmxkXUGrVys2+BOd8Kb+aC6eOBSjBdKWIOqA5tgReaYvq7HuxVCdjs\n04VcncMc48Us8cumv3NORC1ptIJKgr08hZnkkSLk0ghmo19Zs4SBV7w6FnzYS5WzNw0BTMJgfR7o\nnhXZ65+Bvga2O1OSSxJFB8nC9/ZoBHtnLSyOikDOa/OlrjbYSe49d7rv+7InYfLT8PBlmPOHw88f\nhD/lZTDyC10D5iSwQ+Iw4XkFMF+cBGc9p/kzjFQnl9+v93gf6AG9n4X8RcqahNpDdin4/VJT5OZD\n4mRo97oIWC4FBIRHuRT4XMDyYPgY3SH8tF6pKkxO9yU7K+NWuV7BS/8ICDQK86oegcQk4WiiUvi6\ntAnOMHp/bxJMVo95MwqmZkog0Eu4Wr1aWbVID2W7cu56bDlFLpTBZle3XSMDraNqwDhjk4oAxLZB\nj56QK+B+N9nrj+oI5Sv0nokO4HcyyUCFMjdZ1wama38ZjXSrlgtkYpcCml63lgjXFTOKtl7SxIZJ\n0HQtmK+orydufRDdAWHXEzUQguQIBTDDdU7mX8jd9ELBTBzO9nKPDVSqqAERshTCyxZ9hfgRZhdK\ns/1l2kZcnX+hVn1xPVRtFBCsadsi459g/Cvh42GJ4yEyPm4GzSz0w9is6sWuz2HHprFXp9Tn5ZaU\nQGCu9PzmgSCs9sCfjayQoyEBQ3VOdsrrpLHnRAtr/AK6xa6YuXtWmns3bFUVZmYA8yPly831Ecy8\nAJTmyd8eJv9UGHvzO9iR7dTk8wIwLVPg6l8JRBYlsWeVivglPdgECvuc94BmmyOrtWDJhFVDMXgt\nxMfC0hFw/zUKA3YFLgV8sKYJCEK2uyz30yWSadiAbGmb+8Ow18FuVeYqN0CTuDejLJe/RZGu0s0i\nHqEmiG5XlifWRQYd8cqinrylk4qqvRlJL0LL1J8sH4SqRQKqZLmLAFZDZSftKxeA5h7aT7xaxxZZ\nJ9lgOiLwyQWgaZVseRMnClh8Kag9wUn/1kL0bWW6sm9K1jciK6lF/XyRsdLN2i7zwapmaNlSbMSd\n6CApReod1bw1/s25OGfkEDkoqAhgvhledTOOLyUwCTrvgnC9yFgwJkD0ZFCz7o7Abr1PJiKgbR4K\n8R9q68lJB588BS0oyt1tF9DTGa5cBqQgNRNqdhdVTRmKdkqFv7MIHMMPaTHhX6o6xOh6OZm96Wrr\n2napXv3lT5+PfMYujicAlwCnGGOWudsZBzznoz7UrdbaV4CJ1tpXDrj9izYg+Ccf64cDYOY+iDnh\nK/r7x3rIFqzup35//9esHgjrNraSIPsW8OZoSQPnXAj/+WDxNY9fqH06OSJNfTDXDNdcORe49H34\n0dNw8jTN+wA/vkrb6WdjpoG9y6iv2kYwD6m2iu1dMd9r84PZiMxBjsoUO9lOoEggB6/B/CiiOrNX\nESF7BvVYA+yVIpi8hH7I7nXm+Yhqt1Z7JFMEeMLAs8CMpHAI4MqcLPxnReQG+ZJHtVldS+U8/JVk\nkRj5LeZnIdWgVYCZ48fsExGzvUVY7fEpzHo/1odkkV2Ac7KQMa3taMwoRA5fD2BqfJI4zvSL7K0M\nKBNW44V3gPERtQaIeVrruQE9L34bvB4VOVznl2FJNA+ds2oofs1qYe1badjcIPI9/Tq5It/tzld5\nDjtzuf4e9Rh2z/Mi0IXm5g9fBhv6SpFyzUP6/6bp8NIkfV8JoJ8MMvxlIhvJKsiUFdUUmWZhZK63\n6tS8A4WR1l02ea8wK7hHxhalrqbZeoVzwVjRjCntmlh7nRyy/Xt6n4qXnWxwrN7T5PQ664EhPaHq\nbQVAQ5uFkdm+ztSrg8vQDRI+5j3gSYPvdWFkboAChXmvMDKyUO1ezGqIPS0siy8DHofa+ZB8X0S1\n4R0RsB6/g8+Xat3gS4oYhveAZwnsWwHNKxT4A2FsLgWRVe7z5+Bv3WV5n2ko1o9lQyK0viSkR+sz\ntlsEZbegTFhSJM6TkTNk8wRoGOJq5nNt6t0LBK3QguBbRVUPIaj5vkhZgVmUohiEQfiYAkrGAlug\n/XLIvQzpBbD3ZbjrKDljL/ZB1wCQhsxMPrNxKODjYYJ2iIyPU4Pm+VlCE3XSgUKNF3OfAMVsEsGy\nC8HcZLFXZJQVq0cykpOArqgAvMYLj2cknQhZ7HUGe66Knu3QtOQZWYO9OS1i5mQf+XvD5O91aHsq\n5L8bJv/Fktbjs387Tn+U5mFcBvu1FOabIVgTwfw+AGsbBHJLvXLtCiMZTgWK3C69WpmzsphAa3tX\nuPh1+OU98MBN8H5f7X8AUAVz3y86F4YaFR3scIIKm8vXgOcYTXzejsoala0TaSnZo/sDMW2T5QKQ\nRHtFAcu3SHIRjCm6tW+AJl+Tk14/eBTs+ZImj8QkaHKujmXbBFR766HJ2RpnIiI3ZUug42MiLM1D\nZZiRKgfPYkUcy7ZpsrefV2Qu6ySUmQiSAq6FdtOdRfKLkiC2vK1Jv+FVaOkH+e2yCB5cKflF/ESI\nnahIZsN4HWPOr74xLc/K6TG7F5IvQtI10Ty2k2uQGYHUMCdP7CvAyQwRqcw7kE60R5G+nmA2qMUB\nCKitVxm1TInLPk5Hco1r3GvGArOdDNTJJFOInMXczYcmwIKZ1X4tW47R+U+V6TP5y9Se4akNcuui\nCWXk/mdlnR97fMYujq9Zaz3W2uHW2hHu9sIBzzniQ6KDAJ2NMccDZxljjjrw9umejcOjMNKPHeTO\np86B3Eb1Hnu0UnK2p6fAm50xe9vUmx2sa096OWwbjvkqUFmrxtNjMuovBti7XMqr4JgAsHqQJJPT\nr9Pt8yGZf1znXlMgiy8YzBAwJ1Vjr/LCkQtVI3V2cVdmCJjLncxuUqbo9FhoxDvSZcc21cLTnmIv\nzAq9j70kDu3zmu9R4NBeFYcscpUE+KLF/CYENUbNoUensOelhQldkFnGEmBlXPVmPwtJpVHrhdsC\nMMqRo9NkoGV/4sUsCUiG/0xAEsMGD2a9X1jTF1jgw6z3Y34WgtOzcsacjUhn+5yyg6OAq/8qe/6b\nI+ADs9kPL8dbHR7xu3Vgg0fHc+otmDdkLGKW+LVv595ofykzGAZlij3VKi7E/Nt4viRYAAAgAElE\nQVRwzK8V1OTEvOrF7x+svnZzLhQOAlRUw4R5kkJOvw5unwqzL4TBa/Scp86Bd8dK0ZCCqj8IG/JZ\nla35WtQLs6G/5s5oH/XZBM35FX/WXJqOCCNNnlZzrULD5WzIGVcFhY+ejDDC5ISh3gwkvigSVXux\nnp+YJKUIFF+/Zgtkc9A0SiUJZUsgskw46E23kQouVhbP65LDpbvgSSB6lMoYKjahwN8W1btleomM\n5crUaNsfdyTqLbBpOSZuuVy1aukY1B8PqUEKkhYknt4MJJ+ExDtaNyR2SUVCCuYn4MSMHCZNQGUT\nnm5qaRCM6fwVyNbuScj8pBw4VvVnvmTxFt0h4pspce1r/gh2vPs8HWk1Hgs/pc/HWkHadmSg5UOK\nknL3dx6VKgKtvURbXLYT4JGt4P8lDIork0bBtr/gqPwpj0MBHw8TtENklJWVfWgGzfMH5xrVAKz0\nYtb5RYQuysLLXnjJqGD6ujy8aeDHftjmw34nLvJ1VAbKF8rI403n4jgK7F0e9aUp9DwrWAt3rITu\n6plme2fxfFngZ5Y3YRY1Y2c9Xzy22xN4ZiTwvNqM+foMaPRiq/NQkVfh+CDX8LpzTuB7VVwAlQDe\nm6hFwChE2D63BX7wIKwcru1GdLv1RwKhNAKgfjBxbrHnVvYEZb6a35M9sO/LqqEKNSpa6NmpOjJv\nRk5JgZiAwHqL2+bumthBxCEV1UTsDaqRsjej/cU2acK2MQj+xZ2DXDHb1CUIJUdC1Vzt178B9Uyb\nUJyc00NFcnZf7oxJ7lbN2Z4orCyRLCVQ4ax2zwbK1WOtfI2yW9V/czr3r+j1pe/p2F5u1CTcboQA\nuLQnbDvOEawyMFGZjOT8sti1XgFdtqOAvNwjl8bSgQJtE5BeP9ZV56e5u6Sd1uPAty+K9BVua4sm\nK+E6kblgzD32hB5nLXKjOlbfA3Ngx5ziesyLJBoRJNlIuf+zQPuzUMatk+yffUllPxM75PIV6yCj\nkMOD24BbUd757oPcDo9Pe/zufADsHLC/eRW+dpUIVLoP9O8Ds8+AP7sVx1OqIrEbwVzVR3Lw8oVS\nOEyYJ0J32mNajBdq2Ja6C/tyN1dfDnTbgUlfredNmKds2q13wrgHisf1di32VWTxv1aZJjrLCdK2\nNQV+d4yCf2eBvRvsdxweFQjdc37Yk8T0HYPdUHyZGQXm36sVBHzah/mt3BDNXXk1gn7II8liUkYi\nZk5Yig6A8y1U56EP2LltjqW33BjtNxPCuQvTMLLEyQyB8hxmaQB+lmqVZZo5YdWVXZeHcgthi/1G\nHNs7KxKZRI6OC1GdWoNMUWzIivB2QWRvjh/7C6DUYi4+U1h7qmreuN8rSebEuM5Vb1e3ljXCOB/q\nrRay0D0vC//ng7DSJ8J1tJXV/gNBzGafgqgn5BQovSWBPT6peraMwX4to9fsHY7pK7LMr38E866W\nWcibo6HjRpG0+RNh7njo7IL/Q4EAfDWjrJH1ikhk41Dp2rokdkHuLRdMex9qxyn7Vf2u5u1EpTDF\nk3OZnryMPaI7hKHejDDXdBYmlDkHRvuqZOcep0bJLtUhBWMOd5MwogISnYSRJS8AIdW9+VdpH8ly\nGXTtvlw145VzIbwX0kPgPITJka4ieM1DkRnZFvXr9G9RC4CG8VDxQ+Fkzi8Dj2BMhlpbq6H9SGXI\nghXCyGyFjENAeOdvgdRSCJTp89e9Dce4nmelPV3zbp+OJZ+TI2P9UJmahBohsssdV1DBzJZT1GYn\n8L5zagQ8K9ToOrAESLFfux56Ah0h/ywkdkPNViWSC/LGLCJlTQgro0DHAXoNZ4PtBh1WKrhMOZy9\nl9Zo55qEro/Moxyq41PBx8ME7RAZjY2NH5hB8/y5hfxFETXohNat/ZUH+32fIqKjkCvVQx4VZY8C\nao3co95wPVweHyVwSgDf9sORbtJPAm/44SGvatB8FrOoWSQqaVTUfH4ez/wYZp0fs80HN0/Y/yAb\nUOTvpkvVFHufirfNlCzmNxnoFNn/+es9mBvc5yhEZd9t04y1izvO7b00Y1/wGJy8QARt/kVKp5wC\nc7dKX+0vg/YnSf627zhpxvNhgUqkK7Q7TQW6Jg/xDiI+BaljIKaMUahRZhhQtJE3adeU0iUOMyW6\nr2KVipLTUTV9DuwRGOX8sDUAzatVNB3dCfUXSdbgjxc/XlN3vW/lekdoemoS7/EoDFoJ3kWutgr3\n/YQUEcuUKKpotrvC7FuVGQxv0Htv3wd0Vnbxjb1w0l+gbwropmiULyJZiy/psnNefZ58XHLHVINk\nLZlmESCzT1HHbImildbrzltcRd+ZCCQmo6hfCHjASVkaoeEoSV1SUchej6bDJAr59QKG6bktKAJY\nIGMF+UYvoO8xmkHLgMqC4taBcdk2fY68H6qPFzGt66uWA9SB/R3Y8Z9NudX/hovjPzKstU9aa08H\n7rLWnnLg7bN518OjNDxp/ztWuSZKc8HU/EXGG8f+EH72J7j1UTj1NvUFA5EsKErZAPP5iWCrsQNu\ngL9+Qzb8ox5rdX40Y10BSf8+2j58qRbqb46Wk+TqgZBwxTf3HKvtplpMtzHKTPV6QKoL5+ZoL3Pq\njCGotnkDcKrm8dbRF/hlGjNGjxXmb/sIkg7+yYgEvuSWL/VgpoRUb1wY38pgv++TxBG9lzkb7PFJ\nye4nRSRHfNRNvAu9sNtNykNzcl4cnZKN/pEIp2qQe+NKjyz+F4RUb7bEDwl0zjbovXjBA4Nzai3w\njh8uymH6oOctex4zAezdK9Sc8xWPXCt/EFQrm+qcMnINHrkZd89CNC+XxmcCwscagx2axp6QhMGO\nDC57XsZcIQun5uWS3D8r2eYxKdjUZjJwDpF2Ia096uxsRJDPRseaHCNCfsrzMqAZvAZ6hqFlhJQD\nE2DWsbDeCyX9JM8DyJ2jOqzm3nLzDfcWjlUNVz/RdJnmVJN3AbZGvS4dVTuaXEDP4XiXndkO8SPk\nZFxQpHR8Dah2GNlFTaprBzucnQ/LmtTrK98OcIG+eA+od8uKXEAGXQWVC4uFj6WLdDO/F0aCU240\nQmI8RFYKT32bXf13T/XtLN8qKWUgJlwc9qRO0cnzhIud1onIRno4V8WKYtPupGuFYNJaa2Sdw3Gh\nl1puLYTfFvlrv1yZynC9Mne14yB7hnOfXISki1P1WT15qD9LAdPEiQgfdwELUF32buSQ2VpZJVLW\nFRGyfkDXs2DwMSJuXiiSuwVgXtOfqXLh+31B2DcGXukD0RyQhpIuSK7yGYxDAR8PE7T/o8Nze0KN\npWckMD0TlJWV4fMd/Gq0/TJ4/twiotTbivy86FPm6TQEKrNd0fUQ9As+IQdH5FVPtskDL3gwXw/r\n8S+6mfpdVAvQGexpafiilT3/kHLsca45dY0X27ES2z4nkgfkz49gj1OqyfOHuLT14zLqZ7PPYFY1\nqolnrVfRy8EZuXkNciStwQOD8yKDfYBYEttvGuae9piH8pjLBdaYWjhxHlwxU7dnJ8GfL9JCZmg9\nPHUTM0JK3adqVfibdSSoR0HTfTQkNquppC+pSTOyxxGUEoFJIK5oYLBRcsOm7rD2bPB1Ad8J0qo3\njRJYUe0kEJ1lORzrocmvYYiz+/VD/93QqRw8D8De4cB2RcyauzszkRLp+tttU/Gz/zXk0IReH92h\nCd6kHTh2QqQEvd6ORCTnTVcHtlYE0HoVXcxMloRh7la95tkWOTOWD1Y00LdBpM4fF2EzOWXKjNcV\nPu9Sds2X1LnJlEgis3e4zh+4rKPLSIYL/cxmI0fGWeB7Bny7RZzC9a4nXDkicVsQwf4t+K4vFjf3\nBjoG9X8lwDBgl4CnEsTengVmQM29qN6tu+oQVzXDt9w5nLuVoi7yMxqfpcTxUx4/NsZ82RhzK4Ax\npocxZvRHvejw+ATj2CVw7BL1Qhu8FlP/IFz1EPzpBFm2jxqmhfq/t5nnnzpH28VO6rgyKSORbjtg\n8tPYvz6vTBpI1tZ2HG1lmDEb1bH97moZToDIGcAD10HHjZp/756lxye8jOWx/e3nz7eYG9GPvnsb\n04tRyNl3mtQYZrImVvN6QO/7NwPPJUTCCrLLhU7euAp41gOPpIpujA1ArYHn/JjTkHX9y149vlNK\nDjs2Cb9JYf/THcRiDwS8ECnD/C4od8eYR4HHo6XU4E9emZxE8wpCnhfBTjXCxl+4fd+AMnuXg70m\nrWzmTpRtyxrsha6lzMSJcHIeM22I8PHkfLG5dtboVutVOUChNc2CkHCtvws6jk3L8n9eWPfvAHPt\n6dhz45i9XgU/e2e1rfFqP0dk9VzANHgwNV7Mr/LYHYiETr1NJQPjc8p8NrDftcL1v1KrhjNehxeA\nM96AW6czbDnUlouQtRuhzFmyFqobnXX+XmFAwzvO1TAvghSu09wfr5YUz5dU4HJfR2WY0s0ifowD\njwvcGWfXn2mvXp4N78hB0re7mEFrPksZNBsWptSfpfePvF/E5XS5y3pt02O1UxWE9DuS73XSSl9E\nNV90gvDv9ViyXNmjdtOR1G+qgprpHpA9SzL+3JVq6L19nxpgF/qaeXzCQu/mYtlBvk7zdNkw4WNi\nlz6XdzPgMoypcndMlTrueLUyauUr9Fgm8v/YO/M4q8u6/b/vs58ZZgMGhk2WYRcBcQEXwAUxpUR7\nFKWFTA213NNKTVAsqeRRzNxQsTADFTNKM1AycAnQABEHEBCQbWCY9TBnP+f+/XHd5xywfHp6kn6Z\n3K/Xec1ZvvuZc1/f6/O5PtcH4d4SYLir4XbS/pyRF44I08M9apHZVkhBzDTQvjv0DgoTvZB3emzB\nYWROqeLctRp7OxlmEdy5WtgernKcLMzBNpKf8Pgs4ONhgvYfNEx9vYjZS84WKddXpu3/XINmNvrh\nAw+2MqMI3FEpONLVEsTA9EYylDDwubSAZKVXzgp1Xk3kx4GtXyqZyTQv5qYs9ud1IlXvCRDYbzBt\nwfNqIaRij1Xljz2hVGDyu0JRtOel/flGnfZH/kIkOG4wC4OSoDist9+JwjrHni4IKeoImO+l1GD1\n0qt0Y5ACRqcFkHdWQvtqAdCIFYoKj1ihG5VZVyliWPt5Lq+F8hUyiGg+ArZXKbJVNkCRQlMig41k\nETQeqX4svrikfaGhAoDcJNlukyJb3WqhZCD4d0kqGKiA5tGSQKRDypalihVJiw1zNVvD5Xy4PawM\nXvJqZeTKt8GekzXhl+wUSEWOENAV74XoWNWxJUu07USpiFsuQ0QT8CfgaWj3kLJnhGTdz1agSjK/\nXKQz8qKkn11L4LqjBUJnLhBJjdZK7w9Q8Ta0+62Ils2IvKVaJPmwRzjAzSjL50sosxZsUnQ11wMu\nUQL2ZEj2RXVlFyJi1dFJHDMQXovI2Y9RBPEd8tFBhiopmsWVi1WJi3YCqIX47a6/S79C02pq1aw6\n1t7JQypUd3ezB4LrnOz1sMwxNx4ETgC+5F7vd+8dHp/wsB33FF7UuIjKAdkwFp0uc4ifTtCc9uRk\nNSzufMBG7p5zUF2auewsmDlec17uBnzeRKkLQFmw3LJ3AudcKxnctJvh5J8ro7anurBM54mY8y6E\n+edhRqLPoZDJSoewY33wNgUzkgPjhiOBna62LLfNJSHdGB6HyAZgrpTU0K5FNWBuH2Y8wqO2QC8w\nc+Xma+8Be24SZvnzNc/msYz29Z0o/CAD9a2SPX4zAU0Gerparg882GsT0BbVi12swKW5EswVVu6T\ng1BwZ3IKNoG5L6Br97k0dmAS/uLBrPBr+RiSR97gg5B6ppmLwP4oqszZG0E4wpEpR9jsmTH1cqvM\nYnulVAOXNtAmi70R7CUpSR9BZMwH5pdFMjuJGxmO+CTLBLC9FNi0ndIiyfcn4Yxbsd+I6xxHiWia\nDffq/+i+64SNXXfCriJ4/A8i6ec/D/3h5Gdh8mLV526vUrPiQIVqtkGSdl8RJDoUXI6tF7wnSjmx\nv6OkiYGo8DHs+k8Gu0oWH6hQrXLzaBGnxiOFkf4WqTJSRcqkNffQdtfUQdwrfMz4hSl7TnYlCBE1\nf84EtN+c43Gst2SLGb/wsWwrlP0IAmsQmVkPlEPJL1Fj5+6QOhm4UKQwEwD7ivDxzX3CidIAzK6B\nz78qnC/qKnlk2Vw12i56X/hITIqWRAP4S4T78XInD82IcGUdSfWmhOMhV9tlMpKEcgpwDdBRJmPN\nZ4Gvr0oPwg+gQOxQHTdnuh9XD2CbsLEYd549BG95dfK1TtoIZPe49bdqG4FWBa3bdIfkV8Bb5zKp\nSSAA9gAl9Gd0/FP4eJig/YcMU+9mtNEiJvaHqlXgbb397VtCH5tBozwDw1KYRwIiZNt92D6uUPwY\nC8eizNNVinRSAQzLCCCaDIxOqwHn/GPhDDA3ZWG/B3NLJQzPKHq536NI4rHIJORjRvbJA2yDY0ba\n+YEpzHUZOHkpZosfU+eFSou92WB+GZK18oHX4icZkbG0wV73niKRFVls37TkI3EXpeyThvkxOOX7\nMNiBz2BH1GZeJTORcS9o4vPCzNVw9gKYuQoCvxDZyCSg4lhI7HIujxmIdZHxh69IevmmU+V02NwD\nIqdqci2pluW8zThbXR+EV0LZIAi018Tsi4P5UE0x22/QPkwGKkvVcyVVrAJfT7OMOsKvaKIO73PE\nroMcq8INmtiLawu1XeF69XnxzYTo8WiiHi5Slu7prmNGxh3xAYrc+eJQcRWUjJFhRm4s2y0gmrxD\nRLX5BGUAbXvYc6rq6UxSEdHWjc5q2WXQvCmBq/UUCsJzmcLmbjr2VO7rdVIUqiA7XHUJDavcZ1UU\n+p8NpxAtLIfijtC+o7Jou7eJrLW47fniep3aIK39TiCW0LYinSUPATlQklG0sKaBfGTQ/PKjDrqf\nzDjELo6f5Bhurf0mDs9d4fTft4s9PP7PIzUHGLgee1EP1YVdNAeaV6v32XEBue79wZGwwS5acu0E\naJBLo90F1AyARWOwN3ng8+4HNu8xt85qZeQeeg1+i6SGzdXY3yyVdD03RuzWdvqshrfAjJwod8jl\nuq2w18Qxi7wQVi2avSyKudWteyywC+w8sFO8rlm0O45dwElF6lm21C1/XlbL+MA87LxXX5Jtvr3X\nwBCrrE9nETE7ypGVszJ5IxJABDBtZALyhhcuSKm27Ibb4M1SGXCsDMBbiMAMTmJPT8gG/+qozLJ8\nVnXOm1FD6zOBsWBfq4P7/ApS5urqdnsxNcJVKi22b0pW/XVezD1peNavGrLPp5Sx65SBX3l0niuD\nEDOSNT5WJCljGh37gBTM8ap59q3ArX7MyqCaaDd64BEvdgFyRt7tk0TyuaI8GTaPhnWePdPu2qcx\nH/oUEE0D3Sx2VErf4Z1AdVSN0afdAtfOLPzfDV8CG0ezcwi8XF4w1ioNwLqEatFa+8P+9SIn3haR\nNDtUGBmrFWHL9IPmaskUfd2VQSuphuhmZ6JRKolgVRIqhup1IAq2ShhpA3JtNBnN6f0GQMVu4WPl\niyIzgSbhYHifCBgII1u6iVyFmkQ4Yu0UzIx0QdG8hcBysN+WZDJ7JthB5FvggPYZXgtFjyKpBlBT\nrwDmjJHKph35lJwvW0dD/Cxgjkhh0CluYrVS4/hLlT0LRJwJyR4RzFSRlCWelEobss5F2WSdJHQB\neat82xvKVsmBGFAAswkFMZsQwQq552VSlhQDWxOwe4Ni8RnAflnLtmyAdajV4Lpt2kekq7uX2Eu+\nqXYwohp3+oP9FZgzwJ50aMoAPgv4eJigfQpG3sDjY4apr1cU8fNFKnJ+3+Vth83CLtsMl13P3T+I\n/s0MmucbMdV21XnhnKwaUvosZq5fGbjfGb1X44HlHlkIV2agzuOKmDNkTy2B7V7JLdqoiSedMmpy\n/WsvdMoqqveywd4G2UnhvzoOIO/Y6PlGDHN0TKSu0SPAeMYLRaP0+k9eeMdg7rYwPHOwlAZkWbww\nrIjhor76u8UHEaPI4YAUdropSEj6pjA/7AXXqSGGubhSzlcrXoK2UXhtNJwFkRIVv9Y0QIkX7t4j\nEPKkVZ9WNlr68JJqmXCEKnVD7w2KrPkr5OzUppds6kHRv3CVatvajRIIhasEOPFySTGyrv4r3KD3\n97VA62pNznu/q8LhUBM0TICGvtLqe52k0HoEQMEWiDkTkUAr+NZBZIxqt4rWoMl6xAEXsVbZK/8O\nCM2TwUhrFTTcpH4wm8YXzDKmDIex3eHR7mD2O2tjR5z8rSKDOYOT4j6KFKadEUguOpgqViPqaCXY\n02SJHGoSIKWKXX1df0hdKpD0bNDxeYMUjEH6Q+sa2HMl1P8IGn6IgKhcRdC5DFkQuVRlt7lo7Tl6\nnULAFO6nBtjBiADds0KulqUDoP1fYF0v97s742/+G38iw+P95B6HeCSNKQhFjDGVFLoXHB6f9Jh2\nC8m5YIZMhsYNsHgyZvYR+uzRCQqevToOMy0rg4euO+GhswpSx1zG7fXT9bfCZUp+3BN6zFJQakcX\n2L4Zc+4BDYwiMu/IBfzyaoMDhn0CGUOtfxF2ZOHEwh2sqSZPcugMzD7AFXLfZsxlVk2Tb6tTQA+g\nEfUse8zAGkf6vmx0e+Oz2B+4GH8YORi6+iKz2wt9Ra4A1WONT0C3tAhkyMIcL7byeuy8J2S4EmqB\n6v1qHF1isd9y/cpeDGPuCyo75nqf2btNIWvX1+oRA/OY0z13dtnB99x51Bi5J/rA1Hkx7/pl1FEn\n6aV9AhluDUhiv+NVk2+fVVuadwMKclZQaG3gs7DYDxOy8IAPemUxl1nYYrAPgn3YyIzkImCEhTZZ\nSTtvq8u7O9qLEurZNqwIO9XdX+zPbR+VOVRmFNj8ShQWJNXmYcQKZUePnYK5shLzG1e3dwzQSfg4\n8ll4olWk5M61+tt+pG6G0yGRNOOVNBH+NkaGOro+mo0idoFSYaSvWHNxuEr4mCs58Du3XeuF/V1g\n29uQNJA8R47I+4a6GvDXhZH+KKTa6ThKdgofTdplo1qFSdYjYw++hlx9n5Lk32S0bPwE8O9DhlIR\nBSZTX9acWxoQOevaBs7vLZI27yxgAyS3i1wxuRB89KaUJSx21yLRwRmDtcjRMufemBiilgWRU1Xr\n7U3peFo76BjTRwPlYF5B5KsrVL6LcHAhJP8b0qdC5HxovBUablPfz1wJRBDo1FEKkyzCczsISs9x\nPUJRLJeOOr54O30P8X0KWD/UV0Fs2hxai334bODjYYL2KRlmbVP+uee7MTzfjR28wAqFAsz8MPR1\nYaRnJ4O3Gvuth//KxdHz4xie2w/YRtzAbz1qXrrd/c0VcYNSC2FE2tZ4sde5/jI1TnIyJ6AeaT/z\nKCo604tZF1CR98MOXG+NY8ai2rgff+T4Ac8zUbk5DnLgshuZfSwOyCWsdjX2aRedPDmrAuvlXi2b\nNvn2AOa5IuyJiYM33jOt6GBINXbmZtUFAMrodUpjfp/BjAsJMNceEPG9cbokM6cDR8LydhDxiqgN\nmQfPbFXzZY9XLo37twlIgl3l4pSO6hFc52qw1rtLvkkg1ZSV3W7rBtW6Na/T5/4K8jbusbYiMJXD\noeJEaHeOIn3eoKSK0UpJHUt2qR0AGwSEGb/AJtIF7CpN5Bm/smaxLiIo+YjaL8CzR+QtOhhCLwHr\nIXKpjsEXlzwj+iFkojD7PZjQA/pk4OKuknt6sjqW2LFgroG2d2vddEiEtXWHXluvjstkRSB9JdB+\nDLQ9RtfPOrDMBESS9lep8BlcXdxWnW/ZWzIRaf46NA+AeA0quM644u9+eoQn6SvsVCaiVgp4JhV6\nwBSj4KMHYIjOwWQEQtFeOrcFzUBX952UIyezwX/1b/xZG/cDzwMdjDF3AW8A0///HtJnZCz+nswm\n5j0B24fC5AcKssFfeGTwAJj9zhH37ToIDBURm3kVpmVynnDZ+18/eNulEdWzdZwlwpAOHeSCaM4P\nyaL9yErMSUM1P+/bXOhl9vkiSQ3v88NQt/+cPHE88F/rsS/GJaW7TIzMPgHm/vaqLcv1bduMMmrD\nM9jbUK+1uzyw3WAWhjG5Hmi93X676d7H3FGEWRKAl7zYKZ68izCAne4V4cmN3aPAa6BTKWw3unbd\n0lJmlDuyNtId/2tgrgd7p0/n+pIRgTzAoMTeg15XI4zpDLZnSjXRK5UtNPODkiyGwTyUVj+0N0Pw\nfFw9Pl8sgl/5hFc5nBqYVQD0117h08MefSeNHjknvwXm2yK1eWfMl4zuDVb6Mfe3LxxkGhGwldH8\n9bLj3PMVAdWy7fbmvzPaZOGPSUzTHZixlXp/eEYB4Stfgh/dpOW+QL6A6fI3hZET3f9N61YwAWf0\nsU+Eq7i7iFusFkI5W/acaUijnkd3CCOTDXoeKBVG+uKFRsnpfcJIEEb2/QKkzpOc3xsEm3R9Qk8Q\nRiZLhI8l1cKinDFJqFk9T7OOEDUMdiStHEn7Vjt5o3NLplYSRV8MyrZoncwH6v1Z0yBpfEtSdWn9\nfQqchnoXnIhL75c002QK5iAt70tZEy8XRoIyhaEjJCUMV4nkmQ8pNPluEmYnS3RsjAC2QsVS3Tcw\nHppvF976GnWtrMfVf0+C+BUQHAKdglo/hmMRtTrf1t8WSspSQOtZuo8ItXdkt1Hfz6JtujfCxUYO\n4+M/h4+HCdq/8TDdRcRs35RkEh/9/GhnAnJdkQDBgZW9xZuPbtr1evNv1qD1tZKyTHeT8CSnhV+E\n5CevIZBe5J43IFnHJjDfdOAZF9ljhKKI9o9h2SJ/zWLveR1eN9K4P+OFaxfDl7JweQY2cRBJMye4\n553RuWwC+32wH87CXlcEX5uquou2c6V7edlTcJ4CAfBur0jagBTmTWfJF3H/4rnebiCr4tNjklA6\nuaUdmFJU8xwKNznkSNpgqNoGng7QH94ZCpwKz8XhWmeiNnUvfO5FTZ7BCgjfJXlGslGRpfBeTbiZ\nhOrIMs4EI+Nqr5ZkVU8VcDcOnqC2FWpSvZb3ONVpRX4P17wDH37gdOn10JUAiQwAACAASURBVHKC\n0813lOV+Qx8IuPeCEUj2kp6+qE4ZqRwpsUmRnfTRyjoxXN9neoAApP7ragJa8jiwWttr6QsdV+m4\nZ7t+NzYtC/3YbgGNf4COhdF6pIrVDDSy1tkX16ruIPS83Kn8UZ1rosERzr6qxYu1U4QxJ+0INzjD\nkxtkDez5iWSj4X1Qtg7MkXKDDDmJqL8jEILU54E9rlzxQmFtDGido+Pdc70+C+CKpU9VRLT0USi7\nGIo+gMC18IWVkDgRKIKkyzbawf/e8o1DKeEwxniALcB3EejsAsZba585dHv9jI9Fp9PmtnexHSfA\nHfdi310Ksydhnj7ABfHmqdhTr4dnLpA0cUyl6sxCrvjH1a9Z5kKPzcoq5WrWWkrAVorIuWFGIZn7\nccgMxN3U52zlASheDaURSRJ/P06Eqcfm/Dbs1Cj2XuAClzJYMxTzSEiYtVYZsPzYpffMr7OF+rnl\nXtgYg0qrzzcBv1Wwzrgbc7sAZdqWO1nlbeQLaezzT6smC+DPcWHciBWYysnCtOt+CIPqC+f8XBH0\nTAl7t/vkAnlfAM7JYu9bnZdngvZv3wY7IYltQM3DB4G9Ni77/deQO/GKoCt+dccVcue60ScS1SaL\nWedXDfhrKKLks7DbA90ywqvdPr3fGalZYmBfnou9ejD2tTqVNixybsWv6frY/3b7gYLjcsjKwTKH\ni2cdcG8wIKXygIEH3HdUZPOtCRgE9kZE2jpl1Nuu3R0wWq5RA8qQNLArPLdPGHnDn2U9/5U9EO4i\nY6zwXR+Pj/F9DlscRi6rdZm0ClePFhT+hauEj9YrjGw3Shj5zp8h1iDsy9arbCBXW+bfpKxZxg8s\nUgAu2UvBy/0dZRxSVKdtdliiv3vOEi5QDmxzlvkhSQnNi5Bqo4cvDm3fV6uc09pAF6Maut4uXpze\npkDpvn7AL4AzhS/WCzYCTX9xPePeh3brDzDR2qWfr9fd1pQMU01eS1+n9HAKmyJXL5e6BTkt5gzd\nm3S+qbMheaWOM12h68JCtSCgibz5RxZHDhbKvn//q8JIL1BaVqiFK/ueWg10fB5KXoXnhkBigv4v\n/2bPxk9wfBbw8TBB+zcfdhFqlvk7IzJWX69C5kWoWec9brmZRfBeHfZnm7GrwthXz8TuLUgJD3Rx\n9PwqKrdGwD7kAK7OwDp/PhJoqt3jShQJDIF9NI59ug67aSk2MUMF1jn3qVcPANgxkkaYb4wUEHUW\nwTP+cdgrPAVC6FyfzWkxgY6LKNrXwK5cLYDvuhMeu1xuUumQpDpFo0QYXX9Ne1pccpaUpCf2Gr+0\n/JC3E865YdGoZqfmJ0VOa3/AjQ0oitgI9uaTBNKAPSKNubASLtkKs25imXMa5Fi4b4OiY5ccKUnD\nbdvBrIbs3RBaKvAp6gLmaCg+VmQm3AVKXFYmHZVOf2xHSO5T9ihQoWxcfIUIRNEq8BZpQk6UwE/L\n4Ijeyu54shBaq+W8KWi+WLLC6Co1jDZZ7aO5h5MPejWht1aK/LV2gvojNcFGxuh79q06oEfNHmi+\nQsXQRXXqRdPaSRFPm4aGRYX6AF8x0FV2+gCtV0DEGdUEI5JneoLOWrgM7BiIH63XAXeTlc2oHi/U\nkXy9XEnu5mIH2GMdeHQELncRxgXQ2lPHREYSmVzkkWbw3wYsg/BkoL/8Q0DR2+YegBfaniPs7eK6\nVYdfRwXTI6B4iyyWW4+AXR7UyRMK2YJDMDKf4ONQDWttFnjAWrvOWvsz91h3CHd5eLhhAu7uZ/co\nOC0Es30w42bs1YMxTXeohvbD3oUVLnlV1npvo+DdyskyeRi7GLZvVq+rm6dq22fqYX8cF5nbiebE\ntwqbs8s2a95fH5fEPOsInbNptzOjmC9Xa5m94/RZZ0Qa1haeA3CW+pqxC+wUA8+6bdzzuvAol+06\nNyyZ4FjyTbfN3TLpyB9bBdgzU2pEfSSy8M+5P94InKFzpN+LMOINbeMHwKMhWNkO+mSx30tKPeID\n80AAe68yVXYT8FsP5ttDRNhGIVyMoWvUISTyl52q4y4J6RwHobqyl7xQmRGx64xckIdnhJG51ja3\neFXn98WMzvFXPpHgRo9q7V4WztqHwG5aCjOv13dY2iKMTMWhpQTboDgmDchV8hvCSHtUEt76lqSL\n49QPDVAfudxIG5gYhpOKFMgENdJ22TT7X3FlMqf8WTXio+PKuF1YCT+/iXVfga9vRT7tCbhvX8FM\n6rqjhY8l1ch8aqlz+j0aAn2FiyYg4pXDSJsWPsbroHmtMNLjlfwv7hpQVwwVjtYvFUYOGwXhSqBO\nbsl2mzASrzAyfbxzSy6XesW/VeUEAHVHCSs9Sag7xtWrecG3G7L9gOEUasT3QfKCgruif7+IWnCd\nsnwt+hcj0BZWROG5rpLvZwOQfAAi7ieaw6u2G2V8kirW8Xm2iUBmO4uotu5Qdq3lfYjv0blGK2Ww\nsr8KbFegySWwztTDHwVqdXyRD6D5fd0XgMtSXu6+9/GQuhu4R4bG5WXAhVqvTS106afsWfpHkooW\nv4TAtEqPff0g6M6To6HNMXwqMPJQjU8CHw8TtH9gmG/d96/dYa+lkuLF0KMCGFNUsJO3S+HnZ2H/\n271uKcFuHPQ3N9XU1ETbCa2FN9LA+wYzSABjFyHwrFkNf1SE0b6FwDeE5CvnP18A9REr4ARJV+xt\ndXCGzUcrs2e0wf5R9VycYVWD9suk5BxfR/tcAFQrS2gudufXU7bOdEY3BpfMgZvu1v6GrIKzX4SG\nLnDRZXDDZQKfe9IquF4RBL9VUfV4CvUHTh5iflKk5XK1A59PYXtXYNu1ww4tw7Zrp0bci8PYmVHo\n8TLZyWG1AuiZxjbOzctpLv8QyMkxOsm56ih3wzC9P6S+4IjG1xWhCbaFcCdnBFKlSGBkqfT1AG+E\nJW0sHQDvtdXDVwT0BDtQZhXN66Q5Lx0Ce+OwbSOETwFPV8kXy08QgUhGJIcwp2hfqSLVxRXVQcl5\nrkDbKxv+QASibeVo1TJKksf6c4BtmtSDTbIOzpE/X6OcDb1xreNZK1D1FcONK+C8vwA7wCQV+UuH\nRPJychEyAtbyMxTty/hd37cSZ8Hsc1LPHSKV3l5ar7GXqwnoCuZ1gccHe4CnocJZ8MfLBeglfaD9\nchdVzUDDj1ENQa6+bj10PBO6uZcm4xp+jtB2GSEgZgPsfgf2LITYLcrSBfeqCP6wg+NB4xVjzPnG\nmEPjlnJ4HDTsxkHQdSeJs0ugbDPm7izceJbm7dwy5VMxV1XLkbEt0L8ezn8eM7/8YGkfSObYplqZ\nsd/egamYiF22ueCY21KK/dNmzcuBzcqgjH0flnUqZLfGz1WD6hZnE5uTJz7kGljfn5Tpx1sU1gmj\nfmfnZbE3u3+dEJhbgK+GCjd2bcFcbUWIbnJ3lV/MYL+eUNuW3HmMR+QzDOZBP6YmIJz53TzsdSfB\n9Eth/gQRGYD0OKiV0Ye9OAataUi0QonF7Papn9xGP/aSlLYd07HYlauxL82Di+bqOj2N4uJxXR9m\nyQ3TblkNe4qU4XKZNgaBvdsrw5IKsO0z2J+8IeytM5Lzj1Wm0l7hVTPv18jjqrkIuSX/NA4nL4Wm\nUaoXPP95GcPMukq1hVGBkTkOEamJSWhSzbh5OgRn/1QGJJ0yELaqHZ9UMNqyg5Pw0wS8Ec3b8tvv\nxuXE3Ohx5C4qU7ADlCn21picjwNRnhij64Vr03OjI+Of6wy+UcLH9HeVtfp7GPlGWOQrFSlgpK8I\nUsOAnsLHn+8QPvorhJFrV0PrXuFjqBJKRjmHyKAz0NopjPT0Fj5mAhCqF0aC6qa9Kede3KI6rtYB\n0Dhcn/viwol0J63riysL1dxTzxMlwhXTCbI+OPsPMtaaWCSiZTIywcoZfeQk9Y3DhI9J16In2VcZ\ntmy9iGm4k4K+JdUKYoKCrZ4MlOwAkwb2ODXIM4VjTX1e2cg23SG4XXVtvkYnEV2PgpEhVxN3nwKW\nxPWZyepn3bIB2uOwPA775kBiOSQWQuIWBWHXpyVztN/j8ND4p/Dx0HuYfIqH6RCDN+MwIAQjwZw5\nGXPMn7F/OeGQ79sz64AarQGzsIsnQ/1SzE9OwlZk4OwATFgMRy8pNK0YsYKCf+oB2/pVlAfLysB4\nXd2ZyUcmreuDQSPYk5fCjOkCsa47YfIDKgYvbYE21cCKgh3zyS9o4y0l8N4o7HRl3DxzYmQnhTHl\nN8J2JEU5x8J3Atjm1bBzqOoOxqNGnsd5lW27wkjO0cXAz4E1x8slKleI3lICD18A87+kmrCKStWH\n7fbCmTGssxK2JyVgtBXhqq/XNquKsLVRFXg//zSMHq8C6Y+MXLQQEDE78PmTl7hXP8EsvxsqgWRv\nOHETO16Blz4U+GQSkF4Jye6aSGdG4fvAjhSUhqBNW5EQby9X4FwHXbsIoFqScFJAUZ2Gdc4k4zgw\nEbDdgC3QvAeqBoF3hySBNiPnx3UJ6OR+zdkMFD8HZoyLEpaqNs4+D3RSOwBwGvekpITpYjAxqFgN\n6XOlZQ9EBKCejADH58AERODig6C0k8CzpgF+c7widOEqnXu7W4HuYG4EolBUIpBNPwmRPq4xtcf1\nsBmm7diM2g+k6mD/O+B1fW88GTCbgOWK4pUD9FPWLzbB2STX6prG+sgOuHwb+J8DliGwyf00tgKT\ngA3KzrV0U1PsLrhlFkBiSUHmEURZQ4CJ6+ByC4GXgW0oSnwIRkvy7y/zbzKuAG4AMsaYnPODtdaW\n/n88pv/4kasyt1/0wMnO7rylRPb3LaVwNJqjrr7+4BUjcd3MHwn4atRAOW3gLx4F4aiWNf/kBzAD\nh2IXDIWFKfgBMEc/AjNwKHYMmIvBrkaNq90wF4Htdz3m8nuxYxdgmIgF2BaHVQYmBuHZaEHqGLZK\njTxRBmNd8KytAnjmolEiPy8ZYdTzHuw3U5gmD5wbFFnZsRRz1CjJFHNyxqXAPKBtHWy5tGBJBzKC\nciTVvBmS6/A9Yey6IFSWqHa6M8rmPQTE/dj1cWXHBs+A2jHCpIHrdZ3nTVIvTX9IAcXZk0SaFp+o\nncy+DJYdj31zMsxzuNfGqsZtvweOGgWnZGG5B/vKasx3h0j1scCrWuzj0LlhRJIB85UQdtQc+L4j\naC0l+v6XHQ/HLoG+78JWsD02Y0ZWSyXyvh9SYC+Mq/fosJzmzuHe6e76pB0O5xQoTZ78+3nMrFMm\nECgoU0Dr1QyAmsGwtwP49gojo5vY4Rc+Zn2ut1iDMOLe9jCiDMbwMRi5Tfjo8UHpUa6eKwDNuyHT\npHnf64eLNris1jpoboTeXaHtAMhs0742eqFbb8DVefkrIPScyE0g7sxLklC3TMHHVqc2Aak9Yv3l\n6JtxQctUsX5fiRIRmFCTnIsDEWXTiupcW5tWZQ4XjodHa8g38/aWQ3i9kyQuhNjjUs6kIlD3JoST\nwr1kiZNiloN3MKSanSFKQjJQwiKYJotIVhzYKtOrTmVAD1fyEAGzXySvNKrAb+YDZR/Zo2MAKBoN\n9tsQvwbCQblNpop0LSoAz2hgFbBBJDBBPnahHq0vwPXHOoOQr4Idc2hKAOBTg5H/FD4ezqB9zDCl\nLrX/qPPifg3sh/+apg6e82PQKYuZOCrfF8zcn8H0HoW9zM0a3VaLKIHA57kv/e2NuXHlXXXQMatJ\nfifYF+PYt4FvzdACNYOVHcsRotRe/V1znMjSH3rAI5cKCM5/XoTwtBd0DJfOgP2btc3bwHNfTAXN\nA7OSd7RxhdvDhiqi+CBwqlXBdDVyp6qRU5ddimQa88+D107UDcfMq6DvKvj15fCtx+DYSviDZBV2\nYEp1ZiGrR8Rdr1MXKjvWrh02FRaoRDyY2MT8+x8d+eX/xmcHjR4dINMbAptgE7x+AczfKPAZ9wq0\nPVpZJeODW/oo5V8agFLrJH4tIhLGB/5qGFisCOO05foqE05Clw5B5i1I9ZBEwieTSx7cCntd/xFv\nQIYdA4uBJmg3FIq7QHiipCGJDooMVhwlGUn5QNRcc7D+rT1ZkS5PTKQrUaKMlb9V7yfLnd1vq4An\n18MmWqnC7ESdjmvGSNhbouxZqkUkMXsOpI7V/ZfxQpseApbIUEUWc85U3iRENmo9T1DkzHih+EjY\neSwwWPV3AGyDUqfDb3lGUos2tc4uP6P1sm5W8+8AOiL//JB7nIKAbDmwVc1HPUXAIy6DVouafSLg\nqUMBxt0TXQ+fKg4ZKfs0DmttG2utx1rrt9aWuMdhcnaIR/IZNF+f/aLIwsB1mP335o1BAM2hs78m\nUvLDx7ANX9b7l/wiX7fFr73Yy+qxT21WEC4GZvob8NSAwnYenA9nXw8DW+UUPOIA0tc/JMwYu1jZ\nttJZ8HunnlgwUeYa45oLuFK+FEbslusg5DM0VGYUcPttYdP2gqR+hLlebGdoOXu9F3OXw5RzR4lI\nIcJIn7TuFk9eCju7a0Kt2qa/s7+m2q1voxrpL3sKNXQnxmFAo4yoFiBy9xV3npfMgSumKmj5+3OF\nf8cu0d8Bq5S1enICXPILmDIdvne3NGYrjy40eb7hMu1rLdinDGzxywk57M7pLTBjhkrGWOeF55KS\nUDa48z5Qxgkigr2WOoLmfm4jl8Bd06BbNeYpi/lyNfb4lCz6KzO6xreFRBBBteuhj9xA+6wMVlyv\nuJy5lnkwWFimMvOxrXJM4g7h4va9uuaBTcw9RRiZ9cnxeNwrwrMcPpYGwFP61xiZSRbwcdTCAkYm\ndqCJGefcWCWM9BVD0QS9v3k3JBw+FnXVNjxe4WO8Tni4v6eyStneziV5LBR/CK19oKiBfC/O4jow\ntQpsWmdaFW0LxX8WZoaahJVZP8QGO9mkw7birmrY/WiNsmeJRtWaZeudrHEJ7H3QBXajwq+2x0gG\nCdp/OqSaPd92kc1cg+VAhconio+F2ClIalgL+7a5eus4sAzavaRtBSMKnvricob2RyH0KtAEsRxG\nbgPzAISv0evQ7dD2fuF/vk/oO9CyRM2ot7ivYpv7yJuCxz8dxOlfMv5ZfDxM0P6nUbZZjn5jF2si\nLW2BgevwfDVWMLU4RMNe6r6azmDaTMZe7ZV2P14Hy78Lb54I/hhkwJZb7Fefwr56cPbM8w1nMnK/\ngVNLwOPDrnX1XfMmwREvwowb4cilIkLLjlc0DgSMsy+A7F5oikHJXr13w93SRCbdMuNeEPjOukqa\n+15LBZgRAy97BEjjfZJtbEaSlafTiop2cvKPGNiLEurhtgtFK6ffAeP+IDex9Dg45w+KzlUjN7KS\nbAF4SrKYNQEVWPd24dH5x+r8vxGTjHJNoBD1+2dHdi94N0nqFgESMLOHmnXODQtc/KVQVC3QmL9R\n0R5vUG6PvAlFNdpUuApmb1Yzy64lkG6Q4YavvSbUTD+gTmQrk4CNfeHqfnCEy8DF92miblilwuGm\nrMiOdaV1XjertrxfaIRJk9bNlEoCkvG7YuQiZ+6B3K+ae4i4hZokvUgVK+MU2u0khYNkbmLTapPT\nOeuijnVQPgCSEwo931J7dF3uz7j+cGl9lg6JFIY6uqJdBz7RncqIdXlbcpR4GWpC3RFaEspqlQ6B\nyMkFp8d2b+vYghFlz9gK9ID4ExC7HxG1PyEgi+szypTBoz8EJwHvOAMRNDlmUaSw9zW6ltOzkBwG\nlBz66OAn9Tg8/vNG6YN/0JMPx+Xfs9mpmsdnnaYa4T9+Dqbcpb5mudE9BL+6EXvMVCkKfhTFjHRR\nlyOcZD08F0a3YM+foIzPzomYunuhohI+qIPn7oV7zlJ91edmwH+79EtpC+bcS+HtOmzHWbL5B4hV\nghklI6eB6yFZjX3MYG+PY6d+kN+/fQjsDxKYtuQt8+0ihL3HueM/PQSL1YLF7nRZrmlT82ZRduZC\n1YONWKHfexiRqqYOENG1sktRvXPuui0AAn74RVthfDVgK0V+dtUJE2dPgm8+AuV7hYUAY34lhUhp\nBE54QZq64Uu03zpg2CoYvESyv5YSuH0CxF3t2gcIAxvAjvML92KScdrL45JZPoHS+iGLPT5Z6Bu3\nC+Etbt9fWCO1yWuj4fEbMdXAX4zq2/qmZExSkZU0cWqhvYGdGZX0f4sP3vdL6rjFh70kiT0QKyMe\n+IJ7nSNmaQrkGldCANglSxW8TaCWvG1gYlyvT34W7llbwMgD8XHH/oMx0uOCZDl8nDK8gJHZtAJ8\nOYws7i4ZY87MfGNfGDQYEq8IH9NR4Y2/VL04Y1tg71JllLwtIm7ecmhcIeIVai/rf7zOlbnK2eBH\nVR8W6QztNkHkWuGS182xqWFgdxdqvK1rn7PK52R/GZGzYGdodwr4LoT4Czr2cJXIXEm1cDpYoX5p\nyWLhbnK7ziFYCaZYtXhZF7TMJoSVrZ2AZv3LlyGcpIecK0GBVtY4TN4JJWtRL7ZvQ/QlYDxyqqxF\nPdXKgf4Qv0i15+WTyQc6/QiDQfjYASliIsfDTQ3OIKSEQzo+C/j4H0fQzPTv/HPrH/NnPRk/F7zV\n8NJJIi6dgYHrMWddhO06Q0B1iIZ9JArrotBTk6L9eR0Ur5YW//znFdGD/ARoXj3l47eVi7rNb4Zy\n1+csMPQgWUo+K3bZI5IlDFqlm9gYioJ9iABnPwKkDe6zne7vOS8oUhhHvXIqgO1GdWwLkNGIw2oz\nHnjely8SNxe5ZVYGFDlcH1ed2gzg9VGY/xKomitHwguTFVGsKcYsCalOYoADlcFJ7LBkoWE3YC9J\nYX8UhTdjBa3817J4fhU9aLl/eKwC3nTXoz/4l8HYlfpoeX8RkKnLwf8gXPRHOL+Pon+5zFlOX15x\nlF7viMiOd/5GmFML9e0hsweSg7QtTwpa31P0bVkt7Lfwdq2Apa2LCJb2VSbNt11F1vWrXUPo7pDu\nJvKTcRO5t6PWzWXksmEdj7ccKNfrWBdN5MGICqWzCYHi/iqIXSL3J3+l+rzc/hcBTDYNsWEifQDv\nA9N7u+MbIrACuPQDtSowZxWilDkHy0CpZJI27Zwsj1LfuNQwoAxa94g01QFr34HQBCiZUPhq4p2c\njXM56u2y1UUO3waC0PJbBEBN7rFcBJTuKAzYUfKQBAK6aly92lad130t/EvGYYJ2eHzsaClVturX\nLygjVdPfZcsmKYNz8i7Nx4DJzMMMHArnzVCT4R9P0Jz98B35zdnxl2FGVMOIFao5Lo0USNOyzdC8\nWjjSADRUFow2Jlg1xP72YhGQKdPV/BkcKSnFfv+A497ugTcnq7/ZTRlZ9XurRZCODsMTKczKgEjI\nImSO1VEuk3Yecjkcj4jEBxQMrXbfoUzdqVZZrKfPgrum6MP6DoV2A2eCuRW5Li7UcyqtZIQ3t8KF\n31EQEBTAqRkghcglc0SEQHejMTQ57EfZuuSmvOEVte46tUHvBXQ9c3Vidi3Y34B9arOMPBagDGFO\nVvkzj+SSz3hUS/0W8IzBLAlg7lavODaDWe9I9+pxmJGSnTL+JcxTGZUrfCcqO/4xRdi7nJzzfb+I\nmjPOMiuDWq5nGpOTOraxuk9w5iE0eqAki+2TUg3bwoAIXc90oUBmi0/bAdWljYlqgs5hZEAYCXDT\n2wWM/Cg+zn6vgJEfxcdF2woYmcPHHEbG/izi1fqecGNZLby5EVJuP3vCwqB0VBjpLRdGthmiGupt\nlcLH0iHKnEU/FGkKtXc1WmH1+/Kk5FBMOdT31vayARfMrBYZ9HVXHzZfZx2/r0jH/swZ2qavvWrl\nMgnJH7+yHBo6irxl07BoD/R7GW7dIjLmTUou6WmnY0zUKQtZdIQwN7rT1aT1gvQQIKh/vyTystr9\nUyieqIyedc6LLT1chq8TMEllBuF6hH/vQEszxLZB6zYkH0G15zQhZ+ZlCmCGUU/u7kDpJAV6S3rp\n+27Tk0M+Pgv4+B9F0P5ZcpbfzqlOkNvvRck93HO+9Rj2uCECpUM0PD+OYe4vUgPJKV5F0QDOfx57\nt5s0PbE8ebJBiz31T3+9nfNj2LVOw/5QEo55F55y4NnB9aWp6Q+TZykyOPoF/bLTHfSZFwHMNnS3\nuhHtswtwFKpvcI53xMPQcxM8OUEF5WFk7duAwOIhsO9sFqC+5QqfNyOL/7fAjARK1MiaoSEHcFYy\nkziYC8E+LPMPMwrMY0XYn3kwFwJborIgjnjk4JiTJ+72Yjb6pak/UDOfQrKOXH+Xf2CYnSoCsGdb\nAfB6dKNQDstbYXkPGPchBJ6FO7dDl7Aigzv2C4SaaqDsXah4G4r36vWTdfp8eYP+XlItqUeot7JO\n0Z1QPg6+3Kqs1LV9oG05HOlxAFCsiFsumuYrUiSuTXd9VlIt4hOuUsTQZhywbS9k2Xyt6kOWaYJs\nVMXNwQrXLLSd1gtXCVACQ2SXb3ur7qw0oKhmrBZa3oF2C91x75ChxrJaRUXfTqpQenaN1hvRLCLW\n9hg157QZAc2HcfWGMz6tl0mImPqKITUGiqdA+TUiaSHAHwSugZK35ThZ+n5BdkITxE+VNDPtgNWP\n/hLS3+w2CL8AsZMh+g1In6B7qjAKPPtwmbUeUHEr2FfA/9dlnofH4fEvG7ka6NQcoOtOzOhRhQ+7\n7pTiY9otsK0Be7FHtustpcokAWwtktPQKc4edewr2CmDRaoA87nLsPVLMfOfgb7VeVt+QPqo+efB\nLS9hfzdPGaYPx4mATP4ZdvRZMOtbmGX3Qq9KzMNZCNdJUjkwo+VHgj3Hi701odo0kOHJbD/2awbz\nOLAyKpOLB3tgcr+37a61Sq5f1yjkUPioK0GYZkSqZk8SJs26SXXUpfXQdSsMzuSNSkxbNAeArs/w\nYrjrbmX73kMy+1xd38glsPxE9fr67ec1QSSRycl+FMRsQpNGWxTcfHiaMmk7kQKlw15l9Y54UVLH\nvtU6ljjCwu2bsVtWY67P6vpsQsvFUPawkxpX596zD3vgL6OUaWwEJmUU1HzGC1+0mJ8Uqb782bgM\numJGJlq5kTbYYQllNSMeWeovDmsf3wop8wYidBEP7FMZgb0sWpBIZRGrdAAAIABJREFUbheGmogH\nc3kR9FQWzdwA9IIuCXT/sBthZA9gRAEjP4qPd64TJoa2HYyPd66D+1YXMLI0AMGeBYwsHwdlfYWR\nmQTcMAhGDoI2HYWRJRuFkd6gSNCBdvW+YujRCP4SPY+2VfAxul4uiYFSkTRPUEHL2BbwOPljvsnx\nMbola+N6upmknt+zVrXhlxwpZUvzCu3H/zthX21A+Hj2Ah1P7wUioKUBZ0b1AhSdDaHPOfyNCs8z\nCedumXGBUXdr43tHZiDdJkD7a8T5Ow0B+kHZ8xDeJBJVtlk1a9YL9FeGLtSM2uyMFkaGz4TifkAP\nCM2E8FpIjpU1f0uzkmM+lEVLAAwF33Xg+znYuzg8PqHxH0XQ7M0/AT4houYiabbPDAHblLvgyQmY\naWv0+XU/w/j/7zLHXI3b3xxhJHcYhCbnIytloftGEfzwOi2T5O+6yZkpGck5LtgOY56D05/VtltK\nFGl8/XTJOGIITHYdrRWzYYFOGWrMC/pF5lQPt02DJy6HxqO1bk1/eOdoZRoHrofm1QKeS2fAjTOg\nz2rMiGpJO9rKTMTmIo6bgQscGJxq1T9nF9grDOaGSoHoywiAH40rO/cWmJsz2EtSklzUuSjfYJdB\n2y23RntiHLMygKnzYgekMDV+sl8sxo4uyDz+3vDcF8OsbcJsaoSYwfO7VmVZ+yOCVgfUo1DSQPT+\ncMALO3vB8pSig4u2CSz8UUh11QSZTcBlfQVOdIVZp8PMddLh+0sh3FOEq/FdmHuEJnuAul3QFBBx\nSbdKY59ymZ1bNin6BiqM/tCdqk0rqtfhJNVzFfWHsoGwMOBkjgnp6wMRAVEy4jTxAS2baBT4tO5Q\nw8yZq7TdHft1bt4iHa8dKsIV7qSI59jucN1WGPlreHmnIlazToeT16hWLidr9BWJjHaIiFAGK/R+\nfJ+OPV4jMw9qRQ57uX/J1gQkfgps1XXN+mUosr9KxdyRzg5Ms8AeV2e/DdbtgX3LXdFz/4Ipim83\ndCyD8o4QmwPmMXTD9TQwFPacB2MvhzN2/a//hf5P49OSQTPGPPm/ee/w+ORHci5QMwAbnApvTYSt\n1ZLotZSIuN3jGEjXnZqj+2yVcuK+m6ChC2ZItT6ffx7m5jWYN58BJy3kl8cpa9Vjlub0XQg7zn8+\nn51j3kTMxZUKvC0aowwaKFM0owd85XrsFR44slKOwHe9AcdWYsLI2v7bQfCH1O9smXPhCYO9c3PB\nPXCKV0SmkXx/TrPEBfEGgX3zRUniAdYVCSfHLoZIDC6+W8c7FsxvHXhNSsExVs7FDzlSN3AdVM2F\nM56BcB1Md9b1UDinBnRtR6yAOZcXMmjumDkSBeuSkLf77RUudPfN1aN13SnMfA2VFoTrdG1HrIDW\nofC6R+f7NnK1rAZOTKk2bY0PjsmKlL0G9Fmta/AW2O94YRHYWUXYKwx8Lg3rYqolmxpQdqxnWmQr\nbjCLQ8LIbhn1DE2bgqX+FVllyUABzt1ezO4Das4qM5LHDkwp+LnGJ9XPFuf62CkLGdh5EvlaMeoR\nPkIeIw/Ex7FHwNe7CSPb1B6Mjy+ehiZ8h5GlVhiRjgpzGt8tYCSIDNWshWhEuFXUVQoT0Dq3btHr\njQ53jFeEJ7JZGOnZ5GqevfC7uI4jUELe4MNfre88GVHLnFitMLJ1ozJ1NiCyd12hlSAgQhnfBynX\nTiDXyPqdc3VMO5PwxAbN2esMpEYVjjlWq3qzRKPKFTIJkU1vUCQyk1DW0WQQXi1UHHL3O5DY4A5g\niTCupZtqyFNFUsd4Uq6Wr72W2QM0LIQ9G2DfEv0ftrpbQ18jlJ4JwTLwzBI+BkFGXD1UrpBYKHy0\nww9dCQB8NvDxP4agmenfwTxwhSa66Xf8/RU+buzoosl54wG/rkVjJPubdosse99wDlSTZxX2X1+P\nZ84/Rtg81xSWN/1jynrdHlfULRLH/jIOJ0+F9+rUsyWAnApy5GnQux+7bbtltSbjRlRMvvEY+No8\nEZwdXeDs32A77hGADXkXXrgcvvyGoqPTbobffAne+hIcgXLYR1GYYK+fAhc8ogLpMgrAkwO185+H\nU18sPMdJOSZYyVXWgrnaYm8Dc28WFvtVr/ZrI+vitS5rNg9lu2KoJ9ppITgvrXNo9MDX/ZiIgXFB\nmXt0bKsMWhuLPTMmcAlZuUuVZMmOVG2mbdcOO6j8734/pr4e+5WoHK3iRqDUKY35wlDJHEuAvfBI\nFUQHoqyj7aBrdjoypBgOO/fDE5tguXNS8k+WrKBkNTQvhJFZuLa0MFl4vPCzD90x+MC7RZGzlqS2\n8YaRsxVogvYGFeHLpmXzP+N4AUWPRhGeUKUAo3wgtGwAb399vqwRzkxq234XKUw7G+LyAVqvfKCW\nDVaIKOX08lOGi6Tt2A/PnlU43lkRrf9KE4xbAMt2K4J4hjve0gCMXCBpZK73mb9U59DhJB1n4wpX\nL+AK+NNRgUjJToifIFeqyDPKovlwk9gC8L8gAhyISBqyv0pAmxgA+4aqn5uHghqpBYg5ACrd7uz1\nH9FrrtU20p8D4q6Gbhr4T/27/zafyPi0EDRkA5QfxhgfcMwh3+tnfQSG0ua1mSIN5z8v6aIbZtBE\n2D4U++5Sycv3HYBlWydLtrfseOyT7WQUcfO92BcPqE261IP5XhimTZUNP8CJBazjz+Pgm5cp07PL\n9WMb+4r+5px3z/4N1AxQ9qsRzGPu7vbkqXIP/i/1OmNhSmYdw4rgS2lJFQFODGGvjmKud9maixzv\nGYmygu8uLahLGoFpU+HBy0Umc4YkbcHEHpOVfs+0eoyBAnlhZMl/A+old/4LEAjm8YqB64STR9dD\nKgwnvStsPHkx5tV74dxnIdoB+qH5viQs58w2QH1MxG7R6fqsDB1X150KYl4xFb46S1nNgevgorn6\nbJeOmTgFonxWFtsrLSUMYG9fIxlqhb5XOy4O7Vfrez4HzJNR59DpTLMqsiJccaM6sVwm7Ex371GS\nhZcM5n2f7hfqvNj2GZE5gJbhql07Uf8fpsavvytdCqoiC8NSklDu9ur1XzywCm5rhDM88PU0xIai\nIHBvhJFdKOBjJ2j3yF9j5EfxMYeRM9cJb7LOTCuHjyXVWr9NHPZ1UlnhAzuFLcsjwseSamFkshG6\n1Urp4Q0qC1U+UM/NkbLfrzhKOJWOuLY5QRHCTAJssoCR/lIFKAkLv9o5YyyAAUE4IiTlSLAttD9W\n6+XI3pThEPiFyBoZoAh29oEzimWqRdDJLSu1vq9Ix7N/m5OC1kC0VgTOdALfq9AwFiJ3FmSIwY7A\nHGSjX6xasWCTSLCnnYKaLcOgqQc0z1QGrQHdzuS8hIpXQWCv+99cre8yHYJdF0DzG8iJcgNEvw6Z\nX/IvGZ8FfPzUEzTPfR8hRb2W5Btu/p/HzKtUfDJwnSbl0hZYezT86URNqjfNFIkBzM13FrJhLysz\n5nnt7xeqmBnkv7rc+tZp6u0iFBXsEBJIXPczaHHSvdKIkOqZL2HOqj5om56vxvB89QCr+F8YON1F\nQKNJsFa9WgC7R+Em+9Wn1FvnJ/dKUvLqvbD8RgHJjdMl6Uj21i/9T6MFNmUIkDagSTe4V8XTALO+\nJRcr0HHXDIAdXTBXgr16n/Lu1cCrBjPdYh/3wEkZ7Burdd6XzFFd2mvIlctnFZ17vivmugz2Th98\nKa0s2G8SkmZ8HWW59jRg1jYp4rcyiJkXhNk+bEUWszDMPzr+lqujWRzAbp6LnWixl1jslZaxR0Cf\nF1BW8aI5UNsb+m2DHlGR6u7ickenwbYR32waAIFFUPFL8C+F+ZsUTRtRJT09FFyd6OcIVa3eP60U\nNtdIW9+0TsCRjirS1/K+nJ5Cla6XzEYY8hs1WH7X2TTnCqaHWQFF47uuxsyrSGA6KhAKVMhUxJdr\nu5fUMpmEjuW+avivTqq3u24AvOMC2fu36TzqL5e748hnRcwGlAmQzqgSsVvWWKg5KOoKsd0QXwwd\n1kDgIWej7DJpgVYZgQSicpQqWSsJYnCIc6yqAoYWooNFdVJjefvLbdJmoOxHAp4Eii5mgfB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Bptk/wPAh3/acNxnIeB94DfAjNb3L4b3/TI3kVcTF