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Analysis of DC violent crimes by neighborhood
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import modules\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as tkr\n",
"import matplotlib.dates as mdates\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"# use fivethirtyeight plot style\n",
"plt.style.use('fivethirtyeight')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# loading multiple csv files into python list\n",
"# the code below will depend on the file path of the DC crime data files on your machines\n",
"tmp_lst = []\n",
"for filename in range(1,10):\n",
" path = 'c:/users/mra/desktop/admin/chart-it/DC violent crime 2015 spike/ASAP__from2000_01_01__to2015_08_16({}).csv'.format(filename) \n",
" frame = pd.read_csv(path, header = 0, low_memory = False) \n",
" tmp_lst.append(frame)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# use concat method to convert python list into pd dataframe\n",
"dc_crime = (pd.concat(tmp_lst, ignore_index = True))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# convert 'Start Date' column data into datetime data type\n",
"dc_crime['START_DATE'] = pd.to_datetime(dc_crime['START_DATE'], infer_datetime_format = True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# drop unused columns\n",
"dc_crime = dc_crime.drop([\"BLOCKXCOORD\", \"BLOCKYCOORD\", \"CENSUS_TRACT\", \"CCN\", \"SHIFT\", \"BLOCKSITEADDRESS\", \"REPORTDATETIME\", \"ANC\", \"DISTRICT\", \"PSA\", \"LASTMODIFIEDDATE\", \"BUSINESSIMPROVEMENTDISTRICT\", \"BLOCK_GROUP\", \"VOTING_PRECINCT\", \"END_DATE\"], 1) "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# set start date as dataframe index\n",
"dc_crime = dc_crime.set_index(\"START_DATE\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create dataframe for each homicide offense type and all violent crimes\n",
"dc_violent_crime_homicide = dc_crime[dc_crime[\"OFFENSE\"] == \"HOMICIDE\"]\n",
"dc_violent_crime = dc_crime[((dc_crime[\"OFFENSE\"] == \"SEX ABUSE\") | (dc_crime[\"OFFENSE\"] == \"ASSAULT W/DANGEROUS WEAPON\") |\\\n",
" (dc_crime[\"OFFENSE\"] == \"ROBBERY\"))]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#slicindg desired dates\n",
"dc_violent_crime_homicide_slice = [dc_violent_crime_homicide['2011-01-01': '2011-08-19'],\\\n",
" dc_violent_crime_homicide['2012-01-01': '2012-08-19'],\\\n",
" dc_violent_crime_homicide['2013-01-01': '2013-08-19'],\\\n",
" dc_violent_crime_homicide['2014-01-01': '2014-08-19'],\\\n",
" dc_violent_crime_homicide['2015-01-01': '2015-08-19']]\n",
"\n",
"dc_violent_crime_slice = [dc_violent_crime['2011-01-01': '2011-08-19'],\\\n",
" dc_violent_crime['2012-01-01': '2012-08-19'],\\\n",
" dc_violent_crime['2013-01-01': '2013-08-19'],\\\n",
" dc_violent_crime['2014-01-01': '2014-08-19'],\\\n",
" dc_violent_crime['2015-01-01': '2015-08-19']]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dc_violent_crime_homicide_slice = pd.concat(dc_violent_crime_homicide_slice)\n",
"dc_violent_crime_slice = pd.concat(dc_violent_crime_slice)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create data sets by ward for the period from January to August 19 to compare across years\n",
"\n",
"dc_violent_crime_homicide_ward1 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 1]\n",
"dc_violent_crime_homicide_ward2 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 2]\n",
"dc_violent_crime_homicide_ward3 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 3]\n",
"dc_violent_crime_homicide_ward4 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 4]\n",
"dc_violent_crime_homicide_ward5 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 5]\n",
"dc_violent_crime_homicide_ward6 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 6]\n",
"dc_violent_crime_homicide_ward7 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 7]\n",
"dc_violent_crime_homicide_ward8 = dc_violent_crime_homicide_slice[dc_violent_crime_homicide_slice[\"WARD\"] == 8]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create data sets by ward for the period from January to August 19 to compare across years\n",
"\n",
"dc_violent_crime_ward1 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 1]\n",
"dc_violent_crime_ward2 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 2]\n",
"dc_violent_crime_ward3 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 3]\n",
"dc_violent_crime_ward4 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 4]\n",
"dc_violent_crime_ward5 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 5]\n",
"dc_violent_crime_ward6 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 6]\n",
"dc_violent_crime_ward7 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 7]\n",
"dc_violent_crime_ward8 = dc_violent_crime_slice[dc_violent_crime_slice[\"WARD\"] == 8]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# re-sample all offenses by month for Jan to Aug 19 dataset\n",
"dc_violent_crime_homicide_ward1_annual = dc_violent_crime_homicide_ward1[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_homicide_ward2_annual = dc_violent_crime_homicide_ward2[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_homicide_ward3_annual = dc_violent_crime_homicide_ward3[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_homicide_ward4_annual = dc_violent_crime_homicide_ward4[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_homicide_ward5_annual = dc_violent_crime_homicide_ward5[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_homicide_ward6_annual = dc_violent_crime_homicide_ward6[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_homicide_ward7_annual = dc_violent_crime_homicide_ward7[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_homicide_ward8_annual = dc_violent_crime_homicide_ward8[\"OFFENSE\"].resample('A', how='count')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# re-sample all offenses by month for Jan to Aug 19 dataset\n",
"dc_violent_crime_ward1_annual = dc_violent_crime_ward1[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_ward2_annual = dc_violent_crime_ward2[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_ward3_annual = dc_violent_crime_ward3[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_ward4_annual = dc_violent_crime_ward4[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_ward5_annual = dc_violent_crime_ward5[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_ward6_annual = dc_violent_crime_ward6[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_ward7_annual = dc_violent_crime_ward7[\"OFFENSE\"].resample('A', how='count')\n",
"dc_violent_crime_ward8_annual = dc_violent_crime_ward8[\"OFFENSE\"].resample('A', how='count')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# remove incidents from before 2010 as these appear to be errors in the original data set\n",
"dc_violent_crime_homicide_ward1_annual = dc_violent_crime_homicide_ward1_annual[dc_violent_crime_homicide_ward1_annual.index.year > 2010]\n",
"dc_violent_crime_homicide_ward2_annual = dc_violent_crime_homicide_ward2_annual[dc_violent_crime_homicide_ward2_annual.index.year > 2010]\n",
"dc_violent_crime_homicide_ward3_annual = dc_violent_crime_homicide_ward3_annual[dc_violent_crime_homicide_ward3_annual.index.year > 2010]\n",
"dc_violent_crime_homicide_ward4_annual = dc_violent_crime_homicide_ward4_annual[dc_violent_crime_homicide_ward4_annual.index.year > 2010]\n",
"dc_violent_crime_homicide_ward5_annual = dc_violent_crime_homicide_ward5_annual[dc_violent_crime_homicide_ward5_annual.index.year > 2010]\n",
"dc_violent_crime_homicide_ward6_annual = dc_violent_crime_homicide_ward6_annual[dc_violent_crime_homicide_ward6_annual.index.year > 2010]\n",
"dc_violent_crime_homicide_ward7_annual = dc_violent_crime_homicide_ward7_annual[dc_violent_crime_homicide_ward7_annual.index.year > 2010]\n",
"dc_violent_crime_homicide_ward8_annual = dc_violent_crime_homicide_ward8_annual[dc_violent_crime_homicide_ward8_annual.index.year > 2010]\n",
"\n",
"dc_violent_crime_ward1_annual = dc_violent_crime_ward1_annual[dc_violent_crime_ward1_annual.index.year > 2010]\n",
"dc_violent_crime_ward2_annual = dc_violent_crime_ward2_annual[dc_violent_crime_ward2_annual.index.year > 2010]\n",
"dc_violent_crime_ward3_annual = dc_violent_crime_ward3_annual[dc_violent_crime_ward3_annual.index.year > 2010]\n",
"dc_violent_crime_ward4_annual = dc_violent_crime_ward4_annual[dc_violent_crime_ward4_annual.index.year > 2010]\n",
"dc_violent_crime_ward5_annual = dc_violent_crime_ward5_annual[dc_violent_crime_ward5_annual.index.year > 2010]\n",
"dc_violent_crime_ward6_annual = dc_violent_crime_ward6_annual[dc_violent_crime_ward6_annual.index.year > 2010]\n",
"dc_violent_crime_ward7_annual = dc_violent_crime_ward7_annual[dc_violent_crime_ward7_annual.index.year > 2010]\n",
"dc_violent_crime_ward8_annual = dc_violent_crime_ward8_annual[dc_violent_crime_ward8_annual.index.year > 2010]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# change dataframe name to Ward number for purposes of chart legend\n",
"dc_violent_crime_homicide_ward1_annual.name = \"WARD 1\"\n",
"dc_violent_crime_homicide_ward2_annual.name = \"WARD 2\"\n",
"dc_violent_crime_homicide_ward3_annual.name = \"WARD 3\"\n",
"dc_violent_crime_homicide_ward4_annual.name = \"WARD 4\"\n",
"dc_violent_crime_homicide_ward5_annual.name = \"WARD 5\"\n",
"dc_violent_crime_homicide_ward6_annual.name = \"WARD 6\"\n",
"dc_violent_crime_homicide_ward7_annual.name = \"WARD 7\"\n",
"dc_violent_crime_homicide_ward8_annual.name = \"WARD 8\"\n",
"\n",
"dc_violent_crime_ward1_annual.name = \"WARD 1\"\n",
"dc_violent_crime_ward2_annual.name = \"WARD 2\"\n",
"dc_violent_crime_ward3_annual.name = \"WARD 3\"\n",
"dc_violent_crime_ward4_annual.name = \"WARD 4\"\n",
"dc_violent_crime_ward5_annual.name = \"WARD 5\"\n",
"dc_violent_crime_ward6_annual.name = \"WARD 6\"\n",
"dc_violent_crime_ward7_annual.name = \"WARD 7\"\n",
"dc_violent_crime_ward8_annual.name = \"WARD 8\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xc4a03c8>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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ZLlbJsTi1Fif+xd67NQDwxhtvwMnJCevWrYNEIsH8+fMxatQovP7663o7x8G8RuzOqddq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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd90db70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot data\n",
"\n",
"# formatter function to format y-axis labels to have a comma at the thousands place \n",
"def func(x, pos): \n",
" s = '{:0,d}'.format(int(x))\n",
" return s\n",
"\n",
"y_format = tkr.FuncFormatter(func) # make formatter\n",
"\n",
"plt.subplot(3,3,1)\n",
"\n",
"ax10 = dc_violent_crime_homicide_ward1_annual.plot(kind='line', figsize=(10,6),fontsize=14)\n",
"ax10.set_xlabel('', size='medium')\n",
"ax10.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax10.yaxis.set_major_formatter(y_format)\n",
"ax10.set_title(\"Ward 1\", fontsize = 14)\n",
"ax10.set_ylim(0, 8)\n",
"plt.yticks(np.arange(0,10,2))\n",
"ax10.grid(False)\n",
"\n",
"plt.subplot(3,3,2)\n",
"\n",
"ax11 = dc_violent_crime_homicide_ward2_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax11.set_xlabel('', size='medium')\n",
"ax11.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax11.yaxis.set_major_formatter(y_format)\n",
"ax11.set_title(\"Ward 2\", fontsize = 14)\n",
"ax11.set_ylim(0, 6)\n",
"ax11.grid(False)\n",
"\n",
"# plt.subplot(3,3,3)\n",
"\n",
"# ax12 = dc_violent_crime_homicide_ward3_annual.plot(kind='line',figsize=(8,4), fontsize=14)\n",
"# ax12.set_xlabel('', size='medium')\n",
"# ax12.tick_params(\n",
"# axis='both',\n",
"# which='both', \n",
"# bottom='off', \n",
"# top='off', \n",
"# right='off',\n",
"# left='off',\n",
"# labelbottom='on')\n",
"\n",
"# ax12.yaxis.set_major_formatter(y_format)\n",
"# ax12.set_title(\"Ward 3\", fontsize = 14)\n",
"# ax12.set_ylim(0,6)\n",
"\n",
"plt.subplot(3,3,3)\n",
"\n",
"ax13 = dc_violent_crime_homicide_ward4_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 4\", fontsize = 14)\n",
"ax13.set_ylim(0, 10)\n",
"ax13.grid(False)\n",
"\n",
"plt.subplot(3,3,4)\n",
"\n",
"ax13 = dc_violent_crime_homicide_ward5_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 5\", fontsize = 14)\n",
"ax13.set_ylim(0, 18)\n",
"ax13.grid(False)\n",
"plt.yticks(np.arange(0,18,4))\n",
"\n",
"plt.subplot(3,3,5)\n",
"\n",
"ax13 = dc_violent_crime_homicide_ward6_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 6\", fontsize = 14)\n",
"ax13.set_ylim(0, 12)\n",
"ax13.grid(False)\n",
"\n",
"plt.subplot(3,3,6)\n",
"\n",
"ax13 = dc_violent_crime_homicide_ward7_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 7\", fontsize = 14)\n",
"ax13.set_ylim(0, 20)\n",
"ax13.grid(False)\n",
"\n",
"plt.subplot(3,3,7)\n",
"\n",
"ax13 = dc_violent_crime_homicide_ward8_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 8\", fontsize = 14)\n",
"ax13.set_ylim(0, 40)\n",
"ax13.grid(False)\n",
"plt.yticks(np.arange(0,50,10))\n",
"\n",
"plt.tight_layout()\n",
"\n",
"ax13.text(0.5, -0.7, \"From: chart-it (MikeRAzar.com/chart-it) | Source: http://data.octo.dc.gov/metadata.aspx?id=3\", \n",
" fontsize=12, transform=ax13.transAxes) "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xcc3bb70>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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CG8EO/PDDD+XSF962fft2zGZzhW1ufVFksbDyVB7fXSwod8xLJeWm3jYkfriZAx/2cBbr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YAAAg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\nQpiV/wMRkRrpJ0301QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd901e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot data\n",
"\n",
"# formatter function to format y-axis labels to have a comma at the thousands place \n",
"def func(x, pos): \n",
" s = '{:0,d}'.format(int(x))\n",
" return s\n",
"\n",
"y_format = tkr.FuncFormatter(func) # make formatter\n",
"\n",
"plt.subplot(3,3,1)\n",
"\n",
"ax10 = dc_violent_crime_ward1_annual.plot(kind='line', figsize=(8,4),fontsize=14)\n",
"ax10.set_xlabel('', size='medium')\n",
"ax10.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax10.yaxis.set_major_formatter(y_format)\n",
"ax10.set_title(\"Ward 1\", fontsize = 14)\n",
"# ax10.set_ylim(0, 8)\n",
"ax10.grid(False)\n",
"\n",
"plt.subplot(3,3,2)\n",
"\n",
"ax11 = dc_violent_crime_ward2_annual.plot(kind='line', figsize=(8,4), fontsize=14)\n",
"ax11.set_xlabel('', size='medium')\n",
"ax11.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax11.yaxis.set_major_formatter(y_format)\n",
"ax11.set_title(\"Ward 2\", fontsize = 14)\n",
"# ax11.set_ylim(0, 6)\n",
"ax11.grid(False)\n",
"plt.yticks(np.arange(280,460,40))\n",
"\n",
"plt.subplot(3,3,3)\n",
"\n",
"ax12 = dc_violent_crime_ward3_annual.plot(kind='line',figsize=(8,4), fontsize=14)\n",
"ax12.set_xlabel('', size='medium')\n",
"ax12.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax12.yaxis.set_major_formatter(y_format)\n",
"ax12.set_title(\"Ward 3\", fontsize = 14)\n",
"# ax12.set_ylim(0,6)\n",
"ax12.grid(False)\n",
"\n",
"plt.subplot(3,3,4)\n",
"\n",
"ax13 = dc_violent_crime_ward4_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 4\", fontsize = 14)\n",
"# ax13.set_ylim(0, 10)\n",
"ax13.grid(False)\n",
"\n",
"plt.subplot(3,3,5)\n",
"\n",
"ax13 = dc_violent_crime_ward5_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 5\", fontsize = 14)\n",
"# ax13.set_ylim(0, 14)\n",
"ax13.grid(False)\n",
"\n",
"plt.subplot(3,3,6)\n",
"\n",
"ax13 = dc_violent_crime_ward6_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 6\", fontsize = 14)\n",
"# ax13.set_ylim(0, 12)\n",
"ax13.grid(False)\n",
"plt.yticks(np.arange(420,600,40))\n",
"\n",
"\n",
"plt.subplot(3,3,7)\n",
"\n",
"ax13 = dc_violent_crime_ward7_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 7\", fontsize = 14)\n",
"# ax13.set_ylim(0, 20)\n",
"ax13.grid(False)\n",
"\n",
"plt.subplot(3,3,8)\n",
"\n",
"ax13 = dc_violent_crime_ward8_annual.plot(kind='line', figsize=(10,6), fontsize=14)\n",
"ax13.set_xlabel('', size='medium')\n",
"ax13.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax13.yaxis.set_major_formatter(y_format)\n",
"ax13.set_title(\"Ward 8\", fontsize = 14)\n",
"# ax13.set_ylim(0, 35)\n",
"ax13.grid(False)\n",
"\n",
"plt.tight_layout()\n",
"\n",
"ax13.text(-0.9, -0.7, \"From: chart-it (MikeRAzar.com/chart-it) | Source: http://data.octo.dc.gov/metadata.aspx?id=3\", \n",
" fontsize=12, transform=ax13.transAxes) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#create dataframe with homicides and violent crimes\n",
"sum_homicides = dc_violent_crime_homicide[(dc_violent_crime_homicide.index.month < 9) & (dc_violent_crime_homicide.index.year > 2010)]\n",
"sum_violence = dc_violent_crime[(dc_violent_crime.index.month < 9) & (dc_violent_crime.index.year > 2010)]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#create dataframe with 2015 homicides and violent crimes\n",
"sum_homicides_2015 = sum_homicides['2015'].groupby(\"WARD\").count()['OFFENSE']\n",
"sum_violence_2015 = sum_violence['2015'].groupby(\"WARD\").count()['OFFENSE']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#create dataframe with 2014 homicides and violent crimes\n",
"sum_homicides_2014 = sum_homicides['2014'].groupby(\"WARD\").count()['OFFENSE']\n",
"sum_violence_2014 = sum_violence['2014'].groupby(\"WARD\").count()['OFFENSE']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#create dataframe comparing 2014 and 2015 homicides and violent crimes\n",
"sum_homicides_2014_2015 = pd.concat([sum_homicides_2014,sum_homicides_2015], axis=1)\n",
"sum_violence_2014_2015 = pd.concat([sum_violence_2014,sum_violence_2015], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#change column names for clarity\n",
"sum_homicides_2014_2015.columns = ['2014', '2015']\n",
"sum_violence_2014_2015.columns = ['2014', '2015']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#add column with change in homicide incidents and violent crime incidents\n",
"sum_homicides_2014_2015['Increase'] = sum_homicides_2014_2015['2015'] - sum_homicides_2014_2015['2014']\n",
"sum_violence_2014_2015['Increase'] = sum_violence_2014_2015['2015'] - sum_violence_2014_2015['2014']"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