3UUj/XDa8p+2N+RzOBMRf1hsVTcX5+CYwN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scX16tReRX6yMvZkZha08CTvUUYS5e9k8F4DRY5wAvMNzHKRcQzD7khB8Y+BGGaTLFjr4VRSk\nbAzxBJ0ax2ZYa8usta8Cl/7Sx3JqfHfYrrrXouaLTIdiZdOBUDDVghCJQxQMOw1BrXe6KlltWFIm\nFtX0PFiQLRbU7g10j9EAJZG2QJ9IVCHzlnQPWpngtnNCyzQCtjeQYznldiUlAXYklvcFUWBUgfCI\napMzVBUIAi+khNArnpCxSxISj+t3WwYLe8gW9I6Cfn455F8LKk+kD86KVhA3LxzTEcH4e4NZGqXP\nJYC5S8dt0qOw1wVEvz/KYtKiRaXeHLivWFWpTYRQD575HTxbzIdviObeXILYGZNQX20boFuZArhl\nEUKIvFcAYdeEftvg2dhGR7BDApK6edoHjUpEzf+2D7M1Entescgt6pRinqgEWWWyj57odwX7aP4Y\nHbKRVyNnv3eqGJUHH4G1kZjVkVI2ji0QzK6gFkTWEuGIS05n1IG3dsO+a9yc1ydEBhLu/tZGidht\n7vx7CXPc+fub1nTTMlHf9dR9MOlN2b32KKFeC+gE2YcctLbAzXNr99cHvPganBar/6+fB/PeUu92\nFjTzQcyzQfW6rasB+bHqic9Woav8PAMM9omxczcKuvcGMC+Wii6/divs4lydw4OJEAk2eayIdSIK\nNZebO8GkkZhvwoXomdoF3i5R0DRjsgg/1sSxtxac54de70FpL1hTF3r2cWXIawMMi4LAVYQYUGMX\nkRwH9rZi7EfrFXzNmFxOMGdSA5gLE2H4ZMivofPdfWk5Oc+SDjD+dCUtx62CURsFbZy52Z2r2hC4\nHAYuPcXceKzGf2Mff1UBmu1qYQuYoUBQAsAx4UAXdPNvJVR9ygJ86iMD5NgFKXeO5291DZThcuoW\n+BWczdoGCUVQEAN9ot1n9+F6o5Dh+dx93yHgwXGCZ3ztsYd8/yj7m09B2sXRupGueEl9Z48Nh4Up\n2nc4UClHN+hBFAyGU87wV05i8e19l5Vx8OB3K1nHbERGYy8agTljqAxQ0jbM16f9eyjhp2GYZ90E\n9n1FmcaLNn5HvPrfjbDrg/CmUYYwD+xYV32be4MgkYNnQ5tl2GcV+NqL/VqofsIwWT+hty/GYqtZ\nbJOtP/mYy/ff2z3O7gTvFkI1H9Tx6fp0vRsxUZQbI5qiHo+LAvB/m/niiy/+4+/8r8abFSCnM366\n4Le97SmxcA5/jEHJUDsAGf3hm4bwtY9yUhTznmH7eRD82tDtK2S4A51JyYYlz76kBuv5V2rjSjlg\n9mF2zNPzqgEZpO5LYfgGsLXgT8+Uw2gKGsNAl6XNL4ZVnlZMVOg4T1jjc5JU0IwxfSo8+hljHiKU\nwjo1TrSxBdgD+24FmrqEhddDHUTBlEd/Xg2mfoPgiTlQUMO9ngWDakK2gWntRak/8H2hS9p+5r4n\n3CWYitzzPAThrkpIPgbkML7VC/q5tXv2TfobFDLDXILEmz96GnYWYl93PQWLu8rJXXCNkCaHgNyk\nEFKmBXLc3yQkmt1kGUzxw4JizIDQGsxrYdAyH1oUYR8uxL6fib2tWAmmINghAeyDYC8pCfWNrdFh\nmHPBXhKENY5ExAdmbRQ2KDp3ooHNudAqzWmw7VNAct0cGDMBm5kKjcugfYmc/keLdTyvoUSsD5gW\nLhKU966BIStEt+4JR38CNAZbvUzVn/eMgpQIi9keidkeqaDvqwidVx+qJnr2MTlD9vHZ79pIe3U4\ndnyEoKXXBdR/lt5UyeIaOUoc26CuGSdE7vU1bb/BwWR3ufeO1FLVqswn/2VPZ/C74O5MQj2Pb/US\nY2UQqBsv2OEmoFaebGR9t/06oJZLGnQA2um65C8BzB0BOLQD6h4AX67IWL7sBBMDEO6QKZ2AL90y\ntWMotskkRWcRaaqSLQT7WSYmUZBEdudit6+Hdnuwd+5QkF4XzB/iseMLVUWd9yKMuQ/q78T8OVq9\na4Wac/vWDAwDoXM0PD8Lcz+qYm4phJq1qO2DSm/CkstEdd8zHQV0LdfBdX789cC/DOmebulMtwOw\npH8ANlWDO9tj1k+Gv/XXfTFmAjZiBLbxWGixXtfOyvOU7B8Uso9shp671LPWLVZok/Q8FSjsv6Dy\niQ5U/w3Yx18Fi+NR+3nMQRE+QYQU2zka71wJ3cjFaEHYS4jyNwkJ+SWILnhYQ5V8O74ksoT52yC7\nAYJ5HEDGzssK9kALajEigFjVWVk+LyO48rzyyo1p7c5P2Hrsp+frtUj32oBUzDX9hflObagbrt1q\n3az5MXr++zmQimApUZT33FEDKAU78Oh5PHDgAHPnzuXmm2/+2fP77XFUEJacoaqGJ4xYz4f1BX7o\no5qHumgBzinEBn9alS9sXgDmGQUArzYs71kgeYsCvCvGCBYx5XaJfs+8EXNOK6qc45ee2OwV5fN+\n1PHcM17/jByjLPLe787lsRymQVC9ciWF6t163a9b92zk0HiwkLReGH8qzA7Hkgr7uzB/0Br69Olz\n3I7t2yOsr5NBmP/TyEiMbz/saAo1cjCPO2jkBR2gx0ewswa8F4QPxsG9Y8jbCXErgD7od68CnnkL\nYgsoeaUfkW+Bvcz1XJy2C3NvPGXDdBzmcXdND56tSuzS9nIANhISnd8CfVzBccEusCvB/BV4B+wt\nx+b8HnOGqrRjsSc3eh57lipvGGOeBbxJPALsAJ621uYcj+/7rY5jZR/B2cjWCGXyHmQ3JuS0NYBm\nGZARge4fT6bGs5e1odmXouZu+QLQGvoUuGCsqfbTdp96P7MP6X1AjnoXQpDJXPedpSgxNeV2rd1Z\nCaqgRPaE/hY+FeuvnQvMHAJ/miFGxcQh2v7yRfBaL2mgBYFXr4HL5siB3+q+o9gdW677vo7AuQEF\nfzeUwrxw7Jd/gZV9obg1bCyCP1WCLzKlx5niL+83I9MRSfQsEv383X4FRvGOMThPrJN0c2QSneSL\nkAk83B+zYx52QENJibzWS8mly17B/LEj9pKgWBkPGfW93WyxL7yBadATcNC6iELMM5W07SN+bL9i\naHQE84gTxY4GepQpqJ0ahIY+yGp4tI28YbiqWbe9VG4fyUrAXB5/lI0kvSmmb7RstAf/A2zRpKNs\nJFGE4JeOQPyDS9XLlFEPJQC8pJhHz1+FkEZeKaooejJE9bKhxWrMPH3IjnLU9/cPgZZz5Kd9Tgil\n9JlEys38eaqAAdRfCumNMGMay8ZuAjvFH9LfA0h5RsimetkKPNOb6fUxD2IiJ2P/Sqhqu9udx91o\nPkozMdMbYHtHwPYAJB+Wnt5+916PRAW0eUoy2Gz396/Ipv/OMXW3Ww3tVmM2vIkd7IcLVyhAXtwF\ns2ge9lE/gQDcswWmNke+5grA3wuAYdUWMbUZcPZOVURn3qjfs76n2CrTmyogXzBZ/mR6M2i9TPOw\n1B3LFhGULNiv4GxVJTh8GCqfB7bJsfN/TlgbeQLbx4jjcVC/5LC3W8wCg7kcCEK36soKZMegykQd\nFFR9QqhH7RC60R2T1ZJcoBJMXe8EFtvA1F2ULyiD8mFWOMra5Oh94tBC40OZogbLYH4KPD4Svq4h\n2uB/XYu9/gXsOh+mdRDzp5YSWr58LKx8R4to96XYRQXwwVC47FVY1V5GZm1r3XyT7mH0eshPdgFj\nFfjsbLjsS+heXdkrs9NgG4RurOPRg1Y+3wv7YmKDcM9YBWhBzTtD3xK8rffR24fdKUfffqBmW3to\nOqbuUOgYjTk/iP34xwMAO9koO/luDXgjCGmXKiCOzVdPwl5gdYwWqu5LYdA0bHYSBfEoYP99e0L3\njIYCzWbweD8FCEEgCuo9Y8i66TgFafuBs8B0ilb2NHUczBkptq8xD8Ld92JaDhVsc2IZdhvYjwez\nY8cOUlP/RxW0CuOnBmcANlgd03MHzA5g7y6UY9AwQW822KeexUcvhaXQoQAYDObxADZuiDJ+wL4X\n+5H1LV25761E18tWhjA5A9oFYK1fWdZsSFiuvAm4akGdCvs6RsHZb3lYa//wSx/DqfEfDgfvL74A\nouagNbGL+1sqqBrOBpYzOcbro91yYOUhkQlQB/ad56CO+SjJmYBozcNRwnAdCtJau315Djpum+XX\nwCVz9HzwbN37M2/ELivCLJfKr01fD7QSkZYPbGYqJjhDZCE9Fymo256kIO+vw2EDjK4MyV0E0QIY\nFglTcxwF+Qrg6f7Y+VfClIHw9yIY1Bb+WATLgEsqQVQm5qpEbG3gOYupbsqZbakO/LGStNqa4kSm\nTbnYtF2ci7myBhCGfbgQc2e0iERGTsB+2FB2/Y0rdOyJAVG9I1ZGtmkOAWz8ZfDGm9i6rtLXMBNz\ne6L0Vu/xC1lRxUqI+voizBeRsCEM27kQJvrhtBrQJgAf5sBbl8IKZyOzeslGVrCPwHdtZB0ftuc+\nmBJQkLrOecSPjym3kQkByA4nRMBSX/93fBmhPxprO75qDaev03VUBZ2v2HwFqocQigZ0Lqfcjrkz\nGj50ifbeSBPvgRkQnAHv+WHGFG2fnIGJnAx1ypSw9SrBJTuhUTWoY2G3S+b+bmfoHqgbD81THQLj\nHT2cvAP3TcZ6gVld9aNRHcwtCCK6GzgrEZs+AvPUo9gOfszlfuxDLhndNRH7osP1No/HZi2HPZ3U\nzxiXK3K0TZ3UYzfzIujlxxYCIwOCIy7uAtPnYef6ITFAkwl+obnOAL5pDUeawfUzYJGf4a1gapOd\n+h2DZ+v4v2kFNddjhrXCZo/VcSSkyn9Mbwqzl2mOmgLF0HaP0wMthdh4KL4Sov737sWvcvxc+/ir\nq6ABmAUKhov7ugvNq5jtRRCyPEJEHgEUqHk46Dpu+y+BWtCsSNnAdrUdTCoCEg5AdjFqSIUQ/t2j\nio0CInzCmp+xET45G54cpWxJfgycvUiL8KbWDvZQAJPv02vNU/VarWgt3NkNtP95yjClNVrG4p3C\n/K/cA7GV1NyZXANS3kcLYi7YG0NzuX//fubNm0dKSsoxmd+j5rpWUJnFL3Nhw7kShmyL4AgN9mFz\nQk59uXD07HDsNpSV80ace+4Tg+APjbCprtJYHawZAmfMCWHbPwX2Oh2d5C2weRl7m8PXzdVHuCQb\nhp0NUx06hDZAnTw43zFFvtoQ0nPodkDNs+3q6JyPPOf43SNhDweltXNltLKfX+ZC9XgdT3PF+gDc\nWFbeu5ebm8uyZcvo2/fn9dIdz2FqBSEjAI0cLLLJevhK2jM2x4e5OKj+xZf9mCUB7KN+MbFd9iom\nuRW2jb+838BjoPve73n8Zsk03PCMDFRWghyPxstEGBQObIBu7v5d+QbU6wTZnU7w7OBbx2JPblx6\nXDOE/0TZDm//R/1vrb3zeHzvb20cS/sIIRtJFLKFF3K0iHQtZAP3InTIOvdeNSAL2h6BVTUI6XwG\nULo3gVDvVx66h+ujwKxmLTA5Ib3R/ap80HOR2Hw9eHtsgdj7CuIUwNRF6yKoujDA3eceXL1etmzt\n7VNg0HDSaipozC+C8Rth+4CQ1uG4VTAr1v3uFSnQ5SHM3yth310CN9XE7G2t5GFHsIOL4dwo6A5m\n5BFsTBlmbRR8AHZsoYiuHvFjn80FG6/1rpdfQVom6s3uG63Ky/5cJSyHTQwFYRs6w8rzMJ/eJ0hk\nc+Q/dCjFXhsORp8xRfdJamCNez8aqKoqDQlgr5OGmb1DgRSJqOLT3dlH11NEbXdOPnY20k4rt48t\nl0G3sG/ZyG/bRzjKRvZtonkdle2ugTjkPxUQYjoORz5JFXddZDna97bL1MfmzxHRU/N1MCNF53Jx\nVwVeuZOxLzgnYUkCDIyWcHd+rJJxD54P90cKgVPXwU0/QBm5cYvhm0TM7xqqdy6BkBzCNmC9Sxpu\naAVTRsAjk6FTKnwyEOoooPLOh/1TMeb+KOy+5TC5nQhkfEg77Qkw48FOVo+7TRGxibn5Uuzl4QrI\nkjPgybZwdiXJKE26R31xb5fAvR/LL1zcFfOnltgXDOZaq9aNxU4LL72FfMJREyExoKrsctj3uZ8a\nZcDUUTDvvhAktuZ6qJeNad1T89dutWwjKEBL3qL599AqOdCnfkgLLbmGWByPdWL6hLWRJ7B9/FX1\noHnD9rEQB1Hvgr2fEJlHODJGceiGjUeLh8eS15gQ1evvoU9lyOikfjOAVSX6bLYfVc+KKc8algd+\nO1rrtSNBKPSJRtyHMoOLu2gBWoVupnrZ8Ocx2rnJ1QLSbjUciYbPHBb8kDv+rnNg6zJmbg4ZmjFt\n4bYEeDDJVQdi+F506/GsoFE1U4tm3XiVz+shw74hCJlH9yyVdQtRgplBqKl6EsokVUeGON2PCXz/\nvWIaBFVNcsxZNJ+jCubrwMvue2MXqZqyuAslrp2h4X549TzIuxAeOkvZ1GGR6Dy3Ww2je8CtQyA/\nh4P1YH5zeCgM+jrNn+M6soEW0YLO3GExz8ZhJpdqLtwom+8rD84AAoEAn39+ItMrcVRgDmA/PR+b\n41NwdttU+EAZSVaMw17ZA6alYL7YCrH52H/64WYgqvOPf89tT8G942S0V54nxwN0j1dC0KoasGS/\nGOfqOZKBYxmcHZdxkvSgIXfxHOALBCprTYgu6dPj/u2nxn81PBt5OB/Zp63IIa+C7MgWZA8boWRb\nLQRV2wkEYZVjSSUc+lRHSU4fxHyJ7OEht31t1BPVCPjGBWd5bpvkLXp80FnOZ71sl7DMh6fjtD6/\n3AGSUlXhGP6YArfkDFGN19+m7Y8Etd9bh8NaJS6T40ReMq29kCbJcULDzN/mfkMCkDIN80AlbGEq\n5B+GRSWqlmwC+2Ym5v6oUGXoqwi4Iwr7JNgpAegVLVhhHhAdr56kFdEwq0j/31cCraIFVWzutpl/\npbbPQr1xrZdBelPB8vLAXlGiOZ8drv64ifFaD19fr4CiLvD4EPU1Tb8de+YIAMyGKNmP4X647YjQ\nGNFAO1eZ9MTA1yNYW7SzkY7dtuF+ONjiuzbyKPv44NE28vnYkH3c182d/xqEzm8D5At4+p2gNbnF\nMgUHPmB/DgRqCfWyqTUMmabAZcVQuGey7HzyFtiRCM30eXNhohKYezphUiNFonIXmEdK1K8c7c7t\n09lqUPOYMINA/zKduzgwf4sWQUddRKRRmAsLB6o6FlugIDutEJsHJjVK2/k7iWDmzDSdrweAVmmh\n4Ox2BMtc3AVWhcPISZjL49X/1qmS2Dc9XTuAhxZL8/bxkboORnwjQfBLgupr6/6Ogse4lbonYvKg\n8XJdL3/xU8P6Qq00i4K6Lv7RX+es+zua476v6LmrTIqmv2mIUCeoc7TALxv5ZHcFZyfF+A3Yx19l\ngAZgL5IDVjIbLXoebbCPUMDmVb1AuPRDFZ5vhgVfo6xPC/WklZ/MOEICnx5bVBAFCC3XaYHajgzH\nQRzG+krdJF+vE6nICiAtJxSofdhFGPva0+BTvz6zdZv27QPel0ioNz7sB2FHYOFBWOvFMwVu2wFH\nz8XxYnH8zuj+pgxxY+B3HC1+6kZZtyq6ZD8RY5SdjKqFa4Adfi2uc7/7OQC704eZaKGbFV47HgZF\ny58YVB26RUNxG2DuQAAiF0DkYYhYrcxaeCU4vEaUsvViUAYztkAC4L+fQ7OVatC1R+CcHVAjXbwU\nx3OU/cNXrjlWdo0CWjsuHPuxD5vm+15YYeXKlTnjjDOO74Edg+EJWX+7ImofHya4yLPuhfSmgqm8\nFAlvXMm0LATHabHs31bPysf7I9WDcOA+3Uv1loXE5x2Mgyow83klN44bZPW3OVoAF1lr/2mt/Qdw\nMdDKWvuctfa5X/jYTo0fGZXrIke6MXRbiypkG9BZ3Yoc7C+DcvK3IDuaoNdjtgJ7hDBhC1DfJTO/\n1P4IoHuvAZDRWmzGVZCtTHf7nX+laORLfPq/3Wo4sohulZE9zl8H990E02+H2yfCh53gjzeFmJm7\nTQz1yG3QvsdvVAVtbg8FEMNbgT0MKenu+FonyQfo8xI2djpkD4QzfVA7GtNRiSMzOlH7d31n7FeF\nxrQBcsMFz1+MUA4znGEu1ebgngAAIABJREFUMHBeJSURV0SKnj8R2eS1AUHPbntJcNL27phn3aQE\n5WYwR4DNCgCtq/aY9ZMxf2uBeTlS89ZyDsz1K8jpMU29eUGj4Lo58FaEeuReBGqrHSMmC2JehbSa\n8Fk3KG6PElqlIfvIhu/ayKPsYwUbWdE+1ngFamzSuS93eCtKNHhM1p4J8JLau9x7tXLUT5a8RQQi\np2VC/TT4V3+sL1UBRndEnhZYrv3koSRmAhKSfnU5zI6UIPSigLgHDjaErDj1r7XRZ+y1YZilPlVD\nry7Uds3dOewYr4TC75YrKP4sU7ZkTS52uev/+8BduwU91Zs2w2Ka9lRyeYkCRftgmALzZcsVBN9Q\nouAvDvklR6Jhx1CxZH7c0wX4aJ67v6M52h6hgHRxVxjwAAxfCOPuxbwWCbEFjJ7rZ1p74IDLxv9x\nnqgpr5+u57H5kN4M+2iukuaLuyqxkdNTPmjfV9SjnY1+cxywHewyaD37lI08xuNn2cdfbYAGQEeI\nKoHic9Gi0JFQYLYXGRFvUVmMFowqHA3bKEDwjlLk7G1w+/kSXdiersv7hFTsc9z7u9wjCt3s/7zJ\nNUVDTAEcvgRlMRrPkZZLhKsIHYDRX7jvdDcPUTqUBdtUgl68C8oioO/p0LIQRq1BWc4ehOCabpSW\nlh4/FsevE/Vw8ESaBTQfn7X+wSpF2dNOwwxn8NKKMC+XwSJoO4/yfofvHa8bPbLhgzCY0Rn2XQcT\nIuGVIgjuBXL9mu/WUKUDHGoEtIc3LIz3wagSPYjNV8+ZYy7LOAg1P4azVkC2Y/OsXP9/u1CV9ajy\no314wWDwhK+geeOH4Kpl//DBbZO0zW1PYX/XATvcz7TMICklQJtlP/1Llvph8RnYPxbK+GR11v1b\nBd1fZ4FNg+SuP7KfE2mcPBW0akBFDZEY99qpcYIPe5GVLYsH4mBJO2ibSUi/ytMKPISgax3d8wba\npsBVSVbVQYGcR8gVg5KQxcgWeBT+jaYJ3hdPyIG/e6JscUFQwUr/MeVi9NO8oDAXCAvC3iR4wQ/F\n0GcTxMwkJLC9CAUJteSr1quiSlrWIQVn4zdBQgFKXtbLhufHqZIw8wYlB7ODkL4RuwnsxuWy383B\nPFiq37Df/YYeZaKtv7AMnihRkqnAiJZ/pcG8WAoRhQoi0qMElXwbQb2bI/KGIc/A36doHmLy5LTX\nBZZGSn8rxS8CkyXuez8Mg2fCFex5QJggcK1o9pkTpspPkPK2AXMX8Lkqhjs7wtZm0HklJL7jbORz\n/eBgSrl9DNbgOzay3D56pF/ORp61whHL1EbBz5mAraVzvxMF9lnovBajc74NrUVVK1wPBxFRSvVa\nav1YeZ6rqmZg5s8TFBDEF3DZCMx5nQQXTQTmhIs1s0WpJH72C5VjUvyqvF22ExbmCYY4GQl7JwIH\nDPauCDgvWkHXJ8A7AexC9ZyZ8zopiNmRCMWJqnxuRuQsjyHUT4LOi334M+0TkYDYR90+HJkKhZ2w\n/xcJaVU1N/P7C+IYWA7LB+ocFwKXpGKujlei4PT1kHGrC4ydI5Q3EPLisV1jYHI/6sVASiWg00a4\n+s3Q9fDYcCU5Hh+pa6B7vOtNew72xamnLjlDDJwHOuv+i6O8RzTQXbtKv/4kCc5+A/bxVx2geRpL\nAIQnqbrjJ6TLshMZI0/JPhfIgmFOmDAmHC0yCe6xB6gHMc+hm/Ygcga3oEzIFjVcEo6Csp0oyDsI\n2GCo760LFHjov1cRdv9NFIHVgZJzHWQxFwWEWehmqqbjSt/nNGeAsHCIzUJwrs61xOLoP/oGCwsL\no1q14+MzedUfc4uDrBUUYqYE4KKPfvpO7qyEvSpMxt6Je5t53w9ztFMCgin8fiMdX1KQaipD1R1Q\nXBkGf4EYLttPg2KotAYCzSGquqigp34OMWuh7Q6g8joS3gbWQZ+16FpoIPpo28keVwbHnzP8fv9J\nUUH7sWEnjMZOGC3IY/V1UAopiT7dj/DThbijgFU1ITJOjlybZXD9W3o9iJrqkZjub874HH8D9BCw\n1hjznDHmOUSmPuG4f+upcexGfcGzCIdVzVAgtgYFKaXIbiahJOZHQBb0iUUJQa+/uwohAogLkZOe\n695bDOxeJyc1i5Bd3IsSmh61/yH3XXthSUP1DVOKbO0aYM028Engfm4PVcbK7WwxoaoMgmn1dUm2\nqFUw/ivIPtdtM/MGObHJW4TeADgzGqafgRlhYbMIHewdAdnmuSXYW4LYTLAPbAhV/m+NFLTtmTDs\nQwG93+iIqj0XlGHHZwpu6NHtu0ocw/ookdQroKqPV6VrUYZ5pDQklpytKp39xDEIPuEvlwHiIAoW\nesu1sHcGBX1LJCR0PXAjBcUQkyz7GIj7ro307GPc7yDcH7KRFe3jUTaygWPnDEeuZrw7f4U58o+a\nIl/KgzZ6QVo8kNla56uijisoMBv6ONz7JqZpT8zhkSKGyY9VRfVrvypCh6brWtitHizTEYh2OprT\nAtgH3N9NwCsN4c6q2OsKVe3sEpS49EIUDBcCiwrVYtHBj5mEgvN+xZh7OyiQ2k2o56+Wo82v7uY4\nD3jjzNA5/bgQeqdi+xWLvbNeJ0wSmCvALIqC3bmYLfPg9XqCSkZlqsesdypMusddJ6kSsb5thqSC\n7HJVz2YHBHXMqQV1fKTc8RZctlGB5Bd+GDEWZg7BvHRYME1XSbPJY0UCE1ugCuKtfl3ziQGhnaLc\nedgOhefAQ405ucZvwD7+qgM0ANIh6jOw92/TTeUJGtZ2f2uhi7QeyswlwNSaQB4UnKvXqEWITjZO\nva0Uw/bLga3LlIVoD+yDVV4c5BmdpqjnzOmmEIWyfW2gsoMxTKssXRmKIWYPUEkQDVqjhbgOWtSq\naH9T1iszmJ4H4e8RohH25xwdlLpx5MgR8vOPr6hF2cM+yh5WtcSD7f3YsH8uxP65UJCNuFxSK8PX\n1cB2sNj+3+9Ie7A5MzoRBtQico6qiXuvgoizJJwaA8qsBoCOCnZLi2DYmUA9ZX5XuXObfY7+zu0B\nJXdAnzBCGb4TdJxMFbSfNAY/B43A3BOQ8/TYKGzMTwukzD3j5djVg+3VUXLg1WvgtUuV0X8H7EoY\nc+wVJk4NwFo7C3Xkvuwe7awtd2FPjRN82AgL6WD+BHYU4PXctkdQPK9ik4psVydgi6PV9yCQbQlR\n2IMCO5dMbPuFq1wF3fZx0DYHRke77Q661790+3+fcvHqgqbILgNtnfZaQo7o20tyYXy0+36vD7wA\nyBa1e2yUbGSjDLff1m4bH4J8vdcBc/Vl+q4JI8A46ZanDCwsUfVsgR87wXlw5/lUmUlvKlr1rXKd\nbIqqZfTzS7PrkkoidiowmKsSBYXrjSo8YyzmD2CmRUnLLA/MOCuGwEmIffH1cGhbqkDgiSJB0ABW\nN+RgFdi3FsouhYkHgPpp2PNKdFxdfKrqOJ+CNsDKOnAdXPASrOkO0UlwpFIFG/kt+wghG1luH889\n2kb2CUMVVK8HMRcFZrm4nbrz58FOo9z7QcTimEdIQqYq0HlRSEZoM9C2TAyIG1pB8gYFIbc7xsbu\n7wiC2Hg5FOaKiOVQGPZvRaqcVQfqOZf2tF3w54OYd6OV8H3eHwqsPtDvpkNBOWzRvpsG7xaKBOb1\ncMztie685SqYaw4mUdevnYsgq4N9CuLeCUDfVzA9BkDPg9Iei0ZSC/PWK9Bvt1rC4tdmKKirkqgK\nZbvVQAbmVvT50zJ1bFkJmFs66rUqNaH9NMxtO0IkWFUSFXG/fo0I5oY+hl33d4mxLx2q/c68QULf\nrZcp0f8+sDKIuRXY5ZefswbYDJ+7osVJk8A8ScbPtY+/+gDNtnD6SZ4BuhAtTLXQgrAHLSRvAzu1\neLEVqA8xn0CCJZRJdJpKAB9cpSxd26rQrQBSneB1QpYzPgV63vYdQvogHn27ZyicEHHKpw7HHwMF\nxRD5Giyp5I7NyxB6dMe7hKNfvAtafujebwrEJf2gU3s8K2jfHjZYnbDrg4RdH8SsOYhp+sOafDap\nOvSKhuYBSGtA/4bK5uUs/feEOma4X+fu0h3QHXpWg0a7oe1HCrALqgGFvVRVLPOxci9cuDQkPE44\nmksni9CtOhRlAUUyVvaKE3uR+rVU0EABlpnwEcYTqs2P/fcfqPjZ03bBvWNkZNfoGrD/55dxOgSs\nArs2tP2480/s83rUOEkqaMaYMMRn29JauxCIMsacd3y/9dQ4lsOzkSWzkaMdhwglQPZqF3ARagnY\niWxnG/d+MfA8CoA8rbFdQFX44HcwvLUIOhIKUDB1CFYdhPFf67NpyU5sOIjcF1CVaBUK8g4BCUpG\nziqE7DqCL/o/BfZAs02EiL4auM/uFeFBz08JaSEmuL//GCfH+KKPJH7dHJgxGT71QfcwkXs8P0v9\nSSDh6FsdGcWtYHpGY9IcjG6LWBpNApg4V+VqDvbxIFQvUy+a0+CiI9gZRkHU4BJ40+m7PWcgE+xI\nBYQA9sZw7Bqgc7401YLA4B1UPQg1stUDNsgC/fqJKGOi1TZ1xfprl2tuTJofugZYdRF0zIdBGXBL\niea9oBrwTS8o89HyXdnHo2ykm3fPV+lWHSjS3A+rjvyOLH0PfveZfSgxegj5O8WEkt+e/1QFJQEq\nBnCLuyrwvW0S9uYwQfOau8A2K0Hr+Uq3pLRbLdKN/Bj5UCWICOQqq/PzWqQIVeo3gPrO56kO9u5C\nTHMHT8wIOs24eGnGxQFv9ITIaFUuP1qP7VyM9aWGetNaTIJrjkjgvCPQbo/60xYi+GryFnjZwNJY\n/ZY4sKNRRayDX7/jBrVemO4OBnl6IubQZMyzPuzfCnVNVElUAPfoDIgpg5cN5toA3BqAlFLMCxs0\nB2HrISqga/mmSdA0WhI9LRaIhTI5Q8f0mV/oLEejT1Owwx18y/mjxX/gO3I2J8X4DdjHXyXN/vfu\nf7PBPgLmHrSI1KBcpI8G7m8LtOhkS7APYFWZ0/qop9cpAvwSsV68Czb0haJcQehMBKzcDx0XQdkQ\n3Q+Ld8m4jFqFjIhnqKqihWsPIZ2ZbLRYeNnIOEKVO1DTr4VZ36DFLhwZxQSw9X547vbu3cucOXO4\n6667fvY8/tQRdn0QmwfEpWpx3ZEIQ6fD2qGYBEE3zGMog5PgGJG2+7VodOc7lcCwPwbV+NzEiS/G\nRGMes/ChEfRgrx+W9KJb2SKWtER6WCvdQuSxlFWjPMCmFJ3vPe49YFASzOx2YtwP/27s3r2bp59+\nmrFjx/7Sh/KzhrnobTggWkWT+J/prAGYf12rrOq7OTL87SnXr8PBo2wa8NzxP6fHnEJ45bHYkxvt\njiuN8FNAGWqEbmaMiQMWW2vb/MhHT43/YPwv7CNAYXOI/opQ/9ghyqH3rIGYABSEUw5hGxYNUw9D\nzE5X8XKO3rBYePR8mLdDEjX1qkDkNJjm+tjmb4W0rtDxDb03+Czo+S4hen4fIajcl4RsZB4he3gA\n2W7veTF0K1KQseAbQsQUTZEtfe8aMdllJYi+fK+jxR9Vhp0yEs69Fu5tFqqGBV3QlVoIw5aKne/8\naMwA1LuEs2Pngi1Jg5ye+v9ZMN2B247AsghVXKoDs4owf60kp97TNNsP9slciRzvBnpNx3w9VNtf\ncwQ2RGCvC0Azv8g/7vDrd3UNwLwWYvS4JQpzBQou8oC968sZCs2NFjvZKKH5r/50K1sEcLSN9Owj\nhGykZx+h3EYOSoJZldxrB0XQNatmhYtoOfJxvPORgIJ5T/euoi/jvT9mihAUANd+VN7TZZq7ShUI\nxleK6OTzY6TnZZ0odAW2Y3Dz3hzs3/8FVzWBre0EESw0cOFuJQCrx8OiQjgnGvMHwR7NBAsvSy/N\nNAe7GwVKOwvVI1gdQUrvAho7xsYFpZAejn0sU8HWACTBQCosFyOkuR/splRM/6thXpiuJ9y1uaVQ\ncNi96zGNWokUJT8Wur8joeyRaD76jy0X0jb3nw25iips9lhVyeplK2htvBw2d9K1/kIHwUYPt4Ln\n/DT7AjLOwUktJEHpNvlaPkhz4cJlDY+vnTxhbeQJbB9/9RW0isP8HxQ3A+okhVTsu6PFqSnCwzsc\n/KoIZfoogOwI0QfHFAGVoFmYcNr5bh9R1WFVAZQVQYfaUHK923cRXBgJf2olw9StMiR4uN88lHGq\nhgLGz4EoaLbH4fsPoGMMoJu5NiTsdJmOHOSQOkZx/jUKM+H/fvB3/y8raOb8oASnSVUz7NyBwttP\nGaHF9cEe2jBDAZddjALT0zL1e09rHerVqzDsblT639AKc2e0yvRHnIHsDebpAEydx5LdKXBWHrzY\nQw6Gl9FtgObbEcAMqokMf7FsSgzK0J4Mw+fz0bRp01/6MH72MDUUnNl1389W+W8/G/SHxEUvBOq2\nDlWZq4qrBh+YE1cq7tcy2lprb0VAIKy1eUDkL3tIp8Z/OuxZcs5Knwf7F3Qf1awlh64asjVdoKAU\nVUwOQdsDjrb+S0m+DAuDaY30fOVe2JQvEquhTix6YltV0wDe6gmzdkL3+pDaSX+pCs0OINu2DyUg\ni1z17QhQLNvbzP1PNRQ4biUElQQWHEZ20wvqPBbBledhuraCdqsxj4OZIc0y3gyDzhdD40qYu10V\nazGyH2uAztEw8yLMddGYmy1UsyHYWyf1inF3d/gyVw74aZnqR7s3AvtkLubBMgDM2ijs3XIazO2J\ncuYHODmdxzrAJ35IzsDO8GNfy8X2jtBxdPArGMl0c7OjtZgO+2+Au6IU5M1ywVkQweN6g7nWYseo\namd6U24fl5RxtI307GMFG1luHyvYyFmFbttqer2vpx/rEEITO8pHivHYpHe593IJMWdXcY9GOt88\nOhzqrhP88S9DYMAQHZOrCJLph/o7ITsJhk5UT9XcG2T/66Ig6C/a3kwuw76fqbmKOwC1q2N8YP7p\nh+t9UJyIebKGg6RGq/+suSqjdsQ3unh8iAimOVCaiXkhCrYWSuOuDZI8eA0FdkPCVfm8KhE+LsT+\nFfXOLR8IC4uVaH40FxZ3xT4VJv21+UU6F2HrMdOiBYcd86DOXWwB5uKemF2Tda57p8Jav4Kz+VcK\n9jknHDt1PXbVdFXSarqI9uaxUOjEr+8YIfbGN65QNS2iNRldKe/PpP42Bexxuj96fnj8g7Pf8PhZ\n9vE3E6DZsyy8NoqoB0dRfMY2ZQXzEN1vPRT0tKe8aTImh3JMdZ+aorWvVwUmthLZQNuqsOFayDcQ\nyBKT4rNZUJQnCIKH6TYR+tu9fsg4cQDahkGzncBOSAiHtIth9Gmw6Wqn6h4F05Jg3xDUG+f2t3Iv\ntK1NuX4FAZRB+Tfjf9GD5g37sQ+uGwFP3KQFI90PXcbAoGkw4yaosQx7fX/MUh98kQl3jGDRXX4Y\ndbYydx+vE+XwHkPY9CBh8xz0zcPW10WQkCfAPv2BYCLp68XItBvMoclOP26ZDPYeIAsS3oGYLZB6\ngR7DWysDSBUoqAEFdZBD/+VxSaQc01FYWEhGRsYvfRg/e5TN92HX/WeBWfnIj1E2vO46sVk1XCIo\nRyPgIKQ7xmL7+5PU8JwkEEeg2BhT/i3GmHiUMTw1TrJhz7JU3jUK8yTgS4KSnJBm6PvIyT7LPXyw\naq8IlSiFURtharojr4qRTtrQpZI6ebu39v+nVkowDjkDykrh+ni4pzEUfKn3S67UutwnWsK5HklW\ndjWgEoyuq+rYmLYK1Aiqx41iGBSrAAEItS3Eo+u/inve9xVVS669DLsQ7Io0kXvsBuYG4dkS7EMB\n2YGOKLAZoAoZ5/hV0QJYYgSN3I/6xxKAAZGqyq0BqiRiEvVZouNhVZigihGO2XEAqpZBqKJUL1sC\n0tdOE2v0mAfVGzc7AGv9JDzlx17oVyD64TqY3E8VskR3DN2RjfzAfecmsAOdduZuIBrMiDLZx4at\nJQLu2UhnHyvayOGt4bPzK9hIZx/Lma590PNN5Oh/Aqnny8cpKIZJnmSRD3gPBn0Dn53ufquXGPcS\npwcJadMOnAMjJ2BGH8GODWjffV6C9Q0gfBv4fQpKOi7DrkuDwlxR2z/qXaNhmAcaQ+cjUDkOgvsk\nND3XzU0QbJ1S9RjeNURBUM0yadU9WaM82WtvDtM18ERD+CpMAtnNBGs1f2kBN5ViP1ovaGkblyTu\nGw3np2EatYL7h8DpRzC3l6oadnm8iHKSUjEPV8K+owqnHZWLTSvExL0Aw/rDzBuwc8HuTlWVdXFX\n6BJQwFcrulwSgK2tZP+ufhPOmS4Uyrz7dO31fSVEq5+VICbM2ALdC5PGad7f6qXjno+S4yfr+A3Y\nx99MgAZg73kk9CQcZXB8SQrW2qCFsSrgc3opCUCMjELLuZBxSALR4zfCG70FX4zcDbFnQqU4GJwI\nYRGQshKySsSwGOGXDEbVXSFtFoq1AOYXAzmw63otbn9tBv2XqGJXNkSHOfIDKO6l3rjpXRQoroog\n1GS7Heg65+jf9q3xP62gPX4zPDIZZqfAaL/muRR4Di0OZwOXLsJe75e6/cwbWbkH7GYFugcvgZjN\nwFaw14sQJGxOQE25oIBsIfBRC3jsUqiLRJC3+OCjgBpxB8wWPCMHOAipZ8NXAdjeCLoH9Ugqglnb\nURDexD08iNwJPnw+H2eeeeYvfRi/6LCnfS0mtvoBNcODzmEU5TCrkzY4g5MpQPsn8ApQyxjzIOL5\nO8XieDKPqaMoPGObHOdtyInz9ARroSqLD/V+VfzrdzatAozts0ulLXn4czj/JQVwRwIw/yvFEZHx\nUKm6bGPWISUxF5TBgl1iU+5WGzgkVEr3BrDiSvV+5xdDyUD4uB8U3QjPdNH3TeoIw1pBszjk9DdF\nQUhYLTms56Zilz2NcX1H5m6/IIGNfHB4h/qENiECiqscQ2CeKNxtcgkccH1knUqwj4J90zE1Aqwr\nUtDUHawvFXtdAPNUqbZbDnaUS/5dWYZdvRz7eEP5HOu2wdc5UHOR5rqt29/K83Q848eRbSBQFQ6u\ngeLBEPM2MMMPHcHcW4b9AMyzR8rZB+1ClHye6ofrp2PXrsfeHiZo5oDZ0GyjbGQF+1jRRiYVfY+N\nrIdsZBTwe3ctVAHCxSzdPBZKb3Hsm17vXDV4ojWcvpcQWVo4oUAvxieafq/S9mEX7B8ioKtfqJpJ\n96hvsDQJioJi6W22UVUlG6/ErJN4tZ+IrdGeUQLN68PH1bGfuCDbq2FccQA6RarH68JS7B/ClPC9\ntli+xFx0fj8Be224jjfTXUsZAZgThn3kI33/HiMCkuaIWXrmRYJILp4BjfzYceEQW4BtNULEIQsH\nQhsw57RSwPx8HDSIxl4cKdjm4NnScWs+UAQyd8VLJLxTKmYQgkR6/snCgdBvOuwYqmTCv4ogLlcV\nxuGPKUldYymM+UjBf7e3VIV7bhycv4jihmCu1u+y152kdvI3YB9/UwEaALdMJGo/FF+AKmil2yC/\nlwyR5+AVo+CnWHCK9DzKIQAFdQA/1JghnZWJX6tq9s4BREqBqlwJbi0+ElBF7WB9BXXta8qg9E3S\nY98geGs3BHfAP75U4PZNcwV16fuULcwokoHyqPUphmYFhARFwx3k6wdGaWnpca+gmYuDmIuDarj+\nl19Bmg9Gf01IR2cnMhrbgTM2CtOf3pTxOb3Iexh8a6BKAyhoghb/nCD2WoO90WCe92PiHAZ8ux/Y\nporbo37M16cJWrksWo9RiVAfSoZo3nqUgrkT/D0gbo4eh7MRt86QFXo09kF4COpzIo+ioqJfRQXt\nZ48KwbStUQPb1MIusPf9cof0WxvW2ueBPyOjsxvoba2d98se1anx3w57zyNwy0Si94N9BDnSGdco\nAEtGDk0uoQRnVdSPVF2vLalLuQbaqhIFXUV5ULmh9j/9AiUt+54OIz4QG2NYJWhSCnXLoE0UpJ0N\neR1ld1fuhbTOCvA61IYV3wjJkhynJOhGxyZoj0iUOj0PpmYqmUoecrAPoipCcga8PRD+NVSOuA/o\nZjHDWoE5DHUbK4BZEXRzYaAb8ECJkBuL78BOWy/Y4f2R6g+aXR9zv+B1ZnukHN531ut7tkdIRDrR\nVeH2qz+NVWFw36WwJIeEAzAtGfVv7SJEsjFkmqpFn6BE1NZxBPtBdBfJBhTE6HfZlELYH6YK0bII\nwe22+8XS5yWrxjyoykpd4Joy2ccqzkZWsI8VbeTh7G/ZyMY+aOBTIFWVUI98HFAL7msr1FBpkTu2\nbDe/jeDwTij+BvbWg7QL3LXjc5/NSpDv5enF5sfAyx2g2nIFGiMnOKr4fDEW9n/JaaVtke7ZXwgR\nsWQiOGgXH2R/BTXzFKzeIQp+LkqD6HjJAdUFe3O4etpBwVEi8E2moI+LgaqZ2PmCN5qOQFc/dvt6\nzMBOEqO+Wz0RNj8Npj8M9cIwV4D9swJ1NqPjzo9VL1hJoYL/F4H1hdjr8qDLdAXVWQmY4qEw5nls\n2VhIBDsjEy57Bd4eiL0jgHkoGvuEu1G3BzQ/Dafrenz9QZGr1MuGKbdj5n0ETZyERHpTKOyEGZKo\nuatQNbOXnfj+zsk8fq59/M0FaLaaLsior6G4HVps8mNVRatPeZBFFLALMmq454eArZCwT+/FhMPU\nr+DeJrA3SgZj/jYw4YIp5n+h4AwkDBl/QMamINMtYukKxmKjXDawLgxvBmdkQGQs9F4BDybpvaQi\nCO5RI/U4x7yTUQ0FjUXAXrC+wA/+5rCwMKpWrfqD7x+LYd91ULXqwItTaHa/n5J2qgoWdYHPLtBx\nkoMW+uQt8GkneD0Ozl5EpRzY10OQ0NGVEVnL2QEYN1Z9gq4B2r6cCa+j4DQOLfSHc6B4PbwfpofD\nsBfv0cM7DzuAjEcg+AjU2gQ08WkBjc2HAbOxlU6OxSo6OppmzZr90ofxiw4T65yoEl9IeuGe8dgx\nboP/ATHIcR0nTwUNa22GtfYx9ziVOTjJh2cjzbMoGLtkjiBSILvoUb87+GAzC7NikQ11JEwJVaBP\ndSUxK9eDoR8q2TgqXpspAAAgAElEQVSlwtVxXy0ocND1YFU4FA3vHoIe9VVdA8hLEbqktBHkfy4b\nOreH7GLdMkk67Q5TQrNamJKagOy4l2ytA7wThMtehb8PUWUmQtUI+5TBTl0PO/xQu5Kc9Ggr+P2C\nYhE1nFGCTUuDpCcVQHREAcEmoHekbFPnQuy1YZiOiMihaiZcFwWvhAmGl6e+JV5yxuj/2Tv3+Kiq\nc/1/10wymQlJgEAQSQQxqBAQ4xWUAiqCaFREEYkXKoig1gt4oFZbQKCKFhRoadWI4KUIIpTaGlAo\nVYMioGJECFQJ94ASSSCJmcl1/f541mSw5yj+zoESJO/nsz+ZTGb23rP2ZL/rWe/zPs/4t+hgYUs/\nuCVBzJGxpxLxCVvVUxTui7JheSz7fzaO6nVQnCvVaMpRjhw3WRN+9D67AViE/FXDOfLbfXDTlEiO\nDFJH+Ts0P4JyZDg/fidHDnpZ262vQ1RA7KJLkHpnDewogwILi/aiv1VSZ1IdaAnxp4OpgcviEHBZ\nBuxvoX6o1kTAZPNP1T+18HxYMk2gOqVA1Z+ua6E0A6ozYFkv7O9DsAUBrvMBU6jeu37AttZQutrJ\n6Wvx2lyWIePqj9xrmucKIAYK4dYh8JsqzNmpool2QiC261rMe5Lqx+EdO7ZQYxnvh48L4Z0MzN4J\nkFKAjRsFl8UIeAUKI7L4O0IR4+1Ls7Vg0CoJ03eYwHPLdFVtF/aH5RPgTQfAov2q/A2OxT6GzK1B\nKpKzB2v/+WA+maB+utlLoSQeuzZHff8Lm2k87xmGvbwbnPsq3+bBiGUc/3EC5McTDqDBd0EaScDA\nV6HROboxtkX/7GGaRtB5m7mVogKDQAbyQTt3KbT2K2H0aQ1n/1V/Wx4vUBWdJBGR2mpVz0yUkk98\nKjTaDPZbVdLiq7QimHQpxKbCQudG3ylBr/fGiNqxuwzRBxy1gI763VR/f+/Uf6oHzSS7rWQ4mx4r\nJ/FF+O0mOLhWQHRIAqJLVARhwY3i2F8CvA0VxdDsQwHYsZ2Ar1xCe3YCZmSNPmMi4pifDlPaEmlQ\nfm0MfJOuvoI30A0283UarYZGq6GgDXCFmDgAgXEI2FYHBRTHTYaEEsx5H2LO+/Coj9P/NSoqKsjL\nyzvWp3FMw5YEsFX/1r92wzjMfRz/4AyOK4DWED/BqAQKofJs93shEf+qSrTY1gyIhU1nA5+he7EP\naBLxIQN49BP93i0oAHWgFiqLBLri2kPZDqhaDTH73AQe0d3nJkN5vr7CCT546gCMaAK1JfDOJVD9\nrRZEkw6oCjfrC0iIISKsVVelQYt8Wb8QbfCMVLjIDz/L0cESSqD2W1hRqYnx4FiJL9zgE9i4MRZ2\nZsCQaFU6EiUOQmKhepzycuHcWAlHfKTKjOmaKsr9A5XYPiHR6D7IjfhxbezBpkvhnk+1YFtR7Ma5\nDapadnkPsu4VIDkPvKdBVDGc9A7Maw3xTrbenDke00NqgVxfg2mF5gSVsD8DhlS7a/LaGPApR5IP\nXJVdlyPD+TGcI+vyYzhHuvyoalC8aIYFaHM9ZQk+bZnvE+n/SwQugcDJ2hLOVitIbW/qfFupBB6d\nCJXttJUBF7wKhU4eMq8DdHYy+1ctFjX1IyQr396PHSEQbR6plTpjWCjllu3wvjOSDormaJ9G1cz7\nLCwDMyId1sfpcyXVYOZFSwEyLFy2p1B99Es9EWXRuR0E2h5IV5/ai4kQKMQWz5OS4xlTwRTqfNM2\nYYKZmJxZsNIDgx7DvpsvM+4+SfB8JfbkalgzWkBvYyFkN1ZP4/obtI+miMZ6kzv+wFoBxSX9BeCX\n9YqYZ28Am5ClgsPyvrCwPyZrP/xmlsyvu35AqFPkX/y4r56dAPnxhARoALaF+3Ku7Knk0/xT3VS+\nQhftIFKOqqFO6re33yk5elUxm/6pwNWjn8BZieDdpurZ0xvg2kZamfJUQ+gb9aZtKBFYO5AnunvU\n11C8ViCtulxgomiZEpA3RmBuQ4kqcI3ehjlbJPvPNvchNiJe96Emof9D/CcqaAA2ZSoMrcbuzpHR\n94K3GN0cbBwEPhM1JfkzNMa1AbjnOSiCkivA3xy2T4SEG8HMhZWXgS2eh90RgHX6L7ILQ9jrG0GK\nqyQWgL3cYh/+nV4Xju5gWmQI/F2ia1X0JNR+Bad+BQktUSaqBCaO1CrUuMnYTy466mN0JMLv9/8k\nVByPZJiPtUBhzz/Ok05DNEQ9iHB+9J27isqTUH755hzlHBAI2I1o6xsRJT0sy14uq6lxXSAlHtKa\naXs+KNBWs175Lr5KNDpbCr7m4I2VZU1wr2iQ/h2qjJXm637/yOkuL3r10+NypPHCx5UOFJYSWVwt\nRJP1Q+T/TdJwEY1eL4f3ncVH3x7gCUBJDJR6BL6+ydfEfozVhDksxFHtF+3RD2ZWM8yzNeopAuzn\nOfDXIPSYJ4rb4FgYkQedXQXGly6K4x7UP/TwC8z5eAzbmwIHYFQ8qn6FwU+X9+CNWCgQq6bkaghO\nhGYvOyGOGrCPrVeFsAj4wvmnpWgMTp0Dc/YAzQKYZhNEBVxXLlGLFhkCnZdE8mM4R9blx3CO3J0s\nmuG4yZr872wHX/YUeN0JtIRmn0Czt9ACN2gO1QU4CJ5N4HlL4AzAM4uID2kQeHycqkxJTvq9EtFk\nv8gXPa+NX+dQkoD9bYUA0D/KBaAmLVeFcmAl1OQLYD0DrDoVep2mylWxU7hMBTsa7M9FO6VDFVwY\nEOXzAT/2ngroUiP7n4uy4fwk2Fqo97+br+rcn2IwbTKw0wBfPqz3yiJh2eVwwyjsPdv1e226xDsC\nbtH4Tp9okAMWC6ynTNUCwJjVcDBXYO7FRCi4VYO0rJfEQQCuGiVPNV8+9g4PLKmEhDVaXO6zQmqc\ne8B2HSWT6sbpkFguleRiRBl9eDz25liS/gQtS44PO6GGOIEBGgBXroLRkyUrHJa/T3Y/TwN6oBtN\nuX5f7uiOTtyRoR3VuAxKJDFJAlQ/r4Yndmpl6uAX0PQs/S1sLWxroLIUvJUQXQ5FuaJExp8LUW2U\nkLyxSkKn14gi8Nm17rjh/jgvEZPqOH6QnlddXU1paekRG7b/KTwzgpiWo2F9FGT2VQIa2Jf3o1UB\nTHTUl4JY9xl2BOHdnmBb4NhqnNoFtn4GDIez1oIhE8+AoEwcAXbX6tq0dJ47l3z3HOyOgLYbYrHj\nY/nmI7BDISoTar7WiiuoV4EmKKkVIerEsl6YhydJur2eR2Vl5QlfQfvJR0MFrSGOcRwK0kgCTvpU\nk/8Aqq6cjBYyg+65Mi1i4tVzq/dqERO0kAkSump+vgBHZbFyI0CTNOXFmrZa0PQlQtwNECiA0E4Y\n9Q2UfKa/VZUo3/oSJDASOFm9Z5nvw4xGUkWuo7+3oc5ahT4rsPnzRO9bcn+kAvYnYH85JFTBczOx\nfwVzdqqA2lKDfagCM6pW/mZ3g70DuAAoNdi7vAJdicDeHtAzAKsv1KS8CMzgdIHZf5Rrsl+AVAY7\njYRr7oDdybT8AppuhdrdbuADKEfudI+TodEGOLWn9E62/h5u8gITytVfttxV7p5xx2kLdHQ5snVP\nSNgva4APymVe3RTs+Fj4u3JkOD+Gc2RdfgznyK5r63Ik8wdD3BZo/57ONQ4tYDdDc6Zm7nvRHi0k\nh0FYADyvaKOZ+478y/3tkYmi9oW91yp1/qw+WVsqENsDWiVBZgwUJUkF+rQkzDkZGuv2fswtqU5i\nvxC2roDd+yVl3899jg1AVUhKjWtzsDdEQ6gQm52NuQnMrihdzzk5qlx+XIiZ1QybHRI47FWhcV6a\nL5uDr1OxTwH+HBlaT5iGGZMqQY++UwXSmyJAWYAqXq+OFg11+r0AmJQe8E067O2Bvb4KU/EaNm08\n5svR+iz7QvCHaQJZCaWizo73wewnNP5DXxLds+so6PYk/L2zFCH7zcMGkS3AuZ/CyJmY9mMorR1B\nyd0/EXB2AuTHExqg2U8ugncvxswE+yv3ZCURkLYXJaQwle6gZGRLvfJI+9nrMGipgFpMUyiPU5Xo\nlgI9N/1TiE+D2fmQ961WkKISITpeTc3+XlAdA/E7wXdAnpOeGK0e2hr9fKVQq41XveEMQh19gaB7\nnPTfPtZ/C6/Xe9QraLUPHFLBeqyc7DEj2XaLpJbjUyF4iVQvSUTc+CDQ7D3xWgavotFeSPsFnFIC\n0bOg0bVgd+fo5thHRjpmvU9Nws+MwV5qsW2+e6MxgWJMoBhOL8I8XE7jPhDqD/t/CR+dAdsS4Euv\negdJPOSNee3h+ufg0XFqFq7nERMTQ1pa2rE+jXoV9nzbUD1riIY4whHOkXXCIGEvqzbIqzMZUe7L\n9PflUUAQ4n0w6RtVtUBVnwHtoE0cjF8D7wShsIm8Q5ucpx6m2z7W6/3NoWSTQJj1QPweqIqF6JME\nIELNlBvD1MDiSrgzDWgshkZBJ5S3TybihdYaRwm7HPNrp7SXUiAKXAAwAZjpgTvuhRDYlYWquMwH\nHorB3uERhXE+df5bNK2Fq7PUEvEP18hVBJySqvwWQkqO7cBMiFVlJRkZDV8KNdfB2FtH8tcE8A6H\nRSmHWAWEFQ93AmMnwuAdmNnQoQSaL4TXaoA9hZiFC7ALQ5grwEyrkSrl02Mkzz61HPosFb2zGszb\nAezjQG4I83A5jFCODOfHcI6csQshwXCOzGsfyZG48drcU48PmRvVCccEifSb74Mhxe65JLSofBB5\nzjo6K3e5xuH3r4bSnpAO5iVnzv2r2Ehf2fOV2sLm1E5h0VzggPMz8qEzNyWBvzckJWIzyiX0kRtS\nj9bfouECMHd3h7UhGLAYk5EBe8FO92ImIbPnlcDQl7C3eASMgkmYVTGStT8jVTTHZ20dMLdFYGZa\neaHNy5GwywawrxVKrbFxvqiUzXP1neuzQv5rOeiL3isL85Af+97zsHyCnm+l1/HrYTD756JkDn1Z\n1gugKp37TjP9Xmh0PgyfiVm1ADZk6jPsSofBL1D2RlDHb4jjKoy19WNSY4yxx+pcTKnB3use30Ok\nQhVG17uBMkjeCwXN0I2nCXVm0skBKKiElf2ktJjWTD1pCWHxkC/h0fNEVfQ4ikZ5AcQmQ005eN6D\n8iTwnQFVpaqYhQoF7hZugetbwNXvaJ9rmulcOIdIM24QbLPvH7vt27ezZMkS7rnnnqMyfv8epk1Q\nUsO/bwZLglT1l1oXwHnLnMDJyUDHgPw6AD7KhP5T9f7do+sU+uylo+r2a3/x7A8ft717U6NcceWX\nnKVEV0MkGQTRCmO4rxDE+XcUGMpbSMK9Hsf+/ft56qmnePzxx4/1qTSEC2MM1tojYqJnjLF8cST2\n5OIMjti5NcSxiWOdHwHsvS4/guxSwj1IjYlY1FQiymPY1aUC3X+9sPJq5cSJa5QXU+KV0yaugYIr\n1AZQW63qWm2FetNANEavE++q+RpqfRAVD7EpMNkZJp+dDQ+cCjNSEe0yLFcfXjf0AXNHYMqmiZGR\nthlzTTr2yRDmTj92/Vzomg5PSQHFLKmRyl7TJPjA9aU9VYFZG4PNKMesi4F1XqkDhkVTioGTczAp\nPWRevcd5oTnVPPtEOeYPsTrmQ37l7WGxdFgN76bqM2ZsgTUhoG8L2LNPEvQZb4lamFCinixQD1PQ\nAcmUAjE/3hoNZfkCiB8D28p1vqO8omf2rsUO26/XlzgeUDhH7kegFiKGxuEcGf69DChvocfN9kXa\nQSqJADRQrk0CXoIuibJG8L7jni8gsqgcNkCvQTYIsfsgDsxwARCb7zzmUqXESIoH0v0CW+l+ydu/\nCAyaB1syBdaGuEriSa/D1haw7wLM72KhbzWEDHamVwIlneVFZp6uxl5Vre9Dt3Ts+HKJgrzqwQZR\nb1jTpMj3aI8b7y/TJUDyRb5A0+QJqs7NcNd1LBIJAcyUJEiqwY7yQnGhrmPaZkxChuT5b4wVUhv6\nMgydpX65hCzM18N1fRNKBBIBlp0BI98QKGulnjvTB2xonuiTYTGVbk/CU4sAqPzzHfjOLIf7Rh12\nDnU0o97myHqcH0/oClo4bLzFPHbIE2En+xp082mmnwWno5tUDM7DA+gs4ZAuiaqm5RWpEXrZDlWP\n0hJVTTtQq4QSnaREFBUr+dlvv4TyNGh6mUBbVCxUfigaR2me3v/7rVKvWlMLHfaim8VudLNbo3P6\nIXGL/0QF7dAI94OZJ/dDZ/D9vJx3gqJxbioDBhZBOrA/iH1lqLbNkQqcXVYoCskFAmXh7XBhhrht\nYLpuoj6UAFqjpLAbJSIQuA5TXwqQMlVUAMy+IzUMRy1iYmJOeBXHn3w0UBwbop6EjbdwEMxjEOrq\nntxGZFK+kUhlpBDR1GpQnopH/WkoHy7bKfXiSZ9o4XJAO4lrVbVSG0Dcqcp9nhgBtUDLQ85jL8S0\nhdjW+ntVCfyqtf7WO8G9aA0Qi4DFQfd72I4jbbMkzAHy2ovamO76w672wTSk7hgVwn4xGmwSzJcK\nhrm3BvNYjEDqiFjsZC92DnBblqiQAVfJGdIDuztHVZ8HXcUt5MBZXjSkWUm0v6c+bc4rZxM3E3Uu\nNGoDa2IQ0F19oYBRIPw4Hbb2UK/Sn7WyaJchc2zAfDta4GHeKeqdux3Mr2IFCppKoIQqMI8kaV9f\np343R55DJEeGI5wjwz1xUQGI2qcceRDNjcrcz9bofFu639cA7aFrS9gZgm0XoXlUGMSHLY3CfYKr\nL4R/Xg1/GgHJYNflqvdrgxP36BaLudUvEbIMP3yMDMa/KscMHgj+HOiYo2tyei7sTIL7m8lg/DwL\n70XBu16YUClglVAiKf9XozC3+iWDfz6YRbGwXkbVFKEx+qBc9MgCMM80EwC6LUvWCX1WwCW/gVcG\nwkV+bHY2dmY+3JYlwNQxCTaC/c3nAusjZ8LXqepjS8iCtG/hhlFSeJx+L3QHyzzIGi56ZDBJgDtt\nk+isQ9+RB1pKAXSaB3eNx65Ex8rroD7BP0yDPj5YdjnW48AZh1/gPu7iBMiPDRW08PF3u1XCSWBu\nQTeNFCI3KYisKO1wj/choHZQW3KFANXqr/Tyri0FzgaeKoWpAe2gqQ/KtmuFMCZJPPraKCj+BzTq\nCBXrROcwNUpGgZYwZ4eS2fKoQ84lyR03EeylFnPeh98rcrFt2zbefvtt7rrrriM7aD8Q5nTpJ5sx\nqXB6NbZ7AskvQcFpqNu59ZsCaY9MxE4ee2SPHW5q+2dspKm9nLqJAqejG/BWlHjORDQV0HU/qIf/\nTqGsL1FcXMyTTz7JE088caxPpSFcHPHVwfwjsScXqfV3hbAhflwc6/wIypFl70MgE7yr0WR7H86z\nEoGxAwicFSDq4zY0eXe9aGGVx896R1gmnRKUH7u2hLRGQIzk32sqlCdNVEQKvvpb9TSHdoL1QYtu\n8JSzshoTFsioRJPr1kQqfN9crRcN/6Mm56eox8zOATMJ7L1/gqd7Ymo7Yi+shB4++VaNm4zJm4Cd\nXo6ZFYv9jQNec4BTclV1OXm8Jun9oqFjDiazB/ZpLRbyEXAtAhu/CkG0X9WsgVYiZO8YLUTuHw99\npwggtW8nSmH6mwJPU8bASxNgRwgzwF9njG2mogpeIvCPctEbA66q1F20wDq/twmVmCfEN7Wf5Qug\nQSRHhqX2W6D8iHvfSpQfQTkyDHYPHvK4JRFhli2HPAYe+Fq+dPhQRXMvmsOc7q5Lofue4J4fN12P\nVw0XpXSZxtAMElgznVRZY3YQ9nohykIUcGEMZlEN9pdeUQNLFsLe5hB/CSYVgZ1k9erxERACM7dW\n5t2hQoHxUlVWmfow3L5U9FWQ8Eb/qapatUyHQcMw+bM09uGICmEy/Brb5qnQbh7Mz1Q/XFESrO4m\n0+h/hmClB7POh/0FsHAgzFgAu9w12VauCtnuZPU0hqNXFua6O1QFHOcRWJxusRONqnJNXVmyUa7A\nW1577MVnYd514OxQEbVjFPU2R9bj/Bh1rE+gvoRNsZh9RjfrsWCuR4kmBt20aoisFnlRJSasnHiI\nguLynfBcLxi90jUur1VFLSVedMVhZ4jKENVI9MbqKPh2t8CYiYLaaPBWgSdF1bQ1pTDiXeidjG5u\n8UQ43/uALmC2Guwn35+8PR4PCQkJ3/v3oxH2S+m5erKC2KQa6PA5BTlnkbwECm5eoZvkNlTq33xA\n72nf5Pt3+CPC87LLGOflaEVpL8S7xFPqjMYB2HI1bH9T1JRS6sy+yXW/d/k/ncZRD5/P19CD9lOP\no7yyZ4x5GLgVqAU+B4ZYayuO7lEb4ngOm2Ixlxn4Gvmd7UX30BqUi1ojgNbmkDclwpAK6NpLi5dX\nvQFd/aI1Du0I3f+p9zzgxEISWsNJroLmS5TcfpzbX9kO8PoEzmJKobad/p6WqFxLDMrRLRF4CFMt\nOwPnvAmTJmry2jgdZofgRT9mZi2UGvi2Jfy2EGuBfbUQDeZWP/bZCdgVIcwqv8Dc3a5vqBi4IF3K\njRsmwALgjSro10MKj4OAj8A+5xJQiiMr+YGba1WlCQLtwF4WgupHoN0ULfgmbhHXcRXgbwE3TxFI\nDPil/td9tOT0HdPH3A2MjMXeUwU9oiM+bc4cm2LgVh82iAy1r03FlrrWAZcjS8PXrA3Kj6AcCaJL\nDkV3ibBtQRsEHkFiH4dSSQ8BaDMceK7rB4wnMocKz6Pi3Hv+MgJeHy7fL1Q5My9Ww/ooCbSMrcXe\nvV9VwN/ogPYNd+x+87Avg/ndQOx9XvjFyTAqAZOpMbJ7wFxbCwOqJAZyrRe2eiS7f1dzbK9yaBkL\n69MlhhIA0+1O7BQjUPjqaJ3U7kL450zs9iCmICAAGRLt0b6RDp1SJQaT21+gsCgJ+oCZ/gF20DBM\np1nYNaOwI6fJ5uHRBaKlfp0K/ebBgGQtMjzTBe7OUbVu8WhI2yTKLUBKAWb62dgzfwYX3CsgONS1\nh3wzWudxxTwqXkb/kz/VOIo5sr7kxwaK4yERVq0y96N/kjh0EwrfSNqi5tkgESPGAupoAQUpMO9K\nPe7aEgqC0CUaJm2X/G9aInifkQAIRHrNGqVEOPb+AxDYr+c8MXCeF7okqSp3g+Nrx5fqmGE1yUNv\niP9T/CdUHL8v7BzgzhhonooZV07BdcD6oMa0NbrxtG/yfwZngG7sITCZPWDCNEh0oi6VSFXqTLdd\n9qaOvxkN4kpgBbBf1Ugbq62+RlVVFRs3bjz8Cxvi+I2jSHE0xpwK3Amca609y71q0NH7MA3xU4k6\ne5pmaOFyM6qCxKOqFQi4hRc0dyt3jdgiimOf1rC8ChZ9CxlvAj6Bsxmf662Nd6pq5onSz+pyCYEc\n2KSKmokCAlK79ycpR07/FGZvJEKf+xQBxQARA+Tuq+DV0eodCgLzJmMfQyIQ8RZOLQPTElqBedSv\nCf8T5RKn6FYKeR5JmT8jKiPno0XG3cnKH/nAGVWqhr3o1POc55R5OwC7azEz/JiXarB374ee1dhn\nwA4rh9v8cI6F+CIYtEoLlzvRHCS0D25/S7T/Ak3AyVfPkR2L6JPnVgo09osWTS/ZVc8qpqqyNrYW\n0x0Zb0/yYNMqlR8X9gfvIfkxnCMvezOSI+OJ5Mj9iD1Ug4BjWCCkHXWCIJS5MQ+gcT4dgfjOaD5V\nTmTiUkkEzMchMNIxR9W/N6oFLKsNJFnMKORH1jEJO9/RGy9A/m/zqwRSCjKx11apgvb4Z3DSZ6JI\nZgvx2Wc9mDwf/MUrj9UNyFdtgZG6JcBw5yOWD/bzp2BzSCAsFfUUTkmC7RbzmwD2b4Ww1i0K16br\nuGX5mNuT9AU1hfosF6j6x9OzBPwKp8HE8fq8FzsqZCo6rumh79Tix/U4bbPUN3dOg6kPY2ZJStz+\nwYgSuTsZ8jqLavn4OOyfQ5h7oCzjDvz/lJBLfaieHZU4AfJjA0D7t7AtLLajS0J7UQLajXjtoEsV\nh+hxlYBP1a3n0lHTLKp4pSVCh8bw4Y3QxaMK2uqvYEga/D0k1cfqclXJgnvVi2a8UH02eK6AiiIl\nqTcOSpq41CtKCDVOsrgFlJ4GfKqVzR+K/3QP2qFhPwzA0litPDafKgVHL7pxf3YOAOai4A/t4kdH\n7fAAdko+9tZYqSZFAde4raYd9H5LW1SgbqVv3s9g/2WwvyfEbxWNJ0x3ra8RHR1Nx44dD//ChmiI\n/zlKgCog1hgThe5uBcf2lBriuImvgBqo7IQm33vRQmYZAm7xRMARsMnxdDaVwaIovf6Bk7TwOKQp\nzPha7xu9El4H4hZDmV95MTpevWaBllq0rCqBRjtlR1NbEcmPCT7oUAodLJGJV0d3DluRPHzzXPmZ\n9UAiGd2BbeXYaR74pgbOlEqffboQ48/ETIiVpHpTgQL6zdN7AkjF8QLAm6qJ+Un50DZWdLpd+aLC\nFalfjaVGCoq/AXuXV8d+L0ogpEOsjJMv8suY+vGLoRS6fISrpgEHekiZcFM5dl2uAOLC/gJjvwYz\nww10SK+3v64QuHlrNAyOxb7gwY6o1OvygSojG5wn+up6hfNjOEeG+7FPU34M58j4rRC/GYGtkgQo\nbCcmz0533X1oAftTBNZrEMg8H4HnOFTlBIH6g+494Ypah08FSPYAu6Iwvwb7S69ofBv1+Uwn1+t3\nV60AbnY2psyDeRAZPaf79bOiLXzdGTyuhLdHm51mRHX8WyH26oHYlYXY/TkSiWsKrBsOYwZLROSt\n0RJzuTQb4rPlsbYoG1IC+r53XQudXc9JEPnfvX4K9vchmDgec3sSJhns3HzM4zXgyRW4rJgKEx+G\nvA4Y73ABrU7zYEkGZpiVOfbEhzGdgDmzYP6vJXyStgm76mnwpWs87hmg78w162HSUrjsLRj6MhUT\nYomvrD/UxuMw6k1+bABo3xdeoJnjc3ck0gzdxv2MR/+Ue2W+ufBL6OARdeO5SwSmhnaEjnNhjVMw\n3F2q565LVONsRZGqaTWVEGirfrQdSZHVw99uEhg7ezlMOV/HWdnb0TlKgTKw1xy+0lNVVXXMKmiA\nfNoujIWHxotQ458AACAASURBVJHdFqZ4YGU12PPWafvwCN5EBiwWlWXAYlFdXhujrd0WzHU9MNf1\ngA+C9E6C3kka3wSrrbQSJcTEwxzjGEd1dTUbNmw41qfREEczjmIFzVpbBDyFplZ7gAPW2vpvANgQ\n9SJsZws+8G2CUHPkf/UVqrCE76Eb0SQ9HuIPQG8Lz3WHG6rVApDWTLkwrwhqB8DYU1VdK6mEmru1\nmBnVCMp3aiGzqkSeogC+DGh0Jpy5XEyVBJ/UjXeXwbor4Ya2MKQtJG9GOeBKoPUWTagTnejEH0fL\nrDrFo8l3YwMffS2K3YDF2JWu6rH6ZBkg+4ENmZhE9/xpOQI7H5RLmj2hFPNiLXYlmK7q77LPh0TB\nawXEW8xMq8ct/Nj7yrG3R6kC9BejifkFr7K/J2w7Twu8JAJftYMCd8wvotVb9Ibol+zKx/aogoG1\n6mtrCuYm4ByLXVaI+V2NAGMRMkW+uUbiJTnRTkmQSI5st6UuR/JBUDmyAvo1juTIOjbKyBcEEBf2\n1zmGRdU+harroOpyIgvYle67EUR3m/D9KCyAEkTgbfnVsKYnvDKccG+R7VVZB8js7x3vMVWbvdj9\nXp2BHedVlatjElTK9JnmIbi9UNL0LfywOQRvBrWvue4As38Of3XMnYAMqQmB+TIavknHPGyxT4Zc\nlaq9qIgl8aImAubnV0qwpakUNdmSCZ/fg3kpBqZNwM5xFMzKVOz92zEPpIuO+u5oCZ10Xau+uvv9\nUnA8NR9WG32GdD/2zFHa74DFUojMf1/n0m6eFgTW/VLiKq1cJTClACbeSsxLDpxd+u6P+n8+buME\nyI8NIiE/EGazwU4GMx6tDu5DN5tKoAKGtIM5xe55L3SI04110RYlodV7YWp39Z6NeBf2D4PZefBg\nJyhcI5PO4lxI6CCq44dOCv6yOHmq7S6D9lFw2iIn718ENIbPesLZr0OyD3bfcfgx27p1Kx999BE3\n3XTTURmnw0WdaMf8WFama6U0LfHouNnXSe2DxqvYPQ4A73TT47WfUjVCD0sqI2aqkzzA469D1i/q\ntdR+MBjktdde4/bbbz/Wp9IQLo54A/SR/Pqd9N0maGNMKvB3VA84iAoXC621c4/gURviCEZ9zY8A\nlR3AtxrdY/ehteZS2c8k+ASiVn8Fmwzc0DTy/j5tdP8N/z3BJz+zUCFUFsmAuqZCVMawsuPotfB4\nO/CdDNNzde8uqBH9P69I1brkcuWX5TuBy1Frghd4dCKMvke9RjiRjfnuZDa+DvEXQNSpkOgogn93\nk/2CTPUhvYEAQjECESflY8amYtdkYZKGSxVyUzkM+FheWiH3epCce78oSd//2Yl5PJcrIPAgUv57\nuTV83pgujWH+ldD2lRFwy3Nw3wuQkylwke/2NTVKfWiJYK+rgkHR6kf7Ndh+Iegrg2Y7pAKui1HV\nDbBb3OctJkJJLSbSQ/ZON1irhFg1wrF20DhPOsVd21NbQJN9mhMVtYMi7TR+Hey/VYvOMTuJKHke\n2gcVpr4ePOS5cC/bra9DbgbcPBWmup6vxEJM9yQJg4CAcjGYZ6rhvShVpyachr3L6G620ol/DHgf\nahpD2zjMvaka31NSRdVsiq5NTiXcuAt2J2OWaaZu74/WcTYAg+ZhumfWvcc+AwzPwvS8U6+tuFPV\nr2/SBdjzOggkdsiC7cO1n48L9QUGWFeOGRkrX7YWfgmfvFio9+wB3gphHvOLGntKLezyqEDQCsn5\nb0+F6aOk0rgH+OMwVdquT8W+WEjlvjb4/tkTs2sptYPrX+Ws3ubIepwfGypoPxRBMCPBTgBKncQ9\n1CHvObWiaMR7gUrdzFLi4IZ2MGKFEtDolVqB2j9MHPyRHaCyROpTNRUQf7qqZY1S9F6Aj92+Vn8l\ncJbgg/i9SAoXOHs7fHajvGPMp4f/vh/LHjQAWxKA+UqK3XNhTcrRAWfAd+T6MYVa7fpZjhJP8adQ\n/Ck1d0Dxx9qaeCT5POkT955nb4Ta+i2131BBa4gfjA+AKYds/z3OB1ZZa/dba6uBvwAX/8fOryF+\nEmHbWwiCbx1UdoX4D9ACpg/oGMlnc4phU1Aqx4u2RSb9y3YoH4Jy3NBUmU7/sQAanSrxLG8MWOeN\nlvctTDkX/vatFi8TfAJn8y5RrkxLBGqgoAUsjwW6oeoMwLAXVGEY+Yb6ypqC/SJLYh7FQGwZXFUN\nvnxMKthnCmFmR/lPdZmqSlF6tgQzOs2TlPspqdjRwMRbBdgSgbaxMK0rrAoJTIQ90CZFSfp+Uawm\n30HAly4Pq1+FNPnuFw09YE0stN0EjHgOM7lc5zC/SibMY2ux/aIwt1jso0GBs37REv+YaTWh7+8X\nAFyGlCIBrrEwtFrVl3bzVMkxhZEc+U63uhx58HLqcmQTTyRH1pG8avfBqp6wvic03qIeNB+UxoLv\nL/JspTUCxnGopw70mQvRAne4fy0ZuGSVtgGL4bfDZCb+W2R5kLYJ+2iFjl2WL9pfcSG2X5T621MK\nsLOMKmAvl0MrsHd4YPw+GP01bE9VxRQwE6pECf0tksE/pVrj3saPvSwaO+5DVdqCyFsNdF27qxpq\nHgSyhmPfex57i4HRkzFp6QLhe3vA+gswPcAMGoL5VZXAWUqBAGUfYMn9ktZPKAVfPjYHzN1JquDW\n5MMDK6T2eepA7E0e7BdZ6lELg9PhWbCwv/Z1aTbmjVkCZ2VZ2L4RZZ76CM7qVRxH+bGhgnaYCAMg\nOx1Mb/dkDYw9AyZ9g0r5LoakyedlQDutNs0rVxIZcArMzheFo2kBxKdCbY1WCffFQ+LXEJuqFcEx\nq2DKxdrHoKVKZpsOAqfBDRYWFSAedx90s6sBe84Pj1t+fj7r1q3jxhtvPOLjU1/D85AqafYv7u42\n9WHJFgPBdyDYzL2wLbzt0ziP2Eld34S9vP59F8PRUEGrf3HEVwe/ORJ7ctH8v60Qng3MRV00IeBF\nYK219o9H8KgNcQSjvuZHcDlyPVTeDr4/IMU+L3WiHUM8MCcPercWsNp0UP1n79+oloAw2BrXRYuY\nU7vruQtjpdwY10bVs0H/1Gtmb9Tf518Jp85RrpyRq4XRRc0RvbIzsqIpBG4thw2xEuHYXSuQFERC\nEM+4StrMBdDpazh4n6onbo5rpljss0ZVkbz2mojvAfrNw/gzsU+Uq4/shVrJnz9fCff54Jt89adB\nhAK4qVwAzu+OOaYQBizGVA7H7ssWXS1tkyb1d90BlxdBe79en+zUIy8AorIxt1wFTWslK+9ABPlu\nC6gCCMDQauxw5/h9RiomALZI+6OVnrZ/yVd+BEh/k+A77rSbIZCFSggj8hDgauyucSIQ4wbqQFCq\nk8CQBJjTiki/fthvNA5oE4DNwYiwWVjC/4bXRd8DjTOoHy2vvQQ3Jsq/zjSboL9tcLL7o2rVQxif\nrUpYt3Ts8yGo/RhOjgN/OlwgsGWaZurz9hoIty0Q0MHZDgBmWCp2VTa8k4F51mJHGjglV8qOn1dg\n/hQj1c45F2Ie82M/RlXI05w5+XRRC82IWOx542WNcHKOrucbmbIN2KH988pAUUTzOmj/iYWiT0b7\n4eHxmOQJcEo1dvrbuja7k2FZL8w5/4X99VbZP3ifh6WGWk8sZoMG//uslupD1NscWY/zY0MF7TBh\nz7GwHsxlYJc7ika5aIscRDfy9ko+I88RCEuJh4RVkBkr+sbsfK0OJvgEzh50ntJ/LlaiCbQEKpR4\nurhqeNtcvbdrS9SAuxUWxQBnEym8Nj48OIN60IN2LKKj2+adoq3rWngXeBdq/ND0KW1VJZARpWsV\nvwPiv6zf4AygpqaGzz///FifRkMczTi6PWifAS+jesB693TW0fooDfHTjnAO8r3o2CT7gSCM9QMH\nxTTp3VrgKiUOOA1WXuUYJ/ERQDY7D15O1/MXN4ebPwB/c1hbDrurJAaybGdE8TF6Fmwf4qTcvarO\nUYru+wEdm/OAJwdK/KLaD133Sv7+VyFNtqNCqoKVlkJOV9HZAHMPqnIMc/O214fDxh6i1m0rh5IE\n9YJNiBXYmrVUoiAzfJjrUO/anysFAprnCjRNiFXlLSBwZronwToHzlIKRHXL66AqyfvlsqA5H8wQ\nsAWoKgeiAf7dYCd7VaVrhYBZU+BLp1g43wG696LkRXpGqsDEtQgo5PDdHNl1bV2OrPFHcmRVibbM\nWCg5XzmSrTfDBzfDe1eDPwg2GPFCS4Q5W4jcdyoRMGur78R3wFmAiEDIuMcFVPr8Ay5TT5YZnI55\nwo+ZazGxE2DyBAmt/AIZbx/MxT64DXblY87JwLRNx75WiHkhBprvhwNiwphgpvoKP8gVeBs9GVZV\nqIpala1zKUkQMF99oYRj/mBgU7l62JqCeTsG2/9KKURf6Lzoho2CX46SgiLA1bGwKwr7m0qBL2cy\nbppmwvqQAO38wfB1M1UJrxwEQ19yx4/Xd+B0+ZjZSfmwOApm9FKlbu1SzJejVRHtuhaGzoqAs11h\nE7sTLE6A/NhQQfuRYf7sKmnLBdZIBAIR2uOmKOjtVwJauAWKRijptImDHWXQqhY8CUowl0SLp/3z\nz5V0mnhk1Ll6r+PqO3WjB84Ur355MaIMFKAvVDLQBuxpP268tmzZQm5uLgMGDDjSw1JvwzPAVdAu\nHaVm4CXXwYe6YRd5oeltel3t41ATDZXxMNfJIg/vVH+/hwAVFRXMnTuXoUOHHutTaQgXR3x1sPjw\nr/vR0ZR6a8TZED8u6nt+hH/LkV2BJtCh2ol4FEHvlgJjo1cq783eCI+eB8Peg64nR8QoPF4IfiVa\nI0BtdYT2v7tM71tTJbuZUq/EnlZ/BaXdkOJygIiCYEtU8Xm3Jwx6GVolRfrH/nSqZNnb+CH0Glx0\nMfzTL9GHv1bAWTGSSU9EBsfIR4z1HtHmUoEmOZrcg6pfu5MhtgdEZUNphvrAHi+EYBImuwqyoqF/\nLfax9ar4XJuE/XUIhqzVfvLaiwJnkyBZ1Dg7B5koX+TXcXbp3AFY4JUdwNsBWG1EgxxTi02rwsyP\ngdOQQmGq6127O0r0xrQa+LvruzpzlOiFCaXwdpAiN2k9NEcGnXBWLwNrLkfT1kuImFWH6YsQ6Wfz\nEqFFNnbPh33qDqJ5zIKbBcpm/7yuSgTA16karwNe7LOqI5hwxc+XJbXFrYWiCG4Auy1XYxftxih9\nOtzYHR48T+99ycIXRn2CAFe4Cugb7vH5maqoNpqK6TMSm9ATrv9AtNYS5wtQkgCL2kMHHEh6Gf40\nS397/EqYPVhCIeHPH++Ad0KJPltRJswIwYTFmGsGYf8+H9M0E/vzbpiXPtCCQUoBjJssgPfShLpq\nLSUJsCSjrs+O6aP03PIJMFbeTvVdFKTe5sh6nB8bKmg/MuytziOtN7oxeYFgREK4QzUsN0ogA9rp\nubwigbPWflE0aksE4P70tZJPVi9o6lNPWt5+JaAEn9t3pVYKlxci75Cv0A3NVf/xgln/475TVVVV\nlJWVHZFxOF6idmEAuz9HN7qEEiUft2LYaB8UHNTmmQjRYyF0uiiOI3Ye6zM/fDRU0E6AOIoVtIZo\niKMR4RxZ9TLY1UCchEF2l0GXRFgegrMSYcDpEp1Y/ZUWJpfthGFnKBcSAwu2w33/gr+4VmBPlEBd\n15bKkWtCyOcMIOjAWQ3qLWmDRLEDiOIYRPLjCSWa/BZI6dA82RZ7z3ZRDKfUyvl6ezXm2iQoLoRr\ndqs36/VyKSgGgVAhtmcIO19CFDTJwQzpgbkmXZP4jzI1Ef8iHz7MkKjHWPUZmQfB3hmtyWDPkHyz\nTkvC/q0Q87lKSmagUxxMKMFMRdY0DpyZGVJtNFc4Kt5VXuzMt7APVECpBzvRSEgjNyQT7GongLJR\ndEfz+yrsI1Hq0XoD7DBvxMR6YX/lR6jLj/+eI0Ona1sTD2w/BxLOUfXr0EhCC8lBIiC5HZH7UKV7\nvgABtoPAFa9Cs32qJJXEy7C5JEFqlC9Hw0se2SLs0bnad/MFzt4s1/i97cRbFrWHDz0at9uBohBM\n/hw+rISvyrEjBV7p5Maze6Z69DqB6ZQpT7yl+RoLgK4fqHfs4gyNzfs9MJenC5yBnntslsD+tePh\nqsUwqb+orbjP+WGGVCkX9sdcM0i+eTdtho8ysXOX6BxeK8TsWCnq4z/7wsiZ0G29vq81+eqt250M\n/8rAvAAMGQaruwkcrr4QbpDheH0HZ0clToD82ADQ/j/C3mrrklDYR4s4gbRNbeTDstxhppJKGXGm\nOFGg4e+rgtamUDTIPR69ZkcZXLJCSQxgzbdKZskBmNdHcsEPhK3ynDoWPqBFINIAfZjwer3Ex8cf\n/oU/tcjroC1tM7TZX2egWdVbi32FADdpa/4FkmO+Esxsg9mqrT6G1+vlrLPOOtan0RBHMxoAWkMc\nh2FvtfhecyBtIdAYCmIk4BF/ADx/kjjI785WNS0lDnYNgQ0lEs2iAm5qJxB3fQstZN71kXLlVW/A\nmJ1owdIrUPZAuoBbvBdZ33yKfNk6E6novNVXk9yuayNG0o94MLekRoCJZxd8FI19DQGF3cmYB3+G\nWeXHTKnFLKrCPJIkGtuZ2RA0mAd/JrPoNIvJaqMJdTg2lcvY+FmLvT2EfcxRFZ8PweWxEqO4ZCoE\nk+BvHpjWFdt+INw8FXNlKnYasMz5nL0Qgz2rChanYB8NYedI6MK0z5CRc1+/FABTwWT4BUTirJQb\n9zg6Y7VRN83rjg63LyThkGtsJD+22V+XH/89Rzb/5pAcWf0pfPapAFYZksgPe5oVEJHPD3ucVaDK\nZnjxOQzO4oBO5RAoh2WX6zzWVkCrJF2jlaJ22rfddysfXcdiNIbnZsHHAmTmMT9cVIt9IKQxPqUW\nGvvVOHd1rKqdUc7PLKEU+xtkj7AB7Kx87fMMgUNbcrOqjJNHYa+ohAm9YXgWtvUo+BwpMF6cgflD\njQDzmeNFc3xupqpaGwuhPEderH4kp//MSsx/IbPpRxAQWwmMexy7+DVV/wa/IGB4UTZMfEQgrChJ\niqB7wK7Jkn/awv5UxgZVeeQEBWdwQuTHBoD2vwjbxoE0L3WTfrbApiJgn3rRonbpJR4vvLUHsn4G\nlXvhqQOSzg8rWY1eqUSVVyTVxxtOEoAbmgaZ72p1MK8IbkhAN7qD6Ka3OigKxCu3HPZ8T8QK2ndi\nYX8whcQvgfi/Q6P7IP1aOAk4eJc2AsAct1UAb/94Cul/Ompra1m/fv3hX9gQDdEQDXEMwvcn0Rzt\nQqA1zNnlqlxNBMrWlisPzt6oHqcEnxYxiYGSfykH/uMAjF+jHDignYDYA02RtxqAF2b8S3L6CT7X\nHx7OyUtRRadJQAqOEx9RNWMZdT5Wdj6YpOHwkge2JcMF1RAolMfYRwZ7hwd7iwcWeGBdNLbzlTJJ\njs7AfvoUvO+RVPq9Bnt3FLzbCrypAoIj33A+agZ+4ce8ZLFxozBP+OVbdatfUvLn5QgcPOiD+xfA\npHHYZa4K1AfXx2TgugPw3q8wf4lWL1nQ0S4f9EmIItOPPXm8qkL5QO8CHR8Exgq9mIct3BgrABHv\nh4DVtqxXnaJjOD82uk/5MZwj66pic5AIC+73AAJWYfpiW8T0CRtVl6Gq5ulE6JC4x/8C9sTqekx8\nWOex+HH5jZ2aD8WFUm3sMhVau16xizR+/KNcfYFRIayrrtHCj1nmla9Y0kZomQtzO0CLbIHTtE06\nhlNWtKN1rub6VMyD6FhZv4h8ttGTYYgPLqqFTcMFvl6frGroqmzsfV6pMi71OF+5zXBakvbx+EWY\n61PhomxJ9a++UNXQhBLsJhlV2xxUBVt9oWiNeR2k1LgzA/Nios63u/tsfxwGbw7XuXddi++rFvoO\n/+LZ/59/y4Y4zqIBoP0v4ztCEo73Pu9KGHuWlKm2N4UhZwqcAXhjwETJnDM6QcmpbTYs+lYrigk+\nmV2DM6IGsvtIqWr5V0pSz12MVgfDN7oyIPPVw55rVFQUCQkJR+aDH0dhv26N/bq1VkRL4hmZDiPT\nEQe+JZw8Tl47gZOB9vDZtdqCAW31NRoqaCdANFTQGuI4DTswkhu/vRLsC0CFWCGf9RWo6v6eGCWv\ndpMYyO4yUf69gC9Rfdzhfu6wofXCsIfXASeeVQnbBsG2IZLb312GGCZhGXeAfUFNakEU99FTMXd2\nhxVBVcI614oi57NQ9iXYJGyPYZgZMap4nJQP50tUxET9TcAoCEwdLXnzf+TCskrMOAsDArBD9DnS\nNqs683kO5t4aSKrFRE/Tsb7IlwLh7cDWHnquy1RYVAmp5eo7ewb1uRU76l2rJFgyDXthhapBaVbC\nI8nA+z0EKP42AfNircDGlanqqcoB2vuxvYLYXkF4vRzzTDPMs7XYgZUCn/CdHHlofgznSFL4To4s\nC9NIA0DSFlXDmgQg+2pt5Wh+UkOkFBcEvgR2IPCcDFxQDgd6aLxKElS5vFeVLM5P0v7/OFqVsw0a\nJ3MFmPf8en9CKSbV0Rz3gM2Ixm7Oho2XwNobodgDv+oj8BWuou5Kh/PR9e07VZTP1qMEKm9bgHlz\ngca+zwrXr7cZc5fVGC3rJVsE0LWOGy6AOD8TfjFLFMf7F8BlNfpu5LWXf9qS/tB/Klz6gaq2oR6S\n7V/YX1L/IJC3rBd0moc9N1XAbcgwyOuMiXke0wfKRvRVtZEGcHYi5McfFAkxxviB95Cwuw94w1r7\nsPvbfcA96F8w21r7kHt+NnAu8GtrbbYx5lQkRn+/tXame81M4CNr7UuHHKveN0H/e3R9TRS4vCIo\nPR2tLMXDlHRVwJp4RN0AaFcBj2yRalW4R232Rpi0XdSPUi8kuy9LSpwoHgPaKemE/WQGLXX8+zgi\nNIJ+YKN+eNw+//xzcnNzue22247o56/vYU46pKGsYxJMkhdbycl6yhMFhU30uO0eqDlNjx91nmiT\nTgGbUv++k6FQiIceeogZM2Yc61NpCBdHvAG66kjsyUV0/W2CPt7jP5Ujj+f8CMqR47poMXJqdy04\n3pmmgakukmhWdAIs3AU3uPtzVCNV0MILlrvLYE2h6IylMbCyr6iTC7doX55ZMqxeVIsqD/9Cqnct\n2knAIa8D5rIMVU46Rc7TjK3FzsqC5gfgwxthe6oMn58MwYceVVDa+JVv7xovYYaNzmD4Y+Sa9GEI\nPjJwcg0mOxbiarE3eTTB7+Tk/H9XI1n8i7MwXw+XguCyXqpmhZy64uBK6F6rSXxJPLT3S9yjc6UU\nKE9JVT9WEFVWOqK+s2g/VIVEcXyuHLrFqtK0B1XsEsGulGmymVYLazx1/mD0IbLoOykWCqTY6HH9\n9YVNlB/huzly0inuPeHFzLDwx1cIgMF3Tam97ncvkR61QavUfxbu/crrIBC1M0Og6hqLvcVg5lrs\nGCPRjp0Zem0rIkbQ+zQGZioQZ7HPL4FzDKb0KlUVg0hopdovSfsBi3UNckQ9BV0jTsrHzG6NXTID\nXhiNmW6xDxuN9em5kV7GnEydy8iZEp/pmAS78qU++ckEbGCeAGGfFQJaIJGP6aNg+r2Yp9tiW3WH\nq/6Kmd8U2ydK3mtbMqHSGZi/WA1ro7B/BTMIKkbG4rtnus71jw9wvEW9zZH1OD/+YAXNWhsCLrXW\npiNW96XGmJ8ZYy4FrgU6W2s7AVMBjDGdgJ1I3HbwIbvaB9xvjIkO7/rIfoxjE6tv0sfI6gX2FcAL\nvZuqGlZSqYoZwNlzYZ3RSuC9rdUUvbsMftOBSKNtMygIitqR4JNoSLiqlmykBjm1O/ROQCtc4djW\nDlP9w9+t6OjoE7IHra6CFnSJNEpbQo22uKQAbaOgrbtO3q3gzYFJZ8OkUE945+ZjefrfGw0VtIZo\niPoRDTny+2P1TfY7OfK+80TjX7ZTgOr5PKgqBF8CGC/YalXGTBQs2gvffKz9JPhgUXitrUZUyW0D\nlENLKpUrO86FKec7MFeEwNkBZFHTeosoZENfws7Mx8y14MmVyMfdYG/3wDtxsM5djoDbRxs/9PBp\nQl6TL7GNvc6Hq+tamVynospO2ma4LQbaxmIzyrEPbYNT87GNpkqsowBY4JU0+/bh2CCYFk6AwhQK\n5O0BEzSi8TVNgnQ/pjsQZaFbrCpLQQRM0rP1nhbZOs+mwCYHwm4UODOdEN1xJQJteY1gVVCUvCIE\nzFohUROboy0KaBPJj+EcGQ5vjsuRoZ7wydUwebp6yhoj6f0gWkA+eMhWRqQnLRGBOKcKWacemddB\nVbNWSQJgnlzs7hzsfd9ojD8x6qlbkgEDnSdaD/T68H6ausrj0vkQWA+/byHj7tNyoGMOtPGrd+/a\nJEzJcL3PFOo9zyD7hLTN2JGOw3nHVOwUI5B7aXZECn/AYv29NAPz5VLMTQ6cubD7cwTKBizGVEzQ\ndR73OIyeCr+bhrkkFfvWrAiwWxwln7fVF8I9w2DkTPUrDirGPl2oXrski+9LrVQcj+CsIf53cViK\no7U2bLIQbvEsBu4CJltrq9xrwu241UAjtJp4aBQCK4CfH4FzrlcRTkDzfy1O+JuXCmS1iYNQoapf\nNXfL12XZTlEdh7RRYkn/OyRXQ2ksdCiHG9rK0POtDCWliW71cMw6eM3RO5Z/S50HGu2B2YMPW0Gr\nrKw8sXvQ/G57ebq23T21Xbwfuq/StmKEttOANT0BsLfNPZZn/b1hreWzzz471qfREEczGiiOx000\n5MgfjnCOXPwvgbQEHzyVq2raJzVQUay86I3RYmTJF9CvMaxoApPWwpxv1HO25iCsvBGq7lD+7NpS\nxtYJzlfr/tO0CEoFagXoghZAK4HqoKoZ807Bvvc8fJkO7/fATsmHt6vg0SKYWwZxopvZHa7nqaMU\nH8m6F/uaJvPcMEpAAQeCrpinqk8BcHouZmSslAivTFVl6GNUidmSgymbhvlVFfxsvBQJ8xphZjWD\njYWi2vWpEZg5OUeVlE5gb4iGM7MF9D4GkwrMvhQzthoTnYGZaCFUKKGM88OlKyeq0VSm2Jwbqwpc\nr4DA8fJlMgAAIABJREFUUQipCgYdHTJts7ZDc+TC/pEceYvLkd9ere3upfDQAvVJAcy7WdWl967W\n/psEIoIgyURUNcvcczXu+QemwK2vCvx8ky4Q3AkJYIR61KkZ2jnCj+aPwLQJsKcQ2lq9toeqVaYP\n8OhAnc+uKPDskqrjWT3kR9YPqTWudOOzDNkZnJej6lifL6S8OGCxqmcxo3VNmwpMmzGpML1fna2C\nSQU7JV8y+dc7muLyCbDwfKlRDliMTR0m+uXER7Tf5rm6Lst6QfNUmBeUqMnswZiuqVKGnPgwNnWY\n3t+3DabbndjiB3XODeAsEidAfow63AuMMR5gHVrHeMZau9EYcwbQwxjzOPpXH22t/dhau9kYE4Uo\nH//1b7v6HbDU0Tt+UjHoDEvWBkPWGBg+ApaNhRErYEp36FqjRLJwCzzYCarLodr1nXVt6Vb8ypxc\nfxEcvAp2hpRoEnwCdQPaSYK4qgQ6WGC/VCPZixzlD4zDNvl+kBYVFXVCVtDCYfcFMJcF1egLMMjR\nKQZkiaIAdTQEe5oVSKvH4fF46Ny587E+jYY4mhE6kk2QwcO/pCH+19GQIw8f4Rz50ufw87PgsVXy\nPpv+qWiKQztCSo1ojf7mUFshEPZAunJgiQ8GtFbePKcarloeed/olbD+FlhbpJz5wJkw41tgCwIB\nlUBsAE7ar0pIn02qlvT5Qid33QEoioOL/fBmEPP7APYv7WF3CPCr4rRsKVAIV2XDH6ZhXpCEPolA\nMFPg4B6wf3d9X2mbIDEJ0zUVuwXsU59B3oXYWGBYFYzrJR+2ll5sVAiIVzUr4FfFxpcMQ1/GPpau\nSl1yBvYx6mTy2RAL1yGqZjUSn1hXIPn+zeqRMgP8MNc14/m262dNPuxJ/c61sfOBxr30yynub4P6\nC1BcnCWQBKoePbRA+3YUUbtkWsQHbrej8l34JviC8OHVeuxFSxdhq6AioLoFePfBznYCK1Mfhun3\nYue647w9Gi4A9qSKkvkq2OwQdt5a0Tr39oAFUnk0yZnY1SHs6hCGBTL1nr4KamuxkxF7JqEU+4/N\nkJqO6a4xNHeDXeuqXU58wzzaGXvHBCkpFmSo8vZ+D2xoHlSthZEXwoZZWn3vlIQJpWJXX6hrklKA\n6ZeKzXoSThosULhnFjYRAdBhH2g8V6RrbPM6wFkZME4VQbtgAvTKwiQNx058GHvxWZgLR0AYnLVr\n+mP+1U6cOGI5sv7mx8MCNGttLZBujGkMvG2MucS9r6m1tqsx5gJgAW5aa60d9T372WaMWQN8L2+s\nqKiIrKwsRowYwezZs7njjjt45ZVXGDx4MPPmzSMzM5PXX3+dgQMHsnjxYq6//nrefPNNrr76at5+\n+2369u3LihUruPzyy/l/7J17WFT12v4/X44DAoqKqWBqaAl5QEsjTbQ0SqnMIpUOlmhkauYBLTNR\n0DSVkvKQIZqaiaFlVh5CLcVDeCZStBSPoMhJHBSQ0/f3x7MG6t17/3b7fTXZOvd1cS2YWbNmrTXD\nPHOv537ue/v27XTv3p3du3fTuXNn9u7dywMPPMDBgwe57777SE1NpV27dhw9ehQfHx9OnDhBy5Yt\nOX36NM2bNyczMxNPT08uXrxIw4YNycvLo379+ly+fJk6depQVFSEs7MzZWVlODg4MMS3grg0W2In\nwaA24PabFBgLEetTW2yE9xbJ8MHUJlDLC2xjgQp4oCFs6AMbzsKzCpLzq2fVTHlwqR4kF8DO56De\nTuAY0kErKobUbqjAYnTZP3/DlpaWcvXq1X/3Ut/asNgLA+wUpya1NQGdIXKFmtot+2e4lTpolrma\n0tJS7O3tKS4uxmQyUVhYiIuLC5cuXcLd3Z3s7Gw8PDw4f/48jRo14ty5c3h5eXHq1CmaNWvG8ePH\nadmyJUePHqVVq1akpqbSpk0bDh06hJ+fH/v27aNjx478/PPP+Pv7k5SURNeuXfnpp5/o3r07mzdv\n5tFHH2XDhg306tWLb7/9lieffJKvv/6avn37kpCQQL9+/Vi5ciUhISEsW7aMgQMHEhcXx+DBg2/y\nWbTiZuLvqpE7d+4kNzeXBg0akJeXR4MGDcjPz6d+/fpcunSpalmvXj0uX76Mu7s7ZrMZd3d3CgsL\nqVOnDoWFhdSuXZsrV65Qu3Ztrl69iqurK0VFRbi4uFBcXIyLiwslJSU4Oztz7do1nJ2dKS0txWQy\nUVZWhslkory8HAcHByoqKrC3t6eyshJ7e3u01tjZ2aG1xtbWFqUUNjY2KKV49d5KFh0R0c7EzjD3\ngBAqN0chYaHesPEs9LpTFCMDG8Jy4MMHofPa6guWbo5wehDoqxC0RWbbQFQqYVvFRZlLiFGFC9Kb\ntC2G481kxWbHICYT5e+HNhthwt+XAGWw/H1oNwVGNoYfR0g347AhLezoIWQiNBY2h0n4sydgQkw/\nWoN6qx06Z7QYejxdijpph/KoQPveDc+ZpGu1aiK49UTPsBWZYaJJnAzdCqE8SGSJtQplbm7aEHSY\ncRGxlRHCfKwENcCEfuwaaq8j/K6kC/aQBjS6R6W8n7xKYJ0xO4e3XD4A9BrD5MJ/ryxXdYSHjDeZ\npU7+sUYmi+slZjchVyDB0AC5fnA4Ep6Prp61qmN8D3no++oOhRNC0jCWttnwS3txSgzcKs81yVus\n5GPDxMVxnzd4IqHex/1kzk4FVG/vGQ1DFTo3HfBEfWQSEjt0Mnx6AZ4MFFv9lp5w3A/V00M6cauA\nwhL0+Ergfhi1FhUWDMu9oaxS7PEzPNFu8agWIZLlNjAYCEYzXrqjgcYsoxPg5SkS2BYB6P3xYO4k\nr2X4DHQp0t1sHYBWyPvHd710J0OXy2sQESlzcRohcsHllEa2QfEr7NIwah5lQ+dTXl5ORUUFSinK\nysqwtbWltLQUOzs7rl27VlU/HRwcKC4uxtHRkaKiIkwmE1euXMHJyYkrV67g7OxMYWEhtWrVwmw2\n4+LiQkFBAa6urhQUFODm5salS5eoXbs2eXl51KlTh7y8PNzd3cnNzaVu3brk5ORQr149Ll68SP3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KMx23YYcEhHtfMWZ8ZLNqgVJvSh9Si3IHRyOiryLvQoObeqj5hhqNlaHBEBFVcPPdhG9jEg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\nSEwoQRMREREREYkJJWgiIiIiIiIx8f/iBzBtV2n/qAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cd29910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display = pyart.graph.RadarMapDisplay(radar)\n",
"fig = plt.figure(figsize = [15,7])\n",
"plt.subplot(1,2,1)\n",
"display.plot_ppi_map('reflectivity_masked', sweep = 2, resolution = 'i',\n",
" mask_outside = False,\n",
" cmap = pyart.graph.cm.NWSRef,\n",
" vmin = -10, vmax = 64)\n",
"plt.subplot(1,2,2)\n",
"display.plot_ppi_map('reflectivity', sweep = 2, resolution = 'i',\n",
" mask_outside = False,\n",
" cmap = pyart.graph.cm.NWSRef,\n",
" vmin = -10, vmax = 64)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"gatefilter = pyart.correct.GateFilter(radar)\n",
"gatefilter.exclude_masked('reflectivity_masked')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"corr_vel = pyart.correct.dealias_region_based(\n",
" radar, vel_field='velocity', keep_original=False, \n",
" gatefilter = gatefilter, nyquist_vel=nyq, centered = True)\n",
"radar.add_field('corrected_velocity', corr_vel, replace_existing = True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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vAnfa/3eiaQtGFbLmF6jwqxrhc+5FK5ifRGSJiFxrzU9Fp0AchYhcICJzReQf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2cm70trGTkuWIGhvf+bHFMqfXIryXGgpV6c+DUZkyhP2itygvbA+B49kcBZXEyqvJycAQPI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\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kd/l6yMfKGqZhVe3scBYNWL1uOR3Z7Uxpvd3cn53MRAVXKsCy/4wUjAQEdaQwA8dTLGtI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\nXAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cd29cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display = pyart.graph.RadarMapDisplay(radar)\n",
"fig = plt.figure(figsize = [15,7])\n",
"plt.subplot(1,2,1)\n",
"display.plot_ppi_map('velocity', sweep = 2, resolution = 'i',\n",
" mask_outside = False,\n",
" cmap = pyart.graph.cm.NWSVel,\n",
" vmin = -nyq, vmax = nyq)\n",
"plt.subplot(1,2,2)\n",
"display.plot_ppi_map('corrected_velocity', sweep = 2, resolution = 'i',\n",
" mask_outside = False,\n",
" cmap = pyart.graph.cm.NWSVel,\n",
" vmin = -1.5*nyq, vmax = 1.5*nyq)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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| bsd-2-clause |
jdespraz/deep_generative_networks | autoencoder.ipynb | 1 | 8895 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"np.random.seed(123)\n",
"\n",
"import os\n",
"from keras.models import Model\n",
"from keras.layers import Input, Convolution2D, MaxPooling2D, BatchNormalization\n",
"from keras.layers import Flatten, Dense, Reshape, UpSampling2D\n",
"from keras.utils.layer_utils import print_summary\n",
"\n",
"import cv2\n",
"import h5py\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def autoencoder(weights_path=None):\n",
" # encoder\n",
" inputs = Input(shape=(3,224,224), name='input')\n",
" \n",
" enc1 = Convolution2D(32, 3, 3, activation='relu')(inputs)\n",
" enc1 = BatchNormalization(mode = 0 , axis = 1)(enc1)\n",
" enc1 = Convolution2D(32, 3, 3, activation='relu')(enc1)\n",
" enc1 = BatchNormalization(mode = 0 , axis = 1)(enc1)\n",
" enc1 = MaxPooling2D((2,2), strides=(2,2))(enc1)\n",
" \n",
" enc2 = Convolution2D(64, 3, 3, activation='relu')(enc1)\n",
" enc2 = BatchNormalization(mode = 0 , axis = 1)(enc2)\n",
" enc2 = Convolution2D(64, 3, 3, activation='relu')(enc2)\n",
" enc2 = BatchNormalization(mode = 0 , axis = 1)(enc2)\n",
" enc2 = MaxPooling2D((2,2), strides=(2,2))(enc2)\n",
" \n",
" enc3 = Convolution2D(128, 3, 3, activation='relu')(enc2)\n",
" enc3 = BatchNormalization(mode = 0 , axis = 1)(enc3)\n",
" enc3 = Convolution2D(128, 3, 3, activation='relu')(enc3)\n",
" enc3 = BatchNormalization(mode = 0 , axis = 1)(enc3)\n",
" enc3 = MaxPooling2D((2,2), strides=(2,2))(enc3)\n",
" \n",
" enc4 = Convolution2D(256, 3, 3, activation='relu')(enc3)\n",
" enc4 = BatchNormalization(mode = 0 , axis = 1)(enc4)\n",
" enc4 = Convolution2D(256, 3, 3, activation='relu')(enc4)\n",
" enc4 = BatchNormalization(mode = 0 , axis = 1)(enc4)\n",
" enc4 = MaxPooling2D((2,2), strides=(2,2))(enc4)\n",
" \n",
" enc5 = Convolution2D(512, 3, 3, activation='relu')(enc4)\n",
" enc5 = BatchNormalization(mode = 0 , axis = 1)(enc5)\n",
" enc5 = Convolution2D(512, 3, 3, activation='relu')(enc5)\n",
" enc5 = BatchNormalization(mode = 0 , axis = 1)(enc5)\n",
" enc5 = MaxPooling2D((2,2), strides=(2,2))(enc5)\n",
" \n",
" enc6 = Flatten()(enc5)\n",
" enc6 = Dense(2048, activation='relu')(enc6)\n",
" enc6 = Dense(5*5*128, activation='linear', name='encoded_img')(enc6)\n",
" \n",
" # decoder (generator)\n",
" g2 = Reshape((128,5,5))(enc6)\n",
" \n",
" g3 = UpSampling2D(size=(2, 2))(g2) # 10x10\n",
" g3 = Convolution2D(512,2,2,activation='relu',border_mode='valid')(g3) # 9x9\n",
" g3 = BatchNormalization(mode = 0 , axis = 1)(g3)\n",
" g3 = Convolution2D(512,2,2,activation='relu',border_mode='same')(g3) # 9x9\n",
" g3 = BatchNormalization(mode = 0 , axis = 1)(g3)\n",
" \n",
" g4 = UpSampling2D(size=(2, 2))(g3) # 18x18\n",
" g4 = Convolution2D(256,3,3,activation='relu',border_mode='valid')(g4) # 16x16\n",
" g4 = BatchNormalization(mode = 0 , axis = 1)(g4)\n",
" g4 = Convolution2D(256,3,3,activation='relu',border_mode='same')(g4) # 16x16\n",
" g4 = BatchNormalization(mode = 0 , axis = 1)(g4)\n",
" \n",
" g5 = UpSampling2D(size=(2, 2))(g4) # 32x32\n",
" g5 = Convolution2D(256,3,3,activation='relu',border_mode='valid')(g5)# 30x30\n",
" g5 = BatchNormalization(mode = 0 , axis = 1)(g5)\n",
" g5 = Convolution2D(256,3,3,activation='relu',border_mode='same')(g5) # 30x30\n",
" g5 = BatchNormalization(mode = 0 , axis = 1)(g5)\n",
" \n",
" g6 = UpSampling2D(size=(2, 2))(g5) # 60x60\n",
" g6 = Convolution2D(128,3,3,activation='relu',border_mode='valid')(g6) # 58x58\n",
" g6 = BatchNormalization(mode = 0 , axis = 1)(g6)\n",
" g6 = Convolution2D(128,3,3,activation='relu',border_mode='same')(g6) # 58x58\n",
" g6 = BatchNormalization(mode = 0 , axis = 1)(g6)\n",
" \n",
" g7 = UpSampling2D(size=(2, 2))(g6) # 116x116\n",
" g7 = Convolution2D(128,4,4,activation='relu',border_mode='valid')(g7) # 113x113\n",
" g7 = BatchNormalization(mode = 0 , axis = 1)(g7)\n",
" g7 = Convolution2D(128,4,4,activation='relu',border_mode='same')(g7) # 113x113\n",
" g7 = BatchNormalization(mode = 0 , axis = 1)(g7)\n",
" \n",
" g8 = UpSampling2D(size=(2, 2))(g7) # 226x226\n",
" g8 = Convolution2D(64,3,3,activation='relu',border_mode='valid')(g8) # 224x224\n",
" g8 = BatchNormalization(mode = 0 , axis = 1)(g8)\n",
" g8 = Convolution2D(64,3,3,activation='relu',border_mode='same')(g8) # 224x224\n",
" g8 = BatchNormalization(mode = 0 , axis = 1)(g8)\n",
" g8 = Convolution2D(3,3,3,activation='relu',border_mode='same')(g8) # 224x224\n",
" g8 = BatchNormalization(mode = 0, axis = 1, name='image')(g8)\n",
" \n",
" return Model(input=inputs, output=[g8,enc6])\n",
"\n",
"\n",
"# compile model\n",
"model = autoencoder()\n",
"print_summary(model.layers)\n",
"model.compile(optimizer='adadelta', loss='mean_squared_error')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def transform_image(im):\n",
" im = cv2.cvtColor(im.astype(np.uint8), cv2.COLOR_RGB2BGR)\n",
" im = im.astype(np.float32)\n",
" im[:,:,0] -= 103.939\n",
" im[:,:,1] -= 116.779\n",
" im[:,:,2] -= 123.68\n",
" im = im.transpose((2,0,1))\n",
" im = np.expand_dims(im, axis=0)\n",
" return im\n",
"\n",
"def reconstruct_image(im):\n",
" im2 = np.squeeze(im)*1\n",
" im2 = im2.transpose((1,2,0))\n",
" im2[:,:,0] += 103.939\n",
" im2[:,:,1] += 116.779\n",
" im2[:,:,2] += 123.68\n",
" im2 = im2.astype(np.uint8)\n",
" return cv2.cvtColor(im2,cv2.COLOR_BGR2RGB)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get all images and corresponding filter responses\n",
"path_all = 'n02084071/'\n",
"f_imgs = os.listdir(path_all)\n",
"train_pics = []\n",
"for i, pic in enumerate(f_imgs):\n",
" img = cv2.resize(cv2.imread(path_all+pic), (224, 224)).astype(np.float32)\n",
" img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)\n",
" img = transform_image(img)\n",
" train_pics.append(img)\n",
" \n",
"train_pics = np.squeeze(np.array(train_pics))\n",
"dummy_labels = np.zeros(shape=(len(f_imgs),5*5*128))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# train auto-encoder\n",
"model.fit(train_pics, [train_pics,dummy_labels], nb_epoch=400, batch_size=24, class_weight=[1.,0.])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# test\n",
"idx = np.random.choice(train_pics.shape[0])\n",
"pic = train_pics[idx:idx+1]\n",
"out = model.predict(pic)\n",
"\n",
"code = np.squeeze(out[1])\n",
"print('code: ',code)\n",
"plt.figure()\n",
"plt.plot(code)\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=(10,10))\n",
"plt.subplot(1,2,1)\n",
"plt.imshow(reconstruct_image(pic))\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(reconstruct_image(out[0]))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save the decoder weights for later use\n",
"model.save_weights('decoder_weights.h5')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
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"nbformat": 4,
"nbformat_minor": 0
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| mit |
xArchange/XGBoost-Analysis | XGB_RF_GB_exp.ipynb | 1 | 53855 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from timeit import default_timer as timer\n",
"\n",
"# Pre-splits the dataset into training and testing folds for all the classifiers to use.\n",
"def splitting_dataset(folder_name, dataset_name, num_folds):\n",
" from numpy import genfromtxt\n",
" from sklearn.model_selection import StratifiedKFold\n",
"\n",
" # Loads the dataset.\n",
" dataset = genfromtxt(folder_name + '/' + dataset_name, delimiter=\",\")\n",
" # Splits the dataset into the data and the labels.\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" y = dataset[:, len(dataset[0]) - 1]\n",
"\n",
" print(\"Splitting the whole dataset into training and testing folds...\")\n",
"\n",
" # Creating folds with StratifiedKFold.\n",
" skf = StratifiedKFold(n_splits = num_folds, random_state = None, shuffle = True)\n",
" skf.get_n_splits(X, y)\n",
" # Opening the dataset file for copying lines.\n",
" f_ds = open(folder_name + \"/\" + dataset_name)\n",
" # Creating a list from its lines.\n",
" dataset_lines = []\n",
" for line in f_ds:\n",
" dataset_lines.append(line)\n",
"\n",
" ctr = 0; # For naming the files.\n",
"\n",
" # For each fold...\n",
" for train_index, test_index in skf.split(X, y):\n",
" train_name = folder_name + '/' + 'fold' + str(ctr) + '_train.data'; # file for train instances\n",
" test_name = folder_name + '/' + '/fold' + str(ctr) + '_test.data'; # file for test instances\n",
" f_train = open(train_name, 'w')\n",
" f_test = open(test_name, 'w')\n",
"\n",
" # Selecting the training and testing instances + labels.\n",
" X_train, X_test = X[train_index], X[test_index]\n",
" y_train, y_test = y[train_index], y[test_index]\n",
"\n",
" # Printing the training data (+labels) into the train file.\n",
" for i in train_index:\n",
" f_train.write(dataset_lines[i])\n",
"\n",
" # Printing the testing data (+labels) into the test file.\n",
" for i in test_index:\n",
" f_test.write(dataset_lines[i])\n",
"\n",
" ctr += 1;\n",
" f_train.close()\n",
" f_test.close()\n",
"\n",
" print(\"%.2f %%\" % (ctr / num_folds * 100))\n",
"\n",
" f_ds.close()\n",
" print(\"The whole dataset has been split into folds!\") \n",
" print(\"\\n\")\n",
" \n",
" return\n",
"\n",
"\n",
"\n",
"# Performs the grid search for all of the classifiers, including the extended ones. \n",
"def general_grid_search(folder_name, num_folds, missing):\n",
" # Grid search and extension for XGBoost.\n",
" parameters_xgboost = grid_search_xgboost(folder_name, num_folds, missing)\n",
" # Grid search and extension for RF.\n",
" parameters_rf = grid_search_rf(folder_name, num_folds, missing)\n",
" # Grid search and extension for GB\n",
" parameters_gb = grid_search_gb(folder_name, num_folds, missing)\n",
" \n",
" return parameters_xgboost, parameters_rf, parameters_gb\n",
"\n",
"\n",
"\n",
"# Grid search for XGBoost. Checks also if the extension of the grid search\n",
"# is necessary and if it is, performs it by calling the extended grid search function.\n",
"def grid_search_xgboost(folder_name, num_folds, missing):\n",
" from numpy import genfromtxt\n",
" from xgboost import XGBClassifier\n",
" from sklearn.model_selection import GridSearchCV\n",
" from sklearn.model_selection import StratifiedKFold\n",
" from sklearn.metrics import accuracy_score\n",
" from sklearn.preprocessing import Imputer\n",
" \n",
" # Initializes the parameters' list.\n",
" #print(\"Initializing the parameters to test for XGBoost...\") \n",
" learning_rate = [0.05, 0.1, 0.2]\n",
" max_depth = [3, 5, 6, 8]\n",
" subsample = [0.5, 0.8, 1]\n",
" gamma = [0, 0.1, 0.2, 0.3]\n",
" min_child_weight = [1, 3, 5]\n",
" #print(\"The parameters to test for XGBoost have been initialized!\\n\")\n",
" \n",
" # Initialize the arrays that'll contain the best parameters for each fold (10). Will be returned at the end.\n",
" best_learning_rate = []\n",
" best_max_depth = []\n",
" best_subsample = []\n",
" best_gamma = []\n",
" best_min_child_weight = []\n",
" \n",
" imputer = Imputer(missing_values = missing) \n",
" xgb_model = XGBClassifier(n_estimators = 200)\n",
" \n",
" # For each fold, loads the training dataset in and perform the grid search on it.\n",
" for i in range(0, num_folds):\n",
" # Loads the dataset (training fold).\n",
" #print(\"Loading training dataset...\") \n",
" dataset = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_train.data', delimiter=\",\")\n",
" #print(\"Training dataset was loaded in!\")\n",
"\n",
" # Splits the dataset into the data and the labels.\n",
" #print(\"Splitting the dataset into data and labels...\")\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" Y = dataset[:, len(dataset[0]) - 1]\n",
" X = imputer.fit_transform(X, Y)\n",
" #print(\"The data and labels from the dataset have been split!\")\n",
"\n",
" # Timed grid search.\n",
" start = timer()\n",
" param_grid = dict(learning_rate = learning_rate, max_depth = max_depth, \n",
" subsample = subsample, gamma = gamma, min_child_weight = min_child_weight)\n",
" #print(\"Starting the grid search...\")\n",
" kfold = StratifiedKFold(n_splits = num_folds, shuffle = True, random_state = 7)\n",
" grid_search = GridSearchCV(xgb_model, param_grid, scoring = \"neg_log_loss\", n_jobs = -1, cv = kfold, verbose = 1)\n",