}
],
"source": [
"#manually adjust number in ward 3 as no murders reported in 2014\n",
"sum_homicides_2014_2015['Increase'][3] = 5"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#add column with pct change in homicide rate\n",
"sum_homicides_2014_2015['Pct Increase'] = sum_homicides_2014_2015['2015'] / sum_homicides_2014_2015['2014'] - 1\n",
"sum_homicides_2014_2015['Pct of Total Increase'] = sum_homicides_2014_2015['Increase'] / sum_homicides_2014_2015['Increase'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>Increase</th>\n",
" <th>Pct Increase</th>\n",
" <th>Pct of Total Increase</th>\n",
" </tr>\n",
" <tr>\n",
" <th>WARD</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>8</th>\n",
" <td>23</td>\n",
" <td>37</td>\n",
" <td>14</td>\n",
" <td>0.608696</td>\n",
" <td>0.388889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10</td>\n",
" <td>17</td>\n",
" <td>7</td>\n",
" <td>0.700000</td>\n",
" <td>0.194444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>0.138889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>4.000000</td>\n",
" <td>0.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>0.571429</td>\n",
" <td>0.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0.142857</td>\n",
" <td>0.027778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0.250000</td>\n",
" <td>0.027778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2014 2015 Increase Pct Increase Pct of Total Increase\n",
"WARD \n",
"8 23 37 14 0.608696 0.388889\n",
"5 10 17 7 0.700000 0.194444\n",
"3 NaN 5 5 NaN 0.138889\n",
"2 1 5 4 4.000000 0.111111\n",
"6 7 11 4 0.571429 0.111111\n",
"1 7 8 1 0.142857 0.027778\n",
"4 4 5 1 0.250000 0.027778\n",
"7 18 18 0 0.000000 0.000000"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#view new dataframe sorted by largest contributor to homicide rate increase\n",
"sum_homicides_2014_2015.sort('Pct of Total Increase', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#add column with % increase in violent crimes\n",
"sum_violence_2014_2015['Pct Increase'] = sum_violence_2014_2015['2015'] / sum_violence_2014_2015['2014'] - 1"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2014 3854.000000\n",
"2015 3821.000000\n",
"Increase -33.000000\n",
"Pct Increase -0.089585\n",
"dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#sum of violent crime in the city\n",
"sum_violence_2014_2015.sum(0)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>Increase</th>\n",
" <th>Pct Increase</th>\n",
" </tr>\n",
" <tr>\n",
" <th>WARD</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>450</td>\n",
" <td>531</td>\n",
" <td>81</td>\n",
" <td>0.180000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>540</td>\n",
" <td>613</td>\n",
" <td>73</td>\n",
" <td>0.135185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>365</td>\n",
" <td>404</td>\n",
" <td>39</td>\n",
" <td>0.106849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>434</td>\n",
" <td>460</td>\n",
" <td>26</td>\n",
" <td>0.059908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>304</td>\n",
" <td>300</td>\n",
" <td>-4</td>\n",
" <td>-0.013158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>772</td>\n",
" <td>707</td>\n",
" <td>-65</td>\n",
" <td>-0.084197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>925</td>\n",
" <td>761</td>\n",
" <td>-164</td>\n",
" <td>-0.177297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>64</td>\n",
" <td>45</td>\n",
" <td>-19</td>\n",
" <td>-0.296875</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2014 2015 Increase Pct Increase\n",
"WARD \n",
"6 450 531 81 0.180000\n",
"5 540 613 73 0.135185\n",
"4 365 404 39 0.106849\n",
"1 434 460 26 0.059908\n",
"2 304 300 -4 -0.013158\n",
"8 772 707 -65 -0.084197\n",
"7 925 761 -164 -0.177297\n",
"3 64 45 -19 -0.296875"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#view new dataframe sorted by largest change in violent crimes\n",
"sum_violence_2014_2015.sort('Pct Increase', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create new crime dataframe for use to calculate stats by neighborhood cluster\n",
"dc_crime_slice = pd.concat([dc_violent_crime_slice, dc_violent_crime_homicide_slice])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n"
]
},
{
"data": {
"text/plain": [
"NEIGHBORHOODCLUSTER\n",
"1 84.2\n",
"2 323.8\n",
"3 138.6\n",
"4 31.4\n",
"5 24.4\n",
"6 110.8\n",
"7 96.2\n",
"8 183.4\n",
"9 60.0\n",
"10 7.6\n",
"11 19.6\n",
"12 6.6\n",
"13 4.0\n",
"14 9.2\n",
"15 15.6\n",
"16 10.6\n",
"17 102.8\n",
"18 233.4\n",
"19 56.2\n",
"20 37.2\n",
"21 156.6\n",
"22 119.0\n",
"23 205.0\n",
"24 33.4\n",
"25 187.2\n",
"26 116.6\n",
"27 12.4\n",
"28 74.6\n",
"29 22.4\n",
"30 91.0\n",
"31 184.2\n",
"32 159.4\n",
"33 186.4\n",
"34 134.4\n",
"35 36.4\n",
"36 89.6\n",
"37 115.0\n",
"38 137.2\n",
"39 300.2\n",
"dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#worse off neighborhood cluster\n",
"dc_crime_slice[(dc_crime_slice[\"OFFENSE\"] == \"HOMICIDE\") | (dc_crime_slice[\"OFFENSE\"] == \"SEX ABUSE\") |\\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ASSAULT W/DANGEROUS WEAPON\") | \\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ROBBERY\")].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].unstack().mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n",
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
}
],
"source": [
"#worse off neighborhood cluster\n",
"dc_crime_by_neighborhood = dc_crime_slice[(dc_crime_slice[\"OFFENSE\"] == \"HOMICIDE\") | (dc_crime_slice[\"OFFENSE\"] == \"SEX ABUSE\") |\\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ASSAULT W/DANGEROUS WEAPON\") | \\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ROBBERY\")].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].pct_change().unstack().sort('2015-12-31 00:00:00', ascending=True)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n"
]
}
],
"source": [
"dc_crime_by_neighborhood[\"Avg Annual Crimes\"] = dc_crime_slice[(dc_crime_slice[\"OFFENSE\"] == \"HOMICIDE\") | \\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"SEX ABUSE\") |\\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ASSAULT W/DANGEROUS WEAPON\") | \\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ROBBERY\")].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].unstack().mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dc_crime_by_neighborhood = dc_crime_by_neighborhood[dc_crime_by_neighborhood[\"Avg Annual Crimes\"] > dc_crime_by_neighborhood['Avg Annual Crimes'].quantile(0.2)]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>START_DATE</th>\n",
" <th>2011-12-31 00:00:00</th>\n",
" <th>2012-12-31 00:00:00</th>\n",
" <th>2013-12-31 00:00:00</th>\n",
" <th>2014-12-31 00:00:00</th>\n",
" <th>2015-12-31 00:00:00</th>\n",
" <th>Avg Annual Crimes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NEIGHBORHOODCLUSTER</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>19</th>\n",
" <td>-0.789700</td>\n",
" <td>0.020408</td>\n",
" <td>0.200000</td>\n",
" <td>-0.183333</td>\n",
" <td>0.489796</td>\n",
" <td>56.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>-0.115044</td>\n",
" <td>-0.090000</td>\n",
" <td>-0.483516</td>\n",
" <td>0.489362</td>\n",
" <td>84.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.317647</td>\n",
" <td>0.120690</td>\n",
" <td>-0.007692</td>\n",
" <td>-0.286822</td>\n",
" <td>0.391304</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>13.714286</td>\n",
" <td>0.135922</td>\n",
" <td>-0.170940</td>\n",
" <td>-0.134021</td>\n",
" <td>0.345238</td>\n",
" <td>102.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>5.333333</td>\n",
" <td>0.251462</td>\n",
" <td>-0.172897</td>\n",
" <td>-0.096045</td>\n",
" <td>0.337500</td>\n",
" <td>187.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.312500</td>\n",
" <td>0.267857</td>\n",
" <td>-0.070423</td>\n",
" <td>0.010101</td>\n",
" <td>0.230000</td>\n",
" <td>205.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-0.500000</td>\n",
" <td>0.261682</td>\n",
" <td>-0.251852</td>\n",
" <td>0.079208</td>\n",
" <td>0.201835</td>\n",
" <td>116.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3.628571</td>\n",
" <td>-0.012346</td>\n",
" <td>-0.075000</td>\n",
" <td>-0.033784</td>\n",
" <td>0.188811</td>\n",
" <td>156.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>-0.460733</td>\n",
" <td>0.650485</td>\n",
" <td>-0.152941</td>\n",
" <td>-0.187500</td>\n",
" <td>0.179487</td>\n",
" <td>134.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5.227273</td>\n",
" <td>0.087591</td>\n",
" <td>-0.268456</td>\n",
" <td>-0.321101</td>\n",
" <td>0.148649</td>\n",
" <td>110.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.314286</td>\n",
" <td>-0.107527</td>\n",
" <td>0.111446</td>\n",
" <td>-0.295393</td>\n",
" <td>0.100000</td>\n",
" <td>323.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.411765</td>\n",
" <td>-0.066667</td>\n",
" <td>-0.223214</td>\n",
" <td>-0.103448</td>\n",
" <td>0.076923</td>\n",
" <td>96.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.787611</td>\n",
" <td>0.267327</td>\n",
" <td>0.000000</td>\n",
" <td>-0.140625</td>\n",
" <td>0.059091</td>\n",
" <td>233.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1.452381</td>\n",
" <td>0.121359</td>\n",
" <td>-0.255411</td>\n",
" <td>-0.127907</td>\n",
" <td>0.053333</td>\n",
" <td>183.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-0.424658</td>\n",
" <td>-0.309524</td>\n",
" <td>0.586207</td>\n",
" <td>-0.260870</td>\n",
" <td>0.029412</td>\n",
" <td>37.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>0.400000</td>\n",
" <td>0.302521</td>\n",
" <td>-0.077419</td>\n",
" <td>-0.062937</td>\n",
" <td>0.007463</td>\n",
" <td>137.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>9.142857</td>\n",
" <td>0.380282</td>\n",
" <td>-0.306122</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>74.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-0.849593</td>\n",
" <td>0.027027</td>\n",
" <td>-0.026316</td>\n",
" <td>-0.243243</td>\n",
" <td>-0.035714</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2.416667</td>\n",
" <td>0.146341</td>\n",
" <td>0.031915</td>\n",
" <td>-0.072165</td>\n",
" <td>-0.055556</td>\n",
" <td>89.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>1.429630</td>\n",
" <td>0.009146</td>\n",
" <td>0.000000</td>\n",
" <td>-0.196375</td>\n",
" <td>-0.078947</td>\n",
" <td>300.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.653061</td>\n",
" <td>-0.092593</td>\n",
" <td>0.401361</td>\n",
" <td>0.029126</td>\n",
" <td>-0.084906</td>\n",
" <td>184.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>-0.064286</td>\n",
" <td>0.213740</td>\n",
" <td>0.484277</td>\n",
" <td>-0.088983</td>\n",
" <td>-0.111628</td>\n",
" <td>186.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>4.692308</td>\n",
" <td>-0.081081</td>\n",
" <td>0.529412</td>\n",
" <td>0.067308</td>\n",
" <td>-0.117117</td>\n",
" <td>91.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.103448</td>\n",
" <td>0.230769</td>\n",
" <td>-0.468750</td>\n",
" <td>0.470588</td>\n",
" <td>-0.120000</td>\n",
" <td>24.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.612903</td>\n",
" <td>-0.194444</td>\n",
" <td>0.034483</td>\n",
" <td>0.100000</td>\n",
" <td>-0.121212</td>\n",
" <td>31.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.575949</td>\n",
" <td>0.059701</td>\n",
" <td>-0.169014</td>\n",
" <td>-0.050847</td>\n",
" <td>-0.160714</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.416084</td>\n",
" <td>-0.149701</td>\n",
" <td>0.253521</td>\n",
" <td>-0.365169</td>\n",
" <td>-0.176991</td>\n",
" <td>138.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>0.458824</td>\n",
" <td>0.185484</td>\n",
" <td>-0.238095</td>\n",
" <td>-0.044643</td>\n",
" <td>-0.205607</td>\n",
" <td>115.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>-0.319588</td>\n",
" <td>0.098485</td>\n",
" <td>0.337931</td>\n",
" <td>-0.041237</td>\n",
" <td>-0.247312</td>\n",
" <td>159.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>-0.753623</td>\n",
" <td>0.235294</td>\n",
" <td>0.023810</td>\n",
" <td>-0.093023</td>\n",
" <td>-0.384615</td>\n",
" <td>36.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-0.867647</td>\n",
" <td>1.222222</td>\n",
" <td>0.450000</td>\n",
" <td>0.413793</td>\n",
" <td>-0.682927</td>\n",
" <td>22.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"START_DATE 2011-12-31 00:00:00 2012-12-31 00:00:00 \\\n",
"NEIGHBORHOODCLUSTER \n",
"19 -0.789700 0.020408 \n",
"1 NaN -0.115044 \n",
"22 -0.317647 0.120690 \n",
"17 13.714286 0.135922 \n",
"25 5.333333 0.251462 \n",
"23 0.312500 0.267857 \n",
"26 -0.500000 0.261682 \n",
"21 3.628571 -0.012346 \n",
"34 -0.460733 0.650485 \n",
"6 5.227273 0.087591 \n",
"2 4.314286 -0.107527 \n",
"7 0.411765 -0.066667 \n",
"18 0.787611 0.267327 \n",
"8 1.452381 0.121359 \n",
"20 -0.424658 -0.309524 \n",
"38 0.400000 0.302521 \n",
"28 9.142857 0.380282 \n",
"24 -0.849593 0.027027 \n",
"36 2.416667 0.146341 \n",
"39 1.429630 0.009146 \n",
"31 0.653061 -0.092593 \n",
"33 -0.064286 0.213740 \n",
"30 4.692308 -0.081081 \n",
"5 -0.103448 0.230769 \n",
"4 -0.612903 -0.194444 \n",
"9 -0.575949 0.059701 \n",
"3 -0.416084 -0.149701 \n",
"37 0.458824 0.185484 \n",
"32 -0.319588 0.098485 \n",
"35 -0.753623 0.235294 \n",
"29 -0.867647 1.222222 \n",
"\n",
"START_DATE 2013-12-31 00:00:00 2014-12-31 00:00:00 \\\n",
"NEIGHBORHOODCLUSTER \n",
"19 0.200000 -0.183333 \n",
"1 -0.090000 -0.483516 \n",
"22 -0.007692 -0.286822 \n",
"17 -0.170940 -0.134021 \n",
"25 -0.172897 -0.096045 \n",
"23 -0.070423 0.010101 \n",
"26 -0.251852 0.079208 \n",
"21 -0.075000 -0.033784 \n",
"34 -0.152941 -0.187500 \n",
"6 -0.268456 -0.321101 \n",
"2 0.111446 -0.295393 \n",
"7 -0.223214 -0.103448 \n",
"18 0.000000 -0.140625 \n",
"8 -0.255411 -0.127907 \n",
"20 0.586207 -0.260870 \n",
"38 -0.077419 -0.062937 \n",
"28 -0.306122 0.000000 \n",
"24 -0.026316 -0.243243 \n",
"36 0.031915 -0.072165 \n",
"39 0.000000 -0.196375 \n",
"31 0.401361 0.029126 \n",
"33 0.484277 -0.088983 \n",
"30 0.529412 0.067308 \n",
"5 -0.468750 0.470588 \n",
"4 0.034483 0.100000 \n",
"9 -0.169014 -0.050847 \n",
"3 0.253521 -0.365169 \n",
"37 -0.238095 -0.044643 \n",
"32 0.337931 -0.041237 \n",
"35 0.023810 -0.093023 \n",
"29 0.450000 0.413793 \n",
"\n",
"START_DATE 2015-12-31 00:00:00 Avg Annual Crimes \n",
"NEIGHBORHOODCLUSTER \n",
"19 0.489796 56.2 \n",
"1 0.489362 84.2 \n",
"22 0.391304 119.0 \n",
"17 0.345238 102.8 \n",
"25 0.337500 187.2 \n",
"23 0.230000 205.0 \n",
"26 0.201835 116.6 \n",
"21 0.188811 156.6 \n",
"34 0.179487 134.4 \n",
"6 0.148649 110.8 \n",
"2 0.100000 323.8 \n",
"7 0.076923 96.2 \n",
"18 0.059091 233.4 \n",
"8 0.053333 183.4 \n",
"20 0.029412 37.2 \n",
"38 0.007463 137.2 \n",
"28 0.000000 74.6 \n",
"24 -0.035714 33.4 \n",
"36 -0.055556 89.6 \n",
"39 -0.078947 300.2 \n",
"31 -0.084906 184.2 \n",
"33 -0.111628 186.4 \n",
"30 -0.117117 91.0 \n",
"5 -0.120000 24.4 \n",
"4 -0.121212 31.4 \n",
"9 -0.160714 60.0 \n",
"3 -0.176991 138.6 \n",
"37 -0.205607 115.0 \n",
"32 -0.247312 159.4 \n",
"35 -0.384615 36.4 \n",
"29 -0.682927 22.4 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#sort by most regressed \n",
"dc_crime_by_neighborhood.sort(pd.Timestamp('2015-12-31 00:00:00', offset='A-DEC'), ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>START_DATE</th>\n",
" <th>2011-12-31 00:00:00</th>\n",
" <th>2012-12-31 00:00:00</th>\n",
" <th>2013-12-31 00:00:00</th>\n",
" <th>2014-12-31 00:00:00</th>\n",
" <th>2015-12-31 00:00:00</th>\n",
" <th>Avg Annual Crimes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NEIGHBORHOODCLUSTER</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>29</th>\n",
" <td>-0.867647</td>\n",
" <td>1.222222</td>\n",
" <td>0.450000</td>\n",
" <td>0.413793</td>\n",
" <td>-0.682927</td>\n",
" <td>22.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>-0.753623</td>\n",
" <td>0.235294</td>\n",
" <td>0.023810</td>\n",
" <td>-0.093023</td>\n",
" <td>-0.384615</td>\n",
" <td>36.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>-0.319588</td>\n",
" <td>0.098485</td>\n",
" <td>0.337931</td>\n",
" <td>-0.041237</td>\n",
" <td>-0.247312</td>\n",
" <td>159.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>0.458824</td>\n",
" <td>0.185484</td>\n",
" <td>-0.238095</td>\n",
" <td>-0.044643</td>\n",
" <td>-0.205607</td>\n",
" <td>115.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.416084</td>\n",
" <td>-0.149701</td>\n",
" <td>0.253521</td>\n",
" <td>-0.365169</td>\n",
" <td>-0.176991</td>\n",
" <td>138.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.575949</td>\n",
" <td>0.059701</td>\n",
" <td>-0.169014</td>\n",
" <td>-0.050847</td>\n",
" <td>-0.160714</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.612903</td>\n",
" <td>-0.194444</td>\n",
" <td>0.034483</td>\n",
" <td>0.100000</td>\n",
" <td>-0.121212</td>\n",
" <td>31.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.103448</td>\n",
" <td>0.230769</td>\n",
" <td>-0.468750</td>\n",
" <td>0.470588</td>\n",
" <td>-0.120000</td>\n",
" <td>24.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>4.692308</td>\n",
" <td>-0.081081</td>\n",
" <td>0.529412</td>\n",
" <td>0.067308</td>\n",
" <td>-0.117117</td>\n",
" <td>91.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>-0.064286</td>\n",
" <td>0.213740</td>\n",
" <td>0.484277</td>\n",
" <td>-0.088983</td>\n",
" <td>-0.111628</td>\n",
" <td>186.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.653061</td>\n",
" <td>-0.092593</td>\n",
" <td>0.401361</td>\n",
" <td>0.029126</td>\n",
" <td>-0.084906</td>\n",
" <td>184.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>1.429630</td>\n",
" <td>0.009146</td>\n",
" <td>0.000000</td>\n",
" <td>-0.196375</td>\n",
" <td>-0.078947</td>\n",
" <td>300.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2.416667</td>\n",
" <td>0.146341</td>\n",
" <td>0.031915</td>\n",
" <td>-0.072165</td>\n",
" <td>-0.055556</td>\n",
" <td>89.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-0.849593</td>\n",
" <td>0.027027</td>\n",
" <td>-0.026316</td>\n",