" grid_result = grid_search.fit(X, Y)\n",
" #print(\"The grid search is over!\")\n",
" end = timer()\n",
" time_grid_xgb.append(end - start) \n",
"\n",
" # Summarizes results. \n",
" print(\"Best: %f using %s \\n\" % (grid_result.best_score_, grid_result.best_params_))\n",
"\n",
" best_learning_rate.append(grid_result.best_params_['learning_rate'])\n",
" best_max_depth.append(grid_result.best_params_['max_depth'])\n",
" best_subsample.append(grid_result.best_params_['subsample'])\n",
" best_gamma.append(grid_result.best_params_['gamma'])\n",
" best_min_child_weight.append(grid_result.best_params_['min_child_weight'])\n",
" \n",
" print(\"%.2f %%\\n\" % ((i + 1) / num_folds * 100))\n",
" \n",
" print(\"Done with the normal grid search for XGBoost!\\n\")\n",
" \n",
" # Grid extension.\n",
" parameters_xgboost = grid_extension_xgboost(folder_name, num_folds, missing, best_learning_rate, \n",
" best_max_depth, best_subsample, best_gamma, best_min_child_weight)\n",
" \n",
" return parameters_xgboost\n",
"\n",
"\n",
"\n",
"# Grid search for Random Forest. Checks also if the extension of the grid search\n",
"# is necessary and if it is, performs it by calling the extended grid search function.\n",
"def grid_search_rf(folder_name, num_folds, missing):\n",
" from numpy import genfromtxt\n",
" from sklearn.ensemble import RandomForestClassifier\n",
" from sklearn.model_selection import GridSearchCV\n",
" from sklearn.model_selection import StratifiedKFold\n",
" from sklearn.metrics import accuracy_score\n",
" from sklearn.preprocessing import Imputer\n",
" \n",
" # Initializes the parameters' list.\n",
" #print(\"Initializing the parameters to test for Random Forest...\") \n",
" max_features = [\"auto\", \"log2\", None]\n",
" min_samples_leaf = [1, 25, 50, 70]\n",
" max_depth = [None, 5, 8, 10]\n",
" min_samples_split = [2, 5, 8, 10]\n",
" #print(\"The parameters to test for Random Forest have been initialized!\\n\")\n",
" \n",
" # Initialize the arrays that'll contain the best parameters for each fold (10). Will be returned at the end.\n",
" best_max_features = []\n",
" best_min_samples_leaf = []\n",
" best_max_depth = []\n",
" best_min_samples_split = []\n",
" \n",
" imputer = Imputer(missing_values = missing) \n",
" rf_model = RandomForestClassifier(n_estimators = 200) \n",
" \n",
" # For each fold, loads the training dataset in and perform the grid search on it.\n",
" for i in range(0, num_folds):\n",
" # Loads the dataset (training fold).\n",
" #print(\"Loading training dataset...\") \n",
" dataset = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_train.data', delimiter=\",\")\n",
" #print(\"Training dataset was loaded in!\")\n",
"\n",
" # Splits the dataset into the data and the labels.\n",
" #print(\"Splitting the dataset into data and labels...\")\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" Y = dataset[:, len(dataset[0]) - 1]\n",
" X = imputer.fit_transform(X, Y)\n",
" #print(\"The data and labels from the dataset have been split!\")\n",
"\n",
" # Timed grid search.\n",
" start = timer()\n",
" param_grid = dict(max_features = max_features, min_samples_leaf = min_samples_leaf, \n",
" max_depth = max_depth, min_samples_split = min_samples_split)\n",
" #print(\"Starting the grid search...\")\n",
" kfold = StratifiedKFold(n_splits = num_folds, shuffle = True, random_state = 7)\n",
" grid_search = GridSearchCV(rf_model, param_grid, scoring = \"neg_log_loss\", n_jobs = -1, cv = kfold, verbose = 1)\n",
" grid_result = grid_search.fit(X, Y)\n",
" #print(\"The grid search is over!\")\n",
" end = timer()\n",
" time_grid_rf.append(end - start) \n",
"\n",
" # Summarize results \n",
" print(\"Best: %f using %s \\n\" % (grid_result.best_score_, grid_result.best_params_))\n",
"\n",
" best_max_features.append(grid_result.best_params_['max_features']) \n",
" best_min_samples_leaf.append(grid_result.best_params_['min_samples_leaf'])\n",
" best_max_depth.append(grid_result.best_params_['max_depth'])\n",
" best_min_samples_split.append(grid_result.best_params_['min_samples_split'])\n",
" \n",
" print(\"%.2f %%\\n\" % ((i + 1) / num_folds * 100))\n",
" \n",
" print(\"Done with the normal grid search for Random Forests!\\n\")\n",
" \n",
" # Grid extension.\n",
" parameters_rf = grid_extension_rf(folder_name, num_folds, missing, best_max_features, best_min_samples_leaf,\n",
" best_max_depth, best_min_samples_split)\n",
" \n",
" return parameters_rf\n",
"\n",
" \n",
" \n",
"# Grid search for Gradient Boosting. Checks also if the extension of the grid search\n",
"# is necessary and if it is, performs it by calling the extended grid search function.\n",
"def grid_search_gb(folder_name, num_folds, missing):\n",
" from numpy import genfromtxt\n",
" from sklearn.ensemble import GradientBoostingClassifier\n",
" from sklearn.model_selection import GridSearchCV\n",
" from sklearn.model_selection import StratifiedKFold\n",
" from sklearn.metrics import accuracy_score\n",
" from sklearn.preprocessing import Imputer\n",
" \n",
" # Initializes the parameters' list.\n",
" #print(\"Initializing the parameters to test for Gradient Boosting...\") \n",
" learning_rate = [0.05, 0.1, 0.2]\n",
" max_depth = [3, 5, 6, 8]\n",
" subsample = [0.5, 0.8, 1]\n",
" max_features = [\"auto\", \"log2\", None]\n",
" min_samples_split = [2, 5, 8, 10]\n",
" #print(\"The parameters to test for Gradient Boosting have been initialized!\\n\")\n",
" \n",
" # Initialize the arrays that'll contain the best parameters for each fold (10). Will be returned at the end.\n",
" best_learning_rate = []\n",
" best_max_depth = []\n",
" best_subsample = []\n",
" best_max_features = []\n",
" best_min_samples_split = []\n",
" \n",
" imputer = Imputer(missing_values = missing) \n",
" gb_model = GradientBoostingClassifier(n_estimators = 200) \n",
" \n",
" # For each fold, loads the training dataset in and perform the grid search on it.\n",
" for i in range(0, num_folds):\n",
" # Loads the dataset (training fold).\n",
" #print(\"Loading training dataset...\") \n",
" dataset = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_train.data', delimiter=\",\")\n",
" #print(\"Training dataset was loaded in!\")\n",
"\n",
" # Splits the dataset into the data and the labels.\n",
" #print(\"Splitting the dataset into data and labels...\")\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" Y = dataset[:, len(dataset[0]) - 1]\n",
" X = imputer.fit_transform(X, Y)\n",
" #print(\"The data and labels from the dataset have been split!\")\n",
"\n",
" # Timed grid search.\n",
" start = timer()\n",
" param_grid = dict(learning_rate = learning_rate, max_depth = max_depth, subsample = subsample, \n",
" max_features = max_features, min_samples_split = min_samples_split)\n",
" #print(\"Starting the grid search...\")\n",
" kfold = StratifiedKFold(n_splits = num_folds, shuffle = True, random_state = 7)\n",
" grid_search = GridSearchCV(gb_model, param_grid, scoring = \"neg_log_loss\", n_jobs = -1, cv = kfold, verbose = 1)\n",
" grid_result = grid_search.fit(X, Y)\n",
" #print(\"The grid search is over!\")\n",
" end = timer()\n",
" time_grid_gb.append(end - start) \n",
"\n",
" # Summarize results \n",
" print(\"Best: %f using %s \\n\" % (grid_result.best_score_, grid_result.best_params_))\n",
"\n",
" best_learning_rate.append(grid_result.best_params_['learning_rate'])\n",
" best_max_depth.append(grid_result.best_params_['max_depth'])\n",
" best_subsample.append(grid_result.best_params_['subsample'])\n",
" best_max_features.append(grid_result.best_params_['max_features']) \n",
" best_min_samples_split.append(grid_result.best_params_['min_samples_split'])\n",
" \n",
" print(\"%.2f %%\\n\" % ((i + 1) / num_folds * 100))\n",
" \n",
" print(\"Done with the normal grid search for Gradient Boosting!\\n\")\n",
" \n",
" # Grid extension.\n",
" parameters_gb = grid_extension_gb(folder_name, num_folds, missing, best_learning_rate, best_max_depth,\n",
" best_subsample, best_max_features, best_min_samples_split)\n",
" \n",
" return parameters_gb\n",
" \n",
" \n",
" \n",
"# Checks whether the grid extension is needed for XGBoost and performs it if it's the case.\n",
"# In any case, also groups all the parameters into one 2D array to avoid having too\n",
"# many parameters going around.\n",
"def grid_extension_xgboost(folder_name, num_folds, missing, best_learning_rate, \n",
" best_max_depth, best_subsample, best_gamma, \n",
" best_min_child_weight): \n",
" from numpy import genfromtxt\n",
" from xgboost import XGBClassifier\n",
" from sklearn.model_selection import GridSearchCV\n",
" from sklearn.model_selection import StratifiedKFold\n",
" from sklearn.metrics import accuracy_score\n",
" from sklearn.preprocessing import Imputer\n",
" \n",
" need = False # Initializing the boolean that'll be use to know if the extension is needed. \n",
" \n",
" # Checks whether the grid extension is needed.\n",
" if (best_learning_rate.count(0.05) >= num_folds / 2 or best_learning_rate.count(0.2) >= num_folds / 2 \n",
" or best_max_depth.count(3) >= num_folds / 2 or best_max_depth.count(8) >= num_folds / 2 \n",
" or best_subsample.count(0.5) >= num_folds / 2 or best_subsample.count(1) >= num_folds / 2 \n",
" or best_gamma.count(0) >= num_folds / 2 or best_gamma.count(0.3) >= num_folds / 2\n",
" or best_min_child_weight.count(1) >= num_folds / 2 or best_min_child_weight.count(5) >= num_folds / 2): \n",
" need = True\n",
" \n",
" # If the extension is needed...\n",
" if need == True:\n",
" #print(\"\\nStarting the grid expansion...\") \n",
"\n",
" imputer = Imputer(missing_values = missing)\n",
"\n",
" # For each fold, load the training dataset in and perform the grid search on it.\n",
" for i in range(0, num_folds):\n",
" # Load the dataset (training fold)\n",
" #print(\"Loading training dataset...\") \n",
" dataset = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_train.data', delimiter=\",\")\n",
" #print(\"Training dataset was loaded in!\")\n",
"\n",
" # Split the dataset into the data and the labels\n",
" #print(\"Splitting the dataset into data and labels...\")\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" Y = dataset[:, len(dataset[0]) - 1]\n",
" X = imputer.fit_transform(X, Y)\n",
" #print(\"The data and labels from the dataset have been split!\")\n",
"\n",
" # Initializes the model with the best found parameter for each fold (individually).\n",
" # The parameters on the extreme of the grid will be added to the parameters' dictionary.\n",
" xgb_model = XGBClassifier(n_estimators = 200, learning_rate = best_learning_rate[i], max_depth = best_max_depth[i],\n",
" subsample = best_subsample[i], gamma = best_gamma[i], \n",
" min_child_weight = best_min_child_weight[i])\n",
"\n",
" param_grid = dict()\n",
"\n",
" if best_learning_rate[i] == 0.05 or best_learning_rate[i] == 0.2:\n",
" if best_learning_rate[i] == 0.05:\n",
" learning_rate = [0.01, 0.03, 0.05, 0.07]\n",
" else:\n",
" learning_rate = [0.15, 0.2, 0.25, 0.3]\n",
" param_grid['learning_rate'] = learning_rate\n",
" bool_lr = True\n",
" else:\n",
" bool_lr = False\n",
"\n",
" if best_max_depth[i] == 3 or best_max_depth[i] == 8:\n",
" if best_max_depth[i] == 3:\n",
" max_depth = [1, 2, 3, 4]\n",
" else:\n",
" max_depth = [7, 8, 9, 10]\n",
" param_grid['max_depth'] = max_depth\n",
" bool_md = True\n",
" else:\n",
" bool_md = False\n",
"\n",
" if best_subsample[i] == 0.5 or best_subsample[i] == 1:\n",
" if best_subsample[i] == 0.5:\n",
" subsample = [0.4, 0.5, 0.6]\n",
" else:\n",
" subsample = [0.9, 0.95, 1]\n",
" param_grid['subsample'] = subsample\n",
" bool_s = True\n",
" else:\n",
" bool_s = False\n",
"\n",
" if best_gamma[i] == 0 or best_gamma[i] == 0.3:\n",
" if best_gamma[i] == 0:\n",
" gamma = [0, 0.03, 0.05]\n",
" else:\n",
" gamma = [0.25, 0.3, 0.4]\n",
" param_grid['gamma'] = gamma\n",
" bool_g = True\n",
" else:\n",
" bool_g = False\n",
"\n",
" if best_min_child_weight[i] == 1 or best_min_child_weight[i] == 5:\n",
" if best_min_child_weight[i] == 1:\n",
" min_child_weight = [0, 1, 2]\n",
" else:\n",
" min_child_weight = [5, 6, 7, 8] \n",
" param_grid['min_child_weight'] = min_child_weight\n",
" bool_mcw = True\n",
" else:\n",
" bool_mcw = False\n",
"\n",
" # Timed extended grid search.\n",
" start = timer()\n",
" #print(\"Starting the (expended) grid search...\")\n",
" kfold = StratifiedKFold(n_splits = num_folds, shuffle = True, random_state = 7)\n",
" grid_search = GridSearchCV(xgb_model, param_grid, scoring = \"neg_log_loss\", n_jobs = -1, cv = kfold, verbose = 1)\n",
" grid_result = grid_search.fit(X, Y)\n",
" #print(\"The (expended) grid search is over!\")\n",
" end = timer()\n",
" time_grid_xgb[i] += (end - start)\n",
"\n",
" # Summarizes results.\n",
" print(\"Best: %f using %s \\n\" % (grid_result.best_score_, grid_result.best_params_))\n",
"\n",
" # Replaces the old parameters on the extreme of the grid by the newly found values.\n",
" if bool_lr == True:\n",
" best_learning_rate[i] = grid_result.best_params_['learning_rate']\n",
" if bool_md == True:\n",
" best_max_depth[i] = grid_result.best_params_['max_depth']\n",
" if bool_s == True:\n",
" best_subsample[i] = grid_result.best_params_['subsample']\n",
" if bool_g == True:\n",
" best_gamma[i] = grid_result.best_params_['gamma']\n",
" if bool_mcw == True:\n",
" best_min_child_weight[i] = grid_result.best_params_['min_child_weight']\n",
"\n",
" print(\"%.2f %%\\n\" % ((i + 1) / num_folds * 100))\n",
"\n",
" print(\"Done with the grid expansion for XGBoost!\\n\") \n",
" \n",
" parameters_xgboost = [best_learning_rate, best_max_depth, best_subsample, best_gamma, best_min_child_weight]\n",
" \n",
" return parameters_xgboost \n",
"\n",
"\n",
"\n",
"# Checks whether the grid extension is needed for Random Forest and performs it if it's the case.\n",
"# In any case, also groups all the parameters into one 2D array to avoid having too\n",
"# many parameters going around.\n",
"def grid_extension_rf(folder_name, num_folds, missing, best_max_features, best_min_samples_leaf,\n",
" best_max_depth, best_min_samples_split): \n",
" from numpy import genfromtxt\n",
" from sklearn.ensemble import RandomForestClassifier\n",
" from sklearn.model_selection import GridSearchCV\n",
" from sklearn.model_selection import StratifiedKFold\n",
" from sklearn.metrics import accuracy_score\n",
" from sklearn.preprocessing import Imputer\n",
" \n",
" need = False # Initializing the boolean that'll be use to know if the extension is needed. \n",
" \n",
" # Checks whether the grid extension is needed.\n",
" if (best_min_samples_leaf.count(1) >= num_folds / 2 or best_min_samples_leaf.count(70) >= num_folds / 2 \n",
" or best_max_depth.count(10) >= num_folds / 2 or best_min_samples_split.count(1) >= num_folds / 2 \n",
" or best_min_samples_split.count(10) >= num_folds / 2):\n",
" need = True\n",
" \n",
" # If the extension is needed...\n",
" if need == True:\n",
" #print(\"\\nStarting the grid expansion...\") \n",
"\n",
" imputer = Imputer(missing_values = missing)\n",
" # For each fold, loads the training dataset in and perform the grid search on it.\n",
" for i in range(0, num_folds):\n",
" # Loads the dataset (training fold).\n",
" #print(\"Loading training dataset...\") \n",
" dataset = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_train.data', delimiter=\",\")\n",
" #print(\"Training dataset was loaded in!\")\n",
"\n",
" # Splits the dataset into the data and the labels.\n",
" #print(\"Splitting the dataset into data and labels...\")\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" Y = dataset[:, len(dataset[0]) - 1]\n",
" X = imputer.fit_transform(X, Y)\n",
" #print(\"The data and labels from the dataset have been split!\")\n",
"\n",
" # Initializes the model with the best found parameter for each fold (individually).\n",
" # The parameters on the extreme of the grid will be added to the parameters' dictionary.\n",
" rf_model = RandomForestClassifier(n_estimators = 200, max_features = best_max_features[i],\n",
" min_samples_leaf = best_min_samples_leaf[i], max_depth = best_max_depth[i],\n",
" min_samples_split = best_min_samples_split[i])\n",
" \n",
" param_grid = dict()\n",
" \n",
" if best_min_samples_leaf[i] == 1 or best_min_samples_leaf[i] == 70:\n",
" if best_min_samples_leaf[i] == 1:\n",
" min_samples_leaf = [1, 5, 10, 15]\n",
" else:\n",
" min_samples_leaf = [60, 70, 80]\n",
" param_grid['min_samples_leaf'] = min_samples_leaf\n",
" bool_msl = True\n",
" else:\n",
" bool_msl = False\n",
" \n",
" if best_max_depth[i] == 10:\n",
" max_depth = [9, 10, 15, 20]\n",
" param_grid['max_depth'] = max_depth\n",
" bool_md = True\n",
" else:\n",
" bool_md = False\n",
" \n",
" if best_min_samples_split[i] == 1 or best_min_samples_split[i] == 10:\n",
" if best_min_samples_split[i] == 1:\n",
" min_samples_split = [1, 2, 3, 4]\n",
" else:\n",
" min_samples_split = [9, 10, 11, 15]\n",
" param_grid['min_samples_split'] = min_samples_split\n",
" bool_mss = True\n",
" else:\n",
" bool_mss = False\n",
"\n",
" # Timed extended grid search.\n",
" start = timer()\n",
" #print(\"Starting the (expended) grid search...\")\n",
" kfold = StratifiedKFold(n_splits = num_folds, shuffle = True, random_state = 7)\n",
" grid_search = GridSearchCV(rf_model, param_grid, scoring = \"neg_log_loss\", n_jobs = -1, cv = kfold, verbose = 1)\n",
" grid_result = grid_search.fit(X, Y)\n",
" #print(\"The (expended) grid search is over!\")\n",
" end = timer()\n",
" time_grid_rf[i] += (end - start)\n",
"\n",
" # Summarizes results.\n",
" print(\"Best: %f using %s \\n\" % (grid_result.best_score_, grid_result.best_params_))\n",
"\n",
" # Replaces the old parameters on the extreme of the grid by the newly found values.\n",
" if bool_msl == True:\n",
" best_min_samples_leaf[i] = grid_result.best_params_['min_samples_leaf'] \n",
" if bool_md == True:\n",
" best_max_depth[i] = grid_result.best_params_['max_depth']\n",
" if bool_mss == True:\n",
" best_min_samples_split[i] = grid_result.best_params_['min_samples_split']\n",
"\n",
" print(\"%.2f %%\\n\" % ((i + 1) / num_folds * 100))\n",
"\n",
" print(\"Done with the grid expansion for Random Forests!\\n\") \n",
" \n",
" parameters_rf = [best_max_features, best_min_samples_leaf, best_max_depth, best_min_samples_split] \n",
" \n",
" return parameters_rf\n",
"\n",
"\n",
"\n",
"# Checks whether the grid extension is needed for XGBoost and performs it if it's the case.\n",
"# In any case, also groups all the parameters into one 2D array to avoid having too\n",
"# many parameters going around.\n",
"def grid_extension_gb(folder_name, num_folds, missing, best_learning_rate, \n",
" best_max_depth, best_subsample, best_max_features, \n",
" best_min_samples_split): \n",
" from numpy import genfromtxt\n",
" from sklearn.ensemble import GradientBoostingClassifier\n",
" from sklearn.model_selection import GridSearchCV\n",
" from sklearn.model_selection import StratifiedKFold\n",
" from sklearn.metrics import accuracy_score\n",
" from sklearn.preprocessing import Imputer\n",
" \n",
" need = False # Initializing the boolean that'll be use to know if the extension is needed. \n",
" \n",
" # Checks whether the grid extension is needed.\n",
" if (best_learning_rate.count(0.05) >= num_folds / 2 or best_learning_rate.count(0.2) >= num_folds / 2 \n",
" or best_max_depth.count(3) >= num_folds / 2 or best_max_depth.count(8) >= num_folds / 2 \n",
" or best_subsample.count(0.5) >= num_folds / 2 or best_subsample.count(1) >= num_folds / 2 \n",
" or best_min_samples_split.count(1) >= num_folds / 2 or best_min_samples_split.count(10) >= num_folds / 2): \n",
" need = True\n",
" \n",
" # If the extension is needed...\n",
" if need == True:\n",
" #print(\"\\nStarting the grid expansion...\") \n",
"\n",
" imputer = Imputer(missing_values = missing)\n",
"\n",
" # For each fold, load the training dataset in and perform the grid search on it.\n",
" for i in range(0, num_folds):\n",
" # Load the dataset (training fold)\n",
" #print(\"Loading training dataset...\") \n",
" dataset = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_train.data', delimiter=\",\")\n",
" #print(\"Training dataset was loaded in!\")\n",
"\n",
" # Split the dataset into the data and the labels\n",
" #print(\"Splitting the dataset into data and labels...\")\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" Y = dataset[:, len(dataset[0]) - 1]\n",
" X = imputer.fit_transform(X, Y)\n",
" #print(\"The data and labels from the dataset have been split!\")\n",
"\n",
" # Initializes the model with the best found parameter for each fold (individually).\n",
" # The parameters on the extreme of the grid will be added to the parameters' dictionary.\n",
" gb_model = GradientBoostingClassifier(n_estimators = 200, learning_rate = best_learning_rate[i], \n",
" max_depth = best_max_depth[i], subsample = best_subsample[i], \n",
" max_features = best_max_features[i], min_samples_split = best_min_samples_split[i])\n",
"\n",
" param_grid = dict()\n",
"\n",
" if best_learning_rate[i] == 0.05 or best_learning_rate[i] == 0.2:\n",
" if best_learning_rate[i] == 0.05:\n",
" learning_rate = [0.01, 0.03, 0.05, 0.07]\n",
" else:\n",
" learning_rate = [0.15, 0.2, 0.25, 0.3]\n",
" param_grid['learning_rate'] = learning_rate\n",
" bool_lr = True\n",
" else:\n",
" bool_lr = False\n",
"\n",
" if best_max_depth[i] == 3 or best_max_depth[i] == 8:\n",
" if best_max_depth[i] == 3:\n",
" max_depth = [1, 2, 3, 4]\n",
" else:\n",
" max_depth = [7, 8, 9, 10]\n",
" param_grid['max_depth'] = max_depth\n",
" bool_md = True\n",
" else:\n",
" bool_md = False\n",
"\n",
" if best_subsample[i] == 0.5 or best_subsample[i] == 1:\n",
" if best_subsample[i] == 0.5:\n",
" subsample = [0.4, 0.5, 0.6]\n",
" else:\n",
" subsample = [0.9, 0.95, 1]\n",
" param_grid['subsample'] = subsample\n",
" bool_s = True\n",
" else:\n",
" bool_s = False\n",
"\n",
" if best_min_samples_split[i] == 1 or best_min_samples_split[i] == 10:\n",
" if best_min_samples_split[i] == 1:\n",
" min_samples_split = [1, 2, 3, 4]\n",
" else:\n",
" min_samples_split = [9, 10, 11, 15]\n",
" param_grid['min_samples_split'] = min_samples_split\n",
" bool_mss = True\n",
" else:\n",
" bool_mss = False\n",
"\n",
" # Timed extended grid search.\n",
" start = timer()\n",
" #print(\"Starting the (expended) grid search...\")\n",
" kfold = StratifiedKFold(n_splits = num_folds, shuffle = True, random_state = 7)\n",
" grid_search = GridSearchCV(gb_model, param_grid, scoring = \"neg_log_loss\", n_jobs = -1, cv = kfold, verbose = 1)\n",
" grid_result = grid_search.fit(X, Y)\n",
" #print(\"The (expended) grid search is over!\")\n",
" end = timer()\n",
" time_grid_gb[i] += (end - start)\n",
"\n",
" # Summarizes results.\n",
" print(\"Best: %f using %s \\n\" % (grid_result.best_score_, grid_result.best_params_))\n",
"\n",
" # Replaces the old parameters on the extreme of the grid by the newly found values.\n",
" if bool_lr == True:\n",
" best_learning_rate[i] = grid_result.best_params_['learning_rate']\n",
" if bool_md == True:\n",
" best_max_depth[i] = grid_result.best_params_['max_depth']\n",
" if bool_s == True:\n",
" best_subsample[i] = grid_result.best_params_['subsample']\n",
" if bool_mss == True:\n",
" best_min_samples_split[i] = grid_result.best_params_['min_samples_split']\n",
"\n",
" print(\"%.2f %%\\n\" % ((i + 1) / num_folds * 100))\n",
"\n",
" print(\"Done with the grid expansion for Gradient Boosting!\\n\") \n",
" \n",
" parameters_gb = [best_learning_rate, best_max_depth, best_subsample, best_max_features, best_min_samples_split]\n",
" \n",
" return parameters_gb \n",
"\n",
"\n",
"\n",
"# Trains all the models with the final parameters and tests them.\n",
"def train_and_test(folder_name, num_folds, missing, parameters_xgboost, parameters_rf, parameters_gb):\n",
" from numpy import genfromtxt\n",
" from xgboost import XGBClassifier\n",
" from sklearn.ensemble import RandomForestClassifier\n",
" from sklearn.ensemble import GradientBoostingClassifier\n",
" from sklearn.metrics import accuracy_score\n",
" from sklearn.preprocessing import Imputer\n",
" from sklearn.model_selection import GridSearchCV\n",
" from sklearn.model_selection import StratifiedKFold\n",
" \n",
" # Initializes the default models.\n",
" print(\"Initializing all the default models...\")\n",
" xgb_d_model = XGBClassifier(n_estimators = 200)\n",
" rf_d_model = RandomForestClassifier(n_estimators = 200)\n",
" gb_d_model = GradientBoostingClassifier(n_estimators = 200)\n",
" print(\"All the default models have been initialized!\")\n",
" \n",
" # Will store the accuracy for each fold. An average will then be computed.\n",
" xgb_d_results = []\n",
" xgb_results = []\n",
" rf_d_results = []\n",
" rf_results = []\n",
" gb_d_results = []\n",
" gb_results = []\n",
" \n",
" imputer = Imputer(missing_values = missing)\n",
" \n",
" depth_rf_d = []\n",
" depth_rf = []\n",
" \n",
" # For each fold, loads the training/testing dataset in and fits the models before testing.\n",
" for i in range(0, num_folds):\n",
" # Loads the dataset (training fold).\n",
" #print(\"Loading the training set...\")\n",
" dataset = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_train.data', delimiter=\",\")\n",
" #print(\"Training set was loaded in!\")\n",
" \n",
" # Split the dataset into the data and the labels\n",
" #print(\"Splitting the dataset into data and labels...\")\n",
" X = dataset[:, 0 : len(dataset[0]) - 1]\n",
" Y = dataset[:, len(dataset[0]) - 1] \n",
" X = imputer.fit_transform(X, Y)\n",
" #print(\"The data and labels from the dataset have been split!\")\n",
"\n",
" print(\"Training all the default models over the fold with the best parameters...\")\n",
" start = timer()\n",
" xgb_d_model.fit(X, Y)\n",
" end = timer()\n",
" time_fit_xgb_d.append(end - start) \n",
" \n",
" start = timer()\n",
" rf_d_model.fit(X, Y)\n",
" end = timer()\n",
" time_fit_rf_d.append(end - start)\n",
" \n",
" start = timer()\n",
" gb_d_model.fit(X, Y)\n",
" end = timer()\n",
" time_fit_gb_d.append(end - start)\n",
" print(\"All the default models have been trained!\")\n",
" \n",
" print(\"Initializing all the tuned models and training them over the fold with the best parameters...\")\n",
" xgb_model = XGBClassifier(n_estimators = 200, learning_rate = parameters_xgboost[0][i], \n",
" max_depth = parameters_xgboost[1][i], subsample = parameters_xgboost[2][i],\n",
" gamma = parameters_xgboost[3][i], min_child_weight = parameters_xgboost[4][i])\n",
" \n",
" rf_model = RandomForestClassifier(n_estimators = 200, max_features = parameters_rf[0][i], \n",
" min_samples_leaf = parameters_rf[1][i], max_depth = parameters_rf[2][i],\n",
" min_samples_split = parameters_rf[3][i])\n",
" \n",
" gb_model = GradientBoostingClassifier(n_estimators = 200, learning_rate = parameters_gb[0][i], \n",
" max_depth = parameters_gb[1][i], subsample = parameters_gb[2][i],\n",
" max_features = parameters_gb[3][i], min_samples_split = parameters_gb[4][i])\n",
" \n",
" start = timer()\n",
" xgb_model.fit(X, Y)\n",
" end = timer()\n",
" time_fit_xgb.append(end - start)\n",
" \n",
" start = timer()\n",
" rf_model.fit(X, Y)\n",
" end = timer()\n",
" time_fit_rf.append(end - start)\n",
" \n",
" start = timer()\n",
" gb_model.fit(X, Y)\n",
" end = timer()\n",
" time_fit_gb.append(end - start)\n",
" print(\"All the tuned models have been initialized and trained!\")\n",
" \n",
" # Loads the dataset (testing fold).\n",
" #print(\"Loading testing dataset...\") \n",
" testing = genfromtxt(folder_name + '/' + 'fold' + str(i) + '_test.data', delimiter=\",\")\n",
" #print(\"Testing dataset was loaded in!\")\n",
"\n",
" # Splits the testing fold into the data and the labels.\n",
" #print(\"Splitting the testing set into data and labels...\")\n",
" X_test = testing[:, 0 : len(dataset[0]) - 1]\n",
" Y_test = testing[:, len(dataset[0]) - 1]\n",
" X_test = imputer.fit_transform(X_test, Y_test);\n",
" #print(\"The data and labels from the testing set have been split!\\n\")\n",
" \n",
" # Makes predictions for test data.\n",
" xgb_d_Y_pred = xgb_d_model.predict(X_test)\n",
" xgb_d_predictions = [round(value) for value in xgb_d_Y_pred]\n",
" xgb_Y_pred = xgb_model.predict(X_test)\n",
" xgb_predictions = [round(value) for value in xgb_Y_pred]\n",
" rf_d_Y_pred = rf_d_model.predict(X_test)\n",
" rf_d_predictions = [round(value) for value in rf_d_Y_pred]\n",
" rf_Y_pred = rf_model.predict(X_test)\n",
" rf_predictions = [round(value) for value in rf_Y_pred]\n",
" gb_d_Y_pred = gb_d_model.predict(X_test)\n",
" gb_d_predictions = [round(value) for value in gb_d_Y_pred]\n",
" gb_Y_pred = gb_model.predict(X_test)\n",
" gb_predictions = [round(value) for value in gb_Y_pred]\n",
"\n",
" # Evaluates predictions.\n",
" xgb_d_accuracy = accuracy_score(Y_test, xgb_d_predictions)\n",
" xgb_accuracy = accuracy_score(Y_test, xgb_predictions)\n",
" rf_d_accuracy = accuracy_score(Y_test, rf_d_predictions)\n",
" rf_accuracy = accuracy_score(Y_test, rf_predictions)\n",
" gb_d_accuracy = accuracy_score(Y_test, gb_d_predictions)\n",
" gb_accuracy = accuracy_score(Y_test, gb_predictions)\n",
"\n",
" # Saves the predictions results.\n",
" xgb_d_results.append(xgb_d_accuracy)\n",
" xgb_results.append(xgb_accuracy)\n",
" rf_d_results.append(rf_d_accuracy)\n",
" rf_results.append(rf_accuracy)\n",
" gb_d_results.append(gb_d_accuracy)\n",
" gb_results.append(gb_accuracy)\n",
" \n",
" #print(\"Default XGBoost accuracy %.2f%%\" % (xgb_d_accuracy * 100.0))\n",
" #print(\"XGBoost accuracy: %.2f%%\" % (xgb_accuracy * 100.0))\n",
" #print(\"Default Random Forests accuracy: %.2f%%\" % (rf_d_accuracy * 100.0))\n",
" #print(\"Random Forests accuracy: %.2f%%\" % (rf_accuracy * 100.0))\n",
" #print(\"Default Gradient Boosting accuracy: %.2f%%\" % (gb_d_accuracy * 100.0))\n",
" #print(\"Gradient Boosting accuracy: %.2f%% \\n\" % (gb_accuracy * 100.0))\n",
"\n",
" print(\"%.2f %%\\n\" % ((i + 1) / num_folds * 100))\n",
" \n",
" # Saving the depth of the trees for random forests. \n",
" tmp = [estimator.tree_.max_depth for estimator in rf_d_model.estimators_]\n",
" depth_rf_d.append(sum(tmp) / len(tmp)) \n",
" tmp = [estimator.tree_.max_depth for estimator in rf_model.estimators_]\n",
" depth_rf.append(sum(tmp) / len(tmp))\n",
" \n",
" return xgb_d_results, xgb_results, rf_d_results, rf_results, gb_d_results, gb_results, depth_rf_d, depth_rf\n",
"\n",
"\n",
"\n",
"# Finds the most frequent parameters of a classifier out of its parameters array. \n",
"def find_most_frequent_parameters(parameters):\n",
" n_parameters = len(parameters) # Number of different parameters\n",
" most_frequent_parameters = [] # 1D array containing the most frequent parameters.\n",
" \n",
" for i in range(0, n_parameters):\n",
" for j in range(0, len(parameters[i])):\n",
" if parameters[i][j] == None:\n",
" parameters[i][j] = -1\n",
" \n",
" (values, counts) = np.unique(parameters[i], return_counts = True)\n",
" ind = np.argmax(counts)\n",
" most_frequent_parameters.append(values[ind]) # Stores the most frequent value for parameter i\n",
" \n",
" if most_frequent_parameters[i] == -1:\n",
" most_frequent_parameters[i] = None\n",
" \n",
" return most_frequent_parameters\n",
"\n",
"\n",
" \n",
"# Prints the final accuracy results.\n",
"def print_results(num_folds, xgb_d_results, xgb_results, rf_d_results, rf_results, gb_d_results, gb_results):\n",
" print(\"\\t\\t XGB d.\\t\\t XGB t.\\t\\t RF d.\\t\\t RF t.\\t\\t GB d.\\t\\t GB t.\")\n",
" for i in range(0, num_folds):\n",
" print(\"Fold \", i + 1, \"\\t %.2f%%\" % (xgb_d_results[i] * 100.0), \"\\t %.2f%%\" % (xgb_results[i] * 100.0), \n",
" \"\\t %.2f%%\" % (rf_d_results[i] * 100), \"\\t %.2f%%\" % (rf_results[i] * 100), \n",
" \"\\t %.2f%%\" % (gb_d_results[i] * 100), \"\\t %.2f%%\" % (gb_results[i] * 100))\n",
" print(\"Average\", \"\\t %.2f%%\" % (sum(xgb_d_results) / float(len(xgb_d_results)) * 100.0), \n",
" \"\\t %.2f%%\" % (sum(xgb_results) / float(len(xgb_results)) * 100.0), \n",
" \"\\t %.2f%%\" % (sum(rf_d_results) / float(len(rf_d_results)) * 100.0), \n",
" \"\\t %.2f%%\" % (sum(rf_results) / float(len(rf_results)) * 100.0), \n",
" \"\\t %.2f%%\" % (sum(gb_d_results) / float(len(gb_d_results)) * 100.0), \n",
" \"\\t %.2f%%\" % (sum(gb_results) / float(len(gb_results)) * 100.0))\n",
" \n",
" print(\"Std dev\", \"\\t %.2f\" % (np.std(xgb_d_results)), \"\\t\\t %.2f\" % (np.std(xgb_results)),\n",
" \"\\t\\t %.2f\" % (np.std(rf_d_results)), \"\\t\\t %.2f\" % (np.std(rf_results)), \"\\t\\t %.2f\" % (np.std(gb_d_results)),\n",
" \"\\t\\t %.2f\" % (np.std(gb_results))) \n",
" print(\"\\n\")\n",
" print(\"\\n\")\n",
" return\n",
"\n",
"\n",
"\n",
"# Prints the time for finding the optimum parameters.\n",
"def print_timing(num_folds):\n",
" print(\"\\t\\tXGB d.\\t XGB t.\\t\\t RF d.\\t RF t.\\t\\t GB d.\\t GB t.\")\n",
" for i in range(0, num_folds):\n",
" print(\"Fold \", i + 1, \"\\t %.2f\" % time_fit_xgb_d[i], \"\\t %.2f\" % time_grid_xgb[i], \"+ %.2f\" % time_fit_xgb[i], \n",
" \"\\t %.2f\" % time_fit_rf_d[i], \"\\t %.2f\" % time_grid_rf[i], \"+ %.2f\" % time_fit_rf[i],\n",
" \"\\t %.2f\" % time_fit_gb_d[i], \"\\t %.2f\" % time_grid_gb[i], \"+ %.2f\" % time_fit_gb[i]) \n",
" \n",
" print(\"Average\", \"\\t %.2f\" % (sum(time_fit_xgb_d) / float(len(time_fit_xgb_d))), \n",
" \"\\t %.2f\" % (sum(time_grid_xgb) / float(len(time_grid_xgb))), \"+ %.2f\" % (sum(time_fit_xgb) / float(len(time_fit_xgb))),\n",
" \"\\t %.2f\" % (sum(time_fit_rf_d) / float(len(time_fit_rf_d))), \n",
" \"\\t %.2f\" % (sum(time_grid_rf) / float(len(time_grid_rf))), \"+ %.2f\" % (sum(time_fit_rf) / float(len(time_fit_rf))),\n",
" \"\\t %.2f\" % (sum(time_fit_gb_d) / float(len(time_fit_gb_d))), \n",
" \"\\t %.2f\" % (sum(time_grid_gb) / float(len(time_grid_gb))), \"+ %.2f\" % (sum(time_fit_gb) / float(len(time_fit_gb)))) \n",
" print(\"\\n\")\n",
" print(\"\\n\")\n",
" return\n",
"\n",
"\n",
"\n",
"# Prints the average depth of each model. \n",
"def print_depth(parameters_xgboost, parameters_gb, depth_rf_d, depth_rf):\n",
" print(\"Depth of default XGBoost: \", 3) # Default value.\n",
" print(\"Depth of tuned XGBoost: \", sum(parameters_xgboost[1]) / len(parameters_xgboost[1])) # Average max_depth value.\n",
" print(\"Depth of default Random Forest: \", sum(depth_rf_d) / len(depth_rf_d)) # Since default value is \"None\", mean of all the depths.\n",
" print(\"Depth of tuned Random Forest: \", sum(depth_rf) / len(depth_rf)) # Average value between the \"None\" and the \"fixed\" depths. \n",
" print(\"Depth of default Gradient Boosting:\", 3) # Default value.\n",
" print(\"Depth of tuned Gradient Boosting: \", sum(parameters_gb[1]) / len(parameters_gb[1])) # Average max_depth value.\n",
" print(\"\\n\")\n",
" print(\"\\n\")\n",
" return\n",
" \n",
" \n",
" \n",
"# Prints the mode of the parameters.\n",
"def print_mode(parameters_xgboost, parameters_rf, parameters_gb):\n",
" # XGBoost\n",
" most_frequent_xgboost = find_most_frequent_parameters(parameters_xgboost)\n",
" print(\"XGBoost most frequent parameters\")\n",
" print(\"Learning rate: \", most_frequent_xgboost[0])\n",
" print(\"Depth: \", most_frequent_xgboost[1])\n",
" print(\"Subsample: \", most_frequent_xgboost[2])\n",
" print(\"Gamma: \", most_frequent_xgboost[3])\n",
" print(\"Min child weight: \", most_frequent_xgboost[4])\n",
" \n",
" # Random Forests\n",
" most_frequent_rf = find_most_frequent_parameters(parameters_rf) \n",
" print(\"\\n\")\n",
" print(\"Random Forest most frequent parameters\")\n",
" print(\"Max features: \", most_frequent_rf[0])\n",
" print(\"Min samples leaf: \", most_frequent_rf[1])\n",
" print(\"Max depth: \", most_frequent_rf[2])\n",
" print(\"Min samples split: \", most_frequent_rf[3]) \n",
" \n",
" # Gradient Boosting\n",
" most_frequent_gb = find_most_frequent_parameters(parameters_gb)\n",
" print(\"\\n\")\n",
" print(\"Gradient Boosting most frequent parameters\")\n",
" print(\"Learning rate: \", most_frequent_gb[0])\n",
" print(\"Depth: \", most_frequent_gb[1])\n",
" print(\"Subsample: \", most_frequent_gb[2])\n",
" print(\"Max features: \", most_frequent_gb[3])\n",
" print(\"Min samples split: \", most_frequent_gb[4])\n",
" \n",
" return \n",
" \n",
" \n",
" \n",
"# Compare the three classifiers. \n",
"def compare_classifiers(folder_name, dataset_name, num_folds, missing):\n",
" # Initializing all the variables for the time measurements.\n",
" global time_grid_xgb, time_grid_rf, time_grid_gb\n",
" global time_fit_xgb_d, time_fit_xgb\n",
" global time_fit_rf_d, time_fit_rf\n",
" global time_fit_gb_d, time_fit_gb\n",
" time_grid_xgb = []\n",
" time_grid_rf = []\n",
" time_grid_gb = []\n",
" time_fit_xgb_d = []\n",
" time_fit_xgb = []\n",
" time_fit_rf_d = []\n",
" time_fit_rf = []\n",
" time_fit_gb_d = []\n",
" time_fit_gb = []\n",
" \n",
" # Splitting the dataset\n",
" splitting_dataset(folder_name, dataset_name, num_folds) \n",
" # Learning the parameters over the basic grid for XGBoost, RF and GB\n",
" parameters_xgboost, parameters_rf, parameters_gb = general_grid_search(folder_name, num_folds, missing) \n",
" # Training and testing with the best parameters of each fold for each classifier.\n",
" xgb_d_results, xgb_results, rf_d_results, rf_results, gb_d_results, gb_results, depth_rf_d, depth_rf = train_and_test(folder_name, num_folds, missing, parameters_xgboost, parameters_rf, parameters_gb)\n",
" # Printing stuff (accuracy, most frequent parameters, depth, std).\n",
" print_results(num_folds, xgb_d_results, xgb_results, rf_d_results, rf_results, gb_d_results, gb_results)\n",
" print_timing(num_folds)\n",
" print_depth(parameters_xgboost, parameters_gb, depth_rf_d, depth_rf)\n",
" print_mode(parameters_xgboost, parameters_rf, parameters_gb)\n",
"\n",
" return"
]
}
],
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"display_name": "Python [conda root]",
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"name": "conda-root-py"
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| mit |
google/referring-manipulation | third_party/blended_diffusion/CLIP/notebooks/Interacting_with_CLIP.ipynb | 1 | 3599403 | null | apache-2.0 |
JarronL/pynrc | dev_utils/test_speckle_bar.ipynb | 1 | 1277710 | null | mit |
jaidevd/inmantec_fdp | notebooks/day4/05_intro_keras.ipynb | 1 | 5932 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"os.environ['KERAS_BACKEND'] = \"theano\"\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation\n",
"from keras.optimizers import SGD, RMSprop\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.datasets import load_digits\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"import numpy as np\n",
"from utils import backprop_decision_boundary, backprop_make_classification, backprop_make_moons\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"plt.rc('figure', figsize=(8, 6))\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting started with the Keras Sequential model\n",
"\n",
"## The Sequential model is a linear stack of layers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# simulate data\n",
"X, Y = backprop_make_classification()\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y.argmax(1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Same neural network architecture as before, but now in keras:\n",
"### 1. Input layer has two neurons\n",
"### 2. Hidden layer has three neurons\n",
"### 3. Output layer has two neurons"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(3, input_dim=2)) # input layer is implicit\n",
"model.add(Activation('sigmoid'))\n",
"model.add(Dense(2)) # input dimensions are inferred\n",
"model.add(Activation('sigmoid'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compilation Step:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sgd = SGD(lr=0.1)\n",
"model.compile(optimizer=sgd, loss=\"binary_crossentropy\", metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model.fit(X, Y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# make dummy data\n",
"backprop_decision_boundary(model.predict, X, Y)\n",
"y_hat = model.predict(X)\n",
"print(\"Accuracy: \", accuracy_score(np.argmax(Y, axis=1), np.argmax(y_hat, axis=1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Q: What went wrong?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(3, input_dim=2)) # input layer is implicit\n",
"model.add(Activation('sigmoid'))\n",
"model.add(Dense(2)) # input dimensions are inferred\n",
"model.add(Activation('sigmoid'))\n",
"# Why design the NN again?\n",
"model.compile(optimizer=sgd, loss=\"binary_crossentropy\", metrics=['accuracy'])\n",
"model.fit(X, Y, epochs=10000, verbose=0)\n",
"backprop_decision_boundary(model.predict, X, Y)\n",
"y_hat = model.predict(X)\n",
"print(\"Accuracy: \", accuracy_score(np.argmax(Y, axis=1), np.argmax(y_hat, axis=1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Q: How do we reduce epochs?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(3, input_dim=2)) # input layer is implicit\n",
"model.add(Activation('sigmoid'))\n",
"model.add(Dense(2)) # input dimensions are inferred\n",
"model.add(Activation('sigmoid'))\n",
"sgd.lr = 0.4\n",
"model.compile(optimizer=sgd, loss=\"binary_crossentropy\", metrics=['accuracy'])\n",
"model.fit(X, Y, epochs=1000, verbose=0)\n",
"backprop_decision_boundary(model.predict, X, Y)\n",
"y_hat = model.predict(X)\n",
"print(\"Accuracy: \", accuracy_score(np.argmax(Y, axis=1), np.argmax(y_hat, axis=1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise: Make a neural network to classify MNIST data\n",
"## Hints: \n",
"### 1. Two hidden layers, first of size 128, second of size 63.\n",
"### 2. Use \"categorical_crossentropy\" loss function\n",
"### 3. Use the RMSprop optimizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"digits = load_digits()\n",
"X = digits.data\n",
"X /= 255\n",
"y = digits.target\n",
"y = OneHotEncoder().fit_transform(y.reshape(-1, 1)) # What is this?\n",
"y = y.todense()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# enter code here"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
sevenval/history-diagnostics | benchmark_get_windows.ipynb | 1 | 10837 | {
"metadata": {
"name": "",
"signature": "sha256:9912e70c71d78314e8430de20fc1c9f9ebd6cc5fddc6455abfba2940bc51aafa"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Benchmarking `searchsorted` (1) vs. `unique` (2) based get_windows\n",
"\n",
"tl;dr `unique`-based is much faster if there are a lot of empty windows (i.e. sparse data)\n"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Old version `searchsorted()`-based."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_windows_1(times, window_size, start=None, end=None):\n",
" t = times\n",
" if start is None:\n",
" start = times[0]\n",
" if end is None:\n",
" end = times[-1]\n",
" n_windows = 1 + int((end - start) / window_size)\n",
" windows = []\n",
" idx_last = 0\n",
" num_requests = 0\n",
" n_empty = 0\n",
" for i_window in range(0, n_windows):\n",
" idx_included = np.searchsorted(t[idx_last:] , start + (i_window + 1) * window_size, \"right\")\n",
" if idx_included == 0:\n",
" n_empty += 1\n",
" else:\n",
" windows.append(np.arange(idx_last, idx_last + idx_included))\n",
" idx_last += idx_included\n",
" num_requests += len(windows[-1])\n",
" assert num_requests == len(t), (num_requests, len(t))\n",
" assert n_windows == len(windows) + n_empty, (n_windows, len(windows), n_empty)\n",
"\n",
" return windows\n",
" \n",
"get_windows_1(np.concatenate((np.zeros(4), np.arange(8))), 0.1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"[array([0, 1, 2, 3, 4]),\n",
" array([5]),\n",
" array([6]),\n",
" array([7]),\n",
" array([8]),\n",
" array([9]),\n",
" array([10]),\n",
" array([11])]"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## New version `unique()`-based."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_windows_2(times, window_size, start=None, end_unused=None):\n",
" if start is None:\n",
" start = times[0]\n",
" window_idx = ((times - start) / window_size).astype(int)\n",
" #print(times)\n",
" #print(window_idx)\n",
" #print(window_size)\n",
" first_idx = np.unique(window_idx, return_index=True)[1]\n",
" # print(first_idx)\n",
" windows = []\n",
" for i, j in enumerate(first_idx):\n",
" if i < len(first_idx) - 1:\n",
" end = first_idx[i+1]\n",
" else:\n",
" end = len(window_idx)\n",
" #print(i, j, end, np.arange(j, end)\n",
" windows.append(np.arange(j, end))\n",
" return windows\n",
"\n",
"get_windows_2(np.concatenate((np.zeros(4), np.arange(8))), 0.1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"[array([0, 1, 2, 3, 4]),\n",
" array([5]),\n",
" array([6]),\n",
" array([7]),\n",
" array([8]),\n",
" array([9]),\n",
" array([10]),\n",
" array([11])]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data1 = np.concatenate((np.zeros(4), np.arange(8)))\n",
"data2 = np.random.rand(20)\n",
"data2.sort()\n",
"data3 = np.random.rand(1000)\n",
"data3.sort()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Verify that both algorithms give the same results"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r1 = get_windows_1(data3, 1e-3)\n",
"r2 = get_windows_2(data3, 1e-3)\n",
"print(\"same length\" if len(r1) == len(r2) else \"different length!\")\n",
"for i1, i2 in zip(r1, r2):\n",
" if len(i1) != len(i2) or (i1 != i2).any():\n",
" print(i1, i2)\n",
" break\n",
"else:\n",
" print(\"equal items\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"same length\n",
"equal items\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Benchmarking results"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for n, data in enumerate((data1, data2, data3), 1):\n",
" print(\"data #{}\".format(n))\n",
" for sz in (0.1, 0.01, 0.001, 1e-4):\n",
" print(\" window size: {}\".format(sz))\n",
" print(\" 1:\", end=\"\")\n",
" %timeit get_windows_1(data3, sz, 0, 7)\n",
" print(\" 2:\", end=\"\")\n",
" %timeit get_windows_2(data3, sz, 0, 7)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"data #1\n",
" window size: 0.1\n",
" 1:10000 loops, best of 3: 185 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:10000 loops, best of 3: 61.2 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.01\n",
" 1:1000 loops, best of 3: 1.84 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:1000 loops, best of 3: 283 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.001\n",
" 1:100 loops, best of 3: 17.4 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:1000 loops, best of 3: 1.59 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.0001\n",
" 1:10 loops, best of 3: 160 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:100 loops, best of 3: 2.5 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data #2\n",
" window size: 0.1\n",
" 1:10000 loops, best of 3: 196 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:10000 loops, best of 3: 63.6 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.01\n",
" 1:100 loops, best of 3: 1.9 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:1000 loops, best of 3: 283 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.001\n",
" 1:100 loops, best of 3: 18 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:1000 loops, best of 3: 1.61 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.0001\n",
" 1:10 loops, best of 3: 167 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:100 loops, best of 3: 2.38 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data #3\n",
" window size: 0.1\n",
" 1:10000 loops, best of 3: 196 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:10000 loops, best of 3: 62 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.01\n",
" 1:100 loops, best of 3: 1.87 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:1000 loops, best of 3: 279 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.001\n",
" 1:100 loops, best of 3: 18 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:1000 loops, best of 3: 1.6 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" window size: 0.0001\n",
" 1:10 loops, best of 3: 165 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2:100 loops, best of 3: 2.37 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 13
}
],
"metadata": {}
}
]
} | mit |
facaiy/book_notes | 健身/动作整理/硬拉/Untitled.ipynb | 2 | 761 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"硬拉知识整理\n",
"======="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 动作要领"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 生理学基础"
]
}
],
"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
}
| cc0-1.0 |
cgpotts/cs224u | setup.ipynb | 1 | 8424 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Course set-up"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"__author__ = \"Christopher Potts\"\n",
"__version__ = \"CS224u, Stanford, Spring 2022\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook covers the steps you'll need to take to get set up for [CS224u](http://web.stanford.edu/class/cs224u/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Contents\n",
"\n",
"1. [Anaconda](#Anaconda)\n",
"1. [The course Github repository](#The-course-Github-repository)\n",
"1. [Main data distribution](#Main-data-distribution)\n",
"1. [Additional installations](#Additional-installations)\n",
"1. [Jupyter notebooks](#Jupyter-notebooks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Anaconda\n",
"\n",
"We recommend installing [the free Anaconda Python distribution](https://www.anaconda.com/products/individual), which includes IPython, Numpy, Scipy, matplotlib, scikit-learn, NLTK, and many other useful packages. This is not required, but it's an easy way to get all these packages installed. Unless you're very comfortable with Python package management and like installing things, this is the option for you!\n",
"\n",
"Please be sure that you download the __Python 3__ version, which currently installs Python 3.9. __Our codebase is not compatible with Python 2__.\n",
"\n",
"One you have Anaconda installed, create a virtual environment for the course. In a terminal, run\n",
"\n",
"```conda create -n nlu python=3.9 anaconda```\n",
"\n",
"to create an environment called `nlu`.\n",
"\n",
"Then, to enter the environment, run\n",
"\n",
"```conda activate nlu```\n",
"\n",
"To leave it, you can just close the window, or run\n",
"\n",
"```conda deactivate```\n",
"\n",
"If your version of Anaconda is older than version 4.4 (see `conda --version`), then replace `conda` with `source` in the above (and consider upgrading your Anaconda!).\n",
"\n",
"[This page](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) has more detailed instructions on managing virtual environments with Anaconda."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The course Github repository\n",
"\n",
"The core materials for the course are on Github:\n",
"\n",
"https://github.com/cgpotts/cs224u\n",
"\n",
"We'll be working in this repository a lot, and it will receive updates throughout the quarter, as we add new materials and correct bugs.\n",
"\n",
"If you're new to git and Github, we recommend using [Github's Desktop Apps](https://desktop.github.com). Then you just have to clone our repository and sync your local copy with the official one when there are updates. \n",
"\n",
"If you are comfortable with git in the command line, you can type the following command to clone the course's Github repo:\n",
"\n",
"```git clone https://github.com/cgpotts/cs224u```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Main data distribution\n",
"\n",
"The datasets needed to run the course notebooks and complete the assignments are in the following zip archive:\n",
"\n",
"http://web.stanford.edu/class/cs224u/data/data.tgz\n",
"\n",
"We recommend that you download it, unzip it, and place it in the same directory as your local copy of this Github repository. If you decide to put it somewhere else, you'll need to adjust the paths given in the \"Set-up\" sections of essentially all the notebooks.\n",
"\n",
"We recommend you to check the `md5` checksum of the `data.tgz` after the download. The current version (as of March 25, 2022), the checksum is `5e4a4e4c6b1aca47d711e25cb306a3aa`. If you see the different checksum, then please report this to the teaching staff."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional installations\n",
"\n",
"Be sure to do these additional installations from [inside your virtual environment](#Anaconda) for the course! Before you proceed from here, perhaps run\n",
"\n",
"```conda activate nlu```\n",
"\n",
"to make sure you are in that environment.\n",
"\n",
"If you are running Anaconda, then you can simply run\n",
"\n",
"```pip install -r requirements.txt```\n",
"\n",
"from inside the course virtual environment to install the core additional packages.\n",
"\n",
"People who aren't using Anaconda should edit `requirements.txt` so that it installs all the prerequisites that come with Anaconda and then run the above `pip` command from inside the course virtual environment to install the core additional packages."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our most important and finicky installations relate to our deep learning code. The following will check that you have the desired versions of the core libraries ([PyTorch](https://pytorch.org/) and [Hugging Face](https://huggingface.co/) `transformers`):"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"assert torch.__version__ == '1.10.0',\\\n",
" f\"torch version is {torch.__version__}\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import transformers\n",
"\n",
"assert transformers.__version__ == '4.17.0',\\\n",
" f\"transformers version is {transformers.__version__}\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the above tests didn't pass, you *might* be okay, but it is probably best to change your versions inside the `nlu` virtual environment. These are fast-changing libraries and we can't ensure complete backward compatibility.\n",
"\n",
"If you have a [CUDA-enabled GPU](https://developer.nvidia.com/cuda-gpus), we recommend following the instructions posted here for installing PyTorch in a way that will let you take advantage of this:\n",
"\n",
"https://pytorch.org/get-started/locally/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jupyter notebooks\n",
"\n",
"The majority of the materials for this course are Jupyter notebooks, which allow you to work in a browser, mixing code and description. It's a powerful form of [literate programming](https://en.wikipedia.org/wiki/Literate_programming), and increasingly a standard for open science.\n",
"\n",
"To start a notebook server, navigate to the directory where you want to work and run\n",
"\n",
"```jupyter notebook --port 5656```\n",
"\n",
"The port specification is optional. \n",
"\n",
"This should launch a browser that takes you to a view of the directory you're in. You can then open notebooks for working and create new notebooks.\n",
"\n",
"A major advantage of working with Anaconda is that you can switch virtual environments from inside a notebook, via the __Kernel__ menu. If this isn't an option for you, then run this command while inside your virtual environment:\n",
"\n",
"```python -m ipykernel install --user --name nlu --display-name \"nlu\"```\n",
"\n",
"(If you named your environment something other than `nlu`, then change the `--name` and `--display-name` values.) \n",
"\n",
"[Additional discussion of Jupyter and kernels.](https://stackoverflow.com/a/44786736)\n",
"\n",
"For some tips on getting started with notebooks, see [our Jupyter tutorial](tutorial_jupyter_notebooks.ipynb)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
datactive/bigbang | examples/git-analysis/Git Loading Tutorials.ipynb | 1 | 134780 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading From Git\n",
"This will familiarize you with the different ways to access a GitRepo (or MultiGitRepo) object and how to use its data.\n",
"\n",
"\n",
"* Single Repo:\n",
" * remote `get_repo(\"https://github.com/sbenthall/bigbang.git\", in_type = \"remote\" )`\n",
" * local `get_repo(\"~/urap/bigbang/archives/sample_git_repos/bigbang\", in_type = \"local\" )`\n",
" * name `get_repo(\"bigbang\", in_type = \"name\")`\n",
"* Multiple Repos:\n",
" * With repo names: `get_multi_repo(repo_names=[\"bigbang\",\"django\"])`\n",
" * With repo objects: `get_multi_repo(repos=[{list of existing GitRepo objects}]`\n",
" * With Github Organization names `get_org_multirepo(\"glass-bead-labs\")`\n",
"\n",
"# Repo Locations\n",
"As of now, repos are clones into `archives/sample_git_repos/{repo_name}`. Their caches are stored at `archives/sample_git_repos/{repo_name}_backup.csv`.\n",
"\n",
"# Caches\n",
"Caches are stored at `archives/sample_git_repos/{repo_name}_backup.csv`. They are the dumped `.csv` files of a GitRepo object's `commit_data` attribute, which is a pandas dataframe of all commit information. We can initialize a GitRepo object by feeding the cache's Pandas dataframe into the GitRepo init function. However, the init function will need to do some processing before it can use the cache as its commit data. It needs to convert the `\"Touched File\"` attribute of the cache dataframe from unicode `\"[file1, file2, file3]\"` to an actual list `[\"file1\", \"file2\", \"file3\"]`. It will also need to convert the time index of the cache from string to datetime.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Single Repos\n",
"Here, we can load in three ways. We can use a github url, a local path to a repo, or the name of a repo. All of these return a `GitRepo` object.\n",
"\n",
"## Remote\n",
"A remote call to `get_repo` will extract the repo's name from its git url. Thus, `https://github.com/sbenthall/bigbang.git` will yield `bigbang` as its name. It will check if the repo already exists. If it doesn't it will send a shell command to clone the remote repository to `archives/sample_git_repos/{repo_name}`. It will then return `get_repo({name}, in_type=\"name\")`. Before returning, however, it will cache the GitRepo object at `archives/sample_git_repos/{repo_name}_backup.csv` to make loading faster the next time.\n",
"\n",
"## Local\n",
"A local call is the simplest. It will first extract the repo name from the filepath. Thus, `~/urap/bigbang/archives/sample_git_repos/bigbang` will yield `bigbang`. It will check to see if a git repo exists at the given address. If it does, it will initialize a GitPython object, which only needs a name and a filepath to a Git repo. Note that this option does not check or create a cache.\n",
"\n",
"## Name\n",
"This is the preferred and easiest way to load a git repository. It works under the assumptions above about where a git repo and its cache should be stored. It will check to see if a cache exists. If it does, then it will load a GitPython object using that cache.\n",
"\n",
"If a cache is not found, then the function constructs a filepath from the name, using the above rule about where repo locations. It will pass off the function to `get_repo(filepath, in_type=\"local\")`. Before returning the answer, it will cache the result."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Commit Message</th>\n",
" <th>Committer Email</th>\n",
" <th>Committer Name</th>\n",
" <th>HEXSHA</th>\n",
" <th>Parent Commit</th>\n",
" <th>Time</th>\n",
" <th>Touched File</th>\n",
" <th>Person-ID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 2015-04-13 22:49:33</td>\n",
" <td> Merge pull request #195 from jesscxu/master\\n\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> e6f985d15ff4736a08e2112b6c7ff0c0d0836a75</td>\n",
" <td> [02d30c7ba4b02e899c4f098531812ca390983c0b, 5b5...</td>\n",
" <td>2015-04-13 22:49:33</td>\n",
" <td> [examples/viz/git/glass.json, examples/viz/git...</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2015-04-13 22:44:21</td>\n",
" <td> Adding d3 visualization of GitDiff.ipynb graph\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Jessica Xu</td>\n",