" <td>-0.243243</td>\n",
" <td>-0.035714</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>9.142857</td>\n",
" <td>0.380282</td>\n",
" <td>-0.306122</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>74.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>0.400000</td>\n",
" <td>0.302521</td>\n",
" <td>-0.077419</td>\n",
" <td>-0.062937</td>\n",
" <td>0.007463</td>\n",
" <td>137.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-0.424658</td>\n",
" <td>-0.309524</td>\n",
" <td>0.586207</td>\n",
" <td>-0.260870</td>\n",
" <td>0.029412</td>\n",
" <td>37.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1.452381</td>\n",
" <td>0.121359</td>\n",
" <td>-0.255411</td>\n",
" <td>-0.127907</td>\n",
" <td>0.053333</td>\n",
" <td>183.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.787611</td>\n",
" <td>0.267327</td>\n",
" <td>0.000000</td>\n",
" <td>-0.140625</td>\n",
" <td>0.059091</td>\n",
" <td>233.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.411765</td>\n",
" <td>-0.066667</td>\n",
" <td>-0.223214</td>\n",
" <td>-0.103448</td>\n",
" <td>0.076923</td>\n",
" <td>96.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.314286</td>\n",
" <td>-0.107527</td>\n",
" <td>0.111446</td>\n",
" <td>-0.295393</td>\n",
" <td>0.100000</td>\n",
" <td>323.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5.227273</td>\n",
" <td>0.087591</td>\n",
" <td>-0.268456</td>\n",
" <td>-0.321101</td>\n",
" <td>0.148649</td>\n",
" <td>110.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>-0.460733</td>\n",
" <td>0.650485</td>\n",
" <td>-0.152941</td>\n",
" <td>-0.187500</td>\n",
" <td>0.179487</td>\n",
" <td>134.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3.628571</td>\n",
" <td>-0.012346</td>\n",
" <td>-0.075000</td>\n",
" <td>-0.033784</td>\n",
" <td>0.188811</td>\n",
" <td>156.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-0.500000</td>\n",
" <td>0.261682</td>\n",
" <td>-0.251852</td>\n",
" <td>0.079208</td>\n",
" <td>0.201835</td>\n",
" <td>116.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.312500</td>\n",
" <td>0.267857</td>\n",
" <td>-0.070423</td>\n",
" <td>0.010101</td>\n",
" <td>0.230000</td>\n",
" <td>205.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>5.333333</td>\n",
" <td>0.251462</td>\n",
" <td>-0.172897</td>\n",
" <td>-0.096045</td>\n",
" <td>0.337500</td>\n",
" <td>187.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>13.714286</td>\n",
" <td>0.135922</td>\n",
" <td>-0.170940</td>\n",
" <td>-0.134021</td>\n",
" <td>0.345238</td>\n",
" <td>102.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.317647</td>\n",
" <td>0.120690</td>\n",
" <td>-0.007692</td>\n",
" <td>-0.286822</td>\n",
" <td>0.391304</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>-0.115044</td>\n",
" <td>-0.090000</td>\n",
" <td>-0.483516</td>\n",
" <td>0.489362</td>\n",
" <td>84.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-0.789700</td>\n",
" <td>0.020408</td>\n",
" <td>0.200000</td>\n",
" <td>-0.183333</td>\n",
" <td>0.489796</td>\n",
" <td>56.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"START_DATE 2011-12-31 00:00:00 2012-12-31 00:00:00 \\\n",
"NEIGHBORHOODCLUSTER \n",
"29 -0.867647 1.222222 \n",
"35 -0.753623 0.235294 \n",
"32 -0.319588 0.098485 \n",
"37 0.458824 0.185484 \n",
"3 -0.416084 -0.149701 \n",
"9 -0.575949 0.059701 \n",
"4 -0.612903 -0.194444 \n",
"5 -0.103448 0.230769 \n",
"30 4.692308 -0.081081 \n",
"33 -0.064286 0.213740 \n",
"31 0.653061 -0.092593 \n",
"39 1.429630 0.009146 \n",
"36 2.416667 0.146341 \n",
"24 -0.849593 0.027027 \n",
"28 9.142857 0.380282 \n",
"38 0.400000 0.302521 \n",
"20 -0.424658 -0.309524 \n",
"8 1.452381 0.121359 \n",
"18 0.787611 0.267327 \n",
"7 0.411765 -0.066667 \n",
"2 4.314286 -0.107527 \n",
"6 5.227273 0.087591 \n",
"34 -0.460733 0.650485 \n",
"21 3.628571 -0.012346 \n",
"26 -0.500000 0.261682 \n",
"23 0.312500 0.267857 \n",
"25 5.333333 0.251462 \n",
"17 13.714286 0.135922 \n",
"22 -0.317647 0.120690 \n",
"1 NaN -0.115044 \n",
"19 -0.789700 0.020408 \n",
"\n",
"START_DATE 2013-12-31 00:00:00 2014-12-31 00:00:00 \\\n",
"NEIGHBORHOODCLUSTER \n",
"29 0.450000 0.413793 \n",
"35 0.023810 -0.093023 \n",
"32 0.337931 -0.041237 \n",
"37 -0.238095 -0.044643 \n",
"3 0.253521 -0.365169 \n",
"9 -0.169014 -0.050847 \n",
"4 0.034483 0.100000 \n",
"5 -0.468750 0.470588 \n",
"30 0.529412 0.067308 \n",
"33 0.484277 -0.088983 \n",
"31 0.401361 0.029126 \n",
"39 0.000000 -0.196375 \n",
"36 0.031915 -0.072165 \n",
"24 -0.026316 -0.243243 \n",
"28 -0.306122 0.000000 \n",
"38 -0.077419 -0.062937 \n",
"20 0.586207 -0.260870 \n",
"8 -0.255411 -0.127907 \n",
"18 0.000000 -0.140625 \n",
"7 -0.223214 -0.103448 \n",
"2 0.111446 -0.295393 \n",
"6 -0.268456 -0.321101 \n",
"34 -0.152941 -0.187500 \n",
"21 -0.075000 -0.033784 \n",
"26 -0.251852 0.079208 \n",
"23 -0.070423 0.010101 \n",
"25 -0.172897 -0.096045 \n",
"17 -0.170940 -0.134021 \n",
"22 -0.007692 -0.286822 \n",
"1 -0.090000 -0.483516 \n",
"19 0.200000 -0.183333 \n",
"\n",
"START_DATE 2015-12-31 00:00:00 Avg Annual Crimes \n",
"NEIGHBORHOODCLUSTER \n",
"29 -0.682927 22.4 \n",
"35 -0.384615 36.4 \n",
"32 -0.247312 159.4 \n",
"37 -0.205607 115.0 \n",
"3 -0.176991 138.6 \n",
"9 -0.160714 60.0 \n",
"4 -0.121212 31.4 \n",
"5 -0.120000 24.4 \n",
"30 -0.117117 91.0 \n",
"33 -0.111628 186.4 \n",
"31 -0.084906 184.2 \n",
"39 -0.078947 300.2 \n",
"36 -0.055556 89.6 \n",
"24 -0.035714 33.4 \n",
"28 0.000000 74.6 \n",
"38 0.007463 137.2 \n",
"20 0.029412 37.2 \n",
"8 0.053333 183.4 \n",
"18 0.059091 233.4 \n",
"7 0.076923 96.2 \n",
"2 0.100000 323.8 \n",
"6 0.148649 110.8 \n",
"34 0.179487 134.4 \n",
"21 0.188811 156.6 \n",
"26 0.201835 116.6 \n",
"23 0.230000 205.0 \n",
"25 0.337500 187.2 \n",
"17 0.345238 102.8 \n",
"22 0.391304 119.0 \n",
"1 0.489362 84.2 \n",
"19 0.489796 56.2 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#sort by most improved \n",
"dc_crime_by_neighborhood.sort(pd.Timestamp('2015-12-31 00:00:00', offset='A-DEC'), ascending=True)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>START_DATE</th>\n",
" <th>2011-12-31 00:00:00</th>\n",
" <th>2012-12-31 00:00:00</th>\n",
" <th>2013-12-31 00:00:00</th>\n",
" <th>2014-12-31 00:00:00</th>\n",
" <th>2015-12-31 00:00:00</th>\n",
" <th>Avg Annual Crimes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NEIGHBORHOODCLUSTER</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <td>14</td>\n",
" <td>4</td>\n",
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" <th>36</th>\n",
" <td>23</td>\n",
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" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>21</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
" <th>37</th>\n",
" <td>18</td>\n",
" <td>20</td>\n",
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" <td>21</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
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" <tr>\n",
" <th>32</th>\n",
" <td>11</td>\n",
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" <td>3</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>20.0</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>28.0</td>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"START_DATE 2011-12-31 00:00:00 2012-12-31 00:00:00 \\\n",
"NEIGHBORHOODCLUSTER \n",
"19 3 11 \n",
"1 NaN 4 \n",
"22 12 16 \n",
"17 30 18 \n",
"25 28 24 \n",
"23 15 27 \n",
"26 7 25 \n",
"21 24 9 \n",
"34 8 30 \n",
"6 27 14 \n",
"2 25 5 \n",
"7 17 8 \n",
"18 20 26 \n",
"8 22 17 \n",
"20 9 1 \n",
"38 16 28 \n",
"28 29 29 \n",
"24 2 12 \n",
"36 23 19 \n",
"39 21 10 \n",
"31 19 6 \n",
"33 14 21 \n",
"30 26 7 \n",
"5 13 22 \n",
"4 5 2 \n",
"9 6 13 \n",
"3 10 3 \n",
"37 18 20 \n",
"32 11 15 \n",
"35 4 23 \n",
"29 1 31 \n",
"\n",
"START_DATE 2013-12-31 00:00:00 2014-12-31 00:00:00 \\\n",
"NEIGHBORHOODCLUSTER \n",
"19 24.0 10 \n",
"1 12.0 1 \n",
"22 17.0 5 \n",
"17 9.0 12 \n",
"25 8.0 15 \n",
"23 15.0 25 \n",
"26 5.0 28 \n",
"21 14.0 23 \n",
"34 11.0 9 \n",
"6 3.0 3 \n",
"2 23.0 4 \n",
"7 7.0 14 \n",
"18 18.5 11 \n",
"8 4.0 13 \n",
"20 31.0 6 \n",
"38 13.0 19 \n",
"28 2.0 24 \n",
"24 16.0 7 \n",
"36 21.0 18 \n",
"39 18.5 8 \n",
"31 27.0 26 \n",
"33 29.0 17 \n",
"30 30.0 27 \n",
"5 1.0 31 \n",
"4 22.0 29 \n",
"9 10.0 20 \n",
"3 25.0 2 \n",
"37 6.0 21 \n",
"32 26.0 22 \n",
"35 20.0 16 \n",
"29 28.0 30 \n",
"\n",
"START_DATE 2015-12-31 00:00:00 Avg Annual Crimes \n",
"NEIGHBORHOODCLUSTER \n",
"19 31 7 \n",
"1 30 10 \n",
"22 29 18 \n",
"17 28 14 \n",
"25 27 27 \n",
"23 26 28 \n",
"26 25 17 \n",
"21 24 22 \n",
"34 23 19 \n",
"6 22 15 \n",
"2 21 31 \n",
"7 20 13 \n",
"18 19 29 \n",
"8 18 24 \n",
"20 17 6 \n",
"38 16 20 \n",
"28 15 9 \n",
"24 14 4 \n",
"36 13 11 \n",
"39 12 30 \n",
"31 11 25 \n",
"33 10 26 \n",
"30 9 12 \n",
"5 8 2 \n",
"4 7 3 \n",
"9 6 8 \n",
"3 5 21 \n",
"37 4 16 \n",
"32 3 23 \n",
"35 2 5 \n",
"29 1 1 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#ranking of neighborhoods by average annual crime rate\n",
"dc_crime_by_neighborhood.sort(pd.Timestamp('2015-12-31 00:00:00', offset='A-DEC'), ascending=False).rank(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n",
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n",
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
]
}
],
"source": [
"#dataframe of homicides and other violent crimes by neighborhood by annual change\n",
"dc_crime_by_neighborhood_homicide = dc_crime_slice[dc_crime_slice[\"OFFENSE\"] == \"HOMICIDE\"].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].diff().unstack().sort('2015-12-31 00:00:00', ascending=True)\n",
" \n",
"dc_crime_by_neighborhood_violence = dc_crime_slice[(dc_crime_slice[\"OFFENSE\"] == \"SEX ABUSE\") |\\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ASSAULT W/DANGEROUS WEAPON\") | \\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ROBBERY\")].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].diff().unstack().sort('2015-12-31 00:00:00', ascending=True)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n"
]
}
],
"source": [
"#add column with average annual of crime incidents\n",
"dc_crime_by_neighborhood_homicide[\"Avg Annual Crimes\"] = dc_crime_slice[dc_crime_slice[\"OFFENSE\"] == \"HOMICIDE\"].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].unstack().mean(axis=1)\n",
" \n",
"dc_crime_by_neighborhood_violence[\"Avg Annual Crimes\"] = dc_crime_slice[(dc_crime_slice[\"OFFENSE\"] == \"SEX ABUSE\") |\\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ASSAULT W/DANGEROUS WEAPON\") | \\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ROBBERY\")].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].unstack().mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#extract 2014-2015 change only\n",
"dc_crime_by_neighborhood_homicide = pd.DataFrame(dc_crime_by_neighborhood_homicide[pd.Timestamp('2015-12-31 00:00:00', offset='A-DEC')])\n",
"dc_crime_by_neighborhood_violence = pd.DataFrame(dc_crime_by_neighborhood_violence[pd.Timestamp('2015-12-31 00:00:00', offset='A-DEC')])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#change column names for clarity\n",
"dc_crime_by_neighborhood_homicide.columns = ['Change in Homicides']\n",
"dc_crime_by_neighborhood_violence.columns = ['Change in Other Violent Crimes']"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#drop NA\n",
"dc_crime_by_neighborhood_homicide_violence = dc_crime_by_neighborhood_homicide.join(dc_crime_by_neighborhood_violence).dropna()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xd796898>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
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buRVoz54967S4QQWbzcaKXQAAADCUW4G2c+fOmjNnjn799VdH28GDB5WWlqZb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\nNAlohRBCCCFEjSYBrRBCCCGEqNH+HyC78tqseYcxAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xcbc2cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot data\n",
"\n",
"# formatter function to format y-axis labels to have a comma at the thousands place \n",
"def func(x, pos): \n",
" s = '{0:.0f}'.format(float(x))\n",
" return s\n",
"\n",
"y_format = tkr.FuncFormatter(func) # make formatter\n",
"\n",
"ax10 = dc_crime_by_neighborhood_homicide.join(dc_crime_by_neighborhood_violence).\\\n",
"plot(kind='scatter', x='Change in Homicides', y='Change in Other Violent Crimes',figsize=(10,6), fontsize=14, s=60)\n",
"ax10.set_xlabel('', size=14)\n",
"ax10.tick_params(\n",
" axis='both',\n",
" which='both', \n",
" bottom='off', \n",
" top='off', \n",
" right='off',\n",
" left='off',\n",
" labelbottom='on')\n",
"\n",
"ax10.yaxis.set_major_formatter(y_format)\n",
"ax10.xaxis.set_major_formatter(y_format)\n",
"\n",
"ax10.axhline(0, linestyle='-', color='grey', alpha=0.5)\n",
"ax10.axvline(0, linestyle='-', color='grey', alpha=0.5) \n",
"\n",
"ax10.set_xlabel(\"Change in Homicides\", fontsize=14)\n",
"ax10.set_ylabel(\"Change in Other Violent Crimes\", fontsize=14)\n",
"\n",
"ax10.text(0.1, -0.2, \"From: chart-it (MikeRAzar.com/chart-it) | Source: http://data.octo.dc.gov/metadata.aspx?id=3\", \n",
" fontsize=12, transform=ax10.transAxes) "
]
},
{
"cell_type": "code",
"execution_count": 44,
"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>Change in Homicides</th>\n",
" <th>Change in Other Violent Crimes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Change in Homicides</th>\n",
" <td>1.000000</td>\n",
" <td>0.077621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Change in Other Violent Crimes</th>\n",
" <td>0.077621</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Change in Homicides \\\n",
"Change in Homicides 1.000000 \n",
"Change in Other Violent Crimes 0.077621 \n",
"\n",
" Change in Other Violent Crimes \n",
"Change in Homicides 0.077621 \n",
"Change in Other Violent Crimes 1.000000 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#correlation between percent change in homicide and percent change in other violent crimes\n",
"dc_crime_by_neighborhood_homicide_violence.corr()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#add column to show neighborhoods where murder and other violent crime moved in different directions\n",
"dc_crime_by_neighborhood_homicide_violence[\"Sign Diff\"] = (dc_crime_by_neighborhood_homicide_violence[\"Change in Homicides\"] < 0) & \\\n",
"(dc_crime_by_neighborhood_homicide_violence[\"Change in Other Violent Crimes\"] > 0) |\\\n",
"(dc_crime_by_neighborhood_homicide_violence[\"Change in Homicides\"] > 0) &\\\n",
"(dc_crime_by_neighborhood_homicide_violence[\"Change in Other Violent Crimes\"] < 0)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Change in Homicides</th>\n",
" <th>Change in Other Violent Crimes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Sign Diff</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Change in Homicides Change in Other Violent Crimes\n",
"Sign Diff \n",
"False 14 14\n",
"True 9 9"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#count neighborhoods where homicide changed in different direction than other violent crime\n",
"dc_crime_by_neighborhood_homicide_violence.sort('Sign Diff').groupby('Sign Diff').count()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Change in Homicides</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NEIGHBORHOODCLUSTER</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5</td>\n",
" </tr>\n",
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" <th>22</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3</td>\n",
" </tr>\n",
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" <th>32</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
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" <th>25</th>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>1</td>\n",
" </tr>\n",
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" <th>9</th>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" </tr>\n",
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" <th>18</th>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-1</td>\n",
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" <th>33</th>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>-4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>-6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" </tr>\n",
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" <th>4</th>\n",
" <td>NaN</td>\n",
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" <th>6</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <th>30</th>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>34</th>\n",
" <td>NaN</td>\n",
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" <th>35</th>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Change in Homicides\n",
"NEIGHBORHOODCLUSTER \n",
"36 8\n",
"38 8\n",
"7 5\n",
"22 3\n",
"23 3\n",
"32 3\n",
"15 3\n",
"28 2\n",
"8 2\n",
"17 2\n",
"39 2\n",
"2 2\n",
"25 1\n",
"29 1\n",
"9 1\n",
"21 0\n",
"20 0\n",
"3 0\n",
"18 -1\n",
"14 -1\n",
"33 -1\n",
"31 -4\n",
"37 -6\n",
"1 NaN\n",
"4 NaN\n",
"6 NaN\n",
"19 NaN\n",
"24 NaN\n",
"26 NaN\n",
"30 NaN\n",
"34 NaN\n",
"35 NaN"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# change in homicides by neighborhood\n",
"dc_crime_by_neighborhood_homicide.sort(['Change in Homicides'], ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n",
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" from IPython.kernel.zmq import kernelapp as app\n"
]
}
],
"source": [
"# dataframe to create ranking of neighborhoods by violent crime\n",
"ranking = dc_crime_slice[((dc_crime_slice[\"OFFENSE\"] == \"SEX ABUSE\") | (dc_crime_slice[\"OFFENSE\"] == \"ASSAULT W/DANGEROUS WEAPON\") |\\\n",
" (dc_crime_slice[\"OFFENSE\"] == \"ROBBERY\"))].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].unstack().sort('2015-12-31 00:00:00', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>START_DATE</th>\n",
" <th>2011-12-31 00:00:00</th>\n",
" <th>2012-12-31 00:00:00</th>\n",
" <th>2013-12-31 00:00:00</th>\n",
" <th>2014-12-31 00:00:00</th>\n",
" <th>2015-12-31 00:00:00</th>\n",
" </tr>\n",
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" <th>NEIGHBORHOODCLUSTER</th>\n",
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" <th>23</th>\n",
" <td>33.0</td>\n",