" <td> 5b54cc96d652a07b12b5c31d4f5ad5269e1aec37</td>\n",
" <td> [02d30c7ba4b02e899c4f098531812ca390983c0b]</td>\n",
" <td>2015-04-13 22:44:21</td>\n",
" <td> [examples/viz/git/glass.json, examples/viz/git...</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 2015-04-10 21:59:33</td>\n",
" <td> Merge pull request #194 from vsporeddy/master\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 02d30c7ba4b02e899c4f098531812ca390983c0b</td>\n",
" <td> [3723718c356155a8c2c2104e813d61263a1f23c7, 2ec...</td>\n",
" <td>2015-04-10 21:59:33</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 2015-04-10 18:19:22</td>\n",
" <td> Changed to directed graph</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 2ec31ee60878a08e5738dfa40245740e79dde97c</td>\n",
" <td> [f5316bf07da3d4d51ac3bc1875b24d10693daa02]</td>\n",
" <td>2015-04-10 18:19:22</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 2015-04-10 18:18:13</td>\n",
" <td> Merge pull request #3 from sbenthall/master\\n\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> f5316bf07da3d4d51ac3bc1875b24d10693daa02</td>\n",
" <td> [9aacab2a8eb5e7eabcb227caea5a82d99e5f8835, 372...</td>\n",
" <td>2015-04-10 18:18:13</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 2015-04-10 17:54:34</td>\n",
" <td> Merge pull request #192 from Aryan-Barbarian/m...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 3723718c356155a8c2c2104e813d61263a1f23c7</td>\n",
" <td> [a22c55ea0887bdff8f62e50d2abdca02f6fdbce6, ed6...</td>\n",
" <td>2015-04-10 17:54:34</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 2015-04-10 17:53:13</td>\n",
" <td> Merge pull request #193 from vsporeddy/master\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> a22c55ea0887bdff8f62e50d2abdca02f6fdbce6</td>\n",
" <td> [2b1f678c8ad75458b6a6b7484bed0ca72baee298, 9aa...</td>\n",
" <td>2015-04-10 17:53:13</td>\n",
" <td> [bigbang/get_dependencies.py, examples/File De...</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 2015-04-10 17:30:29</td>\n",
" <td> Fixed an issue where git repos with hyphens in...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> ed60740e26981e216542a258c0c5aa0afa50af95</td>\n",
" <td> [8dac7fc397738b057d7fbdcd2bea1552e6f88339]</td>\n",
" <td>2015-04-10 17:30:29</td>\n",
" <td> [bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 2015-04-10 16:55:36</td>\n",
" <td> Update File Dependency Network.ipynb</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 9aacab2a8eb5e7eabcb227caea5a82d99e5f8835</td>\n",
" <td> [465c3a275bc341e2dab9d43c0363c2a7fff59b15]</td>\n",
" <td>2015-04-10 16:55:36</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 2015-04-10 16:54:44</td>\n",
" <td> Create get_dependencies.py</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 465c3a275bc341e2dab9d43c0363c2a7fff59b15</td>\n",
" <td> [95e074b3e32017adf92e74a8fb19e471bf95f1ee]</td>\n",
" <td>2015-04-10 16:54:44</td>\n",
" <td> [bigbang/get_dependencies.py]</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 2015-04-10 16:53:57</td>\n",
" <td> Update requirements.txt</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 95e074b3e32017adf92e74a8fb19e471bf95f1ee</td>\n",
" <td> [68a5743f1cfe1241cb2608739418850b0b285360]</td>\n",
" <td>2015-04-10 16:53:57</td>\n",
" <td> [requirements.txt]</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 2015-04-10 16:53:31</td>\n",
" <td> Create File Dependency Network.ipynb</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 68a5743f1cfe1241cb2608739418850b0b285360</td>\n",
" <td> [be536710f94ec072e04431e7cd043ad24f5f1afb]</td>\n",
" <td>2015-04-10 16:53:31</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 2015-04-10 16:18:26</td>\n",
" <td> Merge pull request #2 from sbenthall/master\\n\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> be536710f94ec072e04431e7cd043ad24f5f1afb</td>\n",
" <td> [3287f61619d148ccb7deb77c4821812d1dc9cff0, 2b1...</td>\n",
" <td>2015-04-10 16:18:26</td>\n",
" <td> [.gitignore, README.md, bigbang/archive.py, bi...</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2015-04-10 11:06:56</td>\n",
" <td> Warning people how long git diffs will take\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 8dac7fc397738b057d7fbdcd2bea1552e6f88339</td>\n",
" <td> [0db0b375fcb90522f6a8700d87820e8fd91e5343]</td>\n",
" <td>2015-04-10 11:06:56</td>\n",
" <td> [bigbang/git_repo.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 2015-04-10 10:56:57</td>\n",
" <td> Fixed another bug with repo loading logic\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 0db0b375fcb90522f6a8700d87820e8fd91e5343</td>\n",
" <td> [a121a04579461d4a520fbe4113f0cd0b3a052911]</td>\n",
" <td>2015-04-10 10:56:57</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 2015-04-10 10:35:54</td>\n",
" <td> Fixed repo loading bug. The answer fetched was...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> a121a04579461d4a520fbe4113f0cd0b3a052911</td>\n",
" <td> [2b1f678c8ad75458b6a6b7484bed0ca72baee298]</td>\n",
" <td>2015-04-10 10:35:54</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 2015-04-06 23:30:06</td>\n",
" <td> Merge pull request #190 from dwins/setting_wit...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 2b1f678c8ad75458b6a6b7484bed0ca72baee298</td>\n",
" <td> [48dfc9b5472471b5a8768f56566c6246c63aa3fe, c03...</td>\n",
" <td>2015-04-06 23:30:06</td>\n",
" <td> [bigbang/archive.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 2015-04-06 23:21:00</td>\n",
" <td> Merge branch 'raj4-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 48dfc9b5472471b5a8768f56566c6246c63aa3fe</td>\n",
" <td> [ff0a46b3afac4995517d7dc0ad1281f457e818b4, bc5...</td>\n",
" <td>2015-04-06 23:21:00</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 2015-04-06 23:20:37</td>\n",
" <td> Merge branch 'master' of https://github.com/ra...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> bc5ccc1fe3034f939ef2f74789a949d2f3604694</td>\n",
" <td> [ff0a46b3afac4995517d7dc0ad1281f457e818b4, 039...</td>\n",
" <td>2015-04-06 23:20:37</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 2015-04-06 23:13:58</td>\n",
" <td> Merge branch 'cool9210-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> ff0a46b3afac4995517d7dc0ad1281f457e818b4</td>\n",
" <td> [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 505...</td>\n",
" <td>2015-04-06 23:13:58</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 2015-04-06 23:13:27</td>\n",
" <td> Merge branch 'master' of https://github.com/co...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 505689d8494bab11e69f0687364dbba2a461b532</td>\n",
" <td> [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 3fa...</td>\n",
" <td>2015-04-06 23:13:27</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 2015-04-03 21:41:36</td>\n",
" <td> Avoid SettingWithCopyWarning\\n\\nfixes #162\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> David Winslow</td>\n",
" <td> c03e3d20fae49a6d2f0458a4132af557b7ec355b</td>\n",
" <td> [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e]</td>\n",
" <td>2015-04-03 21:41:36</td>\n",
" <td> [bigbang/archive.py]</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 2015-04-02 23:45:44</td>\n",
" <td> committing twopeople\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> 3fa34b21dc5e7d6c7a7154fcda9473f4b0f18f93</td>\n",
" <td> [e57bd1d4a81466b73027808d1f55fb9b4c671072]</td>\n",
" <td>2015-04-02 23:45:44</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 2015-04-02 23:26:23</td>\n",
" <td> updated robustness notebook\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Raj Agrawal</td>\n",
" <td> 039df37b77929fe52b183dfbf436254b95a4742d</td>\n",
" <td> [a69e75b9e36afaf1a1b7af1f51ef00e9c3468095]</td>\n",
" <td>2015-04-02 23:26:23</td>\n",
" <td> [bigbang/twopeople.py, examples/Collaboration ...</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 2015-04-01 04:14:15</td>\n",
" <td> Merge branch 'dwins-email_character_sets'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e</td>\n",
" <td> [05d773f13331693d796a75daac2529b2efb8ccff, 561...</td>\n",
" <td>2015-04-01 04:14:15</td>\n",
" <td> [bigbang/mailman.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 2015-03-31 20:34:42</td>\n",
" <td> Consistently represent email data as Unicode\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> David Winslow</td>\n",
" <td> 56140670a9f627e226d449c17d29544be6f5598d</td>\n",
" <td> [05d773f13331693d796a75daac2529b2efb8ccff]</td>\n",
" <td>2015-03-31 20:34:42</td>\n",
" <td> [bigbang/mailman.py]</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 2015-03-31 04:50:46</td>\n",
" <td> changing type attribute to be keyed to string ...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 05d773f13331693d796a75daac2529b2efb8ccff</td>\n",
" <td> [3e1c1f07f1b0d4a55751405b65004bd2b469945f]</td>\n",
" <td>2015-03-31 04:50:46</td>\n",
" <td> [examples/Git Diffs.ipynb]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> 2015-03-30 01:08:56</td>\n",
" <td> Merge pull request #182 from Aryan-Barbarian/g...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 3e1c1f07f1b0d4a55751405b65004bd2b469945f</td>\n",
" <td> [11905640d44377fb0c007cd340ab780e408f2d10, a71...</td>\n",
" <td>2015-03-30 01:08:56</td>\n",
" <td> [.gitignore, README.md, bigbang/git_repo.py, b...</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> 2015-03-24 04:43:47</td>\n",
" <td> Added the option to override the cache and for...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> a713fad3a49cbb803cac33b01cfa3283fe20840f</td>\n",
" <td> [225b0ee0c3b4db0cda06155eacc1b7d945572306]</td>\n",
" <td>2015-03-24 04:43:47</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py, ...</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 2015-03-24 04:17:58</td>\n",
" <td> Fixed bugs relating to caching the data.\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 225b0ee0c3b4db0cda06155eacc1b7d945572306</td>\n",
" <td> [d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f]</td>\n",
" <td>2015-03-24 04:17:58</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py, ...</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 2015-03-24 03:55:41</td>\n",
" <td> Repo Loader wasn't importing pandas\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f</td>\n",
" <td> [c5919b8d0fc2482b172923e58e51dad54ff209f9]</td>\n",
" <td>2015-03-24 03:55:41</td>\n",
" <td> [bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> 2015-03-24 03:54:51</td>\n",
" <td> Repo Loader tries to cache now?\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> c5919b8d0fc2482b172923e58e51dad54ff209f9</td>\n",
" <td> [fa5688b0711d68ec0ffa436d7f31c73907c81e35]</td>\n",
" <td>2015-03-24 03:54:51</td>\n",
" <td> [bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 2015-03-24 03:40:36</td>\n",
" <td> Git Repo takes flags for initialization now. N...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> fa5688b0711d68ec0ffa436d7f31c73907c81e35</td>\n",
" <td> [c886ee31fbd48f17afc1b3158983591a17389dfd]</td>\n",
" <td>2015-03-24 03:40:36</td>\n",
" <td> [bigbang/git_repo.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> 2015-03-22 19:51:47</td>\n",
" <td> Fixed issues in the ipython notebooks regardin...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> c886ee31fbd48f17afc1b3158983591a17389dfd</td>\n",
" <td> [d5187fadf9a8529bfc57ac9bade890cd7167a20b]</td>\n",
" <td>2015-03-22 19:51:47</td>\n",
" <td> [examples/Committer Dominance.ipynb, examples/...</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> 2015-03-22 19:32:33</td>\n",
" <td> Moved git files into the main bigbang library....</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> d5187fadf9a8529bfc57ac9bade890cd7167a20b</td>\n",
" <td> [89de558656441f4f4e2ec16cc96d757c073d4772]</td>\n",
" <td>2015-03-22 19:32:33</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py, ...</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> 2015-03-17 21:26:03</td>\n",
" <td> Fixing the readme\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 89de558656441f4f4e2ec16cc96d757c073d4772</td>\n",
" <td> [befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d]</td>\n",
" <td>2015-03-17 21:26:03</td>\n",
" <td> [README.md]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> 2015-03-17 21:14:42</td>\n",
" <td> One more tweak to the README\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d</td>\n",
" <td> [d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501]</td>\n",
" <td>2015-03-17 21:14:42</td>\n",
" <td> [README.md]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> 2015-03-17 21:10:21</td>\n",
" <td> Updated README\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501</td>\n",
" <td> [b7c4d709b0a07972c90b336a0f7a667981416b7a]</td>\n",
" <td>2015-03-17 21:10:21</td>\n",
" <td> [README.md]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 2015-03-17 20:41:16</td>\n",
" <td> The repo loader can now correctly fetch files.\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> b7c4d709b0a07972c90b336a0f7a667981416b7a</td>\n",
" <td> [974c7a2e1765365dd40705e6ae7b41d9f984a118]</td>\n",
" <td>2015-03-17 20:41:16</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> 2015-03-17 20:27:58</td>\n",
" <td> Small bug with repo loader\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 974c7a2e1765365dd40705e6ae7b41d9f984a118</td>\n",
" <td> [598cf71c6697e4e346894bb58dfbeb30bda3c4aa]</td>\n",
" <td>2015-03-17 20:27:58</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> 2015-03-17 20:26:12</td>\n",
" <td> RepoLoader generates the sample git directory ...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 598cf71c6697e4e346894bb58dfbeb30bda3c4aa</td>\n",
" <td> [a0f02f7f9a401c79815df5f5f52ca483dd6c007b]</td>\n",
" <td>2015-03-17 20:26:12</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> 2015-03-17 20:25:37</td>\n",
" <td> Moved a lot of git repo loading functionality ...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> a0f02f7f9a401c79815df5f5f52ca483dd6c007b</td>\n",
" <td> [8c102702f168ba86a8bb81802fe61db70361dfb0]</td>\n",
" <td>2015-03-17 20:25:37</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> 2015-03-17 20:06:49</td>\n",
" <td> Very rough first draft of repo loader\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 8c102702f168ba86a8bb81802fe61db70361dfb0</td>\n",
" <td> [296dd9a35d2aa006b8f8e9c32852b073e961b3bd]</td>\n",
" <td>2015-03-17 20:06:49</td>\n",
" <td> [bin/collect_git.py, git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> 2015-03-17 19:14:08</td>\n",
" <td> collect git now imports from Repository Loader\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 296dd9a35d2aa006b8f8e9c32852b073e961b3bd</td>\n",
" <td> [0ff39bd05a7b4b792459b991a0f726422c7d2ef0]</td>\n",
" <td>2015-03-17 19:14:08</td>\n",
" <td> [bin/collect_git.py, git_data/GitRepo.py, git_...</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> 2015-03-17 18:53:17</td>\n",
" <td> Slight cleanup in collect git script\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 0ff39bd05a7b4b792459b991a0f726422c7d2ef0</td>\n",
" <td> [4f5104300b17035460a9f5e7819f8999da72e75b]</td>\n",
" <td>2015-03-17 18:53:17</td>\n",
" <td> [bin/collect_git.py]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> 2015-03-17 18:31:44</td>\n",
" <td> Merge remote-tracking branch 'upstream/master'...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 4f5104300b17035460a9f5e7819f8999da72e75b</td>\n",
" <td> [f54194242ea036274d788039e77b2619020434dd, 119...</td>\n",
" <td>2015-03-17 18:31:44</td>\n",
" <td> [bigbang/mailman.py, bigbang/twopeople.py, req...</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> 2015-03-17 00:03:27</td>\n",
" <td> Merge branch 'raj4-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 11905640d44377fb0c007cd340ab780e408f2d10</td>\n",
" <td> [00f13d97385763b699b52b562fc204d80149098b, 9f6...</td>\n",
" <td>2015-03-17 00:03:27</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> 2015-03-17 00:03:11</td>\n",
" <td> Merge branch 'master' of https://github.com/ra...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 9f6c74e01dbbdd14468befa8cde1de82d08d7935</td>\n",
" <td> [00f13d97385763b699b52b562fc204d80149098b, a69...</td>\n",
" <td>2015-03-17 00:03:11</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> 2015-03-16 23:56:02</td>\n",
" <td> functions to create df\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Raj Agrawal</td>\n",
" <td> a69e75b9e36afaf1a1b7af1f51ef00e9c3468095</td>\n",
" <td> [847720442d7cab223a6c83f0bd9db37ca28bdfbd]</td>\n",
" <td>2015-03-16 23:56:02</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 2015-03-14 20:18:01</td>\n",
" <td> fixing variable reference in data collection e...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 00f13d97385763b699b52b562fc204d80149098b</td>\n",
" <td> [701212ecb79f1b400c2e293d98ff582c750532d0]</td>\n",
" <td>2015-03-14 20:18:01</td>\n",
" <td> [bigbang/mailman.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> 2015-03-12 21:51:25</td>\n",
" <td> adding jsonschema as a pip requirement\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 701212ecb79f1b400c2e293d98ff582c750532d0</td>\n",
" <td> [aef98ed18e82a52ca4dfc593769f99f4618f8edb]</td>\n",
" <td>2015-03-12 21:51:25</td>\n",
" <td> [requirements.txt]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> 2015-03-10 20:33:56</td>\n",
" <td> git will now ignore the git_locals.json file, ...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> f54194242ea036274d788039e77b2619020434dd</td>\n",
" <td> [aef98ed18e82a52ca4dfc593769f99f4618f8edb]</td>\n",
" <td>2015-03-10 20:33:56</td>\n",
" <td> [.gitignore]</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td> 2015-03-10 00:06:20</td>\n",
" <td> Merge branch 'cool9210-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> aef98ed18e82a52ca4dfc593769f99f4618f8edb</td>\n",
" <td> [a87af8aed3e0e2fb964579b8a7144361d4c19d2f, e57...</td>\n",
" <td>2015-03-10 00:06:20</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td> 2015-03-10 00:01:08</td>\n",
" <td> Merge branch 'master' of https://github.com/co...</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> e57bd1d4a81466b73027808d1f55fb9b4c671072</td>\n",
" <td> [0547569578a496cf80d153ca9cf2d20849c1736c, 4ba...</td>\n",
" <td>2015-03-10 00:01:08</td>\n",
" <td> []</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td> 2015-03-09 23:52:56</td>\n",
" <td> This change is adding duration, reciprocity, a...</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> 0547569578a496cf80d153ca9cf2d20849c1736c</td>\n",
" <td> [a87af8aed3e0e2fb964579b8a7144361d4c19d2f]</td>\n",
" <td>2015-03-09 23:52:56</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td> 2015-03-09 23:40:02</td>\n",
" <td> Merge branch 'raj4-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> a87af8aed3e0e2fb964579b8a7144361d4c19d2f</td>\n",
" <td> [8c450a41c5446db94c0cff7151a8ef2297c43a07, 847...</td>\n",
" <td>2015-03-09 23:40:02</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td> 2015-03-09 23:31:02</td>\n",
" <td> first commit\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Raj Agrawal</td>\n",
" <td> 847720442d7cab223a6c83f0bd9db37ca28bdfbd</td>\n",
" <td> [8c450a41c5446db94c0cff7151a8ef2297c43a07]</td>\n",
" <td>2015-03-09 23:31:02</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td> 2015-03-09 23:20:54</td>\n",
" <td> Create twopeople.py</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> 4ba2d1df3cb06eec91795ff22489b5533690dcfa</td>\n",
" <td> [8c450a41c5446db94c0cff7151a8ef2297c43a07]</td>\n",
" <td>2015-03-09 23:20:54</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td> 2015-03-05 22:47:56</td>\n",
" <td> Merge branch 'vsporeddy'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 8c450a41c5446db94c0cff7151a8ef2297c43a07</td>\n",
" <td> [0b47f504de03817db97e0d3556c98f7c252bc0f9, fef...</td>\n",
" <td>2015-03-05 22:47:56</td>\n",
" <td> [examples/Git Diffs.ipynb]</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td> 2015-03-04 05:48:45</td>\n",
" <td> Update Git Diffs.ipynb\\n\\nAdded node colors an...</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> fefb82dbc2b827cafb47edea9678f43f2a411681</td>\n",
" <td> [0b47f504de03817db97e0d3556c98f7c252bc0f9]</td>\n",
" <td>2015-03-04 05:48:45</td>\n",
" <td> [examples/Git Diffs.ipynb]</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>372 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Commit Message \\\n",
"0 2015-04-13 22:49:33 Merge pull request #195 from jesscxu/master\\n\\... \n",
"1 2015-04-13 22:44:21 Adding d3 visualization of GitDiff.ipynb graph\\n \n",
"2 2015-04-10 21:59:33 Merge pull request #194 from vsporeddy/master\\... \n",
"3 2015-04-10 18:19:22 Changed to directed graph \n",
"4 2015-04-10 18:18:13 Merge pull request #3 from sbenthall/master\\n\\... \n",
"5 2015-04-10 17:54:34 Merge pull request #192 from Aryan-Barbarian/m... \n",
"6 2015-04-10 17:53:13 Merge pull request #193 from vsporeddy/master\\... \n",
"7 2015-04-10 17:30:29 Fixed an issue where git repos with hyphens in... \n",
"8 2015-04-10 16:55:36 Update File Dependency Network.ipynb \n",
"9 2015-04-10 16:54:44 Create get_dependencies.py \n",
"10 2015-04-10 16:53:57 Update requirements.txt \n",
"11 2015-04-10 16:53:31 Create File Dependency Network.ipynb \n",
"12 2015-04-10 16:18:26 Merge pull request #2 from sbenthall/master\\n\\... \n",
"13 2015-04-10 11:06:56 Warning people how long git diffs will take\\n \n",
"14 2015-04-10 10:56:57 Fixed another bug with repo loading logic\\n \n",
"15 2015-04-10 10:35:54 Fixed repo loading bug. The answer fetched was... \n",
"16 2015-04-06 23:30:06 Merge pull request #190 from dwins/setting_wit... \n",
"17 2015-04-06 23:21:00 Merge branch 'raj4-master'\\n \n",
"18 2015-04-06 23:20:37 Merge branch 'master' of https://github.com/ra... \n",
"19 2015-04-06 23:13:58 Merge branch 'cool9210-master'\\n \n",
"20 2015-04-06 23:13:27 Merge branch 'master' of https://github.com/co... \n",
"21 2015-04-03 21:41:36 Avoid SettingWithCopyWarning\\n\\nfixes #162\\n \n",
"22 2015-04-02 23:45:44 committing twopeople\\n \n",
"23 2015-04-02 23:26:23 updated robustness notebook\\n \n",
"24 2015-04-01 04:14:15 Merge branch 'dwins-email_character_sets'\\n \n",
"25 2015-03-31 20:34:42 Consistently represent email data as Unicode\\n \n",
"26 2015-03-31 04:50:46 changing type attribute to be keyed to string ... \n",
"27 2015-03-30 01:08:56 Merge pull request #182 from Aryan-Barbarian/g... \n",
"28 2015-03-24 04:43:47 Added the option to override the cache and for... \n",
"29 2015-03-24 04:17:58 Fixed bugs relating to caching the data.\\n \n",
"30 2015-03-24 03:55:41 Repo Loader wasn't importing pandas\\n \n",
"31 2015-03-24 03:54:51 Repo Loader tries to cache now?\\n \n",
"32 2015-03-24 03:40:36 Git Repo takes flags for initialization now. N... \n",
"33 2015-03-22 19:51:47 Fixed issues in the ipython notebooks regardin... \n",
"34 2015-03-22 19:32:33 Moved git files into the main bigbang library.... \n",
"35 2015-03-17 21:26:03 Fixing the readme\\n \n",
"36 2015-03-17 21:14:42 One more tweak to the README\\n \n",
"37 2015-03-17 21:10:21 Updated README\\n \n",
"38 2015-03-17 20:41:16 The repo loader can now correctly fetch files.\\n \n",
"39 2015-03-17 20:27:58 Small bug with repo loader\\n \n",
"40 2015-03-17 20:26:12 RepoLoader generates the sample git directory ... \n",
"41 2015-03-17 20:25:37 Moved a lot of git repo loading functionality ... \n",
"42 2015-03-17 20:06:49 Very rough first draft of repo loader\\n \n",
"43 2015-03-17 19:14:08 collect git now imports from Repository Loader\\n \n",
"44 2015-03-17 18:53:17 Slight cleanup in collect git script\\n \n",
"45 2015-03-17 18:31:44 Merge remote-tracking branch 'upstream/master'... \n",
"46 2015-03-17 00:03:27 Merge branch 'raj4-master'\\n \n",
"47 2015-03-17 00:03:11 Merge branch 'master' of https://github.com/ra... \n",
"48 2015-03-16 23:56:02 functions to create df\\n \n",
"49 2015-03-14 20:18:01 fixing variable reference in data collection e... \n",
"50 2015-03-12 21:51:25 adding jsonschema as a pip requirement\\n \n",
"51 2015-03-10 20:33:56 git will now ignore the git_locals.json file, ... \n",
"52 2015-03-10 00:06:20 Merge branch 'cool9210-master'\\n \n",
"53 2015-03-10 00:01:08 Merge branch 'master' of https://github.com/co... \n",
"54 2015-03-09 23:52:56 This change is adding duration, reciprocity, a... \n",
"55 2015-03-09 23:40:02 Merge branch 'raj4-master'\\n \n",
"56 2015-03-09 23:31:02 first commit\\n \n",
"57 2015-03-09 23:20:54 Create twopeople.py \n",
"58 2015-03-05 22:47:56 Merge branch 'vsporeddy'\\n \n",
"59 2015-03-04 05:48:45 Update Git Diffs.ipynb\\n\\nAdded node colors an... \n",
" ... ... \n",
"\n",
" Committer Email Committer Name \\\n",
"0 [email protected] Sebastian Benthall \n",
"1 [email protected] Jessica Xu \n",
"2 [email protected] Sebastian Benthall \n",
"3 [email protected] Venkata Poreddy \n",
"4 [email protected] Venkata Poreddy \n",
"5 [email protected] Sebastian Benthall \n",
"6 [email protected] Sebastian Benthall \n",
"7 [email protected] Aryan Falahatpisheh \n",
"8 [email protected] Venkata Poreddy \n",
"9 [email protected] Venkata Poreddy \n",
"10 [email protected] Venkata Poreddy \n",
"11 [email protected] Venkata Poreddy \n",
"12 [email protected] Venkata Poreddy \n",
"13 [email protected] Aryan Falahatpisheh \n",
"14 [email protected] Aryan Falahatpisheh \n",
"15 [email protected] Aryan Falahatpisheh \n",
"16 [email protected] Sebastian Benthall \n",
"17 [email protected] sb \n",
"18 [email protected] sb \n",
"19 [email protected] sb \n",
"20 [email protected] sb \n",
"21 [email protected] David Winslow \n",
"22 [email protected] Ki Deuk Kim \n",
"23 [email protected] Raj Agrawal \n",
"24 [email protected] sb \n",
"25 [email protected] David Winslow \n",
"26 [email protected] sb \n",
"27 [email protected] Sebastian Benthall \n",
"28 [email protected] Aryan Falahatpisheh \n",
"29 [email protected] Aryan Falahatpisheh \n",
"30 [email protected] Aryan Falahatpisheh \n",
"31 [email protected] Aryan Falahatpisheh \n",
"32 [email protected] Aryan Falahatpisheh \n",
"33 [email protected] Aryan Falahatpisheh \n",
"34 [email protected] Aryan Falahatpisheh \n",
"35 [email protected] Aryan Falahatpisheh \n",
"36 [email protected] Aryan Falahatpisheh \n",
"37 [email protected] Aryan Falahatpisheh \n",
"38 [email protected] Aryan Falahatpisheh \n",
"39 [email protected] Aryan Falahatpisheh \n",
"40 [email protected] Aryan Falahatpisheh \n",
"41 [email protected] Aryan Falahatpisheh \n",
"42 [email protected] Aryan Falahatpisheh \n",
"43 [email protected] Aryan Falahatpisheh \n",
"44 [email protected] Aryan Falahatpisheh \n",
"45 [email protected] Aryan Falahatpisheh \n",
"46 [email protected] sb \n",
"47 [email protected] sb \n",
"48 [email protected] Raj Agrawal \n",
"49 [email protected] sb \n",
"50 [email protected] sb \n",
"51 [email protected] Aryan Falahatpisheh \n",
"52 [email protected] sb \n",
"53 [email protected] Ki Deuk Kim \n",
"54 [email protected] Ki Deuk Kim \n",
"55 [email protected] sb \n",
"56 [email protected] Raj Agrawal \n",
"57 [email protected] Ki Deuk Kim \n",
"58 [email protected] sb \n",
"59 [email protected] Venkata Poreddy \n",
" ... ... \n",
"\n",
" HEXSHA \\\n",
"0 e6f985d15ff4736a08e2112b6c7ff0c0d0836a75 \n",
"1 5b54cc96d652a07b12b5c31d4f5ad5269e1aec37 \n",
"2 02d30c7ba4b02e899c4f098531812ca390983c0b \n",
"3 2ec31ee60878a08e5738dfa40245740e79dde97c \n",
"4 f5316bf07da3d4d51ac3bc1875b24d10693daa02 \n",
"5 3723718c356155a8c2c2104e813d61263a1f23c7 \n",
"6 a22c55ea0887bdff8f62e50d2abdca02f6fdbce6 \n",
"7 ed60740e26981e216542a258c0c5aa0afa50af95 \n",
"8 9aacab2a8eb5e7eabcb227caea5a82d99e5f8835 \n",
"9 465c3a275bc341e2dab9d43c0363c2a7fff59b15 \n",
"10 95e074b3e32017adf92e74a8fb19e471bf95f1ee \n",
"11 68a5743f1cfe1241cb2608739418850b0b285360 \n",
"12 be536710f94ec072e04431e7cd043ad24f5f1afb \n",
"13 8dac7fc397738b057d7fbdcd2bea1552e6f88339 \n",
"14 0db0b375fcb90522f6a8700d87820e8fd91e5343 \n",
"15 a121a04579461d4a520fbe4113f0cd0b3a052911 \n",
"16 2b1f678c8ad75458b6a6b7484bed0ca72baee298 \n",
"17 48dfc9b5472471b5a8768f56566c6246c63aa3fe \n",
"18 bc5ccc1fe3034f939ef2f74789a949d2f3604694 \n",
"19 ff0a46b3afac4995517d7dc0ad1281f457e818b4 \n",
"20 505689d8494bab11e69f0687364dbba2a461b532 \n",
"21 c03e3d20fae49a6d2f0458a4132af557b7ec355b \n",
"22 3fa34b21dc5e7d6c7a7154fcda9473f4b0f18f93 \n",
"23 039df37b77929fe52b183dfbf436254b95a4742d \n",
"24 6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e \n",
"25 56140670a9f627e226d449c17d29544be6f5598d \n",
"26 05d773f13331693d796a75daac2529b2efb8ccff \n",
"27 3e1c1f07f1b0d4a55751405b65004bd2b469945f \n",
"28 a713fad3a49cbb803cac33b01cfa3283fe20840f \n",
"29 225b0ee0c3b4db0cda06155eacc1b7d945572306 \n",
"30 d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f \n",
"31 c5919b8d0fc2482b172923e58e51dad54ff209f9 \n",
"32 fa5688b0711d68ec0ffa436d7f31c73907c81e35 \n",
"33 c886ee31fbd48f17afc1b3158983591a17389dfd \n",
"34 d5187fadf9a8529bfc57ac9bade890cd7167a20b \n",
"35 89de558656441f4f4e2ec16cc96d757c073d4772 \n",
"36 befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d \n",
"37 d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501 \n",
"38 b7c4d709b0a07972c90b336a0f7a667981416b7a \n",
"39 974c7a2e1765365dd40705e6ae7b41d9f984a118 \n",
"40 598cf71c6697e4e346894bb58dfbeb30bda3c4aa \n",
"41 a0f02f7f9a401c79815df5f5f52ca483dd6c007b \n",
"42 8c102702f168ba86a8bb81802fe61db70361dfb0 \n",
"43 296dd9a35d2aa006b8f8e9c32852b073e961b3bd \n",
"44 0ff39bd05a7b4b792459b991a0f726422c7d2ef0 \n",
"45 4f5104300b17035460a9f5e7819f8999da72e75b \n",
"46 11905640d44377fb0c007cd340ab780e408f2d10 \n",
"47 9f6c74e01dbbdd14468befa8cde1de82d08d7935 \n",
"48 a69e75b9e36afaf1a1b7af1f51ef00e9c3468095 \n",
"49 00f13d97385763b699b52b562fc204d80149098b \n",
"50 701212ecb79f1b400c2e293d98ff582c750532d0 \n",
"51 f54194242ea036274d788039e77b2619020434dd \n",
"52 aef98ed18e82a52ca4dfc593769f99f4618f8edb \n",
"53 e57bd1d4a81466b73027808d1f55fb9b4c671072 \n",
"54 0547569578a496cf80d153ca9cf2d20849c1736c \n",
"55 a87af8aed3e0e2fb964579b8a7144361d4c19d2f \n",
"56 847720442d7cab223a6c83f0bd9db37ca28bdfbd \n",
"57 4ba2d1df3cb06eec91795ff22489b5533690dcfa \n",
"58 8c450a41c5446db94c0cff7151a8ef2297c43a07 \n",
"59 fefb82dbc2b827cafb47edea9678f43f2a411681 \n",
" ... \n",
"\n",
" Parent Commit Time \\\n",
"0 [02d30c7ba4b02e899c4f098531812ca390983c0b, 5b5... 2015-04-13 22:49:33 \n",
"1 [02d30c7ba4b02e899c4f098531812ca390983c0b] 2015-04-13 22:44:21 \n",
"2 [3723718c356155a8c2c2104e813d61263a1f23c7, 2ec... 2015-04-10 21:59:33 \n",
"3 [f5316bf07da3d4d51ac3bc1875b24d10693daa02] 2015-04-10 18:19:22 \n",
"4 [9aacab2a8eb5e7eabcb227caea5a82d99e5f8835, 372... 2015-04-10 18:18:13 \n",
"5 [a22c55ea0887bdff8f62e50d2abdca02f6fdbce6, ed6... 2015-04-10 17:54:34 \n",
"6 [2b1f678c8ad75458b6a6b7484bed0ca72baee298, 9aa... 2015-04-10 17:53:13 \n",
"7 [8dac7fc397738b057d7fbdcd2bea1552e6f88339] 2015-04-10 17:30:29 \n",
"8 [465c3a275bc341e2dab9d43c0363c2a7fff59b15] 2015-04-10 16:55:36 \n",
"9 [95e074b3e32017adf92e74a8fb19e471bf95f1ee] 2015-04-10 16:54:44 \n",
"10 [68a5743f1cfe1241cb2608739418850b0b285360] 2015-04-10 16:53:57 \n",
"11 [be536710f94ec072e04431e7cd043ad24f5f1afb] 2015-04-10 16:53:31 \n",
"12 [3287f61619d148ccb7deb77c4821812d1dc9cff0, 2b1... 2015-04-10 16:18:26 \n",
"13 [0db0b375fcb90522f6a8700d87820e8fd91e5343] 2015-04-10 11:06:56 \n",
"14 [a121a04579461d4a520fbe4113f0cd0b3a052911] 2015-04-10 10:56:57 \n",
"15 [2b1f678c8ad75458b6a6b7484bed0ca72baee298] 2015-04-10 10:35:54 \n",
"16 [48dfc9b5472471b5a8768f56566c6246c63aa3fe, c03... 2015-04-06 23:30:06 \n",
"17 [ff0a46b3afac4995517d7dc0ad1281f457e818b4, bc5... 2015-04-06 23:21:00 \n",
"18 [ff0a46b3afac4995517d7dc0ad1281f457e818b4, 039... 2015-04-06 23:20:37 \n",
"19 [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 505... 2015-04-06 23:13:58 \n",
"20 [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 3fa... 2015-04-06 23:13:27 \n",
"21 [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e] 2015-04-03 21:41:36 \n",
"22 [e57bd1d4a81466b73027808d1f55fb9b4c671072] 2015-04-02 23:45:44 \n",
"23 [a69e75b9e36afaf1a1b7af1f51ef00e9c3468095] 2015-04-02 23:26:23 \n",
"24 [05d773f13331693d796a75daac2529b2efb8ccff, 561... 2015-04-01 04:14:15 \n",
"25 [05d773f13331693d796a75daac2529b2efb8ccff] 2015-03-31 20:34:42 \n",
"26 [3e1c1f07f1b0d4a55751405b65004bd2b469945f] 2015-03-31 04:50:46 \n",
"27 [11905640d44377fb0c007cd340ab780e408f2d10, a71... 2015-03-30 01:08:56 \n",
"28 [225b0ee0c3b4db0cda06155eacc1b7d945572306] 2015-03-24 04:43:47 \n",
"29 [d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f] 2015-03-24 04:17:58 \n",
"30 [c5919b8d0fc2482b172923e58e51dad54ff209f9] 2015-03-24 03:55:41 \n",
"31 [fa5688b0711d68ec0ffa436d7f31c73907c81e35] 2015-03-24 03:54:51 \n",
"32 [c886ee31fbd48f17afc1b3158983591a17389dfd] 2015-03-24 03:40:36 \n",
"33 [d5187fadf9a8529bfc57ac9bade890cd7167a20b] 2015-03-22 19:51:47 \n",
"34 [89de558656441f4f4e2ec16cc96d757c073d4772] 2015-03-22 19:32:33 \n",
"35 [befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d] 2015-03-17 21:26:03 \n",
"36 [d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501] 2015-03-17 21:14:42 \n",
"37 [b7c4d709b0a07972c90b336a0f7a667981416b7a] 2015-03-17 21:10:21 \n",
"38 [974c7a2e1765365dd40705e6ae7b41d9f984a118] 2015-03-17 20:41:16 \n",
"39 [598cf71c6697e4e346894bb58dfbeb30bda3c4aa] 2015-03-17 20:27:58 \n",
"40 [a0f02f7f9a401c79815df5f5f52ca483dd6c007b] 2015-03-17 20:26:12 \n",
"41 [8c102702f168ba86a8bb81802fe61db70361dfb0] 2015-03-17 20:25:37 \n",
"42 [296dd9a35d2aa006b8f8e9c32852b073e961b3bd] 2015-03-17 20:06:49 \n",
"43 [0ff39bd05a7b4b792459b991a0f726422c7d2ef0] 2015-03-17 19:14:08 \n",
"44 [4f5104300b17035460a9f5e7819f8999da72e75b] 2015-03-17 18:53:17 \n",
"45 [f54194242ea036274d788039e77b2619020434dd, 119... 2015-03-17 18:31:44 \n",
"46 [00f13d97385763b699b52b562fc204d80149098b, 9f6... 2015-03-17 00:03:27 \n",
"47 [00f13d97385763b699b52b562fc204d80149098b, a69... 2015-03-17 00:03:11 \n",
"48 [847720442d7cab223a6c83f0bd9db37ca28bdfbd] 2015-03-16 23:56:02 \n",
"49 [701212ecb79f1b400c2e293d98ff582c750532d0] 2015-03-14 20:18:01 \n",
"50 [aef98ed18e82a52ca4dfc593769f99f4618f8edb] 2015-03-12 21:51:25 \n",
"51 [aef98ed18e82a52ca4dfc593769f99f4618f8edb] 2015-03-10 20:33:56 \n",
"52 [a87af8aed3e0e2fb964579b8a7144361d4c19d2f, e57... 2015-03-10 00:06:20 \n",
"53 [0547569578a496cf80d153ca9cf2d20849c1736c, 4ba... 2015-03-10 00:01:08 \n",
"54 [a87af8aed3e0e2fb964579b8a7144361d4c19d2f] 2015-03-09 23:52:56 \n",
"55 [8c450a41c5446db94c0cff7151a8ef2297c43a07, 847... 2015-03-09 23:40:02 \n",
"56 [8c450a41c5446db94c0cff7151a8ef2297c43a07] 2015-03-09 23:31:02 \n",
"57 [8c450a41c5446db94c0cff7151a8ef2297c43a07] 2015-03-09 23:20:54 \n",
"58 [0b47f504de03817db97e0d3556c98f7c252bc0f9, fef... 2015-03-05 22:47:56 \n",
"59 [0b47f504de03817db97e0d3556c98f7c252bc0f9] 2015-03-04 05:48:45 \n",
" ... ... \n",
"\n",
" Touched File Person-ID \n",
"0 [examples/viz/git/glass.json, examples/viz/git... 1 \n",
"1 [examples/viz/git/glass.json, examples/viz/git... 2 \n",
"2 [examples/File Dependency Network.ipynb] 1 \n",
"3 [examples/File Dependency Network.ipynb] 3 \n",
"4 [bigbang/git_repo.py, bigbang/repo_loader.py] 3 \n",
"5 [bigbang/git_repo.py, bigbang/repo_loader.py] 1 \n",
"6 [bigbang/get_dependencies.py, examples/File De... 1 \n",
"7 [bigbang/repo_loader.py] 4 \n",
"8 [examples/File Dependency Network.ipynb] 3 \n",
"9 [bigbang/get_dependencies.py] 3 \n",
"10 [requirements.txt] 3 \n",
"11 [examples/File Dependency Network.ipynb] 3 \n",
"12 [.gitignore, README.md, bigbang/archive.py, bi... 3 \n",
"13 [bigbang/git_repo.py] 4 \n",
"14 [bigbang/git_repo.py, bigbang/repo_loader.py] 4 \n",
"15 [bigbang/git_repo.py, bigbang/repo_loader.py] 4 \n",
"16 [bigbang/archive.py] 1 \n",
"17 [examples/Collaboration Robustness.ipynb] 1 \n",
"18 [examples/Collaboration Robustness.ipynb] 1 \n",
"19 [bigbang/twopeople.py] 1 \n",
"20 [bigbang/twopeople.py] 1 \n",
"21 [bigbang/archive.py] 5 \n",
"22 [bigbang/twopeople.py] 6 \n",
"23 [bigbang/twopeople.py, examples/Collaboration ... 7 \n",
"24 [bigbang/mailman.py] 1 \n",
"25 [bigbang/mailman.py] 5 \n",
"26 [examples/Git Diffs.ipynb] 1 \n",
"27 [.gitignore, README.md, bigbang/git_repo.py, b... 1 \n",
"28 [bigbang/git_repo.py, bigbang/repo_loader.py, ... 4 \n",
"29 [bigbang/git_repo.py, bigbang/repo_loader.py, ... 4 \n",
"30 [bigbang/repo_loader.py] 4 \n",
"31 [bigbang/repo_loader.py] 4 \n",
"32 [bigbang/git_repo.py] 4 \n",
"33 [examples/Committer Dominance.ipynb, examples/... 4 \n",
"34 [bigbang/git_repo.py, bigbang/repo_loader.py, ... 4 \n",
"35 [README.md] 4 \n",
"36 [README.md] 4 \n",
"37 [README.md] 4 \n",
"38 [git_data/RepoLoader.py] 4 \n",
"39 [git_data/RepoLoader.py] 4 \n",
"40 [git_data/RepoLoader.py] 4 \n",
"41 [git_data/RepoLoader.py] 4 \n",
"42 [bin/collect_git.py, git_data/RepoLoader.py] 4 \n",
"43 [bin/collect_git.py, git_data/GitRepo.py, git_... 4 \n",
"44 [bin/collect_git.py] 4 \n",
"45 [bigbang/mailman.py, bigbang/twopeople.py, req... 4 \n",
"46 [bigbang/twopeople.py] 1 \n",
"47 [bigbang/twopeople.py] 1 \n",
"48 [bigbang/twopeople.py] 7 \n",
"49 [bigbang/mailman.py] 1 \n",
"50 [requirements.txt] 1 \n",
"51 [.gitignore] 4 \n",
"52 [examples/Collaboration Robustness.ipynb] 1 \n",
"53 [] 6 \n",
"54 [examples/Collaboration Robustness.ipynb] 6 \n",
"55 [bigbang/twopeople.py] 1 \n",
"56 [bigbang/twopeople.py] 7 \n",
"57 [bigbang/twopeople.py] 6 \n",
"58 [examples/Git Diffs.ipynb] 1 \n",
"59 [examples/Git Diffs.ipynb] 3 \n",
" ... ... \n",
"\n",
"[372 rows x 9 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from bigbang import repo_loader # The file that handles most loading\n",
"\n",
"repo = repo_loader.get_repo(\"https://github.com/sbenthall/bigbang.git\", in_type = \"remote\" )\n",
"# repo = repo_loader.get_repo(\"../\", in_type = \"local\" ) # I commented this out because it may take too long\n",
"repo = repo_loader.get_repo(\"bigbang\", in_type = \"name\")\n",
"repo.commit_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MultiRepos\n",
"These are the ways we can get MultiGitRepo objects. MultiGitRepo objects are GitRepos that were created with a list of GitRepos. Basically, a MultiGitRepo's `commit_data` contains the commit_data from all of its GitRepos. The only difference is that each entry has an extra attribute, `Repo Name` that tells us which Repo that commit is initially from.\n",
"\n",
"## List of Repos / List of Repo Names (`get_multi_repo`)\n",
"This is rather simple. We can call the `get_multi_repo` method with either a list of repo names `[\"bigbang\", \"django\", \"scipy\"]` or a list of actual GitRepo objects. This returns us the merged MultiGitRepo. Please note that this will not work if a local clone / cache of the repos does not exist for every repo name (e.g. if you ask for `[\"bigbang\", \"django\", \"scipy\"]`, you must already have a local copy of those in your sample_git_repos directory.\n",
"\n",
"## Github Organization's Repos (`get_org_multirepo`)\n",
"This is more useful to us. We can use this method to get a MultiGitRepo that contains the information from every repo in a Github Organization. This requires that we input the organization's name *exactly* as it appears on Github (edX, glass-bead-labs, codeforamerica, etc.)\n",
"\n",
"It will look for `examples/{org_name}_urls.txt`, which should be a file that contains all of the git urls of the projects that belong to that organization. If this file doesn't yet exist, it will make a call to the Github API. This requires a stable internet connection, and it may randomly stall on requests that do not time out.\n",
"\n",
"The function will then use the list of git urls and the `get_repo` method to get each repo. It will use this list of repos to create a MultiGitRepo object, using `get_multi_repo`.\n",
"\n",
"\n",
"Note that the examples below will not work if you don't have an internet connection, and may take some time to process. The first call may also fail if you do not have all of the repositories"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Commit Message</th>\n",
" <th>Committer Email</th>\n",
" <th>Committer Name</th>\n",
" <th>HEXSHA</th>\n",
" <th>Parent Commit</th>\n",
" <th>Time</th>\n",
" <th>Touched File</th>\n",
" <th>Person-ID</th>\n",
" <th>Repo Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 2015-04-13 22:49:33</td>\n",
" <td> Merge pull request #195 from jesscxu/master\\n\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> e6f985d15ff4736a08e2112b6c7ff0c0d0836a75</td>\n",
" <td> [02d30c7ba4b02e899c4f098531812ca390983c0b, 5b5...</td>\n",
" <td>2015-04-13 22:49:33</td>\n",
" <td> [examples/viz/git/glass.json, examples/viz/git...</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2015-04-13 22:44:21</td>\n",
" <td> Adding d3 visualization of GitDiff.ipynb graph\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Jessica Xu</td>\n",
" <td> 5b54cc96d652a07b12b5c31d4f5ad5269e1aec37</td>\n",
" <td> [02d30c7ba4b02e899c4f098531812ca390983c0b]</td>\n",
" <td>2015-04-13 22:44:21</td>\n",
" <td> [examples/viz/git/glass.json, examples/viz/git...</td>\n",
" <td> 2</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 2015-04-10 21:59:33</td>\n",
" <td> Merge pull request #194 from vsporeddy/master\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 02d30c7ba4b02e899c4f098531812ca390983c0b</td>\n",
" <td> [3723718c356155a8c2c2104e813d61263a1f23c7, 2ec...</td>\n",
" <td>2015-04-10 21:59:33</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 2015-04-10 18:19:22</td>\n",
" <td> Changed to directed graph</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 2ec31ee60878a08e5738dfa40245740e79dde97c</td>\n",
" <td> [f5316bf07da3d4d51ac3bc1875b24d10693daa02]</td>\n",
" <td>2015-04-10 18:19:22</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 2015-04-10 18:18:13</td>\n",
" <td> Merge pull request #3 from sbenthall/master\\n\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> f5316bf07da3d4d51ac3bc1875b24d10693daa02</td>\n",
" <td> [9aacab2a8eb5e7eabcb227caea5a82d99e5f8835, 372...</td>\n",
" <td>2015-04-10 18:18:13</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 2015-04-10 17:54:34</td>\n",
" <td> Merge pull request #192 from Aryan-Barbarian/m...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 3723718c356155a8c2c2104e813d61263a1f23c7</td>\n",
" <td> [a22c55ea0887bdff8f62e50d2abdca02f6fdbce6, ed6...</td>\n",
" <td>2015-04-10 17:54:34</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 2015-04-10 17:53:13</td>\n",
" <td> Merge pull request #193 from vsporeddy/master\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> a22c55ea0887bdff8f62e50d2abdca02f6fdbce6</td>\n",
" <td> [2b1f678c8ad75458b6a6b7484bed0ca72baee298, 9aa...</td>\n",
" <td>2015-04-10 17:53:13</td>\n",
" <td> [bigbang/get_dependencies.py, examples/File De...</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 2015-04-10 17:30:29</td>\n",
" <td> Fixed an issue where git repos with hyphens in...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> ed60740e26981e216542a258c0c5aa0afa50af95</td>\n",
" <td> [8dac7fc397738b057d7fbdcd2bea1552e6f88339]</td>\n",
" <td>2015-04-10 17:30:29</td>\n",
" <td> [bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 2015-04-10 16:55:36</td>\n",
" <td> Update File Dependency Network.ipynb</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 9aacab2a8eb5e7eabcb227caea5a82d99e5f8835</td>\n",
" <td> [465c3a275bc341e2dab9d43c0363c2a7fff59b15]</td>\n",
" <td>2015-04-10 16:55:36</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 2015-04-10 16:54:44</td>\n",
" <td> Create get_dependencies.py</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 465c3a275bc341e2dab9d43c0363c2a7fff59b15</td>\n",
" <td> [95e074b3e32017adf92e74a8fb19e471bf95f1ee]</td>\n",
" <td>2015-04-10 16:54:44</td>\n",
" <td> [bigbang/get_dependencies.py]</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 2015-04-10 16:53:57</td>\n",
" <td> Update requirements.txt</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 95e074b3e32017adf92e74a8fb19e471bf95f1ee</td>\n",
" <td> [68a5743f1cfe1241cb2608739418850b0b285360]</td>\n",
" <td>2015-04-10 16:53:57</td>\n",
" <td> [requirements.txt]</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 2015-04-10 16:53:31</td>\n",
" <td> Create File Dependency Network.ipynb</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> 68a5743f1cfe1241cb2608739418850b0b285360</td>\n",
" <td> [be536710f94ec072e04431e7cd043ad24f5f1afb]</td>\n",
" <td>2015-04-10 16:53:31</td>\n",
" <td> [examples/File Dependency Network.ipynb]</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 2015-04-10 16:18:26</td>\n",
" <td> Merge pull request #2 from sbenthall/master\\n\\...</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> be536710f94ec072e04431e7cd043ad24f5f1afb</td>\n",
" <td> [3287f61619d148ccb7deb77c4821812d1dc9cff0, 2b1...</td>\n",
" <td>2015-04-10 16:18:26</td>\n",
" <td> [.gitignore, README.md, bigbang/archive.py, bi...</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2015-04-10 11:06:56</td>\n",
" <td> Warning people how long git diffs will take\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 8dac7fc397738b057d7fbdcd2bea1552e6f88339</td>\n",
" <td> [0db0b375fcb90522f6a8700d87820e8fd91e5343]</td>\n",
" <td>2015-04-10 11:06:56</td>\n",
" <td> [bigbang/git_repo.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 2015-04-10 10:56:57</td>\n",
" <td> Fixed another bug with repo loading logic\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 0db0b375fcb90522f6a8700d87820e8fd91e5343</td>\n",
" <td> [a121a04579461d4a520fbe4113f0cd0b3a052911]</td>\n",
" <td>2015-04-10 10:56:57</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 2015-04-10 10:35:54</td>\n",
" <td> Fixed repo loading bug. The answer fetched was...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> a121a04579461d4a520fbe4113f0cd0b3a052911</td>\n",
" <td> [2b1f678c8ad75458b6a6b7484bed0ca72baee298]</td>\n",
" <td>2015-04-10 10:35:54</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 2015-04-06 23:30:06</td>\n",
" <td> Merge pull request #190 from dwins/setting_wit...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 2b1f678c8ad75458b6a6b7484bed0ca72baee298</td>\n",
" <td> [48dfc9b5472471b5a8768f56566c6246c63aa3fe, c03...</td>\n",
" <td>2015-04-06 23:30:06</td>\n",
" <td> [bigbang/archive.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 2015-04-06 23:21:00</td>\n",
" <td> Merge branch 'raj4-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 48dfc9b5472471b5a8768f56566c6246c63aa3fe</td>\n",
" <td> [ff0a46b3afac4995517d7dc0ad1281f457e818b4, bc5...</td>\n",
" <td>2015-04-06 23:21:00</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 2015-04-06 23:20:37</td>\n",
" <td> Merge branch 'master' of https://github.com/ra...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> bc5ccc1fe3034f939ef2f74789a949d2f3604694</td>\n",
" <td> [ff0a46b3afac4995517d7dc0ad1281f457e818b4, 039...</td>\n",
" <td>2015-04-06 23:20:37</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 2015-04-06 23:13:58</td>\n",
" <td> Merge branch 'cool9210-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> ff0a46b3afac4995517d7dc0ad1281f457e818b4</td>\n",
" <td> [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 505...</td>\n",
" <td>2015-04-06 23:13:58</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 2015-04-06 23:13:27</td>\n",
" <td> Merge branch 'master' of https://github.com/co...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 505689d8494bab11e69f0687364dbba2a461b532</td>\n",
" <td> [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 3fa...</td>\n",
" <td>2015-04-06 23:13:27</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 2015-04-03 21:41:36</td>\n",
" <td> Avoid SettingWithCopyWarning\\n\\nfixes #162\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> David Winslow</td>\n",
" <td> c03e3d20fae49a6d2f0458a4132af557b7ec355b</td>\n",
" <td> [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e]</td>\n",
" <td>2015-04-03 21:41:36</td>\n",
" <td> [bigbang/archive.py]</td>\n",
" <td> 5</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 2015-04-02 23:45:44</td>\n",
" <td> committing twopeople\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> 3fa34b21dc5e7d6c7a7154fcda9473f4b0f18f93</td>\n",
" <td> [e57bd1d4a81466b73027808d1f55fb9b4c671072]</td>\n",
" <td>2015-04-02 23:45:44</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 6</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 2015-04-02 23:26:23</td>\n",
" <td> updated robustness notebook\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Raj Agrawal</td>\n",
" <td> 039df37b77929fe52b183dfbf436254b95a4742d</td>\n",
" <td> [a69e75b9e36afaf1a1b7af1f51ef00e9c3468095]</td>\n",
" <td>2015-04-02 23:26:23</td>\n",
" <td> [bigbang/twopeople.py, examples/Collaboration ...</td>\n",
" <td> 7</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 2015-04-01 04:14:15</td>\n",
" <td> Merge branch 'dwins-email_character_sets'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e</td>\n",
" <td> [05d773f13331693d796a75daac2529b2efb8ccff, 561...</td>\n",
" <td>2015-04-01 04:14:15</td>\n",
" <td> [bigbang/mailman.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 2015-03-31 20:34:42</td>\n",
" <td> Consistently represent email data as Unicode\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> David Winslow</td>\n",
" <td> 56140670a9f627e226d449c17d29544be6f5598d</td>\n",
" <td> [05d773f13331693d796a75daac2529b2efb8ccff]</td>\n",
" <td>2015-03-31 20:34:42</td>\n",
" <td> [bigbang/mailman.py]</td>\n",
" <td> 5</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 2015-03-31 04:50:46</td>\n",
" <td> changing type attribute to be keyed to string ...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 05d773f13331693d796a75daac2529b2efb8ccff</td>\n",
" <td> [3e1c1f07f1b0d4a55751405b65004bd2b469945f]</td>\n",
" <td>2015-03-31 04:50:46</td>\n",
" <td> [examples/Git Diffs.ipynb]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> 2015-03-30 01:08:56</td>\n",
" <td> Merge pull request #182 from Aryan-Barbarian/g...</td>\n",
" <td> [email protected]</td>\n",
" <td> Sebastian Benthall</td>\n",
" <td> 3e1c1f07f1b0d4a55751405b65004bd2b469945f</td>\n",
" <td> [11905640d44377fb0c007cd340ab780e408f2d10, a71...</td>\n",
" <td>2015-03-30 01:08:56</td>\n",
" <td> [.gitignore, README.md, bigbang/git_repo.py, b...</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> 2015-03-24 04:43:47</td>\n",
" <td> Added the option to override the cache and for...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> a713fad3a49cbb803cac33b01cfa3283fe20840f</td>\n",
" <td> [225b0ee0c3b4db0cda06155eacc1b7d945572306]</td>\n",
" <td>2015-03-24 04:43:47</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py, ...</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 2015-03-24 04:17:58</td>\n",
" <td> Fixed bugs relating to caching the data.\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 225b0ee0c3b4db0cda06155eacc1b7d945572306</td>\n",
" <td> [d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f]</td>\n",
" <td>2015-03-24 04:17:58</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py, ...</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 2015-03-24 03:55:41</td>\n",
" <td> Repo Loader wasn't importing pandas\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f</td>\n",
" <td> [c5919b8d0fc2482b172923e58e51dad54ff209f9]</td>\n",
" <td>2015-03-24 03:55:41</td>\n",
" <td> [bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> 2015-03-24 03:54:51</td>\n",
" <td> Repo Loader tries to cache now?\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> c5919b8d0fc2482b172923e58e51dad54ff209f9</td>\n",
" <td> [fa5688b0711d68ec0ffa436d7f31c73907c81e35]</td>\n",
" <td>2015-03-24 03:54:51</td>\n",
" <td> [bigbang/repo_loader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 2015-03-24 03:40:36</td>\n",
" <td> Git Repo takes flags for initialization now. N...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> fa5688b0711d68ec0ffa436d7f31c73907c81e35</td>\n",
" <td> [c886ee31fbd48f17afc1b3158983591a17389dfd]</td>\n",
" <td>2015-03-24 03:40:36</td>\n",
" <td> [bigbang/git_repo.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> 2015-03-22 19:51:47</td>\n",
" <td> Fixed issues in the ipython notebooks regardin...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> c886ee31fbd48f17afc1b3158983591a17389dfd</td>\n",
" <td> [d5187fadf9a8529bfc57ac9bade890cd7167a20b]</td>\n",
" <td>2015-03-22 19:51:47</td>\n",
" <td> [examples/Committer Dominance.ipynb, examples/...</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> 2015-03-22 19:32:33</td>\n",
" <td> Moved git files into the main bigbang library....</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> d5187fadf9a8529bfc57ac9bade890cd7167a20b</td>\n",
" <td> [89de558656441f4f4e2ec16cc96d757c073d4772]</td>\n",
" <td>2015-03-22 19:32:33</td>\n",
" <td> [bigbang/git_repo.py, bigbang/repo_loader.py, ...</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> 2015-03-17 21:26:03</td>\n",
" <td> Fixing the readme\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 89de558656441f4f4e2ec16cc96d757c073d4772</td>\n",
" <td> [befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d]</td>\n",
" <td>2015-03-17 21:26:03</td>\n",
" <td> [README.md]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> 2015-03-17 21:14:42</td>\n",
" <td> One more tweak to the README\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d</td>\n",
" <td> [d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501]</td>\n",
" <td>2015-03-17 21:14:42</td>\n",
" <td> [README.md]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> 2015-03-17 21:10:21</td>\n",
" <td> Updated README\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501</td>\n",
" <td> [b7c4d709b0a07972c90b336a0f7a667981416b7a]</td>\n",
" <td>2015-03-17 21:10:21</td>\n",
" <td> [README.md]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 2015-03-17 20:41:16</td>\n",
" <td> The repo loader can now correctly fetch files.\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> b7c4d709b0a07972c90b336a0f7a667981416b7a</td>\n",
" <td> [974c7a2e1765365dd40705e6ae7b41d9f984a118]</td>\n",
" <td>2015-03-17 20:41:16</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> 2015-03-17 20:27:58</td>\n",
" <td> Small bug with repo loader\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 974c7a2e1765365dd40705e6ae7b41d9f984a118</td>\n",
" <td> [598cf71c6697e4e346894bb58dfbeb30bda3c4aa]</td>\n",
" <td>2015-03-17 20:27:58</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> 2015-03-17 20:26:12</td>\n",
" <td> RepoLoader generates the sample git directory ...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 598cf71c6697e4e346894bb58dfbeb30bda3c4aa</td>\n",
" <td> [a0f02f7f9a401c79815df5f5f52ca483dd6c007b]</td>\n",
" <td>2015-03-17 20:26:12</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> 2015-03-17 20:25:37</td>\n",
" <td> Moved a lot of git repo loading functionality ...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> a0f02f7f9a401c79815df5f5f52ca483dd6c007b</td>\n",
" <td> [8c102702f168ba86a8bb81802fe61db70361dfb0]</td>\n",
" <td>2015-03-17 20:25:37</td>\n",
" <td> [git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> 2015-03-17 20:06:49</td>\n",
" <td> Very rough first draft of repo loader\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 8c102702f168ba86a8bb81802fe61db70361dfb0</td>\n",
" <td> [296dd9a35d2aa006b8f8e9c32852b073e961b3bd]</td>\n",
" <td>2015-03-17 20:06:49</td>\n",
" <td> [bin/collect_git.py, git_data/RepoLoader.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> 2015-03-17 19:14:08</td>\n",
" <td> collect git now imports from Repository Loader\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 296dd9a35d2aa006b8f8e9c32852b073e961b3bd</td>\n",
" <td> [0ff39bd05a7b4b792459b991a0f726422c7d2ef0]</td>\n",
" <td>2015-03-17 19:14:08</td>\n",
" <td> [bin/collect_git.py, git_data/GitRepo.py, git_...</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> 2015-03-17 18:53:17</td>\n",
" <td> Slight cleanup in collect git script\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 0ff39bd05a7b4b792459b991a0f726422c7d2ef0</td>\n",
" <td> [4f5104300b17035460a9f5e7819f8999da72e75b]</td>\n",
" <td>2015-03-17 18:53:17</td>\n",
" <td> [bin/collect_git.py]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> 2015-03-17 18:31:44</td>\n",
" <td> Merge remote-tracking branch 'upstream/master'...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> 4f5104300b17035460a9f5e7819f8999da72e75b</td>\n",
" <td> [f54194242ea036274d788039e77b2619020434dd, 119...</td>\n",
" <td>2015-03-17 18:31:44</td>\n",
" <td> [bigbang/mailman.py, bigbang/twopeople.py, req...</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> 2015-03-17 00:03:27</td>\n",
" <td> Merge branch 'raj4-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 11905640d44377fb0c007cd340ab780e408f2d10</td>\n",
" <td> [00f13d97385763b699b52b562fc204d80149098b, 9f6...</td>\n",
" <td>2015-03-17 00:03:27</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> 2015-03-17 00:03:11</td>\n",
" <td> Merge branch 'master' of https://github.com/ra...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 9f6c74e01dbbdd14468befa8cde1de82d08d7935</td>\n",
" <td> [00f13d97385763b699b52b562fc204d80149098b, a69...</td>\n",
" <td>2015-03-17 00:03:11</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> 2015-03-16 23:56:02</td>\n",
" <td> functions to create df\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Raj Agrawal</td>\n",
" <td> a69e75b9e36afaf1a1b7af1f51ef00e9c3468095</td>\n",
" <td> [847720442d7cab223a6c83f0bd9db37ca28bdfbd]</td>\n",
" <td>2015-03-16 23:56:02</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 7</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 2015-03-14 20:18:01</td>\n",
" <td> fixing variable reference in data collection e...</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 00f13d97385763b699b52b562fc204d80149098b</td>\n",
" <td> [701212ecb79f1b400c2e293d98ff582c750532d0]</td>\n",
" <td>2015-03-14 20:18:01</td>\n",
" <td> [bigbang/mailman.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> 2015-03-12 21:51:25</td>\n",
" <td> adding jsonschema as a pip requirement\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 701212ecb79f1b400c2e293d98ff582c750532d0</td>\n",
" <td> [aef98ed18e82a52ca4dfc593769f99f4618f8edb]</td>\n",
" <td>2015-03-12 21:51:25</td>\n",
" <td> [requirements.txt]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> 2015-03-10 20:33:56</td>\n",
" <td> git will now ignore the git_locals.json file, ...</td>\n",
" <td> [email protected]</td>\n",
" <td> Aryan Falahatpisheh</td>\n",
" <td> f54194242ea036274d788039e77b2619020434dd</td>\n",
" <td> [aef98ed18e82a52ca4dfc593769f99f4618f8edb]</td>\n",
" <td>2015-03-10 20:33:56</td>\n",
" <td> [.gitignore]</td>\n",
" <td> 4</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td> 2015-03-10 00:06:20</td>\n",
" <td> Merge branch 'cool9210-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> aef98ed18e82a52ca4dfc593769f99f4618f8edb</td>\n",
" <td> [a87af8aed3e0e2fb964579b8a7144361d4c19d2f, e57...</td>\n",
" <td>2015-03-10 00:06:20</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td> 2015-03-10 00:01:08</td>\n",
" <td> Merge branch 'master' of https://github.com/co...</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> e57bd1d4a81466b73027808d1f55fb9b4c671072</td>\n",
" <td> [0547569578a496cf80d153ca9cf2d20849c1736c, 4ba...</td>\n",
" <td>2015-03-10 00:01:08</td>\n",
" <td> []</td>\n",
" <td> 6</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td> 2015-03-09 23:52:56</td>\n",