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" <th>34</th>\n",
" <td>20.0</td>\n",
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" <td>25.5</td>\n",
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" <td>25.0</td>\n",
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" <td>29.0</td>\n",
" <td>26.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>24.0</td>\n",
" <td>23.0</td>\n",
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" <td>26.5</td>\n",
" </tr>\n",
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" <th>17</th>\n",
" <td>21.0</td>\n",
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" <th>6</th>\n",
" <td>30.0</td>\n",
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" <th>37</th>\n",
" <td>27.0</td>\n",
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" <td>26.0</td>\n",
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" <td>19.0</td>\n",
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" <th>19</th>\n",
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" <td>9.0</td>\n",
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" <td>14.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>12.0</td>\n",
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" <tr>\n",
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" <td>10.0</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <td>3.0</td>\n",
" <td>7.0</td>\n",
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" <td>14.0</td>\n",
" <td>8.0</td>\n",
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" <td>9.0</td>\n",
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" <td>7.5</td>\n",
" <td>7.0</td>\n",
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" <th>16</th>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
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" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"START_DATE 2011-12-31 2012-12-31 2013-12-31 2014-12-31 \\\n",
"NEIGHBORHOODCLUSTER \n",
"2 39.0 39.0 39 38.0 \n",
"23 33.0 34.0 34 34.0 \n",
"39 38.0 38.0 38 39.0 \n",
"18 36.0 37.0 37 37.0 \n",
"25 35.0 35.0 31 32.0 \n",
"31 32.0 28.0 35 35.0 \n",
"33 28.0 31.0 36 36.0 \n",
"21 31.0 32.0 29 30.0 \n",
"8 37.0 36.0 30 31.0 \n",
"34 20.0 33.0 27 28.0 \n",
"32 29.0 26.0 33 33.0 \n",
"26 22.0 24.0 22 25.5 \n",
"38 25.0 29.5 28 29.0 \n",
"22 24.0 23.0 26 23.0 \n",
"17 21.0 22.0 21 21.0 \n",
"30 18.0 16.0 23 25.5 \n",
"3 34.0 25.0 32 27.0 \n",
"6 30.0 29.5 24 19.0 \n",
"37 27.0 27.0 25 24.0 \n",
"7 26.0 21.0 18 20.0 \n",
"36 19.0 18.0 20 22.0 \n",
"19 15.0 15.0 16 16.0 \n",
"1 23.0 20.0 19 15.0 \n",
"28 17.0 19.0 17 18.0 \n",
"9 16.0 17.0 15 17.0 \n",
"20 14.0 9.0 14 12.0 \n",
"4 12.0 10.5 11 11.0 \n",
"24 13.0 13.0 12 10.0 \n",
"35 11.0 14.0 13 13.0 \n",
"5 10.0 12.0 8 9.0 \n",
"11 8.0 10.5 9 7.5 \n",
"29 3.0 7.0 10 14.0 \n",
"15 9.0 5.0 7 7.5 \n",
"16 5.5 7.0 4 3.0 \n",
"27 5.5 7.0 6 6.0 \n",
"13 1.0 2.0 2 1.0 \n",
"10 4.0 3.0 1 5.0 \n",
"12 2.0 1.0 5 2.0 \n",
"14 7.0 4.0 3 4.0 \n",
"\n",
"START_DATE 2015-12-31 \n",
"NEIGHBORHOODCLUSTER \n",
"2 39.0 \n",
"23 38.0 \n",
"39 37.0 \n",
"18 36.0 \n",
"25 35.0 \n",
"31 34.0 \n",
"33 33.0 \n",
"21 32.0 \n",
"8 31.0 \n",
"34 30.0 \n",
"32 29.0 \n",
"26 28.0 \n",
"38 26.5 \n",
"22 26.5 \n",
"17 25.0 \n",
"30 24.0 \n",
"3 23.0 \n",
"6 22.0 \n",
"37 21.0 \n",
"7 20.0 \n",
"36 19.0 \n",
"19 18.0 \n",
"1 17.0 \n",
"28 16.0 \n",
"9 15.0 \n",
"20 14.0 \n",
"4 13.0 \n",
"24 12.0 \n",
"35 11.0 \n",
"5 10.0 \n",
"11 9.0 \n",
"29 8.0 \n",
"15 7.0 \n",
"16 5.5 \n",
"27 5.5 \n",
"13 3.5 \n",
"10 3.5 \n",
"12 2.0 \n",
"14 1.0 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ranking\n",
"ranking.rank()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# dataframe to calculate dc monthly murder rate\n",
"dc_monthly_crime = dc_crime[dc_crime[\"OFFENSE\"] == \"HOMICIDE\"].resample(\"M\", how=\"count\")[\"OFFENSE\"]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# exclude errors in dataset\n",
"dc_monthly_crime = dc_monthly_crime[dc_monthly_crime.index.year > 2010]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# exclude Navy Yard victims\n",
"dc_monthly_crime['2013-09-30'] = dc_monthly_crime['2013-09-30'] - 12"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"START_DATE\n",
"2011-01-31 8\n",
"2011-02-28 6\n",
"2011-03-31 7\n",
"2011-04-30 12\n",
"2011-05-31 12\n",
"2011-06-30 9\n",
"2011-07-31 8\n",
"2011-08-31 14\n",
"2011-09-30 6\n",
"2011-10-31 16\n",
"2011-11-30 6\n",
"2011-12-31 6\n",
"2012-01-31 5\n",
"2012-02-29 6\n",
"2012-03-31 6\n",
"2012-04-30 9\n",
"2012-05-31 6\n",
"2012-06-30 9\n",
"2012-07-31 8\n",
"2012-08-31 6\n",
"2012-09-30 8\n",
"2012-10-31 6\n",
"2012-11-30 3\n",
"2012-12-31 10\n",
"2013-01-31 5\n",
"2013-02-28 6\n",
"2013-03-31 5\n",
"2013-04-30 3\n",
"2013-05-31 6\n",
"2013-06-30 13\n",
"2013-07-31 13\n",
"2013-08-31 9\n",
"2013-09-30 10\n",
"2013-10-31 6\n",
"2013-11-30 10\n",
"2013-12-31 6\n",
"2014-01-31 13\n",
"2014-02-28 9\n",
"2014-03-31 8\n",
"2014-04-30 5\n",
"2014-05-31 9\n",
"2014-06-30 12\n",
"2014-07-31 11\n",
"2014-08-31 3\n",
"2014-09-30 6\n",
"2014-10-31 9\n",
"2014-11-30 5\n",
"2014-12-31 11\n",
"2015-01-31 9\n",
"2015-02-28 7\n",
"2015-03-31 10\n",
"2015-04-30 8\n",
"2015-05-31 16\n",
"2015-06-30 18\n",
"2015-07-31 16\n",
"2015-08-31 22\n",
"Freq: M, Name: OFFENSE, dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# dc monthly murder rate\n",
"dc_monthly_crime"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n"
]
},
{
"data": {
"text/plain": [
"array([[ -2., 2., -1., nan],\n",
" [ -2., 0., 0., 2.],\n",
" [ nan, 0., 1., 0.],\n",
" [ -1., 0., 1., nan],\n",
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" [ nan, nan, -1., 2.],\n",
" [ nan, 1., -1., 1.],\n",
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" [ 0., -1., 1., 2.],\n",
" [ nan, -3., 1., 1.],\n",
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" [ -3., 3., 2., -4.],\n",
" [ -2., 3., -4., 3.],\n",
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" [ 0., 0., 3., 2.]])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diff(dc_crime_slice[dc_crime_slice[\"OFFENSE\"] == \"HOMICIDE\"].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].unstack(), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" return np.sum(name == np.asarray(self.names)) > 1\n",
"C:\\Users\\MRA\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
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" <td>0</td>\n",
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],
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"START_DATE 2011-12-31 2012-12-31 2013-12-31 2014-12-31 \\\n",
"NEIGHBORHOODCLUSTER \n",
"38 2 3 -3 -1 \n",
"36 0 -2 2 0 \n",
"7 1 -1 -1 0 \n",
"22 -2 1 -2 -1 \n",
"23 2 -3 -1 4 \n",
"32 2 -2 3 -4 \n",
"15 NaN NaN NaN NaN \n",
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"8 NaN NaN -3 -1 \n",
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"START_DATE 2015-12-31 \n",
"NEIGHBORHOODCLUSTER \n",
"38 8 \n",
"36 8 \n",
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]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dc_crime_slice[dc_crime_slice[\"OFFENSE\"] == \"HOMICIDE\"].groupby(\"NEIGHBORHOODCLUSTER\").\\\n",
" resample(\"A\", how=\"count\")['OFFENSE'].diff().unstack().sort('2015-12-31 00:00:00', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1cfdc4a8>"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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By8Y8Nep0zSG7j0LA//bmqAu5nycSfNGyPmOVVsTHZ1n3iBzbziINqrTSPvy+\nWNUNnuEaGLwAGBOlQsscPhfrDLhQ6/q1Tq5qjNhktshrarw3K+ySW4rwlmNqvDRw33yxEZdZ94gc\nlCiKrfrw+2NV8Fa4fh/u+lfYAd08ZfivEOn8oPniJ1e0Pq9BkkY6UClgkotVHiXX8fXXX2PatGmY\nPn06Zs+ejdOnT1v9PabG+yDMq7lbNIjA6pP1rHtEDulopQ7nW9xoywBMcuHt0S0xePnV+BizWkdF\nGpfusErVhlbzpNP7+rhFxE7O5/Lly3j77bfx1ltvITMzE4899hieffZZq7+PSiHgSbO6RwfLtPix\nlHWPyPGYlwIYGaFEpI97rFfkN9Wv7orwlMx3X1EbcarKdYeL1+U2oOVaxHAvGSb04KgLOSalUom0\ntDQEBwcDAPr374/Kykro9db/Gx0T5YnbgqWL+FefrIeWdY/IgRQ3GFoVE3X17dEtMXj5lY+HDCPC\nW4++uKL8ej2yCqTXNqOfD5Ry116dTs4rMjISI0eOBHBtnn/lypUYPXq0TarUC79unW7ZORY1GFrd\n5RLZ05aLarQMp+P9Fa2CblfG4KUF84R1u4qboDe63t3WR2caJMXouvvKcXeM669OJ+fX2NiIJUuW\noKioCM8995zN3ic+wAMPmI1EfpyrdtvyIeRYGnRGbM83T0rnBcHFt0e3xOClheHhnvBRNP/jVzUZ\ncbTCtdKEn6vRYZdZCYRZ/X2gcMGS6eRarly5glmzZkGhUODdd9+Fr6/vzZ/UCY/194GvR/PfRaNB\nxD9Pc+s02V9WgQZqvXSzhXnCVVfH4KUFT7mAUZGuPXW01qzz7ROgwGizayZyNDU1NZgzZw5SUlKQ\nnp4OpdL22UO7ecrwmFndo28KNDhV5Vo3NORcDKLYqo7Rg3Febjftz+DFjPnU0d6SJjS5yEK9Xyq1\n2F8mXeA1u7+Py2diJOe3efNmlJWVYdeuXZg+fbrpv5qaGpu+74NxXojzk+7eeJt1j8iO9pdqUaxu\nnr70kF37nLob1y05aaGhIUoEKgVTxsIGvYiDZdpWIzLORhRFrDEbdRkc5IGkMNeuf0Gu4bHHHsNj\njz3W5e+rkAmYP8gPT/1YbXrsdLUe3xZo8NtY9/vCIPvbZLZwfFy0yi3LuXDkxYxCJmCM2dyhK0wd\nHSrX4per0uHuxwf4uNUCLyJL/FeoEqMipDcv/zzdALWedY+oa52v0eOI2TrMKW6aWJTBSxvGmwUv\nOVea0ODdQPCgAAAgAElEQVTEBdraGnVJClNiSDBHXYg6Yu5AX3i06C2vNhmRkcut09S1Nl+UfuZu\nC/ZAnwD32R7dksXBS15eHp544gk88sgjSE1NxZkzZ1qds2/fPvzhD3/A7373OyxZsgQNDc6xUj8h\nUIEI7+ZfjdYI/HCl6QbPcGx7S5qQWyNN5jW7v087ZxORuUgfOX5vVrD08/NqFNa7biJLcixVTUZ8\nV2i+Pdp9ktKZsyh40Wg0mD9/PlJTU5GRkYFZs2YhLS1Nck5VVRXS09Pxyiuv4PPPP0d0dDRWr15t\nlUbbmiAIGBclHX3ZUeScwYtBFLH2jDRoHBPlib7d3DNaJ7LUtD4+CFE1d5n6X+seEXWFLy81SrKi\nR3jLMDLCfUfPLQpe9u/fj+7du2PEiBEAgFGjRmH58uWScw4cOICEhATExMQAACZPnoysrKxONrfr\njDdL2vZTuRbVTc43dfRtgQb59c0r02UAZvbjqAvRrfJSCHjCrO7Rj6VaHChzzhsbch46o4h/m9Wi\nm9zTG3I3XrNoUfCSn5+PoKAgpKenIzU1FfPnz4fBIM08WVpairCwMNNxaGgoGhoaoFY7xzxxL3+F\nZIukUQT2FDvXwl2tQcS/zkpHXe7trkIPP24yI7JESrQnBgVJRy1XnaiHzgUzcZPj2FXUhKstbp69\n5ALui3WvpHTmLApe9Ho9cnJyMGnSJKxbtw4PP/wwFi5cCJ2ueRV0exWZZTLnWSNsvnDX2aaOvspv\nRGlj8wdeIQCpHHUhspggCFgwyBct73cL6g3YerGx3ecQdYYoiq22R98Xq4Kvh/N8l9qCRVcfFhaG\nuLg4JCQkAABGjx4No9GI4uJi0zkRERGoqKgwHZeXl8PPzw8qlfNEi+bplo9f1aFU7Ry1TRr1Yqvd\nEA/GeSHC2/3yARBZU99uHrjf7K533dkGXNU437QyOb7jV3WSDRcCgEk93XN7dEsWBS8jRoxAcXGx\naYfR4cOHIQgCoqKiTOckJSXhxIkTKCgoAABs2bIFycnJVmhy14nykSMhUDrFstNJcr5suahGVYth\nRpUcmNbHfVemE1nT7AG+kjpoDXoRa85w8S5Z3yazUgAjwpWI8eXUv1BdXW3RZO2RI0fw9ttvo7Gx\nEUqlEk899RQUCgWWLVuGzMxMAEBOTg5Wr14NvV6PmJgYLF26FH5+fla9AFvbfEGNt080d0rx/gqs\nGRNkxxbdXJ3OiKk7KlGva/6nndbHG48PsG0hOyJ38vl5tWS3kQDgvdGB6MedfGQlV9QG/GFHJVqO\n6b0+ohv+K9R9dxldZ3Hw4i4qNQb87lvph2fd2CCHXvS65nQ9Ms81Txn5egj4NCUYfkr3niMlsia9\nUcSs3VdxucVuvkGBHnj7rm7MXE1W8e7Jemw839yX9/ST48MxQfx8gRl2bypYJcfQEOmdlCOXC7iq\nMbYaZpwa783AhcjKFDIB/zdIOpp5okrndAv7yTGp9UZ8dVnal0/p5c3A5Vf8RuuAlBjzWkdN7e6m\nsrf15xqgaVEFO1ApYFJPrnUhsoVhYZ640yxR2Pun6ln3iDrtmwINGvTNfXmAUmiVf8ydMXjpgFGR\nnpK6JkUNBpytdry04FfUBnxhFqk/0tcHXgpG6kS2Yl73qEJjxCfnnCOfFTkmoyhis9kI+oQeXvCU\nsy+/jsFLB/h5yDA8THp35YhTRx/nNkjSR4d7yfBAD26pI7KlaB8FfmdWY2bjeTWKG5wjrQI5noNl\nWhS2+PzIBeAhbo+WYPDSQSlmOV92FjXB4EBTR/n1emTlSwOqGf18oGSkTmRz0/t6I9izuTvVGYF3\nTtbZsUXkzMyT0o2N8kSIijm6WmLw0kEjwj3h1SIQqGwy4pdK3Q2e0bU+OtMg2RHV3VeOuzk/StQl\nvBUyzEmQZq/ed0WLn8q1dmoROauLtXr8VC79bpnSm+sWzTF46SCVQsBdkY45dXSuRoddxdIdDrP6\n+0Ah46gLUVcZH6NqldRy1Yk66Fn3iG7B5ovSUZdBQR7oz9xBrTB4uQXmtY72FDc5REG2taelxRf7\nBCgwOtLTTq0hck8yQcCCQdIknJfqDPjPJdY9oo6p0RrxbYH0pnhKL651aQuDl1vwX6FK+CubRzPq\ndCIOldl3WPiXSi32m7Vhdn8fyJgLgKjL9Q/0wH3dpTc5H55pQHUTt07TzX11uRHaFh+VMC8Z7org\njWhbGLzcAoVMwJhI80rT9ps6EkURa8xGXYYEeSApjKmjiexl9gAfeJvVPVrLukd0E3qj2Ko6+aSe\nXpz+bweDl1s0PkYaBedcaUKj3j5TRwfLtfjlqnRh1+wBPszASGRHwSo5Hu0rXbz71WUNztU4zgJ/\ncjx7iptQoZEW0/3vWE4ZtYfByy0aFOSBUFXzr01juBbAdDVjG6Muw8OUGBLMURcie5vcywvdfZq3\ntooA3jpe77CZucn+zLdH39vdi2VdboC/mVskE4RWOV/sMXW0t6QJ52qkWX5n9fdp52wi6koeMgHz\nzOoeHb+qw85i1j2i1k5e1eG0Wdb2SUxKd0MMXiyQYjZ1dLBMixpt1y3I0xtFfHhGOuoyJsoTfbmd\njtyEKIp44YUXsH79ens3pV13hHviDrP1Z++drLfbNDM5LvNRl+FhSvTwU7RzNgEMXiwS769ArG/z\nkLBBBPZ24R3Vd4Ua5Nc3p46WAZjZj6Mu5B4uXryIuXPnIjs7295Nual5g3zRsrRYucaIT/Ma2n8C\nuZ2yRgP2lEi/P7g9+uYY2llA+HXq6KOzzZ1QdpEGE+Js/4HTGkT866y087u3u4pROrmNTZs2YeLE\niYiMjLR3U26qu68Ck3t5Y+P55jvrDXlq3BfrhUhvpnvvCJ1RhAOk07KZLRcaJdfXw1eOxFCuXbwZ\nfuNZKCXaUxK8HKvUobzRgFAv23ZIX+U3orSxeYrKQwakctSF3MjTTz8NADh48KCdW9Ixj/b1xreF\nGlT9mutFawTePVmPF4cF2Lllju1YpRYfnG7Ayas6uHDs0srkXt7cMdoBnDayUIyvAv26Ncd+ItAq\nRb+1NepFZORK50Yf7OGFCN7BETksHw8Z/t8A6Q3G3pImHKlg3aO2lDQY8LdDNfjjD9U44WaBi5+H\ngHtYk65DGLx0gvmuI1vXOtpyUW26ewOu5QGY1oejLkSO7t7uKvTvJh3ofus46x61pNYb8cHpejy6\nq7LVGhB3MTHOCyoFR106wuJpo5UrV2Lnzp3w9/cHAMTFxSE9Pf2Wz3FmY6M88e7JetOdwdlqPQrr\n9Yjxtf5sXJ3WiE/zpKMuU3p5I0jF+JPI0ckEAfMH+WHevirTYxfrDPjyciP+p6d7Vww2iiK+KdDg\ng9MNuNpOGQVXT3fiIRMwIlzZKrkhtc/ib9njx4/jpZdewuDBgzt1jjML9ZLjtmAPHK1szpyZXdSE\n1H7WD142nlejXtd8l+brIeD3LJNO5DQGBnngnhgVvi1sHqH98EwDxkWrEODq387t+KVSi1Un6pFr\nlrPqun7dFJg/yA+DgpgGgqSE6urqWx631Gq1SElJwciRI1FYWIju3bvjT3/6E8LDw2/pHFfw5aVG\nvP5Lnem4u68cH48NsuqCq6saI/6QXQFN8+5oPD7Ah1NGRE6mQmPA9Oyr0Biau92H4rywcIjfDZ7l\nekrUBvzzVH276wSDPWX4fwk+uDtGxSKz1CaLwv2KigokJiZi3rx5WL9+PQYNGoRFixbd8jmuYHSU\npySPQ0G9AXm1bd9FWGr9uQZJ4BKoFDDJzYeaiZxRiEqOR/pK/3a/uNSI8+2MPLgatd6INafr8ejO\nyjYDFw8ZML2PNzJSgnBvdy8GLtQui0Ze2jJ27Fh88sknN8y90JFznNGSA9X4sbR558D/9vbGEwN9\nb/CMjruiNuCRnZXQtZgKXjDIF5N6MXghckZag4gZu66iWN18R3J7sAdWjOzmsltkjaKIb39d11LZ\nzrqWsVGemJPgy92T1CEWjbzk5eVh+/btpuPrxcYUCsUtneMqxrex68hopQJsH+c2SAKXcC8ZHujB\n7ItEzkopb1336GilzmV32Byv1OLJ76uw/Ghdm4FL3wAF3rqzG/6WGMDAhTrMouBFEAS88cYbKC4u\nBgBs3rwZ8fHxCA0NvaVzXMXICE+oWvzNlWuMOHFV1/4TOii/Xo+sfOn26xn9fKCUu+bdGZG7GBmu\nxDCzLKrvnqxHk8F1tk6Xqg148ecazP+hGmerW0+LBXnK8OztfnhvdCCGBDOjLN0ai6eNsrKysG7d\nOhiNRoSFhSEtLQ2VlZVYtmwZMjMz2z3H1RbsXvf3n2uQXdR85/RgDy/8+bbOLcJb+lMNdreYF471\nlePDMUFQyBi8EDm7y3V6PLb7KlrGKzP7+Th9xuxGvYhP8xqwIU+NturVesiA3/f2xh/6eMNb4Z67\nrKjzrLbmxd3lXGnCXw7WmI79lQK23BNicaCRW63D/9tbJXlsaaI/xkQx+yKRq1h1og6bLjSajj3l\nwMdjgxHuhNMnRlHEjkIN/nm6ARWatte1jInyxJwBvoj0cb7rI8fCsNdKhoUp4efRHKjUakX8XG55\n+u+1Z6TFF/sGKDA60tPi1yMix5PazwfdlM39RpMBeO9UvR1bZJmTV3WY930Vlh2pazNw6ROgwJt3\ndsPSxAAGLmQVDF6sxEMmIDlKGlzssLBcwC+VWhwokwY+swb4cNsgkYvx85Bh9gDp4t1dxU04Vukc\ndY/KGg1I/7kG8/ZV4XQb61oCPWV45td1LbdxXQtZEYMXKzKvdbSvRAuN/tZm5URRxAenpaMuQ4I8\nkMQS6UQu6b5YFfoEmNc9qofBSjsWbUGjF/HRmXo8srMSO4raztcyNd4bmeOCcH+sF+S88SIrY/Bi\nRUOCPRDSotZQo0HEj6W3tv3xYLkWx812Ks0e4OOy+R+I3J1cEDDfbOv0+Vo9tl22baFXS4iiiO8K\nNXhkZyXW5arRZGh9zuhIT6wbG4w5Cb7w8eBXDNkGP1lWJBcEjDWbOrqVStNGUcQas1GX4WFKbiMk\ncnFDgpVIiZb2HWvO1KOure06dnKqSod5+6rw0uFalLexrqW3vwIrRnbDi8MCEMV1LWRjDF6szHzq\n6ECZFnW6jnVAe0uacM4sTfjsAc69bZKIOmZOgq8kX1StVsS/zja0/4QuUt5owEuHazD3+yqcqmq9\nrqWbUsCi2/zwz+RADA3hjRZ1DQYvVtavmwLRLe46dEbg+w5kztQbxVY7jMZGeaJPAKupErmDMC85\n/mBWbHXrpUZctHKttI7S6EWsO9uAR3ZW4rvC1n2YQrhWCiUzJRgP9OC6FupaDF6sTBCEVsO/2YU3\nnzr6rlCDgvrmCWQZgJn9OepC5E5+39sbEd7N3bJRvJYLRuzCxbuiKCK7SINHd1Xio7PSorDX3RWh\nxL/GBuGJgb7w5boWsgN+6mzAfOroSIUOlW31AL/SGloPD/82VoVYX9erA0VE7fOUC5ibIF28+3OF\nDvuudM3W6TNVOszfV42//1yLssbW0929/OR4Y0Q3pCd1Qwz7J7IjBi820MNPgXj/5j9sIyBJ82/u\nq8uNKG3RUXjIgEf7ctSFyB2NivTEb0Kk08XvnKyzad2j8kYDXj5ciye+r8KJqtZ12QKUAv48xA8f\njAnCb5i2gRwAgxcbGR/TsYR1jXoRGbnSUZcHe3ixuiqRmxIEAf83yA8tK4uUqI34/Lza6u/VZBDx\nce61dS3ftDG9rRCAh3t7YX1KMB6M47oWchwMXmxkrFkNotNVehQ3tJ462nJRjSpt8x2VSg5M68NR\nFyJ31stfgYlxXpLHMs+pUd7Y/vTzrRBFETuLNHh0ZyU+PNP2upY7f13XMnegH9e1kMPhJ9JGwr3l\nGBwkHfrdaTb6Uqc14tM86d3UlF7eCFLxn4XI3c3s5wP/FnWPNAYR71uh7tHZah0W/FCNF3+ulUxX\nX9fTT47XRnTDS1zXQg6M35I2ND76xlNHG8+rUa9rHnXx9RDw+97eXdI2InJs/koZZvWTjsLuKGrC\niaut16R0RKXGgOVHavHE3qpWWbyvvZ+APw32xQfJQUjkuhZycAxebCg5SiWZt75UZ8CFX3M2VGoM\n2HRBOuoyNd4bfkr+kxDRNQ/EeaG3v3ndozoYb2HrdJNBRGZuA6ZlX0VWgQbmz5QLwO96XVvXMrGn\nNxQyrmshx8dvShvq5inDMLM7mB2/Lopbf04tmWcO9JRhUk+OuhBRs7bqHuXW6PF1/s1zR4miiN3F\nGqTuqsSaMw3QtLFbaUT4tXUt8wb5wY/rWsiJcELTxlKiPXGgrDlHw85iDSbEeeGLS42S8x7p6w0v\nBe94iEjq9hAlxkR5StItrDldj+Qoz3YX0p6r0WHViXocq2x7iinOT455A30xLMyzzZ8TOTqhurra\nceuuuwC13oiHsirQsr5a3wAFclvUMAr3kiFjXDCUcgYvRNTaFbUBj+6slPQjD/fywtxBfpLzKjUG\nrD3TgK/zW08PAYC/h4CZ/X0woYcXp4fIqVk88rJy5Urs3LkT/v7+AIC4uDikp6dLztm3bx/eeecd\n6HQ6xMfHIy0tDT4+7rUN2Fshw8gI6V1TrlnxxRn9fBi4EHWAu/YpEd5yTI33xrrc5nVymy824r97\neKGHnwJNBhGbLqiRmatGYxvTQ3IBeKinF1L7+sCf6+rIBVg88jJr1iwsXLgQgwcPbvPnVVVVmDp1\nKtasWYOYmBisWrUKarUazzzzTKca7Iy+L2nCXw/VtPmzWF85PhwTxLsgoptw9z5Foxfx6K5KSdr+\nYaFKPNBDhfdO1aNE3Xb1+jvClHhyoC96+HGVALkOi0JwrVaL3NxcZGZmYtq0aVi8eDFKS0sl5xw4\ncAAJCQmIiYkBAEyePBlZWVmdb7ETSgpTwqed9SyP9fdh4ELUAe7ep6gUAp40q3t0qFyLv/1U22bg\nEusrxz/uCMDyO7oxcCGXY1HwUlFRgcTERMybNw/r16/HoEGDsGjRIsk5paWlCAsLMx2HhoaioaEB\narX1U1w7Ok+5gNGRrRfG9Q1QtPk4EbXGPgUYE+WJ24I9bniOn4eABYN88eGYIAznglxyURaF41FR\nUVixYoXpePr06Vi7di1KSkoQGRkJAO2WcJfJ2o+XVq5caUlznIIngIfaePytI13dEqJbt3DhQns3\n4Zb6FFfuS3r++t+N5J8FVnVFY4gsYI3+xKKRl7y8PGzfvt10fL1TUSiaY6GIiAhUVFSYjsvLy+Hn\n5weVSlrzh4ioI9inENF1FgUvgiDgjTfeQHFxMQBg8+bNiI+PR2hoqOmcpKQknDhxAgUFBQCALVu2\nIDk52QpNJiJ3xD6FiK6zeLdRVlYW1q1bB6PRiLCwMKSlpaGyshLLli1DZmYmACAnJwerV6+GXq9H\nTEwMli5dCj8/v5u8MhFR29inEBHAJHVERETkZJitiIiIiJwKgxciIiJyKgxeiIiIyKl0SdrFr7/+\nGpmZmRAEASqVCk899RT69euHFStW4MCBAzAYDJg2bRomTZoked4XX3yBPXv24PXXXzc9JooiXnzx\nRcTHx2PatGld0fybsub1rV+/Hl9++SXkcjkCAwOxZMkSREdHd/UltWKtaxRFEe+99x52794NAEhI\nSMCzzz7rENtdrfnveN2GDRvwn//8B59++mlXXcZNWfM6n332WeTl5cHLywsAkJiYaPOcMK7cn7hD\nXwKwP3GV/sSefYnNg5fLly/j7bffRkZGBoKDg5GTk4Nnn30Wjz76KAoLC7FhwwY0NDRg1qxZ6N+/\nPxISElBTU4N33nkHWVlZSExMNL3WxYsX8corr+DkyZOIj4+3ddM7xJrXd/DgQXzxxRf46KOP4O3t\njU2bNuHFF1/E+++/b8crtO417t69G4cOHcL69euhUCiwZMkSbNy4EampqXa8Qute43XHjh1DRkYG\nAgIC7HBFbbP2dZ44cQLr1q1DSEiI07Xf0foTd+hLAPYnrtKf2Lsvsfm0kVKpRFpaGoKDgwEA/fv3\nR2VlJbKzszFhwgTIZDL4+fnh7rvvxtdffw0AyM7ORlhYGBYsWCDJqrlp0yZMnDgR48ePt3WzO8ya\n1xccHIzFixfD29sbADBgwABcuXKl6y/KjDWvcezYsfjnP/8JhUKB+vp6VFVVOcQfozWvEQAqKyvx\n6quvYv78+e1mhrUHa15nUVER1Go1li9fjj/84Q/4+9//jtraWqdpv6P1J+7QlwDsT1ylP7F3X2Lz\nkZfIyEhJyYCVK1di1KhRuHDhAsLDw03nhYaGIi8vDwBMQ0xfffWV5LWefvppANfuKhyFNa+vd+/e\npv/XarVYtWoVUlJSbH0JN2XNawSuZWL+7LPP8P777yMsLAxjxoyx/UXchDWv0WAw4Pnnn8eCBQsk\nWacdgTWvs7q6GklJSXjmmWcQGBiIN954A3//+9/x6quvOkX7Ha0/cYe+BGB/4ir9ib37ki5bsNvY\n2IglS5agqKgIaWlpMBpbV0G9Ud0jR2fN66uqqsL8+fPh4+ODuXPnWrupFrPmNT788MPIzs5GcnIy\nFi9ebO2mWswa17h69WoMHToUSUlJDnOXZM4a1zlw4ED84x//QHBwMGQyGR5//HH88MMP0Ov1tmq2\niSv3J+7QlwDsT65z9v7EXn1Jl/x1X7lyBbNmzYJCocC7774LX19fREREoLy83HROeXm5JFpzJta8\nvnPnzmHGjBkYMGAAXn31VYeJtK11jefOnUNubq7p+MEHH8TZs2dt1u5bYa1rzMrKwq5duzB9+nQs\nW7YMhYWFeOSRR2zd/A6z1nUePXoUe/fuNR2LogiZTAa5XG6ztgOu3Z+4Q18CsD+5ztn7E3v2JTYP\nXmpqajBnzhykpKQgPT0dSqUSADB69Gh8+eWXMBgMqKurw44dO5yyTok1r6+goABPPvkkHn/8cSxc\nuBCCIHTFJdyUNa8xLy8PL774IjQaDQBg+/btbS5O62rWvMbt27dj/fr1yMzMxHPPPYeYmBhkZGR0\nxWXclDWvU61W4/XXXzfNTWdkZCAlJcWmn1tX7k/coS8B2J+4Sn9i777E5qH45s2bUVZWhl27dmHX\nrl0ArhV2fPPNN1FYWIhp06ZBp9Nh0qRJGDp0aKvnO9IfXVuseX0ff/wxtFotNmzYgA0bNgC4tijq\nww8/7JqLaYc1r/G+++5DQUEBUlNTIZfL0bt3b6SlpXXZtbTHVp9TURQd6jNszescOXIkHn74YTz+\n+OMwGo2Ij4/Hc8895zTtdzTu0JcA7E9cpT+xd1/C2kZERETkVJxzRRsRERG5LQYvRERE5FQYvBAR\nEZFTYfBCREREToXBCxERETkVBi9ERETkVBi8EBERkVNh8EJEREROhcELERERORUGL0RERORUGLwQ\nERGRU2HwQkRERE6FwQsRERE5FQYvRERE5FQYvBAREZFTYfBCREREToXBCxERETkVBi9kMnXqVNxz\nzz2m44yMDAwfPhwTJ040PbZixQoMHz4c58+f79R7jRs3Dk8++WSrxw0GA9577z1MmDAB48ePx1//\n+lfU19d36r2IqGs5Ql/S0qpVqzB8+HAcPny4U+9FjoPBC5kkJiaipqYGly5dAgDk5OQAAEpLS1FQ\nUAAAOHr0KAIDA9G7d+9OvZcgCG0+npWVhY8++ggTJkzAY489hm+//RYffPBBp96LiLqWI/Ql1+Xk\n5CAjI+Om55FzUdi7AeQ4EhMT8dlnn+Ho0aMIDQ3FsWPHMHz4cBw4cAAHDhxASEgIcnNzkZKSAo1G\ng2XLliEnJwcajQZxcXFYsmQJBg4ciCeeeALl5eUICwvDmTNnsG7dOly6dAlvvfUWqqurMWHCBBiN\nxjbbcO+992LIkCGIjIzEoUOHAAAeHh5d+Wsgok5yhL4EAMrKyrB06VLEx8cjLy+vC38DZGsceSGT\noUOHQiaT4ciRIzh48CAMBgNmzJiBkJAQHDx4EMePH4fRaERiYiJ+/PFHnDx5ErNmzcILL7yAS5cu\nISMjw/RahYWFGDBgAP7yl7/A29sbaWlpkMvleO6551BXVwe1Wt1mGxQKBbp3746NGzdi4cKF6NGj\nB1JTU7vqV0BEVuAIfYnBYMBzzz2H/v37Y+rUqV116dRFOPJCJv7+/ujTpw+OHTsGpVIJX19fDBky\nBHfccQd2795tGt4dNmwYoqOjER4ejkOHDuG7776DIAiora01vZZMJsPcuXOhUCiwd+9eNDU1ITU1\nFWPHjsVdd92Fbdu23bAto0aNQmxsLNLT07FkyRKsWrXKptdORNbjCH3J+++/j/z8fKxbtw779+8H\nAOh0OoiiyCkkF8CRF5JITExESUkJdu3ahWHDhkGhUODOO+9EfX09tm7dioiICERHR2Pjxo2YOXMm\nZDIZHn30UQQHB0MURdPreHl5QaG4Fhtf7yj0ev1N3//ChQv49ttvERsbi1GjRmHo0KH46aefYDAY\nbHPBRGQT9u5LvvnmG1RXV2PixIl4+eWXAQALFizAkSNHbHC11NU48kISiYmJWL9+Perq6jBy5EgA\nQFJSEmQyGaqqqvDAAw8AAA4dOgSZTAY/Pz/s378fpaWlCA8PN71OyzubwYMHw9fXFxkZGfDx8cEP\nP/zQ7jz1zz//jNdeew3nz59Hjx49cODAAdx+++2Qy+U2vGoisjZ79yWvvvoqdDodAGDfvn348MMP\n8cwzz6Bfv362umTqQhx5IYnrgYIgCBgxYgQAwNfXF7fddhsEQcCwYcMAADNmzEBsbCzefPNNnDt3\nDiNHjsTly5eh1+shCIKkw+nWrRtefvllyGQyLF++HJ6enujVq1eb7z9lyhTMmDEDX375JV577TUM\nHz4c6enptr9wIrIqe/clffv2xcCBAzFw4EBER0dDEAT07NkTPj4+tr94sjmhurpavPlpre3btw/v\nvPMOdDod4uPjkZaW1upDkZ2djbVr15qi6rS0NERHR1ul4UTk+kRRxIsvvoj4+HhMmzYNAFBXV4c5\nc0XucpUAACAASURBVObgr3/9KwYMGGDnFhKRPVg08lJVVYX09HS88sor+PzzzxEdHY3Vq1dLztFo\nNFi6dCleffVVZGZmYvTo0Xjttdes0mgicn0XL17E3LlzkZ2dbXrshx9+wIwZM5Cfn89Fl0RuzKLg\n5cCBA0hISEBMTAwAYPLkycjKymp1nkqlQl1dHQBArVbD09OzE00lIneyadMmTJw4EePHjzc99tln\nn2Hp0qUIDg62Y8uIyN4sWrBbWlqKsLAw03FoaCgaGhqgVqvh7e0N4Frg8sc//hGzZ89GQEAADAYD\n1qxZY51WE5HLe/rppwEABw8eND325ptv2qs5RORALBp5abmNTfJisuaX++WXX/Dee+9h48aN2LZt\nG2bOnIlnn33WslYSERER/cqi4CUiIgIVFRWm4/Lycvj5+UGlUpkeO3bsmCkBEXBtF8mFCxdQU1PT\nySYTERGRO7MoeElKSsKJEydMBba2bNmC5ORkyTmDBg3C4cOHcfXqVQDAnj17EBUVhYCAgE42mYiI\niNyZRWtegoKC8Pzzz2Px4sXQ6/WIiYnB0qVLcerUKSxbtgyZmZkYOnQoUlNT8eSTT0KhUCAgIIC7\njYioy435ogz/HB2Ivt1Y4JPIVVic54WIyBmM+aIMD/f2wtyBfvZuChFZCTPsEpHL21nUBGM7Gw2I\nyPkweCEil1ehMeKXSp29m0FEVsLghYjcQnaRxt5NICIrYfBCRG5hT3ETdEZOHRG5AgYvROQWanUi\nfirX2rsZRGQFDF6IyG1kF3LqiMgVMHghIrex74oWGj2njoicHYMXInJpIarmbk5jEJFT2mTH1hCR\nNTB4ISKXNi7KU3LMXUdEzo/BCxG5tHExKsnxgVIt6rRGO7WGiKyBwQsRubR+AQrE+MhNx3oR2FvC\nqSMiZ8bghYgcjiiKeOGFF7B+/XoAgMFgwOuvv46HH34YkydPxpYtWzr8WoIgICWaU0dEroTBCxE5\nlIsXL2Lu3LnIzs42PbZ161YUFhZiw4YN+Ne//oUNGzbg1KlTHX7NcdHSqaMjFTpUagxWazMRdS0G\nL0TkUDZt2oSJEydi/Pjxpsd2796NCRMmQCaTwc/PD3fffTe+/vrrDr9mDz8F+gQoTMcirhVrJCLn\nxOCFiBzK008/jd/+9reSx8rKyhAeHm46Dg0NRVlZ2S29rvnU0U5OHRE5LQYvROTwjMbWu4Nkslvr\nvsynjk5X61HUoO9Uu4jIPhi8EJHDi4iIQHl5uem4vLxcMhLTEWFecgwJ8pA8ls2pIyKnxOCFiBze\n6NGj8eWXX8JgMKCurg47duxAcnLyLb9OilnOl+xCDUSR5QKInI3i5qcQEdnX5MmTUVhYiGnTpkGn\n02HSpEkYOnToLb9OcqQn3jpeB8Ov8crlegPO1+oRH+Bx4ycSkUMRqquredtBRG7j2f3VOFCmNR1P\njffGnARfO7aIiG4Vp42IyK2kmC3c3VmkgZFTR0ROhcELEbmVuyKVULbo+UobjTh5VWe/BhHRLWPw\nQkRuxVshw50R5uUCuOuIyJkweCEit2Oe82V3sQZ6I6eOiJwFgxcicjvDw5TwUQim42qtiMMV2hs8\ng4gcCYMXInI7SrmA5ChOHRE5KwYvROSWzKeOvi9pQpOBU0dEzoDBCxG5paEhHgj0bO4C1XoR+0s5\n+kLkDBi8EJFbkgsCxnHqiMgpWVweYN++fXjnnXeg0+kQHx+PtLQ0+Pj4SM7Jy8vDa6+9hoaGBshk\nMixZsgT9+/fvdKOJiKxhXLQKmy82mo5/LG1Cvc4IXw/e1xE5Mov+QquqqpCeno5XXnkFn3/+OaKj\no7F69WrJORqNBvPnz0dqaioyMjIwa9YspKWlWaXRRETWkBCoQKR3czeoM15b+0JEjs2i4OXAgQNI\nSEhATEwMgGtF07KysiTn7N+/H927d8eIESMAAKNGjcLy5cs72VwiIusRBKGNcgEMXogcnUXTRqWl\npQgLCzMdh4aGoqGhAWq1Gt7e3gCA/Px8BAUFIT09HefOnYOfnx/mz59vnVYTkdvZuHEjNm/eDIVC\ngV69euGZZ56Bv79/p183JVqFzHNq0/HPFVpc1RgRpOLUEZGjsuivU2yniJlM1vxyer0eOTk5mDRp\nEtatW4eHH34YCxcuhF6vt6ylROS2fvrpJ2RmZuLdd9/FJ598gkGDBmHZsmVWee2e/gr08pObjo0i\nsLtEY5XXJiLbsCh4iYiIQEVFhem4vLwcfn5+UKmah1/DwsIQFxeHhIQEAMDo0aNhNBpRVFTUySYT\nkbs5e/YskpKSEBwcDABITk7Gvn37rHYzlBJjNnVUyKkjIkdmUfCSlJSEEydOoKCgAACwZcsWJCcn\nS84ZMWIEiouLcebMGQDA4cOHIQgCoqKiOtlkInI3AwYMwE8//YTS0lIAwPbt26HT6VBTU2OV1zdP\nWHeiSocStcEqr01E1idUV1dblFIyJycHq1evhl6vR0xMDJYuXYqCggIsW7YMmZmZAIAjR47g7bff\nRmNjI5RKJZ566ikMGTLEqhdARO7h3//+NzZt2gSlUomHHnoIL7/8Mr755hurrHsBgHnfX8XJquaR\nnMcH+GBaH58bPIOI7MXi4IWIqKtoNBpUVlYiOjoaAHD58mXMmjULO3bssNp7bLmoxlvH603Hvf0V\nWDsmyGqvT0TWw+X0ROTwrly5gjlz5qChoQGiKOLDDz/Eb3/7W6u+x5hIlaRDPF+rx8VabjAgckQM\nXojI4cXFxSE19f+zd+fRUVX52vifU3OGSmUmE0JDRGaMMghImPvX7avte4PNexUVcbgKLQIOzCog\ngjIogii200UGQRHvlUZoJTLIDAJhFhCETCQhSVWGSs3n90dhJScQQkJVTlXq+azFWtShKmefkJx6\nau/v3nsknnzySTz00EPQaDR44YUXvHqOaJ0Cd8dpJMcycznriMgfcdiIiOiqTZeq8PaRcs/jpFAF\nVg2OgSAIMraKiGpjzwsR0VX9ErWoua1RntmFU0YOHRH5G4YXIqKrwtUK9G5Ra6fpHA4dEfkbhhci\nohoGJUvDy9Y8K5x1rCpORPJgeCEiqqF3Cy1CVdU1LiVWF45cscvYIiKqjeGFiKgGrVJAv4RaQ0ec\ndUTkVxheiIhqGZQiDS/b86ywOTl0ROQvGF6IiGq5O1aDSE310FGlQ8S+QpuMLSKimhheiIhqUSkE\nDEiqtdM0h46I/AbDCxHRdQyuNetod4EVZodLptYQUU0ML0RE19EpWo0WIdW3SKsT2JnPoSMif8Dw\nQkR0HQpBwKBkDh0R+SOGFyIKCJmZmXjkkUfw6KOPYvTo0cjNzfX5OWsPHR0ossFo5dARkdwYXojI\n71ksFsyYMQPz58/HypUrkZ6ejgULFvj8vG0jVGgVrvQ8dorA9nyrz89LRDfG8EJEAUGn06G83L3j\ns9lshlarrecVt04QBAxO4dARkb9Ryd0AIqL66HQ6jBs3Dk8//TQMBgOcTic++eSTJjn3oCQtPjtd\n6XmcVWxHYZUT8SHKG7yKiHyJPS9E5PeOHj2KZcuWYe3atdi4cSNGjRqFSZMmNcm5U8JV6BAp/Zz3\nUy6HjojkxPBCRH4vKysLPXr0QHJyMgDgoYcewvnz52EymZrk/LVnHXGvIyJ5MbwQkd/r3LkzDh06\nhJKSEgDA9u3bkZSUBIPB0CTnH5ishVDj8VmTAxfLHU1ybiK6FmteiMjvpaWlYeTIkRg9ejRUKhUM\nBkOTzDb6Q6xOibRYNQ5dsXuO/ZRrwaj24U3WBiKqJhiNRm6VSkRUj39drMKCrHLP45QwJVYMioYg\nCDd4FRH5AoeNiIhuQv9ELVQ1ckpOpRNnTBw6IpIDwwsR0U3QaxTo1UIjOZaZw8JdIjkwvBAR3aRr\n9jrKs8IpcuSdqKkxvBAR3aQ+LbTQKavHjq5YXDhabL/BK4jIFxheiIhuUohKwL0J0qEjbhdA1PQY\nXoiIGqD2Xkfb86ywuzh0RNSUGh1edu7ciUceeQR///vfMWXKFFRWVtb53G3btmHgwIGNPRURkd/o\nHqdBhLp66KjMLuJAoU3GFhEFn0aFl9LSUsyePRvz5s3D119/jeTkZCxduvS6z7106RIWL158S40k\nIvIXaoWA/knSHa05dETUtBoVXvbt24eOHTsiJSUFADBs2DBs3rz5mudZLBbMmDEDEyZMgMiKfCJq\nJgbXmnW087IVVQ7e44iaSqPCS0FBAeLj4z2P4+LiUFlZCbPZLHne3LlzkZGRgdTU1FtrJRGRH+ka\no0asrvr2aXECuwu40zRRU2lUeKmrF0WhqP5y69atg0qlwv33389eFyJqVhSCgEHJHDoikkujwktC\nQgKuXLnieVxUVAS9Xg+drrordePGjTh58iQeffRRTJgwAVarFY899pjkdUREgar20NG+AhvKbC6Z\nWkMUXBq1q3TPnj2xaNEiZGdno2XLlli/fj369+8vec7nn3/u+Xt+fj4efvhhrFix4tZaS0RB6/vv\nv8fq1as9j8vLy1FUVISNGzciKiqqydvTzqBCyzAlsiudAACHCOzIt+L+ViFN3haiYNOo8BIdHY3X\nXnsNkydPhsPhQEpKCmbMmIGTJ09izpw5WLlypeT5oihy51UiuiX33Xcf7rvvPgCAw+HAs88+i1Gj\nRskSXABAuDp0tPxMda1fZq6F4YWoCQhGo5EFKUQUUD799FOcPn0a8+fPl7UdlyocePynEs9jAcDX\nf45BrE4pX6OIggBX2CWigGI0GvHll1/ixRdflLspuC1chXaG6g5sEcDWXM46IvI1hhciCijffvst\n+vfvj8TERLmbAuDanaYzOeuIyOcYXogooGzZsgX333+/3M3wGJSsRc2KvtNGB3IqHLK1hygYMLwQ\nUcAoKytDTk4OunbtKndTPOJDlOgao5Yc+4lDR0Q+xfBCRAEjJycHsbGxUCr9qyC29tDRllwLF+ck\n8iHONiIiukVGqwvDfrgCZ4276cf9o3C7QV33i4io0djzQkR0iyK1CvSI00iOceiIyHcYXoiIvGBw\ninTo6KdcC1wcOiLyCYYXIiIv6JuggbZGKU5BlQvHS+zyNYioGWN4ISLyglCVAn1a1N5pmkNHRL7A\n8EJE5CW1d5remmeBw8WhIyJvY3ghIvKSnvEahKurl6wz2UT8csUmY4uImieGFyIiL9EoBaQnSoeO\nMnM4dETkbQwvREReVHvo6Od8K6xODh0ReRPDCxGRF90Zq0a0tvrWWuUUsaeAvS9E3sTwQkTkRUpB\nwMBkDh0R+RLDCxGRl9UeOtpbaEW53SVTa4iaH4YXIgoI586dw3PPPYfHHnsMI0eOxOnTp+VuUp06\nRKqQFFp9e7W7gJ357H0h8haGFyLyexaLBWPHjsXIkSOxYsUKPPXUU5g+fbrczaqTIAjX7DSdmWuR\nqTVEzY9K7gYQEdVn7969aNmyJXr37g0A6NevH5KSkmRu1Y0NSdFh5Vmz5/GhIjtKLC5E6/iZkehW\n8beIiPzepUuXEB0djdmzZ2PkyJEYO3YsnE6n3M26odZ6FdpGVH8+dAHYlsfeFyJvYHghIr/ncDiw\ne/duZGRkYPny5Rg+fDjGjx8Ph8Mhd9NuaFDtWUccOiLyCoYXIvJ78fHxaN26NTp27AgASE9Ph8vl\nQm5urswtu7HadS8nSh3Ir/TvHiOiQMDwQkR+r3fv3sjLy/PMMDp06BAEQfD7upfEUCU6R6klx37i\n0BHRLROMRiPXrSYiv3f48GEsWbIEVVVV0Gg0eOmll9C1a1e5m1Wvby+Y8d6xCs/jNnolPhsYI2OL\niAIfwwsRkQ+VWl0Y9sMVuGrcaT8bEI02EZzsSdRYHDYiIvKhKK0Cd8dqJMd+YuEu0S1heCEi8rHB\n15l1JIrs9CZqLIYXIiIf65eohbrG3Tbf7MLJUv+e5k3kzxo96Lpz50588MEHsNvtSE1NxfTp0xEW\nFiZ5zqZNm7By5UoIggCdToeXXnoJHTp0uOVGExEFkjC1Ar1baLGjxv5GmbkWdIpW3+BVRFSXRvW8\nlJaWYvbs2Zg3bx6+/vprJCcnY+nSpZLnXLx4EUuWLMHixYuxcuVKPPnkk5g0aZJXGk1EFGhqDx1t\nzbPC4eLQEVFjNCq87Nu3Dx07dkRKSgoAYNiwYdi8ebPkORqNBtOnT0dMjHtKYPv27VFcXOz3K2IS\nEfnCPS20CFMJnselVheOFNtlbBFR4GpUeCkoKEB8fLzncVxcHCorK2E2V29ClpiYiD59+gAARFHE\nokWLkJ6eDpWK0wOJKPholQLuTaxVuJvDWUdEjdGo8FJXlbxCce2Xq6qqwpQpU5Cbm4tp06Y15nRE\nRM1C7aGjHflWWJ0cOiJqqEaFl4SEBFy5csXzuKioCHq9HjqddB+Py5cv46mnnoJKpcKHH36I8PDw\nW2stEVEAuytWg0hN9dBRpUPE/kKbjC0iCkyNCi89e/bE8ePHkZ2dDQBYv349+vfvL3mOyWTCs88+\ni8GDB2P27NnQaDTX+1JEREFDpRAwIEn6IY87TRM1XKO3B9i9ezeWLl0Kh8OBlJQUzJgxA9nZ2Zgz\nZw5WrlyJzz77DB9//DHatm0red3SpUthMBi80ngiokBzvMSO53eWeh5rFMD//CUWoSouu0V0s7i3\nERFRExJFEf+5pRgFVS7Psalpevy5ZYiMrSIKLJz6Q0QBYdGiRfjpp58QEREBAGjdujVmz54tc6sa\nThAEDErW4ctz1bMzM3OtDC9EDcDwQkQB4dixY3jzzTfRpUsXuZtyy4bUCi8HimwwWl2I1HLoiOhm\n8DeFiPyezWbDmTNnsHLlSowYMQKTJ09GQUGB3M1qtDYRSrTWKz2PXSKwPY+Fu9T8iKKIY8Xen1HH\n8EJEfu/KlSvo3r07/vGPf2DVqlXo3LkzXn75Zbmb1WiCIGBwcu1ZR9Y6nk0UeC6WO/DJqQo8klmM\nsbuMXv/6LNglooA0cOBArF69GomJiXI3pVFyKx0YkVkiObZ2SAxahCrreAWRfyu2OPFTrhU/5lhw\nxiTdCmjb3+LreFXjsOaFiPzeuXPncObMGdx3330Aqlf5DuTtRpLDVOgQqcIpY/VNfmueBf+ZGiZj\nq4gaxuxw4ed8d2A5VGSHq/6XeEXg/uYTUdAQBAHvvPMO7rzzTiQlJeGbb75Bamoq4uLi5G7aLRmc\nosMpY4Xn8ZYcK8ML+T2HS8SBIht+zLFg12UrrM4bP1/jgwIVDhsRUUDYvHkzli9fDpfLhfj4eEyf\nPh0tWrSQu1m3pNjixN9/KJZ8Wl0+MBqt9PxcSf5FFEWcLHXgxxwLtuZZYLLdODoIANJi1RiaokO/\nRC3C1d5NMAwvREQyenF3KQ5dsXseP94uFE+25z5w5B+yKxzYkmPBjzlW5Jnr6WIBkBqhwtAUHQYl\naxEX4rv6LcZ7IiIZDU7WScJLZq4Vo+4IgyAIN3gVke+UWFzYmmfBjzkWnDY66n1+ixAFhqToMCRZ\nhz9FNE2sYHghIpJReqIWi46Vw3517Ci30olfTQ60j1TL2zAKKmaHCzvzbdiSa8HBIhtc9YzJhKsF\nDEzSYmiKDp2j1VA0cdhmeCEikpFeo0DPeA12Xa5eyCszx8LwQj7ncIn45Wrh7c7LVljqGRVSK4De\nLdyBpVe8BhqlfL2DDC9ERDIbnKyThJeteVY81ykcSg4dkZeJoojTRncdy0+5FpTeROFttxh34W16\nkhZ6LxfeNhbDCxGRzPq00EKnFGBxut9IrlhcOFpsR1qsRuaWUXORU+HAllwrtuRYkFNZf+Ft2wgV\nhiRrMThFh3gfFt42FsMLEZHMdCoB9yZosKXGFgGZuRaGF7olRqsLP+VZsCXHgpOl9RfexumqC2/b\nGvw7Hvh364iIgsSQFJ0kvGzPs2JcFxFqBYeO6OZZHCJ2Xbbix1wL9hfWX3gbphIwIEmLISk6dItp\n+sLbxmJ4ISLyA93jNIjQCCi7WoNQbhdxoNCGPglamVtG/s7hEnH4ig0/5ljxc74VVc4bJxaV4C68\nHZKixT0ttNDKWHjbWAwvRER+QKUQ0D9Riw0XLZ5jmbkWhhe6LlEUccbkXvH2p1wrSqz17yrULUaN\nIck69E/SIsIXa/Y3IYYXIiI/MSRFJwkvuy5bUeUQEaIKvE/G5Bv5lU78mOuuY7lUUX/hbWu9EkNT\ndBicrENCM9qxnOGFiMhPdIlWI06nQJHF/Sna4gR2X7ZicIpO5paRnEw2F7blupfoP15qr/f5sToF\nBifrMCRFi9QIVbNcrZnhhYgCyrZt2zBz5kxs3bpV7qZ4nUIQMChZh7W/mT3HtuRaGF6CkNUpYvdl\nK37MsWBfoQ31lLEgVOUedhyaokO3WHWzXyOI4YWIAsalS5ewePFiuZvhU4OTtZLwcqDQhjKbK+Br\nFKh+TlHEkSt2/JhjwY58K8yOGycWpQDc00KDoSk69A7QwtvGYnghooBgsVgwY8YMTJgwAa+++qrc\nzfGZ2w0qtAxTIvvqQmIOEdiRb8X9rUJkbhn5giiKOFdWXXh7xVJ/4W3naPeKtwOStDAEaahleCGi\ngDB37lxkZGQgNTVV7qb4lCAIGJyiw3//Wuk5tiXHwvDSzFw2O5GZ6965+ffy+gtvbwt3F94OSdYh\nMaz5FN42FsMLEfm9devWQaVS4f7770deXp7czfG5wclaSXjJKrajqMqJOD9cpp1uXpnNhW157iX6\nj5bUX3gbrVVgcLK7juV2Q/MsvG0swWg01lMGREQkr1GjRsFisUCpVMJut+PSpUtITU3Fu+++i9jY\nWLmb5xP/tb0EZ0zVS7qP6RSO4W1DZWwRNYbVKWJPgTuw7C2woZ4yFoQoBaQnaTE0WYe0uOZfeNtY\nDC9EFFDy8/Px8MMPY9u2bXI3xafWnjPjw5MVnsd3RKrwUXq0jC2im+USRWQVuwtvt+dZUXkThbc9\n4t2Ft31baKHjuj714rAREQUUURSDovt8YLIWy05W4I+3vV+NDuRUOJASztu2v/rt6oq3mbkWz1o9\nN9IpSoUhKToMTNIhUhuchbeNxZ4XIiI/NW5XKbKKq2sjRt0RhpF3hMnYIqqtsMqJLTnuFW/P30Th\nbcuwqyvepmiRHMYg2liN/s7t3LkTH3zwAex2O1JTUzF9+nSEhYU1+DlERHR9g5N1kvCyJdeCx9uF\nBkXPkz8rt7uwPc+9gNzRYjvq6wGI0ggYlKLD0GQd7ohk4a03NKrnpbS0FA8//DA++eQTpKSk4P33\n34fZbMbEiRMb9BwiIqqbyeZCxr+vSFZX/bh/FG43qOVrVJCyOUXsK7ThxxwL9hRYYa9nVEinBPol\najEkRYe7YzVQKRhYvKlRg2z79u1Dx44dkZKSAgAYNmwYNm/e3ODnEBFR3QwaBXrEayTHMnOsMrUm\n+LgLb21YkFWGjB+u4NUDJuzIrzu4KASgV7wG0+6KwPr/LxbT7jKgV7yWwcUHGjVsVFBQgPj4eM/j\nuLg4VFZWwmw2IzQ09KafQ0RENzYkWYe9BTbP45/yLPivjmFQcOjBZ86XObDlauFtQVX9hbcdIlUY\nmqLDwGQdolh42yQaFV5E8fojTQqFokHPISKiG+uToIFWCViv1oIWVrlwvMSOrjGaG7+QGqSoyonM\nXHcdy29ljnqfnxSqxNAU9wJynAHW9Br1HU9ISMDx48c9j4uKiqDX66HT6Rr0HCIiurFQlQJ9Wmix\nNa96uCgz18rw4gUVdhd+zncHlsNX6i+8NWjcu34PSdahYxQLb+XUqPDSs2dPLFq0CNnZ2WjZsiXW\nr1+P/v37N/g5RERUvyEpOkl42ZpnwdjO4aylaAS7S8S+Ahu25Fqw63L9hbdaJXBvgrvwtkccC2/9\nRaPXedm9ezeWLl0Kh8OBlJQUzJgxA9nZ2ZgzZw5WrlxZ53P0er1XL4CIqLmzOUVk/HAFFfbq2/Xb\nvQzo1UIrY6sCh0sUcbzEji05VmzLs6DMfuO3PQWAu+M0GJqixb2JWoSqWO7gb7hIHRFRAJh/pAwb\nL1k8j4emaDHtLoOMLfJ/F8vdK95uybXgsrn+wtt2Bnfh7aBkLWJ03ATTn7HKiIgoAAxO1knCy858\nGywOkfvg1FJscRfebsmxSDa2rEtCqAJDU9x1LK30fEsMFPyfIiIKAN1i1YjRKlBsdfcgVDlF7C20\nYkASJ0GYHS7syLNiS64Fh4rsqK+PJUItYGCyDkNTdOjEwtuAxPBCRBQAlIKAgclarDtf5Tm2JccS\ntOHF4RKx/+qKt7sLrJ6p5HXRKIC+Ce6pzT3iNVCz8DagMbwQUUD46quvsH79egiCgOTkZEybNg1R\nUVFyN6tJDU7WScLLvkIbyu0u6NXBUVAqiiJOlrrrWH7Ks6DMduOSTQFAWqwaQ1N0SE/UIixIvk/B\ngOGFiPzeqVOnsGrVKqxevRphYWFYvHgxli1bhilTpsjdtCbVPlKFpFAl8szubga7C/g534r7bguR\nuWW+danC4dm5Oe8mCm9TI6oLb+NCWHjbHDG8EJHf69ChA9avXw+lUgmr1YrCwkIkJyfL3awmJwgC\nBqdoseKM2XNs5ZlKHL5iu8GrAtulCid+NdZfeNsiRIEhVwtv/xTBt7bmzq+mSi9atEjuJhDRdYwf\nP17uJgAAtm3bhjlz5kCj0eDDDz9Ey5Ytr/s83kuI/Jc37iccACSigDFgwAD88MMPePrpp/HCCy/I\n3RwikgnDCxH5vZycHBw5csTz+IEHHsDly5dRVlYmY6uISC5+NWxERHQ9hw8fxquvvoqVK1ciMjIS\nGzduxJdffunZioSIggvDCxEFhG+++Qbr1q2DUqlEXFwcJk6ciMTERLmbRUQyYHghIiKigMKaFyIi\nIgooDC9EREQUUBheiIiIKKBwGUIiCiibNm3CypUrIQgCdDodXnrpJdxxxx149913sW/fPjidTowY\nMQIZGRmS13333XfYvn07Fi5c6DkmiiJmzZqF1NRUjBgxoqkv5RrevLZVq1Zhw4YNUCqViIqKeuFd\n4AAAIABJREFUwpQpU/xmVWJvXacoili2bBm2bdsGAOjYsSMmTZoEnU7+zSq9+X/5hzVr1uB///d/\n8eWXXzbVZdyQN69x0qRJOHfuHEJC3FtddO/e/YaL2TG8EFHAuHjxIpYsWYIVK1YgJiYGu3fvxqRJ\nk/D4448jJycHa9asQWVlJZ566im0b98eHTt2hMlkwgcffIDNmzeje/funq914cIFzJs3DydOnEBq\naqqMV+XmzWvbv38/vvvuO3z++ecIDQ3FunXrMGvWLHz00UcyXqGbN69z27ZtOHDgAFatWgWVSoUp\nU6Zg7dq1GDlypIxX6N1r/ENWVhZWrFgBg8EgwxVdy9vXePz4cSxfvhyxsbE3dX4OGxFRwNBoNJg+\nfTpiYmIAAO3bt0dxcTEyMzPxwAMPQKFQQK/XY+jQodi0aRMAIDMzE/Hx8XjhhRcgitWTK9etW4cH\nH3wQQ4YMkeVaavPmtcXExGDy5MkIDQ0F4N4b6vLly01/UdfhzescOHAg/vnPf0KlUqGiogKlpaV+\n8ebuzWsEgOLiYsyfPx9jx4695t/k4s1rzM3NhdlsxltvvYVHHnkEb7zxRr0LULLnhYgCRmJiomdt\nF1EUsWjRIvTr1w/nz59HixYtPM+Li4vDuXPnAMDTZf2vf/1L8rVeeeUVAO5eCn/gzWtr27at5+82\nmw3vv/8+Bg8e7OtLuCnevE4AUKlU+Oqrr/DRRx8hPj4eAwYM8P1F1MOb1+h0OvHaa6/hhRdegErl\nP2/Z3rxGo9GInj17YuLEiYiKisI777yDN954A/Pnz6/z/Ox5IaKAU1VVhSlTpiA3NxfTp0+Hy+W6\n5jkKRWDe3rx5baWlpRg7dizCwsIwZswYbzf1lnjzOocPH47MzEz0798fkydP9nZTG80b17h06VKk\npaWhZ8+eftPrUpM3rrFTp054++23ERMTA4VCgWeeeQa7du2Cw1H3buKB+dtNREHr8uXLeOqpp6BS\nqfDhhx8iPDwcCQkJKCoq8jynqKhI8ukvUHjz2s6ePYsnnngCHTp0wPz58/3qU7u3rvPs2bM4c+aM\n5/Hf/vY3/Prrrz5rd0N46xo3b96MrVu34tFHH8WcOXOQk5ODxx57zNfNvyneusYjR45gx44dnsei\nKEKhUECpVNb5GoYXIgoYJpMJzz77LAYPHozZs2dDo9EAANLT07FhwwY4nU6Ul5djy5Yt6N+/v8yt\nbRhvXlt2djZGjx6NZ555BuPHj4cgCE1xCTfFm9d57tw5zJo1CxaLBQDw/fffX7fYtal58xq///57\nrFq1CitXrsS0adOQkpKCFStWNMVl3JA3r9FsNmPhwoWeOpcVK1Zg8ODBN/y59Z8oTkRUj2+++QaF\nhYXYunUrtm7dCgAQBAHvvfcecnJyMGLECNjtdmRkZCAtLe2a1/vTm3ht3ry2L774AjabDWvWrMGa\nNWsAuAssP/vss6a5mBvw5nX+9a9/RXZ2NkaOHAmlUom2bdti+vTpTXYtdfHVz6koin7zM+zNa+zT\npw+GDx+OZ555Bi6XC6mpqZg2bdoNz8+9jYiIiCigcNiIiIiIAgrDCxEREQUUhhciIiIKKAwvRERE\nFFAYXoiIiCigMLwQERFRQGF4ISIiooDC8EJEREQBheGFiIiIAgrDCxEREQUUhhciIiIKKAwvRERE\nFFAYXoiIiCigMLwQERFRQGF4ISIiooDC8EJEREQBheGFiIiIAgrDCwEAHn74Yfz5z3/2PF6xYgV6\n9eqFBx980HPs3XffRa9evfDbb7/d0rkGDRqE0aNHX/ffpk6dil69enn+PProo7d0LiJqev5yPzly\n5AieeOIJDBgwAE8++SQuXrx4S+ci/8HwQgCA7t27w2Qy4ffffwcA7N69GwBQUFCA7OxsAO4bQVRU\nFNq2bXtL5xIEoc5/O3r0KPr06YMlS5ZgyZIlmDp16i2di4ianj/cT/Ly8jB27FjExsZi8uTJuHTp\nEubOnXtL5yL/oZK7AeQfunfvjq+++gpHjhxBXFwcsrKy0KtXL+zbtw/79u1DbGwszpw5g8GDB8Ni\nsWDOnDnYvXs3LBYLWrdujSlTpqBTp0547rnnUFRUhPj4eJw+fRrLly/H77//jsWLF8NoNOKBBx6A\ny+W6bhvy8/NRVFQEi8WCvXv3ol27dnj99deb+DtBRLfKH+4nmZmZsNlseP7559GqVSt06tQJISEh\nTfydIF9hzwsBANLS0qBQKHD48GHs378fTqcTTzzxBGJjY7F//34cO3YMLpcL3bt3x549e3DixAk8\n9dRTmDlzJn7//XesWLHC87VycnLQoUMHTJ06FaGhoZg+fTqUSiWmTZuG8vJymM3m67ahqKgIqamp\neOSRR/D222+joKAAU6ZMaapvARF5iT/cT3JycgAAH330EdLT0zFp0iQUFBQ0yfWT77HnhQAAERER\nuP3225GVlQWNRoPw8HB07doV99xzD7Zt2+bp2u3RoweSk5PRokULHDhwAD/++CMEQUBZWZnnaykU\nCowZMwYqlQo7duyA1WrFyJEjMXDgQNx7773YuHHjddvQtWtXrFq1yvP4l19+wZo1a1BQUIAWLVr4\n9htARF7jD/cTURQBAAaDAfPmzcNbb72FyZMnY8OGDb7/BpDPseeFPLp37478/Hxs3boVPXr0gEql\nQt++fVFRUYFvv/0WCQkJSE5Oxtq1azFq1CgoFAo8/vjjiImJ8dwoACAkJAQqlTsX/zEe7XA46j3/\niRMn8PHHH3tuXH+8Rq1We/tSicjH5L6fJCYmAgAyMjLQu3dv3HPPPSgsLJQEIwpcDC/k0b17dwBA\neXk5+vTpAwDo2bMnFAoFSktLPf9+4MABKBQK6PV67N27FwUFBZJx55oFdF26dEF4eDhWrFiBzMxM\nzJ07t84x6rKyMnzyySeYP38+Nm/ejH//+9/o2bMnoqOjfXXJROQjct9PBg0aBKVSiY8//hhbt27F\n3r170bJlS0RERPjqkqkJMbyQx5133gmlUglBENC7d28AQHh4OLp16wZBENCjRw8AwBNPPIHbbrsN\n7733Hs6ePYs+ffrg4sWLcDgcEARBcrOJjIzE3LlzoVAo8NZbb0Gr1aJNmzbXPX/v3r3x4osvIisr\nC2+++Sa6deuGmTNn+v7Cicjr5L6ftGrVCgsXLkRubi5mzpyJhIQEvP32276/cGoSgtFoFOt/Wv1E\nUcSsWbOQmpqKESNGAADWrVuH7777DlarFe3bt8f06dM5BEBE9ap9P7FYLJg/fz5OnToFl8uFTp06\nYeLEidBqtXI3lYhk4JWelwsXLmDMmDHIzMz0HNu6dSu+/vprLF26FGvWrIHVapUUYxIRXc/17ief\nf/45XC4XVq9ejdWrV8NqtWL58uUytpKI5OSV2Ubr1q3Dgw8+6CmQAoCNGzdixIgR0Ov1AIDJkyfD\nZrN543RE1Ixd735y1113ISkpCYB79km7du08C6ARUfDxSnh55ZVXAAD79+/3HMvOzkZJSQnGjRuH\noqIipKWlYezYsd44HRE1Y9e7n/Tq1cvz9/z8fKxdu5arLxMFMZ8V7DocDuzfvx9z587FF198AZPJ\nhA8++MBXpyOiIHDq1Ck8++yzGD58OPr27St3c4hIJj4LL3FxcRgwYABCQ0OhUqnwl7/8BcePH/fV\n6Yiomfvhhx8wduxYPP/88xg5cqTczSEiGfksvAwaNAhbtmyB1WqFKIrYvn07Onbs6KvTEZEXfHyq\nAgO+K8SA7wrlbopEZmYm3nnnHbz//vuS3YqJKDj5bHuAhx56CGVlZXj88cfhcrnQvn17TJgwwVen\nI6JbdLTYhi/PXn+fGLn9MeQ8e/Zsz7Fu3bp56mOIKLh4bZ0XIgpclXYXntpegsvm6tVKt/0tXsYW\nERHVjSvsEhHeP14hCS5ERP6M4YUoyO3Is2BTtkVybFibEJlaQ0RUP4YXoiBWbHFiwdFyybHWeiX+\nq0O4TC0iIqofwwtRkBJFEfOOlKPMVl32phKAaXdFQKsUbvBKIiJ5MbwQBan//b0K+wqlW3aMah+G\n2w3cPJWI/BvDC1EQulThwIcnKyTHukar8Z+poTK1iIjo5jG8EAUZh0vEm4fKYHVWHwtVCZhyVwSU\nAoeLiMj/MbwQBZkvzlTiV6NDcmxs53AkhiplahERNVeiKOI3k6P+JzYQwwtREDlRYsfKM9JVdNMT\ntfhLS51MLSKi5sopiliQVY7nfi7x+tf22fYARORfzA4X3jxUhppL0UVrFXixqx4Ch4uIyIscLhFv\nHS7DllyrT74+wwtRkFh6vAJ5Zqfk2KQ0PSK17IAlIu+xOUXM+sWEnZdt9T+5kRheiILArstWbLwk\nXUX3/7YOQa94rUwtIqLmyOIQ8eoBEw4U+S64AAwvRM1eicWF+UfKJMdahivxXEeuoktE3lNpd2HK\nPhOOltglxxNDvd+7y/BC1IyJooj5WWUw1lhFV3l1FV2dinUuROQdZTYXJu014lStmYy3hSuxsHek\n18/H8ELUjP3rogV7CqTdtyPvCEP7SK6iS0TeUWp14eU9RvxWJg0ubSNUWNA7ElE+qKtjeCFqpnIq\nHFh6QrrpYqcoFR7hKrpE5CWFVU68tMeI7ArpZIAOUSrM6xUJvcY3EwIYXoiaIYdLxJuHy2CpcT8J\nUQqYelcEVAoOFxHRrcurdOLFPaW4bHZJjneLUWNuLwNCVb6bycjwQtQMrTxrxqlSaRfu853DkRzG\nX3kiunUXyx14aY8RVyzS4NIzXoNZ3Q0+r6nzWiwSRREzZ87EqlWrrvm3iRMnYv78+d46FRHdwKlS\nO744Uyk51jdBg/tuC5xVdGvfT5xOJxYuXIjhw4dj2LBhWL9+vcwtJApev5kcGLer9Jrg0i9Bi9k9\nfB9cAC+FlwsXLmDMmDHIzMy85t+++OILZGVlcQVPoiZQ5XBvuuiqnlyEKI2Al7tFBMzv4PXuJ99+\n+y1ycnKwZs0a/Pd//zfWrFmDkydPythKouB0qtSO8btLJTMYAWBIshavd4+ARtk09xmv9CGvW7cO\nDz74IBITEyXHDx48iL179yIjIwNlZWV1vJqIvOXDExXIqZQWzk1Mi/BJtb+vXO9+sm3bNmRkZECh\nUECv12Po0KHYtGkTOnbsKGNLiYJLVrENk/eaUOWUBpf7W+kwoau+SXel98od7ZVXXsFf/vIXybGi\noiK88847eOONN6BQBM6NkyhQ7Smw4ruLVZJjD7TSoXeLwFpF93r3k8LCQrRo0cLzOC4uDoWFhU3d\nNKKgtb/Qiol7jdcEl4fahOClJg4ugI92lXY4HJg2bRpeeuklxMTEQBTF+l9UyyEfLy1M1JwYrS7M\nOyKdFp0SpsSYTnqZWuRdLpfrmmP8UETUNH7Ot2LafhOs0k5dPNYuFP/oFC7LkLRPph6cOnUK+fn5\nePfddwEAxcXFcLlcsNvtmDp16k19jdcOmvBhvyi0DOfsCKIb+WMV3VJr9Ru84uoquiHNZBXdhIQE\nFBUVeR4XFRVJemKIyDe25Fgw57C0jg4AnukQhhG3h8nTKPgovHTp0gUbNmzwPP74449hMpnw8ssv\n3/TXqLCLmLLPHWB8tcgNUXOwKduCXbV2b328XRg6RDWfVXTT09OxYcMG9OvXD2azGVu2bMHkyZPl\nbhZRs/avi1VYmFWO2mMnL3QOR0YbeRe79OtujZxKJ14/aMK8eyK5sBbRdeRVOrHkWIXkWIdIFR69\nvXmtojts2DDk5ORgxIgRsNvtyMjIQFpamtzNImq21p034/3j0nuLAsDLd+px320h8jSqBsFoNDa8\nIKUJDPiuuhjvgVY6vNhVHzBTPYmagsMlYvwuI46XVu/gqlMCn/SPRgqHW4mokVaeqcQnp6VrRf2x\noeugZP9YLyogxmM2XLRg/YWq+p9IFES+PGeWBBcAGNNJz+BCRI0iiiI+PlVxTXBRK4BZPQx+E1wA\nPw4vCaHSpi09XoF9BVaZWkPkX04b7fjvX6U3mN4tNHiglf/cXIgocLhEEe8fr8Cqs2bJca0SmNsz\nEn0T/GvJBb8NL3N6RiK0xkwJF4CZv5ThQq0tt4mCjeXqKro1l1uI1Ah4JYBW0SUi/+EURSzIKsc3\ntUY4QlUC5t8Tie7xGplaVje/DS9tIlR47e4ISQPNDhFT9xthtF675gNRsPjoVMU128+/3C0C0Tq/\n/XUmIj/lcImYc6gM31+ySI5HqAW80ycSXWP8L7gAfhxeAOCeFlqM7hQuOZZvduHVAybYnH5ZZ0zk\nU/sLrfi21qej+27T4d5E/+rSJSL/Z3OKeP2gCZm50pKMKI2ARX2j0D7Sf5db8OvwAriXHr6/1jj+\nsRI73jla3qiVe4kClcnmwtuHpavoJoUq8Hzn8DpeQUR0fRaHiGn7TdesERWnU2DxvVFoE+Hfhf9+\nH14EQcD4LnrcGSNNgJuzLVhzzlzHq4iaF1EUsTCrHMU1V9EFMO0uA0JVfv9rTER+pNLuwsS9Rhyo\ntQ1PUqg7uATCyvYBcddTKQTM6mFAcphScvyfpyqxM58zkKj5+3e2BTtq/ayPaBeKTtH+261LRP6n\nzObCS3uMOFoiXWahVbgSi++NQmKoso5X+peACC8AEKFRYG4vA8LV1bMpRACzD5XhrMle9wuJAlx+\npROLa610eUekCiPbybevCBEFnhKLC+N3leK0UTprNzVChff6RiFWFxjBBQig8AIAt4WrMKO7ATV3\nCrA43eN2xRZn3S8kClBOUcTcw2UwO6rru7RK90qX3DKDiG5WYZUT43aV4ny59L2yQ5QK7/aJRKQ2\noOJAYIUXAOgep8G4WgWKhVUuTD9ggpUzkKiZWXvOfE337nMdw3FbAIxJE5F/yKt04oVdpciulAaX\nO2PUWNg7MiA3Pw68FgN48E+hyPiTdGOoU6UOzDtSxhlI1GycNdnxWa1lunvGa/B/W8u/KRoRBYaL\n5Q68sKsUl83S9dF6xWvw9j2RAVvwH5itBjCmUzh6xEkXz8nMtWLFGc5AosBndbpX0a0xWoQIjYBJ\nd3KDUiK6OedMdozbVYorFmlwSU/U4o0eBmiVgXsvCdjwolIIeL17BFqFSwuMPvu1EltzLXW8iigw\n/PNUBX6vNTb9clc9YgKooI6I5HOy1I7xu40w2qSjEUNTtHjt7ghoAji4AAEcXgAgXK3AnF4GRGik\n/wlzD5fhdClnIFFgOlhowzfnpavo/qWlDulJ3HSRiOp35IoNL+02osIuDS4PtNJhSlrzKPYP6PAC\nAMlhKrzRw4AaezjC5gKm7TehsIozkCiwlNlceOtImeRYQqgCY7mKLhHdhH2FVkzca0RVrQksf28T\nghe76qFoJsPOAR9eAKBbjAYvdtNLjhVbXZi234QqBwt4KTCIooh3j5ZLxqcFAFPTIhCmbha/qkTk\nQz/nWzFtnwm2WnsXP94uFGM6hTererlmc0e877YQ/L+2oZJjZ00OzD1cBhdnIFEA2JJrxdY86Sq6\nD6eG+u2urkTkP7bkWPD6QRNqf17/rw5heLJ98wouAOC1xSJEUcSsWbOQmpqKESNGwGKxYP78+Th1\n6hRcLhc6deqEiRMnQqv13e63/9UxDNkVDuwuqN6vYUe+FZ+drsTTHdjtTv6rwOzEoqPSTRdvN6gw\nqj1X0f1DZmYmPv30UygUCuj1ekyfPh3JyclyN4tIdv+6WIWFWeWo/TF9XJdw/MefQq/7mkDnlZ6X\nCxcuYMyYMcjMzPQc+/zzz+FyubB69WqsXr0aVqsVy5cv98bp6qQUBEy/OwJta+2GufKsGT9kcwYS\n+SfX1VV0K2t8ZNIo3KvoqptBYZ03WCwWzJgxA/Pnz8fKlSuRnp6OBQsWyN0sItl9/ZsZC2oFFwWA\nSXfqm21wAbzU87Ju3To8+OCDSExM9By76667kJSUBABQKBRo164dfv/9d2+c7oZCVQq82dOA0TtK\nUFpjitj8rDIkhSnRmRvZkZ/56rcqHCmWzo57tmM4Wuu5im5NOp0O5eXu3imz2ezTXlyiQLDiTCU+\nrbWQpVJwf/AZlNy8Zyd65e74yiuvAAD279/vOdarVy/P3/Pz87F27VpMnTrVG6erV0KoErN7RmL8\n7lLYrxYu2V3A9P1GfJgeHTC7ZlLz95vJgU9PSzddvDtWjf/4E1fRrUmn02HcuHF4+umnYTAY4HQ6\n8cknn8jdLCJZiKKIT05XYtVZ6aKsagUws7sBfRKaf7D3ecHuqVOn8Oyzz2L48OHo27evr0/n0Sla\njYl3RkiOGW0ipu4zwuxw1fEqoqbjXkXX5AnYAKBXC5icFtFspjN6y9GjR7Fs2TKsXbsWGzduxKhR\nozBp0iS5m0XU5FyiiCXHK64JLjolMLdXZFAEF8DH4eWHH37A2LFj8fzzz2PkyJG+PNV1DU3R4bF2\n0jG/C+VOzPqlDE7OQCKZfXq64podXl/sqkdcCHsGa8vKykKPHj08BboPPfQQzp8/D5PJJHPLiJqO\nUxSxIKsc6y9IF7EMUwmYd08kuscFz8xEn4WXzMxMvPPOO3j//ffx5z//2VenqdeoO8LQP1GaRPcW\n2PDRyYo6XkHke4ev2PD1b9Ib0NAULQY283HqxurcuTMOHTqEkpISAMD27duRlJQEg8Egc8uImobD\n5d7v7PtL0sknEWoBC/tEBt2SCj6rCPzggw8AALNnz/Yc69atm6c+pqkoBAFT0iKQby7FGZPDc/yr\n36rQKlyF/9OKtQXUtMrtLsw9XCaZHdAiRIFxXfR1vibYpaWlYeTIkRg9ejRUKhUMBgNnG1HQsDpF\nzPrFhF2XbZLjUVoFFvaORJuI4CvuF4xGY1CMnxRVOTH6Z+numkoBWNA7EmmxwZVYSV6zfzFhS271\nYnQCgHf7ROJO/hwSUS1VDhGvHjDiYJF0RmJ8iDu4tAwPvuACNKMVdusTF6LEmz0N0NYoJ3CKwOsH\nTMipcNT9QiIvysy1SIILAAxvG8rgQkTXqLS7MHHvtcElKVSJxX2jgja4AEEUXgDgjkg1pqRJZyCV\n2UVM3W9CuZ0zkMi3CquceLfWKrptI1R4iqvoElEtZTYXXtxjxLESaXBpFa7E4nsjkRDkS34EVXgB\ngAFJOjxZ683iUoUTMw+a4HAFxQgaycAlinjrcJlki3r11VV0NUpOiyaiaiUWF8bvKsWvRumoQGqE\nCu/1jUKsLriDCxCE4QUAHrs9FEOSpTOQDhbZ8f5xzkAi3/jmfBUOXZF+gnqmQ3hQFtoRUd0Kq5wY\nt6v0mmUUOkap8G7fSERqg/Jt+xpB+V0QBAGv3BmBjlHSN47/+b0K314w1/EqosY5X+bAP09Jg/Fd\nsWo81IYz3YioWm6lA2N3liK7Uhpc0mLVWNA7Enp1UL5lX1fQfie0SgGze0QiPkT6LVhyvAIHC211\nvIqoYWxO99oMNUuqwlRcRZeIpC6WOzBulxEFVdL6y3viNXirVyRCVUH7dn1dQf3diNYpMLdnJHQ1\nag5cIvD6QRMulnMGEt26z3+txG9l0p+lCV31iOcqukR01VmTHeN2SZfyAID0RC3e6GmAlnVx1wjq\n8AIAbQ0qvHZ3BGr+aFQ6REzZZ4LJxhlI1HhZxTasOScdhhyUrMWQFK6iS0Tuntm9BVZM2G2E0Sad\nMPLnFB1euzsCagWDy/UEzSJ19VlzzoxltbYM6BbjHmfkDw81VIXdhae2lUi6gON0Cnw2IBp6TdB/\nZiAKSmaHCydK7DhaYsfRYjtOldpxvc/If2sVgvFdwzm0fAOc6nDV/2sbgovlDmzKrt43IqvYjneP\nluOVbnoI/CGiBlhyrOKasevJaREMLkRBpNTqwrFimyesnDM5UF9//vA2IRjdKZzvOfVgeLlKEAS8\n2E2PPLMTWcXVU1q/v2RBa70Kw9uG3uDVRNW25Vnw7xzp5ml/bxOCu4Nox1eiYCOKIi6bXThaYsPR\nYnfvSnaFs/4X1jCyXSieuCOMweUmcNioFqPVhTE/lyDPXJ2PBQBv9jSgT4K27hcSAbhiceLJrSUo\nq7EYXWu9Eh+lR7PojqgZcYkifi934lixDVlXe1ZqF9zejDidAl1j1PhLSx16xPM95mYxvFzH7+UO\n/OPnUlQ6qr81IUoBS/tFcVExqpMoipi414QDRdVT7VUCsCw9CqkGtYwtI6JbZXeJOGN04NjVnpVj\nJXaU2xv+9nlbuBJdY9ToGq1GlxgNEkIU7GlpBIaXOuwvtGLyXpNkfLJFiALL0qMRxRUO6TrWXzBj\n8TFp0fezHcLw8O3cu4go0JgdLpwsdXhqVk6W2mFt2CgQFABSDSpJWOH7h3cwvNzA+vNmLK61ZUDn\nKDUW9onkEABJXCx34JntJZKZA91i1HinTySU/FRF5PeMVheOldg9YeWMyYGGbnenUQAdotSesNIp\nWs3F5XyE4eUGRFHEomMV+N/fqyTHh6ZoMTUtgl19BMDdnfyPn0txxlS9GF2YSsCnA6KDfudXIn9V\nYHZenQXkHga62MDiWsD9e97lalDpGqNBO4OKG602ERZw3IAgCBjbORzZFQ7Jpno/5ljRKtyMR9tx\nOICA5b9WSoILAIzrEs7gQuQnRFHExQqnexZQsQ3HSuzXLGVwM2K07uLaLjFqdIvW4E8RSq7FIhOG\nl3qoFAJmdjdgzM/SzbI+OV2J28KVSE/iaqnB7FixDavPSlfRHZCkxVCuokskG4dLxFmT4+qUZXdY\nKbM1fJAhJcxdXNsl2j0UlBSqZI+7n/DasJEoipg1axZSU1MxYsQIOJ1OLFq0CPv27YPT6cSIESOQ\nkZHhjVPJIqfCgdE/l0qqy3VKYHHfKLSL5EySYGR2uFfRza8xrT5Gq8BnA6Nh4GJ0Xnfu3DksWLAA\nlZWVUCgUmDJlCtq3by93s8gPWBwiThnd05Wzim04WeqAxdmwtzYF3NvFuAtr3YElRsfeU3/llZ6X\nCxcuYN68eThx4gRSU1MBAN9++y1ycnKwZs0aVFZW4qmnnkL79u3RsWNHb5yyyaWEqzDchpEeAAAg\nAElEQVSrhwEv7zHij98JixOYut+EZelRiOUPedB5/3iFJLgAwOQ0PYOLD1gsFowdOxavvfYaevfu\njR07dmD69OlYt26d3E0jGZTZXDh+dW2VoyU2nDE64Gjgx3C1AugQqfYMA3WKUiNczd/dQOGV8LJu\n3To8+OCDSExM9Bzbtm0bMjIyoFAooNfrMXToUGzatClgwwsApMVqMKGrHguyyj3HrlhcmLbfhPf6\nREGnYndisPg534rvL0lX0c34UwgXmfKRvXv3omXLlujduzcAoF+/fkhKSpK5VdRUCqucOFZs9xTY\nXihvXHFtp+g/imvVuCNSzVmjAcwr4eWVV14BAOzfv99zrLCwEC1atPA8jouLw7lz57xxOlnd3yoE\nv5c7sO589QykX40OvHWkDK/dHcHirSBQbHFiQVaZ5FircCWe7RguU4uav0uXLiE6OhqzZ8/G2bNn\nodfrMXbsWLmbRT4giiIuVThxrEbPymVzw4tro7QKT1DpGqNGmwgVly1oRnxWsOtyXfvDplA0jy65\n0Z3CkVPhxN7C6pVUt+VZ0Sq8EqPa8w2sObM5Rcw8WAZTjeI/pQBMuyuCn+J8yOFwYPfu3Vi2bBk6\nduyIHTt2YPz48diwYQNUKs47CGQOl4jfyhyeVWuPFttgbERxbVKo0hNUukarkRzG4trmzGe/9QkJ\nCSgqKvI8LioqkvTEBDKlIODVuyPwj52l+L1G9+XyM2bcFq7CYM40aZZEUcS7R8txtMQuOf5k+zAW\nbftYfHw8Wrdu7Rl2Tk9Px5tvvonc3Fy0atVK5tZRQ1idIk6VuoeAjhXbcbzEjqoGFtcKANpEqDyz\ngLrGqFl3GGR8Fl7S09OxYcMG9OvXD2azGVu2bMHkyZN9dbomF6ZWYG7PSDz3c4nkU/hbR8qQGKZE\nxyi+mTU3a38zY1O2tM4lLVaN/0zljuO+1rt3byxatAinT59G+/btcejQIQiCwLqXAFBud+FEiR1Z\nxe6w8qvJDnsDR4FUAtA+0l1Y2zVGjc7RauhZXBvUfBZehg0bhpycHIwYMQJ2ux0ZGRlIS0vz1elk\nkRimxBs9DHhpj9Hzy2h3AdP2m7CsXxRacJGyZmPXZSs+OlkpOZYcpsSM7gaOozeBmJgYzJ8/H/Pm\nzUNVVRU0Gg3mzZsHtZofEvxNseXqYnBXa1bOlznQ0EGgEKWAzlenLHeNVqNDFItrSYrbA3jBv7Or\nMPdwueRY2wgVltwbyX0tmoFzJjue32mUrBsRphLwQb8otNKz3oKClyiKyK10eoLK0WI78swNnwkU\nqRHQJVrjGQJqG6GCSsGwQnVjePGSf56swOpz0pVW+yZo8EYPA2cgBbBiixOjfy5FYY2lxBUCMK9X\nJLrHa2RsGVHTc4oizl8trv2jwLbE2vCZQAmhCnS9Gla6RKtxWziLa6lh+LHRS57uEIZLFQ7svFw9\nA2nXZRs+PlXJKbQByuoUMf2ASRJcAOCFzuEMLhQUbE4Rp412z7Tl4yV2VDZ0NTgAf9Ir0TVG41m9\nNj6EQ+p0a9jz4kVmhwsv7DTiXJl0k75Jd+rx19tCZGoVNYYoiph9qAyZuVbJ8f/4UwjGddHL1Coi\n36q0u3C81F1Ye7TYjlPGhhfXKgWgXaTK07PSOVrNVafJ6xhevKywyonndpRKulJVAvBOn0h0jeGn\n9UDxxa+V+OxXaYFujzgN5vYycCyemo0SiwvHSmyeacvnTA40dBBIpwQ6Rqk9PSsdotQI4Wrj5GMM\nLz5wqtSOcbtKYatxF4jQCFjWLxpJYewu9Xfb8iyYcVC6gu5t4Uos7RfF6ZkUsERRRL7ZhaPF1WEl\nu7LhxbURauHqLCB3z8rtBhbXUtNjePGRn3ItmPWL9A2wtV6J9++N4uZffuy00R08rTXu6RFqAR+m\nRyE5jCViFDhcoogLZU4cLbF5imuvWBpeXBsforhaq+LuWWmlV3ISAsmO4cWHPj9dgeVnpDOQesZr\nMKcnhx78UdHVIb/iWkN+C3pH4s5YDvmRf7O7RJwxOjw9K8dL7Ci3N/z23ipceXUWkLtnJYHrVZEf\n4kdJH3rijjBcqnBia1510ef+Qhs+PFmBsZ1Z9OlPqhwipu03SYILALzYTc/gQn7J6hSv7rRs8xTX\nWhs4CqQQgNsNKnS72rPSJVqNSC17hsn/Mbz4kCAImJwWgXxzKU4bq2cgfXO+ComhSjzUhsvK+wOX\nKGLu4TKcMUlnif2/tqG4j7PEyA8dLLJh7qGya8J2fTSKP4pr3TUrHaNVXEiTAhKHjZpAscWJZ3eU\nXjPe/FzHMPxnaphMraI/fHKqAivPSof3erfQYHZPLv1P/mf3ZSteP2i6qSnM4WrBvXlhtHs2ULtI\nFdQcsqZmgOGliZw12TF2Zykstbp1R7YLxRN3hHF1SZn8kG3BnMPSwuo2eiXe7xfFT6Tkd37KteDN\nQ2WoaxPmWJ3iaq+KO6y0ZnEtNVMML01of6EV0/ebJFOoAWB4mxCM7hTOANPETpTYMX53qeQTbJRG\nwIfp0SxSJL+z6VIV5h8pv2Ydlj+n6HBXrDusJIYqeB+hoMDw0sQOX7Fhyj6TZJM/AHiglQ4Tuur5\nKamJXDY7MXpHCUpt1f8PagWwqE8UOkVzp2LyL+svmLH4WIXkmADgpW563N+KdVkUfNgv3sTSYjVY\n2DsS4WppSNlw0YK3DpfD4WKW9DWzw4Wp+4yS4AIAE++MYHAhv7P6bOU1wUUhANPuimBwoaDF8CKD\nTtFqvNsnEgaNNMD8kONe2M7OAOMzTlHEG7+U4Xy5tPjo0dtDMTRFJ1OriK4liiI+PV2Bf56SblOh\nVgAzuxswhD+vFMQYXmRyu0GNxX2jEFNrTYUd+e66GGtdFXl0Sz46WYE9BTbJsfRELZ5sz1lf5D9E\nUcQHJyqwotYil1ol8GZPA/olamVqGZF/YHiRUSu9CovvjUSLEOl/w75CGybtNcLsaPhS3lS3jRer\n8NVvVZJj7QwqTEmLYK0R+Q2nKGLh0XJ8fV76sxqiFDDvnkj0jGdwIWJ4kVlymApL7o1Cy1obNh4p\ntuPlPUaUN3Q/erquI1dseOdoueRYjFaBN3sauAMu+Q2HS8TcQ2X410WL5Hi4WsA7fSLRjTvTEwFg\nePEL8SFKvNc3Cn/SSwPMyVIHJuwywtjAVTRJKrfSgdcOmCRrY2iVwJu9DIgL4ZRo8g82p4iZB8uw\nJdcqOR6pEbCoTyQ6RLGYnOgPPg8vmZmZeOSRR/Doo49i9OjRyM3N9fUpA1K0ToFFfaNwR6R0x4Zz\nZQ6M21WKK7VXt6ObUm53Yco+E8pqbVA3JS0C7SP5ZhCItm3bhoEDB8rdDK+yOERMP2DCz5elwSVW\np8B7faOQauDPKlFNPg0vFosFM2bMwPz587Fy5Uqkp6djwYIFvjxlQDNoFFjYOxJdak3XvVjhxAs7\nS5FvZoBpCIdLxKyDZbhUIf2+Pdk+DAOSOFMjEF26dAmLFy+WuxleZXa4MGmfEfsLpYXkiaEKLOkb\nhVZ6bkFHVJvPe150Oh3Ky921BmazGVoti81uJFytwLx7ItE9Thpg8swujNtViuwKRx2vpNqWnqjA\ngSLpG8KQZC0eu50bYgaiPz4MTZgwAaLYPGbjldtceGmPEVnFdsnxluHuoeTEMA5rEl2PTyO9TqfD\nuHHj8PTTT8NgMMDpdOKTTz7x5SmbhRCVgDd7RmLWLybsulz95ltY5cILu4xY2DsSbSL4aexG/ueC\nGd9ekM7W6BClwit3RnD59AA1d+5cZGRkIDU1Ve6meEWp1YWX9xjxW5n0A0nbCBXm3xOJaB1LEonq\n4tPfjqNHj2LZsmVYu3YtNm7ciFGjRmHSpEm+PGWzoVUKmNndgIFJ0p6qUqsL43eV4rTRXscr6WCh\nDYuPS1ckjQ9RYHYPA7RKBpdAtG7dOqhUKtx///3NotelqMqJcbtKrwkuHSJVeLcPgwtRfXz6G5KV\nlYUePXogOTkZAPDQQw/h/PnzMJlMvjxts6FSCJh+dwT+2lJan1FmF/HibiOOFtvqeGXwuljuwOsH\nTai5SLFOKWBOTwNidOyCD1QbN27EyZMn8eijj2LChAmwWq147LHHcOXKFbmb1mD5ZndwqV2L1S1G\njQW9IxGhYXAhqo9Pxx46d+6MdevWoaSkBNHR0di+fTuSkpJgMBh8edpmRSkIeOVOPUJUAtbXGAYx\nO0RM3GvE7J6R6B7HtR8AwGRzzyyqdFQnFwHAq3dHcLZGgPv88889f8/Pz8fDDz+MFStWyNiixrlU\n4cBLu40oskiXP+gRp8EbPQzQcc0hopvi0/CSlpaGkSNHYvTo0VCpVDAYDJxt1AgKQcDYzuHQKQWs\nPle9XLjFCUzZZ8TM7gb0SQjuQmi7S8RrB0zIqzUj69mOYegb5N+b5kYUxYCsW/rN5MDLe0qv2RC0\nb4IGr99tgIZDmkQ3TTAajYE/gBxEVpypxKenpRu1Ka/uMDsoOTin/4qiiAVZ5dh4Sboq6V9b6jDx\nTn1AvtFR83K61I5X9hpRXmu9oSHJWkxOi4BKwZ9Roobg4GqAeaxdGP7RKVxyzCkCs38pw6ZLVXW8\nqnlbd77qmuDSNVqNF7sxuJD8jhbb8OKea4PL/7lNhyl3MbgQNQbDSwD6e9tQvNxNj5q3PBeAt4+U\n49sL5rpe1iztKbDigxPSmUWJoQrM6mGAmm8KJLODhTa8stcIs0MaXIa1CcHL3fRQMlwTNQrDS4C6\nv1UIpt4Vgdrvz+8dq8CXZyuv/6Jm5nyZA7MOlqHm20LY/8/encc1daX/A//cJIQASQCRTdwqWAEt\nikWpS91Q68xoabHjt4La+tPW0Wlt1bpQnZlqtVqtrUqtSmsXxSm1VKd1XDoVQVutS1VcwH1BgwqI\nQBIg+/39gQZuAgiaPc/79err1VxyybmSnDz3nOc8R8Bgabwf/DzprU3s6+AdNVKPVkBtUhh7XGdv\nvNFVTKOChDwGqnTmxIa1FUHEZ7Dwj0rUv7HbcK4KNXoWE7v4uGwHWa42IPVIBWrq7bbIA/DPOCk6\nUjl1YmfZRSosOSHnLNkHgNeifJDS2cc+jSLEhdDtqZN7NtQTS+J9YVoaYtPFanyWr3SJgl6m1HoW\n/zhaieIa7nLTN7qJER9EK4uIfe26UYPFx80Dlze7iSlwIcRCKHhxAfFBnlj+jB+8TJZafn+1Bh+f\nVsDgQgEMy7JYeUqBs+XcCsPPd/DCi0942alVhNTadrUay/MUnKlMBsDs7hKM7kR7ahFiKRS8uIge\nrYVY2dcPYg9uALOjUIWlJ+XQmd4GOql/X67G/2TclUU9W3tg+lOUQ0Dsa8ulKrNtKXgMsKCnFH/p\nQIE1IZZEwYsLifb3wKq+fvATcr/Ef5Gpsei4HBq9cwcwB26p8Pk5bjJyOx8+Fsb50nJTYjcsy+KL\nc0qz96YHD1gU54uEtu5Zf4kQa6LgxcVE+HpgdT9/tDbZ2O3AbTUWHKuE2kkDmEuVWnxwUs45JvFg\nsDTeFxLaC4bYCcuyWJuvRMYlbokCTz7wQW9f9A+lHCxCrIF6fRfUQSLAmn7+CPHm/nmPlmgw93AF\nqnWGRs50THdVeqQeqYSq3pJTPgMsjPNFWzGtLCL2ob9f2TnrKrc4pLeAwYpn/NCLkscJsRoKXlxU\nGx8+1vTzRzsf7k7KeWVavPN7BRQa5whgVDoW849W4q7JRnZvPyVBT9qQktiJzsBi6Qm5WWVniQeD\nlX38EBNA701CrImCFxcW5MXH6n7+6CThBjAF5TrMOFSBCrVjBzAGlsWHeXJcqNBxjr/UyQujOlIC\nJLEPjZ7Fwj/k2Fuk5hz3FzJY1dcfUf60gzkh1kbBi4trJeJhVT9/RPpxp1cuy3WYfrAcpTX6Rs60\nv28uVCHnFvcLIj5IiKkmezsRYisPRgJ/vcN9X7YW8bC6vz/CfWkakxBboODFDUiFvNqh7FbcO8Ib\nSj3eOliO29WOF8BkF6nwzUVuEmRHCR//fFpK+8EQu6jWGTD3SAWOlWo4x0O9eUjr54/2lH9FiM1Q\n8OImfDx4WP6MH3qZ5IncqjZg+m/luKnUNXKm7RWUa7HMZGWRr5DB0t5+8PGgtyyxPYXGgFmHKnCq\njFscsb24Nrcs1CS3jBBiXfRN4EZEAgZLevuiXwg3gClVGTD9YAWuyu0fwJTU6DH/aCW09dJxPHjA\n+7186QuC2EW52oC3D1XgnEnuVbhUgNX9/BHoRe9LQmyNghc3I+QzWBjniyFh3GWc5WoD3j5YjvMV\n2kbOtL5qnQGpRypRbpJIPCtGQqs3iF2U1tROrV4xCeyj/AVY1dcP/rR7OSF2QZ88NyTgMZjfU4o/\nt+dW/pRrWcw8VIHTZZpGzrQeA8tiyQm52ZfE2AhvjGhPK4uI7d2u0mP6wXLcUHJzwroHeGBlHz8q\njkiIHTEVFRVWLbl6+fJlfPTRR6iqqgKPx0NqaioiIyOt+ZKkmQwsi7VnlfjhGrfIlicfWNLLD3FB\nthvt2FCgxLeXuQm6/UOEWNTLFzxK0CUAdu/ejYyMDDAMA5FIhFmzZiEqKsoqr3VDqcPMQxVm9YV6\nBwmxKM4XIgG9JwmxJ6sGLyqVCi+++CL++c9/ok+fPjhw4ADWrFmDrKwsa70kaSGWZfHF+SpsMSlv\n7sGrrWDbN8T6VUJ336jBh3kKzrFwqQBp/f3gLaC7WwIUFhZi6tSp2Lx5MwICAnDo0CEsW7YMP/30\nk8Vf60qlDu/8Xo5yDbdrfDbEE/94WgohnwIXQuzNqmv7Dh8+jHbt2qFPnz4AgGeffRZt2rSx5kuS\nFmIYBq9FieHFZ/DF+bqN5bQG4B/HKjG/pxRDwqy3sdzpMg1WnuIGLv6ePCyN96XAhRgJhUIsWLAA\nAQEBAIDIyEiUlZVBp9NBILBcN3auXIs5hyug0HIDl6FhnpgXK6UNQAlxEFYNXm7cuIFWrVph8eLF\nuHTpEiQSCd58801rviR5ROOe9IEnn8HafKXxmJ4F3j8uh0rP4s9WyDu5VaXHP45VQlfve8KDByzp\n7YsgWsFB6gkNDUVoaCiA2tHCVatWYcCAARYNXE6VaZB6pBLVOm7gMrKDCDNiJFRfiBAHYtVbW51O\nh0OHDiEpKQnffPMNxowZg7fffhs6nf2X5BJzfw33xjvdJajfRbMAlucpsO1adWOnPZIqrQHvHq1A\npcnQ/LxYKaKpvDppRE1NDVJTU1FUVIT58+db7PceK1FjzuEKs8DlpU5emEWBCyEOx6rBS1BQEDp2\n7Ijo6GgAwIABA2AwGFBUVGTNlyWPYWQHL8zvKYXp6PiaM0r8+1JVwye1kM7AYtFxOa4ruKs4XnnS\nGwlWnKIizu3OnTuYNGkSBAIB1q1bB7HYMttE/HZbjXePVkJtUmh6/JPe+HtXMRgKXAhxOFYNXvr0\n6YNbt27h/PnzAIATJ06AYRjKe3FwQ9uKsDDOF6bFbNPPVWHjeSVY9vFyvNcXKHGkhLsce3AbT7zS\nxeexfi9xXZWVlZgyZQoSEhKwePFiCIWWWQmXLVPhn39wiyICwGtRPpgUSYELIY7K6kulT548ibS0\nNNTU1EAoFGLWrFmIiYmx5ksSCzlaosaCo5XQmHTsL3XyeuQ70h3Xa7DyNDdBN9KvtlKpJ63iII34\n8ssv8fnnnyM8PJxzfO3atfD19X2k37mzsAYfnVLAtAOc3k2MpE7ej9hSQogtWD14Ic4t725tEmON\n/vGTGE+UajD7cAXq/6rWIh42DPBHgIgSdInt/HC1GmlnlZxjPADv9JBYJTmdEGJZtBaVNKlHayE+\n7usHsQc3SPlvoQpLT8ihMzQv9r2p1OFff1RyAhcRH1ga70uBC7GpLZeqzAIXPgMseFpKgQshToKC\nF/JQUf4eWNXXD35CbgCzt0iNhX/IodE3HcAoNAa8e6TSrHbG/J6+6OxLK4uIbbAsiy/OKfH5OW7i\nuQcPWNTL16r1jAghlkXBC2mWCF8PrO7nj9Yi7lvm1ztqLDhWCZWu4QBGZ2Dxrz8qcbOKu5TjtSgf\nPBtq/eq9hAC1gcunZ5XIMKkk7ckHlvb2Qz8bVJImhFgOBS+k2TpIBFjTzx+h3ty3zdESDeYeqUC1\njpvZy7Is1pxR4sRd7k7Vw9uKkBxBCZHENvQsi49OKcz28PIWMFjxjG338CKEWAYFL6RF2vjwsbqf\nP9qJuXkqp8q0mPV7BRT1liZtu1aDnwq5Xxjd/D1qC+HRElRiI0tPyLHzhopzTOrB4OO+fogJoMCF\nEGdEwQtpsSAvPlb39Ue4lFua/Vy5Dm8fqkC52oAjJWqsNUmKDPbi4f3evrSxHbGpvUVqzmN/IYNV\n/fwR6Uf5VoQ4K1oqTR6ZXGPAnMMVOF/B3e6hnQ8f99QGVNXLg/HiM1j7rD86Sa26nRYhZgb9VGL8\n/0ARDx/39UM7Mb0PCXFmNPJCHplUyMPKPn7oHsC9g71ZpecELjwA/4yTUuBC7KqNNw9r+vtT4EKI\nC6DghTwWHw8ePoz3Q6/AxnMHpnYVo08wreYg9tNBzMea/v4I9aaaQoS4AgpeyGMTCRgs6e2LfiHm\nAcxf2ovwUicq/EXsJ0IquL/MnwIXQlwF5bwQi9EZWHyYJ8cvstoEyd5BQizp7QsP0y2qCbEhhcYA\niZDu0whxJRS8EItiWRZn7mmh0rOICxSCR0uiCSGEWBgFL4QQQghxKjSWSgghhBCnQsELIYQQQpwK\nBS+EEEIIcSoUvBBCCCHEqVDwQgghhBCnYrPgJTc3F4MHD7bVyxFCXNBvv/2G5ORk/PWvf0Vqaiqq\nqqrs3SRCiB3YJHi5ceMG1qxZY4uXIoS4qPLycixevBjLly/H999/j7CwMKxdu9bezSKE2IHVgxeV\nSoX33nsPM2bMAMtSSRlCyKM5cuQIoqOj0bZtWwDA6NGjsWfPHju3ihBiD1YPXpYuXYqkpCRERERY\n+6UIIS6suLgYQUFBxseBgYGoqqpCdXW1HVtFCLEHq+4Nn5WVBYFAgJEjR+LWrVsPff6qVaus2RxC\nyCN6++237d2ERkdueTzzezDqSwhxXJboT6wavOzcuRMqlQrjxo2DVquFWq3G+PHj8cknn6B169bW\nfGlCiIsJCQnB2bNnjY9LS0shkUggEons2CpCiD1YNXj56quvjP9/+/ZtjB07Fps3b7bmSxJCXFTv\n3r2xatUq3Lx5E+3atcO2bdswcOBAezeLEGIHNtuY8datW0hJSUFOTo4tXo4Q4oIOHTqEtWvXQqfT\noW3btnjvvfcgkUjs3SxCiI3RrtKEEEIIcSpUYZcQQgghToWCF0IIIYQ4FQpeCCGEEOJUrLra6IHd\nu3cjIyMDDMNAJBJh1qxZ6NKlCz755BMcOXIEer0eKSkpSEpK4pz3008/Yf/+/Vi5cqXxGMuyWLRo\nESIiIpCSkmKL5j+UJa9vy5Yt2LFjB/h8Pvz9/ZGamoqwsDBbX5IZS10jy7JYv349cnNzAQDR0dGY\nO3euQyx3teTf8YHMzEz8+OOP+Pbbb211GQ9lyeucO3cuLl++DC8vLwBAXFyc1WvCuHJ/4g59CUD9\niav0J/bsS6wevBQWFiItLQ2bN29GQEAADh06hLlz52LChAmQyWTIzMxEVVUVJk2ahMjISERHR6Oy\nshKfffYZ9uzZg7i4OOPvunbtGpYvX478/HyHqdhryes7evQofvrpJ3z11Vfw9vZGVlYWFi1ahA0b\nNtjxCi17jbm5uTh27Bi2bNkCgUCA1NRUfPfdd3jllVfseIWWvcYHTp06hc2bN8PX19cOV9QwS1/n\n2bNn8c0339isbpMr9yfu0JcA1J+4Sn9i777E6tNGQqEQCxYsQEBAAAAgMjISZWVlyM7OxqhRo8Dj\n8SCRSDBs2DDs3r0bAJCdnY2goCBMnz6dU1UzKysLiYmJGDp0qLWb3WyWvL6AgADMmzcP3t7eAICo\nqCjcuXPH9hdlwpLXOHjwYKSnp0MgEECpVKK8vNwhPoyWvEYAKCsrw4oVK/Dmm2861J5elrzOoqIi\nVFdXY9myZUhOTsb7778PuVzuNO13tP7EHfoSgPoTV+lP7N2XWH3kJTQ0FKGhoQBqh/hWrVqFZ599\nFlevXkVwcLDxeYGBgbh8+TIAGIeY/vvf/3J+1+zZswHU3lU4CkteX3h4uPH/NRoNPv30UyQkJFj7\nEh7KktcIAAKBAFu3bsWGDRsQFBSEQYMGWf8iHsKS16jX6/HPf/4T06dPh0Bgk5nZZrPkdVZUVKB3\n796YM2cO/P398fHHH+P999/HihUrnKL9jtafuENfAlB/4ir9ib37Epsl7NbU1CA1NRVFRUVYsGAB\nDAaDeWMa2KPEWVjy+srLy/Hmm2/Cx8cH06ZNs3RTH5klr3HMmDHIzs7GwIEDMW/ePEs39ZFZ4hrX\nrl2L2NhY9O7d22HukkxZ4jq7du2KDz/8EAEBAeDxeHjttddw8OBB6HQ6azXbyJX7E3foSwDqTx5w\n9v7EXn2JTT7dd+7cwaRJkyAQCLBu3TqIxWKEhISgtLTU+JzS0lJOtOZMLHl9ly5dwquvvoqoqCis\nWLHCYSJtS13jpUuXcPHiRePj559/HhcuXLBau1vCUte4Z88e5OTkYNy4cfjggw8gk8kwfvx4aze/\n2Sx1nXl5eThw4IDxMcuy4PF44PP5Vms74Nr9iTv0JQD1Jw84e39iz77E6sFLZWUlpkyZgoSEBCxe\nvBhCoRAAMGDAAOzYsQN6vR4KhQJ79+51yn1KLHl9N2/exNSpU/Haa6/h7bffBsMwtriEh7LkNV6+\nfBmLFi2CSqUCAOzatavB5DRbs+Q17tq1C1u2bEFGRgbmz5+Ptm3bOsyeXpa8zurqaqxcudI4N715\n82YkJCRY9X3ryv2JO/QlAPUnrtKf2LsvsXoo/sMPP6CkpAQ5OTnGfY0YhsHq1ashk8mQkpICrVaL\npKQkxMbGmp3vSB+6hljy+jZt2gSNRoPMzExkZmYCqE2K+vLLL21zMY2w5DX+6YufEa8AACAASURB\nVE9/ws2bN/HKK6+Az+cjPDwcCxYssNm1NMZa71OWZR3qPWzJ6+zbty/GjBmD1157DQaDAREREZg/\nf77TtN/RuENfAlB/4ir9ib37EtrbiBBCCCFOxTkz2gghhBDitih4IYQQQohToeCFEEIIIU6FghdC\nCCGEOBUKXgghhBDiVCh4IYQQQohToeCFEEIIIU6FghdCCCGEOBUKXgghhBDiVCh4IYQQQohToeCF\nEEIIIU6FghdCCCGEOBUKXgghhBDiVCh4IYQQQohToeCFEEIIIU6FghdCCCGEOBUKXgghhBDiVCh4\nIUZjx47F8OHDjY83b96M+Ph4JCYmGo998skniI+Px5UrVx7rtYYMGYKpU6eaHV+4cCHi4+PN/iOE\nOA9H6EsA4LPPPsOIESMwePBgTJ8+HSUlJY/1WsRxUPBCjOLi4lBZWYnr168DAA4dOgQAKC4uxs2b\nNwEAeXl58Pf3R3h4+GO9FsMwDR6fMGECPv30U3z66af4xz/+AR6Ph+Tk5Md6LUKIbTlCX3L06FF8\n8803GD58ON555x0UFBRg7dq1j/VaxHEI7N0A4jji4uKwdetW5OXlITAwEKdOnUJ8fDyOHDmCI0eO\noHXr1rh48SISEhKgUqnwwQcf4NChQ1CpVOjYsSNSU1PRtWtX/O1vf0NpaSmCgoJw/vx5fPPNN7h+\n/TrWrFmDiooKjBo1CgaDocE2PPHEE3jiiScAANOmTUN4eDjeeOMNW/4zEEIekyP0JZ6engCA6Oho\ndOvWDT4+PhAKhbb8ZyBWRCMvxCg2NhY8Hg8nT57E0aNHodfr8eqrr6J169Y4evQozpw5A4PBgLi4\nOPz+++/Iz8/HpEmTsHDhQly/fh2bN282/i6ZTIaoqCi8++678Pb2xoIFC8Dn8zF//nwoFApUV1c3\n2ZZff/0Vx48fxxtvvAE+n2/tSyeEWJAj9CXdu3fHSy+9hH/9618YPXo09Ho9pkyZYqt/AmJlNPJC\njKRSKTp37oxTp05BKBRCLBYjJiYGzzzzDHJzc43Du7169UJYWBiCg4Nx7Ngx/PLLL2AYBnK53Pi7\neDwepk2bBoFAgAMHDkCtVuOVV17B4MGD0b9/f+zcubPJtmzatAkdO3bEM888Y9VrJoRYniP0JYcO\nHcIPP/yAlJQUxMbGYvHixVi4cCHS0tJs8m9ArItGXghHXFwcbt++jZycHPTq1QsCgQD9+vWDUqnE\n9u3bERISgrCwMHz33XeYOHEieDweJkyYgICAALAsa/w9Xl5eEAhqY+MHc9I6na5Zbbh79y7OnDmD\nhIQEy18gIcQm7N2X/Pbbb2BZFsnJyXj22WfRs2dP4ygQcX4UvBCOuLg4AIBCoUDfvn0BAL179waP\nx0N5ebnx58eOHQOPx4NEIsHhw4dRXFzMmXuun0T31FNPQSwWY/PmzcjOzsbSpUsbnacGgJMnT4Jl\nWTz11FPWuERCiA3Yuy+Jjo4GAHz++ef4+eefcezYMXTp0oWmoV0EBS+Eo0ePHuDz+WAYBn369AEA\niMVidO/eHQzDoFevXgCAV199Fe3bt8fq1atx6dIl9O3bF4WFhdDpdGAYhtPh+Pn5YenSpeDxeFi2\nbBk8PT3RqVOnRttQUlIChmEQGhpq3YslhFiNvfuSkSNHYtKkSTh48CCWLl2KyMhILF682PoXTmyC\nqaioYJt6AsuyWLRoESIiIpCSkgKdTofly5fj1KlTAIB+/fph+vTpZufp9XqsWrUKR44cgV6vR0pK\nCpKSkqxzFYQQl5Obm4uFCxciJyeH+hNCCEeTCbvXrl3D8uXLkZ+fj4iICADAf//7X8hkMmRmZkKv\n12Py5MnIzs42y0/Yvn278XlVVVWYNGkSIiMjjUN5hBDSmBs3bmDNmjXGx9SfEELqa3LaKCsrC4mJ\niRg6dKjxmI+PD1QqFdRqNdRqNbRarXE9fX379+/HqFGjjHOZw4YNw+7duy1/BYQQl6JSqfDee+9h\nxowZxsTN3Nxc6k8IIUZNBi+zZ8/GiBEjOMcGDx4MsViMkSNHYuTIkWjXrh369+9vdm5xcTGCg4ON\njwMDA6k0MyHkoZYuXYqkpCTjaC9QmwdF/Qkh5IEWJ+yuXLkSrVq1wp49e7Bjxw5UVlZiy5YtZs9r\nKAOcx6P8YEJI47KysiAQCDBy5EjOclnqTwgh9bX403/y5Ek8//zzEAgEEIvF+Mtf/oLjx4+bPS8k\nJASlpaXGx6WlpZw7J0IIMbVz504UFBRg3LhxmDFjBtRqNcaNG4egoCDqTwghRi0OXrp164ZffvkF\nQG2hoAMHDjRYj2PAgAHYsWMH9Ho9FAoF9u7di4EDBz5+iwkhLuurr77Ct99+i4yMDKxatQqenp7I\nyMjAoEGDqD8hhBi1eHuAt956CytWrMCYMWPA4/HQu3dvTJgwAQCQnp4OAHj99dcxevRoyGQypKSk\nQKvVIikpCbGxsZZtPSE2Uq0zYHLuPdyqbrgg1vpn/RHp72HjVrk2lmWNNT6oPyGE1PfQOi+EEGB5\nnhy7bqga/flLnbzwRjeJDVtECCHuizLeCHmIX2+rzQKXcCl30DKnSA09S/cBhBBiCxS8ENKEMpUe\nH52Sc461F/PxSV8/eAvqypaXqQ04dVdr6+YRQohbouCFkEawLIsVeQpUaupGVPgMML+nFFIhD/1D\nuMUZs4san1YihBBiORS8ENKInwpVOFyi4Rx7tYsPuvjVJuYmtOUGLwduq6HR09QRIYRYGwUvhDTg\nplKHdfkKzrFu/h4YG+FtfPx0ayF8hXVTRwoti2Ol3GCHEEKI5VHwQogJnYHFkhNyqPR1x7z4DN7t\nKYWAVxesCHgMBrcRcc6lqSNCCLE+Cl4IMbH5YhXOV+g4x958Sow2Pnyz5w4J404dHbyjRrWu4Vow\nhBBCLIOCF0Lqyb+nxeZL1Zxj/UOE+FM7UYPP79bKA0FedR8jtR44dIemjgghxJooeCHkvmqdAR+c\nlMNQL+fW35OHd7pLjZVeTfEYBglhNHVECCG2RMELIfety1eiqErPOTanhwR+nk1/TEynjo6WaFCp\noakjQgixFgpeCAFw6I4aOwq5IybPd/BCn2DPRs6oEyEVoIO4Lh9GzwIHbqkt3kZCCCG1KHghbq9c\nbcCKPG4V3bY+fEztKm7W+UwDU0d7aeqIEEKs5qG7SrMsi0WLFiEiIgIpKSmYN28eZDKZ8edFRUV4\n+umn8dFHH5mdO3z4cAQFBRkfjx8/Hs8995yFmk7I42NZFh+dkqO8XhVd3v0qul6ChvNcGjIkzBNf\nXqgyPj5dpkVJjR5BXuYrlAghhDyeJoOXa9euYfny5cjPz0dERAQAYNmyZcafFxQUIDU1FXPmzDE7\nt7CwEFKpFBkZGRZuMiGWs/OGCgdNVge98qQPovw9WvR72ooFiPQTGJdYs6jdrPH/6hW1I4QQYhlN\nThtlZWUhMTERQ4cONfuZVqvFwoULMXPmTM7oygOnT58Gn8/H1KlTkZycjI0bN8JgoCRG4jhkSh0+\nPavkHIvyFyCl86MFHLTqiBBCbKPJ4GX27NkYMWJEgz/78ccfERQUhIEDBzb4c71ej/j4eKSlpSE9\nPR2HDx/G1q1bH7/FhFiAzsDig5NyqOrtRSTiA/NjuVV0W2JwmCfqn3mxUoebSl2jzyeEEPJoHjlh\nNzMzExMnTmz05y+88AJmzpwJgUAAsViM5ORk5ObmPurLEWJR/75UjYJybmDx964StBU/NA2sUa1F\nfPRozZ1uyi6iVUeEEGJpjxS8XLhwAXq9Hj179mz0Obt27cLly5eNjw0GAwSCR/9iIMRSzpdr8fXF\nKs6xPsFCjOzQcBXdlmho6ohlaadpQgixpEcKXk6cOIG4uLgmn3P16lVs2LABBoMBKpUKWVlZGDZs\n2CM1khBLUelqN12sX0XXT8hgdhNVdFtiYKgn6i9SuqnU41IlTR0RQoglPVLwIpPJ0KZNG7Pj6enp\nSE9PBwBMnjwZUqkUY8eORUpKCmJiYpCYmPh4rSXkMa0vUOKmWRVdKVqJLFPySCLkoXeQkHOMpo4I\nIcSymIqKChrTJm7hSLEac49Uco6N7CDCO92lFn2d7CIV3j9eV/QuUMTDd8MCwLPAyA4hhBCqsEvc\nRIXagA/zFJxjbbz5mNbMKrot0TfYE6J6telKVQacuae1+OsQQoiju1Kpw4YC5cOf2EIUvBCXx7Is\nVp5S4J66rs4QD7VVdL0Flv8IeAkY9A/h7omULaOpI0KIezGwLFaeluPby9UW/90UvBCXt+emCr/e\n4QYP4570RtdWLaui2xJDTFYd5d5WQWegGVpCiPvYUagyK0lhKbR2mbi021V6rDnDHbKM9BNgwpM+\nVn3dXkFCSD0YyLW1AYtcw+KPUg2eacYu1e5u69at2LZtGxiGQVhYGObPnw9/f3/aK40QJ1Km0iPd\nCtNFD1DwQlyWnmWx5KQcNfWq6HrygXd7PnoV3eby4DEY2MYTOwrrtgjYK1NR8PIQ586dw5YtW/Dv\nf/8bPj4+WLNmDdavX4/k5GTaK40QJ7I2X4kqnfVGm2naiLiszMvVOGuSKDstWoz2j1FFtyVMp45+\nu6OByoofZlcQFRWFbdu2wcfHB2q1GiUlJfDz88OZM2dorzRCnMSREjX2WblEBAUvxCVdrNDiy/Pc\nKrrxQUI839HLZm2ICfBA63r1Y1R6Fr8XU+Luw/D5fOTm5mLUqFHIy8vDyJEjodPpaK80QpyASsdi\n1Wnuys7Ovpa/YaTghbgctb62im692SJIhQzm9JBYpIpuc/EZBkPacKeJ9tJO080yaNAg/O9//8Pk\nyZMxffp02iuNECex6WIVbldzV3bO6i6x+OtQ8EJcTnqBEoVKbhXd2d2lCKhffMVGEtpyp46Olmig\n0NB0R2NkMhny8vKMj0eNGoU7d+5g586dtFcaIQ7uqlyH765wl0W/+IQXIv0sv7KTghfiUv4o0eCH\nazWcY39qJ8KzofZJlH3SV4C2PnVBk9YAHLhNU0eNKS0txYIFC1BRUQEA2LNnD8LDw3Ht2jXaK40Q\nB2ZgWXx8SsEZ8W4t4uH/RVpnZSfduhCXIdcYsCxPzjkW6s3Dm09ZvopuczEMg4QwT3xzse5uJLtI\nhb90sF3ujTOJjY3FxIkTMXXqVPD5fAQGBmLFihXw9/fHihUrMHbsWOh0OgwdOpT2SiPEgewsVOFs\nOXeBxPRuYvh4WGeM5KF7G7Esi0WLFiEiIgIpKSmYN28eZDKZ8edFRUV4+umn8dFHH3HO0+v1WLVq\nFY4cOQK9Xo+UlBQkJSVZ5SIIYVkWC4/LkXurblSDB2B1Pz88FSBs/EQbKFTo8ErOPeNjHoDvhwfY\nZRqLEEIs7Z7KgAk5ZVBq68KJvsFCLOnta7U8wyZHXq5du4bly5cjPz8fERERAIBly5YZf15QUIDU\n1FTMmTPH7Nzt27dDJpMhMzMTVVVVmDRpEiIjIxEdHW3hSyAE+EWm4gQuADC2s7fdAxcA6CARoLOv\nAJcqaytNGgDk3FLjpU7e9m0YIYRYwGf5Ck7gIuID05+y7gKJJsdzsrKykJiYiKFDh5r9TKvVYuHC\nhZg5cyan6uUD+/fvx6hRo8Dj8SCRSDBs2DDs3r3bci0n5L471XqsNqmi+6SvAK92sW4V3ZZIMKn5\nkk2rjgghLuBYiRp7TWq6TOwiRoi3dUeWmwxeZs+ejREjRjT4sx9//BFBQUEYOHBggz8vLi5GcHCw\n8XFgYCBKSkoeo6mEmNOzLJaelHMqOQp5tZsueli5im5LDAnjJgyfK9ehqMo6e34QQogtqPUsPjnN\nvXEMlwowupP1c/oeOZMmMzMTEydObPTnDVW/5PFocROxrO+vVONUGTdJ7G/RYnSQOFYuepAXH90D\nuMsFrV2BkhBCrGnzxSrcqq4rS8EAeKe7xOrbrwCPGLxcuHABer0ePXv2bPQ5ISEhKC0tNT4uLS3l\njMQQ8rguV2rxxTluFd1egUK88IRjruQx3S4gu0gFlqXtAgghzue6QofMy9yaLokdvRDlb/maLg15\npODlxIkTiIuLa/I5AwYMwI4dO6DX66FQKLB3795Gp5gIaSm1nsXiE3LU3ypI4sFgbqwEPBtW0W2J\ngaGe4Ndr2nWFHlfl+sZPIIQQB2RgWaw8peD0vwGePEyOsl2e4SONrctkMrRp08bseHp6OgDg9ddf\nx+jRoyGTyZCSkgKtVoukpCTExsY+XmsJue+Lc0pcV3C/+Gd1l6C1Ay8/9vPkoVegEIdLNMZj2UUq\nhPvarw4NIYS01O4bKpwx2fT2zafEEFuppktDHlrnhRBHc7xUg1m/V3CODW8rwrs9pXZqUfP976YK\nH5ysK6QX7MVD5tAAm+65RAghj6pcbcCEfWVQ1Fsa/UyQEEvjrVfTpSGUQUucikJjwLKT3Cq6wV48\nTLdjFd2W6B8qhGe9waHiGgPyy2nVESHEOazLV3ICF08+8FaMbTe9BSh4IU5m1RkFSlV1K9kYAO/2\nlNp0uPJxeAt46BtsstO0jGq+EEIc3/FSDf5n0l9N7OKDUCvXdGmIc/T4hADIlqmQbbK8+OUIb3R3\ngCq6LWG66mj/LRV0Bpq9JYQ4rtqaLgrOsU4Svt0qhVPwQpxCSY0eH5t8cMKlAkx0oCq6zRUfJISP\noG6ItVzD4uRdTRNnEEKIff37UhVkVdyaLrO6S21S06UhFLwQh2dooIqux/0qukK+8yW6CvkMBrYx\nmTqignWEEAdVqNBhyyVuTZfnO3qhayvb1HRpCAUvxOH9cLUGJ+9yl+W9HiVGJ6ljVdFtCdO9jn69\nrYZaT1NHhBDHwrIsPj7NrenSysY1XRpCwQtxaFflOqSf4+6d0bO1h032zrCmHq090Mqz7uNXrWNx\nuJhGXwghjmXPTZXZFixvdBNDYudFEhS8EIel0bNYckIObb1tssQeDObFSh22im5z8RkGg02mjkyT\nkQkhxJ4q1AasK+DePPYOEpr1XfZAwQtxWF+er8IVObcGyowYCYK8HLeKbksktOVOHf1erIZSa76h\nKSGE2MO6AiXkmrr5IiEPePsp29d0aQgFL8Qh5d3V4Lsr3ASxoWGeZrkizizKT4A23nUfQa0B+O02\njb4QQuzv5F0Nfr7JrenyShcftPFxjJtHCl6Iw1FqDVh6Uo766auBIh7eipHYrU3WwDBMAztNU/BC\nCLEvjZ7Fx6e4pSmekPDxf+H2qenSEApeiMNZc0aJ4hru9ElqrNTuCWLWYDqSdPyuBuVqmjoihNjP\nvy9X42aV6ca39qvp0pCHrjVlWRaLFi1CREQEUlJSAABZWVn46aefoFarERkZiQULFsDDw3y99/Dh\nwxEUFGR8PH78eDz33HMWbD5xNbm3VGblp8d08kLPQOeqottcT0gFCJcKjLk9Brb23+DFJxznDocQ\n4j5uKnXYcqmKc2xUBxG62bGmS0OaDF6uXbuG5cuXIz8/HxEREQCAnJwcfP/99/jiiy8gFouRmpqK\nLVu24NVXX+WcW1hYCKlUioyMDKs1nriW0ho9Vp4yLz89Kco5Nl18VEPCPDmJydkyNQUvhBCbe1DT\npf66AX8hg9cdsA9uMnjJyspCYmIiQkNDjcd27tyJlJQUSCS1+Qfz5s2DRmNe2vz06dPg8/mYOnUq\nKisrkZCQgIkTJ4LHc72hf/L4DCyLD/PknN1Ka6vo+sLTCavotsSQMBE+P1d3p3O2XIs71XqE2GGz\nM0KI+/qfTGVWEPTv3SSQCB3ve7vJFs2ePRsjRozgHLt58ybu3buHt956C8nJyfj8888hlUrNztXr\n9YiPj0daWhrS09Nx+PBhbN261bKtJy5j+7Ua/FHK/dBMihQj3Nd5q+g2V6g3H938uUOy+4pop2lC\niO1Uagz4LJ9b0yUu0AMJYfav6dKQFodTOp0OR48exdKlS7Fp0yZUVlbis88+M3veCy+8gJkzZ0Ig\nEEAsFiM5ORm5ubmWaDNxMdcVOmwwKYTUI8ADY8Kdu4puSwxpSwXrHti6dStefvlljB07Fu+88w7K\ny8uh1+uxcuVKjBkzBqNHj8a2bdvs3UxCXMqGAiUqTWq6zIhxjJouDWlx8BIYGIhBgwbB29sbAoEA\nI0aMwNmzZ82et2vXLly+fNn42GAwQCBw/bto0jJaQ20VXU29OVYfgWtU0W2JQaEi1E/kvyLX4ZpJ\ngT53cO7cOWzZsgUbN27Et99+i/bt22P9+vXYvn07ZDIZMjMz8fXXXyMzMxMFBQX2bi4hLuFUmQa7\nbnBHe8c/6YMwH8f9zm5x8DJkyBDs3bsXarUaLMti//79iI6ONnve1atXsWHDBhgMBqhUKmRlZWHY\nsGEWaTRxHV9fqMKlSu6X9FtPSdwu36OViIenW3NXVGW74dRRVFQUtm3bBh8fH6jVapSUlMDX1xf7\n9+/HqFGjwOPxIJFIMGzYMOzevdvezSXE6Wn0rNlCiQ5iPl6OcOxFAy0OXl566SX07t0bEyZMwJgx\nY6BSqTBt2jQAQHp6OtLT0wEAkydPhlQqxdixY5GSkoKYmBgkJiZatvXEqZ0u0+Bbk23WB7fxxLC2\njjnHam1DTOaW9xWpwLLut9M0n89Hbm4uRo0ahby8PIwaNQrFxcUIDg42PicwMBAlJSV2bCUhriHz\nSjVuKE1rukjg4UA1XRrCVFRUuF/vSOyuSmvApP33cKe6br6otYiHLwe1gtQBM9ttQak14MWf73KW\nKX72rD+i/R2rvoIt/ec//8HXX38NgUCAhQsXomvXrsbjR44cwdKlS+3cQkKcl0ypw8Tce5w+58/t\nRZjTw3wRjqNxz28JYndpZ5WcwAUA5sVK3TZwAQCxBw99gk0Td91r6kgmkyEvL8/4+MGoS1BQEEpL\nS43HS0tLOSMxhJCWYVkWn5jUdPETMvhbtOPVdGmI+35TELs5cEuFPSYbfo3u5IU4F62i2xKmyxJz\nitTQu9HUUWlpKRYsWICKigoAwJ49exAeHo5BgwZhx44d0Ov1UCgU2Lt3LwYOHGjn1hLivPYWqXHc\npKbLtK4Sp7mBdNxUYuKSylR6fHSamxzWUcJ3yAqO9vBMsCe8BQyqdbUByz21AXl3tXjaTQK72NhY\nTJw4EVOnTgWfz0dgYCBWrFiBoKAgyGQypKSkQKvVIikpCbGxsfZuLiFOSa4xYO1Zbj/cs7WHU+Ub\nUs4LsRmWZTHvSCWOlNRVZBYwwLoB/ujs6755HaaWnpRztqJ3ljloQohz+OiUHP8trOtjPHjAl4Na\noZ3YecYznGN8iLiEH6/XcAIXAJgY6UOBiwnTqaP9t9TQ6OkegxDy+E6XaTiBCwCM7+zjVIELQMEL\nsZEbSh3WmVTRjWnl4fC1BOyhZ2sh/IR1yxSrdCyOlpjvH0YIIS2hNdRuvFhfOyeo6dIQCl6I1enu\nV9FV1ysl4C1gkNpTCr4bVdFtLgGPwaA2Is4xd1t1RAixvK1XqnFdYVLTJUYCoRNufkvBC7G6TRer\ncKGCW0V3ejcxQt2sim5LmE4dHSpWo1pnaOTZhBDStKIqHb65UMU59qd2IvRo7ZyLASh4IVaVf0+L\njIvcKroDQj3xXDtRI2cQAOjaygPBXnUfT7UeOHiHpo4IIS3HsixWnVZy9pCTChlMcZKaLg2h4IVY\nTbXOgCUn5Kg/XtDKk4eZDrxTqaPgMQyGhJlMHclo6ogQ0nL7bqlxrJR78zMtWgw/T+cNAZy35cTh\nrT2rxK1q7vzq3FiJU39gbMl06uhYqQYVapo6IoQ0n0JjwKdnuYslegR4OP3o90O/RViWxcKFC7Fl\nyxbjsaysLEyYMAH/93//h3/961/QarVm5+n1eqxcuRJjxozB6NGjsW3bNsu2nDi0g3fU2GmyxfoL\nHb0QH+Q8RZDsLVwqQEdJXV6QngX231bbsUWEEGeTfk6J8no3PR48YGZ35x/9bjJ4uXbtGqZNm4bs\n7GzjsZycHHz//fdYu3YtMjMzoVarOYHNA9u3b4dMJkNmZia+/vprZGZmoqCgwPJXQBzOPZUBK/Lk\nnGPtxXyn2TPDUTANTB3to1VHhJBmyr+nxQ6Tmi7JEd5o72Q1XRrS5BVkZWUhMTERoaGhxmM7d+5E\nSkoKJBIJAGDevHnQaMwTCffv348XX3wRPB4PEokEw4YNw+7duxEdHW3hSyCOhGVZrDglR4Wmrqga\nnwHe7SmFSODckb49JIR54svzdSsETpdpUVKjR5AXrdQihDROZ2Dx0SnuTWQ7Hz6SO/vYqUWW1eTI\ny+zZszFixAjOsZs3b+LevXt46623kJycjM8//xxSqXnp8uLiYs6ur4GBgSgpKbFQs4mj+m+hCr8X\nc4PZV7r4INKPqug+ijAfAaL86u4xWAD7imjqiBDStK1XqnHNpKbLzO4SeDphTZeGtDhzUqfT4ejR\no1i6dCk2bdqEyspKfPbZZ2bPMxjMEwt5vOa/nFJLiYnORqbUYW0+t3pjV38Bkp2weqMjSWhLU0eE\nkOa7XaXHNxe5NV2eaytCrJPWdGlIi4OXwMBADBo0CN7e3hAIBBgxYgTOnj1r9ryQkBCUlpYaH5eW\nlnJGYh5m9RnFw59EHIbOwGLJSTlU9QJ9Lz6Dd3tKIeC5RqRvL4PbeHI+qBcrdbih1DX6fEKI+2JZ\nFp+cUXAqmks9GEzt6lo5hy0OXoYMGYK9e/dCrVaDZVns37+/wTyWAQMGYMeOHdDr9VAoFNi7dy8G\nDhzY7Nf5RaZGDt1hOo2MS9U4V879Qn2jmxhhPs6fGGZvASI+erTmTrtRzRdCSENyb6nN9kL7W1fn\nrunSkBZ/s7z00kuQy+WYMGECDAYDIiMjMWPGDABAeno6AOD111/H6NGjIZPJkJKSAq1Wi6SkJMTG\nxrbotT4+rUC3Vh4IpOREh1ZQrsUmkyHK/iFC/Lm9c9cRcCQJYSKcuFtXkmBfkRqvdvFx+uWOhBDL\nUWoNSDOp6dI9wAN/cvKaLg1hKioq2Ic/zfYG/VSb3BsX6IHlz/iBR520hsJOYgAAIABJREFUQ6rR\nsXht/z3IqurGKP2FDL4aHOBykb49KTQGvPjzXejqfVo3DPBHF0qEJoTct+q0Av+5XmN8LGCAjYNa\noYPE9UbAHf7b5Y9SLbZfq3n4E4ldrMtXcgIXAJgTK6XAxcIkQh7ig7nJdrTTNCHkgYJyLX68zv2u\nHNvZ2yUDF8AJghcA2FCgxHUFJSg6mt+L1fipkPthGdVBhD7BVEXXGhLMCtapYWAdcuCUEGJDOgOL\nlacUqN8bhPnwMc5Faro0xGGDF596Bc00BmDxcTm0BuqoHUWF2oDledwVYW19+JjWVWKnFrm+vsGe\nENWr0XBXZcDpMvOtOQgh7iXrag2uyLk3+DNiXKemS0McNnh5O4b7JXhZrsPXF6oaeTaxpQdVdOvv\nl8FjgPk9pfCiKrpWIxIw6B9CU0eEkDp3qvX4+gI3SXdomCfiAl2npktDHDZ4GRrmicFtuNMP316q\nxuky860IiG3tvqnCwTvcv8OEJ30Q5U/Jo9ZmWrBu/y01jUgS4qZYlsWqMwpOfS2JB4O/d3P9EXCH\nDV4YhsGMGAlai+qaaADwwUk5qqj6rt3cqtIj7Qw3yo/yE2BcZ6qiawu9AoWQCutGt+RaFn+UUkBP\niDs6cFuNwybbsUyJFsPfDRZMOPQVSoU8zIvl7pt0p9p8HTuxDZ2BxQcn5KjR193pi/i100VURdc2\nBDwGA0O5I5JUsI4Q91PVQE2Xbq083Ka+lsOvoYoLFGJ0Jy/8cLVuVcuemyr0DRZiQBv3+CM5im8v\nV+NsOTdBdFpXCdq6wPbqziQhTMTZ5v63OxrU6FiXyTfavXs3MjIywDAMRCIRZs2ahaioKAwfPhxB\nQUHG540fPx7PPfecHVtKiP1sPF+Fu6q6WQg+A8yKkbhNTTSn+NZ5PUqM46UaXK+3Q+ZHpxXo2soD\nASKqvmsL5yu0ZgnTfYKFGNWBAkhbiwnwQGsRz9hxqfQsDhWrzZZSO6PCwkKkpaVh8+bNCAgIwKFD\nhzB37lykpaVBKpUiIyPD3k0kxO7Ol5vXPxsb4Y0npE7xlW4RDj1t9IAnn6mdmqgXUMo1LD7MU4Cl\nOhdWp9KxWHJCjnqzRfATMpjdXUrl6e2AxzAYEsadOnKVnaaFQiEWLFiAgIAAAEBkZCTKyspw4sQJ\n8Pl8TJ06FcnJydi4cWODO9cT4up0BhYfmdR0aePNw/gnXbemS0OcIngBgM6+HpgYyf3jHC3RmFUU\nJJa34ZwSN5XcKrrvdJeilchp3j4uZ6jJKMuRYg0UGuf/Mg8NDUXfvn0B3F9JsWoVBgwYAIZhEB8f\nj7S0NKSnp+Pw4cPYunWrnVtLiO1tv1aDyyY1Xd528ZouDXnoGBPLsli0aBEiIiKQkpICAM2ee7b0\nHPXLEd44UqzB6Xt1eRfrCpToGShEe8q7sIqjJWqz4ck/txehfyhV0bWnzr4CtPPh4+b9rRl0LLD/\nthojO3jZuWWWUVNTg4ULF6K0tBSrV6+GWCw2/kwsFiM5ORnfffcdXn75ZTu2khDbKq7WY+N57vR9\nQpgnege5X3/c5Df+tWvXsHz5cuTn5yMiIgJA7Zx0c+aem/u8luAzDFJ7SjEp9x6q7+9Qp9YDS07I\nsba/P614sbBKjQEfnuRW0W3jzcMb3cSNnEFshWEYJLQVcfKQ9hWpXCJ4uXPnDmbOnIlOnTph3bp1\nEAqF2LVrF5588kljP2QwGCAQ0A0LcS9rziqgqjd/7yNgMK2re/bHTY77Z2VlITExEUOHDjUeO336\ndLPmnpv7vJYK9eZjusmX54UKHb65SNV3LYlla/fKKKtfRRfA/J6+8BbQdJEjSDDJezl5V4u7Kn0j\nz3YOlZWVmDJlChISErB48WIIhbVVQq9evYoNGzbAYDBApVIhKysLw4YNs3NrCbGdX2+rzYqDTokW\nu+2ilSZvXWbPng0AOHr0qPGYXq9HfHw8pk+fDpVKhRkzZsDHx8ds+La5z3sUz7UT4VCxBgduq43H\ntlysRnyQJ7q1oiqvlvDzTRXn3xcAUp70Rlf693UY7cQCPOkrwMXK2vlvFkBOkRp/DXfegoE//PAD\nSkpKkJOTg5ycHAC1o0wff/wx1q9fj7Fjx0Kn02Ho0KFITEy0c2sJsY1qnQGrz3BHwbv6CzDSjVd7\nMhUVFQ9drrNo0SKEh4cbc17qy8nJwXfffYf169c3+Tua+7zmqtQY8P9y7nFGBtp48/DFoFY0MvCY\nblfpMWl/3dQcAHTxE9DUnAP67nI11hXUFaqK9BNg/YBWdmwRIcTS0s4qOLXO+Azw+cBW6ORGS6NN\ntfhbfteuXbh8+bLxcWNzz8193qPyFfIwJ5a7f8OtagPWUvXdx6JnWSw9KecELp5URddhDQ7zRP2/\nyvkKHWRKXaPPJ4Q4lwsVWmy/yl00MSbc260DF+ARgpfmzj3bYo46PsgTL3TkJijuvKHCwTvqRs4g\nD/Pd5WrOai4A+Fu0mFZzOaggLz5iArhTeftu0fufEFegM9TmHtbPFg3x5uEVN6vp0pAWBy+TJ0+G\nVCrF2LFjkZKSgpiYGOPcc3p6OtLT0x/6PEuq/WLlJiytyJPjnsr5a17Y2qVKLb40WYbXO0hoFiAS\nx2JaWTdbpqLijYS4gP9crzHmtD0w4ykJRC6yFcjjaFbOi6O7UKHFtF/LORVgnwkWYmlvX6oA20xq\nPYspB+5xtmCQChl8NaiV22azO4tKjQFJP9/lvP+/GOiPCF9KribEWZXU6PHKvnucjXAHt/HEv+J8\n7dgqx+ESma1d/DzwahfuMNrhYg1n8zrStPRzSk7gAgDvxEgocHECvkIeegUJOceyi2jqiBBnlnZG\nyQlcfAQM1diqxyWCF6B2U6pu/tw7zc/yFZS82Ax/lGg4mewAMKKdiHbtdiKmU0f7ilQw0NQRIU7p\nt9tq/GqSu/lalA/dTNbjMsGLgMcgtacEXvX2d1DpgSUn5dAZqBNvjFxjwLI8OedYiDcPb1KE71T6\nhQjhWa9fK64xIN8k8ZoQ4viqdQasOcut6RLlL8Aoyj3kcJngBQDCfARmw2rnynXIuFRtpxY5NpZl\n8clpBe7WS25mALwbK4WPh0u9NVyet4CHvsHcirs0dUSI8/n6fBVKaupVNmeAWTES8Cl/k8PlvqH+\n3F6E/iHc+f9NF6tQUE53oab2FqmRY7KsdmyEN2IChI2cQRzZ0LbcqaPcWyoadSTEiVyq1CLLZAr/\nr528Kfm+AS4XvDAMg3e6S+HvWXdpBhb44IQcNTrqyB8ortZj1Wnu0GRnXwEmRlL9AGfVK1AIsUfd\n3VmFhsXxu5omziCEOAo9y+Ijk5ouwV48s8UopJbLBS8A4OfJw5we3Oq7sio9PstXNHKGezHcr6Jb\nVS+YE/Jqq+h6UBVdpyXkMxgQyp062iejqSNCnMGP12twoYK7wOStpyTwopouDXLJ4AUA+gR7YpTJ\nplU7ClX4vZg6861XapBXxp1GmxItRkcJVdF1dkNNVh0duK2GWk8jjoQ4stIaPb44xy0QOiDUE31D\nPBs5g7hs8AIA07pK0NaHu7RseZ4CFWr3rb57pVKHjee5+z893doDLz5BmeyuoHtrDwTUmzKt0bMU\nsBPi4NLOKjn7yXkLGFrx+RAuHbx4CRjM7ylF/ZmQcrUBK07J3bJ8ulrPYsmJSmjrxW4SDwbzYqXg\nUSa7S+AzDAaHmUwd0aojQhzWoTtqHLjN/YxOjvJBoBfVdGmKSwcvABDl74EJJptYHbyjwa4b7ld9\nd+N5Ja6aVNGdGSOhD4mLMS1Y93uxGkqt+442EuKoTpdp8InJwolIPwESqabLQ7l88AIA4zp7I8qf\nm8+RdlaJoir3qb578q4G31/hLsEb1tYTg8Ooiq6rifQToI13XUCqNQC/3qbRF0Icxe1qPRb+UYnp\nBytQWq/OFg/ArO5U06U5Hhq8sCyLhQsXYsuWLcZjw4cPx7hx44z//fzzz2bn6fV6rFy5EmPGjMHo\n0aOxbds2y7a8BQQ8BvNjpahfWVmlZ/HBCfeovqvQGrD0pBz1rzTYi4e3npI0eg5xXgzDIKGtacE6\n9xtpJMTRVOsM+OKcEhP2lZnV2AKA0Z280JlqujRLk8tLrl27huXLlyM/Px8REREAgMLCQkilUmRk\nZDT5i7dv3w6ZTIbMzExUVVVh0qRJiIyMRHR0tOVa3wJtxQJM6yrBx/WG6PLLdfj2cjXGP+na6+hX\nn1ZwKjYyAFJjpRBTFV2XlRAmwuaLdZWlT5RqcU9lQCsR/c0JsTUDy+J/N1X4/FwVyhpZMPJcOxFe\nj6Yk3eZqsifLyspCYmIihg4dajx2+vRp8Pl8TJ06FcnJydi4cSMMBvM/xv79+zFq1CjweDxIJBIM\nGzYMu3fvtvwVtMCoDiL0CeZWj/36QhXOV7hu9d3sIhX2miRsjgn3Ro/WVEXXlXWUCBAurbs3MQDI\nvU2jL4TY2pkyDab+Wo5leYoGA5cnfQVY088PqbFUZ6slmgxeZs+ejREjRnCO6fV6xMfHIy0tDenp\n6Th8+DC2bt1qdm5xcTGCg4ONjwMDA1FSUmKhZj8ahmEwu7sUfsK6N4ieBZackEPlgtV3S2r0Zslg\n4VIBJlEVXbeQYLLqKFtGwQshtlJcrcei45V482CFWfE5AGjlycPcHhKsH+BPW7I8ghaPIb/wwguY\nOXMmBAIBxGIxkpOTkZuba/a8hkZjeDz7D1m3EvEwu4eUc+ymUo/1BcpGznBOBpbFspNyKLV1QZnH\n/Sq6Qj5F9+5giEkydn65Drer9I08mxBiCTU6Fl+eV2L8vrIGyxR48GoXkWQktMKf2ntRmYpH1OJo\nYteuXbh8+bLxscFggEBgnjoTEhKC0tJS4+PS0lLOSIw99QvxxF/aczv2/1yvwZES11mR8cPVGpy4\ny50Oey1KjE5SqqLrLkK8+ejWipv8t+8Wjb4QYg21eS01GL+vDJsuVkPTQGrLoDae2DQ4AJOjxPAW\n2P9m3pm1+F/v6tWr2LBhAwwGA1QqFbKysjBs2DCz5w0YMAA7duyAXq+HQqHA3r17MXDgQIs02hL+\n3k2MNt7cy//wpGtU370q1yH9HHckKba1B17qRLUD3A1NHRFiffn3tPj7r+X44KQCd1Xm3yGdfQVY\n3c8P78X5ItSH6mpZQouDl8mTJ0MqlWLs2LFISUlBTEwMEhMTAQDp6elIT08HAIwePRphYWFISUnB\nq6++iueffx6xsbGWbf1j8BbwML+nL+cf4J7agI9PK5y6+q5Gz2LJCTmniq6PgEEqVdF1S4PaiDgV\npq8q9Lgqd5/6RoRYU0mNHouPV+Lvv5XjXAN5Lf73NwleP8Af3SmvxaKYiooK5/2mtoCN55WcJaUA\nMK+HBCPaO+coxYYCJb69zL2eBT2lGNqWitG5q9m/V+BYqcb4eFxnb0yOctwlmbt370ZGRgYYhoFI\nJMKsWbPQpUsXfPLJJzhy5Aj0ej1SUlKQlJRk76YSN6XSsfj2chUyr1RD3UAamQcPeKmTN8Z19oYP\nlaSwCrdPgHjlSR8cLdFwssHXnFWie4DQ6Yb3TpVpkGkSuAwJ86TAxc0NbevJCV6yi1SYFOkDxgFH\n4goLC5GWlobNmzcjICAAhw4dwty5czFhwgSHqhtF3BPLsthbpEZ6gZJTGbe+AaGe+Fu0GG2c7PvD\n2bh9SCjg1W7e6FnvfVatY/HBSTn0TjR9pNQa8MEJbhXdQBEPM6iKrtvrH+IJYb1P+u1qAwrKHXPq\nSCgUYsGCBQgICAAAREZGoqysDNnZ2Q5XN4q4l4JyLf7+WzmWnJA3GLiESwX4pK8fFvXypcDFBtw+\neAGA9mIBpppUNjxzT2s2iuHI0s4oUVzD/UDNi5VCIqQ/sbvz8eDhmWDTnaYdM3E3NDQUffv2BVB7\nl7tq1So8++yzKCsrc7i6UcQ9lNboseREJab9Wt5g0O8nZPBOdwnSB/ojlop/2gx9s92X2NELvYO4\nb7yvzlfhUqXjV9/NvaXCzyarSP7ayQtPB9IHidQaarLqaN8ttUPv61VTU4PU1FQUFRVhwYIFDls3\nirgulY7FNxeqMH5fGX6RmZfREDDAy+HeyEgIwMgOXrSZoo3Rp/8+hmEwt4cE0nrVd3X3q++q9Y7b\nyd9V6fHxKW4V3Y4SvkMnZBLbiw/2hI+g7r1drjYgr8wxA/M7d+5g0qRJEAgEWLduHcRisUPXjSKu\nhWVZZBepMCGnDF9dqIKqgYTc/iFCfD24Ff7WVUx7xNkJ/avXEyDi450Ybo7IdYXerGaKo2BZFh+e\nVEBer4qugKldXeRJVXRJPZ58Bs+GOv7UUWVlJaZMmYKEhAQsXrwYQmHt6KGj140iruF8uRZv/laB\n94/LOZvZPhAuFeDjPn5Y3NsPbcVuv97Fruhf38SANiKMaKfBnpt1HfsPV2vQJ8gTcUGONQ2z/XoN\nZxUJAEyK9EEEbalOGpAQ5sl5X++/pcbbT7EOtV3EDz/8gJKSEuTk5CAnJwdA7ajo6tWrIZPJkJKS\nAq1Wi6SkJIeqG0Wc212VHp8XVJlNvz/gJ2QwKVKMP3cQ0fSQg3D7Oi8NqdIaMGn/Pdyprou8W4t4\n+HJQK0gdJAG2UKHDa/vvcUpQdw/wwMd9/ejDRRqkM7D46//uolxT95F/v5ev2YgMIe5CrWex9Uo1\ntlyqhqqB9AABAyR18sKEJ31oesjB0F+jAT4ePLwbK0X9EOCuyoBPHKT6rtZQW0W3fuDifb+KLgUu\npDECHoNBJps1Zjvg1BEh1sayLPYVqTBhXxk2nq9qMHDpdz+vZVpXCQUuDoj+Io2ICRBibIQ351jO\nLXWDWee29s2FKlys5C7Ze+spMUK8qbYAaVqCSfBy6I4a1Trn38+LkOa6UKHF9IMVWHRcblZeAgCe\nkPDxUR8/LKG8FodGf5kmTIz0wbFSDS7VCxRWn1Gge4AHgu0UKJwp0+Dfl7j1ZwaGemI4VdElzdDV\nX4AQb55xSlRjAH67rcbwds65HQYhzVWm0uPzc1X4+aYKDY2fS4UMJnXxwV86eEHAoxFsR/fQnBeW\nZbFo0SJEREQgJSWF87M5c+YgMDAQs2fPbvDc4cOHIygoyPh4/PjxeO655yzQbNu5rtDhdZPckh73\nc0tsvdFhtc6ASbn3cLteLk6AJw9fDm4FXwfJxSGOL71AiX/XK8AYHyTEh8/42bFFhFiPWs/i+yvV\nyGgkr4XPAElPeGFCFx9IaHrIaTQ58nLt2jUsX74c+fn5iIiI4Pxs06ZNOHXqFIYNG9bguYWFhZBK\npcjIyLBca+2go0SAKdFipJ2tWy6dV6bF1is1eNlkWsnaPj2r5AQuADAvVkKBC2mRoW1FnODlWKkG\nFWoD/DzpfURcB8uy2H9bjfUFSs7ii/r6BAsxrasY7Wh6yOk0+RfLyspCYmIiQkNDOcf/+OMPHD58\nGElJSZDL5Q2ee/r0afD5fEydOhWVlZVISEjAxIkTnbIq5otPeOH3YjX+KK0r6rXxvBK9AoUI97XN\nm/7X22rsusFNrkx6wgu9gv5/e3ceV1P+/wH8dbu3TQtNypp9UNRXow1DssYMJWZI2TWWry0GmbKV\ntVJMfNEMhmomvtkTY5CyDMVQ81VkbbRooZNCqns/vz/6ddxbt4jbcnk/H48ej+69n3PO53OWz3mf\n8/mcz6EnRUjNdNAVoZ2OEI8KykbfkjAgJqMIDu3rNhgnpLbczS/B1v8VIqGKgRjb6Qjx727aVH8q\nsWojicWLF8Pe3l7mu5ycHAQEBMDHx6faQEQsFsPa2hpBQUEIDg7GlStXcODAAcXkuo6pCARY2kMX\nOqpvmolKJMCav/LrZPTdp0Vi+CfIBolttIWYYUKj6JL3U7Hj7tn0+u+ITsiHelokhu/N5/guJk9u\n4KKrKsB8U238bPsZBS5Krka3QUpLS+Hp6YlFixZBX1+/2seGHR0dsXDhQohEImhra2P8+PE4f/78\nh+a33hhoCrGwwui7DwvE2FXLo+8yxuB7swD5UmNzCGkUXfKBKgYvic9KkPVSzjjohCiB12KGX+++\nwIRzzxD1T+UOuUIBMLqDJkIH6mNU+0bUIfcjUKM2j+TkZGRmZiIwMBAA8PTpU0gkEpSUlOCHH36Q\nSRsVFYXOnTvzfWUkEglEIuVuV7RrpYHLWbKPSx948Ao2zdTxRS29BPFYahGuZsuOojulixY6N6FR\ndMn7a6klhLGeCMlSb8mNzijCuE5a9ZgrQmqGMYYLma+xPalyf8ByNoZqmNVNG211lPv8Q2TVaGua\nmpri+PHj/OeffvoJ+fn5+P777yulffDgAaKjo7Fx40YUFxcjIiKiUhOUMppvqoPEpyUy4wNsuPkc\nu/p/pvCe6o8LS7H9luxLF7t/pgrnz6lvAvlwg1ppIDnvzZ3Ds+mvKXghSuNufgm2/a+wyheMttUW\nYnZ3bVhT89BHSaFn2+DgYAQHBwMApk+fDl1dXTg7O8PFxQVmZmZwcHBQ5OLqhbaqCpZVGH03+5UE\nWxILqpzmfZT+/yi60m801RQK4Emj6BIF6d9SXaYCuJtfitSC0irTE9IQPCuSwD+hrF+LvMBFR1WA\ned21sav/ZxS4fMTo3UbvacetQoTflx0sbnlP3Up9Cd7XntuF2JsiO/+lPXQwrA0NJkYUZ+HlPPyV\n++YEMKlzI0zpSh3BScNTLGY49PAl9qW8xMvSyqctFQHg2E4Tk7toNZh30JHaQ1v4PU3tqoWOurKt\nboGJBch+9eGdHm89K0FIhVF0+zZXh70RjaJLFGtQhZGZz6S/bhDv7yKkXHm/lsnRz7Aj6YXcwMXK\nUA17+n+GeaY6FLh8IujOywd48LwUM2KfoUSqn9gXTVXh3+v9R999WSrB9PN5yJB68kNPXQV7+n9G\ng4gRhSsokcDp91yZfXhHPz10/Yg6hJ9IfVXfWSDviQE4l14kc3dQmpF22XgtNs2oeehTQ92vP0AH\nXRHcjLXxn1tvOj3+lVuCgw9e4ZuO79epdvutQpnABShrLqLAhdQGHVUVWBuq4eKTN0+0nU0r+qiC\nF78ExfZHI/VPW1WAyV204NiO3kP0qaIz4gca00ETXzSVreiDkwvx4HnNOz5efvIax1NlR9F1aKdJ\nVxWkVlXspxWd8RpiajoiDVB5v5bQAfoY04HGa/mUUfDygVQEAniY60K7wui7a/96juIajL6b91oC\nv5uyo+gaaQkxi0bRJbWsVzN1aEoNeJhbJEFiFY+fElJfLAxUscv2MywwozvRhJqNFMJQUwh3Ux34\n/PUm+Lj/vBR77rx4pyH8GWPwu/kceVKj6KoIAM8vdKEhoisLUrs0RAJ82UJNZvDFs+lFMG9aOwMv\n1rXhbaijuzLTEArQq5kaLAzUIKBhIsj/o+BFQQa21sClrNc4J/WOmPB7L2FtqIYebzkJnPinCJez\nZEfRndRZC131Pp5+B6RhG9hKQyZ4icl4je//VY8ZUqAlPXTrOwuEEAWje28K5G6qAwONN6uUAVh/\n4zkKS+QPWw0AaYWl2Po/2fcjmeiJ4EKj6JI6ZGGgBl21N1e1BSXU54UQ0nBR8KJAOmoq8DCXvcrL\neiVB0N/yX95YKmFYd+M5iqT6xmgIBfjBXJc6opE6JVIRoH8Lal4hhCgHCl4UrKeBGr7pIDsK7u9p\nRTifUVQp7a93XyIpT/appDndtdFam1rzSN0b1JqeaiOEKAcKXmrBdGNttNcRynwXkFCAXKkXFd3O\nK8EvKS9k0vRproavqHMhqSfdP1OVafYkhJCGimqqWqAuFMDzC11IPyj0vIRh440CSBjDq9Kyly5K\npLoVNFET4Pt/6VJvelJvVAQCDFDQu7kIIaQ2vbV9gjEGb29vdOrUCS4uLjK/LVmyBAYGBli8eHGl\n6cRiMTZv3oyrV69CLBbDxcUFTk5Oist5A9epsSqmddXCzuQ3d1fic4px5OErpBaK8fiF7Ci6S3ro\nQo/GLiD1bFBrdeyv8MLR+iCv3hkyZAgMDQ35NBMmTMDQoUPrK4uEkHpUbfDy8OFD+Pr64tatW+jU\nqZPMb/v27UNCQgIGDx4sd9rDhw8jLS0N4eHhePHiBaZNm4auXbvCxMREcblv4L7t1AhXsouRIDXg\n1/akQlR8+Ojrthro3Zz6G5D610lXBCNtIR4XfvgLRt+XvHonNTUVurq6CA0Nrbd8EUIajmov9SMi\nIuDg4IBBgwbJfH/t2jVcuXIFTk5OVb6BNiYmBiNGjICKigp0dHQwePBgnDx5UnE5VwJCgQDLzHWh\nJZIdfVdaKy0hZnejUXRJwyAQCDConpuO5NU7iYmJEAqFmDVrFsaPH49du3ZBIql6CAJCyMet2uBl\n8eLFsLe3l/kuJycHAQEB8PHxgYpK1ZNnZWWhWbNm/GcDAwNkZ2d/YHaVT/NGQsw3lR+cqAiAH8x1\n0UhEzUWk4RjYqn7vAsqrd8RiMaytrREUFITg4GBcuXIFBw4cqKccEkLqW42eyS0tLYWnpycWLVoE\nfX39Ku+6AJB7VVRdsAMAmzdvrkl2lIpjFd//cR/4o05zQsjbOQKQPhwXLFhQb3kBAEfHN0eQtrY2\nxo8fj/3792PcuHFy03/MdQkhyk4R9UmNgpfk5GRkZmYiMDAQAPD06VNIJBKUlJTghx9+kEnbvHlz\n5OTk8J9zcnJk7sQQQsi7ioqKQufOnfk+MBKJBCIRjYdEyKeqRke/qakpjh8/zn/+6aefkJ+fj++/\n/75S2n79+uH48ePo27cvXr58iTNnzsDDw+PDc0wI+eQ8ePAA0dHR2LhxI4qLixEREVGpaYkQ8ulQ\n6KVLcHAwAOC7777D6NGjkZaWBhcXF5SUlMDJyQnm5ubVTl/ft6YJIQ3T9OnT4efnB2dnZ5SWlmLQ\noEFwcHCoMj3VJYR83AQcx9Eb2AghhBCiNOgxF0IIIYQoFQpeCCGEEKJUKHghhBBCiFKpk2cNT548\nidDQUAgEAmhoaGDRokXo0qULAgMDq3330bFjxxATE4NNmzbx31W7jr/CAAAdCElEQVT3rqX6osjy\nhYWF4fjx4xAKhdDT08OyZcvQqlWrui5SJYoqI2MMO3bswPnz5wEAJiYmWLp0KTQ06v+FgIrcjuXC\nw8Nx9OhR/Pbbb3VVjLdSZDmXLl2Ke/fuQVNTEwBgYWFR651lP+b65FOoSwCqTz6W+qQ+65JaD15S\nU1MRFBSEkJAQ6Ovr4/Lly1i6dCkmTpxY5buP8vPz8Z///AenTp2ChYUFP6/q3rVUXxRZvri4OBw7\ndgx79uxBo0aNEBERAW9vb+zcubMeS6jYMp4/fx7x8fEICwuDSCTCsmXLsH//fkyaNKkeS6jYMpZL\nSEhASEgIGjduXA8lkk/R5fzf//6HvXv3omnTpkqX/4ZWn3wKdQlA9cnHUp/Ud11S681Gampq8PLy\ngr6+PgCga9euePr0Kc6ePVvlu4/Onj0LQ0NDzJs3T2YU36retVSfFFk+fX19eHh4oFGjRgAAY2Nj\nPHnypO4LVYEiy2hnZ4fg4GCIRCIUFhYiLy+vQRyMiiwjUDaAo5+fH+bOnVvtSNR1TZHlTE9Px8uX\nL7FhwwaMHz8ePj4+eP78udLkv6HVJ59CXQJQffKx1Cf1XZfU+p2XFi1aoEWLFgDKbvFt3rwZffv2\nxYMHDyq9++jevXsAwN9iioyMlJnX4sWLAZRdVTQUiixfx44d+f+Li4uxdetWDBw4sLaL8FaKLCMA\niEQiHDhwADt37oShoSH69+9f+4V4C0WWUSwWY8WKFZg3b16DGwVWkeXkOA5WVlZYsmQJ9PT0+Hee\n+fn5KUX+G1p98inUJQDVJx9LfVLfdUmdddh99eoVli1bhvT0dHh5eb3Xu48aMkWWLy8vD3PnzoWW\nlhZmz56t6Ky+N0WW8dtvv8XZs2dha2vboEZeVkQZt23bBnNzc1hZWTWYq6SKFFHObt26YePGjdDX\n14eKigrc3Nxw6dIllJaW1la2eR9zffIp1CUA1SfllL0+qa+6pE6O7idPnmDatGkQiUTYvn07tLW1\nP6p3HymyfHfv3sXkyZNhbGwMPz+/BhNpK6qMd+/eRUpKCv955MiRuHPnTq3luyYUVcZTp04hOjoa\nrq6uWLduHdLS0jBhwoTazv47U1Q5b968idjYWP4zYwwqKioQCoW1lnfg465PPoW6BKD6pJyy1yf1\nWZfUevCSn5+PGTNmYODAgVizZg3U1NQAvHn3kVgsRkFBAc6cOQNbW9vazo7CKbJ8jx8/xqxZs+Dm\n5oYFCxZAIBDURRHeSpFlvHfvHry9vVFUVASg7IV78jqn1TVFljEqKgphYWEIDQ2Fp6cnWrdujZCQ\nkLooxlspspwvX77Epk2b+LbpkJAQDBw4sFb324+5PvkU6hKA6pOPpT6p77qk1kPxgwcPIjs7G9HR\n0YiOjgYACAQCbNmy5Z3efdSQDjp5FFm+ffv2obi4GOHh4QgPDwdQ1ilq9+7ddVOYKiiyjMOGDcPj\nx48xadIkCIVCdOzYEV5eXnVWlqrU1n7KGGtQ+7Aiy9m7d298++23cHNzg0QiQadOneDp6ak0+W9o\nPoW6BKD65GOpT+q7LqF3GxFCCCFEqShnjzZCCCGEfLIoeCGEEEKIUqHghRBCCCFKhYIXQgghhCgV\nCl4IIYQQolQoeCGEEEKIUqHghRBCCCFKhYIXQgghhCgVCl4IIYQQolQoeCGEEEKIUqHghRBCCCFK\nhYIXQgghhCgVCl4IIYQQolQoeCGEEEKIUqHghRBCCCFKhYIXQgghhCgVCl4IIYQQolQoeCGEEEKI\nUqHghRBCCCFKhYIXQgghhCgVCl4IIYQQolQoeCGEEEKIUqHghRBCCCFKhYIXQgghhCgVCl4IIYQQ\nolQabPBibW2N8ePHw9XVlf9bt25dfWeLt3r1aoSFhX3QPAoLCzFr1qwqf3d1dUVhYeFb07148QLz\n5s3D69evERwcDGtraxw/flwmzatXr9C/f38sXLgQABAcHIyTJ08CKFvX+fn5Ncq79PaZMGECvvnm\nG0yePBnJycky6e7duwdra2vs3bu3RvNvSCQSCcaPH1/l7xkZGejfv/8HL+fIkSOIiIiQ+9uhQ4f4\ndSidLjk5GevXr69ynjNnzkRmZqbc3/bt2wdXV1e4uLjA2dkZP/74I0pLSz+wFLVn5syZOHfunNzf\n1q5dizt37gAALl68iODg4PdaxtSpU1FYWAgAOHDgAP773/9WSuPu7o7IyMhq5/O2Y1ZRFLEcjuNg\nbW2toBy9n/v378PDw0Mh80pKSsKGDRtqPF1D2q7vg+M4eHh4YNy4cRg7dix+/PFHSCQSAG/OJRWF\nhobC29v7rfPOysrC3Llz+brixIkTCs9/TYnqOwPV2b59Oxo3blzf2ZBLIBB88DyeP39e6WQvLTQ0\nFEDZybG6dFu3bsWoUaOgrq4OAGjevDlOnjyJESNG8GnOnTsHTU1NPt/ffffdB+e/4vYJCwuDv78/\ndu3axX938OBB2NvbIyIiAq6urhAKhR+83Lr2999/o3v37rW+nISEBHTq1Enub05OTnLTGRsbIyIi\nAhcvXsSXX35ZaTqBQCB3Xz1z5gxiYmKwe/duqKmpobi4GB4eHggODsbs2bMVVCLFqu6Yi4uL49dR\nUlJSjYNxoKyC1tLSgra2NgDgwoULWL58udx8vO34f9uxrSh1tZzaFhMTo5ALAAB48OABsrOzazxd\nQ9qu7yMgIAAGBgbYsGEDioqKMG/ePBw8eBDffPMNfy55X35+fvjyyy8xduxYPHv2DKNHj4aVlRUM\nDAwUlPuaa9DBC2NM7vd9+vSBra0t7t69C29vbxQVFSEoKAhFRUVQVVXFzJkz0atXL0RGRuLcuXMo\nLi5GZmYmmjVrhm+++QYHDhzA48eP4ezsDBcXFwBlkamXlxe6du0qs6yXL1/C398fiYmJEAqFsLW1\n5Sv3xMREREdH49mzZ+jQoQPWrFkDDQ0NHDt2DEeOHEFJSQmeP3+OiRMnYvTo0YiMjMTRo0fx+vVr\naGlpAQBev36NCRMmYO/evVBRkb0RZm1tjd9//x0+Pj5VpsvKysKlS5ewePFiAGUHoI2NDWJiYpCd\nnQ1DQ0MAwIkTJzBs2DA8evQIQNmdo06dOvHlB4Dc3FzMmTMHY8aMwZgxY/Dw4UMEBAQgPz8fEokE\nY8eOlQmIpLdPaWkpMjMzZYKZFy9e4NSpU9izZw9SUlJw9uxZDBkyBADg4+PDXymXlJTg0aNH2LZt\nG9q3b4/169cjLy8PT58+RYsWLbBu3Tro6enBwcEB3bt3x7179zB79mzY2tryy7pw4QJ27twJiUQC\nTU1NeHh44PPPP8f58+exa9cuiMViaGlpwd3dHSYmJggODkZ6ejrS09ORk5OD7t27w9raGidOnEBG\nRgbmzp3L5zUmJgb9+vWrcjlaWloQi8XYsGEDkpKSUFBQgHnz5sHOzg5Pnz59p/LMmjULFy5cQHx8\nPNTV1TFmzBiZfSE4OBj5+fmwtLSslG7UqFHYuHGj3OClKk+fPoVEIkFRURHU1NSgpqaGxYsXIy8v\nD0DZFaavry/u3r0LgUCAXr16Yfbs2RAKhbC2tsbp06f5bV3++d69e9i0aRMaNWqEoqIi7NmzBydP\nnsSvv/4KFRUVNGnSBCtXrkSzZs1w4cIF7NmzByUlJdDQ0MC8efNgamqKnJwcuLu7Y/PmzWjatGml\nfMfExCAkJATPnj2DpaUlPD09sX37duTm5mLlypVYuXIlDh06BMYYtLW1YWRkhFOnTkEgECA7OxsG\nBgZYtWoVmjZtitjYWBw+fBiBgYEAgNjYWH47FxQU4OXLlzA0NEROTg5Wr16N3NxcNGvWDBzH8fmp\n6liveMxGRkbKTVfRjRs35NZlAPDLL78gKioKQqEQRkZGWLFiRaXlJCQkVDm9tOjoaOzYsQPq6uow\nNjaW+U3ecsoDunKXLl3Ctm3boKKigs6dOyMuLg4///wzmjdvjl27duH06dMQCoVo06YNFi9ejMLC\nQri5uSEqKgoikQhisRgODg7YunUr2rVrh8uXL2Pz5s01qrOPHj2KgwcPgjGGxo0bY/HixdDQ0MDO\nnTvx4sUL+Pj4wMvLCwEBAbh16xZevHgBAPD09ISZmVmdbteq5pebm4vVq1fzwXafPn0wY8YMREZG\nyt1vtbW1MXnyZL6OPnbsGMLDw7F792707t0bFhYWAAANDQ2Ympri8ePHMseolpYW/P39ER8fjyZN\nmkBfXx86OjoAAA8PD6Slpcnku1WrVti4cSP8/Pz4+j4zMxMikYi/WK43HMexhvjXpUsXNnz4cPb1\n11/zf48ePeJ/Cw8PZxzHsUePHjEbGxt2+fJlxnEc++uvv5iVlRVLSkpioaGhrGfPniwlJYXl5eUx\ne3t7Nnv2bMZxHIuPj2empqZvzcfKlSvZ3LlzWV5eHsvNzWXjxo1j586dY+7u7szJyYllZWWxZ8+e\nsZEjR7LffvuNZWZmstGjR7N//vmHcRzHLly4wMzNzRnHcSw0NJRZWFiwjIwMxnEcS05OZj169Kh2\nHaSmplabLjg4mC1atIj/7Ofnxzw9PZmXlxf78ccfGcdx7Pbt22zUqFEsNDSUTZ06lXEcxxYuXMi2\nbdvGLycuLo4NHTqU7d+/n3Ecx3Jzc9nQoUPZ1atXGcdxLC0tjQ0dOpRdvHhRZvt89dVXrE+fPszO\nzo6tWLGC30Ycx7Gff/6ZOTo6Mo7j2NatW5mTk5PcMsyZM4d5enoyjuPYzp07WVBQEP/blClT+Hza\n2tqygICAStPfv3+f9ezZk8XHxzOO49jhw4fZ5MmTWUJCAuvVqxdLSkpiHMexM2fOsN69e7P09HTm\n6+vL+vfvz9LT01lWVhazsLBg3t7ejOM4duzYMTZo0CB+/iNGjGA5OTlylzNlyhSWnJzMunTpwg4f\nPsw4jmNHjhxhAwYMqHF5pLdJxT8/Pz/m5eVVZTobGxuWnJxcaTpnZ2d2+/btSt+npaWxCRMmsG7d\nurHRo0ez1atXs+joaP73BQsWsBUrVjCO41hOTg6bOHEi27Jli8x+WXE/PXv2LDM2NmZ37txhHMex\nuLg4Zm1tzVJSUhjHcWzHjh3Mw8ODJSYmsuHDh/PHyPXr11nv3r3ZkydPqj0Wx40bx7777juWl5fH\nnjx5wvr06cNiYmL4dXnlypVK6yo0NJT961//YgkJCYzjOLZ27Vo2c+ZMufOfOHEin/fw8HB+27i5\nuTFfX1/GcRy7desWMzc3Z2FhYdUe69LHbEZGRpXppP+qq8uOHTvGBg8ezNLS0hjHcWzVqlVs8+bN\nMsupbnp5x8vNmzf5Y7NLly78vl9xOYGBgZXyaWlpya5du8Y4jmNhYWGsS5cu7Pbt2ywkJISNGTOG\n35Z+fn5s0qRJ/PY7dOgQ4ziORUVFsW+//ZZxHMdSUlKYq6srv73epc4+d+4cGzt2LL+cU6dOsaFD\nh/LzKK/nYmNj+ek5jmNbtmxh06ZNq9PtWl26TZs2MQ8PD8ZxHHvy5AmbM2cOS0tLq3a/vX79OrOy\nsmInTpxgvXr1YomJiZWW+ccff7BevXqx69evyxyjO3bsYC4uLiw3N5c9efKEOTo6ypw/3vbn7OzM\njI2N2Zo1a955mtr6a9B3XqprNurRowcA4NatWzAyMoKJiQkAoEOHDjAzM8Nff/0FADAxMeHvPrRs\n2ZJv223VqhWKi4tRVFQEDQ2NKvMQHx8Pd3d3CAQCiEQi7NixAwAQGRkJW1tbPvrs2LEjnj17Bk1N\nTQQEBODChQtIS0tDSkoKXr16xc/v888/R6NGjQBUfWepourSpaamolWrVpW+Hz58ONasWYOJEyci\nKioKX331VbXLcHd3R7NmzTB06FAAwD///IOMjAz4+PjwaYqLi5GSksI3oZRvn5SUFMyfPx+mpqZo\n0qQJn/7QoUNwdHQEANjb22Pbtm1ITEyEmZkZnyYwMBCvXr3ilzN27FjcuHEDYWFhePz4Me7fvy/T\nZFO+3aUlJiaiQ4cO+PzzzwEAdnZ2sLOzQ0REBKysrNCyZUsAgIWFBfT09HD79m0IBAJYW1vzd8AM\nDAxgY2MDoGzfeP78OYCyW9AtW7aEqqpqlcvJyMiAqqoq7OzsAJRt4/I7GO9TnqpUtx+0bNkSjx49\nQosWLd5pXtra2ggKCkJ6ejquX7+O69evY+HChRg9ejTmzJmDK1eu4OeffwYAqKqqwsnJCeHh4Zg0\naVK18zU0NESzZs0AlB07NjY2/PE3btw4AEBERARyc3NlmqdUVFSQlpZWZbMZUHZXcfDgwRAIBNDQ\n0ICRkRGePXtWKR1jTGZdWVpaom3btgAAR0dHuLq6VpqmvG9Zed5jY2Mxbdo0AMC1a9ewYMECAGX7\nhpWVFQBUe6xLL79Ro0bV1gnlqqvL7ty5g0GDBvF3QMrzk5GR8U7Tlx8DQFmzY8eOHdGuXTt+nQQF\nBQEoa36TtxxpN27cQPv27flt9dVXX2HTpk1gjOHPP//EiBEj+Dp13LhxGDp0KEpLS+Ho6IjIyEgM\nGDAAx48fh4ODA7+uy+94AW+vs1+9eoWLFy8iLS0N06dP56crKCjgj9tyZmZmaNy4MSIiIpCRkYHr\n16/zx3xdbdfq0vXq1Qvu7u7IysqCpaUl/v3vf/Prvqr9tmPHjpg+fToWLVqEVatWoU2bNjLLu3Xr\nFpYvXw5fX1907NiR/54xhri4ONjb20MkEkEkEmHYsGH8HXB5d15atmwJX19f/vP27dvBcRzmzJmD\ndu3a4euvv65U3rrSoIOX6mhqagKQX6FLJBKUlpZCJBJBVVVV5rea9rkQiWRXUXZ2NtTU1Cr9Vt5W\nmpWVhWnTpsHJyQk9evTAgAEDcPHixUr5rig2NpbvZGhgYMDfyn4bFRUVvlOWdF5MTEwgFouRkpKC\nM2fOYOfOnYiJialyPsuWLcPu3bsRFhYGFxcXSCQSaGtry7SV5ubm8rcYpXXu3Bnu7u5Yu3Ytunfv\njhYtWuDmzZt48OABQkJC+I7NqqqqCA8P54OXsLAw3Lx5Ezt37uTXX1BQEJKSkuDg4ABLS0uIxeJK\nFUZFIpGoUlv1/fv3K53EgLL9pbxTasVtW/EzULZdytviq1qOpqZmpX2hfLnvUx6g7KSRm5sLAJgx\nY4bcNNIkEkmN9u29e/fC3NwcZmZmaNWqFUaOHImEhATMnz8fc+bMgUQikcmnRCKBWCzmP5f/VlJS\nIjNf6fJUXJ/lTQESiQSWlpZYu3Yt/9uTJ0/4E1Z15B1z8kj/Jr1exGJxpeZZoKwZpE+fPgDKyvTP\nP/9Uqvgrzu9tx3q5d00nry4r318rbtvyYEtaxXqg/LuKnbCl909Adp1W3Gbly2nevLlMmop5LV+n\nFfcbsVjM7zd2dnYIDAzEo0ePcOPGDaxatQpAWVPsDz/8wE/zLnU2YwzDhg3DnDlz+M9ZWVnQ1dWV\nSXfx4kUEBgbCxcUFtra2aNu2LU6dOiUzn4rLUfR2rS6diYkJjhw5gri4OFy7dg1Tpkzhg4Xq9tv7\n9+9DX18ff//9N+zt7WWWd+zYMYwbN07mIrFcxfOF9DKq6+R89uxZ9OrVC40aNUKTJk1ga2uL27dv\n12vw0mCfNnpX3bt3R2pqKpKSkgCUbdSbN2+iZ8+eCpm/paUlTpw4AcYY36nxxo0bVaa/ffs2Pvvs\nM0ydOhXW1ta4cOECAPkVi1Ao5A/sfv36ITQ0FKGhoZUCF+l0FbVp0wbp6en8Z+kT9vDhwxEYGIi2\nbdtWCjoqVj6mpqZYuXIl9uzZg/v376Nt27ZQU1PjD/SsrCy4urryUXpFQ4YMgampKQICAgCUXV0P\nHz4cx48fx9GjR3H06FEEBAQgOjoaWVlZ+P333xEREYFNmzbJ3Pm6evUqnJ2dYW9vjyZNmiAuLk7u\nupNmYmKCR48e4cGDBwCA8+fPY/ny5bCwsMDVq1f59RMfH4/s7Gx07979ne96Xbp0ie9LUtVyqjuJ\n1qQ8QqGQP9Fs3ryZ3x/69u1bqZKVPiExxpCZmclfpb2L4uJibNu2Taad/+HDh3yfLxsbG/5Jm+Li\nYhw+fJi/MtXT0+M7LUZHR1e5DAsLC8THx/NB2MGDBxEUFMRvl9TUVADAn3/+CVdXVxQXF78131Vt\nN5FIxAdS0v8DwPXr1/kOnIcOHULfvn0rTR8TE8P3oYqPj+f7DgBlV8eHDx8GUHYcxMfHA6j6WGeM\nyRyzycnJVaaTJq8uu3HjBnr27AkrKytER0fz/TaCg4Px66+/8v1HgLJj+F3qwh49euDhw4e4e/cu\nAMg8YSNvORWfqjQzM8Pjx49x7949AGUPAxQUFEBFRQU2NjaIjIxEUVERgLIntr744gu+j8TgwYOx\nevVqDBw4EOrq6igsLERBQQF/x+tdlN81PX36NL9vHTlyBHPnzgUge3zExcXhyy+/hJOTE7p27Yrz\n58/z66uutmtV6SQSCbZu3Ypdu3bB1tYWCxcuRPv27fl+KlXtt+fOnePv5l65cgWxsbEyyxs5ciSG\nDRsmd73Z2NggKioKxcXFKC4uxunTp99pnR86dAgHDhwAUBbQxsbGwtLS8p2mrS0N9s7Lu15VNWnS\nBOvXr4e/vz+KioogEAiwYsUKGBkZISEhodJ8pD9L/19Vh103Nzds2rQJLi4uEIvFGDJkCOzs7Pgd\nsKLyx5THjBkDPT092NraomnTpvwOKb1MAwMDdO3aFWPHjkVwcHClJrLytNLpfvrpJ5mrC1tbW4SE\nhIAxxveWL5/O3t4eO3bsgL+/f6V5ylsPbdu2xdSpU7Fy5Ur88ssv8Pf3R0BAAPbt2wexWIwZM2bw\n0by87fP999/D1dUVf/zxB2JiYio9Hm1hYQFTU1Ps378fBw4cgKGhIRYuXMifzJ2cnDBt2jRs2bIF\nv/zyC/T09DBgwAB+3Umr2LHT29sbq1evhlgshra2NtatW4d27dphyZIlWLp0KcRiMTQ1NbFp0yZo\naWm99ckCgUCA3NxcqKmp8YGfvr6+3OWUr3t52+5dywMAvXv3hp+fHwBUap6Rzm/FdMnJyWjdunWN\nTgDTpk2DiooK3NzcIBAIIJFI0K1bN344gkWLFsHf3x/Ozs4oKSlB7969MWXKFP43X19f6OjoVHri\nQHo9dOzYEfPmzcP8+fMBlO3HXl5eaNq0KZYtWwZPT08wxiASifgg9m0ddqvaZra2tvD09ISXlxcs\nLS2xdOlSqKmpoUuXLjA0NIS3tzdycnLQrl07eHp6AgDfYdfPzw+pqal8M0hsbCzffAoAS5Ysgbe3\nN8aOHQtDQ0O+2bC6Y71169b8Mbt161YYGhrKTdemTRuZuqequszIyAgPHz6Em5sbgLImIU9PT6ir\nq8vUIVVND8jWcT4+PlixYgVUVVVhbm4us2/JW4686VetWgUVFRUYGxtDKBRCXV0dDg4OyM7OxuTJ\nk8EYg5GRkcyjuI6OjoiIiMCyZcsAlAWu5Xe8qtrG8uoqGxsbTJw4EXPnzoVAIIC2tjZ/x8LMzAw7\nduzA0qVLMXv2bCxfvhyurq7Q0dGBra0tH4zV1XYtD+gqpktLS4OzszNWr14NZ2dnqKqqonPnzhgy\nZAh+//13ufttVlYWfH19ERAQwHeAX7JkCYyNjfnj8MiRIzA2NpZ5QrF8vTk5OfHLbdy4MYyMjN7p\nydkVK1Zgw4YN/JARo0aNknlgoj4IOI57t0tQ0mCtX78elpaWGDRoUH1nhdSD1atXY/Dgwejdu3el\n32bNmoWVK1fK3Pb/lERGRuKPP/7Ali1b6jsrH40XL15g9+7dcHNzg4aGBm7fvo1FixY1iLE/Pha0\n375dg73zQt7d3Llz4eHhgX79+vH9ccinITk5GUKhUG7gQsooYkwm8oaWlhZUVVUxefJkvuNnQxpA\n9GNB+2316M4LIYQQQpSK0nfYJYQQQsinhYIXQgghhCgVCl4IIYQQolQoeCGEEEKIUqHghRBCCCFK\nhYIXQgghhCiV/wNOCsW0+TEMUwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x100d95c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lst = [dc_violent_crime_homicide_ward1_annual,\n",
" dc_violent_crime_homicide_ward2_annual,\n",
" dc_violent_crime_homicide_ward3_annual,\n",
" dc_violent_crime_homicide_ward4_annual,\n",
" dc_violent_crime_homicide_ward5_annual,\n",
" dc_violent_crime_homicide_ward6_annual,\n",
" dc_violent_crime_homicide_ward7_annual,\n",
" dc_violent_crime_homicide_ward8_annual]\n",
"\n",
"wards = ['1','2','4','5','6','7','8']\n",
"\n",
"count = 0\n",
"plt.figure(figsize=(8, 12)) \n",
"\n",
"def func(x, pos): \n",
" s = '{:d}'.format(int(x))\n",
" return s\n",
"\n",
"for x in lst:\n",
" if len(x) == 1:\n",
" ax = plt.subplot(4,2 , count + 1)\n",
" count += 1\n",
" plt.plot()\n",
" plt.title('Ward {}'.format(count), fontsize=12, weight='bold')\n",
" plt.tight_layout() \n",
" plt.grid(False)\n",
" ax.axes.get_xaxis().set_visible(False)\n",
" plt.yticks(np.arange(0,1,0.2), fontsize=12)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_color('grey')\n",
" else:\n",
" ax = plt.subplot(4,2 , count + 1)\n",
" count += 1\n",
" plt.plot(x.index.year, x)\n",
" plt.title('Ward {}'.format(count), fontsize=12, weight='bold')\n",
" plt.tight_layout() \n",
" plt.grid(False)\n",
" # formatter function to format y-axis labels to have a comma at the thousands place \n",
" x_format = tkr.FuncFormatter(func) # make formatter\n",
" ax.xaxis.set_major_formatter(x_format)\n",
" plt.xticks(np.arange(2011, 2016, 1), fontsize=12)\n",
" plt.yticks(fontsize=12)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_color('grey')\n",
"# if count < 7:\n",
"# ax.axes.get_xaxis().set_visible(False) \n",
"\n",
"ax.text(-1.2, -0.3, \"From: chart-it (MikeRAzar.com/chart-it) | Source: http://data.octo.dc.gov/metadata.aspx?id=3\", \n",
" fontsize=12, transform=ax.transAxes) "
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1e53d630>"
]
},
"execution_count": 234,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8yapVq7BYLBgMBhISEhg2bJhUfRXX5P627rSscndnNMOmk3J3J4Qj+HO1z2eZ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lu1ahUGDhyIq1ev3tHPuvfee/Hiiy82+3fz58/HwIEDG/5LTk6+o59FCLE9e1lfTp06hUmTJmH4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\nKnIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x215a87f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lst = [dc_violent_crime_ward1_annual,\n",
" dc_violent_crime_ward2_annual,\n",
" dc_violent_crime_ward3_annual,\n",
" dc_violent_crime_ward4_annual,\n",
" dc_violent_crime_ward5_annual,\n",
" dc_violent_crime_ward6_annual,\n",
" dc_violent_crime_ward7_annual,\n",
" dc_violent_crime_ward8_annual]\n",
"\n",
"wards = ['1','2','4','5','6','7','8']\n",
"\n",
"count = 0\n",
"plt.figure(figsize=(8, 12)) \n",
"\n",
"def func(x, pos): \n",
" s = '{:d}'.format(int(x))\n",
" return s\n",
"\n",
"for x in lst:\n",
" if len(x) == 1:\n",
" ax = plt.subplot(4,2 , count + 1)\n",
" count += 1\n",
" plt.plot()\n",
" plt.title('Ward {}'.format(count), fontsize=12, weight='bold')\n",
" plt.tight_layout() \n",
" plt.grid(False)\n",
" ax.axes.get_xaxis().set_visible(False)\n",
" plt.yticks(np.arange(0,1,0.1), fontsize=12)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_color('grey')\n",
" else:\n",
" ax = plt.subplot(4,2 , count + 1)\n",
" count += 1\n",
" plt.plot(x.index.year, x)\n",
" plt.title('Ward {}'.format(count), fontsize=12, weight='bold')\n",
" plt.tight_layout() \n",
" plt.grid(False)\n",
" # formatter function to format y-axis labels to have a comma at the thousands place \n",
" x_format = tkr.FuncFormatter(func) # make formatter\n",
" ax.xaxis.set_major_formatter(x_format)\n",
" plt.xticks(np.arange(2011, 2016, 1), fontsize=12)\n",
" plt.yticks(fontsize=12)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_visible(True)\n",
" ax.spines['bottom'].set_color('grey')\n",
"# if count < 7:\n",
"# ax.axes.get_xaxis().set_visible(False) \n",
"\n",
"ax.text(-1.2, -0.3, \"From: chart-it (MikeRAzar.com/chart-it) | Source: http://data.octo.dc.gov/metadata.aspx?id=3\", \n",
" fontsize=12, transform=ax.transAxes) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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