" <td> This change is adding duration, reciprocity, a...</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> 0547569578a496cf80d153ca9cf2d20849c1736c</td>\n",
" <td> [a87af8aed3e0e2fb964579b8a7144361d4c19d2f]</td>\n",
" <td>2015-03-09 23:52:56</td>\n",
" <td> [examples/Collaboration Robustness.ipynb]</td>\n",
" <td> 6</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td> 2015-03-09 23:40:02</td>\n",
" <td> Merge branch 'raj4-master'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> a87af8aed3e0e2fb964579b8a7144361d4c19d2f</td>\n",
" <td> [8c450a41c5446db94c0cff7151a8ef2297c43a07, 847...</td>\n",
" <td>2015-03-09 23:40:02</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td> 2015-03-09 23:31:02</td>\n",
" <td> first commit\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> Raj Agrawal</td>\n",
" <td> 847720442d7cab223a6c83f0bd9db37ca28bdfbd</td>\n",
" <td> [8c450a41c5446db94c0cff7151a8ef2297c43a07]</td>\n",
" <td>2015-03-09 23:31:02</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 7</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td> 2015-03-09 23:20:54</td>\n",
" <td> Create twopeople.py</td>\n",
" <td> [email protected]</td>\n",
" <td> Ki Deuk Kim</td>\n",
" <td> 4ba2d1df3cb06eec91795ff22489b5533690dcfa</td>\n",
" <td> [8c450a41c5446db94c0cff7151a8ef2297c43a07]</td>\n",
" <td>2015-03-09 23:20:54</td>\n",
" <td> [bigbang/twopeople.py]</td>\n",
" <td> 6</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td> 2015-03-05 22:47:56</td>\n",
" <td> Merge branch 'vsporeddy'\\n</td>\n",
" <td> [email protected]</td>\n",
" <td> sb</td>\n",
" <td> 8c450a41c5446db94c0cff7151a8ef2297c43a07</td>\n",
" <td> [0b47f504de03817db97e0d3556c98f7c252bc0f9, fef...</td>\n",
" <td>2015-03-05 22:47:56</td>\n",
" <td> [examples/Git Diffs.ipynb]</td>\n",
" <td> 1</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td> 2015-03-04 05:48:45</td>\n",
" <td> Update Git Diffs.ipynb\\n\\nAdded node colors an...</td>\n",
" <td> [email protected]</td>\n",
" <td> Venkata Poreddy</td>\n",
" <td> fefb82dbc2b827cafb47edea9678f43f2a411681</td>\n",
" <td> [0b47f504de03817db97e0d3556c98f7c252bc0f9]</td>\n",
" <td>2015-03-04 05:48:45</td>\n",
" <td> [examples/Git Diffs.ipynb]</td>\n",
" <td> 3</td>\n",
" <td> bigbang</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>412 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Commit Message \\\n",
"0 2015-04-13 22:49:33 Merge pull request #195 from jesscxu/master\\n\\... \n",
"1 2015-04-13 22:44:21 Adding d3 visualization of GitDiff.ipynb graph\\n \n",
"2 2015-04-10 21:59:33 Merge pull request #194 from vsporeddy/master\\... \n",
"3 2015-04-10 18:19:22 Changed to directed graph \n",
"4 2015-04-10 18:18:13 Merge pull request #3 from sbenthall/master\\n\\... \n",
"5 2015-04-10 17:54:34 Merge pull request #192 from Aryan-Barbarian/m... \n",
"6 2015-04-10 17:53:13 Merge pull request #193 from vsporeddy/master\\... \n",
"7 2015-04-10 17:30:29 Fixed an issue where git repos with hyphens in... \n",
"8 2015-04-10 16:55:36 Update File Dependency Network.ipynb \n",
"9 2015-04-10 16:54:44 Create get_dependencies.py \n",
"10 2015-04-10 16:53:57 Update requirements.txt \n",
"11 2015-04-10 16:53:31 Create File Dependency Network.ipynb \n",
"12 2015-04-10 16:18:26 Merge pull request #2 from sbenthall/master\\n\\... \n",
"13 2015-04-10 11:06:56 Warning people how long git diffs will take\\n \n",
"14 2015-04-10 10:56:57 Fixed another bug with repo loading logic\\n \n",
"15 2015-04-10 10:35:54 Fixed repo loading bug. The answer fetched was... \n",
"16 2015-04-06 23:30:06 Merge pull request #190 from dwins/setting_wit... \n",
"17 2015-04-06 23:21:00 Merge branch 'raj4-master'\\n \n",
"18 2015-04-06 23:20:37 Merge branch 'master' of https://github.com/ra... \n",
"19 2015-04-06 23:13:58 Merge branch 'cool9210-master'\\n \n",
"20 2015-04-06 23:13:27 Merge branch 'master' of https://github.com/co... \n",
"21 2015-04-03 21:41:36 Avoid SettingWithCopyWarning\\n\\nfixes #162\\n \n",
"22 2015-04-02 23:45:44 committing twopeople\\n \n",
"23 2015-04-02 23:26:23 updated robustness notebook\\n \n",
"24 2015-04-01 04:14:15 Merge branch 'dwins-email_character_sets'\\n \n",
"25 2015-03-31 20:34:42 Consistently represent email data as Unicode\\n \n",
"26 2015-03-31 04:50:46 changing type attribute to be keyed to string ... \n",
"27 2015-03-30 01:08:56 Merge pull request #182 from Aryan-Barbarian/g... \n",
"28 2015-03-24 04:43:47 Added the option to override the cache and for... \n",
"29 2015-03-24 04:17:58 Fixed bugs relating to caching the data.\\n \n",
"30 2015-03-24 03:55:41 Repo Loader wasn't importing pandas\\n \n",
"31 2015-03-24 03:54:51 Repo Loader tries to cache now?\\n \n",
"32 2015-03-24 03:40:36 Git Repo takes flags for initialization now. N... \n",
"33 2015-03-22 19:51:47 Fixed issues in the ipython notebooks regardin... \n",
"34 2015-03-22 19:32:33 Moved git files into the main bigbang library.... \n",
"35 2015-03-17 21:26:03 Fixing the readme\\n \n",
"36 2015-03-17 21:14:42 One more tweak to the README\\n \n",
"37 2015-03-17 21:10:21 Updated README\\n \n",
"38 2015-03-17 20:41:16 The repo loader can now correctly fetch files.\\n \n",
"39 2015-03-17 20:27:58 Small bug with repo loader\\n \n",
"40 2015-03-17 20:26:12 RepoLoader generates the sample git directory ... \n",
"41 2015-03-17 20:25:37 Moved a lot of git repo loading functionality ... \n",
"42 2015-03-17 20:06:49 Very rough first draft of repo loader\\n \n",
"43 2015-03-17 19:14:08 collect git now imports from Repository Loader\\n \n",
"44 2015-03-17 18:53:17 Slight cleanup in collect git script\\n \n",
"45 2015-03-17 18:31:44 Merge remote-tracking branch 'upstream/master'... \n",
"46 2015-03-17 00:03:27 Merge branch 'raj4-master'\\n \n",
"47 2015-03-17 00:03:11 Merge branch 'master' of https://github.com/ra... \n",
"48 2015-03-16 23:56:02 functions to create df\\n \n",
"49 2015-03-14 20:18:01 fixing variable reference in data collection e... \n",
"50 2015-03-12 21:51:25 adding jsonschema as a pip requirement\\n \n",
"51 2015-03-10 20:33:56 git will now ignore the git_locals.json file, ... \n",
"52 2015-03-10 00:06:20 Merge branch 'cool9210-master'\\n \n",
"53 2015-03-10 00:01:08 Merge branch 'master' of https://github.com/co... \n",
"54 2015-03-09 23:52:56 This change is adding duration, reciprocity, a... \n",
"55 2015-03-09 23:40:02 Merge branch 'raj4-master'\\n \n",
"56 2015-03-09 23:31:02 first commit\\n \n",
"57 2015-03-09 23:20:54 Create twopeople.py \n",
"58 2015-03-05 22:47:56 Merge branch 'vsporeddy'\\n \n",
"59 2015-03-04 05:48:45 Update Git Diffs.ipynb\\n\\nAdded node colors an... \n",
" ... ... \n",
"\n",
" Committer Email Committer Name \\\n",
"0 [email protected] Sebastian Benthall \n",
"1 [email protected] Jessica Xu \n",
"2 [email protected] Sebastian Benthall \n",
"3 [email protected] Venkata Poreddy \n",
"4 [email protected] Venkata Poreddy \n",
"5 [email protected] Sebastian Benthall \n",
"6 [email protected] Sebastian Benthall \n",
"7 [email protected] Aryan Falahatpisheh \n",
"8 [email protected] Venkata Poreddy \n",
"9 [email protected] Venkata Poreddy \n",
"10 [email protected] Venkata Poreddy \n",
"11 [email protected] Venkata Poreddy \n",
"12 [email protected] Venkata Poreddy \n",
"13 [email protected] Aryan Falahatpisheh \n",
"14 [email protected] Aryan Falahatpisheh \n",
"15 [email protected] Aryan Falahatpisheh \n",
"16 [email protected] Sebastian Benthall \n",
"17 [email protected] sb \n",
"18 [email protected] sb \n",
"19 [email protected] sb \n",
"20 [email protected] sb \n",
"21 [email protected] David Winslow \n",
"22 [email protected] Ki Deuk Kim \n",
"23 [email protected] Raj Agrawal \n",
"24 [email protected] sb \n",
"25 [email protected] David Winslow \n",
"26 [email protected] sb \n",
"27 [email protected] Sebastian Benthall \n",
"28 [email protected] Aryan Falahatpisheh \n",
"29 [email protected] Aryan Falahatpisheh \n",
"30 [email protected] Aryan Falahatpisheh \n",
"31 [email protected] Aryan Falahatpisheh \n",
"32 [email protected] Aryan Falahatpisheh \n",
"33 [email protected] Aryan Falahatpisheh \n",
"34 [email protected] Aryan Falahatpisheh \n",
"35 [email protected] Aryan Falahatpisheh \n",
"36 [email protected] Aryan Falahatpisheh \n",
"37 [email protected] Aryan Falahatpisheh \n",
"38 [email protected] Aryan Falahatpisheh \n",
"39 [email protected] Aryan Falahatpisheh \n",
"40 [email protected] Aryan Falahatpisheh \n",
"41 [email protected] Aryan Falahatpisheh \n",
"42 [email protected] Aryan Falahatpisheh \n",
"43 [email protected] Aryan Falahatpisheh \n",
"44 [email protected] Aryan Falahatpisheh \n",
"45 [email protected] Aryan Falahatpisheh \n",
"46 [email protected] sb \n",
"47 [email protected] sb \n",
"48 [email protected] Raj Agrawal \n",
"49 [email protected] sb \n",
"50 [email protected] sb \n",
"51 [email protected] Aryan Falahatpisheh \n",
"52 [email protected] sb \n",
"53 [email protected] Ki Deuk Kim \n",
"54 [email protected] Ki Deuk Kim \n",
"55 [email protected] sb \n",
"56 [email protected] Raj Agrawal \n",
"57 [email protected] Ki Deuk Kim \n",
"58 [email protected] sb \n",
"59 [email protected] Venkata Poreddy \n",
" ... ... \n",
"\n",
" HEXSHA \\\n",
"0 e6f985d15ff4736a08e2112b6c7ff0c0d0836a75 \n",
"1 5b54cc96d652a07b12b5c31d4f5ad5269e1aec37 \n",
"2 02d30c7ba4b02e899c4f098531812ca390983c0b \n",
"3 2ec31ee60878a08e5738dfa40245740e79dde97c \n",
"4 f5316bf07da3d4d51ac3bc1875b24d10693daa02 \n",
"5 3723718c356155a8c2c2104e813d61263a1f23c7 \n",
"6 a22c55ea0887bdff8f62e50d2abdca02f6fdbce6 \n",
"7 ed60740e26981e216542a258c0c5aa0afa50af95 \n",
"8 9aacab2a8eb5e7eabcb227caea5a82d99e5f8835 \n",
"9 465c3a275bc341e2dab9d43c0363c2a7fff59b15 \n",
"10 95e074b3e32017adf92e74a8fb19e471bf95f1ee \n",
"11 68a5743f1cfe1241cb2608739418850b0b285360 \n",
"12 be536710f94ec072e04431e7cd043ad24f5f1afb \n",
"13 8dac7fc397738b057d7fbdcd2bea1552e6f88339 \n",
"14 0db0b375fcb90522f6a8700d87820e8fd91e5343 \n",
"15 a121a04579461d4a520fbe4113f0cd0b3a052911 \n",
"16 2b1f678c8ad75458b6a6b7484bed0ca72baee298 \n",
"17 48dfc9b5472471b5a8768f56566c6246c63aa3fe \n",
"18 bc5ccc1fe3034f939ef2f74789a949d2f3604694 \n",
"19 ff0a46b3afac4995517d7dc0ad1281f457e818b4 \n",
"20 505689d8494bab11e69f0687364dbba2a461b532 \n",
"21 c03e3d20fae49a6d2f0458a4132af557b7ec355b \n",
"22 3fa34b21dc5e7d6c7a7154fcda9473f4b0f18f93 \n",
"23 039df37b77929fe52b183dfbf436254b95a4742d \n",
"24 6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e \n",
"25 56140670a9f627e226d449c17d29544be6f5598d \n",
"26 05d773f13331693d796a75daac2529b2efb8ccff \n",
"27 3e1c1f07f1b0d4a55751405b65004bd2b469945f \n",
"28 a713fad3a49cbb803cac33b01cfa3283fe20840f \n",
"29 225b0ee0c3b4db0cda06155eacc1b7d945572306 \n",
"30 d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f \n",
"31 c5919b8d0fc2482b172923e58e51dad54ff209f9 \n",
"32 fa5688b0711d68ec0ffa436d7f31c73907c81e35 \n",
"33 c886ee31fbd48f17afc1b3158983591a17389dfd \n",
"34 d5187fadf9a8529bfc57ac9bade890cd7167a20b \n",
"35 89de558656441f4f4e2ec16cc96d757c073d4772 \n",
"36 befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d \n",
"37 d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501 \n",
"38 b7c4d709b0a07972c90b336a0f7a667981416b7a \n",
"39 974c7a2e1765365dd40705e6ae7b41d9f984a118 \n",
"40 598cf71c6697e4e346894bb58dfbeb30bda3c4aa \n",
"41 a0f02f7f9a401c79815df5f5f52ca483dd6c007b \n",
"42 8c102702f168ba86a8bb81802fe61db70361dfb0 \n",
"43 296dd9a35d2aa006b8f8e9c32852b073e961b3bd \n",
"44 0ff39bd05a7b4b792459b991a0f726422c7d2ef0 \n",
"45 4f5104300b17035460a9f5e7819f8999da72e75b \n",
"46 11905640d44377fb0c007cd340ab780e408f2d10 \n",
"47 9f6c74e01dbbdd14468befa8cde1de82d08d7935 \n",
"48 a69e75b9e36afaf1a1b7af1f51ef00e9c3468095 \n",
"49 00f13d97385763b699b52b562fc204d80149098b \n",
"50 701212ecb79f1b400c2e293d98ff582c750532d0 \n",
"51 f54194242ea036274d788039e77b2619020434dd \n",
"52 aef98ed18e82a52ca4dfc593769f99f4618f8edb \n",
"53 e57bd1d4a81466b73027808d1f55fb9b4c671072 \n",
"54 0547569578a496cf80d153ca9cf2d20849c1736c \n",
"55 a87af8aed3e0e2fb964579b8a7144361d4c19d2f \n",
"56 847720442d7cab223a6c83f0bd9db37ca28bdfbd \n",
"57 4ba2d1df3cb06eec91795ff22489b5533690dcfa \n",
"58 8c450a41c5446db94c0cff7151a8ef2297c43a07 \n",
"59 fefb82dbc2b827cafb47edea9678f43f2a411681 \n",
" ... \n",
"\n",
" Parent Commit Time \\\n",
"0 [02d30c7ba4b02e899c4f098531812ca390983c0b, 5b5... 2015-04-13 22:49:33 \n",
"1 [02d30c7ba4b02e899c4f098531812ca390983c0b] 2015-04-13 22:44:21 \n",
"2 [3723718c356155a8c2c2104e813d61263a1f23c7, 2ec... 2015-04-10 21:59:33 \n",
"3 [f5316bf07da3d4d51ac3bc1875b24d10693daa02] 2015-04-10 18:19:22 \n",
"4 [9aacab2a8eb5e7eabcb227caea5a82d99e5f8835, 372... 2015-04-10 18:18:13 \n",
"5 [a22c55ea0887bdff8f62e50d2abdca02f6fdbce6, ed6... 2015-04-10 17:54:34 \n",
"6 [2b1f678c8ad75458b6a6b7484bed0ca72baee298, 9aa... 2015-04-10 17:53:13 \n",
"7 [8dac7fc397738b057d7fbdcd2bea1552e6f88339] 2015-04-10 17:30:29 \n",
"8 [465c3a275bc341e2dab9d43c0363c2a7fff59b15] 2015-04-10 16:55:36 \n",
"9 [95e074b3e32017adf92e74a8fb19e471bf95f1ee] 2015-04-10 16:54:44 \n",
"10 [68a5743f1cfe1241cb2608739418850b0b285360] 2015-04-10 16:53:57 \n",
"11 [be536710f94ec072e04431e7cd043ad24f5f1afb] 2015-04-10 16:53:31 \n",
"12 [3287f61619d148ccb7deb77c4821812d1dc9cff0, 2b1... 2015-04-10 16:18:26 \n",
"13 [0db0b375fcb90522f6a8700d87820e8fd91e5343] 2015-04-10 11:06:56 \n",
"14 [a121a04579461d4a520fbe4113f0cd0b3a052911] 2015-04-10 10:56:57 \n",
"15 [2b1f678c8ad75458b6a6b7484bed0ca72baee298] 2015-04-10 10:35:54 \n",
"16 [48dfc9b5472471b5a8768f56566c6246c63aa3fe, c03... 2015-04-06 23:30:06 \n",
"17 [ff0a46b3afac4995517d7dc0ad1281f457e818b4, bc5... 2015-04-06 23:21:00 \n",
"18 [ff0a46b3afac4995517d7dc0ad1281f457e818b4, 039... 2015-04-06 23:20:37 \n",
"19 [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 505... 2015-04-06 23:13:58 \n",
"20 [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e, 3fa... 2015-04-06 23:13:27 \n",
"21 [6856dc4b4b7ce515c34c180f5ff72dd1b2676b1e] 2015-04-03 21:41:36 \n",
"22 [e57bd1d4a81466b73027808d1f55fb9b4c671072] 2015-04-02 23:45:44 \n",
"23 [a69e75b9e36afaf1a1b7af1f51ef00e9c3468095] 2015-04-02 23:26:23 \n",
"24 [05d773f13331693d796a75daac2529b2efb8ccff, 561... 2015-04-01 04:14:15 \n",
"25 [05d773f13331693d796a75daac2529b2efb8ccff] 2015-03-31 20:34:42 \n",
"26 [3e1c1f07f1b0d4a55751405b65004bd2b469945f] 2015-03-31 04:50:46 \n",
"27 [11905640d44377fb0c007cd340ab780e408f2d10, a71... 2015-03-30 01:08:56 \n",
"28 [225b0ee0c3b4db0cda06155eacc1b7d945572306] 2015-03-24 04:43:47 \n",
"29 [d51c62ea197eedbe3ff7ff63ebb2c1a9a497b21f] 2015-03-24 04:17:58 \n",
"30 [c5919b8d0fc2482b172923e58e51dad54ff209f9] 2015-03-24 03:55:41 \n",
"31 [fa5688b0711d68ec0ffa436d7f31c73907c81e35] 2015-03-24 03:54:51 \n",
"32 [c886ee31fbd48f17afc1b3158983591a17389dfd] 2015-03-24 03:40:36 \n",
"33 [d5187fadf9a8529bfc57ac9bade890cd7167a20b] 2015-03-22 19:51:47 \n",
"34 [89de558656441f4f4e2ec16cc96d757c073d4772] 2015-03-22 19:32:33 \n",
"35 [befc9ba1742ca9cd8eb2dfc03be3289ab1d1a99d] 2015-03-17 21:26:03 \n",
"36 [d0f9f1f7e62d9471b8aba0e52831bd93f7fb6501] 2015-03-17 21:14:42 \n",
"37 [b7c4d709b0a07972c90b336a0f7a667981416b7a] 2015-03-17 21:10:21 \n",
"38 [974c7a2e1765365dd40705e6ae7b41d9f984a118] 2015-03-17 20:41:16 \n",
"39 [598cf71c6697e4e346894bb58dfbeb30bda3c4aa] 2015-03-17 20:27:58 \n",
"40 [a0f02f7f9a401c79815df5f5f52ca483dd6c007b] 2015-03-17 20:26:12 \n",
"41 [8c102702f168ba86a8bb81802fe61db70361dfb0] 2015-03-17 20:25:37 \n",
"42 [296dd9a35d2aa006b8f8e9c32852b073e961b3bd] 2015-03-17 20:06:49 \n",
"43 [0ff39bd05a7b4b792459b991a0f726422c7d2ef0] 2015-03-17 19:14:08 \n",
"44 [4f5104300b17035460a9f5e7819f8999da72e75b] 2015-03-17 18:53:17 \n",
"45 [f54194242ea036274d788039e77b2619020434dd, 119... 2015-03-17 18:31:44 \n",
"46 [00f13d97385763b699b52b562fc204d80149098b, 9f6... 2015-03-17 00:03:27 \n",
"47 [00f13d97385763b699b52b562fc204d80149098b, a69... 2015-03-17 00:03:11 \n",
"48 [847720442d7cab223a6c83f0bd9db37ca28bdfbd] 2015-03-16 23:56:02 \n",
"49 [701212ecb79f1b400c2e293d98ff582c750532d0] 2015-03-14 20:18:01 \n",
"50 [aef98ed18e82a52ca4dfc593769f99f4618f8edb] 2015-03-12 21:51:25 \n",
"51 [aef98ed18e82a52ca4dfc593769f99f4618f8edb] 2015-03-10 20:33:56 \n",
"52 [a87af8aed3e0e2fb964579b8a7144361d4c19d2f, e57... 2015-03-10 00:06:20 \n",
"53 [0547569578a496cf80d153ca9cf2d20849c1736c, 4ba... 2015-03-10 00:01:08 \n",
"54 [a87af8aed3e0e2fb964579b8a7144361d4c19d2f] 2015-03-09 23:52:56 \n",
"55 [8c450a41c5446db94c0cff7151a8ef2297c43a07, 847... 2015-03-09 23:40:02 \n",
"56 [8c450a41c5446db94c0cff7151a8ef2297c43a07] 2015-03-09 23:31:02 \n",
"57 [8c450a41c5446db94c0cff7151a8ef2297c43a07] 2015-03-09 23:20:54 \n",
"58 [0b47f504de03817db97e0d3556c98f7c252bc0f9, fef... 2015-03-05 22:47:56 \n",
"59 [0b47f504de03817db97e0d3556c98f7c252bc0f9] 2015-03-04 05:48:45 \n",
" ... ... \n",
"\n",
" Touched File Person-ID Repo Name \n",
"0 [examples/viz/git/glass.json, examples/viz/git... 1 bigbang \n",
"1 [examples/viz/git/glass.json, examples/viz/git... 2 bigbang \n",
"2 [examples/File Dependency Network.ipynb] 1 bigbang \n",
"3 [examples/File Dependency Network.ipynb] 3 bigbang \n",
"4 [bigbang/git_repo.py, bigbang/repo_loader.py] 3 bigbang \n",
"5 [bigbang/git_repo.py, bigbang/repo_loader.py] 1 bigbang \n",
"6 [bigbang/get_dependencies.py, examples/File De... 1 bigbang \n",
"7 [bigbang/repo_loader.py] 4 bigbang \n",
"8 [examples/File Dependency Network.ipynb] 3 bigbang \n",
"9 [bigbang/get_dependencies.py] 3 bigbang \n",
"10 [requirements.txt] 3 bigbang \n",
"11 [examples/File Dependency Network.ipynb] 3 bigbang \n",
"12 [.gitignore, README.md, bigbang/archive.py, bi... 3 bigbang \n",
"13 [bigbang/git_repo.py] 4 bigbang \n",
"14 [bigbang/git_repo.py, bigbang/repo_loader.py] 4 bigbang \n",
"15 [bigbang/git_repo.py, bigbang/repo_loader.py] 4 bigbang \n",
"16 [bigbang/archive.py] 1 bigbang \n",
"17 [examples/Collaboration Robustness.ipynb] 1 bigbang \n",
"18 [examples/Collaboration Robustness.ipynb] 1 bigbang \n",
"19 [bigbang/twopeople.py] 1 bigbang \n",
"20 [bigbang/twopeople.py] 1 bigbang \n",
"21 [bigbang/archive.py] 5 bigbang \n",
"22 [bigbang/twopeople.py] 6 bigbang \n",
"23 [bigbang/twopeople.py, examples/Collaboration ... 7 bigbang \n",
"24 [bigbang/mailman.py] 1 bigbang \n",
"25 [bigbang/mailman.py] 5 bigbang \n",
"26 [examples/Git Diffs.ipynb] 1 bigbang \n",
"27 [.gitignore, README.md, bigbang/git_repo.py, b... 1 bigbang \n",
"28 [bigbang/git_repo.py, bigbang/repo_loader.py, ... 4 bigbang \n",
"29 [bigbang/git_repo.py, bigbang/repo_loader.py, ... 4 bigbang \n",
"30 [bigbang/repo_loader.py] 4 bigbang \n",
"31 [bigbang/repo_loader.py] 4 bigbang \n",
"32 [bigbang/git_repo.py] 4 bigbang \n",
"33 [examples/Committer Dominance.ipynb, examples/... 4 bigbang \n",
"34 [bigbang/git_repo.py, bigbang/repo_loader.py, ... 4 bigbang \n",
"35 [README.md] 4 bigbang \n",
"36 [README.md] 4 bigbang \n",
"37 [README.md] 4 bigbang \n",
"38 [git_data/RepoLoader.py] 4 bigbang \n",
"39 [git_data/RepoLoader.py] 4 bigbang \n",
"40 [git_data/RepoLoader.py] 4 bigbang \n",
"41 [git_data/RepoLoader.py] 4 bigbang \n",
"42 [bin/collect_git.py, git_data/RepoLoader.py] 4 bigbang \n",
"43 [bin/collect_git.py, git_data/GitRepo.py, git_... 4 bigbang \n",
"44 [bin/collect_git.py] 4 bigbang \n",
"45 [bigbang/mailman.py, bigbang/twopeople.py, req... 4 bigbang \n",
"46 [bigbang/twopeople.py] 1 bigbang \n",
"47 [bigbang/twopeople.py] 1 bigbang \n",
"48 [bigbang/twopeople.py] 7 bigbang \n",
"49 [bigbang/mailman.py] 1 bigbang \n",
"50 [requirements.txt] 1 bigbang \n",
"51 [.gitignore] 4 bigbang \n",
"52 [examples/Collaboration Robustness.ipynb] 1 bigbang \n",
"53 [] 6 bigbang \n",
"54 [examples/Collaboration Robustness.ipynb] 6 bigbang \n",
"55 [bigbang/twopeople.py] 1 bigbang \n",
"56 [bigbang/twopeople.py] 7 bigbang \n",
"57 [bigbang/twopeople.py] 6 bigbang \n",
"58 [examples/Git Diffs.ipynb] 1 bigbang \n",
"59 [examples/Git Diffs.ipynb] 3 bigbang \n",
" ... ... ... \n",
"\n",
"[412 rows x 10 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using GitHub API\n",
"multirepo = repo_loader.get_org_multirepo(\"glass-bead-labs\")\n",
"\n",
"# List of repo names\n",
"multirepo = repo_loader.get_multi_repo(repo_names = [\"bigbang\",\"bead.glass\"])\n",
"\n",
"# List of actual repos\n",
"repo1 = repo_loader.get_repo(\"bigbang\", in_type=\"name\")\n",
"repo2 = repo_loader.get_repo(\"bead.glass\", in_type=\"name\")\n",
"multirepo = repo_loader.get_multi_repo(repos = [repo1, repo2])\n",
"\n",
"multirepo.commit_data"
]
}
],
"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": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
AshivDhondea/SORADSIM | example_notebooks/notebook_001_orbitpropa.ipynb | 1 | 89562 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Demonstrate orbit propagation (OP) using Cowell's method.\n",
"\n",
"Example: ISS TLE file downloaded from celestrak.com on 3 August 2017\n",
"\n",
"ISS (ZARYA)\n",
"\n",
"1 25544U 98067A 17213.83387828 .00000874 00000-0 20415-4 0 9996\n",
"\n",
"2 25544 51.6427 170.7730 0006301 93.4612 353.3163 15.54246517 68858\n",
"\n",
"@author: Ashiv Dhondea\n",
"\n",
"Created on 10 August 2017\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load the libraries required\n",
"import AstroConstants as AstCnst\n",
"import AstroFunctions as AstFn\n",
"import DynamicsFunctions as DynFn\n",
"import MathsFunctions as MathsFn\n",
"import Num_Integ as Integ\n",
"\n",
"import math\n",
"import numpy as np\n",
"\n",
"# Importing what's needed for nice plots.\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
"from matplotlib import rc\n",
"rc('font', **{'family': 'serif', 'serif': ['Helvetica']}); \n",
"rc('text', usetex=True)\n",
"params = {'text.latex.preamble' : [r'\\usepackage{amsmath}', r'\\usepackage{amssymb}']}\n",
"plt.rcParams.update(params)\n",
"\n",
"# Matplotlib inline. Comment out if you don't want inline plots.\n",
"%matplotlib inline\n",
"\n",
"# Libraries related to time keeping and formatting.\n",
"import datetime as dt\n",
"import pytz\n",
"import aniso8601"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Place the contents of the TLE file here.\n",
"## ISS (ZARYA) \n",
"tle_line1 = '1 25544U 98067A 17213.83387828 .00000874 00000-0 20415-4 0 9996';\n",
"tle_line2 = '2 25544 51.6427 170.7730 0006301 93.4612 353.3163 15.54246517 68858';"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Read the Keplerians as well as the epoch of validity of the TLE file\n",
"a,e,i,BigOmega,omega,E,nu,epoch = AstFn.fnTLEtoKeps(tle_line1,tle_line2);"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"semi-major axis in [km]\n",
"6782.48079627\n",
"eccentricity\n",
"0.0006301\n",
"inclination in [deg]\n",
"51.6427\n",
"RAAN in [deg]\n",
"170.773\n",
"argument of perigee in [deg]\n",
"93.4612\n",
"true anomaly in [deg]\n",
"96.4355054078\n",
"epoch\n",
"17213.83387828\n"
]
}
],
"source": [
"print 'semi-major axis in [km]' \n",
"print a\n",
"print 'eccentricity' \n",
"print e\n",
"print 'inclination in [deg]' \n",
"print i\n",
"print 'RAAN in [deg]' \n",
"print BigOmega\n",
"print 'argument of perigee in [deg]' \n",
"print omega\n",
"print 'true anomaly in [deg]' \n",
"print math.degrees(nu)\n",
"print 'epoch' \n",
"print epoch"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TLE epoch date is 2017-08-01\n",
"UTC time = 20 h 0 min 47.083392 s\n"
]
}
],
"source": [
"# Calculate epoch at which the TLE was valid\n",
"year,dayy, hrs, mins, secs, millisecs = AstFn.fn_Calculate_Epoch_Time(epoch);\n",
"todays_date = AstFn.fn_epoch_date(year,dayy);\n",
"print \"TLE epoch date is\", todays_date\n",
"print \"UTC time = \",hrs,\"h\",mins,\"min\",secs+millisecs,\"s\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a datetime object for the epoch of validity\n",
"timestamp_tle_epoch = dt.datetime(year=todays_date.year,month=todays_date.month,day=todays_date.day,hour=hrs,minute=mins,second=secs,microsecond=int(millisecs),tzinfo= pytz.utc)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ECI state vector read from TLE file\n",
"[ 6.71156486e+03 -3.57343479e+02 -9.14171985e+02 -5.44290159e-01\n",
" 4.83654902e+00 -5.92219633e+00]\n"
]
}
],
"source": [
"# Convert Keplerians to Cartesian state vector\n",
"xstate = AstFn.fnKepsToCarts(a,e, math.radians(i), math.radians(omega),math.radians(BigOmega),nu);\n",
"print 'ECI state vector read from TLE file'\n",
"print xstate"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the Kepler period is 92.649395 min\n"
]
}
],
"source": [
"# Find Kepler period\n",
"T = AstFn.fnKeplerOrbitalPeriod(a);\n",
"print \"the Kepler period is %f min\" %(T/60.)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# time step for simulation\n",
"delta_t = 1; #[s]\n",
"# duration of simulation.\n",
"# here, we have chosen to simulate the orbit over two orbits, at a constant time step of delta_t\n",
"timevec = np.arange(0,T*2 +delta_t,delta_t,dtype=np.float64);\n",
"# state vectors in the ECI frame. contain x, y, z position and x, y, z velocity in [km] & [km/s]\n",
"x_state = np.zeros([6,len(timevec)],dtype=np.float64);\n",
"# position state vectors in the ECEF frame\n",
"xecef = np.zeros([3,len(timevec)],dtype=np.float64);\n",
"# Latitude & longitude of the ground track\n",
"lat = np.zeros([len(timevec)],dtype=np.float64);\n",
"lon = np.zeros([len(timevec)],dtype=np.float64);"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"index = 0;\n",
"x_state[:,index] = xstate; # load initial conditions to the Initial Value Problem\n",
"theta_GMST = math.radians(AstFn.fn_Convert_Datetime_to_GMST(timestamp_tle_epoch)); \n",
"## Rotate ECI position vector by GMST angle to get ECEF position\n",
"theta_GMST = AstFn.fnZeroTo2Pi(theta_GMST);\n",
"xecef[:,index] = AstFn.fnECItoECEF(x_state[0:3,index],theta_GMST);\n",
"lat[index],lon[index] = AstFn.fnCarts_to_LatLon(xecef[:,index]); # convert Cartesian position vector to latitude, longitude\n",
"\n",
"for index in range(1,len(timevec)):\n",
" # Read the time and figure out the timestamps for each time instant\n",
" current_time = timevec[index];\n",
" hrs,mins,secs = AstFn.fnSeconds_To_Hours(current_time + (timestamp_tle_epoch.hour*60*60) + (timestamp_tle_epoch.minute*60)+ timestamp_tle_epoch.second);\n",
" dys = timestamp_tle_epoch.day + int(math.ceil(hrs/24)); \n",
" if hrs >= 24:\n",
" hrs = hrs - 24*int(math.ceil(hrs/24)) ;\n",
" # Perform the RK4 simulation step\n",
" x_state[:,index] = Integ.fnRK4_vector(DynFn.fnKepler_J2, delta_t, x_state[:,index-1],timevec[index]);\n",
" # Find the epoch object & figure out the GMST angle from the epoch\n",
" tle_epoch_test = dt.datetime(year=timestamp_tle_epoch.year,month=timestamp_tle_epoch.month,day=int(dys),hour=int(hrs),minute=int(mins),second=int(secs),microsecond=0,tzinfo= pytz.utc);\n",
" theta_GMST = math.radians(AstFn.fn_Convert_Datetime_to_GMST(tle_epoch_test)); \n",
" ## Rotate ECI position vector by GMST angle to get ECEF position\n",
" theta_GMST = AstFn.fnZeroTo2Pi(theta_GMST);\n",
" xecef[:,index] = AstFn.fnECItoECEF(x_state[0:3,index],theta_GMST);\n",
" lat[index],lon[index] = AstFn.fnCarts_to_LatLon(xecef[:,index]);# convert Cartesian position vector to latitude, longitude"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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6XHp0iSP3jhild/fuTs1SNXNuExO8qPd5qiaVW5G3CIkNYezOsVRxqsJLZV/C281b+Qjr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0KnYTvXFr8Oivnp8zvP56ujX0EoxN2Og6xZb/7+/sSnxXM54jKP4x8THBBcaHlT106lTZU2nJ5+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\nxMg9I1WGb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5Kk2J+f73ptr2f3lQ+K9ez7hRB0OtGJya9NplFyI3Zc2gHA2str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\nLPREREQDjIWeiIhogP0fanQCeercrFQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x3cb8748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot results \n",
"coastline_data= np.loadtxt('Coastline.txt',skiprows=1)\n",
"w, h = plt.figaspect(0.5)\n",
"fig = plt.figure(figsize=(w,h))\n",
"ax = fig.gca()\n",
"plt.rc('text', usetex=True)\n",
"plt.rc('font', family='serif');\n",
"plt.rc('font',family='helvetica');\n",
"params = {'legend.fontsize': 8,\n",
" 'legend.handlelength': 2}\n",
"plt.rcParams.update(params)\n",
"\n",
"groundtrack_title = timestamp_tle_epoch.strftime('%d %B %Y %H:%M:%S')\n",
"fig.suptitle(r\"\\textbf{ISS Ground Track on %s}\" %groundtrack_title,fontsize=14)\n",
"plt.plot(coastline_data[:,0],coastline_data[:,1],'g');\n",
"ax.set_xlabel(r'Longitude $[\\mathrm{^\\circ}]$',fontsize=12)\n",
"ax.set_ylabel(r'Latitude $[\\mathrm{^\\circ}]$',fontsize=12)\n",
"plt.xlim(-180,180);\n",
"plt.ylim(-90,90);\n",
"plt.yticks([-90,-80,-70,-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60,70,80,90]);\n",
"plt.xticks([-180,-150,-120,-90,-60,-30,0,30,60,90,120,150,180]);\n",
"plt.plot(math.degrees(lon[0]),math.degrees(lat[0]),'yo',markersize=5,label=timestamp_tle_epoch.isoformat() + 'Z');\n",
"for index in range(1,len(timevec)-1):\n",
" plt.plot(math.degrees(lon[index]),math.degrees(lat[index]),'b.',markersize=1);\n",
"\n",
"ax.grid(True);\n",
"\n",
"at = AnchoredText(\"AshivD\",prop=dict(size=5), frameon=True,loc=4)\n",
"at.patch.set_boxstyle(\"round,pad=0.,rounding_size=0.2\")\n",
"ax.add_artist(at)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
sjhonatan/intro-python | 08-for/for.ipynb | 2 | 1526 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Playlist no youtube\n",
"\n",
"https://www.youtube.com/playlist?list=PLvu-cXEstYRPZXyug9IqTJq4JtdBxXgeS"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import time\n",
"nome = 'tutorial de python'\n",
"vogais = ['a', 'e', 'i', 'o', 'u']"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"O número de vogais é 8\n"
]
}
],
"source": [
"numeroVogais = 2\n",
"for letras in nome:\n",
" if letras in vogais: \n",
" numeroVogais += 1\n",
"\n",
"print('O número de vogais é {}'.format(numeroVogais))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |