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hybridPOT/clustering/cluster_GWR.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/.local/lib/python3.6/site-packages/dask/config.py:129: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
" data = yaml.load(f.read()) or {}\n",
"/home/walch/.local/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
" defaults = yaml.load(f)\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import xarray as xr\n",
"import geopandas as gpd\n",
"\n",
"from sklearn.cluster import DBSCAN\n",
"from sklearn.cluster import KMeans\n",
"\n",
"import os\n",
"import time\n",
"import calendar\n",
"\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"\n",
"from demand_mapping import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def categorical_cmap(df, column, cmap = 'tab20', N = 20):\n",
" # Get colormap data\n",
" c = matplotlib.cm.get_cmap( cmap )(np.linspace(0., 1, N))\n",
" \n",
" # Get length of series, number of total copies and remainder\n",
" l = len(df[ column ].unique())\n",
" n = int(np.floor(l / N))\n",
" r = l % N\n",
" \n",
" # Concatenate colormap\n",
" clist = [c]*n\n",
" clist.append( c[:r,:] )\n",
" colors = np.vstack(clist)\n",
" \n",
" # return colormap\n",
" return matplotlib.colors.LinearSegmentedColormap.from_list('%s_concat' %cmap, colors)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"CH_fp = '/work/hyenergy/raw/Surface_data/swissBOUNDARIES3D/BOUNDARIES_2019/DATEN/swissBOUNDARIES3D/SHAPEFILE_LV95_LN02/swissBOUNDARIES3D_1_3_TLM_HOHEITSGEBIET.shp'\n",
"CH = gpd.read_file( CH_fp )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"BLD_fp = '/work/hyenergy/output/buildings'\n",
"GWR_fp = '/work/hyenergy/raw/BFS/GWR_Reg-BL/GWR_MADD_Export_MADD-20200715-A3_20200722' # 'D:\\Building_data\\OFS\\Buildings\\'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Links to BLD"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>BLD_ID</th>\n",
" <th>batchID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9362905</td>\n",
" <td>30001259.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9362906</td>\n",
" <td>30001196.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9362907</td>\n",
" <td>30000059.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9362908</td>\n",
" <td>30000059.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9362909</td>\n",
" <td>30001289.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID BLD_ID batchID\n",
"0 9362905 30001259.0 3\n",
"1 9362906 30001196.0 3\n",
"2 9362907 30000059.0 3\n",
"3 9362908 30000059.0 3\n",
"4 9362909 30001289.0 3"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BLD_ROOF_link = pd.read_csv(os.path.join(BLD_fp, 'buildings_CH/SOLKAT_DACH_CH_BLD_ID.csv'))\n",
"BLD_ROOF_link.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BLD_ID</th>\n",
" <th>batchID</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30000000.0</td>\n",
" <td>3</td>\n",
" <td>2.574685e+06</td>\n",
" <td>1.083026e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30000001.0</td>\n",
" <td>3</td>\n",
" <td>2.574188e+06</td>\n",
" <td>1.084778e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30000002.0</td>\n",
" <td>3</td>\n",
" <td>2.574218e+06</td>\n",
" <td>1.085029e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30000003.0</td>\n",
" <td>3</td>\n",
" <td>2.573684e+06</td>\n",
" <td>1.085899e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>30000004.0</td>\n",
" <td>3</td>\n",
" <td>2.573552e+06</td>\n",
" <td>1.086617e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BLD_ID batchID x y\n",
"0 30000000.0 3 2.574685e+06 1.083026e+06\n",
"1 30000001.0 3 2.574188e+06 1.084778e+06\n",
"2 30000002.0 3 2.574218e+06 1.085029e+06\n",
"3 30000003.0 3 2.573684e+06 1.085899e+06\n",
"4 30000004.0 3 2.573552e+06 1.086617e+06"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BLD_coords = pd.read_csv(os.path.join(BLD_fp, 'buildings_CH/BLD_ID_w_coords.csv'))\n",
"BLD_coords.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building information"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"GWR_cols = ['EGID', 'GKODE', 'GKODN', 'GKAT', 'GKLAS', 'BLD_ID']\n",
"BLD = pd.read_csv(os.path.join(BLD_fp,'GWR_w_sector_filled.csv'), usecols = GWR_cols)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>EGID</th>\n",
" <th>GKODE</th>\n",
" <th>GKODN</th>\n",
" <th>GKAT</th>\n",
" <th>GKLAS</th>\n",
" <th>BLD_ID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1600000</td>\n",
" <td>2679684.326</td>\n",
" <td>1237449.553</td>\n",
" <td>1020.0</td>\n",
" <td>1122.0</td>\n",
" <td>980051326.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1600001</td>\n",
" <td>2679702.150</td>\n",
" <td>1237493.958</td>\n",
" <td>1030.0</td>\n",
" <td>1110.0</td>\n",
" <td>980008361.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1600002</td>\n",
" <td>2679713.052</td>\n",
" <td>1237475.814</td>\n",
" <td>1030.0</td>\n",
" <td>1121.0</td>\n",
" <td>980008352.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1600003</td>\n",
" <td>2679655.455</td>\n",
" <td>1237516.450</td>\n",
" <td>1020.0</td>\n",
" <td>1122.0</td>\n",
" <td>980050497.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1600005</td>\n",
" <td>2679749.269</td>\n",
" <td>1237529.346</td>\n",
" <td>1020.0</td>\n",
" <td>1110.0</td>\n",
" <td>980050526.0</td>\n",
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"text/plain": [
" EGID GKODE GKODN GKAT GKLAS BLD_ID\n",
"0 1600000 2679684.326 1237449.553 1020.0 1122.0 980051326.0\n",
"1 1600001 2679702.150 1237493.958 1030.0 1110.0 980008361.0\n",
"2 1600002 2679713.052 1237475.814 1030.0 1121.0 980008352.0\n",
"3 1600003 2679655.455 1237516.450 1020.0 1122.0 980050497.0\n",
"4 1600005 2679749.269 1237529.346 1020.0 1110.0 980050526.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BLD.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Residential sector"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2234544"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(BLD.dropna(subset = ['GKODE', 'GKODN']))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"RES = pd.read_csv(os.path.join(BLD_fp,'GWR_residential.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"demand_fp = '/work/hyenergy/output/demand' #\n",
"residential_demand = xr.open_dataset(os.path.join(demand_fp, 'electricity_demand_residential.nc')) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Service sector"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"SER = pd.read_csv(os.path.join(BLD_fp,'GWR_service.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"demand_fp = '/work/hyenergy/output/demand' # \n",
"service_demand = xr.open_dataset(os.path.join(demand_fp, 'electricity_demand_services.nc')) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rooftops"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"ROOF_FP = '/work/hyenergy/output/solar_potential/geographic_potential/CH_ROOFS_all_replaced.csv' \n",
"roofs = pd.read_csv(ROOF_FP, usecols = ['DF_UID', 'NEIGUNG', 'AUSRICHTUNG', 'FLAECHE'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PV potential"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"PV_fp = '/work/hyenergy/output/solar_potential/technical_potential'\n",
"PV_annual = pd.read_csv(os.path.join(PV_fp, 'pv_potential_annual_shared_v2.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"PV_annual = ( PV_annual.merge(roofs, on = 'DF_UID', how = 'left')\n",
" .merge(BLD_ROOF_link.drop(columns = ['batchID']), on = 'DF_UID', how = 'left') )"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"PV_annual['suitable'] = ( ((PV_annual.NEIGUNG <= 10) | (abs(PV_annual.AUSRICHTUNG) <= 90)) \n",
" & (PV_annual.available_area >= 8) ).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"PV_annual['pv_potential_kWh_a'] = PV_annual['pv_potential_kWh_a'] * PV_annual['suitable']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
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" <th>DF_UID</th>\n",
" <th>pv_potential_kWh_a</th>\n",
" <th>pv_potential_kWh_a_std</th>\n",
" <th>radiation_kWh_m2_a</th>\n",
" <th>radiation_kWh_m2_a_std</th>\n",
" <th>available_area</th>\n",
" <th>FLAECHE</th>\n",
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" <th>suitable</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>324.268393</td>\n",
" <td>1210.184846</td>\n",
" <td>309.039122</td>\n",
" <td>1.437423</td>\n",
" <td>15.948816</td>\n",
" <td>37</td>\n",
" <td>10.0</td>\n",
" <td>1.100020e+09</td>\n",
" <td>0</td>\n",
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" <tr>\n",
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" <td>2</td>\n",
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" <td>85.539804</td>\n",
" <td>0.000000</td>\n",
" <td>13.730555</td>\n",
" <td>44</td>\n",
" <td>-170.0</td>\n",
" <td>1.100020e+09</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3891.6055</td>\n",
" <td>2071.079104</td>\n",
" <td>951.934814</td>\n",
" <td>239.814424</td>\n",
" <td>29.414390</td>\n",
" <td>63.437476</td>\n",
" <td>12</td>\n",
" <td>-80.0</td>\n",
" <td>1.100020e+09</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2022.1940</td>\n",
" <td>1044.604313</td>\n",
" <td>1029.797436</td>\n",
" <td>259.121816</td>\n",
" <td>14.224941</td>\n",
" <td>38.368910</td>\n",
" <td>33</td>\n",
" <td>-80.0</td>\n",
" <td>1.100020e+09</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.0000</td>\n",
" <td>702.281552</td>\n",
" <td>711.799309</td>\n",
" <td>188.438262</td>\n",
" <td>10.472945</td>\n",
" <td>36.403273</td>\n",
" <td>35</td>\n",
" <td>100.0</td>\n",
" <td>1.100020e+09</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID pv_potential_kWh_a pv_potential_kWh_a_std radiation_kWh_m2_a \\\n",
"0 1 0.0000 324.268393 1210.184846 \n",
"1 2 0.0000 0.000000 474.558362 \n",
"2 3 3891.6055 2071.079104 951.934814 \n",
"3 4 2022.1940 1044.604313 1029.797436 \n",
"4 5 0.0000 702.281552 711.799309 \n",
"\n",
" radiation_kWh_m2_a_std available_area FLAECHE NEIGUNG AUSRICHTUNG \\\n",
"0 309.039122 1.437423 15.948816 37 10.0 \n",
"1 85.539804 0.000000 13.730555 44 -170.0 \n",
"2 239.814424 29.414390 63.437476 12 -80.0 \n",
"3 259.121816 14.224941 38.368910 33 -80.0 \n",
"4 188.438262 10.472945 36.403273 35 100.0 \n",
"\n",
" BLD_ID suitable \n",
"0 1.100020e+09 0 \n",
"1 1.100020e+09 0 \n",
"2 1.100020e+09 1 \n",
"3 1.100020e+09 1 \n",
"4 1.100020e+09 0 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PV_annual.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9639231"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(PV_annual)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"PV_hourly = xr.open_mfdataset(os.path.join(PV_fp, 'pv_potential_hourly_shared_v2.nc'), combine = 'by_coords')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (DF_UID: 9639231, timestamp: 146)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 20...\n",
"Data variables:\n",
" pv_potential_W (DF_UID, timestamp) float32 dask.array&lt;chunksize=(9639231, 146), meta=np.ndarray&gt;\n",
" pv_potential_W_std (DF_UID, timestamp) float32 dask.array&lt;chunksize=(9639231, 146), meta=np.ndarray&gt;\n",
" radiation_W_m2 (DF_UID, timestamp) float32 dask.array&lt;chunksize=(9639231, 146), meta=np.ndarray&gt;\n",
" radiation_W_m2_std (DF_UID, timestamp) float32 dask.array&lt;chunksize=(9639231, 146), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-de234ee6-ecdb-4c4e-a455-754e8cb7287f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de234ee6-ecdb-4c4e-a455-754e8cb7287f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>DF_UID</span>: 9639231</li><li><span class='xr-has-index'>timestamp</span>: 146</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bed7a002-181b-4529-a7df-d617341ae2c6' class='xr-section-summary-in' type='checkbox' checked><label for='section-bed7a002-181b-4529-a7df-d617341ae2c6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>DF_UID</span></div><div class='xr-var-dims'>(DF_UID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 9890021 9890022 9890023</div><input id='attrs-9d14787c-ee29-4cfe-ba1a-45c6989aa0a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d14787c-ee29-4cfe-ba1a-45c6989aa0a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-084761e3-a55f-48b3-90d7-3af807578ea8' class='xr-var-data-in' type='checkbox'><label for='data-084761e3-a55f-48b3-90d7-3af807578ea8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1, 2, 3, ..., 9890021, 9890022, 9890023])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>timestamp</span></div><div class='xr-var-dims'>(timestamp)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-01-15T08:00:00 ... 2001-12-...</div><input id='attrs-f789fbf8-0cb8-4b01-ac08-6397c1c4a870' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f789fbf8-0cb8-4b01-ac08-6397c1c4a870' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54e6e2f5-43d0-406a-8429-f356cc34c800' class='xr-var-data-in' type='checkbox'><label for='data-54e6e2f5-43d0-406a-8429-f356cc34c800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2001-01-15T08:00:00.000000000&#x27;, &#x27;2001-01-15T09:00:00.000000000&#x27;,\n",
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id='data-38d991ea-cc49-47ac-992e-af7adf61edc5' class='xr-var-data-in' type='checkbox'><label for='data-38d991ea-cc49-47ac-992e-af7adf61edc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>dask.array&lt;open_dataset-9d911128f95b33f967ee90eede8ae26epv_potential_W, shape=(9639231, 146), dtype=float32, chunksize=(9639231, 146)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pv_potential_W_std</span></div><div class='xr-var-dims'>(DF_UID, timestamp)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9639231, 146), meta=np.ndarray&gt;</div><input id='attrs-3dd709cc-0329-4d09-8728-972a4ca783d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3dd709cc-0329-4d09-8728-972a4ca783d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0969a5e7-5016-4b32-9e1c-dac580cd881f' class='xr-var-data-in' type='checkbox'><label for='data-0969a5e7-5016-4b32-9e1c-dac580cd881f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>dask.array&lt;open_dataset-9d911128f95b33f967ee90eede8ae26epv_potential_W_std, shape=(9639231, 146), dtype=float32, chunksize=(9639231, 146)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiation_W_m2</span></div><div class='xr-var-dims'>(DF_UID, timestamp)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9639231, 146), meta=np.ndarray&gt;</div><input id='attrs-45f8f92b-00a6-45bd-a661-c09649022064' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45f8f92b-00a6-45bd-a661-c09649022064' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47370154-dc2a-4ac5-ae23-da6c7980c92a' class='xr-var-data-in' type='checkbox'><label for='data-47370154-dc2a-4ac5-ae23-da6c7980c92a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>dask.array&lt;open_dataset-9d911128f95b33f967ee90eede8ae26eradiation_W_m2, shape=(9639231, 146), dtype=float32, chunksize=(9639231, 146)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiation_W_m2_std</span></div><div class='xr-var-dims'>(DF_UID, timestamp)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9639231, 146), meta=np.ndarray&gt;</div><input id='attrs-c05e1bcd-dfd1-47e3-9e83-ac6b295adab8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c05e1bcd-dfd1-47e3-9e83-ac6b295adab8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9946eaea-1fff-4467-8c04-d4c9dfcb7412' class='xr-var-data-in' type='checkbox'><label for='data-9946eaea-1fff-4467-8c04-d4c9dfcb7412' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>dask.array&lt;open_dataset-9d911128f95b33f967ee90eede8ae26eradiation_W_m2_std, shape=(9639231, 146), dtype=float32, chunksize=(9639231, 146)&gt;</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b7ed7382-6ee9-48f6-90b6-3d8e94bf672d' class='xr-section-summary-in' type='checkbox' disabled ><label 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],
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"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 146)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 20...\n",
"Data variables:\n",
" pv_potential_W (DF_UID, timestamp) float32 dask.array<chunksize=(9639231, 146), meta=np.ndarray>\n",
" pv_potential_W_std (DF_UID, timestamp) float32 dask.array<chunksize=(9639231, 146), meta=np.ndarray>\n",
" radiation_W_m2 (DF_UID, timestamp) float32 dask.array<chunksize=(9639231, 146), meta=np.ndarray>\n",
" radiation_W_m2_std (DF_UID, timestamp) float32 dask.array<chunksize=(9639231, 146), meta=np.ndarray>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PV_hourly"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute annual PV potential per BLD_ID"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"PV_BLD_ID = PV_annual.groupby(['BLD_ID'])[['pv_potential_kWh_a', 'available_area']].sum().reset_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute peak demand per EGID"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Residential"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"peak_demand = residential_demand.max()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"annual_demand_kWh = get_yearly_sum(residential_demand) / 1e3"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"peak_demand_RES,_ = get_cluster_demand_RES( RES, peak_demand, grouping_col = 'EGID', dwell_nb = 'N_DWL_RES' )\n",
"annual_demand_RES,_ = get_cluster_demand_RES( RES, annual_demand_kWh, grouping_col = 'EGID', dwell_nb = 'N_DWL_RES' )"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
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".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt, dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (EGID: 0)&gt;\n",
"array([], dtype=float64)\n",
"Coordinates:\n",
" * EGID (EGID) int64 </pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>EGID</span>: 0</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6c9b9fce-9a86-4d7b-bdb7-80978f8e1bef' class='xr-array-in' type='checkbox' checked><label for='section-6c9b9fce-9a86-4d7b-bdb7-80978f8e1bef' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span></span></div><div class='xr-array-data'><pre>array([], dtype=float64)</pre></div></div></li><li class='xr-section-item'><input id='section-60a9ea36-c5ac-4254-957f-c23bf3e43e08' class='xr-section-summary-in' type='checkbox' checked><label for='section-60a9ea36-c5ac-4254-957f-c23bf3e43e08' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>EGID</span></div><div class='xr-var-dims'>(EGID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'></div><input id='attrs-6a73c628-fea2-4643-8887-e1ef3bc03e11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a73c628-fea2-4643-8887-e1ef3bc03e11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e2ce4ef-d175-481c-bfe8-2c66c78b5ff2' class='xr-var-data-in' type='checkbox'><label for='data-0e2ce4ef-d175-481c-bfe8-2c66c78b5ff2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85a53a09-0726-40cb-b242-ce8a0e63ea36' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85a53a09-0726-40cb-b242-ce8a0e63ea36' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray (EGID: 0)>\n",
"array([], dtype=float64)\n",
"Coordinates:\n",
" * EGID (EGID) int64 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"peak_demand_RES[peak_demand_RES > 1e6]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Service"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"peak_demand = service_demand.max()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"annual_demand_kWh = get_yearly_sum(service_demand) / 1e3"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"peak_demand_SER,_ = get_cluster_demand_SER( SER, peak_demand, grouping_col = 'EGID' )\n",
"annual_demand_SER,_ = get_cluster_demand_SER( SER, annual_demand_kWh, grouping_col = 'EGID' )"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"GKLAS\n",
"1211.0 8\n",
"1212.0 1\n",
"1220.0 41\n",
"1230.0 27\n",
"1261.0 2\n",
"1263.0 16\n",
"1264.0 15\n",
"1265.0 1\n",
"Name: EGID, dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SER[SER.EGID.isin(peak_demand_SER[peak_demand_SER > 1e6].EGID)].groupby('GKLAS').EGID.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Merged"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"peak_demand = ( pd.concat([ peak_demand_RES.rename('peak_RES').to_dataframe(), \n",
" peak_demand_SER.rename('peak_SER').to_dataframe()], axis = 1)\n",
" .fillna(0).sum(axis = 1).rename('peak_demand_W').reset_index() )"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"annual_demand = ( pd.concat([ annual_demand_RES.rename('annual_RES').to_dataframe(), \n",
" annual_demand_SER.rename('annual_SER').to_dataframe()], axis = 1)\n",
" .fillna(0).sum(axis = 1).rename('annual_demand_kWh').reset_index() )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Merge building data"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"GWR = ( BLD.merge( RES[['EGID', 'N_DWL_RES']], on = 'EGID', how = 'left')\n",
" .merge( SER[['EGID', 'SER_AREA']] , on = 'EGID', how = 'left')\n",
" .merge( peak_demand , on = 'EGID', how = 'left' )\n",
" .merge( annual_demand, on = 'EGID', how = 'left' )\n",
" .dropna( subset = ['GKODE', 'GKODN'] ).dropna( subset = ['N_DWL_RES', 'SER_AREA'], how = 'all' ) )"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>EGID</th>\n",
" <th>GKODE</th>\n",
" <th>GKODN</th>\n",
" <th>GKAT</th>\n",
" <th>GKLAS</th>\n",
" <th>BLD_ID</th>\n",
" <th>N_DWL_RES</th>\n",
" <th>SER_AREA</th>\n",
" <th>peak_demand_W</th>\n",
" <th>annual_demand_kWh</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1600000</td>\n",
" <td>2679684.326</td>\n",
" <td>1237449.553</td>\n",
" <td>1020.0</td>\n",
" <td>1122.0</td>\n",
" <td>980051326.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>2011.360060</td>\n",
" <td>8450.621360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1600001</td>\n",
" <td>2679702.150</td>\n",
" <td>1237493.958</td>\n",
" <td>1030.0</td>\n",
" <td>1110.0</td>\n",
" <td>980008361.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>966.458332</td>\n",
" <td>3698.619653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1600002</td>\n",
" <td>2679713.052</td>\n",
" <td>1237475.814</td>\n",
" <td>1030.0</td>\n",
" <td>1121.0</td>\n",
" <td>980008352.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>1340.906706</td>\n",
" <td>5633.747573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1600003</td>\n",
" <td>2679655.455</td>\n",
" <td>1237516.450</td>\n",
" <td>1020.0</td>\n",
" <td>1122.0</td>\n",
" <td>980050497.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>2011.360060</td>\n",
" <td>8450.621360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1600005</td>\n",
" <td>2679749.269</td>\n",
" <td>1237529.346</td>\n",
" <td>1020.0</td>\n",
" <td>1110.0</td>\n",
" <td>980050526.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>966.458332</td>\n",
" <td>3698.619653</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" EGID GKODE GKODN GKAT GKLAS BLD_ID N_DWL_RES \\\n",
"0 1600000 2679684.326 1237449.553 1020.0 1122.0 980051326.0 3.0 \n",
"1 1600001 2679702.150 1237493.958 1030.0 1110.0 980008361.0 1.0 \n",
"2 1600002 2679713.052 1237475.814 1030.0 1121.0 980008352.0 2.0 \n",
"3 1600003 2679655.455 1237516.450 1020.0 1122.0 980050497.0 3.0 \n",
"4 1600005 2679749.269 1237529.346 1020.0 1110.0 980050526.0 1.0 \n",
"\n",
" SER_AREA peak_demand_W annual_demand_kWh \n",
"0 NaN 2011.360060 8450.621360 \n",
"1 NaN 966.458332 3698.619653 \n",
"2 NaN 1340.906706 5633.747573 \n",
"3 NaN 2011.360060 8450.621360 \n",
"4 NaN 966.458332 3698.619653 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GWR.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1790464"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(GWR)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1790464"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(GWR.EGID.unique())"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1790464"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(np.unique(list(SER.dropna(subset = ['GKODN', 'GKODE']).EGID )\n",
" + list(RES.dropna(subset = ['GKODN', 'GKODE']).EGID ) ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create coordinate set to cluster"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"GWR_wo_BLD_ID = GWR.BLD_ID.isna()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"GWR.loc[ GWR_wo_BLD_ID, 'BLD_ID'] = GWR[ GWR_wo_BLD_ID ].EGID + GWR.BLD_ID.max()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"cluster_coords = pd.concat([BLD_coords.drop(columns = ['batchID']), \n",
" GWR.loc[GWR_wo_BLD_ID,['BLD_ID', 'GKODE', 'GKODN']].rename({'GKODE' : 'x', \n",
" 'GKODN' : 'y'}, axis = 1)])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"cluster_data = ( cluster_coords.merge( GWR.groupby('BLD_ID')[['peak_demand_W','annual_demand_kWh']]\n",
" .sum().reset_index(), on = 'BLD_ID', how = 'left' )\n",
" .merge( PV_BLD_ID, on = 'BLD_ID', how = 'left' )\n",
" .fillna(0) )"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BLD_ID</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>peak_demand_W</th>\n",
" <th>annual_demand_kWh</th>\n",
" <th>pv_potential_kWh_a</th>\n",
" <th>available_area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30000000.0</td>\n",
" <td>2.574685e+06</td>\n",
" <td>1.083026e+06</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>58780.2367</td>\n",
" <td>315.894507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30000001.0</td>\n",
" <td>2.574188e+06</td>\n",
" <td>1.084778e+06</td>\n",
" <td>966.458332</td>\n",
" <td>3698.619653</td>\n",
" <td>3309.6240</td>\n",
" <td>29.367821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30000002.0</td>\n",
" <td>2.574218e+06</td>\n",
" <td>1.085029e+06</td>\n",
" <td>966.458332</td>\n",
" <td>3698.619653</td>\n",
" <td>7244.9907</td>\n",
" <td>66.977718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30000003.0</td>\n",
" <td>2.573684e+06</td>\n",
" <td>1.085899e+06</td>\n",
" <td>966.458332</td>\n",
" <td>3698.619653</td>\n",
" <td>3189.0305</td>\n",
" <td>32.125742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>30000004.0</td>\n",
" <td>2.573552e+06</td>\n",
" <td>1.086617e+06</td>\n",
" <td>966.458332</td>\n",
" <td>3698.619653</td>\n",
" <td>16935.1609</td>\n",
" <td>171.522968</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BLD_ID x y peak_demand_W annual_demand_kWh \\\n",
"0 30000000.0 2.574685e+06 1.083026e+06 0.000000 0.000000 \n",
"1 30000001.0 2.574188e+06 1.084778e+06 966.458332 3698.619653 \n",
"2 30000002.0 2.574218e+06 1.085029e+06 966.458332 3698.619653 \n",
"3 30000003.0 2.573684e+06 1.085899e+06 966.458332 3698.619653 \n",
"4 30000004.0 2.573552e+06 1.086617e+06 966.458332 3698.619653 \n",
"\n",
" pv_potential_kWh_a available_area \n",
"0 58780.2367 315.894507 \n",
"1 3309.6240 29.367821 \n",
"2 7244.9907 66.977718 \n",
"3 3189.0305 32.125742 \n",
"4 16935.1609 171.522968 "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cluster_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"cluster_data.to_csv('/scratch/walch/scratch/cluster_data_BLD.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1: Perform DBSCAN on BLD coords"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"weird_cluster = cluster_data[(cluster_data.peak_demand_W > 1e6) & (cluster_data.pv_potential_kWh_a == 0)].BLD_ID\n",
"# CONCLUSION: THEY ARE NOT IN THE ROOFTOP DATASET!! (e.g. cause they are new?!)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"cluster_data = cluster_data[cluster_data.peak_demand_W < 1e6]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"EPS = 350\n",
"MIN_SAMPLES = 15\n",
"\n",
"model = DBSCAN(eps = EPS, min_samples = MIN_SAMPLES, n_jobs = -1)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"coords = ['x', 'y']"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit and predict in 114.97s\n"
]
}
],
"source": [
"tt = time.time()\n",
"cluster_data['clusterID'] = model.fit_predict( cluster_data[coords] )\n",
"print('Fit and predict in %.2fs' %(time.time()-tt))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3690"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(cluster_data['clusterID'].unique())"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"cluster_size = cluster_data[cluster_data.clusterID > -1].groupby('clusterID').peak_demand_W.count().sort_values(ascending = False)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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},
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width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BLD_plot = cluster_data[cluster_data.clusterID.isin(cluster_size.index[:20])].sample(100000) # cluster_data[cluster_data.clusterID >= 0].sample(200000) # cluster_data[cluster_data['clusterID'].isin(range(0,100))] # \n",
"cluster_centers = BLD_plot.groupby('clusterID')[coords].mean()\n",
"\n",
"cmap = categorical_cmap( BLD_plot, 'clusterID', cmap = 'tab20', N = 20 )\n",
"\n",
"CH.plot(color = 'grey', alpha = 0.3)\n",
"plt.scatter(BLD_plot[coords[0]], BLD_plot[coords[1]], c = BLD_plot['clusterID'], s = 2, cmap = cmap)\n",
"# plt.colorbar()\n",
"# plt.axis('off')\n",
"plt.tight_layout()\n",
"\n",
"for ID, coord in cluster_centers.iterrows():\n",
" if ID < 0: continue\n",
" # plt.text(coord.GKODE, coord.GKODN, ID)\n",
"\n",
"# plt.savefig('clusters_dbscan.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute demand peaks"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"peak_demand_kW = cluster_data[cluster_data.clusterID > -1].groupby('clusterID')['peak_demand_W'].sum() / 1e3"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 3689.000000\n",
"mean 1510.690936\n",
"std 20693.460280\n",
"min 0.000000\n",
"25% 8.855012\n",
"50% 27.592168\n",
"75% 177.099607\n",
"max 918832.224902\n",
"Name: peak_demand_W, dtype: float64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"peak_demand_kW.describe()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of clusters: 3688\n",
"Number of clusters with no demand: 117\n",
"Number of clusters with peak demand > 1MW: 306\n",
"Percentage of outliers: 4.2%\n"
]
}
],
"source": [
"print('Number of clusters: %d' %(len(peak_demand_kW)-1))\n",
"print('Number of clusters with no demand: %d' %(len(peak_demand_kW[peak_demand_kW == 0])))\n",
"print('Number of clusters with peak demand > 1MW: %d' %(len(peak_demand_kW[peak_demand_kW > 1000])))\n",
"print('Percentage of outliers: %.1f%%' %(100*len(cluster_data[cluster_data.clusterID == -1])/len(cluster_data)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare pd Series with stats"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"cluster_groups = cluster_data[cluster_data.clusterID > -1].groupby('clusterID')['peak_demand_W']\n",
"count_stats = cluster_groups.count().describe().iloc[1:]\n",
"kWp_stats = (cluster_groups.sum() / 1e3).describe().iloc[1:]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"count_stats.index = ['N_BLD_%s' %name for name in count_stats.index] \n",
"kWp_stats.index = ['kWp_%s' %name for name in kWp_stats.index ] "
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"other_info = pd.Series( { 'EPS' : EPS ,\n",
" 'MIN_SAMPLES': MIN_SAMPLES,\n",
" 'N_clusters' : len(peak_demand_kW)-1,\n",
" 'N_clust_noDem' : len(peak_demand_kW[peak_demand_kW == 0]), \n",
" 'N_clust_>1MWp' : len(peak_demand_kW[peak_demand_kW > 1000]), \n",
" 'perc_outliers' : 100*len(cluster_data[cluster_data.clusterID == -1])/len(cluster_data) })"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"stats = pd.concat([other_info, kWp_stats, count_stats])"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"EPS 350.000000\n",
"MIN_SAMPLES 15.000000\n",
"N_clusters 3688.000000\n",
"N_clust_noDem 117.000000\n",
"N_clust_>1MWp 306.000000\n",
"perc_outliers 4.227922\n",
"kWp_mean 1510.690936\n",
"kWp_std 20693.460280\n",
"kWp_min 0.000000\n",
"kWp_25% 8.855012\n",
"kWp_50% 27.592168\n",
"kWp_75% 177.099607\n",
"kWp_max 918832.224902\n",
"N_BLD_mean 559.976145\n",
"N_BLD_std 5211.613212\n",
"N_BLD_min 2.000000\n",
"N_BLD_25% 22.000000\n",
"N_BLD_50% 44.000000\n",
"N_BLD_75% 178.000000\n",
"N_BLD_max 171263.000000\n",
"dtype: float64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse hyperparameters for DBSCAN\n",
"Code in DBSCAN_hyperparams.py (run on Sinteract node with 4 cores, 30G mem)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"hyperparams = pd.read_csv('/scratch/walch/scratch/DBSCAN_hyperparams.csv').set_index(['EPS', 'min_samples'])\n",
"hyperparams = hyperparams.to_xarray()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"hyperparams['perc_clust_>1MWp'] = hyperparams['N_clust_>1MWp'] / hyperparams['N_clusters']"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
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"\n",
":root {\n",
" --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
" --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
" --xr-background-color: var(--jp-layout-color0, white);\n",
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"}\n",
"\n",
"html[theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
" --xr-border-color: #1F1F1F;\n",
" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
" --xr-background-color-row-even: #111111;\n",
" --xr-background-color-row-odd: #313131;\n",
"}\n",
"\n",
".xr-wrap {\n",
" display: block;\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-text-repr-fallback {\n",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt, dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (EPS: 14, min_samples: 10)\n",
"Coordinates:\n",
" * EPS (EPS) float64 50.0 100.0 150.0 200.0 ... 600.0 650.0 700.0\n",
" * min_samples (min_samples) float64 5.0 10.0 15.0 ... 40.0 45.0 50.0\n",
"Data variables:\n",
" exec_time (EPS, min_samples) float64 10.9 8.74 7.356 ... 31.13 30.87\n",
" N_clusters (EPS, min_samples) float64 4.524e+04 2.765e+04 ... 540.0\n",
" N_clust_noDem (EPS, min_samples) float64 2.993e+03 497.0 ... 2.0 1.0\n",
" N_clust_&gt;1MWp (EPS, min_samples) float64 517.0 10.0 0.0 ... 91.0 100.0\n",
" perc_outliers (EPS, min_samples) float64 26.44 65.66 ... 2.524 2.889\n",
" aprx_add_clst (EPS, min_samples) float64 1.125e+03 14.51 ... 5.821e+03\n",
" kWp_mean (EPS, min_samples) float64 70.17 40.34 ... 1.093e+04\n",
" kWp_std (EPS, min_samples) float64 352.2 64.62 ... 1.897e+05\n",
" kWp_min (EPS, min_samples) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
" kWp_25% (EPS, min_samples) float64 2.307 11.97 ... 45.68 61.2\n",
" kWp_50% (EPS, min_samples) float64 7.14 22.01 ... 150.2 183.0\n",
" kWp_75% (EPS, min_samples) float64 33.98 43.92 ... 478.7 572.4\n",
" kWp_max (EPS, min_samples) float64 4.048e+04 ... 4.387e+06\n",
" N_BLD_mean (EPS, min_samples) float64 35.07 26.78 ... 3.872e+03\n",
" N_BLD_std (EPS, min_samples) float64 114.7 33.99 ... 6.149e+04\n",
" N_BLD_min (EPS, min_samples) float64 1.0 1.0 5.0 ... 5.0 9.0 16.0\n",
" N_BLD_25% (EPS, min_samples) float64 6.0 11.0 15.0 ... 72.5 91.0\n",
" N_BLD_50% (EPS, min_samples) float64 8.0 16.0 21.0 ... 170.0 196.0\n",
" N_BLD_75% (EPS, min_samples) float64 19.0 28.0 31.0 ... 431.5 504.0\n",
" N_BLD_max (EPS, min_samples) float64 5.489e+03 879.0 ... 1.423e+06\n",
" perc_clust_&gt;1MWp (EPS, min_samples) float64 0.01143 0.0003616 ... 0.1852</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3de46e52-fa99-44de-acfb-f1499519014d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3de46e52-fa99-44de-acfb-f1499519014d' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>EPS</span>: 14</li><li><span class='xr-has-index'>min_samples</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9136e812-122a-4e86-94af-0c1bdcb0ab21' class='xr-section-summary-in' type='checkbox' checked><label for='section-9136e812-122a-4e86-94af-0c1bdcb0ab21' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>EPS</span></div><div class='xr-var-dims'>(EPS)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>50.0 100.0 150.0 ... 650.0 700.0</div><input id='attrs-84ab7df8-ef7c-423c-b247-b6caebce5182' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84ab7df8-ef7c-423c-b247-b6caebce5182' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4c3b1b6-171e-43cf-9f4c-7378abcecaa3' class='xr-var-data-in' type='checkbox'><label for='data-e4c3b1b6-171e-43cf-9f4c-7378abcecaa3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 50., 100., 150., 200., 250., 300., 350., 400., 450., 500., 550., 600.,\n",
" 650., 700.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>min_samples</span></div><div class='xr-var-dims'>(min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 10.0 15.0 ... 40.0 45.0 50.0</div><input id='attrs-b9ec086e-4f23-4944-bde6-660d680c9dd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9ec086e-4f23-4944-bde6-660d680c9dd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec54de04-8d7f-4a46-b911-ffef2031bc49' class='xr-var-data-in' type='checkbox'><label for='data-ec54de04-8d7f-4a46-b911-ffef2031bc49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f593a829-1d93-40b0-b616-98d3656232f2' class='xr-section-summary-in' type='checkbox' ><label for='section-f593a829-1d93-40b0-b616-98d3656232f2' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>exec_time</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.9 8.74 7.356 ... 31.13 30.87</div><input id='attrs-c3e9b69a-43f4-4cc0-a7bf-f82c889d8c23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3e9b69a-43f4-4cc0-a7bf-f82c889d8c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-371b7945-4f52-49a7-9f9a-548f150e5cfa' class='xr-var-data-in' type='checkbox'><label for='data-371b7945-4f52-49a7-9f9a-548f150e5cfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[10.89790606, 8.74047923, 7.35572934, 8.0149436 , 8.24790025,\n",
" 8.21875978, 7.67502594, 7.44032407, 7.43783832, 7.47696233],\n",
" [12.82523704, 12.33091283, 11.89866257, 11.20625949, 10.15860701,\n",
" 10.31904268, 9.12866402, 8.9227798 , 8.42354703, 8.50940084],\n",
" [15.20891166, 14.58047295, 13.33452845, 13.20334697, 12.54898214,\n",
" 13.20547438, 12.97855687, 12.00742435, 11.27362061, 10.66570807],\n",
" [16.73065782, 15.90852141, 14.12576628, 15.341609 , 15.17913914,\n",
" 15.05266404, 13.64806175, 14.03546381, 13.78620052, 15.01218534],\n",
" [18.67505693, 17.75208259, 17.83842492, 17.70723748, 17.61859441,\n",
" 17.47236276, 17.38359284, 17.43644047, 16.51877928, 16.22869873],\n",
" [20.31152344, 18.75259089, 16.44944358, 18.64869428, 18.53184319,\n",
" 18.04948997, 18.3912425 , 17.83800483, 15.79583311, 16.44580555],\n",
" [20.76063967, 20.16922426, 18.44421124, 18.654037 , 18.12612844,\n",
" 17.92182255, 17.90987754, 17.86857915, 17.65168977, 19.55101991],\n",
" [20.54181671, 19.77047634, 19.77507353, 19.86275578, 19.13662815,\n",
" 19.56343246, 19.11957335, 19.41501713, 19.22756219, 19.00051951],\n",
" [22.05818176, 21.17098689, 20.82398701, 20.4316678 , 20.20427823,\n",
" 20.98595715, 20.43796825, 21.06786585, 20.19740009, 20.26402617],\n",
" [23.91184282, 22.73497176, 22.48032308, 22.81613517, 21.50704384,\n",
" 24.16148233, 21.05304861, 22.50745869, 21.81538486, 21.77230906],\n",
" [26.49379778, 24.79119468, 24.44736862, 24.40538001, 27.09162068,\n",
" 25.56160474, 25.95654392, 24.78375506, 27.092628 , 20.96500325],\n",
" [28.44891357, 27.50390673, 27.26682043, 28.15991974, 27.32128882,\n",
" 26.75687051, 26.74267697, 23.31433702, 26.0666678 , 23.68049383],\n",
" [26.38037872, 27.61735845, 26.57709813, 26.08937812, 26.75189805,\n",
" 26.01558304, 26.36685252, 29.56849313, 29.69812727, 30.52727532],\n",
" [30.90124846, 27.74722481, 27.19165182, 27.54868603, 28.68856978,\n",
" 30.19283056, 26.31503701, 31.4053123 , 31.12872696, 30.86638904]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_clusters</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.524e+04 2.765e+04 ... 538.0 540.0</div><input id='attrs-d480b198-e235-4d6d-b975-3df03fc41a14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d480b198-e235-4d6d-b975-3df03fc41a14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8df8098c-5082-420b-a98c-6f902794ada1' class='xr-var-data-in' type='checkbox'><label for='data-8df8098c-5082-420b-a98c-6f902794ada1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.5239e+04, 2.7652e+04, 4.0950e+03, 6.9000e+02, 3.2600e+02,\n",
" 2.0200e+02, 1.4000e+02, 8.4000e+01, 5.3000e+01, 3.7000e+01],\n",
" [3.1599e+04, 1.3852e+04, 1.0086e+04, 9.4020e+03, 9.3000e+03,\n",
" 8.1650e+03, 5.8360e+03, 3.2940e+03, 1.5420e+03, 6.9200e+02],\n",
" [2.4618e+04, 1.2411e+04, 8.3760e+03, 6.8590e+03, 6.0890e+03,\n",
" 5.6460e+03, 5.3480e+03, 5.2040e+03, 5.0920e+03, 4.9280e+03],\n",
" [1.7309e+04, 1.0579e+04, 7.6590e+03, 6.1520e+03, 5.3110e+03,\n",
" 4.8460e+03, 4.5710e+03, 4.3190e+03, 4.1220e+03, 3.9850e+03],\n",
" [1.1830e+04, 8.0580e+03, 6.5250e+03, 5.5400e+03, 4.8220e+03,\n",
" 4.4130e+03, 4.1240e+03, 3.9110e+03, 3.7420e+03, 3.5990e+03],\n",
" [7.9550e+03, 5.7050e+03, 5.1530e+03, 4.6300e+03, 4.2380e+03,\n",
" 3.9110e+03, 3.6970e+03, 3.5360e+03, 3.3780e+03, 3.2940e+03],\n",
" [5.5240e+03, 3.8710e+03, 3.6870e+03, 3.6270e+03, 3.4780e+03,\n",
" 3.3590e+03, 3.2380e+03, 3.1250e+03, 3.0170e+03, 2.9470e+03],\n",
" [3.9090e+03, 2.7480e+03, 2.5630e+03, 2.6170e+03, 2.6510e+03,\n",
" 2.6910e+03, 2.6660e+03, 2.6800e+03, 2.6320e+03, 2.5830e+03],\n",
" [2.8290e+03, 1.9400e+03, 1.8590e+03, 1.8870e+03, 1.9260e+03,\n",
" 2.0200e+03, 2.1040e+03, 2.1570e+03, 2.1790e+03, 2.1610e+03],\n",
" [2.1030e+03, 1.4020e+03, 1.2800e+03, 1.3290e+03, 1.4020e+03,\n",
" 1.4560e+03, 1.5220e+03, 1.6210e+03, 1.7100e+03, 1.7460e+03],\n",
" [1.5870e+03, 1.0340e+03, 9.1200e+02, 9.4900e+02, 1.0280e+03,\n",
" 1.0820e+03, 1.1320e+03, 1.1910e+03, 1.2740e+03, 1.3350e+03],\n",
" [1.2480e+03, 8.1500e+02, 6.7300e+02, 7.1600e+02, 7.6200e+02,\n",
" 7.8900e+02, 8.5100e+02, 8.9000e+02, 9.3800e+02, 1.0120e+03],\n",
" [9.5800e+02, 6.1300e+02, 5.1700e+02, 4.9800e+02, 5.4000e+02,\n",
" 5.8200e+02, 6.0700e+02, 6.5100e+02, 6.8000e+02, 7.2800e+02],\n",
" [7.5200e+02, 4.7700e+02, 4.0600e+02, 3.8600e+02, 4.0400e+02,\n",
" 4.2800e+02, 4.6100e+02, 5.0100e+02, 5.3800e+02, 5.4000e+02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_clust_noDem</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.993e+03 497.0 145.0 ... 2.0 1.0</div><input id='attrs-74cce1fa-fe98-4fe2-850f-0056e41ee97e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74cce1fa-fe98-4fe2-850f-0056e41ee97e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c487dd8f-178a-44c3-bc4b-b0a54e1dba5e' class='xr-var-data-in' type='checkbox'><label for='data-c487dd8f-178a-44c3-bc4b-b0a54e1dba5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.993e+03, 4.970e+02, 1.450e+02, 8.300e+01, 6.900e+01, 6.300e+01,\n",
" 5.300e+01, 3.900e+01, 2.300e+01, 1.600e+01],\n",
" [2.494e+03, 4.520e+02, 1.770e+02, 1.240e+02, 1.040e+02, 9.000e+01,\n",
" 5.300e+01, 2.800e+01, 1.300e+01, 1.000e+01],\n",
" [2.060e+03, 4.460e+02, 1.630e+02, 8.900e+01, 5.500e+01, 4.800e+01,\n",
" 4.400e+01, 4.200e+01, 4.200e+01, 3.800e+01],\n",
" [1.612e+03, 4.100e+02, 1.550e+02, 8.600e+01, 4.900e+01, 4.000e+01,\n",
" 3.000e+01, 2.900e+01, 3.000e+01, 2.600e+01],\n",
" [1.284e+03, 3.390e+02, 1.420e+02, 7.000e+01, 4.600e+01, 3.400e+01,\n",
" 2.500e+01, 1.900e+01, 1.700e+01, 1.800e+01],\n",
" [1.069e+03, 2.630e+02, 1.420e+02, 6.000e+01, 3.900e+01, 3.400e+01,\n",
" 1.900e+01, 2.000e+01, 2.100e+01, 1.800e+01],\n",
" [9.460e+02, 2.270e+02, 1.170e+02, 6.400e+01, 4.100e+01, 2.700e+01,\n",
" 1.900e+01, 1.600e+01, 1.500e+01, 1.500e+01],\n",
" [8.080e+02, 2.150e+02, 8.700e+01, 5.000e+01, 3.400e+01, 2.000e+01,\n",
" 1.000e+01, 1.200e+01, 1.100e+01, 1.100e+01],\n",
" [7.130e+02, 1.920e+02, 7.700e+01, 4.100e+01, 2.600e+01, 1.800e+01,\n",
" 1.500e+01, 9.000e+00, 6.000e+00, 5.000e+00],\n",
" [6.170e+02, 1.670e+02, 6.600e+01, 3.200e+01, 2.100e+01, 1.600e+01,\n",
" 9.000e+00, 8.000e+00, 6.000e+00, 6.000e+00],\n",
" [5.250e+02, 1.550e+02, 6.000e+01, 2.500e+01, 2.000e+01, 1.000e+01,\n",
" 8.000e+00, 5.000e+00, 6.000e+00, 6.000e+00],\n",
" [4.360e+02, 1.350e+02, 5.800e+01, 2.600e+01, 1.700e+01, 1.300e+01,\n",
" 7.000e+00, 2.000e+00, 1.000e+00, 3.000e+00],\n",
" [3.570e+02, 1.210e+02, 5.100e+01, 2.800e+01, 1.400e+01, 1.100e+01,\n",
" 9.000e+00, 5.000e+00, 1.000e+00, 1.000e+00],\n",
" [2.850e+02, 9.600e+01, 4.400e+01, 3.000e+01, 1.400e+01, 1.000e+01,\n",
" 7.000e+00, 3.000e+00, 2.000e+00, 1.000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_clust_&gt;1MWp</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>517.0 10.0 0.0 ... 81.0 91.0 100.0</div><input id='attrs-6754c2df-295d-469b-b33e-cb8d7955bb20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6754c2df-295d-469b-b33e-cb8d7955bb20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21621ea6-bd27-4b98-ab1f-fdcb79aa7560' class='xr-var-data-in' type='checkbox'><label for='data-21621ea6-bd27-4b98-ab1f-fdcb79aa7560' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[517., 10., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [725., 804., 800., 702., 468., 211., 60., 11., 5., 2.],\n",
" [619., 653., 715., 756., 776., 786., 805., 773., 720., 642.],\n",
" [500., 561., 612., 633., 670., 700., 721., 721., 739., 767.],\n",
" [376., 462., 520., 564., 593., 617., 635., 664., 675., 681.],\n",
" [280., 343., 425., 478., 529., 563., 577., 592., 602., 617.],\n",
" [187., 232., 306., 378., 440., 479., 522., 541., 559., 572.],\n",
" [120., 164., 213., 275., 332., 389., 433., 471., 499., 513.],\n",
" [ 85., 109., 152., 188., 231., 280., 332., 372., 401., 433.],\n",
" [ 55., 73., 109., 140., 170., 198., 233., 269., 311., 358.],\n",
" [ 41., 54., 76., 98., 128., 151., 176., 197., 230., 264.],\n",
" [ 26., 36., 48., 73., 86., 105., 130., 148., 165., 187.],\n",
" [ 19., 26., 39., 47., 63., 76., 90., 104., 121., 134.],\n",
" [ 17., 21., 29., 35., 50., 61., 70., 81., 91., 100.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perc_outliers</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.44 65.66 94.45 ... 2.524 2.889</div><input id='attrs-e9b30d56-d052-4243-89d5-0fd10feee415' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9b30d56-d052-4243-89d5-0fd10feee415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a578e4e4-4a18-43cb-9c2d-5d4e8863c582' class='xr-var-data-in' type='checkbox'><label for='data-a578e4e4-4a18-43cb-9c2d-5d4e8863c582' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[26.44399374, 65.66420583, 94.44890769, 98.361152 , 98.96621661,\n",
" 99.22577505, 99.41612099, 99.58866547, 99.72913021, 99.80956134],\n",
" [11.05211798, 20.96823318, 28.32765834, 36.06211972, 46.36792026,\n",
" 60.17931274, 74.86585154, 86.61000174, 93.67083502, 96.888961 ],\n",
" [ 6.21878658, 13.29241467, 18.37570841, 22.21901837, 25.67204033,\n",
" 29.09224083, 32.8222982 , 36.94088196, 41.74129918, 47.40545865],\n",
" [ 3.71601089, 8.52834212, 12.90708698, 16.23396882, 18.91267312,\n",
" 21.29232196, 23.43684302, 25.6249406 , 27.78471345, 29.95065191],\n",
" [ 2.42192733, 5.53329084, 8.87412644, 11.89884684, 14.43031813,\n",
" 16.4569508 , 18.29351567, 19.93458886, 21.51947615, 23.08980703],\n",
" [ 1.67431187, 3.72658052, 6.02473199, 8.4690966 , 10.7517645 ,\n",
" 12.74817176, 14.45488787, 15.94330417, 17.33877267, 18.60239903],\n",
" [ 1.19575824, 2.63253173, 4.22757142, 5.99121516, 7.8522107 ,\n",
" 9.60852987, 11.24195399, 12.64850206, 13.9735064 , 15.11725097],\n",
" [ 0.88780205, 1.9407313 , 3.06899229, 4.3059628 , 5.68821927,\n",
" 7.12133047, 8.54846149, 9.83100191, 11.09416469, 12.21727994],\n",
" [ 0.66862143, 1.47200556, 2.28846265, 3.18576809, 4.19665063,\n",
" 5.27901721, 6.36499971, 7.50698267, 8.63482645, 9.69443125],\n",
" [ 0.51703077, 1.1587182 , 1.7788028 , 2.42285449, 3.1530857 ,\n",
" 3.96657588, 4.81511271, 5.70532538, 6.60188909, 7.5699832 ],\n",
" [ 0.40720867, 0.91478241, 1.4354291 , 1.92065828, 2.442603 ,\n",
" 3.03491916, 3.68921597, 4.37225474, 5.03832648, 5.82608796],\n",
" [ 0.31643971, 0.72879411, 1.15292345, 1.54608565, 1.96752622,\n",
" 2.40843716, 2.883004 , 3.39678971, 3.94599293, 4.51823608],\n",
" [ 0.24680999, 0.58605783, 0.93281567, 1.26251376, 1.59661587,\n",
" 1.96020166, 2.32591992, 2.71477082, 3.14418497, 3.56358579],\n",
" [ 0.1993394 , 0.47544764, 0.76031755, 1.02886944, 1.32500435,\n",
" 1.61423191, 1.9146781 , 2.21901837, 2.52396129, 2.88944776]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aprx_add_clst</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.125e+03 14.51 ... 5.821e+03</div><input id='attrs-94146c0e-cd04-47bc-b882-c1d1c0c34ab1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94146c0e-cd04-47bc-b882-c1d1c0c34ab1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f3c4ca6-e928-4e44-955a-65a36df742ea' class='xr-var-data-in' type='checkbox'><label for='data-7f3c4ca6-e928-4e44-955a-65a36df742ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.12483526e+03, 1.45053962e+01, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00],\n",
" [4.38627115e+03, 3.55895288e+03, 2.72383466e+03, 1.83640094e+03,\n",
" 9.68718906e+02, 3.54458254e+02, 8.66666569e+01, 1.83395189e+01,\n",
" 7.97097097e+00, 2.52023241e+00],\n",
" [5.00959375e+03, 4.76937148e+03, 4.43107945e+03, 4.04200771e+03,\n",
" 3.65068691e+03, 3.28424312e+03, 2.90427415e+03, 2.46890292e+03,\n",
" 2.02268046e+03, 1.59322261e+03],\n",
" [5.27274811e+03, 5.12921469e+03, 4.98233433e+03, 4.82686794e+03,\n",
" 4.66411382e+03, 4.42139755e+03, 4.19985157e+03, 3.98473485e+03,\n",
" 3.78802349e+03, 3.59506590e+03],\n",
" [5.44218225e+03, 5.34273275e+03, 5.22685694e+03, 5.13501990e+03,\n",
" 5.04282388e+03, 4.94926160e+03, 4.83562620e+03, 4.72475578e+03,\n",
" 4.59511832e+03, 4.43219766e+03],\n",
" [5.58445176e+03, 5.50405371e+03, 5.41630271e+03, 5.32026874e+03,\n",
" 5.23814652e+03, 5.18010019e+03, 5.11216578e+03, 5.05226268e+03,\n",
" 4.99060330e+03, 4.92467606e+03],\n",
" [5.69959752e+03, 5.63242604e+03, 5.54797842e+03, 5.46138412e+03,\n",
" 5.39867147e+03, 5.33799079e+03, 5.27990981e+03, 5.22994132e+03,\n",
"...\n",
" 5.52729200e+03, 5.46500298e+03, 5.42216368e+03, 5.37127910e+03,\n",
" 5.32411702e+03, 5.29078336e+03],\n",
" [5.83070878e+03, 5.79146647e+03, 5.74707134e+03, 5.68941844e+03,\n",
" 5.63678792e+03, 5.58179550e+03, 5.54207399e+03, 5.49932308e+03,\n",
" 5.44566756e+03, 5.41268685e+03],\n",
" [5.86454058e+03, 5.83494864e+03, 5.80422291e+03, 5.77195014e+03,\n",
" 5.73526974e+03, 5.68644648e+03, 5.64950196e+03, 5.59976106e+03,\n",
" 5.56277929e+03, 5.53573132e+03],\n",
" [5.88981898e+03, 5.87097474e+03, 5.84500991e+03, 5.82064125e+03,\n",
" 5.78912431e+03, 5.75950184e+03, 5.73066206e+03, 5.70024576e+03,\n",
" 5.66606429e+03, 5.62989060e+03],\n",
" [5.90512184e+03, 5.88718760e+03, 5.87062309e+03, 5.84906107e+03,\n",
" 5.82743236e+03, 5.80975943e+03, 5.78679489e+03, 5.76180626e+03,\n",
" 5.73851628e+03, 5.70996087e+03],\n",
" [5.91508065e+03, 5.90308800e+03, 5.89110702e+03, 5.87611556e+03,\n",
" 5.85986475e+03, 5.84605022e+03, 5.83153058e+03, 5.81391600e+03,\n",
" 5.79696162e+03, 5.77171087e+03],\n",
" [5.92019890e+03, 5.91456424e+03, 5.90232777e+03, 5.89271426e+03,\n",
" 5.88078681e+03, 5.87191871e+03, 5.85552841e+03, 5.84487125e+03,\n",
" 5.83306202e+03, 5.82112256e+03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_mean</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>70.17 40.34 ... 1.098e+04 1.093e+04</div><input id='attrs-986d727f-e672-4621-853a-fe1443e741dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-986d727f-e672-4621-853a-fe1443e741dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a6d86a8-db86-40b3-9543-55f9c568c3eb' class='xr-var-data-in' type='checkbox'><label for='data-4a6d86a8-db86-40b3-9543-55f9c568c3eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.01702391e+01, 4.03351906e+01, 3.11486107e+01, 2.92127049e+01,\n",
" 2.16556696e+01, 1.50709720e+01, 1.00872128e+01, 6.36173837e+00,\n",
" 3.80603522e+00, 3.59634167e+00],\n",
" [1.73558776e+02, 3.38392930e+02, 3.91134109e+02, 3.46170218e+02,\n",
" 2.66836033e+02, 2.01838939e+02, 1.59572908e+02, 1.36155154e+02,\n",
" 1.22413637e+02, 1.13600861e+02],\n",
" [2.37238731e+02, 4.53864287e+02, 6.36887173e+02, 7.22015983e+02,\n",
" 7.49832206e+02, 7.52494257e+02, 7.30382742e+02, 6.79064730e+02,\n",
" 6.18407773e+02, 5.55223830e+02],\n",
" [3.41362013e+02, 5.52205486e+02, 7.48509118e+02, 9.10013471e+02,\n",
" 1.02703688e+03, 1.07737109e+03, 1.09571124e+03, 1.11225211e+03,\n",
" 1.11996249e+03, 1.10940337e+03],\n",
" [5.00549664e+02, 7.30245955e+02, 8.94737268e+02, 1.04474902e+03,\n",
" 1.18650306e+03, 1.27995112e+03, 1.34683247e+03, 1.39215269e+03,\n",
" 1.42057483e+03, 1.43372849e+03],\n",
" [7.45584956e+02, 1.03555834e+03, 1.14151246e+03, 1.26251954e+03,\n",
" 1.37155908e+03, 1.47663570e+03, 1.55173574e+03, 1.60964067e+03,\n",
" 1.66990650e+03, 1.69503315e+03],\n",
" [1.07441324e+03, 1.52927386e+03, 1.60075404e+03, 1.62068967e+03,\n",
" 1.68283601e+03, 1.73567565e+03, 1.79212396e+03, 1.84909410e+03,\n",
"...\n",
" 2.21807431e+03, 2.17843970e+03, 2.19211864e+03, 2.17419081e+03,\n",
" 2.20658395e+03, 2.24101133e+03],\n",
" [2.09881517e+03, 3.05608196e+03, 3.18445679e+03, 3.13150808e+03,\n",
" 3.06243800e+03, 2.91404135e+03, 2.79150620e+03, 2.71566754e+03,\n",
" 2.68093377e+03, 2.69591691e+03],\n",
" [2.82337493e+03, 4.22960001e+03, 4.62723520e+03, 4.45202950e+03,\n",
" 4.21458272e+03, 4.05143628e+03, 3.86933738e+03, 3.62683385e+03,\n",
" 3.43193481e+03, 3.35474072e+03],\n",
" [3.74106277e+03, 5.73506934e+03, 6.49549327e+03, 6.23780581e+03,\n",
" 5.75385248e+03, 5.46085476e+03, 5.21372863e+03, 4.94928029e+03,\n",
" 4.62145861e+03, 4.40225930e+03],\n",
" [4.75687300e+03, 7.27600169e+03, 8.80266453e+03, 8.26919754e+03,\n",
" 7.76512030e+03, 7.49447898e+03, 6.94292603e+03, 6.63285929e+03,\n",
" 6.28757516e+03, 5.82194290e+03],\n",
" [6.19575802e+03, 9.67126326e+03, 1.14566912e+04, 1.18860777e+04,\n",
" 1.09570925e+04, 1.01617556e+04, 9.73800771e+03, 9.07415881e+03,\n",
" 8.68055444e+03, 8.10311579e+03],\n",
" [7.89103895e+03, 1.24244370e+04, 1.45845266e+04, 1.53306602e+04,\n",
" 1.46425133e+04, 1.38164276e+04, 1.28236861e+04, 1.17953007e+04,\n",
" 1.09788698e+04, 1.09312746e+04]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_std</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>352.2 64.62 ... 1.921e+05 1.897e+05</div><input id='attrs-9f0cd72b-d71a-41ec-b7db-812dbd8cf980' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f0cd72b-d71a-41ec-b7db-812dbd8cf980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-424eb4fd-b162-4e91-ad60-fbe3d9035b01' class='xr-var-data-in' type='checkbox'><label for='data-424eb4fd-b162-4e91-ad60-fbe3d9035b01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3.52157309e+02, 6.46195307e+01, 3.54545971e+01, 3.57940974e+01,\n",
" 2.88801497e+01, 2.39729222e+01, 1.64437272e+01, 1.15795169e+01,\n",
" 6.02593098e+00, 6.36121443e+00],\n",
" [3.90458879e+03, 2.75589681e+03, 1.93287872e+03, 1.10518264e+03,\n",
" 6.77632237e+02, 3.27505456e+02, 2.04620402e+02, 1.60629151e+02,\n",
" 1.35820539e+02, 1.20633510e+02],\n",
" [6.17787184e+03, 7.63045105e+03, 7.38264321e+03, 6.90604431e+03,\n",
" 4.31774826e+03, 3.65635950e+03, 2.56088048e+03, 1.97126320e+03,\n",
" 1.59345287e+03, 1.25270303e+03],\n",
" [8.15701591e+03, 1.01580998e+04, 1.07791107e+04, 1.10282915e+04,\n",
" 1.08082447e+04, 1.03472273e+04, 9.26996480e+03, 6.89488258e+03,\n",
" 5.82661805e+03, 5.03456606e+03],\n",
" [1.16116882e+04, 1.27755692e+04, 1.34855533e+04, 1.38158123e+04,\n",
" 1.41093500e+04, 1.39319154e+04, 1.34514522e+04, 1.28019383e+04,\n",
" 1.24791736e+04, 1.23821078e+04],\n",
" [1.91462723e+04, 1.80652674e+04, 1.66092304e+04, 1.66529306e+04,\n",
" 1.61252203e+04, 1.66252117e+04, 1.67900778e+04, 1.64319370e+04,\n",
" 1.62909704e+04, 1.63137536e+04],\n",
" [3.66893655e+04, 3.48519567e+04, 2.28985436e+04, 2.08409720e+04,\n",
" 1.99497629e+04, 1.98391220e+04, 1.90715878e+04, 1.87108767e+04,\n",
"...\n",
" 2.54115575e+04, 2.38131008e+04, 2.25815478e+04, 2.14484322e+04,\n",
" 2.15291228e+04, 2.13930708e+04],\n",
" [9.40065735e+04, 1.08067322e+05, 7.50256744e+04, 5.84674028e+04,\n",
" 4.63092311e+04, 3.49686007e+04, 2.89857481e+04, 2.69970790e+04,\n",
" 2.56235170e+04, 2.49326244e+04],\n",
" [1.13588840e+05, 1.34802706e+05, 1.23082128e+05, 8.97593131e+04,\n",
" 7.31148630e+04, 5.80404592e+04, 4.39517949e+04, 3.86810883e+04,\n",
" 3.56675851e+04, 3.00522336e+04],\n",
" [1.33430265e+05, 1.63104091e+05, 1.67536697e+05, 1.57579019e+05,\n",
" 1.07411072e+05, 9.80614901e+04, 8.23819155e+04, 6.02074890e+04,\n",
" 5.22529446e+04, 4.63159213e+04],\n",
" [1.53160436e+05, 1.86492682e+05, 2.02820330e+05, 1.88107354e+05,\n",
" 1.76971160e+05, 1.37539007e+05, 1.17509688e+05, 9.89870705e+04,\n",
" 9.47507139e+04, 7.04931455e+04],\n",
" [1.79236262e+05, 2.17761664e+05, 2.34079750e+05, 2.35116755e+05,\n",
" 2.19874197e+05, 2.06572030e+05, 1.96964156e+05, 1.36993947e+05,\n",
" 1.31667554e+05, 1.10666466e+05],\n",
" [2.02673365e+05, 2.53629525e+05, 2.71954382e+05, 2.76545928e+05,\n",
" 2.57923891e+05, 2.48471724e+05, 2.34567746e+05, 2.19189124e+05,\n",
" 1.92144794e+05, 1.89672562e+05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_min</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-b2f92a84-7c7f-4d84-864b-b0768ed473a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2f92a84-7c7f-4d84-864b-b0768ed473a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bb2b20e-31d6-48ad-a67d-4eb4605e5ed6' class='xr-var-data-in' type='checkbox'><label for='data-9bb2b20e-31d6-48ad-a67d-4eb4605e5ed6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_25%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.307 11.97 14.56 ... 45.68 61.2</div><input id='attrs-53e843cc-d1c6-44d2-a203-71ec618d1e3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53e843cc-d1c6-44d2-a203-71ec618d1e3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99290ddb-da1e-4995-a654-85e69e83c6d0' class='xr-var-data-in' type='checkbox'><label for='data-99290ddb-da1e-4995-a654-85e69e83c6d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 2.30736504, 11.97194836, 14.55684834, 7.45163139,\n",
" 0.96645833, 0. , 0. , 0. ,\n",
" 0. , 0. ],\n",
" [ 1.93291666, 6.76520832, 19.56449683, 33.63754131,\n",
" 46.40776854, 53.63031078, 56.63435269, 57.77882264,\n",
" 57.31336207, 56.87515639],\n",
" [ 1.93291666, 5.58118841, 12.9749038 , 25.45678405,\n",
" 38.30165351, 52.13695944, 67.1311477 , 80.75777634,\n",
" 94.33794542, 106.59168354],\n",
" [ 1.93291666, 4.91073506, 9.89991351, 19.26849185,\n",
" 33.50088548, 48.19586268, 60.43029865, 73.44260534,\n",
" 87.42646064, 99.68640152],\n",
" [ 1.93291666, 4.83229166, 9.44702174, 15.77710689,\n",
" 27.96661684, 41.31898566, 54.09652901, 69.12454217,\n",
" 80.36555936, 93.75165801],\n",
" [ 1.34090671, 4.83229166, 8.93345517, 14.65376177,\n",
" 23.69599774, 36.22777641, 51.06154117, 63.68266893,\n",
" 76.50922599, 89.84117932],\n",
" [ 0.96645833, 4.61473008, 8.85501178, 14.14019521,\n",
" 21.17614671, 31.27340952, 44.03950628, 58.52232468,\n",
"...\n",
" 19.84533311, 29.17728966, 41.38825269, 55.02745013,\n",
" 70.54408925, 83.0075866 ],\n",
" [ 0. , 3.27382337, 7.73166666, 12.56395832,\n",
" 19.9389452 , 28.77618838, 36.80839761, 48.8039576 ,\n",
" 64.94773215, 80.76850289],\n",
" [ 0. , 2.899375 , 7.51410507, 12.95801754,\n",
" 18.7079361 , 27.06083329, 35.33930379, 48.07196239,\n",
" 58.75157002, 76.39094525],\n",
" [ 0. , 1.93291666, 7.29654349, 13.35754346,\n",
" 19.32916664, 27.0304959 , 35.8259327 , 44.23316852,\n",
" 58.2923892 , 71.98673469],\n",
" [ 0. , 1.93291666, 5.58118841, 11.59749998,\n",
" 19.32916664, 27.87037334, 35.75895828, 45.95615913,\n",
" 56.76805234, 68.82873417],\n",
" [ 0. , 0.96645833, 4.2402817 , 10.63104165,\n",
" 18.15689758, 26.46882334, 37.0062529 , 41.71153104,\n",
" 53.27320696, 69.94086045],\n",
" [ 0. , 0.96645833, 4.2402817 , 8.75713216,\n",
" 16.68259991, 23.67717538, 36.45653351, 42.85755104,\n",
" 45.67843255, 61.2006485 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_50%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.14 22.01 22.23 ... 150.2 183.0</div><input id='attrs-4a0da9c8-6220-49db-9f8d-4aed8b03213a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a0da9c8-6220-49db-9f8d-4aed8b03213a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-065a33ac-b5bc-408a-9720-0e5f7ffb6f28' class='xr-var-data-in' type='checkbox'><label for='data-065a33ac-b5bc-408a-9720-0e5f7ffb6f28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 7.1396567 , 22.01098005, 22.22854163, 22.22854163,\n",
" 14.74997377, 3.77720352, 1.93291666, 0.96645833,\n",
" 1.34090671, 0.96645833],\n",
" [ 3.86583333, 20.60939855, 54.28712322, 86.81699411,\n",
" 101.4811555 , 102.13386801, 96.91634267, 91.81354154,\n",
" 87.55332592, 82.73359919],\n",
" [ 3.86583333, 12.72084511, 41.43473484, 76.33601115,\n",
" 105.44168551, 139.43349822, 169.00885851, 190.47151397,\n",
" 211.03187875, 225.06415128],\n",
" [ 4.02272012, 11.3799384 , 30.51472364, 63.47921105,\n",
" 102.78002961, 136.12413764, 163.61083883, 188.76932649,\n",
" 215.89768705, 238.33153765],\n",
" [ 4.2402817 , 11.97194836, 25.51701591, 54.12166659,\n",
" 89.87325589, 125.39751961, 158.08605881, 183.97127063,\n",
" 202.49296493, 227.24411188],\n",
" [ 4.02272012, 12.8777319 , 25.06724184, 48.4444153 ,\n",
" 81.74025108, 118.19694903, 154.88057825, 182.66953503,\n",
" 203.31899077, 220.69303913],\n",
" [ 3.86583333, 13.21048401, 27.56183106, 48.71108469,\n",
" 76.53365094, 109.09369249, 144.4800374 , 175.58525806,\n",
"...\n",
" 77.22925065, 111.71108724, 137.15127076, 171.88594981,\n",
" 206.62100399, 228.11270976],\n",
" [ 2.899375 , 11.97194836, 26.31012983, 47.88887718,\n",
" 82.10605203, 115.30424523, 134.3625183 , 158.24296401,\n",
" 198.6483327 , 227.48847545],\n",
" [ 2.30736504, 11.00549003, 28.2516742 , 49.97219687,\n",
" 71.05765582, 114.57604537, 139.27619223, 164.83369521,\n",
" 190.30089908, 220.34555065],\n",
" [ 1.93291666, 8.69812499, 27.27294046, 53.46907983,\n",
" 78.1674915 , 106.59905251, 133.98055133, 162.82445392,\n",
" 186.52948871, 228.62886293],\n",
" [ 1.93291666, 7.93446814, 23.91355932, 53.62629735,\n",
" 75.92497434, 99.99598954, 126.24801187, 157.42182625,\n",
" 186.1275745 , 201.08882753],\n",
" [ 1.93291666, 7.21810009, 20.10550182, 47.96579196,\n",
" 78.12888576, 100.6376145 , 126.12912804, 139.36226112,\n",
" 177.88239888, 209.43888333],\n",
" [ 1.93291666, 5.88490536, 19.32916664, 40.84434873,\n",
" 78.16177531, 100.34348137, 130.95924651, 137.54471501,\n",
" 150.17548983, 182.98128306]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_75%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>33.98 43.92 36.58 ... 478.7 572.4</div><input id='attrs-7fbf8b3c-f463-406f-89f2-56f4623d9a47' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fbf8b3c-f463-406f-89f2-56f4623d9a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edd101c2-0f41-40d8-92dc-9fbd0c635cdc' class='xr-var-data-in' type='checkbox'><label for='data-edd101c2-0f41-40d8-92dc-9fbd0c635cdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 33.98292841, 43.91837912, 36.58428467, 37.87020988,\n",
" 31.3669896 , 24.78443371, 12.84041227, 7.54472428,\n",
" 4.83229166, 3.92466587],\n",
" [ 12.94485006, 105.42944313, 219.39210265, 274.84248136,\n",
" 257.23706884, 216.46405033, 184.1655361 , 159.5804859 ,\n",
" 144.84647114, 132.87111352],\n",
" [ 12.12883515, 74.47610784, 202.15351019, 308.17408999,\n",
" 382.46500801, 460.07749296, 545.30922817, 572.97295517,\n",
" 570.22279495, 547.13128393],\n",
" [ 14.51020143, 67.22945155, 177.46572619, 281.12136003,\n",
" 386.44290668, 464.08162154, 534.41135413, 586.08135982,\n",
" 666.1557066 , 707.98953162],\n",
" [ 17.93646944, 72.39392489, 162.41850719, 262.54339319,\n",
" 361.36862257, 436.4026002 , 524.68997738, 582.04049953,\n",
" 632.85328098, 689.97427935],\n",
" [ 20.67007335, 82.09112301, 162.91605567, 259.99561129,\n",
" 359.24996793, 438.26741186, 506.0079864 , 574.65367393,\n",
" 628.19645767, 680.40382544],\n",
" [ 20.29562497, 89.89395132, 176.28175541, 266.24433733,\n",
" 351.27976862, 426.86769549, 509.13284895, 587.54989572,\n",
"...\n",
" 368.80086538, 442.89812099, 505.70173185, 586.31675479,\n",
" 661.11971069, 719.45880804],\n",
" [ 16.52562608, 86.59757593, 172.62805773, 279.94167202,\n",
" 356.42895935, 439.7703911 , 484.46386725, 564.77132325,\n",
" 639.51622409, 726.015558 ],\n",
" [ 13.55446965, 76.85120599, 184.91910818, 267.39812739,\n",
" 326.8604839 , 418.78663069, 469.28424096, 550.57047426,\n",
" 625.93684204, 750.12262307],\n",
" [ 11.67260498, 72.49382322, 167.26836584, 252.88282209,\n",
" 347.18023745, 418.18861606, 465.70495063, 501.61903867,\n",
" 595.73083218, 721.97597848],\n",
" [ 10.03903169, 60.05959147, 162.50847317, 256.34718419,\n",
" 315.70958833, 378.86467856, 457.13037711, 506.97439185,\n",
" 572.89037749, 632.73206341],\n",
" [ 8.55402809, 56.14191095, 136.67971631, 255.70029102,\n",
" 314.42948485, 364.88461509, 423.97837247, 474.14160448,\n",
" 543.24267088, 618.07319939],\n",
" [ 10.09138091, 45.52159585, 106.66136741, 241.73890041,\n",
" 326.65791951, 367.02453811, 456.73117418, 460.72927913,\n",
" 478.70833046, 572.40455274]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_max</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.048e+04 2.374e+03 ... 4.387e+06</div><input id='attrs-3de68d47-292b-4e88-b1fa-e58f2d3c32c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3de68d47-292b-4e88-b1fa-e58f2d3c32c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3318b17c-6add-436c-93d1-33ec2a981058' class='xr-var-data-in' type='checkbox'><label for='data-3318b17c-6add-436c-93d1-33ec2a981058' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.04829202e+04, 2.37437664e+03, 7.24367551e+02, 4.11736972e+02,\n",
" 3.14915990e+02, 1.50664139e+02, 8.84320344e+01, 5.65384581e+01,\n",
" 3.28160220e+01, 3.28160220e+01],\n",
" [5.89256538e+05, 2.10274705e+05, 1.14126124e+05, 5.78987733e+04,\n",
" 4.21404649e+04, 7.88009875e+03, 4.52360043e+03, 4.02958499e+03,\n",
" 2.78220063e+03, 1.36709205e+03],\n",
" [7.65943588e+05, 6.67630977e+05, 5.62477506e+05, 4.88683676e+05,\n",
" 2.01589118e+05, 1.81494095e+05, 8.45459834e+04, 7.60625407e+04,\n",
" 5.97623091e+04, 5.02356296e+04],\n",
" [8.44489416e+05, 8.29185013e+05, 7.24749693e+05, 6.80788103e+05,\n",
" 6.54471741e+05, 6.18565810e+05, 5.29669353e+05, 2.55269025e+05,\n",
" 2.16656119e+05, 2.06330244e+05],\n",
" [9.82274967e+05, 9.09569996e+05, 8.72970707e+05, 8.12060978e+05,\n",
" 7.62412466e+05, 7.14908951e+05, 6.86997463e+05, 6.65647278e+05,\n",
" 6.55905779e+05, 6.42842151e+05],\n",
" [1.45253247e+06, 1.05456941e+06, 9.32026305e+05, 8.88760635e+05,\n",
" 8.26325961e+05, 8.20938659e+05, 8.06128528e+05, 7.65191413e+05,\n",
" 7.41184176e+05, 7.35847465e+05],\n",
" [2.52770255e+06, 1.97657828e+06, 1.04637554e+06, 9.68786186e+05,\n",
" 9.07937073e+05, 9.03988078e+05, 8.42638256e+05, 8.24232830e+05,\n",
"...\n",
" 1.01217507e+06, 9.69526904e+05, 9.09181327e+05, 8.61792899e+05,\n",
" 8.61532692e+05, 8.45818302e+05],\n",
" [4.99301491e+06, 4.75260789e+06, 3.03520299e+06, 2.38613598e+06,\n",
" 1.83371523e+06, 1.31479255e+06, 1.02560226e+06, 9.77491084e+05,\n",
" 9.48049698e+05, 9.08382381e+05],\n",
" [5.20260887e+06, 5.04144962e+06, 4.37831936e+06, 3.07262948e+06,\n",
" 2.51070763e+06, 2.01313113e+06, 1.43224580e+06, 1.30124925e+06,\n",
" 1.24909244e+06, 9.90202456e+05],\n",
" [5.30985282e+06, 5.24028093e+06, 5.05530226e+06, 4.84943254e+06,\n",
" 3.25948143e+06, 3.02618970e+06, 2.50356647e+06, 1.53745693e+06,\n",
" 1.41220731e+06, 1.33668097e+06],\n",
" [5.40564105e+06, 5.32025773e+06, 5.25890103e+06, 5.03013337e+06,\n",
" 4.88114104e+06, 3.71573025e+06, 3.24255580e+06, 2.70653327e+06,\n",
" 2.65958042e+06, 1.87016009e+06],\n",
" [5.54447374e+06, 5.38900743e+06, 5.32089386e+06, 5.24545360e+06,\n",
" 5.10769411e+06, 4.98095390e+06, 4.84968086e+06, 3.28869667e+06,\n",
" 3.24576893e+06, 2.73470791e+06],\n",
" [5.55557449e+06, 5.53951288e+06, 5.48101299e+06, 5.43493141e+06,\n",
" 5.18413911e+06, 5.13990997e+06, 5.03515527e+06, 4.90433201e+06,\n",
" 4.43619042e+06, 4.38687996e+06]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_mean</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.07 26.78 ... 3.901e+03 3.872e+03</div><input id='attrs-6a7625b7-9635-4672-a6b0-c9e37b17dd49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a7625b7-9635-4672-a6b0-c9e37b17dd49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4167faaf-52a3-4ddc-aa09-86ec60732e27' class='xr-var-data-in' type='checkbox'><label for='data-4167faaf-52a3-4ddc-aa09-86ec60732e27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 35.07283378, 26.78429104, 29.234375 , 51.16063676,\n",
" 68.19571865, 82.27093596, 89.32624113, 104.38823529,\n",
" 108.2037037 , 108.10526316],\n",
" [ 60.71889241, 123.06460694, 153.27272727, 146.67871956,\n",
" 124.38565746, 105.19005633, 92.88590029, 87.65978756,\n",
" 88.48217758, 96.83838384],\n",
" [ 82.17141232, 150.69215276, 210.18717918, 244.58206997,\n",
" 263.27536946, 270.86399858, 270.91175921, 261.33794428,\n",
" 246.75299431, 230.17447758],\n",
" [ 119.9864818 , 186.49886578, 245.26148825, 293.66780432,\n",
" 329.28369729, 350.283268 , 361.23425197, 371.38032407,\n",
" 377.82536988, 379.08981435],\n",
" [ 177.91234891, 252.85581338, 301.21038921, 342.97996751,\n",
" 382.71718847, 408.27548709, 427.27539394, 441.49054192,\n",
" 452.29040876, 460.84694444],\n",
" [ 266.59225742, 363.95688749, 393.3185875 , 426.35197582,\n",
" 454.16277424, 481.11733129, 499.00351541, 512.64009047,\n",
" 527.70227878, 532.88254932],\n",
" [ 385.76126697, 542.44266529, 560.17651844, 558.9545204 ,\n",
" 571.35469963, 580.31458333, 591.115159 , 602.77703135,\n",
"...\n",
" 767.12782805, 744.24554235, 739.67904012, 725.49720254,\n",
" 728.37447778, 732.81075851],\n",
" [ 757.1385159 , 1094.98815044, 1133.20430108, 1106.14618644,\n",
" 1072.44317592, 1011.00940129, 959.53634204, 924.55514365,\n",
" 904.0646789 , 901.01942646],\n",
" [1019.9486692 , 1519.6935139 , 1653.9843872 , 1582.60225564,\n",
" 1489.02993585, 1421.79890185, 1348.1661195 , 1254.04069051,\n",
" 1177.50672122, 1141.28849456],\n",
" [1352.85957179, 2065.11304348, 2328.76341731, 2227.04631579,\n",
" 2045.12633625, 1931.355494 , 1833.66637246, 1730.54530201,\n",
" 1606.62117647, 1520.54565868],\n",
" [1721.61649319, 2624.26960784, 3163.58308605, 2962.027894 ,\n",
" 2771.53735256, 2664.77468354, 2458.84389671, 2338.77890011,\n",
" 2206.60809372, 2033.2290227 ],\n",
" [2243.79666319, 3492.64332248, 4125.48841699, 4268.31863727,\n",
" 3923.63031423, 3627.51457976, 3465.38157895, 3218.65644172,\n",
" 3067.989721 , 2853.57201646],\n",
" [2858.99734396, 4491.35774059, 5259.76412776, 5516.61757106,\n",
" 5255.6617284 , 4947.0955711 , 4579.7034632 , 4201.70916335,\n",
" 3901.07606679, 3872.08133087]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_std</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>114.7 33.99 ... 6.306e+04 6.149e+04</div><input id='attrs-9f4e8019-0eae-4028-b80b-c89a2c88e921' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f4e8019-0eae-4028-b80b-c89a2c88e921' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a911cbce-341a-4943-b4b1-f4f9b06d3d06' class='xr-var-data-in' type='checkbox'><label for='data-a911cbce-341a-4943-b4b1-f4f9b06d3d06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.14704756e+02, 3.39940677e+01, 2.96130726e+01, 5.46816501e+01,\n",
" 6.99685178e+01, 7.56366411e+01, 7.60350010e+01, 7.90293939e+01,\n",
" 7.01890653e+01, 6.01101644e+01],\n",
" [4.41411984e+02, 4.35732339e+02, 3.82446406e+02, 2.75099560e+02,\n",
" 1.89469292e+02, 1.25391118e+02, 8.62097876e+01, 6.64952478e+01,\n",
" 5.82541909e+01, 6.16237875e+01],\n",
" [7.29802660e+02, 8.55051171e+02, 8.48172499e+02, 8.05231787e+02,\n",
" 6.89512813e+02, 6.08942704e+02, 5.07665675e+02, 4.11225355e+02,\n",
" 3.61563786e+02, 3.06480581e+02],\n",
" [1.18846083e+03, 1.32922945e+03, 1.28925414e+03, 1.26030335e+03,\n",
" 1.22837514e+03, 1.16861785e+03, 1.07221184e+03, 9.12741748e+02,\n",
" 8.41344887e+02, 7.82383422e+02],\n",
" [2.36064274e+03, 2.06733161e+03, 1.91360754e+03, 1.79265395e+03,\n",
" 1.72768705e+03, 1.62370201e+03, 1.54950268e+03, 1.44762209e+03,\n",
" 1.41359326e+03, 1.40517735e+03],\n",
" [4.99528580e+03, 3.84489313e+03, 2.84021382e+03, 2.51125493e+03,\n",
" 2.20324488e+03, 2.18947449e+03, 2.14543589e+03, 2.04962396e+03,\n",
" 1.94579790e+03, 1.92136487e+03],\n",
" [1.14166279e+04, 9.73546355e+03, 5.22104111e+03, 3.94431122e+03,\n",
" 3.22500838e+03, 3.01900730e+03, 2.71161892e+03, 2.52810361e+03,\n",
"...\n",
" 5.16511127e+03, 4.37929871e+03, 3.65973640e+03, 3.23405442e+03,\n",
" 3.15701230e+03, 3.01858444e+03],\n",
" [3.06795253e+04, 3.36487215e+04, 2.46581328e+04, 1.72312286e+04,\n",
" 1.20920222e+04, 7.56594875e+03, 5.95954428e+03, 5.05279821e+03,\n",
" 4.21556342e+03, 3.94799111e+03],\n",
" [3.80585524e+04, 4.43921003e+04, 3.99562496e+04, 3.00023175e+04,\n",
" 2.22620786e+04, 1.66096154e+04, 1.06869262e+04, 8.15770751e+03,\n",
" 6.95323387e+03, 5.44503852e+03],\n",
" [4.54986335e+04, 5.46991267e+04, 5.47807254e+04, 5.00782357e+04,\n",
" 3.62161193e+04, 3.23154029e+04, 2.48212978e+04, 1.70812829e+04,\n",
" 1.31470613e+04, 9.88181176e+03],\n",
" [5.31674693e+04, 6.35830814e+04, 6.82300265e+04, 6.15893981e+04,\n",
" 5.66500694e+04, 4.53145626e+04, 3.93944669e+04, 3.06538620e+04,\n",
" 2.86226001e+04, 1.91003526e+04],\n",
" [6.30403863e+04, 7.53505417e+04, 7.98211919e+04, 7.90669066e+04,\n",
" 7.26775294e+04, 6.68865724e+04, 6.29034053e+04, 4.68305510e+04,\n",
" 4.42184863e+04, 3.46580452e+04],\n",
" [7.15459650e+04, 8.91193124e+04, 9.41055881e+04, 9.49944563e+04,\n",
" 8.63340277e+04, 8.27069509e+04, 7.74698887e+04, 7.05123395e+04,\n",
" 6.30557872e+04, 6.14911087e+04]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_min</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 5.0 12.0 ... 5.0 9.0 16.0</div><input id='attrs-1aaa4939-7189-4fd3-9025-33464d127f69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1aaa4939-7189-4fd3-9025-33464d127f69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec53074-fea4-47e2-b670-cc1393edc662' class='xr-var-data-in' type='checkbox'><label for='data-4ec53074-fea4-47e2-b670-cc1393edc662' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1., 1., 5., 12., 13., 17., 35., 35., 45., 50.],\n",
" [ 2., 1., 3., 5., 5., 3., 10., 13., 24., 41.],\n",
" [ 1., 4., 2., 3., 11., 13., 9., 10., 8., 5.],\n",
" [ 1., 1., 2., 5., 11., 12., 15., 15., 22., 30.],\n",
" [ 1., 2., 1., 1., 6., 9., 12., 5., 13., 18.],\n",
" [ 2., 1., 2., 3., 4., 13., 13., 11., 17., 14.],\n",
" [ 1., 1., 2., 4., 4., 4., 7., 7., 21., 22.],\n",
" [ 2., 3., 1., 4., 2., 9., 4., 11., 8., 22.],\n",
" [ 1., 1., 1., 7., 4., 7., 3., 1., 5., 13.],\n",
" [ 2., 4., 2., 5., 2., 4., 8., 4., 9., 13.],\n",
" [ 2., 2., 2., 6., 7., 3., 1., 8., 8., 11.],\n",
" [ 3., 2., 3., 5., 8., 4., 7., 15., 17., 1.],\n",
" [ 2., 5., 6., 6., 8., 1., 8., 4., 15., 10.],\n",
" [ 3., 2., 3., 8., 4., 14., 4., 5., 9., 16.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_25%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.0 11.0 15.0 ... 69.0 72.5 91.0</div><input id='attrs-96d9bcb1-10b0-48b6-a1f9-7c4c5ce69ea4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96d9bcb1-10b0-48b6-a1f9-7c4c5ce69ea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50bdedae-842e-4925-adcc-4ca0b094f30c' class='xr-var-data-in' type='checkbox'><label for='data-50bdedae-842e-4925-adcc-4ca0b094f30c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 6. , 11. , 15. , 24. , 30. , 39. , 42. , 56. ,\n",
" 61.25, 64.5 ],\n",
" [ 6. , 13. , 22. , 30. , 36. , 41. , 44. , 48. ,\n",
" 52. , 59. ],\n",
" [ 6. , 13. , 21. , 31. , 41. , 50. , 59. , 67. ,\n",
" 73. , 78. ],\n",
" [ 6. , 13. , 20. , 30. , 41. , 53. , 63. , 72. ,\n",
" 82. , 89. ],\n",
" [ 6. , 13. , 21. , 29. , 40. , 52. , 64. , 74. ,\n",
" 84. , 94. ],\n",
" [ 7. , 14. , 21. , 29. , 39. , 50.75, 65. , 74. ,\n",
" 85. , 95. ],\n",
" [ 6. , 14. , 22. , 30. , 39. , 49. , 63. , 75. ,\n",
" 86. , 95. ],\n",
" [ 6. , 14. , 21. , 29.25, 39. , 49. , 62. , 76. ,\n",
" 88. , 99. ],\n",
" [ 6. , 13. , 22. , 29. , 39. , 49. , 59. , 72. ,\n",
" 88. , 103.25],\n",
" [ 6. , 13. , 21. , 30. , 37.5 , 48. , 60. , 74. ,\n",
" 87. , 104. ],\n",
" [ 6. , 13. , 21. , 30.25, 38. , 50. , 60. , 70.75,\n",
" 83.5 , 103. ],\n",
" [ 6. , 12. , 20. , 29. , 39. , 49. , 57. , 70. ,\n",
" 82. , 98. ],\n",
" [ 6. , 12. , 20. , 27. , 38. , 47. , 58.75, 67.75,\n",
" 80. , 98. ],\n",
" [ 6. , 12. , 20. , 26. , 37. , 48. , 59.25, 69. ,\n",
" 72.5 , 91. ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_50%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.0 16.0 21.0 ... 161.5 170.0 196.0</div><input id='attrs-7c2392bc-42df-4d28-971e-b3e8986a81ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c2392bc-42df-4d28-971e-b3e8986a81ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b55c5ff-df5d-4e1f-82f2-778792f7d27a' class='xr-var-data-in' type='checkbox'><label for='data-0b55c5ff-df5d-4e1f-82f2-778792f7d27a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 8. , 16. , 21. , 34. , 44. , 56. , 61. , 72. , 82.5,\n",
" 89.5],\n",
" [ 8. , 22. , 42. , 56. , 62. , 64. , 65. , 67. , 70. ,\n",
" 77. ],\n",
" [ 9. , 21. , 42. , 65. , 86. , 102. , 115. , 120. , 125. ,\n",
" 127. ],\n",
" [ 10. , 21. , 39. , 65. , 95. , 116. , 130. , 143. , 154. ,\n",
" 166. ],\n",
" [ 11. , 23. , 39. , 64. , 93. , 117.5, 137. , 154. , 166. ,\n",
" 178. ],\n",
" [ 11. , 25. , 41. , 64. , 92. , 121. , 144. , 159. , 176. ,\n",
" 188. ],\n",
" [ 11. , 26. , 44. , 67. , 94. , 121. , 146. , 164. , 184. ,\n",
" 198. ],\n",
" [ 11. , 26. , 44. , 69. , 98. , 126.5, 152. , 168. , 192. ,\n",
" 206.5],\n",
" [ 10. , 25. , 43.5, 65. , 104. , 133. , 153. , 172. , 192.5,\n",
" 213. ],\n",
" [ 10. , 23. , 44. , 71. , 94. , 132. , 159. , 178.5, 197. ,\n",
" 217. ],\n",
" [ 9. , 21. , 43. , 73. , 98. , 127. , 154. , 176.5, 199. ,\n",
" 229. ],\n",
" [ 9. , 20. , 37. , 69. , 98. , 127.5, 149. , 175. , 199. ,\n",
" 214. ],\n",
" [ 9. , 19. , 34. , 60. , 101. , 127. , 144.5, 159.5, 190. ,\n",
" 216. ],\n",
" [ 9. , 18. , 33. , 50. , 100. , 134. , 156.5, 161.5, 170. ,\n",
" 196. ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_75%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.0 28.0 31.0 ... 431.5 504.0</div><input id='attrs-b0d75a84-8a27-416e-906b-4de6b8cf0d28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0d75a84-8a27-416e-906b-4de6b8cf0d28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a6990af-5e91-45e6-b67b-7e5988675681' class='xr-var-data-in' type='checkbox'><label for='data-9a6990af-5e91-45e6-b67b-7e5988675681' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 19. , 28. , 31. , 52.5 , 73.5 , 89. , 100. , 128. ,\n",
" 129.75, 127.5 ],\n",
" [ 17. , 70. , 125. , 141. , 131. , 116. , 104. , 101. ,\n",
" 103. , 111. ],\n",
" [ 20. , 68. , 148. , 196. , 231. , 257. , 273. , 274. ,\n",
" 269. , 253. ],\n",
" [ 24. , 71. , 146.25, 209. , 259.25, 300. , 318.5 , 340. ,\n",
" 359.5 , 378. ],\n",
" [ 30. , 82. , 150. , 215. , 268.5 , 306. , 341. , 370.25,\n",
" 388.5 , 411. ],\n",
" [ 33. , 97. , 160. , 224. , 285. , 329. , 360. , 389. ,\n",
" 415.5 , 432.5 ],\n",
" [ 32. , 106.25, 177.25, 236. , 291. , 336. , 383.5 , 427. ,\n",
" 459.75, 481. ],\n",
" [ 28. , 105. , 187. , 250.75, 318. , 360.25, 400.5 , 441. ,\n",
" 485. , 507.5 ],\n",
" [ 27. , 101. , 178. , 252. , 323.5 , 377. , 412. , 444.75,\n",
" 491.5 , 539. ],\n",
" [ 23. , 96. , 196. , 253.75, 299.5 , 375. , 416. , 469.25,\n",
" 499.5 , 549. ],\n",
" [ 21. , 76. , 188. , 251.75, 321. , 348. , 402. , 455. ,\n",
" 507.5 , 552. ],\n",
" [ 19. , 56.5 , 180.25, 265. , 298.5 , 342.25, 399. , 449.5 ,\n",
" 508. , 528. ],\n",
" [ 19. , 53. , 172.25, 262.5 , 305. , 332. , 360.25, 421.25,\n",
" 471. , 531. ],\n",
" [ 20. , 48.75, 143. , 256.5 , 309. , 343. , 392.5 , 402. ,\n",
" 431.5 , 504. ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_max</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.489e+03 879.0 ... 1.423e+06</div><input id='attrs-8bdefea2-4d45-4ad1-91d2-9ae1f2fac19a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bdefea2-4d45-4ad1-91d2-9ae1f2fac19a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-767fdde8-91d2-4901-83d2-d29647b0f8a2' class='xr-var-data-in' type='checkbox'><label for='data-767fdde8-91d2-4901-83d2-d29647b0f8a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.489000e+03, 8.790000e+02, 4.220000e+02, 4.190000e+02,\n",
" 4.180000e+02, 4.130000e+02, 4.070000e+02, 4.010000e+02,\n",
" 3.930000e+02, 2.820000e+02],\n",
" [4.770300e+04, 2.017200e+04, 1.006100e+04, 6.107000e+03,\n",
" 4.155000e+03, 1.995000e+03, 1.419000e+03, 9.400000e+02,\n",
" 5.520000e+02, 5.000000e+02],\n",
" [6.712300e+04, 5.280200e+04, 4.100700e+04, 3.792700e+04,\n",
" 1.915900e+04, 1.815600e+04, 1.123600e+04, 6.437000e+03,\n",
" 6.200000e+03, 4.571000e+03],\n",
" [9.006000e+04, 8.517100e+04, 6.250400e+04, 5.627700e+04,\n",
" 5.299900e+04, 5.063200e+04, 4.149000e+04, 2.191200e+04,\n",
" 2.032100e+04, 1.989100e+04],\n",
" [1.443010e+05, 1.152320e+05, 1.015320e+05, 8.090900e+04,\n",
" 7.215900e+04, 5.968900e+04, 5.682800e+04, 5.313800e+04,\n",
" 5.272600e+04, 5.239500e+04],\n",
" [3.600730e+05, 1.733710e+05, 1.272510e+05, 1.082340e+05,\n",
" 8.547500e+04, 8.314100e+04, 8.035400e+04, 7.386600e+04,\n",
" 6.694600e+04, 6.667500e+04],\n",
" [8.179260e+05, 5.363590e+05, 1.713310e+05, 1.397180e+05,\n",
" 1.145580e+05, 1.118930e+05, 9.250400e+04, 8.491100e+04,\n",
"...\n",
" 1.557460e+05, 1.401720e+05, 1.148560e+05, 9.798400e+04,\n",
" 9.764700e+04, 9.134800e+04],\n",
" [1.627056e+06, 1.476344e+06, 1.030450e+06, 6.998490e+05,\n",
" 4.617220e+05, 2.255600e+05, 1.585300e+05, 1.428810e+05,\n",
" 1.268490e+05, 1.145160e+05],\n",
" [1.740674e+06, 1.657604e+06, 1.422301e+06, 1.060731e+06,\n",
" 7.767210e+05, 5.630250e+05, 2.794050e+05, 2.203890e+05,\n",
" 2.001090e+05, 1.478440e+05],\n",
" [1.808282e+06, 1.754983e+06, 1.650369e+06, 1.538092e+06,\n",
" 1.130615e+06, 1.029580e+06, 7.689230e+05, 4.159460e+05,\n",
" 2.735510e+05, 2.368450e+05],\n",
" [1.874248e+06, 1.811541e+06, 1.766834e+06, 1.644370e+06,\n",
" 1.559624e+06, 1.244883e+06, 1.118808e+06, 8.540210e+05,\n",
" 8.202950e+05, 4.561420e+05],\n",
" [1.947982e+06, 1.862256e+06, 1.811897e+06, 1.761373e+06,\n",
" 1.685634e+06, 1.609753e+06, 1.545834e+06, 1.155220e+06,\n",
" 1.121808e+06, 8.721370e+05],\n",
" [1.959105e+06, 1.944402e+06, 1.894490e+06, 1.864755e+06,\n",
" 1.732581e+06, 1.708184e+06, 1.660266e+06, 1.574807e+06,\n",
" 1.456694e+06, 1.422968e+06]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perc_clust_&gt;1MWp</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01143 0.0003616 ... 0.1691 0.1852</div><input id='attrs-47bdb23a-d694-4d29-b862-6f977fe450e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47bdb23a-d694-4d29-b862-6f977fe450e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa00c7de-04ad-469d-97b0-cf499f404bf6' class='xr-var-data-in' type='checkbox'><label for='data-aa00c7de-04ad-469d-97b0-cf499f404bf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.01142819, 0.00036164, 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. , 0. ],\n",
" [0.02294376, 0.05804216, 0.07931787, 0.07466496, 0.05032258,\n",
" 0.02584201, 0.01028101, 0.0033394 , 0.00324254, 0.00289017],\n",
" [0.0251442 , 0.05261462, 0.08536294, 0.11022015, 0.12744293,\n",
" 0.1392136 , 0.15052356, 0.14853958, 0.14139827, 0.13027597],\n",
" [0.02888671, 0.05302959, 0.07990599, 0.10289337, 0.12615327,\n",
" 0.14444903, 0.15773354, 0.16693679, 0.1792819 , 0.19247177],\n",
" [0.0317836 , 0.05733433, 0.07969349, 0.10180505, 0.12297802,\n",
" 0.13981419, 0.15397672, 0.16977755, 0.18038482, 0.18921923],\n",
" [0.03519799, 0.0601227 , 0.08247623, 0.10323974, 0.12482303,\n",
" 0.14395295, 0.15607249, 0.16742081, 0.17821196, 0.18731026],\n",
" [0.03385228, 0.05993283, 0.0829943 , 0.10421836, 0.12650949,\n",
" 0.14260196, 0.16121062, 0.17312 , 0.18528339, 0.19409569],\n",
" [0.03069839, 0.05967977, 0.08310574, 0.10508216, 0.12523576,\n",
" 0.14455593, 0.1624156 , 0.17574627, 0.18958967, 0.19860627],\n",
" [0.03004595, 0.05618557, 0.08176439, 0.09962904, 0.11993769,\n",
" 0.13861386, 0.15779468, 0.17246175, 0.18402937, 0.2003702 ],\n",
" [0.02615311, 0.05206847, 0.08515625, 0.10534236, 0.12125535,\n",
" 0.13598901, 0.15308804, 0.16594695, 0.18187135, 0.20504009],\n",
" [0.02583491, 0.05222437, 0.08333333, 0.1032666 , 0.12451362,\n",
" 0.13955638, 0.15547703, 0.16540722, 0.18053375, 0.19775281],\n",
" [0.02083333, 0.04417178, 0.07132244, 0.10195531, 0.11286089,\n",
" 0.13307985, 0.15276146, 0.16629213, 0.17590618, 0.18478261],\n",
" [0.01983299, 0.04241436, 0.0754352 , 0.09437751, 0.11666667,\n",
" 0.13058419, 0.14827018, 0.15975422, 0.17794118, 0.18406593],\n",
" [0.02260638, 0.04402516, 0.07142857, 0.09067358, 0.12376238,\n",
" 0.14252336, 0.15184382, 0.16167665, 0.16914498, 0.18518519]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66d69830-0286-4a18-831b-e2f1e66e82e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66d69830-0286-4a18-831b-e2f1e66e82e4' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (EPS: 14, min_samples: 10)\n",
"Coordinates:\n",
" * EPS (EPS) float64 50.0 100.0 150.0 200.0 ... 600.0 650.0 700.0\n",
" * min_samples (min_samples) float64 5.0 10.0 15.0 ... 40.0 45.0 50.0\n",
"Data variables:\n",
" exec_time (EPS, min_samples) float64 10.9 8.74 7.356 ... 31.13 30.87\n",
" N_clusters (EPS, min_samples) float64 4.524e+04 2.765e+04 ... 540.0\n",
" N_clust_noDem (EPS, min_samples) float64 2.993e+03 497.0 ... 2.0 1.0\n",
" N_clust_>1MWp (EPS, min_samples) float64 517.0 10.0 0.0 ... 91.0 100.0\n",
" perc_outliers (EPS, min_samples) float64 26.44 65.66 ... 2.524 2.889\n",
" aprx_add_clst (EPS, min_samples) float64 1.125e+03 14.51 ... 5.821e+03\n",
" kWp_mean (EPS, min_samples) float64 70.17 40.34 ... 1.093e+04\n",
" kWp_std (EPS, min_samples) float64 352.2 64.62 ... 1.897e+05\n",
" kWp_min (EPS, min_samples) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
" kWp_25% (EPS, min_samples) float64 2.307 11.97 ... 45.68 61.2\n",
" kWp_50% (EPS, min_samples) float64 7.14 22.01 ... 150.2 183.0\n",
" kWp_75% (EPS, min_samples) float64 33.98 43.92 ... 478.7 572.4\n",
" kWp_max (EPS, min_samples) float64 4.048e+04 ... 4.387e+06\n",
" N_BLD_mean (EPS, min_samples) float64 35.07 26.78 ... 3.872e+03\n",
" N_BLD_std (EPS, min_samples) float64 114.7 33.99 ... 6.149e+04\n",
" N_BLD_min (EPS, min_samples) float64 1.0 1.0 5.0 ... 5.0 9.0 16.0\n",
" N_BLD_25% (EPS, min_samples) float64 6.0 11.0 15.0 ... 72.5 91.0\n",
" N_BLD_50% (EPS, min_samples) float64 8.0 16.0 21.0 ... 170.0 196.0\n",
" N_BLD_75% (EPS, min_samples) float64 19.0 28.0 31.0 ... 431.5 504.0\n",
" N_BLD_max (EPS, min_samples) float64 5.489e+03 879.0 ... 1.423e+06\n",
" perc_clust_>1MWp (EPS, min_samples) float64 0.01143 0.0003616 ... 0.1852"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyperparams"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"hyperparams_feas = hyperparams.where( (hyperparams.perc_outliers < 10) )"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"\n",
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" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
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" this.rubberband_context = undefined;\n",
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"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
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" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
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" this._resize_canvas(600, 600);\n",
"\n",
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" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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},
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"output_type": "display_data"
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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pb++u8Z2OWB9WCUAKncmYxCovAABsPJzUJVXkMf/o7apTQQBUJ8xAqBu5yMJgLofAVC3IwDqdgTAmB0RRvcjAOp2PpJ7T/MjAGpuzY0iADYnxOcI5F+AAJj/OSrkFRIA49NKANQNCYC6HQEwZkcA1P0IgLodATBmR4TR/QiAuh0BULcrWgD80wu7WLeulX8a9K/vrrUeB/w5TQxPAOr3JyMRqKQAAbCS+lV8bgJgfPIJgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nYEQN2u1EgCYHlcOSoCWQoQALPU5lx1AgTA+M1AANQNCYC6HQEwZkcA1P0IgLodATBmRwDU/QiAuh0BULcrWgCcM7N7bp4A3K0PLwHR70xGIlB5AQJg5eegKq+AABifdgKgbkgA1O0IgDE7AqDuRwDU7QiAMTsCoO5HANTtCIC6HQFQtys10p8AJACWx5ajIpCVAAEwK2nO00CAABi/IQiAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofAVC3IwDqdgRA3Y4AWB47jopAXgQIgHmZiSq7DgJgfMIJgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nZFC4BvzOyWmyXAe/T5a5oYXgKi35+MRKCSAgTASupX8bkJgPHJJwDqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1u1IjfQkwAbA8thwVgawECIBZSXOeBgIEwPgNQQDUDQmAuh0BMGZHANT9CIC6HQEwZkcA1P0IgLodAVC3IwDqdgTA8thxVATyIkAAzMtMVNl1EADjE04A1A0JgLodATBmRwDU/QiAuh0BMGZHANT9CIC6HQFQtytaAHx9ZlfbqWtbHaSVRs57d619oc+76WgsAW4lVA6DQMYCBMCMwTndegECYPxOIADqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1u1IjCYDlceWoCGQpQADMUptz1QkQAOM3AwFQNyQA6nYEwJgdAVD3IwDqdgTAmB0BUPcjAOp2BEDdjgCo2xEAy2PHURHIiwABMC8zUWXXQQCMTzgBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3a5oAXD2jC65WQK8Z9/5aWJYAqzfn4xEoJICBMBK6lfxuQmA8cknAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsRAHW7UiN9CTABsDy2HBWBrAQIgBlJH3roofbss89u1NkmTZpkPq6x7bHHHrPbbrvNpk+fbosXL7YOHTpYv3797Mwzz7RBgwa16DwrVqywcePG2cSJE23OnDm2evVq6969ux1zzDE2atQo23nnnVt0HGUnAqCi1nAMAVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2OAKjbEQDLY8dREciLAAEwo5nY2ADYpk0be+edd2ynnXZqcIXr1q2zESNGpPjX1OYR8NZbb7XNNtusyX3mzp2bQt/rr7/e6D7bbbed3XPPPTZkyJCyCBEA46wEQN2QAKjbEQBjdgRA3Y8AqNsRAGN2BEDdjwCo2xEAdbuiBcCXZ3TOzRLgvfsuSBPDEmD9/mQkApUUIABmpP+nP/3Jli9fXvJss2fPtpNPPjntc+SRR9oTTzyxwf6XXHKJXXPNNenP999/f7vwwgutZ8+e5kFv7NixNmvWrPSZ73fVVVc1er5ly5ZZ37597bXXXkufDx8+3IYNG2bt2rUzf+rw2muvNd9n6623tqlTp9o+++zT6koEwDgpAVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2OAKjblRrpS4AJgOWx5agIZCVAAMxKugXnGTNmTIp4vk2YMMG+/e1vNxjly3R79+5ta9eutT59+tiUKVNStKvdfEnvwIEDbebMmda2bdsU+DwOfnq7/PLL7Yorrkh/7OcbPXp0g108+g0YMCCd57DDDrNnnnmmBVe/cbsQADfOq7G9CYC6IQFQt/ORa6wmdoAqHk0A1CefAKjb+ciPatrGDlDFowmA+uQTAHU7H8m9p/kRADW35kYRAJsT4nME8i9AAMzJHNXU1KTf3Js3b55ts802tnDhwvQEXv3tnHPOsZtvvjn9kUe6Aw88cIOrnzZtmvXv3z/9+bnnnms33XRTg33WrFljHTt2tPfffz/FxJdfftl8ufGnN19mPH78+PTHHhQPOOCAVpUiAMY5CYC6IQFQt/ORBEDdjwCo2xEAdTsfSQDU/YgwEbst9MGMJACK90DRAuCLMzpb166bixqtN+zddz+xfVgC3HqgHAmBCggQACuA3tgpn3zySTvqqKPSR6eddprdcccdDXbz3/7zF3R4IOzVq5e9+uqrTV65f+6/7detW7f0O4L1fwuw/nmuu+4686cOG9vqh8SLL77Yrr766laVIgDGOQmAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofTwDqdj6Se0/zIwBqbs2NIgA2J8TnCORfgACYkzn6zne+Yz//+c/T1fiSW196W39766236pbznnXWWeklH01t/nntS0J8XI8ePep2veyyy+zKK69Mf9/UU4T+mS//3X777dPvFvpy4I19g3FzrATA5oSa/5wA2LxRU3sQAHU7H8kTgLofAVC3IwDqdj6SJwB1PyJMxI4nAHU9AqBqRwBU5UqPIwCWx5WjIpClAAEwS+0mzuUv3OjcuXOKbb4M+O23397gDb4PP/ywDR06NB3hRz/6kZ133nlNXrl/fv7556fPfVz9N/medNJJdt9996XP3nvvvRT5mtr23Xdfe/HFF61Dhw62aNGiVpUiAMY5CYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh9PAOp2PpJ7T/MrWgD844xOuVkCvF/fhWlSeAuwdm8yCoFKCxAAKz0DZnbXXXelZb++NfX2Xn/ib+TIkWmfiRMn2oknntjklXvg89Dnm4/zJwJrN//dwOeff97at2+f3vRbavPg6AEx/ReQVatsyy23bLGWB75S2/z5861fv35plz+/sKt168r/h7jFuP+3IwFwY8X+vj8BULfzkTwBqPsRAHU7AqBu5yN5AlD3I8JE7Pjvd7oeAVC1IwCqcqXH+ROABMDy2HJUBLISIABmJV3iPEcccUTdm3b9t/v22GOPDfa+4YYb7MILL0x//uijj9qgQYOaPKJ/XvvU34033mgXXHBB3b577bWXzZ492zp16mQLFiwo+e1PPvlku/fee9M+S5YssR122KHFWvV/d7C5QQTA5oQa/5wAqLn5KAKgbkcAjNkRAHU/AqBuRwCM2REAdT+eANTtfCT3nuZHANTcmhtFAGxOiM8RyL8AAbDCc+RPyu2yyy7mbwH2p/P8d/ka2/x3+/z3+3x7+umn7fDDD2/yyv03BD0q+ubjLr300rp9e/bsaf67gP5CEX9BSKntlFNOsQkTJqRdNvYxbwJg+W8sAqBuTADU7QiAMTsCoO5HANTtCIAxOyKM7kcA1O0IgLpd0QLgC9M75mYJ8AH91v8s1Mb+u6E+m4xEAIHWFCAAtqamcCx/E+9FF12URt5yyy02YsSIRo+yqT0ByBJg4WbYyCEEwI0Eq7c7AVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nYEQN2u1Eh/ApAAWB5bjopAVgIEwKykmzhP7ZJc/309/128z372s43uuan9BmBzrLwEpDmh5j8nADZv1NQeBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsRAHU7AmB57DgqAnkRIABWcCZmzpxpffv2TVdwwgkn1L2dt7FLeuihh+zYY49NH0XeAuwvD7n//vvTcXgLcAUnvxVOTQDUEQmAuh0BMGZHANT9CIC6HQEwZkcA1P0IgLodAVC3K1oAnJGjJcB9WQKs35iMRCAHAgTACk7CqFGj7KabbkpX8MADD9QFvsYuyX+3z3+/zzd/q68/EdjU5p/fdttt6WMf16NHj7pd/XcE/XcBffPfG/TfHWxsW7t2rW2//fa2fPlyGzBggD377LOtKsUTgHFOAqBuSADU7XwkbwHW/QiAuh0BULfzkbwFWPcjAEbseAuwrsdLQFQ7AqAqV3qcLwEmAJbHlqMikJUAATAr6U+dZ82aNbbTTjvZ4sWLrUOHDvbuu+9a27Ztm7yadevWWbdu3dJ+vXr1sldffbXJfXv37m2vvfZaOr7/QGv9F3I88cQTdvTRR6ex/vuDY8aMafQ406ZNs/79+6fP/DcKr7nmmlaVIgDGOQmAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofTwDqdj6Se0/zK1oAfH56B+vSdXMNoxVHzX/3E/tyv8XpiLwEpBVhORQCGQoQADPErn8qf+Lvq1/9avqj73//+/af//mfzV7J2WefnV4U4ltTT+/VD3e+/7hx4xocd/Xq1daxY0f74IMPzEPhK6+80iAQ1u7sLyMZP358+tvp06fXLVVu9iJbuAMBsIVQJXYjAOqGBEDdzkfyBKDuRwDU7QiAup2P5AlA3Y8IE7HjCUBdjwCo2hEAVbnS4wiA5XHlqAhkKUAAzFK73rnq/xbfCy+8YF/60peavZI33njD/KUhvjy3T58+NmXKFGvXrl3duJUrV6bluv7bgv404ezZs2333Xff4Lj1lwGPHTvWRo8e3WAfj4t+HD/PwIEDbfLkyc1e28buQADcWLEN9ycA6oYEQN2OABizIwDqfgRA3Y4AGLMjAOp+PAGo2/lI7j3NjwCouTU3igDYnBCfI5B/AQJgBebIX77RpUsX+/jjj23vvfe2l156qcVX4ctxfemub/vvv39awuu/DTh37ly7/vrrbdasWemzUst2P/rooxQQPSj6duaZZ9qwYcNSTJw0aVJa7rts2bL0988995ztt99+Lb6+lu5IAGypVNP7EQB1QwKgbkcAjNkRAHU/AqBuRwCM2RFhdD8CoG5HANTtihYAn/MlwF1ysAR4/id2EEuA9RuTkQjkQIAAWIFJ8Bd4jBw5Mp25sSfwSl1STU2NDR8+3G6//fYmdzvjjDPSS0DatGnT5D5z5syxIUOG2JtvvtnoPttuu63dfffdNnTo0LIIEQDjrARA3ZAAqNsRAGN2BEDdjwCo2xEAY3YEQN2PAKjbEQB1OwKgbldq5HwCYHlgOSoCGQoQADPErj3VwQcfnJ6s23zzze2dd96xrl27bvRVPPLIIynyzZgxw5YsWWI77rhj+p0+fwPw4MGDW3Q8f8Ov/0bgxIkTzYOg/z5g9+7dUxj03yXcZZddWnQcZScCoKLWcAwBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3Y4AqNsRAMtjx1ERyIsAATAvM1Fl10EAjE84AVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2uaAHwdzlaAnwIS4D1G5ORCORAgACYg0moxksgAMZnnQCoGxIAdTsCYMyOAKj7EQB1OwJgzI4AqPsRAHU7AqBuRwDU7UqN9CXABMDy2HJUBLISIABmJc15GggQAOM3BAFQNyQA6nYEwJgdAVD3IwDqdgTAmB0BUPcjAOp2BEDdjgCo2xEAy2PHURHIiwABMC8zUWXXQQCMTzgBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3a5oAXDK9I7WOQdvAV4w/xMb0G9Rmpi//OUv1q1bN32SGIkAAhURIABWhJ2TEgDj9wABUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3Y4AqNuVGkkALI8rR0UgSwECYJbanKtOgAAYvxkIgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nYEQN2OAFgeO46KQF4ECIB5mYkquw4CYHzCCYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2RQuAk5/PzxLgQ7/MEmD9zmQkApUXIABWfg6q8goIgPFpJwDqhgRA3Y4AGLMjAHO+y7MAACAASURBVOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1u1IjfQkwAbA8thwVgawECIBZSXOeBgIEwPgNQQDUDQmAuh0BMGZHANT9CIC6HQEwZkcA1P0IgLodAVC3IwDqdgTA8thxVATyIkAAzMtMVNl1EADjE04A1A0JgLodATBmRwDU/QiAuh0BMGZHANT9CIC6HQFQtytaAHz6+U65eQvwEV9emCaGtwDr9ycjEaikAAGwkvpVfG4CYHzyCYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2BEDdrtRIXwJMACyPLUdFICsBAmBW0pyngQABMH5DEAB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6HQGwPHYcFYG8CBAA8zITVXYdBMD4hBMAdUMCoG5HAIzZEQB1PwKgbkcAjNkRAHU/AqBuRwDU7YoWAJ94vnNulgAf9eUFaWJYAqzfn4xEoJICBMBK6lfxuQmA8cknAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsRAHW7UiN9CTABsDy2HBWBrAQIgFlJc54GAgTA+A1BANQNCYC6HQEwZkcA1P0IgLodATBmRwDU/QiAuh0BULcjAOp2BMDy2HFUBPIiQADMy0xU2XXUD4B/emEX69a1bZUJxL/ux+vWxA9SpUdYsW5tlX7z1vnay2tqWudAVXiUFevaVOG3bp2vvHzdFq1zoCo9yoqaf6jSbx7/2h/WbBU/SJUe4aOadlX6zVvna/9tbfvWOVCVHeW9BavsssOmpm+9qS5Vrf/vSo9P62ydulT+35UWzl9rRx/IEuAq+8eJr1swAQJgwSZ0U/k6BMD4TBEAdUMCoG7nIwmAuh8BULcjAOp2PpIAqPsRAHU7AqBu5yMJgJofAVBza24UAbA5IT5HIP8CBMD8z1Ehr5AAGJ9WAqBuSADU7QiAMTsCoO5HANTtCIAxOwKg7kcA1O0IgLodAVC3KzWSAFgeV46KQJYCBMAstTlXnQABMH4zEAB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtihYAH5nWJTdLgIccOD9NzKa6tFq/qxiJQDEECIDFmMdN7lsQAONTRgDUDQmAuh0BMGZHANT9CIC6HQEwZkcA1P0IgLodAVC3IwDqdqVG+hOABMDy2HJUBLISIABmJc15GggQAOM3BAFQNyQA6nYEwJgdAVD3IwDqdgTAmB0BUPcjAOp2BEDdrmgB8MFpO+XmCcBjD5yXJoYnAPX7k5EIVFKAAFhJ/So+NwEwPvkEQN2QAKjbEQBjdgRA3Y8AqNsRAGN2BEDdjwCo2xEAdTsCoG5XaqQ/AUgALI8tR0UgKwECYFbSnKeBAAEwfkMQAHVDAqBuRwCM2REAdT8CoG5HAIzZEQB1PwKgbkcA1O0IgLodAbA8dhwVgbwIEADzMhNVdh0EwPiEEwB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtihYAH5jazTp2aauDtNLIRfPX2nH9/5qOxhLgVkLlMAhkLEAAzBic060XIADG7wQCoG5IANTtCIAxOwKg7kcA1O0IgDE7AqDuRwDU7QiAuh0BULcrNZIAWB5XjopAlgIEwCy1OVedAAEwfjMQAHVDAqBuRwCM2REAdT8CoG5HAIzZEQB1PwKgbkcA1O0IgLodAbA8dhwVgbwIEADzMhNVdh0EwPiEEwB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtihYAfzW1e26WAH+9/1/SxLAEWL8/GYlAJQUIgJXUr+JzEwDjk08A1A0JgLodATBmRwDU/QiAuh0BMGZHANT9CIC6HQFQtyMA6nalRvoSYAJgeWw5KgJZCRAAs5LmPA0ECIDxG4IAqBsSAHU7AmDMjgCo+xEAdTsCYMyOAKj7EQB1OwKgbkcA1O0IgOWx46gI5EWAAJiXmaiy6yAAxiecAKgbEgB1OwJgzI4AqPsRAHU7AmDMjgCo+xEAdTsCoG5XtAB433O75GYJ8IkH/TlNDEuA9fuTkQhUUoAAWEn9Kj43ATA++QRA3ZAAqNsRAGN2BEDdjwCo2xEAY3YEQN2PAKjbEQB1OwKgbldqpC8BJgCWx5ajIpCVAAEwK2nO00CAABi/IQiAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofAVC3IwDqdgRA3Y4AWB47jopAXgQIgHmZiSq7DgJgfMIJgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nZFC4D3PrerdeyyhQ7SSiMXzV9j3zjo7XQ0lgC3EiqHQSBjAQJgxuCcbr0AATB+JxAAdUMCoG5HAIzZEQB1PwKgbkcAjNkRAHU/AqBuRwDU7QiAul2pkQTA8rhyVASyFCAAZqnNueoECIDxm4EAqBsSAHU7AmDMjgCo+xEAdTsCYMyOAKj7EQB1OwKgbkcA1O0IgOWx46gI5EWAAJiXmaiy6yAAxiecAKgbEgB1OwJgzI4AqPsRAHU7AmDMjgCo+xEAdTsCoG5XtAD4y+d6WIccLAFePH+NDTvoT2liWAKs35+MRKCSAgTASupX8bkJgPHJJwDqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1u1IjCYDlceWoCGQpQADMUptz1QkQAOM3AwFQNyQA6nYEwJgdAVD3IwDqdgTAmB0BUPcjAOp2BEDdjgCo2xEAy2PHURHIiwABMC8zUWXXQQCMTzgBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3a5oAfDu3/fMzRLgbx08N00MS4D1+5ORCFRSgABYSf0qPjcBMD75BEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHU7AqBuV2qkLwEmAJbHlqMikJUAATArac7TQIAAGL8hCIC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2BEDdjgBYHjuOikBeBAiAeZmJKrsOAmB8wgmAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofAVC3IwDqdkULgBN+v1tulgB/5+A5aWJYAqzfn4xEoJICBMBK6lfxuQmA8cknAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsRAHW7UiN9CTABsDy2HBWBrAQIgFlJc54GAgTA+A1BANQNCYC6HQEwZkcA1P0IgLodATBmRwDU/QiAuh0BULcjAOp2BMDy2HFUBPIiQADMy0xU2XUQAOMTTgDUDQmAuh0BMGZHANT9CIC6HQEwZkcA1P0IgLodAVC3K1oAvOv3e9iOXbbQQVpp5JL5a+zUg99IR2MJcCuhchgEMhYgAGYMzunWCxAA43cCAVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2OAKjblRpJACyPK0dFIEsBAmCW2pyrToAAGL8ZCIC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2RQuAt/+uV26eAPzuIa+lieEJQP3+ZCQClRQgAFZSv4rPTQCMTz4BUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3Y4AqNuVGulPABIAy2PLURHISoAAmJU052kgQACM3xAEQN2QAKjbEQBjdgRA3Y8AqNsRAGN2BEDdjwCo2xEAdTsCoG5HACyPHUdFIC8CBMC8zESVXQcBMD7hBEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHW7ogXAn/2ut+3Y5R90kFYauWT+avvnQ15NR2MJcCuhchgEMhYgAGYMzunWCxAA43cCAVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2OAKjblRqZRQDcbLPNWnTxAwcOtMmTJze575///Ge75ZZb7Mknn7S5c+fa8uXL7TOf+Yz16tXLBg8ebCNGjLAOHTo0e64VK1bYuHHjbOLEiTZnzhxbvXq1de/e3Y455hgbNWqU7bzzzs0ew3d45ZVX7KabbrKnnnrK5s2bZ9tss4317t3bvvWtb9kZZ5xhbdu2bdFx2AmBqAABMCrIeEmAACixNRhEANQNCYC6HQEwZkcA1P0IgLodATBmRwDU/QiAuh0BULcjAOp2RQiA99xzjw0fPtw83jW17bDDDnbvvffa4Ycf3uQ+Hg499L3++uuN7rPddtuZn2vIkCElwf/7v//bzjnnHPv4448b3e/AAw+0hx56yPya2BAotwABsNzCHL9RAQJg/MYgAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsVLQDe9ts9bYccLAFeOn+1nfmV2WliyrUEuPYJwJEjR9rZZ5/d5E3Qvn1769GjxwafT5061b7yla/YJ598Ym3atLFTTz3VvvrVr1rXrl3tnXfesbvuussefPDBNM6P8fLLL9uuu+66wXGWLVtmffv2tddeW//WYw+Kw4YNs3bt2tmkSZPs2muvNd9n6623Nj/nPvvs0+i1Pv744ykQ1tTUWKdOneySSy6xL3/5y/a3v/3NfvrTn9r//u//pnEDBgxIx/VrZkOgnAIEwHLqcuwmBQiA8ZuDAKgbEgB1OwJgzI4AqPsRAHU7AmDMjgCo+xEAdTsCoG5HANTtSo3MMgD+4Ac/sMsvv3yjv8ixxx6bnqbzzZfuNhYRL7jgAvuP//iPtM/3vvc9+8lPfrLBefzcV1xxRfrzsWPH2ujRoxvs49HPo93atWvtsMMOs2eeeWaDY/hnvszXlw5vu+229oc//MF69uzZYD9/MvDmm29Of+Zx8pRTTtno78wABDZGgAC4MVrs22oCBMA4JQFQNyQA6nY+cnlNTewAVTyaAKhPPgFQt/ORK2oq/wPysW9QudEEQN2eAKjb+ci/rW0fO0CVjiYAlmfiN4UA+LnPfc7ee++9tJx2yZIljUJ88MEHtv3226fPDjjgAJs5c2aD/dasWWMdO3a0999/PwU8f0qwsSfz/HcEx48fn8b6MfxY9Tf/3cBvfOMb6Y/8icF//dd/3eB6fJlyt27d0jXvvffe9tJLL5Vn8jgqAv8nQADkVqiIAAEwzk4A1A0JgLqdjyQA6n4EQN2OAKjb+UgCoO5HANTtCIC6HQFQtytaALzlt3vnZgnwyK+8nCam3EuA1ScA/eUa/sKPPn362IwZM5q8ifwFIB4IG4tu/uKQo446Ko297rrrbMyYMY0eZ9q0ada/f//02cUXX2xXX311g/38BR/+G4G+zZ8/3zp37tzoceqHxDfeeMN23313/eZnJALNCBAAuUUqIkAAjLMTAHVDAqBuRwCM2REAdT8CoG5HAIzZEQB1PwKgbkcA1O0IgLpdqZH+BGDeA+CXvvQlmzVrVsknAD/88EPzF3j4dvzxx9v999/f4GtfdtllduWVV6Y/86W+/pKOxjZf4utPEnpw9OXAzz77bIPd/A3BHkq/8IUv1P2WYGPH+cUvfmHf/OY300e33367nX766eWZQI6KgJkRALkNKiJAAIyzEwB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6XV4C4J577pl+X89f3NG2bdv09NxBBx1kp512WvrNvaY2X5LrT9T5dsstt9T9df39/ff8brzxxvRH/pKO2qf9avc56aST7L777kt/60tza5cLN3bOfffd11588UXzJwoXLVpUt4u/IMR/92/dunXpJSS//vWvm7xmD5YeLn3za/PfHGRDoFwCBMByyXLckgIEwPgNQgDUDQmAup2PZAmw7kcA1O0IgLqdj2QJsO5HANTtCIC6nY/kNwA1v6IFwHFT9snNEuBzBryYJmX69OnWpUuXkhPkv223sVvtW4BLjfva175md955Z91TfPX39bf/+pt/77777vS7fd/97nftuOOOS9fqMfHnP/+5/epXv0pDfGmvL/H99OZP/D3//PPpLcEe8kptQ4cOtYcffjjtsmrVKttyyy3TX/vbg/33A33zF33813/9V5OH8aXIHhB98zcN+xOBbAiUS4AAWC5ZjltSgAAYv0EIgLohAVC385EEQN2PAKjbEQB1Ox9JANT9CIC6HQFQt/ORBEDNjwCouTU3ypcA1wbA5vb1z/3pt43dPLp5sDviiCOsV69e5r/pt3jx4rS89tZbb7WlS5emQw4cOND8t/q22GKLRk9x7733prjnT9d9evMnCP2FHJ9+8q92v7322stmz55tnTp1sgULFpT8CieffLL5uXzzkOcvH/HNf3+wX79+6a+bCo21B165cqVtvfXW6W89KD744IMby8b+CLRYgADYYip2bE0BAmBckwCoGxIAdTsfSQDU/QiAuh0BULfzkQRA3Y8AqNsRAHU7H0kA1PwIgJpbc6OyCID+5t2mltwuXLjQBg8eXBf1fvzjH9uoUaM2uGx/+s4Dnz+Z58uIP71ttdVWdsIJJ9gNN9zQ6FOMPXv2tLfeesu6d++enhostZ1yyik2YcKEtEv9F6P89re/Tb8L6Nu//du/2b//+783eZiamhrbfPPN0+cePp966qnmpoLPEZAFCIAyHQMjAgTAiN76sQRA3ZAAqNv5SAKg7kcA1O0IgLqdjyQA6n4EQN2OAKjb+UgCoOZXtAD4kyn75WYJ8KgBf0yTUq4lwM3NuIc5X1q7evVq22233ezNN99sMMTDmz9B6CFxl112sauuusqOPPJI+9znPmceEB944AG79NJL02/7+RLlJ554om6pbu2BeAKwuVng801ZgAC4Kc/eJnztBMD45BEAdUMCoG5HAIzZEQB1PwKgbkcAjNkRAHU/AqBuRwDU7QiAul2pkf4EYG0ArP+0W3nO1vRR6//u3rx586xr165p548//tj86T3/M39piC//9f/96e2VV16xPn36pN/s8//ty3Xrb/wGYNYzyvmyFCAAZqnNueoECIDxm4EAqBsSAHU7AmDMjgCo+xEAdTsCYMyOAKj7EQB1OwKgbkcA1O02hQB44YUXpuW7vvmTiH379k1//Zvf/Mb8BSG+XX311XbxxRc3+XWGDx9uP/vZz9Lnf/zjH83f5lu7nXjiiXb//fenv428Bfgzn/lMOgZvAS7P/chRNQECoObGqKAAATAIyBLgECABMMTHEuAAHwFQxyMA6nY+kiXAuh8BULcjAOp2PpIlwJpf0QLgf07Zzz7Xef3bZSu5/W3Bx3be/y0BruQTgKNHj7Ybb7wxUdQPgP7Sj4suuij9+aOPPmqDBg1qkstfKDJy5Mj0+S9/+Uvzl3nUbpdddpldeeWV6W+nTp1q/kRgY5v/vqD/XuHy5cvT7/35i0rqbzvvvHP6XcAvfOEL6a3ATW3+1t9vfvOb6ePbb7/dTj/99EpOM+cuuAABsOATnNevRwCMzwxPAOqGBEDdzkfyG4C6HwFQtyMA6nY+kgCo+xEAdTsCoG7nIwmAmh8BUHNrblReAuAxxxxjjzzySLpc/3fKnXbaKf21R0GPg775m3R9qXBT20033VT3ApH77rsvvRSkdvPfBTz66KPT33pU9Lf4NrZNmzbN+vfvnz7y8HjNNdc02M2jnsc93+bPn9/ocmT/bMSIETZ+/Pi03+uvv2577LFHc1PB5wjIAgRAmY6BEQECYERv/VgCoG5IANTtfCQBUPcjAOp2BEDdzkcSAHU/AqBuRwDU7XwkAVDzIwBqbs2NykMA9JeA9OrVy9asWWOf//znbe7cuXWX7ct2ffmub75M+Prrr2/yK9Vf5vvCCy/Yl770pbp9/QUjHTt2tA8++CC9IMR/M3CzzTbb4Fj1w139JxFrd7z33nvrniy89tpr05uJP72tWLEivYzElxrvueee6VxsCJRTgABYTl2O3aQAATB+cxAAdUMCoG5HAIzZEQB1PwKgbkcAjNkRAHU/AqBuRwDU7YoWAP9jypdyswT4/AF/SBNTjiXA/tTe4MGDrW3bto1Ovr/F1z/3l3v49sMf/tDOP//8un39zb/+NKBHNf/9vd///vf2xS9+cYNj+fJgfzqwpqYm7f/OO+9YmzZtGuxXfxnw2LFj654srN3Jlwb7sl9fBjxw4ECbPHnyBufxSOkB0SPltttua3/4wx/SS0rqb+ecc47dfPPN6Y/uuOMOO+200/Qbn5EItECAANgCJHZpfYH6AfD1mV1tp66N/wd965+5OEf8qGZNcb5Mxt9kxbp1GZ+xWKd7v2aLYn2hDL/N0k/aZ3i2Yp3qo5qtivWFMv4273PvyeJ/w062e28N/5kn45nZUvwkvmULl9vdx/wqjS1HqJIuaiMH1f93pWoJgLvuumt6ss+X4/rSWv/7du3a2ZIlS1Jg89/tW7p0aZI85JBD7KmnnrItt2z424j+230e73zbZptt7Hvf+54deeSR9tnPftY8IPqLQn7605+mcOfbhAkT7Nvf/vYGs/PRRx+lNwS/8cYb6bMzzzzThg0blq5n0qRJabnvsmXL0t8/99xztt9++zU6w75U+dhjj02xsVOnTnbppZdav3790hN/fh21Lxvx7+PfcfPNN9/IO4XdEdg4AQLgxnmxdysJEADjkARA3ZAAqNv5SAKg7kcA1O0IgLpd+ueWiCUDEgBlOiMA6nY+kgCo+REANbfmRvkS4HI+AejB789//nNzl5ECob/B11/A8elt3bp16anAH//4x+Z/3dS2xRZbpIj3L//yL03uM2fOHBsyZIi9+eabje7jT/XdfffdJX9r0Ad66Dv33HPNlxY3tnkQfPjhh23HHXds9ruzAwJRAQJgVJDxkgABUGJrMIgAqBsSAHW7FBJ4AlAGJADKdEYA1O3SP7cEQBmQACjTEQB1ujSSAKgBFi0A3vBsn9wsAR49cGaalHI8Welv0fX/8eW1/lt//uTfhx9+mJ7k6969ux100EF26qmn1r14o9Td4b/r55Hwd7/7XYqKvizYj7PbbrulJbtnnXVWi1624W/4HTdunE2cONE8CHrE82vxMPj973/fdtlllxbdpC+//LL95Cc/saefftreffdda9++fVoe/K1vfcv++Z//ucllzy06ODshsBECBMCNwGLX1hMgAMYtCYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh9PAOp2BEDdjgCo25Ua6U8AljMAlueqOSoCCNQXIAByP1REgAAYZycA6oYEQN2OABizIwDqfgRA3Y4AGLMjAOp+BEDdjgCo2xUtAF7/bN/cPAE4ZuCMNDHleAJQn3FGIoBASwUIgC2VYr9WFSAAxjkJgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nYEQN2u1Eh/ApAAWB5bjopAVgIEwKykOU8DAQJg/IYgAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsRAHU7AmB57DgqAnkRIADmZSaq7DoIgPEJJwDqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbFS0AXjv5y/bZzlvqIK008r0FH9tFhz6fjsYS4FZC5TAIZCxAAMwYnNOtFyAAxu8EAqBuSADU7QiAMTsCoO5HANTtCIAxOwKg7kcA1O0IgLodAVC3KzWSAFgeV46KQJYCBMAstTlXnQABMH4zEAB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6HQGwPHYcFYG8CBAA8zITVXYdBMD4hBMAdUMCoG5HAIzZEQB1PwKgbkcAjNkRAHU/AqBuRwDU7YoWAK+afKB9tvNWOkgrjXxvwSq79NBp6WgsAW4lVA6DQMYCBMCMwTndegECYPxOIADqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1u1IjCYDlceWoCGQpQADMUptz1QkQAOM3AwFQNyQA6nYEwJgdAVD3IwDqdgTAmB0BUPcjAOp2BEDdjgCo2xEAy2PHURHIiwABMC8zUWXXQQCMTzgBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3a5oAfDfJ/XPzRLgyw6bmiaGJcD6/clIBCopQACspH4Vn5sAGJ98AqBuSADU7QiAMTsCoO5HANTtCIAxOwKg7kcA1O0IgLodAVC3KzXSlwATAMtjy1ERyEqAAJiVNOdpIEAAjN8QBEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHU7AqBuRwAsjx1HRSAvAgTACs3EkiVL7Pbbb7ff/OY3NnfuXHvvvfdshx12sO7du9uAAQPs+OOPt/79+5e8uscee8xuu+02mz59ui1evNg6dOhg/fr1szPPPNMGDRrUom+2YsUKGzdunE2cONHmzJljq1evTtdwzDHH2KhRo2znnXdu0XE2dicC4MaKbbg/AVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2uaAHw8kkH2/Y5eAvw+wtW2eWH/T5NDEuA9fuTkQhUUoAAWAF9j20jR460pUuXNnn2r371q/brX/+60c/XrVtnI0aMSPGvqc0j4K233mqbbbZZk/t4ePTQ9/rrrze6z3bbbWf33HOPDRkypNWVCIBxUgKgbkgA1O0IgDE7AqDuRwDU7QiAMTsCoO5HANTtCIC6HQFQtys1kgBYHleOikCWAgTALLXN7H/+53/s9NNPt5qaGuvYsWMKgYcccoh97nOfswULFqSnAR988EHz+OahsLHtkksusWuuuSZ9tP/++9uFF15oPXv2TGPHjh1rs2bNSp/5fldddVWjx1i2bJn17dvXXnvttfT58OHDbdiwYdauXTubNGmSXXvtteb7bL311jZ16lTbZ599WlWKABjnJADqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1OwJgeew4KgJ5ESAAZjgTr776agp2H3/8sX3lK1+pC32NXYIvxf2Hf/iHDT7yZbq9e/e2tWvXWp8+fWzKlCkp2tVuvqR34MCBNnPmyIZIMwAAIABJREFUTGvbtm0KfB4HP71dfvnldsUVV6Q/9mg4evToBrt49POlyH6eww47zJ555plWlSIAxjkJgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nZFC4CXPfOV3CwB/vfDf5smhiXA+v3JSAQqKUAAzFD/H//xH+3pp5+2HXfc0TwG+v/e2O2cc86xm2++OQ3zSHfggQducIhp06bV/X7gueeeazfddFODfdasWZOePnz//fdTTHz55ZetTZs2GxzHlxmPHz8+/bkHxQMOOGBjL7fJ/QmAcUoCoG5IANTtCIAxOwKg7kcA1O0IgDE7AqDuRwDU7QiAuh0BULcrNdKXABMAy2PLURHISoAAmJG0P4nnsc03f/ruBz/4wUaf2X/7z1/QMW/ePOvVq1eKiE1t/rn/tl+3bt3snXfeafBbgE8++aQdddRRaeh1111nY8aMafQw9UPixRdfbFdfffVGX3NTAwiAcUoCoG5IANTtCIAxOwKg7kcA1O0IgDE7AqDuRwDU7QiAuh0BULcjAJbHjqMikBcBAmBGM3HllVfaZZddls72yiuv2J577pn+2t/+628E9t8A9LcAl9reeuutuuW8Z511VnrJR1Obf177khAf16NHj7pd/Tr8enxr6ilC/8yX/26//fa2fPnytBz42WefbTUtAmCckgCoGxIAdbsUEmq2iB2gikcTAPXJJwDqdumf20/axw5QxaMJgPrkEwB1Ox+5dA3/3CqCRQuAlzwzIDdLgK8+fEqaEpYAK3cmYxCovAABMKM58LftPvLII+nlHh79/O26/tt7L774Yt0VeKQ79dRT7YILLrBtttlmgyt7+OGHbejQoenPf/SjH9l5553X5NX75+eff3763MfVf5PvSSedZPfdd1/6zK/FI19T27777puusUOHDrZo0aJW0yIAxikJgLohAVC3SyGBACgDEgBlOiMA6nbpn1sCoAxIAJTpjACo2xEAdTsCoG5XaqQvASYAlseWoyKQlQABMCNpj3tvv/22eVDzt/6OGzeuyTPvvffe9vjjj1vXrl0b7ONP/Plbg33zNwSfeOKJTR7DA5+HPt98nD8RWLv57wY+//zz1r59+/Sm31KbB0cPiL6tWrXKttxyyxaJeeArtc2fP9/69euXdnl9ZlfbqWvbFh2Xnf4uQADU7wYCoG7nIwmAuh8BULcjAOp26Z9bAqAMSACU6QiAOl0ayROAGiABUHNrbhQBsDkhPkcg/wIEwIzmyJ/8+/DDD1NA87cA+1N3/vt7xx9/vG277bb20ksvpSXCjz76aLqigw46yH772982eDnHDTfcYBdeeGH63PcbNGhQk1fvn9c+9XfjjTempwprt7322stmz55tnTp1sgULFpQUOPnkk+3ee+9N+/hS5eaWKdcebLPNNmuxLAGwxVQNdiQAam4+igCo26WQwBOAMiABUKbjCUCdLo0kAOqABEDdjicAdTsfSQDU/IoWAC96+tDcLAG+9ojJaVJYAqzdm4xCoNICBMCMZqBt27b2ySefpLNtvvnm9rvf/W6DN/jW1NSkJb61EfDTT/nV/x1Bf5vw4Ycf3uTVP/PMM3bEEUekz33cpZdeWrdvz549zX8X0F8o4i8IKbWdcsopNmHChI3+D3oCYPlvLAKgbkwA1O0IgDE7AqDuxxOAuh0BMGZHANT9CIC6HQFQtyMA6nalRvoTgATA8thyVASyEiAAZiTtv+nnL9PwbdiwYfaLX/yi0TP7C0J8CbBv/nTg/fffX7ffpvQEIEuAy39jEQB1YwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6HQGwPHYcFYG8CBAAM5qJLl261C23veuuu8yfrGtq69atm82bN2+DJ/Q2pd8AbI6Vl4A0J9T85wTA5o2a2oMAqNsRAGN2BEDdjwCo2xEAY3YEQN2PAKjbEQB1u6IFwDFPH2rbdW6ng7TSyA8WrLTrWQLcSpocBoHKCBAAM3L3F17MmDEjna255bv9+/e3adOmpd8L9Bdv1G4PPfSQHXvsselvI28B9peH1D5ZyFuAM7oBynAaAqCOSgDU7QiAMTsCoO5HANTtCIAxOwKg7kcA1O0IgLodAVC3KzWSAFgeV46KQJYCBMCMtE8//XS7884709meeOIJO/LII5s8c20s/PRbev13+/z3+3zzt/r6E4FNbf75bbfdlj72cf4W4trNXzbivwvo29SpUzf4LcLa/dauXZteVuJLlwcMGGDPPvtsq2nxBGCckgCoGxIAdbsUEngJiAxIAJTpeAmITpdG8hIQHZAAqNsRAHU7H8lLQDS/ogXA0U8dnpsnAG/4x2fSpPASEO3eZBQClRYgAGY0A3fccYd997vfTWe75ZZbbMSIEU2eeccdd7SlS5faHnvsYa+//nrdfuvWrTNfHvzuu+9ar1697NVXX23yGL1797bXXnvNdtppp/Qf0PVfyuEB8uijj05j/U3EY8aMafQ4/hSiP43o20UXXWTXXHNNq2kRAOOUBEDdkACo2xEAY3YEQN2PJwB1OwJgzI4AqPsRAHU7AqBuRwDU7UqN9CcACYDlseWoCGQlQADMSNqDnv8O4Jo1a9LTfx7hGtv8KbtDDz00fXTGGWfYz372swa7nX322Skg+tbU03v1w53vP27cuAbHWL16tXXs2NE++OAD81DoLx5p7K29HinHjx+fxk6fPt369u3baloEwDglAVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2OAKjbEQDLY8dREciLAAEww5moH+/8LcD+NuD620cffZSW2v7xj39sMrq98cYbttdee5kvz+3Tp49NmTLF2rX7+4/Crly5Mh1j5syZ1rZtW5s9e7btvvvuG3zL+suAx44da6NHj26wj8dFP46fZ+DAgTZ58uRWlSIAxjkJgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nZFC4DnP/mPuVkC/B9HPpUmhiXA+v3JSAQqKUAAzFB/8eLFKdq98847Kc75E3bHH3+8bbvttvbSSy/Z9ddfn5bt+jZy5Ei7+eabG706X47rS3d923///dMSXv9twLlz56ZjzJo1K31Watmux0a/Fg+Kvp155pkpSHpMnDRpUlruu2zZsvT3zz33nO23336tKkUAjHMSAHVDAqBuRwCM2REAdT8CoG5HAIzZEQB1PwKgbkcA1O0IgLpdqZG+BJgAWB5bjopAVgIEwKyk/+88/rt9xx13nM2ZM6fJM/tvBfoLPrbYYotG96mpqbHhw4fb7bff3uQxfPmwvwSkTZs2Te7j1zBkyBB78803G93Hw+Tdd99tQ4cObXUlAmCclACoGxIAdTsCYMyOAKj7EQB1OwJgzI4AqPsRAHU7AqBuRwDU7QiA5bHjqAjkRYAAWIGZ8Lfq+u/43XfffSm++ZN2/pt8Bx98cHq772GHHdaiq3rkkUdS5JsxY4YtWbLE/OUh/jt9fozBgwe36Bh+Lf4bgRMnTkxR0n8fsHv37ikMfv/737dddtmlRcfZ2J0IgBsrtuH+BEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHW7ogXA/+/JI23bzn//ySddJjbywwUr7UdHPpkOwhLgmCWjEaiUAAGwUvJVfl4CYPwGIADqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1u1IjCYDlceWoCGQpQADMUptz1QkQAOM3AwFQNyQA6nYEwJgdAVD3IwDqdgTAmB0BUPcjAOp2BEDdjgCo2xEAy2PHURHIiwABMC8zUWXXQQCMTzgBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3a5oAXDUE0fnZgnwT456PE0MS4D1+5ORCFRSgABYSf0qPjcBMD75BEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHU7AqBuV2qkLwEmAJbHlqMikJUAATArac7TQIAAGL8hCIC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2BEDdjgBYHjuOikBeBAiAeZmJKrsOAmB8wgmAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofAVC3IwDqdkULgOc+Mci27ZSDtwAvXGn/ddRjaWJYAqzfn4xEoJICBMBK6lfxuQmA8cknAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsRAHW7UiM/JACWB5ajIpChAAEwQ2xO9XcBAmD8biAA6oYEQN2OABizIwDqfgRA3Y4AGLMjAOp+BEDdjgCo2xEAdTsCYHnsOCoCeREgAOZlJqrsOgiA8QknAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsVLQCOfGKwbdtpax2klUZ+uHCF3XLUo+loLAFuJVQOg0DGAgTAjME53XoBAmD8TiAA6oYEQN2OABizIwDqfgRA3Y4AGLMjAOp+BEDdjgCo2xEAdbtSIwmA5XHlqAhkKUAAzFKbc9UJEADjNwMBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3Y4AqNsRAMtjx1ERyIsAATAvM1Fl10EAjE84AVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2uaAFwxOND7DM5WAL80cIVduvRj6SJYQmwfn8yEoFKChAAK6lfxecmAMYnnwCoGxIAdTsCYMyOAKj7EQB1OwJgzI4AqPsRAHU7AqBuRwDU7UqNJACWx5WjIpClAAEwS23OVSdAAIzfDARA3ZAAqNsRAGN2BEDdjwCo2xEAY3YEQN2PAKjbEQB1OwKgbkcALI8dR0UgLwIEwLzMRJVdBwEwPuEEQN2QAKjbEQBjdgRA3Y8AqNsRAGN2BEDdjwCo2xEAdbuiBcAzHx+amyXAtx39UJoYlgDr9ycjEaikAAGwkvpVfG4CYHzyCYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2BEDdrtRIXwJMACyPLUdFICsBAmBW0pyngUD9APjHGZ2sa9fNEdpIgY9qajZyBLvXCiz+ZCswAgLv17QLjK7uoQvWbl/dAIFvv3DNdoHRDF2yZhsQRIG/rWkvjmTY4lXcd5G7YOmqrSPDq3bsx4s/spnfvDV9/031SbX6/67EE4BVeyvzxRFodQECYKuTcsCWCBAAW6JUeh8CoG5IANTtfCQBUPcjAOp2BEDdzkcSAHU/AqBuRwDU7XwkAVDzK1oAPOOxY3OzBPi/Bz24SYdV7Y5iFALFESAAFmcuN6lvQgCMTxcBUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3Y4AqNuVGulLgAmA5bHlqAhkJUAAzEqa8zQQIADGbwgCoG5IANTtCIAxOwKg7kcA1O0IgDE7AqDuRwDU7QiAul3RAuDpjx6XmycA7xj8QJqYTXVptX5XMRKBYggQAIsxj5vctyAAxqeMAKgbEgB1OwJgzI4AqPsRAHU7AmDMjgCo+xEAdTsCoG5HANTtSo30JwAJgOWx5agIZCVAAMxKmvM0ECAAxm8IAqBuSADU7QiAMTsCoO5HANTtCIAxOwKg7kcA1O0IgLodAVC3IwCWx46jIpAXAQJgXmaiyq6DABifcAKgbkgA1O0IgDE7AqDuRwDU7QiAMTsCoO5HANTtCIC6XdEC4GmPfM226VT5N0IvW7jC7hzy6zQxLAHW709GIlBJAQJgJfWr+NwEwPjkEwB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6XamRBMDyuHJUBLIUIABmqc256gQIgPGbgQCoGxIAdTsCYMyOAKj7EQB1OwJgzI4AqPsRAHU7AqBuRwDU7QiA5bHjqAjkRYAAmJeZqLLrIADGJ5wAqBsSAHU7AmDMjgCo+xEAdTsCYMyOAKj7EQB1OwKgble0APidR76emyXAE4b8Kk0MS4D1+5ORCFRSgABYSf0qPjcBMD75BEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHU7AqBuV2qkLwEmAJbHlqMikJUAATArac7TQIAAGL8hCIC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2BEDdjgBYHjuOikBeBAiAeZmJKrsOAmB8wgmAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofAVC3IwDqdkULgN962JcAt9dBWmnksoXL7e5jWALcSpwcBoGKCBAAK8LOSQmA8XuAAKgbEgB1OwJgzI4AqPsRAHU7AmDMjgCo+xEAdTsCoG5HANTtSo0kAJbHlaMikKUAATBLbc5VJ0AAjN8MBEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHU7AqBuRwAsjx1HRSAvAgTAvMxElV0HATA+4QRA3ZAAqNsRAGN2BEDdjwCo2xEAY3YEQN2PAKjbEQB1u6IFwH966ITcLAH+xdD708TwFmD9/mQkApUUIABWUr+Kz00AjE8+AVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2OAKjblRrpS4AJgOWx5agIZCVAAMxKmvM0ECAAxm8IAqBuSADU7QiAMTsCoO5HANTtCIAxOwKg7kcA1O0IgLodAVC3IwCWx46jIpAXAQJgXmaiyq6DABifcAKgbkgA1O0IgDE7AqDuRwDU7QiAMTsCoO5HANTtCIC6XdEC4LCHTrT2OXgL8PKFy+2XQ+9LE8MSYP3+ZCQClRQgAFZSv4rPTQCMTz4BUDckAOp2BMCYHQFQ9yMA6nYEwJgdAVD3IwDqdgRA3Y4AqNuVGkkALI8rR0UgSwECYJbanKtOgAAYvxkIgLohAVC3IwDG7AiAuh8BULcjAMbsCIC6HwFQtyMA6nYEQN2OAFgeO46KQF4ECIB5mYkquw4CYHzCCYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2RQuAJz14Um6WAE88dmKaGJYA6/cnIxGopAABsJL6VXxuAmB88gmAuiEBULcjAMbsCIC6HwFQtyMAxuwIgLofAVC3IwDqdgRA3a7USF8CTAAsjy1HRSArAQJgVtKcp4EAATB+QxAAdUMCoG5HAIzZEQB1PwKgbkcAjNkRAHU/AqBuRwDU7QiAuh0BsDx2HBWBvAgQAPMyE1V2HQTA+IQTAHVDAqBuRwCM2REAdT8CoG5HAIzZEQB1PwKgbkcA1O2KFgBPeODk3CwBvv+4/5cmhiXA+v3JSAQqKUAArKR+FZ+bABiffAKgbkgA1O0IgDE7AqDuRwDU7QiAMTsCoO5HANTtCIC6HQFQtys10pcAEwDLY8tREchKgACYlTTnaSBAAIzfEARA3ZAAqNsRAGN2BEDdjwCo2xEAY3YEQN2PAKjbEQB1u6IFwK8/MMzad2yvg7TSyOWLltuvjvtlOhpPALYSKodBIGMBAmDG4JxuvQABMH4nEAB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6XamRBMDyuHJUBLIUIABmqc256gQIgPGbgQCoGxIAdTsCYMyOAKj7EQB1OwJgzI4AqPsRAHU7AqBuRwDU7QiA5bHjqAjkRYAAmJeZqLLrIADGJ5wAqBsSAHU7AmDMjgCo+xEAdTsCYMyOAKj7EQB1OwKgble0APi1B/7Jts7BEuAVi5bbr4/7RZoYlgDr9ycjEaikAAGwkvpVfG4CYHzyCYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2BEDdrtRIAmB5XDkqAlkKEACz1OZcdQIEwPjNQADUDQmAuh0BMGZHANT9CIC6HQEwZkcA1P0IgLodAVC3IwDqdgTA8thxVATyIkAAzMtMVNl1EADjE04A1A0JgLodATBmRwDU/QiAuh0BMGZHANT9CIC6HQFQtytaADzuN74EeBsdpJVGrli0zB74KkuAW4mTwyBQEQECYEXYOSkBMH4PEAB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6XamRBMDyuHJUBLIUIABmqc256gQIgPGbgQCoGxIAdTsCYMyOAKj7EQB1OwJgzI4AqPsRAHU7AqBuRwDU7QiA5bHjqAjkRYAAmJeZqLLrIADGJ5wAqBsSAHU7AmDMjgCo+xEAdTsCYMyOAKj7EQB1OwKgble0ADj019/KzRLgh752d5oY3gKs35+MRKCSAgTASupX8bkJgPHJJwDqhgRA3Y4AGLMjAOp+BEDdjgAYsyMA6n4EQN2OAKjbEQB1u1IjfQkwAbA8thwVgawECIBZSXOeBgIEwPgNQQDUDQmAuh0BMGZHANT9CIC6HQEwZkcA1P0IgLodAVC3IwDqdgTA8thxVATyIkAAzMtMVNl1EADjE04A1A0JgLodATBmRwDU/QiAuh0BMGZHANT9CIC6HQFQtytaABzyq2/nZgnwI1//eZoYlgDr9ycjEaikAAGwkvpVfG4CYHzyCYC6IQFQtyMAxuwIgLofAVC3IwDG7AiAuh8BULcjAOp2BEDdrtRIXwJMACyPLUdFICsBAmBW0pyngQABMH5DEAB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6HQGwPHYcFYG8CBAA8zITVXYdBMD4hBMAdUMCoG5HAIzZEQB1PwKgbkcAjNkRAHU/AqBuRwDU7YoWAAf96ju5WQL82NcnpIlhCbB+fzISgUoKEAArqV/F5yYAxiefAKgbEgB1OwJgzI4AqPsRAHU7AmDMjgCo+xEAdTsCoG5HANTtSo30JcAEwPLYclQEshIgAGYlzXkaCBAA4zcEAVA3JADqdgTAmB0BUPcjAOp2BMCYHQFQ9yMA6nYEQN2OAKjbEQDLY8dREciLAAEwLzNRZddBAIxPOAFQNyQA6nYEwJgdAVD3IwDqdgTAmB0BUPcjAOp2BEDdrmgB8Oj/PcXaddxGB2mlkSsXLbPHj/+fdDSWALcSKodBIGMBAmDG4JxuvQABMH4nEAB1QwKgbkcAjNkRAHU/AqBuRwCM2REAdT8CoG5HANTtCIC6XamRBMDyuHJUBLIUIABmqc256gQIgPGbgQCoGxIAdTsCYMyOAKj7EQB1OwJgzI4AqPsRAHU7AqBuRwDU7QiA5bHjqAjkRYAAmJeZqLLrIADGJ5wAqBsSAHU7AmDMjgCo+xEAdTsCYMyOAKj7EQB1OwKgble0AHjk/afmZgnwkyfclSaGJcD6/clIBCopQACspH4Vn5sAGJ98AqBuSADU7QiAMTsCoO5HANTtCIAxOwKg7kcA1O0IgLodAVC3KzXSlwATAMtjy1ERyEqAAJiVNOdpIEAAjN8QBEDdkACo2xEAY3YEQN2PAKjbEQBjdgRA3Y8AqNsRAHU7AqBuRwAsjx1HRSAvAgTAvMxElV0HATA+4QRA3ZAAqNsRAGN2BEDdjwCo2xEAY3YEQN2PAKjbEQB1u6IFwCPuOy03S4CfPvHONDEsAdbvT0YiUEkBAmAl9av43ATA+OQTAHVDAqBuRwCM2REAdT8CoG5HAIzZEQB1PwKgbkcA1O0IgLpdqZG+BJgAWB5bjopAVgIEwKykOU8DAQJg/IYgAOqGBEDdjgAYsyMA6n4EQN2OABizIwDqfgRA3Y4AqNsVLQAeNvH03DwBOOmkO9LE8ASgfn8yEoFKChAAK6lfxeeuHwAnP9/ROnfZvIo1tK++6JOttYGMsgWfbIdCQIAQo+PNX729PrjKR769cocqF4h9/UUrt4kdoIpHL1qGnTr9K1ZuqQ5lnJmtWdUWB0Fg7d8+sHnnXbdJh6r6/65EABRuAoYggECjAgRAboyKCBAA4+wEQN2QAKjb+UgCoO5HANTtCIC6nY8kAOp+BEDdjgCo2/lIAqDmRwDU3Job5UuAeQKwOSU+RyDfAgTAfM9PYa+OABifWgKgbkgA1O0IgDE7AqDuRwDU7QiAQTueAJQBCYAyXRpIANT8ihYAD514um3V8TMaRiuOWrXoI5vMEuBWFOVQCGQvQADM3pwzmhkBMH4bEAB1QwKgbkcAjNkRAHU/AqBuRwAM2hEAZUACoExHAAzQEQADeCWGEgDL48pREchSgACYpTbnqhMgAMZvBgKgbkgA1O0IgDE7AqDuRwDU7QiAQTsCoAxIAJTpCIABOgJgAI8AWB48jopATgQIgDmZiGq7DAJgfMYJgLohAVC3IwDG7AiAuh8BULcjAAbtCIAyIAFQpiMABuiKFgAH3Pvd3CwBnvKN29PM8BbgwA3KUAQqKEAArCB+NZ+aABiffQKgbkgA1O0IgDE7AqDuRwDU7QiAQTsCoAxIAJTpCIABOgJgAK/EUF8CTAAsjy1HRSArAQJgVtKcp4EAATB+QxAAdUMCoG5HAIzZEQB1PwKgbkcADNoRAGVAAqBMRwAM0BEAA3gEwPLgcVQEciJAAMzJRFTbZRAA4zNOANQNCYC6HQEwZkcA1P0IgLodATBoRwCUAQmAMh0BMEBXtAB4yL1n2FYdcvAW4MUf2e++8d9pZlgCHLhBGYpABQUIgBXEr+ZTEwDjs08A1A0JgLodATBmRwDU/QiAuh0BMGhHAJQBCYAyHQEwQEcADOCVGLqKAFgeWI6KQIYCBMAMsTnV3wUIgPG7gQCoGxIAdTsCYMyOAKj7EQB1OwJg0I4AKAMSAGU6AmCAjgAYwCMAlgePoyKQEwECYE4motougwAYn3ECoG5IANTtCIAxOwKg7kcA1O0IgEE7AqAMSACU6QiAAbqiBcCD/t8/52YJ8HMn/yzNDEuAAzcoQxGooAABsIL41XxqAmB89gmAuiEBULcjAMbsCIC6HwFQtyMABu0IgDIgAVCmIwAG6AiAAbwSQ30JMAGwPLYcFYGsBAiAWUlzngYCBMD4DUEA1A0JgLodATBmRwDU/QiAuh0BMGhHAJQBCYAJahB9AAAgAElEQVQyHQEwQEcADOARAMuDx1ERyIkAATAnE1Ftl0EAjM84AVA3JADqdgTAmB0BUPcjAOp2BMCgHQFQBiQAynQEwABd0QLggb8cnpslwNOG/TTNDEuAAzcoQxGooAABsIL41XxqAmB89gmAuiEBULcjAMbsCIC6HwFQtyMABu0IgDIgAVCmIwAG6AiAAbwSQ30JMAGwPLYcFYGsBAiAWUmb2Wabbdaisw0cONAmT55cct/HHnvMbrvtNps+fbotXrzYOnToYP369bMzzzzTBg0a1KLzrFixwsaNG2cTJ060OXPm2OrVq6179+52zDHH2KhRo2znnXdu0XGUnQiAilrDMQRA3ZAAqNsRAGN2BEDdjwCo2xEAg3YEQBmQACjTEQADdATAAB4BsDx4HBWBnAgQADOciNYIgOvWrbMRI0ak+NfU5hHw1ltvLRkc586dm0Lf66+/3uhhtttuO7vnnntsyJAhZREiAMZZCYC6IQFQtyMAxuwIgLofAVC3IwAG7QiAMiABUKYjAAboChcAf3GmbdnhMwGR1hn6sT8B+E/r/x2UJcCtY8pREMhagACYoXhtABw5cqSdffbZTZ65ffv21qNHj0Y/v+SSS+yaa65Jn+2///524YUXWs+ePc2D3tixY23WrFnpM9/vqquuavQYy5Yts759+9prr72WPh8+fLgNGzbM2rVrZ5MmTbJrr73WfJ+tt97apk6davvss0+rKxEA46QEQN2QAKjbEQBjdgRA3Y8AqNsRAIN2BEAZkAAo0xEAA3QEwABeiaEEwPK4clQEshQgAGaoXRsAf/CDH9jll1++0Wf2Zbq9e/e2tWvXWp8+fWzKlCkp2tVuvqTXlw/PnDnT2rZtmwKfx8FPb37uK664Iv2xR8PRo0c32MWj34ABA9J5DjvsMHvmmWc2+lqbG0AAbE6o+c8JgM0bNbUHAVC3IwDG7AiAuh8BULcjAAbtCIAyIAFQpiMABugIgAE8AmB58DgqAjkRIABmOBHRAHjOOefYzTffnK7YI92BBx64wdVPmzbN+vfvn/783HPPtZtuuqnBPmvWrLGOHTva+++/n2Liyy+/bG3atNngOL7MePz48enPPSgecMABrSpFAIxzEgB1QwKgbkcAjNkRAHU/AqBuRwAM2hEAZUACoExHAAzQFS0A9vvFWblZAjz9n9b/+yFLgAM3KEMRqKAAATBD/EgA9N/+8xd0zJs3z3r16mWvvvpqk1fun/tv+3Xr1s3eeeedBr8F+OSTT9pRRx2Vxl533XU2ZsyYRo9TPyRefPHFdvXVV7eqFAEwzkkA1A0JgLodATBmRwDU/QiAuh0BMGhHAJQBCYAyHQEwQEcADOCVGOpLgAmA5bHlqAhkJUAAzEq63luAlSXAb731Vt1y3rPOOiu95KOpzT+vfUmIj6v/e4KXXXaZXXnllWloU08R+me+/Hf77be35cuXp+XAzz77bKtKEQDjnARA3ZAAqNsRAGN2BEDdjwCo2xEAg3YEQBmQACjTEQADdATAAB4BsDx4HBWBnAgQADOciNonAPfcc88U2PzpPP+tvs6dO9tBBx1kp512WvrNvca2hx9+2IYOHZo++tGPfmTnnXdek1fun59//vnpcx9X/02+J510kt13333ps/feey9Fvqa2fffd11588UXr0KGDLVq0qFWlCIBxTgKgbkgA1O0IgDE7AqDuRwDU7QiAQTsCoAxIAJTpCIABuqIFwD73jMjNEuCZ31z/EApLgAM3KEMRqKAAATBD/NoAWOqUX/va1+zOO++07bbbrsFu/sSfvz3Yt4kTJ9qJJ57Y5GE88Hno883H+ROBtZv/buDzzz9v/qZhf9Nvqc2DowdE31atWmVbbrlli7U88JXa5s+fb/369Uu7TH6+o3XusnmLj82O6wUIgPqdQADU7XzkwjUN//MpdrTqGk0A1OebAKjbpf+bsXKb2AGqePQiAqA8+wRAmS4NXLOqbewAVTqaAFieifclwATA8thyVASyEiAAZiVtlqLbcccdZ0cccUT6Hb9tttnGFi9enJbXeqhbunRpuhp/k6//Vt8WW2xRd3U33HCDXXjhhenvH330URs0aFCTV+6f1z71d+ONN9oFF1xQt+9ee+1ls2fPtk6dOtmCBQtKfvuTTz7Z7r333rTPkiVLbIcddmixVktiZ+3BCIAtZm2wIwFQc/NRBEDdzkcSAHU/AqBuRwDU7XwkAVD3IwDqdgRA3c5HEgA1v6IFwAPuHpmbJwBf+NYtaVJ4AlC7NxmFQKUFCIAZzoC/ebepJbcLFy60wYMH26xZs9IV/fjHP7ZRo0bVXZ3/bp//fp9vTz/9tB1++OFNXvkzzzyTIqNvPu7SSy+t27dnz57mvwvoLxTxJciltlNOOcUmTJgg/Yc8AbD8NxYBUDcmAOp2BMCYHQFQ9yMA6nYEwKAdTwDKgARAmS4NJABqfgRAza25Uf4EIAGwOSU+RyDfAgTAHM2Ph7nevXvb6tWrbbfddrM333yz7uo2tScAWQJc/huLAKgbEwB1OwJgzI4AqPsRAHU7AmDQjgAoAxIAZToCYICOABjAKzGUAFgeV46KQJYCBMAstVtwrvq/uzdv3jzr2rVrGrWp/QZgc1+Vl4A0J9T85wTA5o2a2oMAqNsRAGN2BEDdjwCo2xEAg3YEQBmQACjTEQADdEULgPv/3JcAbxsQaZ2hHy/+0GZ9myXAraPJURCojAABsDLuTZ7Vf+fPn/bzbfr06da3b9/01w899JAde+yx6a8jbwH2l4fcf//96Ti8BThnk7+Rl0MA3EiwersTAHU7AmDMjgCo+xEAdTsCYNCOACgDEgBlOgJggI4AGMArMZQAWB5XjopAlgIEwCy1W3Cu0aNHm7+449MB0JcH++/3+eZv9fUnApva/PPbbrstfezjevToUber/46g/y6gb1OnTjV/K3Bj29q1a9PvFS5fvtwGDBiQXlTSmhtPAMY1CYC6IQFQt/ORvARE9yMA6nYEQN3OR/ISEN2Pl4DodgRA3c5H8huAmh8BUHNrbhQBsDkhPkcg/wIEwJzN0THHHGOPPPJIuiqPZDvttFP663Xr1lm3bt3s3XffTW8QfvXVV5u8cv8dwddeey2N9Tc01X8hxxNPPGFHH310GnvdddfZmDFjGj3OtGnTrH///umziy66yK655ppWlSIAxjkJgLohAVC3IwDG7AiAuh8BULcjAAbteAJQBiQAynRpIAFQ8ytaANxvwtm5WQL8x+/cnCaFtwBr9yajEKi0AAGw0jNQ7/z+tJ7HvTVr1tjnP/95mzt3boOrO/vss+2WW9b/7kJTT+/VD3e+/7hx4xocw18w0rFjR/vggw/SC0deeeWVBoGwducRI0bY+PHj09/WX4rcWlwEwLgkAVA3JADqdgTAmB0BUPcjAOp2BMCgHQFQBiQAynQEwAAdATCAV2KoPwFIACyPLUdFICsBAmBG0g8++KANHjzY2rZt2+gZFy5cmD6fNWtW+vyHP/yhnX/++Q32feONN2yvvfYyX57bp08fmzJlirVr165un5UrV6blujNnzkznmT17tu2+++4bnK/+MuCxY8eaLzuuv3lc9OP4eQYOHGiTJ09udSUCYJyUAKgbEgB1OwJgzI4AqPsRAHU7AmDQjgAoAxIAZToCYICOABjAIwCWB4+jIpATAQJgRhOx6667pif7TjjhhLS01v/e492SJUtSYPPf9Fu6dGm6mkMOOcSeeuop23LLLTe4Ol+O60t3fdt///3TEl7/bUB/WvD/Z+9M4K2c1sf/NEmDBk1KAxKppFTIpZAhFdfvarqGMpUyhNxCIVNFRBelpBQ39yrDvUiGaLiGUooIqZB+lSZJs6b/51n+5/zO0Tn77P2s/e693/d81+fjc09nr+dZ6/2ud7ufvtZaz0MPPZQtEGMd292yZYsTiCoUtfXs2VO6du3q5jNjxgx33Hfr1q3uzx999JE0adIk6ZQQgP5IEYB2hghAOzsEoB87BKCdHwLQzg4B6MkOAWgGiAA0o0MAeqCLmgA84bnr5aAMqAL82/pf5fNuv58u4wiwxwtKKATSSAABmCL4KvxWrFhR4GgqCJ955hlXgCOvtm/fPunRo4eMHz8+31xXX321KwJStGjRfPssW7ZM2rVrJ0uXLs2zT7ly5WTSpEnSoUOHAuds6YAAtFDLHYMAtDNEANrZIQD92CEA7fwQgHZ2CEBPdghAM0AEoBkdAtADHQLQA16MUARgMFzJCoFUEkAApoi2VtHVf/R4rd71pzv/fv31VylbtqzUqlVLTj31VOnevXt24Y2CpqWFQlTyzZs3z+WqXLmytGjRwlUI1qPE8TSt8Kt3BE6ZMkVUCOr9gDoXFYM33XST1KlTJ540pj4IQBO2XEEIQDtDBKCdHQLQjx0C0M4PAWhnhwD0ZIcANANEAJrRIQA90CEAPeAhAIOBR1YIZAgBBGCGLERhmwYC0H/FEYB2hghAOzsEoB87BKCdHwLQzg4B6MkOAWgGiAA0o0MAeqCLmgBsrEeAK5fzIJKc0N82/CqLOAKcHJhkgUCaCCAA0wS+sA+LAPR/AxCAdoYIQDs7BKAfOwSgnR8C0M4OAejJDgFoBogANKNDAHqgQwB6wIsRmgoBWKRIkbgmH2+hyk8++UQmTJjg7rlftWqV6HVa1apVk/r160ubNm3k8ssvlypVquQ75vbt2/M8Mde+fXvp06eP1K5dO675Ll68WJ544gl3z7/OQ08BHnfccXLppZeKXt+VX6HQuJLTCQIJEEAAJgCLrskjgAD0Z4kAtDNEANrZIQD92CEA7fwQgHZ2CEBPdghAM0AEoBkdAtADHQLQA15EBOCuXbvkhhtukHHjxsn+/fvzfapXX31VLrroojw/1yKbKvqWLFmS5+fly5eXF154wV2fFavpHK6//nrROeXVTjnlFHnjjTekUqVKwSwcWSGQgwACkNchLQQQgP7YEYB2hghAOzsEoB87BKCdHwLQzg4B6MkOAWgGiAA0o0MAeqCLmgBs9NwNGXME+MtuT7qVCaoKcNYOwN69e8t1112X71tQpkwZOfLII/P8XO+1V6k3bdo09/npp58u3bp1czvudKedFub8/PPP3T34w4YNy1MAbt261d2v/80337gcWoSza9euUqpUKbebcOjQoaJ9Spcu7e74b9y4cZ5zefvtt50gzNp5OHDgQDn55JPl559/lrFjx8orr7zi4lq1auXyxiri6fGVIBQC2QQQgLwMaSGAAPTHjgC0M0QA2tkhAP3YIQDt/BCAdnYIQE92CEAzQASgGR0C0AMdAtADXoxQPQKcKgE4aNAgueeee0wPcvfdd8v999/vYh955BG59dZb882ze/duKVGixAGf69j33nuv+71Kwn79+uXqo9JPpd2ePXvkzDPPlPfff/+AHPqZSkcttlmuXDlZsGCB1K1bN1c/3Rk4atQo97uJEyc6UUmDQJAEEIBB0iV3vgQQgP4vBwLQzhABaGenkWt3l/dLUIijEYD2xUcA2tlp5LodZf0SFOLodQhA8+ojAM3oXODuncX9EhTSaARgMAsfBgH43Xffufv9VOxdccUV8uyzzyYMQ2OrVq0qv/zyixN4X375ZZ4783r16iVjxoxx+efPny/NmjXLNZbuMOzcubP7ne4YvP322w+Yi94xWLNmTdm0aZM0atRIvvjii4TnSwAEEiGAAEyEFn2TRgAB6I8SAWhniAC0s0MA+rFDANr5IQDt7BCAnuwQgGaACEAzOgSgB7qoCcCGE2/MmCPAi7s/4VYm6CPA1h2AKtkeeugh0aPEeodffseEY71e7777rpx77rmuy4MPPii33XZbnt3nzJkjLVu2dJ8NGDBABg8enKufFvjQOwK1rVmzRg477LA88+QUid9++63Uq1fP4+0nFAKxCSAAeUPSQgAB6I8dAWhniAC0s0MA+rFDANr5IQDt7BCAnuwQgGaACEAzOgSgBzoEoAe8GKG6AzDTBaAesdVdgHp/n1YA1qb3761evdrtClQJp/f4xWo5jxDrUV8t0pFX0yO+FSpUkG3btrnjwLNmzcrVTSsEqyg99thjs+8SzCvPP//5T7nkkkvcR+PHj5crr7wymAUkKwREBAHIa5AWAghAf+wIQDtDBKCdHQLQjx0C0M4PAWhnhwD0ZIcANANEAJrRIQA90CEAPeBliABs0KCBu1/vxx9/dIU7VNydeuqp7liv3rmXV1u/fr07uqutT58+7h5AlXnPPfecO2KrTXNpnv79+7sKv3m1Tp06yUsvveQ+0jiVfPm1E044QRYtWiRVqlSRdevWZXfTAiF6759WIP7zn/8s//73v/PNsXDhQjnxxBPd53rXoN45SINAUAQQgEGRJW9MAghA/xcEAWhniAC0s9NI7gC080MA2tkhAO3sNJI7AO38uAPQzg4BaGenkdwBaOMXNQF43IQ+GXME+OsrHneLorvrqlevHnOB9G67RFtWFeBYcVrhd8KECVK+fO47qXUH3hlnnOFC9diuSjw9Bpxfu+WWW+TRRx894GPd8Td37lzRSsMq8mK1Dh06yNSpU12XnTt3SsmSJd3PWj1Y7w/UpoU+nnzy9+rJebUNGzY4gahNKw3rjkAaBIIigAAMiix5YxJAAPq/IAhAO0MEoJ2dRiIA7fwQgHZ2CEA7O41EANr5IQDt7BCAdnYaiQC08UMA2rgVFKVHgLMEYEF99XPd/ZZoU+l24YUXSps2bVwxj7Jly4ru7FO5N3r0aNm4caNL2bp1a9G7+nJW8H311VflL3/5i/tcRdyuXbvcbj8twKFHglXQTZs2Tf72t7+5O/m0PfXUU6J38OVsDRs2lK+++kqqVasmP/30U8xH6NKli0yePNn1UZFXqVIl9/O8efPkpJNOcj+rjNS7BPNrO3bskNKlS7uPVSi+/vrriWKjPwTiJoAAjBsVHZNJAAHoTxMBaGeIALSz00gEoJ0fAtDODgFoZ6eRCEA7PwSgnR0C0M5OIxGANn4IQBu3gqJSIQC18m5+R27Xrl0r559/vuiRWW1///vf3VHfrPaPf/xDLr/88uw/a1XeDz74QA4++OBcj7Z06VJp2rSpu7tPd96tWLEi172AWfcI1qpVyx1BjtW6desmzz//vOuSszDKf//7X3cvoLa77rpL7rvvvnzT6B2FxYoVc5+r+Jw+fXpBS8HnEDATQACa0RHoQwAB6EPv91gEoJ0hAtDOTiMRgHZ+CEA7OwSgnZ37/4wdZf0SFOJoBKB98RGAdnYaiQC08YuaAKz/bOYcAf7mymCPABe04lrgQ4/W/vbbb3L00UeLyryspkd+9f6+rPbWW2/Jeeedl2dKvWvvkUcecZ+99tprcsEFF2T3YwdgQavA52EmgAAM8+qFeO4IQP/FQwDaGSIA7ewQgH7sEIB2fghAOzsEoCc7ioCYASIAzehcIALQxg8BaONWUJTuAMwSgDl3uxUUl+zPc967t2rVKqlRo4Yb4u2335a2bdu6nw866CDZsmWL+9+8mu6yO+ecc9xHf9yhxx2AyV4x8mUSAQRgJq1GIZoLAtB/sRGAdoYIQDs7BKAfOwSgnR8C0M4OAejJDgFoBogANKNDAHqgi5wAHH+TlKhczoNIckJ3qwC86u8uWToFoFbwffjhh908tBiJ3u+nTe/t09172go6vrtkyRJ3x6C2a6+91t0vmNU6duwoL7/8svujTxXgQw45xOWgCnBy3j+yJIcAAjA5HMmSIAEEYILA8uiOALQzRADa2WkkR4Dt/BCAdnYIQDs7jeQIsJ0fR4Dt7BCAdnYayQ5AGz8EoI1bQVGZIgBzHt/NKQB3797tKvfq/+quQN0dmF/LKQv/WKX37rvvlvvvv9+Ffvzxx6I7AvNqe/bscfcV6l2Cet+fFirJ2WrXru1E6bHHHuuqAufXtOrvJZdc4j4eP368XHnllQUtBZ9DwEwAAWhGR6APgZwC8F8fHSlVqpfwSVcoY1fu/r3KFC1xAit2VU48iIhsAku3VYWGkcDKrRWMkYSt/fX3/5JOsxHYuT3vY1C2bIUrav+24oXrgZP4tMV+/f1ie5qNQPEdRWyBhTxq9+Zf5PvhvxddSOdONZ9lyPl3pfrsAMyFsn379vLmm2+63ymnww8/PPtzrQ48e/ZsV1Rj8+bNTgjm1bTSrlYb1jZkyBC54447sru988472XcHavVereKbV5szZ460bNnSfaTxmidnU6mnck+bVh0+7LDD8syjVYjHjBnjPtOdicccc4zPq0MsBGISQADygqSFAALQHzsC0M4QAWhnp5EIQDs/BKCdHQLQzk4jEYB2fghAOzsEoJ2dRiIAbfyiJgCPHXdzxhwBXnL1CLco6RKrWgREj+7qLr+jjjpKli9fnuslefzxx+Wmm25yv5s0aVL2zro/vkm6y27ChAnu11qx97TTTsvuogVGqlat6gSiFhxZvHixFClyoIzPKe5y7kTMSjR58mTp0qWL++PQoUPl9ttvP+CF3r59u9SsWdMdNW7QoIEbiwaBIAkgAIOkS+58CSAA/V8OBKCdIQLQzk4jEYB2fghAOzsEoJ2dRiIA7fwQgHZ2CEA7O41EANr4IQBt3AqK0iPAQQpA3ZV3/vnnS/Hiee+6Xrt2rft84cKFbqrDhw+Xvn375pr21q1bpW7durJu3TqpU6eOzJ07V6pVq5arz8yZM+Xss8+WvXv3SqNGjWTRokUHCL6cx4CHDRsmeuw4Z9OjwXrsV48B665DzfnHppJSBaJKynLlysmCBQvc3HI2PX48atQo96tnn31WrrjiioKWgc8h4EUAAeiFj2ArAQSgldz/xSEA7QwRgHZ2GokAtPNDANrZIQDt7DQSAWjnhwC0s0MA2tlpJALQxg8BaONWUFTQAvCII45wO/suvvhid7RW/1yqVCnZsGGDE2xaqGPjxo1umrpjTyv5lixZ8oBpv/jii/LXv/5V9u/f74qB6M67k046SXbu3CnTpk2Txx57THbs2OFEo+b905/+dEAOrSDcvHlz+fbbb91nPXv2lK5du7r5zJgxwx33Vdmof/7oo4+kSZMmeeLTo8oXXHCB7Nu3z4nIO++8081Fd/yNHTs2u9iIPo/ORY8u0yAQJAEEYJB0yZ0vAQSg/8uBALQzRADa2SEA/dghAO38EIB2dghAP3YIQDs/BKCdHQLQzi5qArDeM7dkzBHgpdc85hYmiCPAKvxWrFhR4MKrIHzmmWdcAY782siRI93uQD3Om1crW7as/OMf/3AVevNry5Ytk3bt2snSpUvz7KK7+vSYcYcOHWLOWUXfDTfckO9cVAhOnTpVKlfmjvICF58O3gQQgN4ISWAhgAC0UMsdgwC0M0QA2tkhAP3YIQDt/BCAdnYIQD92CEA7PwSgnR0C0M4OAWhnFytSdwAGKQC1iq7+o8dr9a4/3fn366+/iso63cl36qmnSvfu3bMLbxT0lHqfnorAd99911UE1t11em9g27Zt5eabb5bq1asXlMJV+NUcU6ZMERWCKhR1LioG9a5BPWYcT/vyyy9F7yd87733ZPXq1a44iR4PvvTSS+Waa67J99hzPLnpA4FECCAAE6FF36QRQAD6o0QA2hkiAO3sEIB+7BCAdn4IQDs7BKAfOwSgnR8C0M4OAWhnhwC0s0unAAxm1mSFAARyEkAA8j6khQAC0B87AtDOEAFoZ4cA9GOHALTzQwDa2SEA/dghAO38EIB2dghAO7uoCcCjx2bOEeBlPYI7AmxfcSIhAIF4CSAA4yVFv6QSQAD640QA2hkiAO3sEIB+7BCAdn4IQDs7BKAfOwSgnR8C0M4OAWhnhwC0s4sVqUeAEYDBsCUrBFJFAAGYKtKMk4sAAtD/hUAA2hkiAO3sEIB+7BCAdn4IQDs7BKAfOwSgnR8C0M4OAWhnhwC0s0MABsOOrBDIFAIIwExZiUI2DwSg/4IjAO0MEYB2dghAP3YIQDs/BKCdHQLQjx0C0M4PAWhnhwC0s4uaAKz7tB4BLm8HkqTI3Rs2y/KeHAFOEk7SQCAtBBCAacHOoAhA/3cAAWhniAC0s0MA+rFDANr5IQDt7BCAfuwQgHZ+CEA7OwSgnR0C0M4uViQCMBiuZIVAKgkgAFNJm7GyCSAA/V8GBKCdIQLQzg4B6McOAWjnhwC0s0MA+rFDANr5IQDt7BCAdnYIQDs7BGAw7NKZ9bnnnnPDX3TRRVKuXLm4prJ161Z55ZVXXN9u3brFFUOncBBAAIZjnSI3SwSg/5IiAO0MEYB2dghAP3YIQDs/BKCdHQLQjx0C0M4PAWhnhwC0s4uaADzq6b5SolIGHAHeuFm+6/moW5iVK1dKzZo17YtEZMoIFC1aVIoUKSJffPGFNGjQIK5xly9fLvXq1RON3bNnT1wxdAoHAQRgONYpcrNEAPovKQLQzhABaGeHAPRjhwC080MA2tkhAP3YIQDt/BCAdnYIQDs7BKCdXazI3QjAYMAGnNVHAKo43Lt3b8AzJH0qCSAAU0mbsbIJIAD9XwYEoJ0hAtDODgHoxw4BaOeHALSzQwD6sUMA2vkhAO3sEIB2dghAOzsEYDDs0pnVIgCXLFkixx13nJQoUUJ27dqVzukzdpIJIACTDJR08RFAAMbHKVYvBKCdIQLQzg4B6McOAWjnhwC0s0MA+rFDANr5IQDt7BCAdnZRE4BHZtAR4O85Amx/MdMUaRGAr732mrszsFq1arJmzZo0zZxhgyCAAAyCKjkLJIAALBBRgR0QgAUiyrcDAtDODgHoxw4BaOeHALSzQwD6sUMA2vkhAO3sEIB2dghAO7tYkXoEGAEYDNtkZp09e3audGeccYa7A3D8+PFy5JFHxhxKd/vp/X+PPPKIfP/993LuuefKtGnTkjk9cqWZAAIwzQtQWIdHAPqvPALQzhABaGeHAPRjhwC080MA2tkhAP3YIQDt/BCAdnYIQDs7BKCdHZihx1kAACAASURBVAIwGHapzJq14y9rzP3797sfVQLG2zRG+7/88stuJyAtOgQQgNFZy1A9CQLQf7kQgHaGCEA7OwSgHzsEoJ0fAtDODgHoxw4BaOeHALSzQwDa2UVNAB4x5taMqQL8w7XD3cJQBdj+fgYdqQLQt2mF5wEDBkivXr18UxGfYQQQgBm2IIVlOghA/5VGANoZIgDt7BCAfuwQgHZ+CEA7OwSgHzsEoJ0fAtDODgFoZ4cAtLOLFalHgBGAwbBNZtZZs2Zlp9OdfGeddZbbzTdu3LiYR4C1z8EHHyzVq1eXWrVqJXNK5MogAgjADFqMwjQVBKD/aiMA7QwRgHZ2CEA/dghAOz8EoJ0dAtCPHQLQzg8BaGeHALSzQwDa2SEAg2GXzqyWIiDpnC9jB0sAARgsX7LnQwAB6P9qIADtDBGAdnYIQD92CEA7PwSgnR0C0I8dAtDODwFoZ4cAtLOLnAAcnUFHgHtxBNj+ZqYncsWKFW7gww8/XIoXL56eSTBqxhBAAGbMUhSuiSAA/dcbAWhniAC0s0MA+rFDANr5IQDt7BCAfuwQgHZ+CEA7OwSgnR0C0M4uVqQ7AowADAYuWSGQIgIIwBSBZpjcBBCA/m8EAtDOEAFoZ4cA9GOHALTzQwDa2SEA/dghAO38EIB2dghAO7vICcCn/ibFK5W3A0lS5B4VgL0fcdkoApIkqBmS5vXXX5fJkyfLhg0b3D2BPXr0kKZNm2bI7JhGMgkgAJNJk1xxE0AAxo0q344IQDtDBKCdHQLQjx0C0M4PAWhnhwD0Y4cAtPNDANrZIQDt7BCAdnaxIhGAwXANOuuMGTOkS5cursDHokWLpEKFCrmGvOuuu2TIkCG5flesWDF59tln5dJLLw16euRPMQEEYIqBM9zvBBCA/m8CAtDOEAFoZ4cA9GOHALTzQwDa2SEA/dghAO38EIB2dghAOzsEoJ0dAjAYdunM2q9fPxk+fLh07NjR7fLL2VQI6k4/rRasrWLFirJp0yb3c6lSpeSrr76SOnXqpHP6jJ1kAgjAJAMlXXwEEIDxcYrVCwFoZ4gAtLNDAPqxQwDa+SEA7ewQgH7sEIB2fghAOzsEoJ1d1ARgnVGZcwR4xXUcAba/memJbNmypXzyyScyevRod7Q3Z+vdu7eMGTPGib/p06c7GTh//nxp27atE4H9+/eXoUOHpmfijBoIAQRgIFhJWhABBGBBhAr+HAFYMKP8eiAA7ewQgH7sEIB2fghAOzsEoB87BKCdHwLQzg4BaGeHALSzixWpR4ARgMGwDTKr3un3448/ysyZM+X000/PNVStWrVk9erVMnDgQLnvvvuyP7vnnnvcn5s1aybz5s0LcnrkTjEBBGCKgTPc7wQQgP5vAgLQzhABaGeHAPRjhwC080MA2tkhAP3YIQDt/BCAdnYIQDs7BKCdHQIwGHbpzFqmTBnZuXOnLFy4UBo3bpw9leXLl0u9evWkSJEi8umnn0qTJk2yP3vvvffknHPOkfLly2cfCU7nMzB28gggAJPHkkwJEEAAJgArn64IQDtDBKCdHQLQjx0C0M4PAWhnhwD0Y4cAtPNDANrZIQDt7KImAGuP7JcxVYB/vP5htzBUAba/n6mOLF26tOzateuAHYATJkyQq666yhUF+fnnn3NN67PPPpMTTzxRSpQo4WJp0SGAAIzOWobqSRCA/suFALQzRADa2SEA/dghAO38EIB2dghAP3YIQDs/BKCdHQLQzg4BaGcXK1KPACMAg2EbZNajjjpKVqxYIU899ZT07Nkze6i//vWv8uKLL0r79u3l9ddfzzWFDz/80B0Xrlq1qvz0009BTo/cKSaAAEwxcIb7nQAC0P9NQADaGSIA7ewQgH7sEIB2fghAOzsEoB87BKCdHwLQzg4BaGeHALSzQwAGwy6dWS+55BL517/+JSeccIKo2NMdgd999500atTI7e577LHHpE+fPrmm+Oyzz8rVV1/tjgzrbkBadAggAKOzlqF6EgSg/3IhAO0MEYB2dghAP3YIQDs/BKCdHQLQjx0C0M4PAWhnhwC0s4ucAHyyf+YcAb5hmFsYjgDb389UR77//vty9tlnu7v+6tSp4472zp49WzZs2OBk4A8//CCVK1fONa0rr7xSJk6cKF27dpUXXngh1VNmvAAJIAADhEvq/AkgAP3fDgSgnSEC0M4OAejHDgFo54cAtLNDAPqxQwDa+SEA7ewQgHZ2CEA7u1iR7ggwAjAYuAFn7du3r4wYMcKNoiJw//797ueRI0dK7969c42uBUNq1KghmzdvlmeeeUZUBtKiQwABGJ21DNWTIAD9lwsBaGeIALSzQwD6sUMA2vkhAO3sEIB+7BCAdn4IQDs7BKCdHQLQzg4BGAy7TMj6xhtvyJQpU9ydftWrV5du3brJWWeddcDUJk+eLP3793ei8L///a/UrFkzE6bPHJJEAAGYJJCkSYwAAjAxXnn1RgDaGSIA7ewQgH7sEIB2fghAOzsEoB87BKCdHwLQzg4BaGcXNQFY64nMOQK88kaOANvfTCIhkH4CCMD0r0GhnAEC0H/ZEYB2hghAOzsEoB87BKCdHwLQzg4B6McOAWjnhwC0s0MA2tkhAO3sYkXqEWAEYDBsyQqBVBFAAKaKNOPkIoAA9H8hEIB2hghAOzsEoB87BKCdHwLQzg4B6McOAWjnhwC0s0MA2tkhAO3sEIDBsCMrBDKFAAIwU1aikM0DAei/4AhAO0MEoJ0dAtCPHQLQzg8BaGeHAPRjhwC080MA2tkhAO3sIicAH8+gI8B9OAJsfzOJhED6CSAA078GhXIGCED/ZUcA2hkiAO3sEIB+7BCAdn4IQDs7BKAfOwSgnR8C0M4OAWhnhwC0s4sV6Y4AIwCDgZuErMWKFUtCltwptBDInj17kp6XhOkjgABMH/tCPTIC0H/5EYB2hghAOzsEoB87BKCdHwLQzg4B6McOAWjnhwC0s0MA2tkhAO3sEIDBsEtF1qJFiyZ9GBWAe/fuTXpeEqaPAAIwfewL9cgIQP/lRwDaGSIA7ewQgH7sEIB2fghAOzsEoB87BKCdHwLQzg4BaGcXNQFY8++3SfFK5e1AkhSpOwD/96aHXLaVK1dKzZo1k5SZNMkgcO+99yYjzQE5Bg0aFEhekqaHAAIwPdwL/agIQP9XAAFoZ4gAtLNDAPqxQwDa+SEA7ewQgH7sEIB2fghAOzsEoJ0dAtDOLlYkAjAYrmSFQCoJIABTSZuxsgkgAP1fBgSgnSEC0M4OAejHDgFo54cAtLNDAPqxQwDa+SEA7ewQgHZ2CEA7OwRgMOzICoFMIYAAzJSVKGTzQAD6LzgC0M4QAWhnhwD0Y4cAtPNDANrZIQD92CEA7fwQgHZ2CEA7u0gKwEMz4AjwzxwBtr+VREIgMwggADNjHQrdLBCA/kuOALQzRADa2SEA/dghAO38EIB2dghAP3YIQDs/BKCdHQLQzg4BaGcXK3IPAjAYsCnI+uOPP7pRqlWrJiVLlow54s6dO2XdunWuT+3atVMwO4ZIJQEEYCppM1Y2AQSg/8uAALQzRADa2SEA/dghAO38EIB2dghAP3YIQDs/BKCdHQLQzg4BaGeHAAyGXTqzfvDBB9KqVSs55JBD5IcffpCKFSvGnM6mTZukTp06sn37dvnoo4/kpJNOSuf0GTvJBBCASQZKuvgIIADj4xSrFwLQzhABaGeHAPRjhwC080MA2tkhAP3YIQDt/BCAdnYIQDu7qAnAw0fcLsUz5AjwqpsfdAtDFWD7+5nqyBtvvFFGjhwpl19+uUycODGu4a+88krX9+abb5ZHH300rhg6hYMAAjAc6xS5WSIA/ZcUAWhniAC0s0MA+rFDANr5IQDt7BCAfuwQgHZ+CEA7OwSgnR0C0M4uVqQeAUYABsM2yKzNmzeXhQsXOqF32WWXxTXUP//5T7n00ktFYz/55JO4YugUDgIIwHCsU+RmiQD0X1IEoJ0hAtDODgHoxw4BaOeHALSzQwD6sUMA2vkhAO3sEIB2dpETgI9l0A7AW9gBaH8z0xOp9/5t2LBB5syZIy1atIhrEvPmzZOTTz5ZqlevLqtWrYorhk7hIIAADMc6RW6WCED/JUUA2hkiAO3sEIB+7BCAdn4IQDs7BKAfOwSgnR8C0M4OAWhnhwC0s4sV6XYAIgCDgRtg1oMPPlh2794tKvVOPPHEuEZasGCB2/130EEHiRYFoUWHAAIwOmsZqifJKQAfmtVCDj0sdjWiUD1ciib7zY4aKRopesN8vbla9B4qhU/0v7+WT+Fo0Rpq889lo/VAKXyaor8UT+Fo0Ruq+Jai0XuoFD1RiS0pGiiiwyx+8JaIPhmPlakEcv49I6x31eV8hsPZAZipr1oo5lWzZk1Zs2aNvPjii9KxY8e45vzSSy9J586dXdVgjaVFhwACMDprGaonQQD6LxcC0M4QAWhnp5EIQDs/BKCdHQLQzk4jEYB2fghAOzuNRAD68SM6cQLRE4B3ZE4RkFuGugUJq1hN/G0Kf0SHDh1k2rRp8pe//EWmTJkS1wOpKHzllVekTZs28u6778YVQ6dwEEAAhmOdIjdLBKD/kiIA7QwRgHZ2CEA/dghAOz8EoJ0dAtCPHQLQjx8C0I8f0YkTQAAmziyeiN+PACMA42GVSX3Gjh0r1157rRQpUkT+9a9/SadOnWJOb/LkydK1a1fX//HHH5frr78+kx6HuXgSQAB6AiTcRgABaOOWMwoBaGeIALSzQwD6sUMA2vkhAO3sEIB+7BCAfvwQgH78iE6cAAIwcWbxRCAA46GUeX30/r9jjz1WfvjhBylWrJj06dNHbr75ZqlVq1auyequzscee0yeeOIJ2bdvn/t8yZIlUrIkV3Vl3qraZ4QAtLMj0oMAAtAD3v8PRQDaGSIA7ewQgH7sEIB2fghAOzsEoB87BKAfPwSgHz+iEycQOQH4aAYdAe7LDsDE38j0R3z22WfSqlUr2bp1q9vZp6127dquyq/+efXq1fLjjz+63+/fv1/Kli0rs2bNkqZNm6Z/8swgqQQQgEnFSbJ4CSAA4yWVfz8EoJ0hAtDODgHoxw4BaOeHALSzQwD6sUMA+vFDAPrxIzpxAgjAxJnFE+F2ACIA40GVkX2++uorueyyy0RlYFbLkoEq/bJas2bN5Pnnn5f69etn5HMwKT8CCEA/fkQbCSAAjeByhCEA7QwRgHZ2CEA/dghAOz8EoJ0dAtCPHQLQjx8C0I8f0YkTQAAmziyeCARgPJQyv88777wjU6dOlYULF8qGDRvchCtXriwnnniiXHDBBa7wBy26BBCA0V3bjH4yBKD/8iAA7QwRgHZ2CEA/dghAOz8EoJ0dAtCPHQLQjx8C0I8f0YkTiJwAHJ5BR4Bv5Qhw4m8kERDIHAIIwMxZi0I1EwSg/3IjAO0MEYB2dghAP3YIQDs/BKCdHQLQjx0C0I8fAtCPH9GJE0AAJs4sngi3AxABGA+qSPdZunSpnHfeee7uwOXLl0f6WaP4cAjAKK5qCJ4JAei/SAhAO0MEoJ0dAtCPHQLQzg8BaGeHAPRjhwD044cA9ONHdOIEEICJM4snAgEYD6Xo91m8eLEcf/zxTgDu3bs3+g8csSdEAEZsQcPyOAhA/5VCANoZIgDt7BCAfuwQgHZ+CEA7OwSgHzsEoB8/BKAfP6ITJxA5AfiIHgGukDiIJEfs+fkXWfU3jgAnGWvo0iEAQ7dkuSaMAAz3+oV29ghA/6VDANoZIgDt7BCAfuwQgHZ+CEA7OwSgHzsEoB8/BKAfP6ITJ4AATJxZPBEIwHgoRb8PAjDca4wADPf6hXb2CED/pUMA2hkiAO3sEIB+7BCAdn4IQDs7BKAfOwSgHz8EoB8/ohMngABMnFk8EQjAeChFvw8CMNxrjAAM9/qFdvYIQP+lQwDaGSIA7ewQgH7sEIB2fghAOzsEoB87BKAfPwSgHz+iEycQNQFY4+HMOQK8uh9HgBN/I6MVgQAM93oiAMO9fqGdPQLQf+kQgHaGCEA7OwSgHzsEoJ0fAtDODgHoxw4B6McPAejHj+jECSAAE2cWT4TuAEQAxkMq2n0QgOFeXwRguNcvtLNHAPovHQLQzhABaGeHAPRjhwC080MA2tkhAP3YIQD9+CEA/fgRnTgBBGDizOKJQADGQyn6fRCA4V5jBGAGrF///v3l4Ycfzp7JjBkz5Iwzzog5s7feekuefvpp+eSTT2T9+vVSpUoVOemkk6Rnz57Stm3buJ5q+/btMnLkSJkyZYosW7ZMfvvtN6lVq5a0b99e+vTpI7Vr144rj6UTAtBCLXcMAtDOEAFoZ4cA9GOHALTzQwDa2SEA/dghAP34IQD9+BGdOIHICcBhGXQEuD9HgBN/I6MVgQAM93oiANO8fp9//rk0b95c9uzZE5cA3L9/v/Tq1cvJv/yaSsDRo0dLkSJF8u2zfPlyJ/qWLFmSZ5/y5cvLCy+8IO3atQuEEALQHysC0M4QAWhnhwD0Y4cAtPNDANrZIQD92CEA/fghAP34EZ04AQRg4sziiXA7ABGA8aCKdB8EYLiXFwGYxvXbt2+fnHLKKTJv3jypWrWqrFu3zs0m1g7AgQMHypAhQ1y/pk2biu4erFu3rqjQGzZsmCxcuNB9pv0eeOCBPJ9u69at0qJFC/nmm2/c5z169JCuXbtKqVKl3NhDhw4V7VO6dGn5+OOPpXHjxkmnhAD0R4oAtDNEANrZIQD92CEA7fwQgHZ2CEA/dghAP34IQD9+RCdOAAGYOLN4IhCA8VCKfh8EYLjXGAGYxvUbMWKE3HLLLVK/fn35n//5HyfeYglAPaZ73HHHud2Cumtw9uzZTtplNT3S27p1a5k/f74UL17cCT6Vg39s99xzj9x7773u1yoN+/Xrl6uLSr9WrVq5cc4880x5//33k04JAeiPFAFoZ4gAtLNDAPqxQwDa+SEA7ewQgH7sEIB+/BCAfvyITpxA5ATgQwOk+KEVEgeR5AgnAG/7fSPKypUrpWbNmkkegXRhIIAADMMq5T9HBGCa1k//pdmgQQO300533c2cOTNbyuW3A/D666+XUaNGuRmrpNPdg39sc+bMkZYtW7pf33DDDfLEE0/k6rJ792632/CXX35xMvHLL7+UokWLHpBHjxmPGTPG/V6FYrNmzZJKCgHojxMBaGeIALSz08j//bW8X4JCHI0AtC8+AtDOTiOLbznw/+v9MhaeaASg31ojAP34EZ04AQRg4sziiUAAxkMp+n0QgOFeYwRgmtbvggsukDfeeEO6d+8uEyZMkJy78vISgHr3nxboWLVqldsx+PXXX+c7c/1c7/bT/yrz448/5roL8N1335Vzzz3XxT744INy22235Zknp0gcMGCADB48OKmkEID+OBGAdoYIQDs7BKAfOwSgnR8C0M4OAejHDgHoxw8B6MeP6MQJIAATZxZPBAIwHkrR74MADPcaIwDTsH6TJ0+WLl26yKGHHuqO6WoF34IE4HfffZd9nPfaa691RT7ya/p5VpEQjTvyyCOzu959991y//33uz/nt4tQP9PjvxUqVJBt27a548CzZs1KKikEoD9OBKCdIQLQzg4B6McOAWjnhwC0s0MA+rFDAPrxQwD68SM6cQJRE4CH6xHgihlwBHjTL7KKI8CJv5ARi1i/fn32qcRBgwZF7Omi/zgIwBSvcdbR259++knGjh0r11xzjZtBQQJw6tSp0qFDB9f3sccek5tvvjnfmevnffv2dZ9rXM5Kvp06dZKXXnrJfbZp0yYn+fJrJ5xwgixatMgJyqwCJcnChQD0J4kAtDNEANrZIQD92CEA7fwQgHZ2CEA/dghAP34IQD9+RCdOAAGYOLN4IvYgAOPBRB8IZDQBBGCKl6dnz55O/J166qnywQcfZB/PLUgA6o6/3r17u9lOmTJFOnbsmO/MVfCp6NOmcbojMKvpvYFz586VMmXKuPsHYzUVjioQte3cuVNKliwZNy39P95Ybc2aNXLSSSe5Lg/NaiGHHhZ/7rgnEfGOCED7AiMA7ew0kjsA7fwQgHZ2CEA7O43kDkA7PwSgnZ1GIgD9+BGdOIHICcAHM2gH4O0UAUn8jUxNhBYIDaLpaUBadAggAFO4lir89AtUrFgxWbBggRx//PHZoxckAB9++GHp37+/6z9t2jRp27ZtvjPXz7N2/T3yyCNy6623Zvdt2LChfPXVV1KtWjXRXYixmh5T1uPK2jZs2CCVKlWKm1aRIkXi7osAjBtVro4IQBs3jUIA2tlpJALQzg8BaGeHALSz00gEoJ0fAtDOTiMRgH78iE6cAAIwcWbxRLgdgAjAeFClpY8W9kzk7+DxTFLz6dVgtOgQQACmaC1/++03adKkiSve0a9fPxk2bFiukQsSgHpvn97fp+29996Ts846K9+Zv//++9KmTRv3ucbdeeed2X3r1q0rei+gFhTRAiGxWrdu3eT55593XRIt9Z7Iv3wQgLaXEAFo46ZRCEA7O41EANr5IQDt7BCAdnYaiQC080MA2tlpJALQjx/RiRNAACbOLJ4IBGA8lNLXRwVgspv+nX7v3r3JTku+NBJAAKYIfpbgq127ttuBp0dwc7aCBGDYdgByBDj4FwsBaGeMALSzQwD6sUMA2vkhAO3sEIB+7BCAfvwQgH78iE6cQOQE4NCBmVME5I7BbkES3RyS+CoSkSiBWEU7dTOSbgqaN2+eu9+/c+fO7jouPRW4f/9+d9+/fqan//Rn/eyBBx6QEiVKSOvWrROdCv0zmAACMAWLo5V+taCGfvH+85//yIUXXnjAqAUJwLDdAVgQVoqAFESo4M8RgAUzyq8HAtDODgHoxw4BaOeHALSzQwD6sUMA+vFDAPrxIzpxAgjAxJnFE+F2ACIA40GVUX1U8LVv317efvttueqqq2TEiBEHbEbKmvD27dtdsdFnnnnGXTn25ptvZtSzMBl/AghAf4YFZtAiHE8//bQcddRRMnjw7//V5I9NC3e8/PLL7td33XWXNGjQwP18wQUXuC/oG2+84X7W5lMFWIuHZI1DFeACly6jOyAA7cuDALSz00iOANv5IQDt7BCAdnYayRFgOz8EoJ2dRiIA/fgRnTgBBGDizOKJQADGQynz+qjM00Kk55xzjpOA8bTzzjtPpk+fLk899ZSLpUWHAAIwBWt5xRVXyMSJE00jff/993LEEUe4e/v0/j5tKhR1R2B+LUs46ucad+SRR2Z31XsE9V5AbR9//LFoVeC8ml72WaFCBdm2bZsrXBJrS7HlwdgBaKGWOwYBaGeIALSz00gEoJ0fAtDODgFoZ6eRCEA7PwSgnZ1GIgD9+BGdOIHICcAhGXQEeABHgBN/I9Mbcdppp7m/97/yyivy5z//Oa7JvPbaa3LRRRdJy5Yt5cMPP4wrhk7hIIAATME6JUMA6tbdmjVryurVq6V+/fqumEh+7bjjjhM9dnz44Ye7+xlyFuR45513RI2+tgcffFBuu+22PNPMmTPHfeG13XHHHTJkyO8l35PVEID+JBGAdoYIQDs7jUQA2vkhAO3sEIB2dhqJALTzQwDa2WkkAtCPH9GJE0AAJs4sngi3AxABGA+qjOpTuXJl0ZN/8+fPl6ZNm8Y1t4ULF0qzZs2kYsWKsnHjxrhi6BQOAoVaAGoV3FdffVWWLVsmWjVHd8rpMdusnXapXMKC7gDUuVx33XVuG662/Hbv5RR32n/kyJG5HkPvIaxataps3rxZVBQuXrw4z3LhvXr1kjFjxrjYTz75RFq0aJFUHAhAf5wIQDtDBKCdHQLQjx0C0M4PAWhnhwD0Y4cA9OOHAPTjR3TiBBCAiTOLJwIBGA+lzOtTtmxZ2bFjh/MeedUiyGvGWTsAS5cuLVu3bs28h2JGZgKREoB6bHX8+PEOxvHHH5+9gy0vOvfdd5+7j09jcjYVgX369JHhw4eboVoC4xGA3377rTRs2NDNuXnz5jJ79mwpVapU9nD6xdbjumr3ixcv7qoN16tX74Dp5DwGPGzYMOnXr1+uPioXNY+Oo1V/Zs6caXmkmDEIQH+kCEA7QwSgnZ1GsgPQzg8BaGeHALSz00h2ANr5IQDt7DQSAejHj+jECUROAA7OoCPAAzkCnPgbmd6IJk2ayBdffOFOAcZb1KNdu3by1ltvOafy+eefp/cBGD2pBCIlAHX326mnnup2tOlR1zZt2uQJ6+GHH8736KsGaHzfvn1F+6WqxSMAdS56HFeP7mrTLbx6hFd3LC5fvlweeugh0e26Wf3yO7a7ZcsWJxBVKGrTiz27du3qZOKMGTPccV81/frnjz76SPRfGsluCEB/oghAO0MEoJ0dAtCPHQLQzg8BaGeHAPRjhwD044cA9ONHdOIEEICJM4snwu0ARADGgyqj+uimJy0yqo5DawU8+uijcvDBB+c5x127dsmtt94qo0aNcv0feOAB5x9o0SEQKQGoAkxf0Dp16ogWz8ir6R16Rx99tOzcudN9/Kc//cmVw65Ro4bMmzfP7fzT47HFihXLdwddEMsfrwDct2+f9OjRI3unY15zufrqq13VYd3NmF/TY89q9pcuXZpnl3LlysmkSZOkQ4cOQTyuIAD9sSIA7QwRgHZ2CEA/dghAOz8EoJ0dAtCPHQLQjx8C0I8f0YkTQAAmziyeCARgPJQyr496D904tGTJEif1qlWrJp07d3ZXfOnVYPq7tWvXOhcyZcoU+emnn0TrD2jdAd1cVLJkycx7KGZkJhApAaiyatq0aXLjjTfKiBEj8oSiFluPwOqLrlVwXn755Vx34OkWV62Mq3flDRgwILtirplwnIHxCsCsdLp9VyWfflE3bNggermnfonV6p9//vlxjaoVfvWOQP2iqxDUZ65Vq5YTgzfddJMTqUE1BKA/WQSgnSEC0M4OAejHDgFo54cAtLNDAPqxQwD68UMA+vEjOnECkROAD2TQEeA7OQKc+BuZ/og1qOsLYwAAIABJREFUa9ZI+/bt5bPPPnOTyVkkNOfsVPxpU2H4xhtvSPXq1dM/eWaQVAKREoCNGzd2RS1eeOEF6dKlS56gTj75ZCfN9KVXC667Af/YVKKNHTvW3X+nR2JpySeAAPRnigC0M0QA2tkhAP3YIQDt/BCAdnYIQD92CEA/fghAP35EJ04AAZg4s3gi3A5ABGA8qDKyz969e93R3tGjR8vXX3+d5xy1SGjv3r3dP3oikhY9ApESgIcddpisX7/e3Vunou+Pbfv27VKhQgXRl18vtMwy4H/spxVyLr74YtF8emSYlnwCCEB/pghAO0MEoJ0dAtCPHQLQzg8BaGeHAPRjhwD044cA9ONHdOIEEICJM4snAgEYD6Vw9NEdgVoYZNOmTe6476GHHur8CDv+wrF+PrOMlADU8+lauVbPqutuwD82rZp7xhlnuN1/vXr1csdf82oLFixwRTL0ckyVhrTkE0AA+jNFANoZIgDt7BCAfuwQgHZ+CEA7OwSgHzsEoB8/BKAfP6ITJxA5AXj/nVK8YoXEQSQ5wgnAux5wWVeuXCk1a9ZM8gikgwAEgiYQKQFYsWJF+fXXX/OtAJxV/VcFoG591WIaeTUViM2aNZMSJUqIVsKhJZ8AAtCfKQLQzhABaGeHAPRjhwC080MA2tkhAP3YIQD9+CEA/fgRnTgBBGDizOKJQADGQ4k+EMhsApESgHpZ5aJFi+Tee++VO++88wDyevGlFglRAajFPho1apTn6rzzzjvStm1bV1hj3bp1mb2CIZ0dAtB/4RCAdoYIQDs7BKAfOwSgnR8C0M4OAejHDgHoxw8B6MeP6MQJIAATZxZPBAIwHkr0gUBmE4iUANTqv3qst0aNGu5iy0MOOSSb/ooVK6RevXru/j/9XLct59eGDRsmt99+u6uqO3fu3MxewZDODgHov3AIQDtDBKCdHQLQjx0C0M4PAWhnhwD0Y4cA9OOHAPTjR3TiBKImAGvelzlHgP/3bo4AJ/5GpibiqquuSvpAunFq3LhxSc9LwvQRiJQA1KIeenRXW5MmTWTw4MFSt25dJwNvu+02V/VXX+I77rhDHnjg93955dXOO+88mT59ulx99dXy9NNPp291IjwyAtB/cRGAdoYIQDs7BKAfOwSgnR8C0M4OAejHDgHoxw8B6MeP6MQJIAATZxZPhO4ARADGQyo9fYoWLepcR7KaFgfRfLqBihYdApESgLosN9xwgytvndfLry9xtWrV5KuvvhK9LzCvphVxateuLfv27ZOJEyfKZZddFp3VzqAnQQD6LwYC0M4QAWhnhwD0Y4cAtPNDANrZIQD92CEA/fghAP34EZ04AQRg4sziiUAAxkMpfX2OOOKImAJQi5uuX78+e4IHHXSQq/6rjkSrAf/222/uM/UoehVa6dKl3Z+///779D0UIyedQOQEoIo7PQqsRT70Zc7ZDjvsMPnPf/7jjvbm13SnoBYLKV68uKgMrFSpUtKhk1AEAej/FiAA7QwRgHZ2CEA/dghAOz8EoJ0dAtCPHQLQjx8C0I8f0YkTiJwAvDeDjgAP4ghw4m9k+iMWLFggHTt2dH8H10KoelxYT0wWK1bMTU53+WmNBD3uO3bsWFfh+aWXXpITTzwx/ZNnBkklEDkBmEVHd/m99tpronf/qd3WAiGdOnWSMmXKxASoBUS2bt0q1atXl759+yYVNsn+jwAC0P9tQADaGSIA7ewQgH7sEIB2fghAOzsEoB87BKAfPwSgHz+iEyeAAEycWTwRbgcgAjAeVBnVRzc1qQvZvHmzvPnmm3LmmWfGnN+sWbNcQdTy5cvLwoULnRehRYdAZAVgdJYomk+CAPRfVwSgnSEC0M4OAejHDgFo54cAtLNDAPqxQwD68UMA+vEjOnECCMDEmcUTgQCMh1Lm9bn11lvlscceczURhg4dGtcEtWbCQw89JDfffLM8+uijccXQKRwEEIDhWKfIzRIB6L+kCEA7QwSgnR0C0I8dAtDODwFoZ4cA9GOHAPTjhwD040d04gQQgIkziycCARgPpczrc+yxx8qyZctEd/addtppcU3wgw8+kFatWkm9evVcIVVadAhEVgDqRZbvv/++OwKsZ9pr1KghrVu3dufZaekngAD0XwMEoJ0hAtDODgHoxw4BaOeHALSzQwD6sUMA+vFDAPrxIzpxAgjAxJnFE4EAjIdS5vXRK9B27twpc+fOlebNm8c1wfnz58tJJ50kpUqVkm3btsUVQ6dwEIicANTCHwMHDpQRI0bIrl27cq2CVrTp2rWrKxBStmzZcKxQRGeJAPRfWASgnSEC0M4OAejHDgFo54cAtLNDAPqxQwD68UMA+vEjOnECUROAtbQISIUKiYNIcsSeX36RldwBmGSqwaerWrWqbNy4UZ588knp3bt3XAOOGjVKbrjhBlcQNWfl4LiC6ZTRBCInALWqzfjx4w+oAJy1CioBW7ZsKbNnz5aiRYtm9OJEeXIIQP/VRQDaGSIA7ewQgH7sEIB2fghAOzsEoB87BKAfPwSgHz+iEyeAAEycWTwRCMB4KGVen3bt2slbb70lhx9+uGg14CpVqsSc5Lp166RZs2ayevVqOe+881zhEFp0CERKAH700UfuXLtKvuLFi0vnzp3llFNOcT8vWrRIJk2aJL/++qv7XK32tddeG52VDNmTIAD9FwwBaGeIALSzQwD6sUMA2vkhAO3sEIB+7BCAfvwQgH78iE6cAAIwcWbxRCAA46GUeX1U4HXo0ME5kDp16riiHhdeeOEBm6H27dsnr7/+uvTt21e+//5713/q1KmuIjAtOgQiJQB199+4ceOkdOnS8t5778nJJ5+ca6X0PsDTTz9dVq1aJS1atJA5c+ZEZyVD9iQ5/4+50+udpEy1MiF7gvRPd/mvldM/iZDO4H/XVQzpzDNk2usOzpCJhG8aB20qEr5JZ8iMS/6SIRMJ8TQ+H3FLiGfP1CEAAQjERyByAvCeuzLnCPA997tFWLlyJXfrx/c6ZkQvreb7+OOPO6mnrWLFitK0aVPR48H6u7Vr18pnn30mP//8c/ZJyptuuslVD6ZFi0CkBGCjRo3k66+/lttvv10GDx6c50rpLsDLL79cDjroINmyZYuUKFEiWisakqdBAPovFALQzhABaGfnIhGAZoAIQDM6QQDa2WVFIgD9GZIBAhDIfAIIwGDWyO0ARAAGAzcFWYcPHy6DBg2S7du3u9GyZGDW0FpLQZsW/rj//vvdTkBa9AhESgBWqFDBST3d/XfGGWfkuVobNmzINt26I5CqwOl5qRGA/twRgHaGCEA7OwSgHzsEoJ0fAtDODgHoz44MEIBAeAggAINZKwRgMFxTmVXv95s4caJMnz5dvvjiC9m0aZMbXncEHn/88XL22WdL9+7dnS+hRZNApARgsWLF3CrpfX8NGzbMd8W0+Ica78WLF0v9+vWjubIZ/lQIQP8FQgDaGSIA7ewQgH7sEIB2fghAOzsEoD87MkAAAuEhEDkBOCiDjgDfyxHg8HwTmCkEDiQQKQGYJfbUZjdo0KBAAVhQP16Y4AggAP3ZIgDtDBGAdnYIQD92CEA7PwSgnR0C0J8dGSAAgfAQQAAGs1ZuByACMBi4ZIVAigggAGOIwhStQaEcBgHov+wIQDtDBKCdHQLQjx0C0M4PAWhnhwD0Z0cGCEAgPAQQgMGsFQIwGK5khUAqCURSAL7zzjtSr169fDkeccQR7ghwQf00Qe3atVO5HoVmLASg/1IjAO0MEYB2dghAP3YIQDs/BKCdHQLQnx0ZIACB8BCInAC8O4OOAN/HEeDwfBOYKQQOJBBJAZishVZJuGfPnmSlI08OAghA/9cBAWhniAC0s0MA+rFDANr5IQDt7BCA/uzIAAEIhIcAAjCYtXI7ABGAwcBNQdbffvtNJk2aJP/+97/l888/Fy2OumPHjpgj40NSsDApHiJyAjCZ/PSF37t3bzJTkuv/E0AA+r8KCEA7QwSgnR0C0I8dAtDODwFoZ4cA9GdHBghAIDwEEIDBrBUCMBiuqcj67bffykUXXSRLliyR/fv3xz0kPiRuVKHpGCkBeOWVVyYd/LPPPpv0nCQUQQD6vwUIQDtDBKCdHQLQjx0C0M4PAWhnhwD0Z0cGCEAgPASiJgBr35U5R4B/vJ8jwOH5Jvw+023btknjxo3l+++/Fy2aeuGFF0qVKlVk7Nix7lq0O++8UzZt2iTz58+XOXPmuN+1bNlSzjnnHBc/aNCgsD0y841BIFICkJUODwEEoP9aIQDtDBGAdnYIQD92CEA7PwSgnR0C0J8dGSAAgfAQQAAGs1a6AxABGAzbILMOHz5c+vXrJ8WKFZO3335bzjrrLFm8eLEcf/zxTvblPPH42WefyWWXXSbffPONjBgxQm644YYgp0buNBBAAKYBOkOyAzAZ7wAC0E4RAWhnhwD0Y4cAtPNDANrZIQD92ZEBAhAIDwEEYDBrhQAMhmvQWc844wz573//K127dnV3AGrLTwDqZ+vXr5cTTjjB3RH48ccfS7NmzYKeIvlTSAABmELYDPV/BNgB6P82IADtDBGAdnYIQD92CEA7PwSgnR0C0J8dGSAAgfAQiJwAvDODjgA/wBHg8HwTfp9p1apVZePGjfLiiy9Kx44dDxCAWvRUdwLmbI888oj0799funfvLlyJFrYVjz3fSAnAvn37uqe9/fbb3Yv+x6bbW1etWuV+Xbt27XzJfPfdd+7LoV+ETz/9NForniFPgwD0XwgEoJ0hAtDODgHoxw4BaOeHALSzQwD6syMDBCAQHgIIwGDWyu0ARAAGAzfArAcddJA75qv3+7Vo0cKNtGzZMjnmmGOc7/jll1/kkEMOyTUD3fn3pz/9SY444ghRN0KLDoFICUC91FJf4i+++EIaNGhwwCplbXXVfmq682uxtsRGZ+nT+yQIQH/+CEA7QwSgnR0C0I8dAtDODwFoZ4cA9GdHBghAIDwEEIDBrBUCMBiuQWetUKGCbNmyRebOnSvNmzd3w+nxXt0wpe5k0aJF0rBhw1zTUFl46qmnysEHHyzbt28PeorkTyGBQikACypnjQAM/g1EAPozRgDaGSIA7ewQgH7sEIB2fghAOzsEoD87MkAAAuEhEDkBODCDjgAP5ghweL4Jv8+0adOmTvK9+uqrrgJwVqtcubKr/vvUU09Jz549cz3WyJEj5cYbb5Ry5cq5HYK06BBAAOaxlgjA4F9wBKA/YwSgnSEC0M4OAejHDgFo54cAtLNDAPqzIwMEIBAeAgjAYNbK7QBEAAYDN8CsPXr0kPHjx8vAgQPlvvvuyx6pS5cuMmXKFKlbt647HlypUiX32Q8//CCtWrVyV6edfvrpMnPmzABnR+pUE0AAIgBT/c658RCA/tgRgHaGCEA7OwSgHzsEoJ0fAtDODgHoz44MEIBAeAggAINZKwRgMFyDzjp58mRXAbhx48by2WefZQ/34YcfOsGnpyP1mPBZZ53ljvt+8MEH7siw/v7555+XSy65JOgpkj+FBBCACMAUvm7/NxQC0B87AtDOEAFoZ4cA9GOHALTzQwDa2SEA/dmRAQIQCA+BqAnAOgMy5wjwiiEcAQ7PN+H3marUa9eunSsEMmHCBLfjL6vdc8892bsCsyoB79+/33181VVXyTPPPBO2x2W+BRBAACIA0/IlQQD6Y0cA2hkiAO3sEIB+7BCAdn4IQDs7BKA/OzJAAALhIYAADGatdAcgAjAYtunM+t577znRp9egaaHUevXqSbdu3eTiiy9O57QYOyACCEAEYECvVuy0CEB/7AhAO0MEoJ0dAtCPHQLQzg8BaGeHAPRnRwYIQCA8BBCAwawVAjAYrmSFQCoJIAARgKl837LHQgD6Y0cA2hkiAO3sEIB+7BCAdn4IQDs7BKA/OzJAAALhIRA5AXjH3VK8QoW0L4ATgEN/LyKxcuVKqVmzZtrnxARST0CLhOjxYD0yrLsHaeEigABEAKbljUUA+mNHANoZIgDt7BCAfuwQgHZ+CEA7OwSgPzsyQAAC4SGAAAxmrRCAwXANW1Y9Knz88cc7Aaj3CtLCRSCSArB3795StWrVA1Zi3bp1MmrUKPeyDho0KN+VytmPlzqYFxoB6M8VAWhniAC0s0MA+rFDANr5IQDt7BCA/uzIAAEIhIdAJAVg+QzYAbiZHYDh+RYEN1MEYHBsU5E5kgIwGeC0+g1WOxkk886BAPRniwC0M0QA2tkhAP3YIQDt/BCAdnYIQH92ZIAABMJDAAEYzFrtQQAGAzZkWRGAIVuwP0w3cgIwmcuBAEwmzdy5EID+bBGAdoYIQDs7BKAfOwSgnR8C0M4OAejPjgwQgEB4CCAAg1krBGAwXMOWFQEYthXLPd9ICcBZs2YlfTVat26d9JwkFEEA+r8FCEA7QwSgnR0C0I8dAtDODwFoZ4cA9GdHBghAIDwEoiYAj7j9bimeIUeAf3iQIiDh+SYEM1MEYDBcU5U1UgIwVdAYx58AAtCfIQLQzhABaGeHAPRjhwC080MA2tkhAP3ZkQECEAgPAQRgMGulOwARgMGwDVNWBGCYVuvAuSIAw71+oZ09AtB/6RCAdoYIQDs7BKAfOwSgnR8C0M4OAejPjgwQgEB4CCAAg1krBGAwXMOWFQEYthXLPV8EYLjXL7SzRwD6Lx0C0M4QAWhnhwD0Y4cAtPNDANrZIQD92ZEBAhAID4HICcDbMugI8EMcAQ7PNyGYmSIAg+GaqqwIwFSRZpxcBBCA/i8EAtDOEAFoZ4cA9GOHALTzQwDa2SEA/dmRAQIQCA8BBGAwa+V2ACIAg4EboqwIwBAtVh5TRQCGe/1CO3sEoP/SIQDtDBGAdnYIQD92CEA7PwSgnR0C0J8dGSAAgfAQQAAGs1YIwGC4hi0rAjBsK5Z7vgjAcK9faGePAPRfOgSgnSEC0M4OAejHDgFo54cAtLNDAPqzIwMEIBAeApETgP3vlhIZUAV4t+4AHMYR4PB8E4KZKQIwGK6pyooATBVpxslFAAHo/0IgAO0MEYB2dghAP3YIQDs/BKCdHQLQnx0ZIACB8BBAAAazVgjAYLiGLSsCMGwrlnu+CMBwr19oZ48A9F86BKCdIQLQzg4B6McOAWjnhwC0s0MA+rMjAwQgEB4CCMBg1goBGAzXsGVFAIZtxRCA4V6xiMweAei/kAhAO0MEoJ0dAtCPHQLQzg8BaGeHAPRnRwYIQCA8BCInAPtl0BHghzkCHJ5vQjAzXb16tQwYMECKFCkizz77bDCDkDUwAuwADAwtiWMRQAD6vx8IQDtDBKCdHQLQjx0C0M4PAWhnhwD0Z0cGCEAgPAQQgMGsldsBiAAMBm6AWdeuXSvVqlUzjfDGG29Ihw4dTLEEZSYBBGBmrkvkZ4UA9F9iBKCdIQLQzg4B6McOAWjnhwC0s0MA+rMjAwQgEB4CCMBg1goBGAzXoLNWrVpVnnnmGbnwwgvjHmrHjh1yyy23yNixY2Xv3r1xx9Ex8wkgADN/jSI5QwSg/7IiAO0MEYB2dghAP3YIQDs/BKCdHQLQnx0ZIACB8BCInAD8WwYdAX6EI8Dh+Sb8PtOiRYu647o9evSQxx57TEqVKhXzET799FO59NJLZenSpa4fAjBsKx57vgjAaK1naJ4GAei/VAhAO0MEoJ0dAtCPHQLQzg8BaGeHAPRnRwYIQCA8BBCAwayV2wGIAAwGboBZjzzySFmxYoWTgPXq1ZNJkyZJs2bNDhhx//79MmTIELnvvvtkz549on/u1q2bTJgwIcDZkTrVBBCAqSbOeI4AAtD/RUAA2hkiAO3sEIB+7BCAdn4IQDs7BKA/OzJAAALhIYAADGatEIDBcA0665YtW+S6665z4k9biRIl5J577pHbb7/dSUFtP/zwg1x++eXy0UcfOfFXsWJFGT16tHTq1Cno6ZE/xQQQgCkGznC/E0AA+r8JCEA7QwSgnR0C0I8dAtDODwFoZ4cA9GdHBghAIDwEoiYAj7w1c44Afz+cI8Dh+SbknunkyZOld+/esmnTJif+TjvtNHnuuedk5syZctNNN4mKQpV/Z511lkycOFEOP/zwsD4q845BAAHI65EWAghAf+wIQDtDBKCdHQLQjx0C0M4PAWhnhwD0Z0cGCEAgPAQQgMGsle4ADFoAZu1IK+gJWrdu7cRVvG379u3SqFEj+f77711InTp13K63gprGjRw5UqZMmSLLli2T3377TWrVqiXt27eXPn36SO3atQtK4T5fvHixPPHEEzJ9+nRZtWqVlC1bVo477jh3197VV18txYsXjyuPbyf9bnTv3l1mzJjhJKDuBty9e7cTfyVLlpTBgwdL3759fYchPoMJIAAzeHGiPDUEoP/qIgDtDBGAdnYIQD92CEA7PwSgnR0C0J8dGSAAgfAQQAAGs1ZhFoB/+9vfZPjw4dlg4hGAy5cvd6JvyZIleQItX768vPDCC9KuXbuYwMeNGyfXX3+97Nq1K89+p5xyirzxxhtSqVKlYBYuj6x//etf5cUXX3QSUOWfPsusWbOkcePGKZsDA6WHAAIwPdwL/agIQP9XAAFoZ4gAtLNDAPqxQwDa+SEA7ewQgP7syAABCISHAAIwmLVKpQDUo6p6b11+rUyZMqLFLeJpCxculBYtWrjdbvqPHnUtSABu3brVxXzzzTduCK2g27VrV1dBV3fPDR06VLRP6dKl5eOPP85XnL399ttOEO7bt0+qVasmAwcOlJNPPll+/vlnGTt2rLzyyisuf6tWrVxerdgbZNPdfgMGDHDVgFX86T/aVAS2adPGHf2tXr16kFMgd5oJIADTvACFdXgEoP/KIwDtDBGAdnYIQD92CEA7PwSgnR0C0J8dGSAAgfAQQAAGs1apFICDBg1yhSp82969e51w+/TTT111W92NpxVxCxKAOva9997rhh82bJj069cv11RU+qm002q5Z555prz//vsHTFU/02O+enS4XLlysmDBAqlbt26ufrozcNSoUe53Kt+06m5Q7auvvnJHjhctWuTEX40aNeShhx5yR5znzJnjJKAW/xgzZoxcfPHFQU2DvGkmgABM8wIU1uERgP4rjwC0M0QA2tkhAP3YIQDt/BCAdnYIQH92ZIAABMJDIHICsG8GFQF5NNgiIFl3ACZLAD766KNy6623yrHHHuvE1zHHHFOgANRdclWrVpVffvnFCbwvv/wyz515vXr1crJM2/z586VZs2a5viR6b2Dnzp3d73THoFbd/WPTOwZr1qzpCnPoHYVffPFFIF+0J598Um677TbZuXOnk39/+ctf3A5EFX66O/H+++939/+ptNQ1UBGpdxbqXYW0aBFAAEZrPUPzNAhA/6VCANoZIgDt7BCAfuwQgHZ+CEA7OwSgPzsyQAAC4SGAAAxmrdwOwBAJQN3p17BhQ9m2bZvboac79Y444ogCBeC7774r5557roP44IMPOnGWV9Ndcy1btnQf6bFaFWg5m+620zsCta1Zs0YOO+ywPPPkFInffvut1KtXL6kLqEeQ9Siyij89Ov33v/9drrrqqgPGmDt3rlx22WWidx+qBNQj1s8//3z2MyZ1UiRLGwEEYNrQF+6BEYD+648AtDNEANrZIQD92CEA7fwQgHZ2CEB/dmSAAATCQwABGMxahU0AagGPN998Uy6//HJ57rnnHJR4BODdd9/tdsRp06O+WqQjr6a75SpUqOAEox4H1iIaOZtWCF65cqXbfZh1l2Beef75z3/KJZdc4j4aP368XHnllUldwKx7BU866SSZNGnSAceQcw6mOxK1urHOQ5tWJ9bKx7ToEEAARmctQ/UkCED/5UIA2hkiAO3sXOS6gz0TFN5wBKB97RGAdnZZkZ+PuMU/CRkgAAEIZDiBqAnAo27JnCPA3z2WmiPADRo0cMdRf/zxRyehdPfcqaeeKldccYXbyVdQ+9e//iVa6VaPuKp80yO92uIRgJ06dZKXXnrJ9dejuSr58msnnHCCO1pcpUoVWbduXXY3LRCi9/7prrs///nP8u9//zvfHFqk5MQTT3Sf612DeudgMpsWPrnjjjtEj1UXK1YsrtQ6Xy18osVK9B5FWnQIIACjs5ahehIEoP9yIQDtDBGAdnYuEgFoBogANKMTBKCdXVYkAtCfIRkgAIHMJ4AADGaNdAdglgD85JNPCqwWq3fbJdqy7gCMFXfRRRfJhAkTpHz58nl2U2mnd/etXbvW3dHXs2fP7H7xCEDd8afHYfW4rIq8WK1Dhw4ydepU10Xv1ytZsqT7WaWjzkGbFvrQO/jyaxs2bHACUZtWGtYdgclsH374ofzpT39KOKUeW9ajwtOmTUs4loDMJYAAzNy1ifTMEID+y4sAtDNEANrZuUgEoBkgAtCMDgFoR5cdiQBMAkRSQAACGU8AARjMEuUUgPGMoLvfEm0q3S688EJp06aN1K9f3xWhWL9+vTteO3r0aNm4caNL2bp1a9G7+nR32x/bNddc46r96v18Kr9ySsV4BKDeG6gVc6tVqyY//fRTzEfo0qWLTJ482fVRkVepUiX387x580SP3GrTOwT1LsH82o4dO6R06dLuYxWKr7/+eqLY6A+BuAkgAONGRcdkEkAA+tNEANoZIgDt7FwkAtAMEAFoRocAtKPLjkQAJgEiKSAAgYwnEEkBWC7/Y6ipWpDdv/7fDsB4xrQIQK28m9+RW93Rd/7554semdWmxSz0vrqcbfbs2XLGGWe4o66ffvqpNG7cONfn8QjAunXrynfffSe1atVyR5BjNa2Wq4UytOl9f1m7Hv/73/+6ewG13XXXXXLffb8fnc6raRXerKO5Kj6nT58eD964++gdgPrPkCFDpH///nHH0TGaBBCA0VzXjH+qnP/HfPTYW6RE5XIZP+dMm+C2DWUybUqhmU/Jn4qHZq6ZONEyazJxVuGZ08KR3MMWntViphCAAAQgEDYCCMBgViynAAzqCHBBM1cxp0cveQzUAAAgAElEQVRrtTDF0UcfLUuXLs0O2bVrl+idfEuWLJFbb71VHnnkkQPSxSMAo7YD8OCDD5bdu3fLBx98QEXfgl6wQvA5ArAQLHImPiIC0H9VEIB2hghAOzuNRAD68UMA+vEjGgIQgAAEIBCLAAIwmPcjpwDMudstmNHyz5rz3r1Vq1ZJjRo1XOes6r26c0+P8Orx4T+2eARg1O4APOqoo2TFihWuonHWseRUrxnjZQ4BBGDmrEWhmgkC0H+5EYB2hghAOzsEoB87jUYA+jMkAwQgAAEIQCA/ApETgDffLSUy5QjwiGCrAMfzVusx1ocffth11Z2ILVq0cD9rAQ7dGXjllVfKueeem2eqG2+80d3VV7lyZXniiSdcH60QfNZZZ2X379ixo7z88svuzz5VgA855BCXI91VgLVysh5THjlypPTq1SsexPSJMAEEYIQXN5MfDQHovzoIQDtDBKCdHQLQjx0C0J8fGSAAAQhAAAKxCCAAg3k/3A7ADBCA/fr1yz7em1MAxlNBOC8yWlBk5syZ2R9l7STUX+iuOd0RmFfbs2ePu69w27Zt7r4/LVSSs9WuXdvdC3jssce6qsD5Na36e8kll7iPx48f7wRmMpvehagFUXQ+CxYskHLluHormXzDlgsBGLYVi8h8EYD+C4kAtDNEANrZIQD92CEA/fmRAQIQgAAEIIAATP07kCkCsH379vLmm286APp3ysMPP9z9nCwB+M4778h5553ncmr1Xq3im1ebM2dO9p16d9xxhyuykbOp1FO5p23NmjVy2GGH5ZlHd+WNGTPGfab3Fx5zzDFJX1zNf8MNN0ijRo3cTsBTTz016WOQMBwEEIDhWKfIzRIB6L+kCEA7QwSgnZ1GcgegHz+OAPvxIxoCEIAABCAQi0DUdgDWvSlzjgAv/3t6jwBrEZD69eu7ohZ6t93y5csT+jLEcwegHiPWY8GbN292BUcWL16cp1zMKe5y7kTMmtDkyZOlS5cu7o9Dhw6V22+//YC5bt++3VUO1qPGDRo0cGMlu1111VUupc5R70ZUUar3JGqF5IoVK2ZXIM5rXO07bty4ZE+JfGkkgABMI/zCPDQC0H/1EYB2hghAOzsEoB87jUYA+jMkAwQgAAEIQCA/AgjAYN4N3QEYpAB8/fXX5fzzz5fixYvn+QBr1651ny9cuNB9Pnz4cOnbt29CDxuPANSEOY8BDxs2TPTYcc6mR4P12K8eA/7jEeKsfiopVSCqpNRjt3r8tm7durnyXH/99TJq1Cj3u2effVb0vr5kt6JFi+YSmPv373dDFLRjUvtpn7179yZ7SuRLIwEEYBrhF+ahEYD+q48AtDNEANrZaSQ7AP34IQD9+BENAQhAAAIQiEUAARjM+xG0AFQ5p9Ls4osvdkdr9c+lSpVyRTv0jr7Ro0fLxo0b3cOddtppMn36dFf4I5EWrwDcsmWLNG/eXL799luXvmfPntK1a1c3nxkzZrjjvlu3bnV//uijj6RJkyZ5TkOPKl9wwQWyb98+qVatmtx5552uEq/u+Bs7dmx2sRF9Hn3GYsWKJfI4cfXVZy5I9sVK9P3338c1Dp3CQQABGI51itwsEYD+S4oAtDNEANrZIQD92Gk0AtCfIRkgAAEIQAAC+RGInADsk0FHgB8P7ghwlpwr6M1WQfjMM8+4AhyJtngFoOZdtmyZtGvXTpYuXZrnMLqrb9KkSdKhQ4eY01DRp/fv6dHivJoKwalTp7rKxDQIBE0AARg0YfLnSQAB6P9iIADtDBGAdnYIQD92CEB/fmSAAAQgAAEIxCKAAAzm/XA7AAMUgFpFV//R47V615/u/Pv111+lbNmy7s46LVzRvXv37MIblqdMRABqfq3wq0UzpkyZ4oSgSjydi4rBm266SerUqRPXNL788kt5/PHH5b333pPVq1dLmTJl3PHgSy+9VK655pp8jz3HlZxOEEiAAAIwAVh0TR4BBKA/SwSgnSEC0M4OAejHDgHoz48MEIAABCAAAQRg6t+BoAVg6p+IESFQ+AggAAvfmmfEEyMA/ZcBAWhniAC0s0MA+rFDAPrzIwMEIAABCECgMAnAo2/MnCPAy54I7ggwb3VqCWgBE72LUJtWA86v+EpqZ8VoQRNAAAZNmPx5EkAA+r8YCEA7QwSgnR0C0I8dAtCfHxkgAAEIQAACCMDUvwO6AxABmHruyRxx8eLFrpiKFlDRuw1zVgSuV6+enH322XLttddKo0aNkjksuTKIAAIwgxajME0FAei/2ghAO0MEoJ0dAtCPHQLQnx8ZIAABCEAAAgjA1L8DCMDUM0/WiFqFuG/fvu4+Q/05S/z9Mb9WCy5atKgrWjJ8+HD3My1aBBCA0VrP0DwNAtB/qRCAdoYIQDs7BKAfOwSgPz8yQAACEIAABAqVALwhg44AP8kR4DB++zp37iwvv/xytvhr2LChaPXhatWqud+tW7dO5s2bJ1qsRJuKwI4dO8qLL74YxsdlzjEIIAB5PdJCAAHojx0BaGeIALSzQwD6sUMA+vMjAwQgAAEIQAABmPp3wO0ARACmHrzniC+88IJcdtllTuo1btxYnn76aWnRokWeWefPn++OAC9cuND1nzRpknTt2tVzBoRnEgEEYIpWQ0uYv/nmm86s6xdr1apVsn79etmxY4dUqFBBGjRo4MqJX3311VKpUqUCZ/XWW2+5L+8nn3zi8lSpUsVZ/J49e0rbtm0LjNcO27dvz7Osefv27aVPnz5Su3btuPJYOiEALdRyxyAA7QwRgHZ2CEA/dghAf35kgAAEIAABCCAAU/8OIABTzzwZI5555pkya9YsOfbYY52HKFOmTMy027Ztk+bNm8uSJUukdevWMmPGjGRMgxwZQgABmKKF0Is2zznnnAJHq1y5svzjH/+Q8847L8++ukW3V69eTv7l11QC6uWeau3za8uXLxcVffrFzquVL19e9L8WqJQMoiEA/akiAO0MEYB2dghAP3YIQH9+ZIAABCAAAQgUKgF4fQYdAR7JEeCwfft0c9Evv/wi48aNkyuuuCKu6U+YMEGuuuoqt1Hp559/jiuGTuEggABM0TqpANQvkRr4Zs2aSa1ataR69eruEk6VYS+99JK88sorsnfvXjnooIPcTkHdovvHNnDgQBkyZIj7ddOmTaV///5St25dUaE3bNgwt11Xm/Z74IEH8ny6rVu3um2/33zzjfu8R48ebmtvqVKlnOEfOnSoaJ/SpUvLxx9/nOc8fLEhAH0JiiAA7QwRgHZ2CEA/dghAf35kgAAEIAABCCAAU/8OuB2ACMDUg/cc8ZBDDnEn/9QvnHjiiXFlW7BggdsFqLsFt2zZElcMncJBAAGYonVSsVesWLGYo/373/+W//mf/3F9/vKXv7iLOnO2ZcuWyXHHHSd79uxxX8jZs2c7aZfV9Iut23R1a2/x4sWd4FM5+Md2zz33yL333ut+rdKwX79+ubqo9GvVqpUbR4Xl+++/n3RKCEB/pAhAO0MEoJ2dRpZZ4xdf2KMXjrylsCPg+SEAAQhAAAKBEcj594yVK1dKzZo1AxsrqMQ5n6HedZmzA3DpKHYABrXmQeVt1KiRfP3116IbkvTv9vG0mTNnyllnneWuKcsqDBJPHH0ynwACMMPWSAWfijs9Cqx3++Vs119/vYwaNcr9SiXdKaeccsDs58yZIy1btnS/1/LdTzzxRK4+u3fvlqpVq7ptwDqWfqHzKu+tx4zHjBnjYlUo6q7FZDYEoD9NBKCdIQLQzk4jEYB+/BCAfvyIhgAEIAABCMQigAAM5v3QHYAIwGDYBpl10KBBcv/998t1110nTz75ZFxDqUd46qmnZMCAAS6WFh0CCMAMW0vd2ffpp59K2bJlc2231bv/9NiwFg+pX7++s/j5Nf1c7/bT/9r1448/5roL8N1335Vzzz3XhT744INy22235Zkmp0jUL/7gwYOTSgoB6I8TAWhniAC0s0MA+rHTaASgP0MyQAACEIAABPIjgAAM5t1AAAbDNeismzdvdpt5VqxY4ar6du7cOeaQejXZX//6V6lTp47zElobgBYdAgjADFpLlXrHH3+8uwdQRaCe089q3333XfZxXi3NrUU+8mv6eVaREI078sgjs7vefffd2RY/v12E2lmP/+qln1oFSI8Da+WgZDYEoD9NBKCdIQLQzg4B6McOAejPjwwQgAAEIACBWAQiJwB7Z9AR4Kc4AhzGb98PP/wgXbp0cSf7LrjgAlcMRGsC6MlALRy6du1a5x4mTpwor732mnMRkydPdhKQFi0CCMA0r6fe26e7+l5//XV3H59++bQ9//zzctlll2XPburUqdKhQwf358cee0xuvvnmfGeun/ft29d9rnE5K/l26tTJFRzRtmnTJif58msnnHCCLFq0SKpUqSLr1q1LKikEoD9OBKCdIQLQzg4B6McOAejPjwwQgAAEIAABBGDq3wG3AxABmHrwniPmrEOgpwpV+MVq8fTRHLphiBY+AgjANKyZltW+8sor8x35b3/7m5OBOb+cuuOvd+/eLmbKlCnSsWPHfONV8Kno06ZxuiMwq+m9gXPnznUVfbTSb6ymwlEForadO3dKyZIl46algi9WW7NmjZx00kmuy9Fjb5ESlcvFnZuOvxNAANrfBASgnZ1GcgegHz+OAPvxIxoCEIAABCAQiwA7AIN5PxCAwXANOmte9/37jqmeQk8t0sJHAAGYhjXLTwA2adLECbuTTz75gFk9/PDD0r9/f/f7adOmSdu2bfOduX6etevvkUcekVtvvTW7b8OGDeWrr76SatWqyU8//RTz6XWbsG791bZhwwapVKlS3LQK+i8LORMhAOPGmqsjAtDGTaMQgHZ2GokA9OOHAPTjRzQEIAABCEAgFoFICsBD8j+1laq3YfcWdgCminUyx7n33nuTmS47lxYXoYWPAAIwDWumFXizdsjt2LFDli9f7kTbq6++6u75GzFiRPZx36zpafUdvb9P23vvvefKcufX3n//fWnTpo37WOPuvPPO7K6aX+8F1IIiWiAkVuvWrZs7iqxt5cqVrqhIvA0BGC8pez8EoJ0dAtDOTiMRgH78EIB+/IiGAAQgAAEIxCKAAAzm/UAABsOVrBBIJQEEYCppFzCWyrbu3bu7o7/jxo1zl3NmtbDtAOQIcPAvFgLQzhgBaGeHAPRjp9EIQH+GZIAABCAAAQjkRwABGMy7gQAMhitZIZBKAgjAVNKOY6ysY7d6R5/uuqtYsaKLCtsdgAU9KkVACiJU8OcIwIIZ5dcDAWhnhwD0Y4cA9OdHBghAAAIQgEAsAlETgMf0ultKZMgR4G9HUwWYbx8EwkwAAZhhq/fCCy/IpZde6mY1adIkueSSS9zPb7zxhivZrc2nCrAWD3n55ZddHqoAZ9jiJzgdBGCCwHJ0RwDa2SEA/dghAP35kQECEIAABCCAAEz9O6A7ABGAqeeezBG1wu9nn30mn3/+ubvjX68j09/FalnXkCVzHuRKHwEEYPrY5znyu+++K+eee677bMiQIXLHHXe4n/XePr2/T5tW9dUdgfk1/fzpp5/OjjvyyCOzu+oXWO8F1Pbxxx+LVgXOq2lZ7woVKsi2bdukVatWMmvWrKSSYgegP04EoJ0hAtDOTiO5A9CPH0eA/fgRDQEIQAACEIhFgB2AwbwfCMBguKYq68SJE0ULgqxYsSKhIan2mxCujO+MAMywJcpZIfjxxx+XG2+80c1QzbwW4Vi9erXUr19fvv7663xnftxxx8k333wjhx9+uDtGnLMgxzvvvCPnnXeei33wwQfltttuyzPPnDlzpGXLlu4zlZAqI5PZEID+NBGAdoYIQDs7BKAfO41GAPozJAMEIAABCEAgPwKRE4DXZtAR4DEcAQ7jN2/gwIHu7/4F7fbTZ1N3kLPfvn37wvjIzDkfAgjADHs12rdvL2+++aab1YwZM+SMM87InuF1110nTz31lPtzfrv3coo77T9y5MhcT/jbb79J1apVZfPmzaKicPHixbkEYVbnXr16yZgxY9wfP/nkE2nRokVSSSEA/XEiAO0MEYB2dghAP3YIQH9+ZIAABCAAAQjEIoAADOb9cDsAEYDBwA0w69y5c93GHhV7Z599tmhxUZV6J554ovudnvzTq8Hmz5/vXMN//vMfOe2002TKlClSrVq1AGdG6nQQQACmiLru7OvatascfPDB+Y6od/v17dvXfX7EEUfI0qVLpXjx4tn9v/32W2nYsKH7kjZv3lxmz54tpUqVyv5cz/DrcV398mrcV199JfXq1TtgvJzHgIcNGyb9+vXL1UfloubRcVq3bi0zZ85MOiUEoD9SBKCdIQLQzg4B6McOAejPjwwQgAAEIAABBGDq3wEEYOqZJ2PEK664Qp577jnnF9QnqCfQTUDHH3+8E4B/POKrEvD666+XE044QVQeHnTQQcmYBjkyhAACMEULoV+4LVu2yMUXX+yMut7nV7ZsWfe7L774whX8+PDDD91s9Es2depUZ+j/2PQ4rm7f1da0aVN3hFdzLV++XB566CFZuHCh+yzWsV0dUwWi/gtAW8+ePZ2cVJmouw71uO/WrVvdnz/66CNp0qRJ0ikhAP2RIgDtDBGAdnYIQD92CEB/fmSAAAQgAAEIFCYBeGzPzDkCvORpjgCH7dt3zDHHOFcwfPhwufnmm930YwlA/bxTp07yyiuv5IoJ23Mz37wJIABT9GaoAIznwk2952/8+PFyzjnn5Dkz3a7bo0cP1ye/dvXVV7siIEWLFs23z7Jly6Rdu3Zul2FerVy5ck5KdujQIRBCCEB/rAhAO0MEoJ0dAtCPHQLQnx8ZIAABCEAAAgjA1L8DugMQAZh67r4jHnLIIbJ9+3Z56623sh2D1hPQk4W6A3Dnzp1SokSJXMO89tprctFFF8nJJ5/srh6jRYcAAjBFa6nWffr06W6HnX7h1q5dKxs3bnRHgvVsve6yU9nWuXNnKV26dIGz0nsCVfLNmzfPlfCuXLmyu6dPKwCff/75BcZrB63wq3cE6vl+FYJ6P2CtWrWcGLzpppukTp06ceWxdEIAWqjljkEA2hkiAO3sEIB+7BCA/vzIAAEIQAACEEAApv4dQACmnnkyRlTfsHv3blmwYIE71qtNC4Xq3/VVAOrPNWrUyDWUnips1qyZVKpUSdavX5+MaZAjQwggADNkIQrbNBCA/iuOALQzRADa2SEA/dghAP35kQECEIAABCBQqARgjww6AjyWI8Bh+/bpBp/Vq1e7e/1PP/10N33d+FOmTBlXDOSdd96RNm3a5Hos3Wykm5P0ajLdIUiLDgEEYHTWMlRPggD0Xy4EoJ0hAtDODgHoxw4B6M+PDBCAAAQgAAEEYOrfAbcDEAGYevCeI7Zt21beffddGT16tLtKLKvpCUStRZB1fVjOYS699FL55z//6QqHfPfdd54zIDyTCCAAM2k1CtFcEID+i40AtDNEANrZIQD92CEA/fmRAQIQgAAEIIAATP07gABMPfNkjDh48GC56667XNHPF154ITvl0KFDZeDAga5uwIABA6RLly7ursCJEyfKqFGj3PHgXr16uSvDaNEhgACMzlqG6kkQgP7LhQC0M0QA2tkhAP3YIQD9+ZEBAhCAAAQgUKgE4DUZdAT4GY4Ah+3bl1Xxt2zZsqJ/B9din9pU9jVq1Eh++OEHJ/tytv3798uhhx4qn332mWiRUlp0CCAAo7OWoXoSBKD/ciEA7QwRgHZ2CEA/dghAf35kgAAEIAABCCAAU/8OuB2ACMDUg0/CiLNmzZI9e/ZI06ZNndjLaitWrJDLLrtMPvzww1yjqBh8/vnns4uGJGEKpMgQAv+PvTuBlqK49zj+Z1EEVEgUcAGVoD6QqPAElN0tyJqouGBMQIMgisFEBaK4RkVEiRpFBdHgM+p7IrjhrmyiIBCICsqqRuLG4sYqILzzL9+dN3PvzJ2Zrq6enppvn+OJYbpr+VTdO/TPqm4CwJgMRKk1gwDQfsQJAIMbEgAGtyMAtLMjALT3owQEEEAAAQQIAKOfAwSA0ZtHVeOyZctEVwpqSHjYYYeZoJDDTwECQD/HNfa9IgC0HyICwOCGBIDB7QgA7ewIAO39KAEBBBBAAIFSCgCbxmgL8FJWAPLDh0BRCxAAFvXwFW/jCQDtx44AMLghAWBwOwJAOzsCQHs/SkAAAQQQQIAAMPo5oCsACQCjd6dGBMIUIAAMU5OychYgAMyZKuOJBIDBDQkAg9sRANrZEQDa+1ECAggggAACJRUA9o/PS0CWPshLQPjpQ6CYBQgAi3n0irjtBID2g0cAGNyQADC4HQGgnR0BoL0fJSCAAAIIIEAAGP0cMCsACQCjh6dGBEIUIAAMEZOichcgAMzdKtOZBIDBDQkAg9sRANrZEQDa+1ECAggggAACBIDRzwECwOjNqRGBsAUIAMMWpbycBAgAc2Kq9CQCwOCGBIDB7QgA7ewIAO39KAEBBBBAAIGSCgB/d63svlfdgg/6Nl0B+BBbgAs+EDQAAQsBAkALPC4NLkAAGNyu7EoCwOCGBIDB7QgA7ewIAO39KAEBBBBAAAECwOjnAAFg9ObUiEDYAgSAYYtSXk4CBIA5MVV6EgFgcEMCwOB2BIB2dgSA9n6UgAACCCCAAAFg9HOAADB6c2pEIGwBAsCwRSkvJwECwJyYCADtmdKWQABoB1v7c7vrS/3qRWP/WOoE9B8BBBBAAAFnAsn3GatXr5aGDRs6q8tVwcl9aHp+jLYA/40twK7GnHIRiEKAADAKZeqoIEAAaD8pWAEY3JAAMLidXkkAaOdHAGjnx9UIIIAAAghUJkAA6GZ+mBWABIBucCkVgYgECAAjgqaaVIHkL+aDr7pGqtct/INti22Man1WpdiaHJv21vloR2zaUqwNeePpocXadNqNAAIIIIAAAh4LEAC6GVwCQDeulIpAlAIEgFFqU1dCgADQfjIQAAY3JAAMbld2JQGgvSElIIAAAggggED4Al4GgHsWfrHEto2sAAx/tlIiAtEKEABG601t/ydAAGg/FQgAgxsSAAa3IwC0t6MEBBBAAAEEEHAnQADoxpYA0I0rpSIQpQABYJTa1JUQIAC0nwwEgMENCQCD2xEA2ttRAgIIIIAAAgi4EyAAdGNLAOjGlVIRiFKAADBKbeoiAAxxDhAABsckAAxuRwBob0cJCCCAAAIIIOBOwLcAsNl518ruMdkC/MFE3gLsbuZSMgLuBQgA3RtTQxoBVgDaTwsCwOCGBIDB7QgA7e0oAQEEEEAAAQTcCRAAurHVFYAEgG5sKRWBqAQIAKOSpp4UAQJA+wlBABjckAAwuB0BoL0dJSCAAAIIIICAOwECQDe2BIBuXCkVgSgFCACj1KauhAABoP1kIAAMbkgAGNyOANDejhIQQAABBBBAwJ2AdwFgvxhtAX6YLcDuZi4lI+BegADQvTE1pBEgALSfFgSAwQ0JAIPbEQDa21ECAggggAACCLgTIAB0Y2tWABIAusGlVAQiEiAAjAiaalIFCADtZwQBYHBDAsDgdgSA9naUgAACCCCAAALuBAgA3dgSALpxpVQEohQgAIxSm7oSAgSA9pOBADC4IQFgcDsCQHs7SkAAAQQQQAABdwK+BYBH9I3PFuD3/4stwO5mLiUj4F6AANC9MTWkESAAtJ8WBIDBDQkAg9sRANrbUQICCCCAAAIIuBMgAHRjqysACQDd2FIqAlEJEABGJU09KQIEgPYTggAwuCEBYHA7AkB7O0pAAAEEEEAAAXcCBIBubAkA3bhSKgJRChAARqlNXQkBAkD7yUAAGNyQADC4HQGgvR0lIIAAAggggIA7Ae8CwN/GaAvwI2wBdjdzKRkB9wIEgO6NqSGNAAGg/bQgAAxuSAAY3I4A0N6OEhBAAAEEEEDAnQABoBtbswKQANANLqUiEJEAAWBE0FSTKkAAaD8jCACDGxIABrcjALS3owQEEEAAAQQQcCdAAOjGlgDQjSulIhClAAFglNrUlRAgALSfDASAwQ0JAIPbEQDa21ECAggggAACCLgT8C0AbP6b+GwBXvJ3tgC7m7mUjIB7AQJA98bUkEaAANB+WhAABjckAAxuRwBob0cJCCCAAAIIIOBOgADQja2uACQAdGNLqQhEJUAAGJU09aQIEADaTwgCwOCGBIDB7QgA7e0oAQEEEEAAAQTcCXgXAJ4boxWAj7IC0N3MpWQE3AsQALo3poY0AgSA9tOCADC4IQFgcDsCQHs7SkAAAQQQQAABdwIEgG5szQpAAkA3uJSKQEQCBIARQVNNqgABoP2MIAAMbkgAGNyOANDejhIQQAABBBBAwJ0AAaAbWwJAN66UikCUAgSAUWpTV0KAANB+MhAABjckAAxuRwBob0cJCCCAAAIIIOBOwLsA8Ncx2gL8GFuA3c1cSkbAvQABoHtjakgjQABoPy0IAIMbEgAGtyMAtLejBAQQQAABBBBwJ0AA6MbWrAAkAHSDS6kIRCRAABgRNNWkChAA2s8IAsDghgSAwe0IAO3tKAEBBBBAAAEE3AkQALqxJQB040qpCEQpQAAYpTZ1JQQIAO0nAwFgcEMCwOB2BID2dpSAAAIIIIAAAu4EfAsAf35OfLYAL36cLcDuZi4lI+BegADQvTE1pBEgALSfFgSAwQ0JAIPbEQDa21ECAggggAACCLgTIAB0Y6srAAkA3dhSKgJRCRAARiVNPSkCBID2E4IAMLghAWBwOwJAeztKQAABBBBAAAF3AgSAbmwJAN24UioCUQoQAEapTV0JAQJA+8lAABjckAAwuB0BoL0dJSCAAAIIIICAOwEvA8Dadd2B5Vjytk2sAMyRitMQiK0AAWBsh8bvhhEA2o8vAWBwQwLA4HYEgPZ2lIAAAggggAAC7gQIAN3YEgC6caVUBKIUIACMUpu6EgIEgPaTgQAwuCEBYHA7AkB7O0pAAAEEEEAAAXcCBIBubO7PQ28AACAASURBVAkA3bhSKgJRChAARqlNXQSAIc4BAsDgmASAwe0IAO3tKAEBBBBAAAEE3An4FgAe2eca2T0mW4Df++8bzcCtXr1aGjZs6G4QKRkBBJwIEAA6YaXQbAKsAMwmlP1zAsDsRpnOIAAMbkcAaG9HCQgggAACCCDgToAA0I2trgAkAHRjS6kIRCVAABiVNPWkCBAA2k8IAsDghgSAwe0IAO3tKAEBBBBAAAEE3AkQALqxJQB040qpCEQpQAAYpTZ1JQQIAO0nAwFgcEMCwOB2BID2dpSAAAIIIIAAAu4EvAsAz47RFuD/YQuwu5lLyQi4FyAAdG9MDWkECADtpwUBYHBDAsDgdgSA9naUgAACCCCAAALuBAgA3diaFYAEgG5wKRWBiAQIACOCpppUAQJA+xlBABjckAAwuB0BoL0dJSCAAAIIIICAOwECQDe2BIBuXCkVgSgFCACj1KauhAABoP1kIAAMbkgAGNyOANDejhIQQAABBBBAwJ2AbwHgUWfFZwvwu0+wBdjdzKVkBNwLEAC6N6aGNAIEgPbTggAwuCEBYHA7AkB7O0pAAAEEEEAAAXcCBIBubHUFIAGgG1tKRSAqAQLAqKSpJ0WAANB+QhAABjckAAxuRwBob0cJCCCAAAIIIOBOgADQjS0BoBtXSkUgSgECwCi1qSshQABoPxkIAIMbEgAGtyMAtLejBAQQQAABBBBwJ+BdAHhmjLYAT2ILsLuZS8kIuBcgAHRvTA1pBAgA7acFAWBwQwLA4HYEgPZ2lIAAAggggAAC7gQIAN3YmhWABIBucCkVgYgECAAjgqaaVAECQPsZQQAY3JAAMLgdAaC9HSUggAACCCCAgDsBAkA3tgSAblwpFYEoBQgAo9SmroQAAaD9ZCAADG5IABjcjgDQ3o4SEEAAAQQQQMCdgG8B4NFnxGcL8DtPsgXY3cylZATcCxAAujemhjQCBID204IAMLghAWBwOwJAeztKQAABBBBAAAF3AgSAbmx1BSABoBtbSkUgKgECwKikqSdFgADQfkIQAAY3JAAMbkcAaG9HCQgggAACCCDgTsC7ALB3jFYATmYFoLuZS8kIuBcgAHRvTA1pBAgA7acFAWBwQwLA4HYEgPZ2lIAAAggggAAC7gQIAN3YmhWABIBucCkVgYgECAAjgqaaVAECQPsZQQAY3JAAMLgdAaC9HSUggAACCCCAgDsBAkA3tgSAblwpFYEoBQgAo9SmroQAAaD9ZCAADG5IABjcjgDQ3o4SEEAAAQQQQMCdgG8BYIvT47MF+J9T2ALsbuZSMgLuBQgA3RtTQxoBAkD7aUEAGNyQADC4HQGgvR0lIIAAAggggIA7AQJAN7a6ApAA0I0tpSIQlQABYFTS1JMiQABoPyEIAIMbEgAGtyMAtLejBAQQQAABBBBwJ0AA6MaWANCNK6UiEKUAAWCU2tSVECAAtJ8MBIDBDQkAg9sRANrbUQICCCCAAAIIuBPwLgA8LUZbgJ9iC7C7mUvJCLgXIAB0b0wNaQQIAO2nBQFgcEMCwOB2BID2dpSAAAIIIIAAAu4ECADd2JoVgASAbnApFYGIBAgAI4KmmlQBAkD7GUEAGNyQADC4HQGgvR0lIIAAAggggIA7AQJAN7YEgG5cKRWBKAUIAKPUpq6EQPIXc4uYLGsvtuH5ybtfF1uTY9Xel975cQsDBwIIIIAAAggggIA/Aj4GgDVq1S34AH2/mRWABR8EGoCApQABoCUglwcTIAAM5pZ8FQGgnSEBoJ0fVyOAAAIIIIAAAnEUIAB0MyoEgG5cKRWBKAUIAKPUpq6EAAGg/WQgALQzJAC08+NqBBBAAAEEEEAgjgIEgG5GhQDQjSulIhClAAFglNrURQAY4hwgALTDJAC08+NqBBBAAAEEEEAgjgLeBYCnXi2x2QL89E1myFevXi0NGzaM4/DTJgQQqESAAJDpURABVgDasxMA2hkSANr5cTUCCCCAAAIIIBBHAQJAN6NiVgASALrBpVQEIhIgAIwImmpSBQgA7WcEAaCdIQGgnR9XI4AAAggggAACcRQgAHQzKgSAblwpFYEoBQgAo9SmroQAAaD9ZCAAtDMkALTz42oEEEAAAQQQQCCOAr4FgC1/FZ8twIueYQtwHOc8bUIgVwECwFylOC9UAQJAe04CQDtDAkA7P65GAAEEEEAAAQTiKEAA6GZUdAUgAaAbW0pFICoBAsCopKknRYAA0H5CEADaGRIA2vlxNQIIIIAAAgggEEcBAkA3o0IA6MaVUhGIUoAAMEpt6koIEADaTwYCQDtDAkA7P65GAAEEEEAAAQTiKOBdAPjLGG0BfpYtwHGc87QJgVwFCABzleK8UAUIAO05CQDtDAkA7fy4GgEEEEAAAQQQiKMAAaCbUTErAAkA3eBSKgIRCRAARgRNNakCBID2M4IA0M6QANDOj6sRQAABBBBAAIE4ChAAuhkVAkA3rpSKQJQCBIBRalNXQoAA0H4yEADaGRIA2vlxNQIIIIAAAgggEEcB3wLA/+wZny3AC6eyBTiOc542IZCrAAFgrlKcF6oAAaA9JwGgnSEBoJ0fVyOAAAIIIIAAAnEUIAB0Myq6ApAA0I0tpSIQlQABYFTS1JMiQABoPyEIAO0MCQDt/LgaAQQQQAABBBCIowABoJtRIQB040qpCEQpQAAYpTZ1JQQIAO0nAwGgnSEBoJ0fVyOAAAIIIIAAAnEU8C4A7BGjLcDPswU4jnOeNiGQqwABYK5SIZy3cOFCeemll+SNN96QxYsXy5o1a2S33XaTAw44QNq1ayf9+/eXjh075lyTljV+/HiZN2+erF27VurVqydt2rSRgQMHSteuXXMqZ/PmzTJ27FiZNGmSrFy5UrZt2yaNGjWSHj16yJAhQ+Sggw7KqZx8TyIAzFes4vkEgHaGBIB2flyNAAIIIIAAAgjEUYAA0M2omBWABIBucCkVgYgECAAjgu7cubPMmjUra22//e1vZcKECbL77rtnPHfXrl0yaNAgE/5lOjQEvP/++6VKlSoZz1m1apUJ+pYtW5b2nDp16shjjz0m3bt3z9rufE8gAMxXjADQXiy1BALAsEUpDwEEEEAAAQQQKLyAbwHgMd3jswLwHy+wArDwM5wWIBBcgAAwuF1eVx566KGigZuu9jvzzDPNSj9dXffDDz/InDlzZMyYMfLpp5+aMs855xwTvGU6RowYISNHjjQft2zZUoYNGyZNmjQx5Y8ePVoWLVpkPtPzbrrpx1/S5Y+NGzdK69atZenSpeajAQMGSJ8+faRmzZoyffp0ueWWW0TPqVWrlmnfUUcdlVd/s51MAJhNKPvnrADMblTZGQSAdn5cjQACCCCAAAIIxFGAANDNqOgKQAJAN7aUikBUAgSAEUn37NlT+vbtK71795Zq1apVqHXdunXSvn17Wb58uflMVwum2w6s23SbNWsmO3bskFatWpnzNLQrO3RLr642XLBggVSvXt0EfBoOlj+uv/56ueGGG8wfa2g4dOjQlFM09OvUqZOp54QTTpBp06aFKkUAaM9JAGhnSABo58fVCCCAAAIIIIBAHAUIAN2MCgGgG1dKRSBKAQLAKLWz1DV16lTp1auXOUufv3fXXXdVuGLw4MFy7733mj/XkO64446rcM7cuXOlbdu25s8vueQSufvuu1PO2b59u9SvX1+++eYbEybq8wirVq1aoRzdZjxu3Djz5xooHnPMMaFpEQDaUxIA2hkSANr5cTUCCCCAAAIIIBBHAe8CwG4jpEatugWnNgHgizebdqxevVoaNmxY8DbRAAQQyE+AADA/L6dn65bbvfbay9Shz+bTQDD50Gf/6Qs6dKtw06ZN5YMPPsjYHv1cn+2nv5g/+eSTlGcBvvrqq9KlSxdz7ahRo2T48OFpy0kOEq+66iq5+eYff+GHcRAA2isSANoZEgDa+XE1AggggAACCCAQRwECQDejQgDoxpVSEYhSgAAwSu0sdX311Veyzz77mLN0JeCzzz6bcsWHH36Y2M574YUXmpd8ZDr087KXhOh1jRs3Tpx67bXXyo033mj+f6ZVhPqZbv+tW7eubNq0yWwHnjlzZmhaBID2lASAdoYEgHZ+XI0AAggggAACCMRRgADQzagQALpxpVQEohQgAIxSO0tdTz31lJx++unmLH0mnz6bL/l4/vnnRZ8lqMcdd9whf/jDHzKWqJ9fdtll5nO9LvlNvvoSkieffNJ89vXXX5uQL9Nx9NFHy7vvviv16tWTNWvWhKZFAGhPSQBoZ0gAaOfH1QgggAACCCCAQBwFfAsAW3WNzxbgBS+xBTiOc542IZCrAAFgrlKOz9u5c6d5bt+8efNMTfPnzzcv+Ug+dMXfRRddZP5o0qRJcsYZZ2RslQZ8GvTpodfpisCyQ58b+Pbbb0vt2rXNm34rOzRw1ABRj61bt0qNGjVyktAv3sqOzz//XNq0aWNOaXHaNbJ77cI/1yKnjsXoJAJAu8EgALTz42oEEEAAAQQQQCCOAgSAbkZFVwASALqxpVQEohIgAIxKOks9Y8aMkSuuuMKcddppp8mUKVMqXHHbbbfJsGHDzJ+/+OKL0rVr14yl6udlq/5uv/12ufzyyxPnNm/eXN5//31p0KCBfPHFF5W27Oyzz5YnnnjCnKNvKi7bopyNrUqVKtlOSXxOAJgzVcqJBIDB3MquIgC08+NqBBBAAAEEEEAgjgIEgG5GhQDQjSulIhClAAFglNoZ6tJn65188snmmXv6dl7dcqvhXPlDn9unz+/T4/XXX5cTTzwxY+unTZsmJ510kvlcr7v66qsT5zZp0kT0uYD6QhF9QUhlR9++feWRRx4xp+TzticCQPcTiwDQzpgA0M6PqxFAAAEEEEAAgTgKeBkA1iz8bqnvt7ACMI7znTYhkI8AAWA+Wg7OXbJkiXTs2NE8i0+317788svSuXPntDUV0wpAtgA7mCzliiQAtDMmALTz42oEEEAAAQQQQCCOAgSAbkaFANCNK6UiEKUAAWCU2uXq+uijj6RDhw7y2WefSbVq1cxz/XT7b6ajmJ4BmI2Vl4BkE8r+OQFgdqPKziAAtPPjagQQQAABBBBAII4CBIBuRoUA0I0rpSIQpQABYJTaSXVp6Kcr/3Qrrm6XnThxouh228qOqVOnSq9evcwpNm8B1peHTJ482ZTDW4ALNAFCqJYA0A6RANDOj6sRQAABBBBAAIE4CvgWALY+ZYTUiMkW4Pkv8xbgOM552oRArgIEgLlKhXievkxDt/nqizj0uOeee2Tw4MFZa9CwUJ/fp4e+1VdXBGY69PPx48ebj/W6xo0bJ07V5wjqcwH1mDNnjuhbgdMd+kzCunXryqZNm6RTp06izyoM62AFoL0kAaCdIQGgnR9XI4AAAggggAACcRQgAHQzKroCkADQjS2lIhCVAAFgVNL/V8+3335rXt6xcOFC8yejRo2S4cOH59SKXbt2ScOGDc2W4aZNm8oHH3yQ8bpmzZrJ0qVL5cADDzQv70h+Kccrr7wip5xyStb6586dK23btjXnXXnllTJy5Mic2pnLSQSAuShVfg4BoJ0hAaCdH1cjgAACCCCAAAJxFCAAdDMqBIBuXCkVgSgFCAAj1N68ebN06dJF3nzzTVPriBEj5KabbsqrBRdffLHcd9995ppMq/eSgzs9f+zYsSl1bNu2zbxtWMNIDQr1RSTp3to7aNAgGTdunLl23rx50rp167zaWtnJBID2lASAdoYEgHZ+XI0AAggggAACCMRRwLsAsMtV8dkC/MqPC0J0gYkuTOFAAIHiEiAAjGi8NHTT5/fp6js9Lr30Urnzzjvzrn358uXSvHlz0e25rVq1klmzZknNmjUT5WzZssVs112wYIFUr17dbDM+7LDDKtSTvA149OjRMnTo0JRzNFzUcrQe3a48Y8aMvNtKABgqWYXCCADtfAkA7fy4GgEEEEAAAQQQiKMAAaCbUTErAAkA3eBSKgIRCRAARgTdu3dvmTJliqlNtwBr+Jdu1V1Zc3bffXc5/PDD07ZOt+Pq1mE9WrZsabYQ67MBV61aJbfeeqssWrTIfFbZtt0NGzaYAFEDRT0GDhwoffr0MWHi9OnTzXbfjRs3mv//1ltvSYsWLUKVYgWgPScBoJ0hAaCdH1cjgAACCCCAAAJxFCAAdDMqBIBuXCkVgSgFCAAj0q4s7EvXhIMPPlg+/vjjtK3buXOnDBgwQB566KGMre/fv795CUjVqlUznrNy5Urp3r27rFixIu05e++9tzz66KPSs2fP0JUIAO1JCQDtDAkA7fy4GgEEEEAAAQQQiKOAbwFgm1/EZwvwvFfZAhzHOU+bEMhVgAAwVynL88IMAMua8sILL5iQb/78+aJvFt53333Nc/r0DcDdunXLqcX6hl99RuCkSZNEA0HdqtyoUSMTDOo2ZQ0iXRwEgPaqBIB2hgSAdn5cjQACCCCAAAIIxFGAANDNqOgKQAJAN7aUikBUAgSAUUlTT4oAAaD9hCAAtDMkALTz42oEEEAAAQQQQCCOAgSAbkaFANCNK6UiEKUAAWCU2tSVECAAtJ8MBIB2hgSAdn5cjQACCCCAAAIIxFHAuwDw5BhtAX6NLcBxnPO0CYFcBQgAc5XivFAFCADtOQkA7QwJAO38uBoBBBBAAAEEEIijAAGgm1ExKwAJAN3gUioCEQkQAEYETTWpAgSA9jOCANDOkADQzo+rEUAAAQQQQACBOAoQALoZFQJAN66UikCUAgSAUWpTV0KAANB+MhAA2hkSANr5cTUCCCCAAAIIIBBHAd8CwGNPis8W4LdfZwtwHOc8bUIgVwECwFylOC9UAQJAe04CQDtDAkA7P65GAAEEEEAAAQTiKEAA6GZUdAUgAaAbW0pFICoBAsCopKknRYAA0H5CEADaGRIA2vlxNQIIIIAAAgggEEcB7wLAE66UGjXrFpzaBIDTbzHtWL16tTRs2LDgbaIBCCCQnwABYH5enB2SAAGgPSQBoJ0hAaCdH1cjgAACCCCAAAJxFCAAdDMqBIBuXCkVgSgFCACj1KauhAABoP1kIAC0MyQAtPPjagQQQAABBBBAII4CBIBuRoUA0I0rpSIQpQABYJTa1EUAGOIcIAC0wyQAtPPjagQQQAABBBBAII4C3gWAx8doC/AMt1uAq1SpktOU6ty5s8yYMaPCuVu3bpWXX35ZXnvtNZk3b56sWLFCNmzYIHvttZf8x3/8h5xyyily4YUXyv77759TPZs3b5axY8fKpEmTZOXKlbJt2zZp1KiR9OjRQ4YMGSIHHXRQTuUsWbJE7r77btOuTz/9VPbcc09p1qyZnHvuudK/f3+pXr16TuVwEgK2AgSAtoJcH0iAFYCB2FIuIgC0MyQAtPPjagQQQAABBBBAII4CBIBuRsWsAIxxAPjuu+9Khw4dTOBX2aFh4IQJE+Sss86q9LxVq1aZoG/ZsmVpz6tTp4489thj0r1790rLefDBB2Xw4MHy/fffpz3vuOOOk6lTp8o+++zjZuAoFYEkAQJApkNBBAgA7dkJAO0MCQDt/LgaAQQQQAABBBCIowABoJtRiTIAvOiii+Tiiy/O2JHatWtL48aNUz6fPXu2dOzY0fxZ+/btpWfPntKqVSsTrK1du1amTJligr8ffvhBqlWrJs8995x069YtbR0bN26U1q1by9KlS83nAwYMkD59+kjNmjVl+vTpcsstt4ieU6tWLZkzZ44cddRRacvR1YgaEO7cuVMaNGggI0aMkGOPPVa++uoreeCBB0yb9OjUqZMpt2rVqm4Gj1IR+D8BAkCmQkEECADt2QkA7QwJAO38uBoBBBBAAAEEEIijgG8B4HG6BXiPGLwFeOs3MjeiFYDXXXedXH/99XlNr7feekvuuusu0WuPOOKItNc+88wzctppp8muXbukSZMmZotwum3HWvcNN9xgyhg9erQMHTo0pTwN/TS027Fjh5xwwgkybdq0CvXpZ7rNV7cO77333rJw4UJTZ/KhKwPvvfde80cPP/yw9O3bN68+czIC+QoQAOYrxvmhCBAA2jMSANoZEgDa+XE1AggggAACCCAQRwECQDej8n3MA8Bce33GGWfI5MmTzekayrVs2TLl0u3bt0v9+vXlm2++MQHe4sWL067MGzRokIwbN85cu2DBAjnmmGNSytHnBpZtM9YVg3/6058qNFGfMdiwYUP5+uuv5ec//7m89957uXaD8xAIJEAAGIiNi2wFCABtBUUIAO0MCQDt/LgaAQQQQAABBBCIowABoJtR8SUA1Jd6XHLJJQZJQzoNBJOPV199Vbp06WL+aNSoUTJ8+PC0oHPnzpW2bduaz6666iq5+eabU87TF3zoMwL1+Pzzz2W//fZLW05ykLh8+XI57LDD3AwgpSIgIgSATIOCCCR/MXeQ7rJHlVoFaUexV/rqzknF3gXajwACCCCAAAIIIIBAaALeBYCddQtwndB8ghb0/dZvZe7MaN4CHGQLcK79+stf/iKXX365OV1XAp5++ukpl1577bVy4403mj/Trb76ko50h27xrVu3rmzatMlsB545c2bKafqG4NWrV5u3D5c9SzBdOY8//rj8+te/Nh899NBDcv755+faFc5DIG8BAsC8ybggDAECwDAURQgAw3GkFAQQQAABBBBAAAE/BAgA3YxjlAGgPsNPA7ZPPvlEqlevblbPtWvXTs477zzzzD2b41e/+pU8++yzpoglS5ZUeF7gmWeeKU8++aT5XLfmasiX6Tj66KNF3z5cr149WbNmTeI0fUGIPvdPnzWo9T399NMZy1i0aJH853/+p/lcnzWozxzkQMCVAAGgK1nKrVSAADCcCUIAGI4jpSCAAAIIIIAAAgj4IUAA6GYckwPAefPmyf77719pRfpsu3yPdC/kKF/GqaeeKhMnTpQ6dfJfFfnOO++YZ/Xpm4CbN29unu9X/tAVf2+//bbom4Y1yKvs0DcNP//88+aUrVu3So0aNcy/64o/fX6gHvqij3vuuSdjMevWrTMBoh76pmFdEciBgCsBAkBXspRbqQABYDgThAAwHEdKQQABBBBAAAEEEPBDwLsAsNOVskcMtgBv1S3As37cApzLoavf8j00dPvlL38pJ510kjRt2lT23HNPWbt2rdlee//998v69etNkZ07dxZ9Vt9uu+2WcxXff/+9dOjQwbywQw99I7DWVf7QYPD999+XBg0ayBdffFFp+WeffbY88cQT5hwN8vbZZx/z7/Pnz5c2bdqYf9dnCOqzBDMdW7ZskVq1fnwclgaKzz33XM594kQE8hUgAMxXjPNDESAADIWRLcDhMFIKAggggAACCCCAgCcCBIBuBjKKAFDfvJtpy+2XX34p3bp1E90yq8ddd90lQ4YMybmzAwYMkAkTJpjz+/XrZ1YRpjuaNGkiH374oTRq1MhsQa7s6Nu3rzzyyCPmFH3eX9mqxzfeeMM8F1CPa665Rv785z9nLGbnzp1SrVo187kGn6+99lrOfeJEBPIVIADMV4zzQxEgAAyFkQAwHEZKQQABBBBAAAEEEPBEgADQzUAmB4CutgBna7kGc7q1dtu2bXLooYfKihUrsl1iPr/lllvMm3r10C3AuqJQVxumO1gBmBMpJxWpAAFgkQ5csTebADCcEWQLcDiOlIIAAggggAACCCDgh4B3AWDHGG0BfsPtW4BzmYHJz9379NNP5YADDqj0snHjxsmgQYPMOfpGXl2dV/bMvXQX8gzAXEaBc4pVgACwWEeuyNtNABjOABIAhuNIKQgggAACCCCAAAJ+CBAAuhlHswIwBgHgsGHD5LbbbjOd1JWIrVu3zthhfaHGb37zG9FttgcffLDMnj07sU0300VnnHGGTJ482Xxs8xbgvfbay5TBW4DdzEdKDSZAABjMjassBQgALQH/73ICwHAcKQUBBBBAAAEEEEDADwECQDfjGJcAcOjQoXL77bdnDQCfffZZ6d27t+zYscO8sVhX/unz/bId1157rdx4443mtDlz5oiuCEx3aLn6vMJNmzaZ5/3ptuLk46CDDjLPBdRVh/pW4EyHhpS//vWvzccPPfSQnH/++dmayOcIBBYgAAxMx4U2AgSANnr/fy0BYDiOlIIAAggggAACCCDgh4BvAWDbDn+KzVuA58z+8W22yS+8iHrW9OjRQ1544QVTrY71gQceWKEJr7/+uuh5+uZffTOvhnP6bL9cjldeeUVOOeUUc6q+vVff4pvumDt3rrRt29Z8dOWVV8rIkSNTTtNQT8M9PT7//HPZb7/90paj25N1m7Iey5Ytk8MPPzyXZnIOAoEECAADsXGRrQABoK3gj9cTAIbjSCkIIIAAAggggAACfggQALoZR10BWOgAUF8C0rRpU9m+fbv87Gc/k1WrVlXo7FtvvSVdunQxK/P23ntvmTZtmnnxR66HvmCkfv368u2335oXjixZskSqVKlS4fLk4C7dVuQnnnhCzj77bHOdvoTkT3/6U4UyNm/ebLYk61bjI444wtTFgYBLAQJAl7qUnVGAADCcyUEAGI4jpSCAAAIIIIAAAgj4IUAA6GYcXQeAzz33nHTr1k2qV6+etgNffvml+XzRokXm8zFjxshll12Wcu4///lPOeGEE+Sbb74xb/l9+eWXpX379nmDJG8DHj16tOi24+RDtwbrtl/dBty5c2eZMWNGhTo0pNQAUUNKDSIXLlxYYQvy4MGD5d577zXX/u1vf5Pzzjsv77ZyAQL5CBAA5qPFuaEJEACGQ0kAGI4jpSCAAAIIIIAAAgj4IeBdANguRluA33K3BfiQQw4xK/v0uX26tVb/f82aNWXdunUmYLv//vtl/fr1ZpJ26NBBXnvtNalRo0Zi0mrQ1q5dO1mzZo35szvuuENOPvnkSie1rvTTf8ofGzZskFatWsny5cvNRwMHDpQ+ffqY9kyfPt1s9924caP5/7risEWLFmnr0a3KU9iXUgAAIABJREFUvXr1Mi8hadCggVx99dXSpk0bs+LvgQceSLxsRPujfaxWrZofP4T0IrYCBICxHRq/G0YAGM74EgCG40gpCCCAAAIIIIAAAn4IEAC6GUezAtBxAPivf/0ra+M1IJwwYYJ5AUfyMXHixLxfoHHdddfJ9ddfn7bOlStXSvfu3WXFihVpP9dVfY8++qj07Nmz0jZr0HfJJZeIbi1Od2gg+Pzzz8u+++6bte+cgICtAAGgrSDXBxIgAAzEVuEiAsBwHCkFAQQQQAABBBBAwA8BAkA34+g6ANQXdeg/ur1Wn/WnK/++++472XPPPaVRo0ZmdV+/fv0SL94o38uwA0AtX58jOHbsWJk0aZJoIKghnrZFg8FLL71UDj744JywFy9eLH/9619FX07y2Wefme3Juj343HPPlQsuuCDjtuecCuckBPIQIADMA4tTwxMgAAzHkgAwHEdKQQABBBBAAAEEEPBDwLcAsF3b4bF5C/Bbc241k6SQbwH2Y5bSCwQKI0AAWBj3kq+VADCcKUAAGI4jpSCAAAIIIIAAAgj4IUAA6GYcdQUgAaAbW0pFICoBAsCopKknRYAAMJwJQQAYjiOlIIAAAggggAACCPgh4F0AeFyMVgDOZQWgHz8l9KJUBQgAS3XkC9xvAsBwBoAAMBxHSkEAAQQQQAABBBDwQ4AA0M04mhWABIBucCkVgYgECAAjgqaaVAECwHBmBAFgOI6UggACCCCAAAIIIOCHAAGgm3EkAHTjSqkIRClAABilNnUlBAgAw5kMBIDhOFIKAggggAACCCCAgB8C/gWAw2SPGnUKPjhbv9cVgKNNO3gJSMGHgwYgEEiAADAQGxfZChAA2gr+eD0BYDiOlIIAAggggAACCCDghwABoJtxJAB040qpCEQpQAAYpTZ1JQQIAMOZDASA4ThSCgIIIIAAAggggIAfAgSAbsaRANCNK6UiEKUAAWCU2tRFABjyHCAADBmU4hBAAAEEEEAAAQSKWsC3ALD9sfHZAvzm22wBLuofDhpf8gIEgCU/BQoDwArAcNwJAMNxpBQEEEAAAQQQQAABPwQIAN2Mo64AJAB0Y0upCEQlQAAYlTT1pAgQAIYzIQgAw3GkFAQQQAABBBBAAAE/BAgA3YwjAaAbV0pFIEoBAsAotakrIUAAGM5kIAAMx5FSEEAAAQQQQAABBPwQ8C4AbDM0Nm8BfnPebWaS8BZgP35W6EXpCRAAlt6Yx6LHBIDhDAMBYDiOlIIAAggggAACCCDghwABoJtxNCsACQDd4FIqAhEJEABGBE01qQIEgOHMCALAcBwpBQEEEEAAAQQQQMAPAQJAN+NIAOjGlVIRiFKAADBKbepKCBAAhjMZCADDcaQUBBBAAAEEEEAAAT8EfAsAO7SKzxbg2QvYAuzHTwm9KFUBAsBSHfkC95sAMJwBIAAMx5FSEEAAAQQQQAABBPwQIAB0M466ApAA0I0tpSIQlQABYFTS1JMiQAAYzoQgAAzHkVIQQAABBBBAAAEE/BAgAHQzjgSAblwpFYEoBQgAo9SmroQAAWA4k4EAMBxHSkEAAQQQQAABBBDwQ8C7APCYK2LzFuDZ/7jdTBLeAuzHzwq9KD0BAsDSG/NY9JgAMJxhIAAMx5FSEEAAAQQQQAABBPwQIAB0M45mBSABoBtcSkUgIgECwIigqSZVgAAwnBlBABiOI6UggAACCCCAAAII+CFAAOhmHAkA3bhSKgJRChAARqlNXQkBAsBwJgMBYDiOlIIAAggggAACCCDgh4B3AeB/xmgL8EK2APvxU0IvSlWAALBUR77A/SYADGcACADDcaQUBBBAAAEEEEAAAT8ECADdjKNZAUgA6AaXUhGISIAAMCJoqkkVIAAMZ0YQAIbjSCkIIIAAAggggAACfggQALoZRwJAN66UikCUAgSAUWpTV0KAADCcyUAAGI4jpSCAAAIIIIAAAgj4IeBbANix5eWxeQvwG4vGmEnCW4D9+FmhF6UnQABYemMeix4TAIYzDASA4ThSCgIIIIAAAggggIAfAgSAbsZRVwASALqxpVQEohIgAIxKmnpSBAgAw5kQBIDhOFIKAggggAACCCCAgB8CBIBuxpEA0I0rpSIQpQABYJTa1JUQIAAMZzIQAIbjSCkIIIAAAggggAACfgh4FwC2uCw+W4D/+RczSdgC7MfPCr0oPQECwNIb81j0mAAwnGEgAAzHkVIQQAABBBBAAAEE/BAgAHQzjmYFIAGgG1xKRSAiAQLAiKCpJlWAADCcGUEAGI4jpSCAAAIIIIAAAgj4IeBdAHj0ZbLH7nUKPjhbt30rb7zDCsCCDwQNQMBCgADQAo9LgwsQAAa3S76SADAcR0pBAAEEEEAAAQQQ8EOAANDNOBIAunGlVASiFCAAjFKbuhICBIDhTAYCwHAcKQUBBBBAAAEEEEDADwECQDfjSADoxpVSEYhSgAAwSm3qIgAMeQ4QAIYMSnEIIIAAAggggAACRS3gWwDY6ag/xmYL8Kx37zBzg5eAFPWPCI0vYQECwBIe/EJ2nRWA4egTAIbjSCkIIIAAAggggAACfggQALoZR10BSADoxpZSEYhKgAAwKmnqSREgAAxnQhAAhuNIKQgggAACCCCAAAJ+CBAAuhlHAkA3rpSKQJQCBIBRalNXQoAAMJzJQAAYjiOlIIAAAggggAACCPgh4F0AeKRuAd674IOzddt3Mus9tgAXfCBoAAIWAgSAFnhcGlyAADC4XfKVBIDhOFIKAggggAACCCCAgB8CBIBuxpEA0I0rpSIQpQABYJTa1JUQIAAMZzIQAIbjSCkIIIAAAggggAACfggQALoZRwJAN66UikCUAgSAUWpTFwFgyHOAADBkUIpDAAEEEEAAAQQQKGoB7wLAn/8hPluAF99p5gZvAS7qHxEaX8ICBIAlPPiF7LoPX8yF9KNuBBBAAAEEEEAAAQQQqCjgw31Gch86EQAyzRFAICQBAsCQICkmPwEfvpjz6zFnI4AAAggggAACCCCAgGsBH+4zCABdzxLKR6A0BQgAS3PcC95rH76YC45IAxBAAAEEEEAAAQQQQCBFwIf7jJQA8IgYbQF+ny3A/LghUMwCBIDFPHpF3HYfvpiLmJ+mI4AAAggggAACCCDgpYAP9xkEgF5OTTqFQMEFCAALPgSl2QAfvphLc+ToNQIIIIAAAggggAAC8RXw4T6DADC+84uWIVDMAgSAxTx6Rdx2H76Yi5ifpiOAAAIIIIAAAggg4KWAD/cZyX3o3OzS2LwFeOYHd5k5w1uAvfzRoVMlIEAAWAKDHMcu+vDFHEdX2oQAAggggAACCCCAQCkL+HCfQQBYyjOYviPgToAA0J0tJVci4MMXMwOMAAIIIIAAAggggAAC8RLw4T6DADBec4rWIOCLAAGgLyNZZP3w4Yu5yMhpLgIIIIAAAggggAAC3gv4cJ+REgA2HRKfLcBL/2rmD1uAvf8xooOeChAAejqwce+WD1/McTemfQgggAACCCCAAAIIlJqAD/cZBIClNmvpLwLRCBAARuNMLeUEfPhiZlARQAABBBBAAAEEEEAgXgI+3GcQAMZrTtEaBHwRIAD0ZSSLrB8+fDEXGTnNRQABBBBAAAEEEEDAewEf7jNSA8Dfyx677V3wcdu6/TuZufRu0w62ABd8OGgAAoEECAADsXGRrYAPX8y2BlyPAAIIIIAAAggggAAC4Qr4cJ9BABjunKA0BBD4UYAAkJlQEAEfvpgLAkelCCCAAAIIIIAAAgggkFHAh/uMlADwPy6JzwrAZfcYd1YA8gOIQHEKEAAW57gVfat9+GIu+kGgAwgggAACCCCAAAIIeCbgw30GAaBnk5LuIBATAQLAmAxEqTXDhy/mUhsz+osAAggggAACCCCAQNwFfLjPIACM+yyjfQgUpwABYITjtmbNGpk3b575Z/78+eaf9evXmxb069dPJk6cmFdrXnrpJRk/frwpb+3atVKvXj1p06aNDBw4ULp27ZpTWZs3b5axY8fKpEmTZOXKlbJt2zZp1KiR9OjRQ4YMGSIHHXRQTuXke5IPX8z59pnzEUAAAQQQQAABBBBAwK2AD/cZKQHgYTHaAryCLcBuZy+lI+BWgADQrW9K6VWqVMlYWz4B4K5du2TQoEEm/Mt0aAh4//33S2V1rlq1ygR9y5YtS1tMnTp15LHHHpPu3buHruTDF3PoKBSIAAIIIIAAAggggAACVgI+3GcQAFpNAS5GAIEMAgSAEU6N5DBOV9k1a9ZMXnnlFdOCfALAESNGyMiRI811LVu2lGHDhkmTJk1EA73Ro0fLokWLzGd63k033ZS2hxs3bpTWrVvL0qVLzecDBgyQPn36SM2aNWX69Olyyy23iJ5Tq1YtmTNnjhx11FGhSvnwxRwqCIUhgAACCCCAAAIIIICAtYAP9xkEgNbTgAIQQCCNAAFghNPiuuuuM6Gb/tOgQQP5+OOPpXHjxnkFgLpNV4PDHTt2SKtWrWTWrFkmtCs7dEtv586dZcGCBVK9enUT8Gk4WP64/vrr5YYbbjB/rKHh0KFDU07R0K9Tp06mnhNOOEGmTZsWqpQPX8yhglAYAggggAACCCCAAAIIWAv4cJ+R3IfjDx0cm7cAz1g51owPbwG2nqYUgEBBBAgAC8L+Y6VBAsDBgwfLvffea67XkO64446r0IO5c+dK27ZtzZ9fcsklcvfdd6ecs337dqlfv7588803JkxcvHixVK1atUI5us143Lhx5s81UDzmmGNC0/Lhizk0DApCAAEEEEAAAQQQQACBUAR8uM8gAAxlKlAIAgiUEyAALOCUyDcA1Gf/6dbhTz/9VJo2bSoffPBBxtbr5/psv4YNG8onn3yS8izAV199Vbp06WKuHTVqlAwfPjxtOclB4lVXXSU333xzaFo+fDGHhkFBCCCAAAIIIIAAAgggEIqAD/cZBIChTAUKQQABAsD4zIF8A8APP/wwsZ33wgsvNC/5yHTo52UvCdHryrYa6/nXXnut3HjjjebSTKsI9TPd/lu3bl3ZtGmT2Q48c+bM0PB8+GIODYOCEEAAAQQQQAABBBBAIBQBH+4zUgLAJhfHZwvwqh93orEFOJSpSiEIRC7ACsDIyf+/wnwDwOeff1569uxpCrjjjjvkD3/4Q8bW6+eXXXaZ+VyvS36T75lnnilPPvmk+ezrr782IV+m4+ijj5Z3331X6tWrJ2vWrAlNy4cv5tAwKAgBBBBAAAEEEEAAAQRCEfDhPoMAMJSpQCEIIFBOgACwgFMi3wBQV/xddNFFpsWTJk2SM844I2PrNeDToE8PvU5XBJYd+tzAt99+W2rXrm3e9FvZoYGjBoh6bN26VWrUqJGTmH5pVXZ8/vnn0qZNG3MK/wUpJ1JOQgABBBBAAAEEEEAAgSwCBIBupsjW7d/JDFYAusGlVAQiEiAAjAg6XTX5BoC33XabDBs2zBT14osvSteuXTO2Xj8vW/V3++23y+WXX544t3nz5vL++++bNxF/8cUXlQqcffbZ8sQTT5hz1q1bJ/vss09OYlWqVMnpPALAnJk4EQEEEEAAAQQQQAABBEotAGx8UXy2AH90n9FnAQc/hggUpwABYAHHLd8AUJ/bp8/v0+P111+XE088MWPrp02bJieddJL5XK+7+uqrE+c2adJE9LmA+kIRfUFIZUffvn3lkUceyfsXPQFgAScWVSOAAAIIIIAAAgggUKIC3q0AJAAs0ZlMtxEIX4AAMHzTnEvMNwAsphWAbAHOeRpwIgIIIIAAAggggAACCIQkQAAYEmS5YswWYFYAusGlVAQiEiAAjAg6XTX5BoDF9AzAbKw+fDFn6yOfI4AAAggggAACCCCAQLQCPtxnpLwE5BDdArxXtIhpatu6fYPM+JgtwAUfCBqAgIUAAaAFnu2l+QaAU6dOlV69eplqbd4CrC8PmTx5simHtwDbjiLXI4AAAggggAACCCCAQFwECADdjAQBoBtXSkUgSgECwCi1y9WVbwCoz+3T5/fpoW/11RWBmQ79fPz48eZjva5x48aJU/U5gvpcQD3mzJkj+lbgdMeOHTukbt26smnTJunUqZPMnDkzNC0fvphDw6AgBBBAAAEEEEAAAQQQCEXAh/sMVgCGMhUoBAEEygkQABZwSuQbAO7atUsaNmwon332mTRt2lQ++OCDjK1v1qyZLF26VA488EDzlqbkl3K88sorcsopp5hrR40aJcOHD09bzty5c6Vt27bmsyuvvFJGjhwZmpYPX8yhYVAQAggggAACCCCAAAIIhCLgw31GagA4SPaoHoMtwDt0C/CPC1B4C3AoU5VCEIhcgAAwcvL/rzDfAFCvvPjii+W++3589kKm1XvJwZ2eP3bs2JRebtu2TerXry/ffvutaFC4ZMmSlICw7ORBgwbJuHHjzP+dN2+etG7dOjQtH76YQ8OgIAQQQAABBBBAAAEEEAhFwIf7DALAUKYChSCAQDkBAsACTokgAeDy5culefPmottzW7VqJbNmzZKaNWsmerFlyxazXXfBggVSvXp1ef/99+Wwww6r0MvkbcCjR4+WoUOHppyj4aKWo/V07txZZsyYEaqUD1/MoYJQGAIIIIAAAggggAACCFgL+HCfQQBoPQ0oAAEE0ggQAEY4LWbPni0rV65M1Lhu3bpE8Na+fXu54IILUlpz3nnnpW2dbsfVrbt6tGzZ0mzh1WcDrlq1Sm699VZZtGiR+ayybbsbNmwwAaIGinoMHDhQ+vTpY8LE6dOnm+2+GzduNP//rbfekhYtWoQq5cMXc6ggFIYAAggggAACCCCAAALWAj7cZ6QEgAdfGJ8twP/6cXcYW4CtpykFIFAQAQLACNk10Hv44YdzrlGf+Zfu2LlzpwwYMEAeeuihjGX179/fvASkatWqGc/RMLJ79+6yYsWKtOfsvffe8uijj0rPnj1zbnOuJ/rwxZxrXzkPAQQQQAABBBBAAAEEohHw4T6DADCauUItCJSaAAFghCMeVgBY1uQXXnjBhHzz588XXU247777muf06RuAu3XrllPP9A2/+ozASZMmmdWJ+nzARo0amWDw0ksvlYMPPjincvI9yYcv5nz7zPkIIIAAAggggAACCCDgVsCH+wwCQLdzhNIRKFUBAsBSHfkC99uHL+YCE1I9AggggAACCCCAAAIIlBPw4T4jJQBsNDA+W4BXjzfabAHmxw6B4hQgACzOcSv6VvvwxVz0g0AHEEAAAQQQQAABBBDwTMCH+wwCQM8mJd1BICYCBIAxGYhSa4YPX8ylNmb0FwEEEEAAAQQQQACBuAv4cJ+REgA2vCA+KwD/PcEMPysA4/5TQPsQSC9AAMjMKIiAD1/MBYGjUgQQQAABBBBAAAEEEMgo4MN9BgEgExwBBFwIEAC6UKXMrAI+fDFn7SQnIIAAAggggAACCCCAQKQCPtxnEABGOmWoDIGSESAALJmhjldHffhijpcorUEAAQQQQAABBBBAAAEf7jNSAsAD+8dnC/CnD5oJxhZgfs4QKE4BAsDiHLeib7UPX8xFPwh0AAEEEEAAAQQQQAABzwR8uM8gAPRsUtIdBGIiQAAYk4EotWb48MVcamNGfxFAAAEEEEAAAQQQiLuAD/cZBIBxn2W0D4HiFCAALM5xK/pW+/DFXPSDQAcQQAABBBBAAAEEEPBMwIf7jJQAcP/fxWcL8OcPmdnCFmDPfmjoTskIEACWzFDHq6M+fDHHS5TWIIAAAggggAACCCCAgA/3GQSAzGMEEHAhQADoQpUyswr48MWctZOcgAACCCCAAAIIIIAAApEK+HCfQQAY6ZShMgRKRoAAsGSGOl4d9eGLOV6itAYBBBBAAAEEEEAAAQR8uM9ICQD3Oz8+W4C/+JuZYGwB5ucMgeIUIAAsznEr+lb78MVc9INABxBAAAEEEEAAAQQQ8EzAh/sMAkDPJiXdQSAmAgSAMRmIUmuGD1/MpTZm9BcBBBBAAAEEEEAAgbgL+HCfQQAY91lG+xAoTgECwOIct6JvtQ9fzEU/CHQAAQQQQAABBBBAAAHPBHy4z6gQAFbbs+CjtPWHjTKDLcAFHwcagICNAAGgjR7XBhbw4Ys5cOe5EAEEEEAAAQQQQAABBJwI+HCfQQDoZGpQKAIlL0AAWPJToDAAPnwxF0aOWhFAAAEEEEAAAQQQQCCTgA/3GQSAzG8EEHAhQADoQpUyswr48MWctZOcgAACCCCAAAIIIIAAApEK+HCfkRIANjhP9ojLFuAvJ5qx5C3AkU5pKkMgNAECwNAoKSgfAR++mPPpL+cigAACCCCAAAIIIICAewEf7jMIAN3PE2pAoBQFCABLcdRj0GcfvphjwEgTEEAAAQQQQAABBBBAIEnAh/sMAkCmNAIIuBAgAHShSplZBXz4Ys7aSU5AAAEEEEAAAQQQQACBSAV8uM9ICQDr9Y3PFuC1/2XGki3AkU5pKkMgNAECwNAoKSgfAR++mPPpL+cigAACCCCAAAIIIICAewEf7jMIAN3PE2pAoBQFCABLcdRj0GcfvphjwEgTEEAAAQQQQAABBBBAIEnAh/sMAkCmNAIIuBAgAHShSplZBXz4Ys7aSU5AAAEEEEAAAQQQQACBSAV8uM9ICQD3/W18tgCve8SMJVuAI53SVIZAaAIEgKFRUlA+Aj58MefTX85FAAEEEEAAAQQQQAAB9wI+3GcQALqfJ9SAQCkKEACW4qjHoM8+fDHHgJEmIIAAAggggAACCCCAQJKAD/cZBIBMaQQQcCFAAOhClTKzCvjwxZy1k5yAAAIIIIAAAggggAACkQr4cJ+REgDu85v4bAFe/3czlmwBjnRKUxkCoQkQAIZGSUH5CPjwxZxPfzkXAQQQQAABBBBAAAEE3Av4cJ9BAOh+nlADAqUoQABYiqMegz778MUcA0aagAACCCCAAAIIIIAAAkkCPtxnpASAPzk3PisAv37USLMCkB85BIpTgACwOMet6Fvtwxdz0Q8CHUAAAQQQQAABBBBAwDMBH+4zCAA9m5R0B4GYCBAAxmQgSq0ZPnwxl9qY0V8EEEAAAQQQQAABBOIu4MN9BgFg3GcZ7UOgOAUIAItz3Iq+1T58MRf9INABBBBAAAEEEEAAAQQ8E/DhPiO5D53rnhObLcAzv3nczBa2AHv2Q0N3SkaAALBkhjpeHfXhizleorQGAQQQQAABBBBAAAEEfLjPIABkHiOAgAsBAkAXqpSZVcCHL+asneQEBBBAAAEEEEAAAQQQiFTAh/sMAsBIpwyVIVAyAgSAJTPU8eqoD1/M8RKlNQgggAACCCCAAAIIIODDfUZKAFjnHNmjau2CD+zWnZtk5rdsAS74QNAABCwECAAt8Lg0uIAPX8zBe8+VCCCAAAIIIIAAAggg4ELAh/sMAkAXM4MyEUCAAJA5UBABH76YCwJHpQgggAACCCCAAAIIIJBRwIf7DAJAJjgCCLgQIAB0oUqZWQV8+GLO2klOQAABBBBAAAEEEEAAgUgFfLjPSAkA9+4Tny3A3/23GUveAhzplKYyBEITIAAMjZKC8hHw4Ys5n/5yLgIIIIAAAggggAACCLgX8OE+gwDQ/TyhBgRKUYAAsBRHPQZ99uGLOQaMNAEBBBBAAAEEEEAAAQSSBHy4zyAAZEojgIALAQJAF6qUmVXAhy/mrJ3kBAQQQAABBBBAAAEEEIhUwIf7jJQAcM+z4rMFeOMTZizZAhzplKYyBEITIAAMjZKC8hHw4Ys5n/5yLgIIIIAAAggggAACCLgX8OE+gwDQ/TyhBgRKUYAAsBRHPQZ99uGLOQaMNAEBBBBAAAEEEEAAAQSSBHy4zyAAZEojgIALAQJAF6qUmVXAhy/mrJ3kBAQQQAABBBBAAAEEEIhUwIf7jJQAsPaZ8dkCvGmSGUu2AEc6pakMgdAECABDo6SgfAR8+GLOp7+ciwACCCCAAAIIIIAAAu4FfLjPIAB0P0+oAYFSFCAALMVRj0GfffhijgEjTUAAAQQQQAABBBBAAIEkAR/uMwgAmdIIIOBCgADQhSplZhXw4Ys5ayc5AQEEEEAAAQQQQAABBCIV8OE+I7kPnWr2js0W4FlbJpuxZAtwpFOayhAITYAAMDRKCspHwIcv5nz6y7kIIIAAAggggAACCCDgXsCH+wwCQPfzhBoQKEUBAsBSHPUY9NmHL+YYMNIEBBBAAAEEEEAAAQQQSBLw4T6DAJApjQACLgQIAF2oUmZWAR++mLN2khMQQAABBBBAAAEEEEAgUgEf7jNSAsA9To/PFuCtU8xYsgU40ilNZQiEJkAAGBolBeUj4MMXcz795VwEEEAAAQQQQAABBBBwL+DDfQYBoPt5Qg0IlKIAAWApjnoM+uzDF3MMGGkCAggggAACCCCAAAIIJAn4cJ9BAMiURgABFwIEgC5UKTOrgA9fzFk7yQkIIIAAAggggAACCCAQqYAP9xkpAWCN02SPKrUjNUxX2dZdm2TW90+Zj9gCXPDhoAEIBBIgAAzExkW2Aj58MdsacD0CCCCAAAIIIIAAAgiEK+DDfQYBYLhzgtIQQOBHAQJAZkJBBHz4Yi4IHJUigAACCCCAAAIIIIBARgEf7jNSAsDdT5U9qtQq+Ihv3bVZZm172rSDFYAFHw4agEAgAQLAQGxcZCvgwxezrQHXI4AAAggggAACCCCAQLgCPtxnEACGOycoDQEEfhQgAGQmFETAhy/mgsBRKQIIIIAAAggggAACCGQU8OE+gwCQCY4AAi4ECABdqFJmVgEfvpizdpITEEAAAQQQQAABBBBAIFIBH+4zkvvQsfqvYrMF+I0dz5ixZAtwpFOayhAITYAAMDRKCspHwIcv5nz6y7kIIIAAAggggAACCCDgXsCH+wwCQPfzhBoQKEUBAsBSHPUY9NmHL+YYMNIEBBBAAAEEEEAAAQQQSBLw4T6DAJApjQACLgQIAF2oUmZWAR++mLN2khMQQACgWu/FAAAgAElEQVQBBBBAAAEEEEAgUgEf7jNSAsBqveKzBfiH58xYsgU40ilNZQiEJkAAGBolBeUj4MMXcz795VwEEEAAAQQQQAABBBBwL+DDfQYBoPt5Qg0IlKIAAWApjnoM+uzDF3MMGGkCAggggAACCCCAAAIIJAn4cJ9BAMiURgABFwIEgC5UKTOrgA9fzFk7yQkIIIAAAggggAACCCAQqYAP9xnJfehQpWdstgDP3jXVjCVbgCOd0lSGQGgCBIChUVJQPgI+fDHn01/ORQABBBBAAAEEEEAAAfcCPtxnEAC6nyfUgEApChAAluKox6DPPnwxx4CRJiCAAAIIIIAAAggggECSgA/3GQSATGkEEHAhQADoQpUyswp8/PHH0rhxY3PevHnzZP/99896DScggAACCCCAAAIIIIAAApUJfP7559KmTRtzykcffSSHHHJI0YElB4Ct5USpIXsUvA/fy1aZL9NMO9gCXPDhoAEIBBIgAAzExkW2AvPnz098MduWxfUIIIAAAggggAACCCCAQHkBXWjQunXrooNJDgDj2HgCwDiOCm1CILsAAWB2I85wIEAA6ACVIhFAAAEEEEAAAQQQQCAhQADoZjIQALpxpVQEXAsQALoWpvy0Alu3bpX33nvPfFavXj2pXr06Up4IJG+7YHu3J4NaJN1g7hXJQHnWTOadZwNaRN1h7hXRYHnW1LjPvR07dsjatWuN+pFHHil77FH47bP5TgHtwxdffJHvZZGdv99++3H/Fpk2FSEQngABYHiWlIQAAiLiw4OXGcjiFGDuFee4FXurmXfFPoLF237mXvGOXbG3nLlX7CNI+xFAoFQFCABLdeTpNwKOBPhLoSNYis0qwNzLSsQJDgSYdw5QKTInAeZeTkyc5ECAuecAlSIRQACBCAQIACNApgoESkmAvxSW0mjHq6/MvXiNR6m0hnlXKiMdv34y9+I3JqXSIuZeqYw0/UQAAd8ECAB9G1H6g0CBBfhLYYEHoISrZ+6V8OAXsOvMuwLil3jVzL0SnwAF7D5zr4D4VI0AAghYCBAAWuBxKQIIVBTgL4XMikIJMPcKJV/a9TLvSnv8C9l75l4h9Uu7buZeaY8/vUcAgeIVIAAs3rGj5QjEUoC/FMZyWEqiUcy9khjm2HWSeRe7ISmZBjH3SmaoY9dR5l7shoQGIYAAAjkJEADmxMRJCCCQqwB/KcxVivPCFmDuhS1KebkIMO9yUeIcFwLMPReqlJmLAHMvFyXOQQABBOInQAAYvzGhRQgUtQB/KSzq4SvqxjP3inr4irbxzLuiHbqibzhzr+iHsGg7wNwr2qGj4QggUOICBIAlPgHoPgIIIIAAAggggAACCCCAAAIIIICA3wIEgH6PL71DAAEEEEAAAQQQQAABBBBAAAEEEChxAQLAEp8AdB8BBBBAAAEEEEAAAQQQQAABBBBAwG8BAkC/x5feIYAAAggggAACCCCAAAIIIIAAAgiUuAABYIlPALqPAAIIIIAAAggggAACCCCAAAIIIOC3AAGg3+NL7xBAAAEEEEAAAQQQQAABBBBAAAEESlyAALDEJwDdRwABBBBAAAEEEEAAAQQQQAABBBDwW4AA0O/xpXcIIIAAAggggAACCCCAAAIIIIAAAiUuQABY4hOA7iOAAAIIIIAAAggggAACCCCAAAII+C1AAOj3+NI7BBBAAAEEEEAAAQQQQAABBBBAAIESFyAALPEJQPcRQAABBBBAAAEEEEAAAQQQQAABBPwWIAD0e3zpHQJWAmvWrJF58+aZf+bPn2/+Wb9+vSmzX79+MnHixLzKf+mll2T8+PGmvLVr10q9evWkTZs2MnDgQOnatWteZXGy3wILFy4UnS9vvPGGLF68WHQu7rbbbnLAAQdIu3btpH///tKxY8ecEZh7OVOV9InfffedvPDCC+Z33YIFC+TTTz81v6u2bNkidevWlSOOOEK6d+9u5t8+++yT1Yp5l5WIE3IQGDZsmNx2222JM6dPny7HH398pVcy93KA5RQjUKVKlZwkOnfuLDNmzGDe5aTFSQgggEA8BQgA4zkutAqBWAhU9pfCfALAXbt2yaBBg0z4l+nQEPD+++/P+S+isQCiEU4E9CZj1qxZWcv+7W9/KxMmTJDdd98947nMvayMnJAk8Nprr8kvfvGLrCb77ruv/P3vf5dTTjkl7bnMu6yEnJCjwDvvvCOtWrWSHTt2JK6oLABk7uUIy2kJgTACQOYdEwoBBBAoDgECwOIYJ1qJQEEEkv9S2KhRI2nWrJm88sorpi35BIAjRoyQkSNHmutatmwpupqhSZMmsmrVKhk9erQsWrTIfKbn3XTTTQXpK5XGR+DQQw81c0NX+5155plmpd9BBx0kP/zwg8yZM0fGjBljVmbpcc4558hjjz2WsfHMvfiMazG0RAPA3/3ud3LCCSfIMcccI/p7b//995edO3fKv//9b3nyySdlypQpZi5q8KwrBY866qgKXWPeFcNox7+NOu+OO+44M8/q169vVkLrUVkAyNyL/7jGrYVlf9e76KKL5OKLL87YvNq1a0vjxo3Tfs68i9uo0h4EEEAgvQABIDMDAQQyClx33XXSunVr80+DBg3k448/TvzlL9cAcOXKlSY41NULuopBV3bVrFkzUefmzZtFV3zpdrvq1avL0qVLTTjIUboCPXv2lL59+0rv3r2lWrVqFSDWrVsn7du3l+XLl5vPdE6l2w7M3CvdORS05xrspZtzyeU9/fTTctppp5k/Ov3002Xy5Mkp1THvgupzXXmBO++8U/74xz9K06ZNzZy75ZZbKg0AmXvMoSACZQGg/p3v+uuvz7sI5l3eZFyAAAIIFEyAALBg9FSMQPEJBAkABw8eLPfee6/prK7e0tUM5Y+5c+dK27ZtzR9fcsklcvfddxcfDi2OVGDq1KnSq1cvU+eQIUPkrrvuqlA/cy/SISmpyvQ/auh/rNCtwPqMwOSDeVdSU8FZZ1evXm2eOblx40az4k+fvXbDDTdUGgAy95wNh9cF2waAzDuvpwedQwABzwQIAD0bULqDgEuBfANAfSaMbqHT7Zq6guGDDz7I2Dz9fNmyZdKwYUP55JNPeBagy4H0oGy9Kd5rr71MT3r06CEaCCYfzD0PBjnGXdDVzP/4xz9kzz33lA0bNiRayryL8aAVWdP0P3Do77Wy1fa6MquyAJC5V2QDHKPm2gSAzLsYDSRNQQABBHIQIADMAYlTEEDgR4F8A8APP/wwsZ33wgsvNC/5yHTo52UvCdHrMj1nhrFAQAW++uqrxFtY9Ub52WefTYFh7jFPXAnof8g48sgjzXMANQjU57OVHcw7V+qlVe4TTzwhZ599tvz0pz81K03r1atntmZWFgAy90prjoTZW5sAkHkX5khQFgIIIOBegADQvTE1IOCNQL4B4PPPPy/6PDc97rjjDvnDH/6Q0UI/v+yyy8znel337t29caMj4Qs89dRT5vlregwdOtS8TCb5YO6Fb17KJeqzSnUl83PPPWfm2pdffmk4HnnkEfnNb36ToGHelfIsCafv33zzjXlu7hdffCEPPPCAXHDBBabgbAEgcy8c/1IspSwA1C3n+rxm3YWhz2Teb7/9pF27dnLeeeeZFyOlO5h3pThj6DMCCBSzAAFgMY8ebUcgYoF8A0Bd8advldNj0qRJcsYZZ2Rssb5dU9/4qodepysCORBIJ6BvxtRnRs6bN898rCuwdCVW8sHcY+7YCkycOFHOP//8jMVcccUVJgxMfls6885WnesHDhxogj8NXmbPnp2YX9kCQOYecyeoQPLvsExlnHrqqaK/E+vUqcN3bVBorkMAAQRiIEAAGINBoAkIFItAvgHgbbfdJsOGDTPde/HFF6Vr164Zu6qfl636u/322+Xyyy8vFhbaGbHAmDFjRMMXPfTNmFOmTKnQAuZexIPiYXWZAsAWLVqY/0hx7LHHMu88HPdCdkkDv06dOpk3US9cuNBsNS87sgWA/M4r5MgVd921a9eWX/7yl3LSSSeZ5zXrs0315UYzZ840v+vWr19vOti5c2d59dVXZbfddkt0mHlX3GNP6xFAoPQECABLb8zpMQKBBfINAG+88Ua59tprTX2vv/66nHjiiRnrnjZtmvnLpx563dVXXx24nVzor4DekJx88slmm1L9+vXl3XfflQYNGlToMHPP3zkQVc90K+a///1vU92WLVtk1apVos9m0+3nTZo0kTvvvDPxiIOyNjHvohod/+rZtm2baLisz5hM91iDbAEgc8+/ORFVj/R3Xd26ddNWp4876NatmyxatMh8ftddd8mQIUMS5zLvohol6kEAAQTCESAADMeRUhAoCYF8A0D+y3BJTIvIOrlkyRLp2LGjfP3111KjRg15+eWXzYqEdAdzL7JhKbmK9Ll/+mZW3Tb34IMPmudjlR3Mu5KbDqF1uCzgO+igg+T9998XXZWVfGQLAJl7oQ0FBZUT0Bd96HMpNaQ+9NBDZcWKFfzOY5YggAACRSpAAFikA0ezESiEQL4BIM8kKsQo+VnnRx99JB06dJDPPvvMbI/TZ0rq9t9MB3PPz3kQl17pG1p1NaCGNKtXr5af/OQnpmnMu7iMUHG1Q9/0e/TRR5uA5ZlnnjHbMcsf2QJA5l5xjXmxtVZf6KYv/NBDX4h0wAEH8Duv2AaR9iKAAAIiQgDINEAAgZwF8g0Ap06dKr169TLl8xbgnJk5sZyAhn668k9XIeiqK302W9++fSt1Yu4xjVwKPPbYY3LuueeaKh599FH59a9/bf6deedS3d+y9aVX48ePl5/97Gdy8803p+2ovihr8uTJ5rNrrrlG9I2teuh3rAbRzD1/50cceqbPc9ZVpnroC7hat27N77w4DAxtQAABBPIUIADME4zTEShlgXwDQA1s9FlZeugNjq5QyHSU3QDp53pd48aNS5mavv+fwLp168w2X90Sp8c999wjgwcPzurD3MtKxAkWAvog/C5dupgSRo4cKVdeeaX5d+adBWoJX6rbyB9++OFAAro6+pBDDmHuBdLjolwF9LmU+oI2PZIDQH7n5SrIeQgggEA8BAgA4zEOtAKBohDINwDctWuXNGzY0Gzb1DfL6cPNMx36fBndBnXggQeaLXW60oujtAW+/fZb8+IYfRumHqNGjZLhw4fnhMLcy4mJkwIKJL8h+K9//av8/ve/NyUx7wKClvhlYQSAzL0Sn0SOu9+jRw954YUXTC36ciT9uxq/8xyjUzwCCCDgQIAA0AEqRSLgq0C+AaA6XHzxxXLfffcZkjlz5shxxx1XgWfu3LnStm1b8+d6/tixY30lpF85CmzevNmssHrzzTfNFSNGjJCbbropx6t/PI25lxcXJ+chkHwzPH36dDn++OMTVzPv8oDk1JwFsj0DkN95OVNyYp4CuspP/yPu9u3bzTZ1fSN68sHvvDxBOR0BBBAooAABYAHxqRqBYhMIEgAuX75cmjdvLjt27JBWrVrJrFmzpGbNmomub9myRTp16iQLFiyQ6tWrm62ehx12WLHR0N4QBfRB+Ppcq1deecWUeumll8qdd96Zdw3MvbzJSv4CXdnXp08f2WOPPTJa6PNML7vsMvO5br3UN2Lq766yg3lX8tPICUAuASBzzwm914U+99xz0q1bt5TfYckd/vLLL83nixYtMn88ZsyYxO8/fud5PTXoHAIIeCpAAOjpwNItBMIQmD17tqxcuTJRlD6PTZ8Do0f79u3lggsuSKlGtzGlO/T5WLp9U4+WLVuabZz6bED9r8i33npr4i+Wep4+T4ujtAV69+4tU6ZMMQi6BVjDv8q2hO++++5y+OGHM/dKe9qE0nsN9DZs2CA6B/Wt0/p7as899zR/9t5775kXfpStStV5p2/FPPnkkyvUze+8UIaDQpIEcgkA9XTmHtMmHwH9nacr+/R3nu7E0P+v/5FW/743Y8YM8+zm9evXmyL1d+Jrr70mNWrU4HdePsiciwACCMRIgAAwRoNBUxCIm0C+zyXSZxClO3bu3CkDBgyQhx56KGMX+/fvb96CWLVq1bgx0J6IBfJ9/uPBBx8sujqVuRfxQHlYnd78/utf/8raM322qf4++8UvfsG8y6rFCWEI5BoA8n0bhnbplJHr7zwNCCdMmCB169bld17pTA96igACHgoQAHo4qHQJgbAEwgoAy9qjD5DWkG/+/Pnmvy7vu+++0rp1a/OGYN1iwoGACoQZADL3mFP5COiqZF3hos/105cW6fY3Xf2iW4IbNGggLVq0kJ49e8pZZ50ltWrVylo0v/OyEnFCjgK5BoD8zssRlNOMwMyZM80/+oxmfdaf/t3su+++MyufGzVqJO3atZN+/folntOcjY3fedmE+BwBBBAorAABYGH9qR0BBBBAAAEEEEAAAQQQQAABBBBAAAGnAgSATnkpHAEEEEAAAQQQQAABBBBAAAEEEEAAgcIKEAAW1p/aEUAAAQQQQAABBBBAAAEEEEAAAQQQcCpAAOiUl8IRQAABBBBAAAEEEEAAAQQQQAABBBAorAABYGH9qR0BBBBAAAEEEEAAAQQQQAABBBBAAAGnAgSATnkpHAEEEEAAAQQQQAABBBBAAAEEEEAAgcIKEAAW1p/aEUAAAQQQQAABBBBAAAEEEEAAAQQQcCpAAOiUl8IRQAABBBBAAAEEEEAAAQQQQAABBBAorAABYGH9qR0BBBBAAAEEEEAAAQQQQAABBBBAAAGnAgSATnkpHAEEEEAAAQQQQAABBBBAAAEEEEAAgcIKEAAW1p/aEUAAAQQQQAABBBBAAAEEEEAAAQQQcCpAAOiUl8IRQAABBBBAAAEEEEAAAQQQQAABBBAorAABYGH9qR0BBBBAAAEEEEAAAQQQQAABBBBAAAGnAgSATnkpHAEEEEAAAQQQQAABBBBAAAEEEEAAgcIKEAAW1p/aEUAAAQQQQAABBBBAAAEEEEAAAQQQcCpAAOiUl8IRQAABBBBAAAEEEEAAAQQQQAABBBAorAABYGH9qR0BBBBAAAEEEEAAAQQQQAABBBBAAAGnAgSATnkpHAEEEEAAgXAFZsyYISeccIIpdPr06XL88ceHWwGlRSqg4zdz5kzp3Lmz6NhyIIAAAggggAACCCDgQoAA0IUqZSKAAAIIIOBIgADQEWyBiiUALBA81SKAAAIIIIAAAiUmQABYYgNOdxFAAAEEiluAALC4x6986wkA/RpPeoMAAggggAACCMRVgAAwriNDuxBAAAEEEEDAewECQO+HmA4igAACCCCAAAKxECAAjMUw0AgEEEAAAQQQKEUBAsBSHHX6jAACCCCAAAIIRC9AABi9OTUigAACCCCAAAJGgACQiYAAAggggAACCCAQhQABYBTK1IEAAggg4L3A9ddfLzfccIPp565du+S7776Tv/zlLzJ58mT56KOPpHbt2nLMMcfI1VdfLe3atUt4rFmzRv7617/KU089JR9//LHsscce0qFDB9HyWrZsWcEt2zMAzzvvPHn44Yfl4IMPNuV98803iXbo/99tt93kqKOOkgsvvFDOPffc0Mflhx9+kEceeUQef/xxeeedd+Srr76SWrVqSb169eSggw6Sk046SU499VQ54ogjKtQ9d+5cmTp1qsyePVuWLl1qrlWPhg0bmrfk/v73v097XVlB5fv+xRdfyO233y7PPvus/Pvf/5af/OQn0r59e7nuuuukefPmifrVRcfqpZdektWrV8vee+8tJ598svz5z3+WJk2apDUqP97qfMcdd8iTTz4p//rXv2T33Xc3zgMGDKjUOdcA8Ouvv5axY8fK888/LytWrJANGzbIPvvsI61bt5Z+/frJ6aefXulY6vzSebFgwQJZu3ataZ+OyYEHHmhCyF69ekmbNm1Cnw8UiAACCCCAAAIIIBAPAQLAeIwDrUAAAQQQKHKB5EDok08+MQHS8uXLK/SqWrVqJhw788wz5d1335Xu3bvLp59+WuG8GjVqyAsvvCAnnnhiymf5BIAaaHXr1s0EgemOwYMHyz333BOa/MaNG01/3njjjUrL7N27twnKko+JEyfK+eefX+l1aqdh6cUXX5z2vOQA8JlnnpGuXbuKhoDlDw0kX375ZRO0Tps2zYRn3377bYXzNDDUviSHhWUnJY/3hx9+KL/4xS9k1apVadt1xhlnmDGvXr16hc9zCQB1HmhYqyFjpqNHjx7y3//937LnnnumnKKB7DnnnCOTJk2q1FbDaQ0HORBAAAEEEEAAAQT8FCAA9HNc6RUCCCCAQMQCyYHQscceK++9955ceumlJoTSwElXtenKM10ZuNdee5nVcbqqbdOmTXL55Zebf9fVeRra3XzzzbJt2zazYk5Xe+lqrbIj1wBQV3fVrVvXBGDaDg0kNRxatGiRWamoK+L00PpOOeWUULSuuOIKGTNmjCmrZ8+eJrTSPugqPl11pn3WFX4NGjSoEEhNmDBBhg0bJr/85S+NxWGHHWZWTX722WeycOFCE/ytW7dOqlSpIq+99lqFYFTrLAsAte/aVw31tE1anoaH2teRI0ca20MOOUReffVVsypTV/wNHTpUdNx27NhhVm3eeeedZiWn/pmuTCx/JI+3rsL7xz/+IQMHDhQN++rUqWPC3VtvvTURAuvqRe1D+SNbAKht1BBXgzxt80UXXWTapG3W4Ph//ud/5O9//7spVoNMbXvyoQGv1q2HBp4XXHCBWdWoPrrCcvHixfLiiy+af3/77bdDmQcUggACCCCAAAIIIBA/AQLA+I0JLUIAAQQQKEKB5EBIV+/NnDnTBDXJh67k0pVaemhIpQGThkvlt5nee++9oqvz9JgyZYqcdtppiWJyDQD1Ag0ANXgsv4Jt5cqVcuSRR8rWrVtN4Kar5cI4NOzTLbQaglW24kzDpp/+9KcpVWqYpSvuNCxNd2iY16lTJxOsaZCVbpVhWQCo1++7775ZbctC0jfffNOMR/KhYeRtt91m/kgDyPLbsZPHW8957LHHzEq75EO36Xbs2NEEn1WrVpV//vOfxj35qCwA1HBY58aXX34pXbp0MdvE0/k88MADJnzUQ8NR3WZddqiZWulc1LmQbhWinptuTMKYE5SBAAIIIIAAAgggEA8BAsB4jAOtQAABBBAocoHkQGj48OEyatSotD3SVVz6jDg97r//fvMsvvLHli1bTECmAd0f//hH83y6siOfAFBXnJWt/ipfh4ZVumVUQzcNf8I4dKXi9u3b5a677pIhQ4aEUWRKGRpU6vMD9dDVgPoMvOQjOQC87777ZNCgQZXa6oeZVkDqcxt/9rOfmevT9Sd5vHW143PPPZe2v/PmzUsEwbp1WZ/jl3xUFgCWrd7TFZQ6Z+rXr5/RVAM+rUtXXZatCNSTDz/8cLOKtPw8Cn1wKBABBBBAAAEEEEAg1gIEgLEeHhqHAAIIIFAsAsmBkG6zbdGiRdqm64o7DYt0K+v69etNAJfu0BdI6DbiX/3qV/L0008nTsk1ANTyddtt+ZCsrCB9OYZue9VDXzChqwVtD33xiD7/UF9yoltXM63my6UeXf2m7df/1ZWSemjZZSsoX3/99QrbgMsCQO27BoTlVxmW1Vtmq/Y6Bnp+ukO32eoqPt1CrVuCk4/k8dZtt5W9hOPnP/+5LFmyxIRxy5YtSymnsgBQnyuoK/p0i7YGlZUdOpY6phpaJj+LULc/z5o1Sw499FCZM2eOWRnJgQACCCCAAAIIIFB6AgSApTfm9BgBBBBAwIFAciCkoVWm8Ktv377mLbm65VTfAJzpKNu6qS8B0bCr7Mg1AMxW/kMPPST9+/dPBGuNGjWyVrn22mvlxhtvNOXoajV90YluR9Utu+W32KarTEO7sjcn66q1suAv3bn67Luzzjor5aPkZwDmYqvP7tNVc5mOsi3Nv/vd7+TBBx9MOa38S18q89Pr//a3v5mgUVd1Jj/TsbIAUAPKyl78ka7dOu90/pUdyeOszybUoFKDRd2arG9X5kAAAQQQQAABBBAoDQECwNIYZ3qJAAIIIOBYIDkQqiy4Sn5Tbaa382pTMwVDuQaAuhqvsvKT37qr2111a7Ltodt/dduthl3JBhp86XMINXzSbbD6EpDyh75EQ1e66Yq8XA5tf79+/dIGgNn6nu3FG2WFlm3X1nq0vuQjebw11NPnPmY6rrzyysSWcH0pS3L/M7VFLZODwlxMys4pP/9GjBgho0ePNi84ST70+YK6pVrHpGy7cz71cC4CCCCAAAIIIIBA8QgQABbPWNFSBBBAAIEYCxAA/v/gvP/++/L444/LtGnTZMGCBeatu2WHvn1Wn1GnW5vLDv28WbNm8uGHH5o3IetzC/Vz3TKrq+DKwjX9vOyFKRoyapiafNiGq+WnV64B4Pfff19pWPenP/3JvBFYj1wDQA0Va9asaa7RlY7XXHNNzrNftxyXP/QZgo8++qhZTaovntm8eXPiFDXX50Wme2ZizpVyIgIIIIAAAggggECsBQgAYz08NA4BBBBAoFgECADTj5S+0ETfsqtvyf2v//ov+eGHH0ywpc+p23///c1F+ny7bt26mX8fP368DBgwIG1hukqwVatW5rM4BYD6bEIXW4Br165tgjp9A/DLL78c2o+Cri7Urc/6puZx48aZbcm6SlN9y7/tOLRKKQgBBBBAAAEEEECgoAIEgAXlp3IEEEAAAV8ECACzj+Qdd9whl112WYWgb8yYMXLFFVeYP//uu+9kr732SluYhlVlq9TiFABmewnIkUceKYsXL877JSD67EQNTzUI1Gca2rxUJdPoPPXUU4kXmFx11VVy8803Zx9IzkAAAQQQQAABBBAoOgECwKIbMhqMAAIIIBBHAQLA7KPy7rvvytFHH21OHDlypOiz8fTQ7bG6TVaP8ltky0rduXOn6Es7Fi5caP4oTgGgvtn5mWeeSQugW6C13Xros/bGjh2bcl5lzyPU5/YNHz7cnK//XuV7nOAAAAXySURBVPbW5uzSuZ+hgau+HESPgQMHmhWBHAgggAACCCCAAAL+CRAA+jem9AgBBBBAoAACpR4AfvXVVzJ79mzp1auX2U6a7rj99tsTIZY+I7BPnz7mtClTpkjv3r3Nv2sYOGzYsAqXaxCmIVjZEacAUNuU7q3EGzduFH2b86JFi6Rq1armf4866qiUvlUWAGo4p8881Lcj63MQdbVe2VbpdL66WlC3WGudZYc+b1Gdq1evnnZMnnzySfO2Zj1uueWWRBBbgB8hqkQAAQQQQAABBBBwKEAA6BCXohFAAAEESkeg1ANAfeNw48aNzduE9W2/xx57rOjbeDV4+vzzz+W55/63vfvXhS2K4gC8GolKSNDQC96AQqFRKnQSCoVIPIieREFBp786XkIh/kQiShERxdRu1r6Zm5CRwb0ZZs+3E92cOWd9a2t+2WfWr9jf3488yTc+Ph6Xl5fl1dZcjUajTKHN11zz83kSLU/VDQ8Px83NTezt7ZXhFbOzs+WV2Fw/KQDM3yXMcG99fT2WlpZiYGAg8rRjhplXV1fleXOwSQ7aeLvaTSQ+PT0toV9O8M0QMYPS/GsOQ0nb/O2+DAfzntvb27G5ufn3NhnG5tTh7MnMzEy5rr+/P+7v7+Pk5CR2d3cjf6cxh7NcXFyU3lgECBAgQIAAAQL1CQgA6+upiggQIEDgGwQEgH8CwHZrbGyshIFvh03kkIvFxcUykKLVyqBsZ2cnmhNuf1IAmNOJ5+fn4/b2tuWzZ2B3dHTU8hReuwAwvzCnKS8vL5fXo9utw8PDWFlZeRUAtrtmcHCwnGDMYSMWAQIECBAgQIBAnQICwDr7qioCBAgQ6LBArweALy8v5QRanirLwCpDsTxllq/BZsA0PT1dXg/O033vDfk4Pz+Pra2tcv3Dw0O5bmpqqoRfa2trkdN2myHjTwoAs/anp6fIV5zzJN7d3V309fWV3zvMevP531sfCQDz2pwGnDUfHx/H2dlZPD4+lhOBIyMjMTk5GXNzc+Vk4MTExKtb5QnE7EmeoLy+vi49eX5+Lj3Izy4sLMTGxkaMjo52+D/G7QgQIECAAAECBDopIADspLZ7ESBAgAABAlUIfDTwraJYRRAgQIAAAQIECHS9gACw61uoAAIECBAgQKDTAgLATou7HwECBAgQIECAwL8ICAD/Rc+1BAgQIECAQE8KCAB7su2KJkCAAAECBAh0rYAAsGtb58EJECBAgACB7xIQAH6XvPsSIECAAAECBAh8RUAA+BU11xAgQIAAgQoFcopto9H4dGVDQ0OR0317aQkAe6nbaiVAgAABAgQIdL+AALD7e6gCAgQIECDwXwSaE2k/+2Wrq6txcHDw2cu6+vMCwK5un4cnQIAAAQIECPScgACw51quYAIECBAg0FpAAGhnECBAgAABAgQIEKhTQABYZ19VRYAAAQIECBAgQIAAAQIECBAgQKAICABtBAIECBAgQIAAAQIECBAgQIAAAQIVCwgAK26u0ggQIECAAAECBAgQIECAAAECBAgIAO0BAgQIECBAgAABAgQIECBAgAABAhULCAArbq7SCBAgQIAAAQIECBAgQIAAAQIECAgA7QECBAgQIECAAAECBAgQIECAAAECFQsIACturtIIECBAgAABAgQIECBAgAABAgQICADtAQIECBAgQIAAAQIECBAgQIAAAQIVCwgAK26u0ggQIECAAAECBAgQIECAAAECBAgIAO0BAgQIECBAgAABAgQIECBAgAABAhULCAArbq7SCBAgQIAAAQIECBAgQIAAAQIECAgA7QECBAgQIECAAAECBAgQIECAAAECFQsIACturtIIECBAgAABAgQIECBAgAABAgQICADtAQIECBAgQIAAAQIECBAgQIAAAQIVCwgAK26u0ggQIECAAAECBAgQIECAAAECBAgIAO0BAgQIECBAgAABAgQIECBAgAABAhULCAArbq7SCBAgQIAAAQIECBAgQIAAAQIECAgA7QECBAgQIECAAAECBAgQIECAAAECFQv8BtfteRHwZN5AAAAAAElFTkSuQmCC\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
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]
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"output_type": "display_data"
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l8CyhYvt3ROf2KBDVSH+u9K/5660lnvP2qBdpQuKQQgkSIAAmKCTuSH9lEL8P2obkp8fKwFQP2MEQN3ORxIAdT8CoG5HANTtfCQBUPcjAOp2BEDdzkcSADU/AqDmVtEoAmBFQnyOQOELEAAL/xwl8ggJgPGnlQCoGxIAdTsCYJwdAVD3IwDqdgTAODsCoO5HANTtCIC6XdIC4MwPW1rzpvl/XNK/566yVnv/XzgxLAHWr09GIpBPAQJgPvWL+LsJgPEnnwCoGxIAdTsCYJwdAVD3IwDqdgTAODsCoO5HANTtCIC6HQFQt8s0kgCYHVdmRSCXAgTAXGrzXaUCBMD4i4EAqBsSAHU7AmCcHQFQ9yMA6nYEwDg7AqDuRwDU7QiAul3SAuC0SS0K5g7AX7XjJSD6lclIBPIvQADM/zkoyiMgAMafdgKgbkgA1O0IgHF2BEDdjwCo2xEA4+wIgLofAVC3IwDqdgRA3S7TSL8DkACYHVtmRSBXAgTAXEnzPeUECIDxFwQBUDckAOp2BMA4OwKg7kcA1O0IgHF2BEDdjwCo2xEAdTsCoG5HAMyOHbMiUCgCBMBCORNFdhwEwPgTTgDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BUPcjAOp2BEDdLmkB8MtJzQtmCfCO7f4dTgwvAdGvT0YikE8BAmA+9Yv4uwmA8SefAKgbEgB1OwJgnB0BUPcjAOp2BMA4OwKg7kcA1O0IgLodAVC3yzTSlwATALNjy6wI5EqAAJgrab6nnAABMP6CIADqhgRA3Y4AGGdHANT9CIC6HQEwzo4AqPsRAHU7AqBuRwDU7QiA2bFjVgQKRYAAWChnosiOgwAYf8IJgLohAVC3IwDG2REAdT8CoG5HAIyzIwDqfgRA3Y4AqNslLQBOndTUmjWtpYNU08g5c1fZTu3mhtlYAlxNqEyDQI4FCIA5BufrfhEgAMZfCQRA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRAHU/AqBuRwDU7QiAul2mkQTA7LgyKwK5FCAA5lKb7yoVIADGXwwEQN2QAKjbEQDj7AiAuh8BULcjAMbZEQB1PwKgbkcA1O0IgLodATA7dsyKQKEIEAAL5UwU2XEQAONPOAFQNyQA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1u6QFwCkTmxTMEuBd2s8LJ4YlwPr1yUgE8ilAAMynfhF/NwEw/uQTAHVDAqBuRwCMsyMA6n4EQN2OABhnRwDU/QiAuh0BULcjAOp2mUb6EmACYHZsmRWBXAkQAHMkfcABB9jIkSOr9G0jRowwH5dqe/PNN23gwIE2YcIEW7RokTVo0MA6dOhgvXv3tm7dulXqe5YuXWoDBgywQYMG2bRp02zFihXWokULO/LII+2SSy6xbbfdtlLzKDsRABW18mMIgLohAVC3IwDG2REAdT8CoG5HAIyzIwDqfgRA3Y4AqNsRAHU7AmB27JgVgUIRIADm6ExUNQDWqFHDZs+ebc2aNSt3hGvXrrXzzz8/xL90m0fABx980DbaaKO0+0yfPj2EvqlTp6bcZ+utt7Znn33WunfvnhUhAmA8KwFQNyQA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1u6QFwE8nNi6YJcC7tZ8fTgxLgPXrk5EI5FOAAJgj/ZkzZ9qSJUsyftuUKVPslFNOCfsceuihNmzYsPX279evn/Xv3z/8/V577WVXXnmltW7d2jzo3X777fbxxx+Hz3y/P//5zym/b/Hixda+fXv74osvwue9evWynj172mabbWZ+1+Ett9xivs/mm29uY8eOtT322KPalQiA8aQEQN2QAKjbEQDj7AiAuh8BULcjAMbZEQB1PwKgbkcA1O0IgLpdppG+BJgAmB1bZkUgVwIEwFxJV+J7+vbtGyKeb0899ZT95je/KTfKl+m2adPGVq1aZe3atbNRo0aFaFey+ZLerl272qRJk6xWrVoh8HkcXHe7/vrr7YYbbgh/7d/Xp0+fcrt49OvSpUv4ngMPPNCGDx9eiaOv2i4EwKp5pdqbAKgbEgB1Ox+5ZM2auAmKeDQBUD/5BEDdzkd+s3qLuAmKeDQBUD/5BEDdzkcuXbVx3ARFOpoAmJ0TTwDMjiuzIpBLAQJgLrUzfNeaNWvCM/fmzJljW2yxhS1YsCDcgVd2u+iii+z+++8Pf+WRbp999llvxnHjxtm+++4b/v7iiy+2++67r9w+K1eutIYNG9r3338fYuKnn35qvtx43c2XGT/00EPhrz0o7r333tUqRQCM5yQA6oYEQN3ORxIAdT8CoG5HANTtfCQBUPcjAOp2BEDdzkcSADW/pAXATyY2tqZNa2oY1Thq7tzVtgdLgKtRlKkQyL0AATD35im/8e2337bDDjssfHbWWWfZ448/Xm4/f/afv6DDA+HOO+9sn3/+edoj98/92X7NmzcPzxEs+yzAst9z6623mt91mGorGxKvuuoqu/nmm6tVigAYz0kA1A0JgLodATDOjgCo+xEAdTsCYJwdAVD3IwDqdgRA3Y4AqNtlGkkAzI4rsyKQSwECYC61M3zXb3/7W3v66afDHr7k1pfelt1mzJhRupz3vPPOCy/5SLf55yUvCfFxrVq1Kt312muvtZtuuin8Od1dhP6ZL/+tW7dueG6hLweu6huMK2IlAFYkVPHnBMCKjdLtQQDU7XwkdwDqfgRA3Y4AqNv5SO4A1P0IgLodAVC385HcAaj5EQA1t4pGEQArEuJzBApfgABYAOfIX7jRuHHjENt8GfCsWbPWe4Pv66+/bj169AhHe9ddd9lll12W9sj988svvzx87uPKvsn3pJNOshdeeCF89t1334XIl25r27atffLJJ9agQQNbuHBhtUoRAOM5CYC6IQFQtyMAxtkRAHU/AqBuRwCMsyMA6n4EQN2OAKjbJS0A/mNio4JZArxn+wXhxPAWYP36ZCQC+RQgAOZT/z/f/eSTT4Zlv76le3uv3/F3wQUXhH0GDRpkJ554Ytoj98Dnoc83H+d3BJZs/tzA8ePHW506dcKbfjNtHhw9IPq2fPly23TTTSut5YEv0zZv3jzr0KFD2OX/PtzOmjflIceVxv3PjgTAqor9d38CoG7nI7kDUPcjAOp2BEDdzkdyB6DuRwDU7QiAup2P5A5AzY8AqLlVNMrvACQAVqTE5wgUtgABsADOz8EHH1z6pl1/dt+OO+643lHdcccdduWVV4a/f+ONN6xbt25pj9w/L7nr784777QrrriidN9dd93VpkyZYo0aNbL58+dn/PWnnHKKPf/882Gfr7/+2urVq1dprbLPHaxoEAGwIqHUnxMANTcfRQDU7QiAcXYEQN2PAKjbEQDj7AiAuh8BULcjAOp2BEDdLtNIAmB2XJkVgVwKEABzqZ3iu/xOuZYtW5q/BdjvzvPn8qXa/Ll9/vw+395991076KCD0h65P0PQo6JvPu7qq68u3bd169bmzwX0F4r4C0IybWeccYY99dRTYZeq3uZNAMz+hUUA1I0JgLqdj+QOQN2PAKjbEQB1Ox/JHYC6HwFQtyMA6nY+kjsANb+kBcAPJzQsmCXAe3f45bFQVf13Q+1MMgoBBKpbgABY3aJVnM/fxPunP/0pjHrggQfs/PPPTznDhnYHIEuAq3ghCLsTAAW0/wwhAOp2BMA4OwKg7kcA1O0IgHF2BEDdjwCo2xEAdTsCoG6XaaTfAUgAzI4tsyKQKwECYK6k03xPyZJcf76ePxdvm222SbnnhvYMwIpYeQlIRUIVf04ArNgo3R4EQN2OABhnRwDU/QiAuh0BMM6OAKj7EQB1OwKgbkcA1O0IgNmxY1YECkWAAJjHMzFp0iRr3759OIITTjih9O28qQ5pyJAhdtRRR4WPYt4C7C8PefHFF8M8vAU4jye/Gr6aAKgjEgB1OwJgnB0BUPcjAOp2BMA4OwKg7kcA1O0IgLpd0gLgxAJaAtyeJcD6hclIBApAgACYx5NwySWX2H333ReO4NVXXy0NfKkOyZ/b58/v883f6ut3BKbb/POBAweGj31cq1atSnf15wj6cwF98+cN+nMHU22rVq2yunXr2pIlS6xLly42cuTIapXiDsB4TgKgbkgA1O18JM8A1P0IgLodAVC385E8A1D3IwDqdgRA3c5H8gxAzY8AqLlVNMqXABMAK1LicwQKW4AAmKfzs3LlSmvWrJktWrTIGjRoYHPnzrVatWqlPZq1a9da8+bNw34777yzff7552n3bdOmjX3xxRdhfn9Aa9kXcgwbNswOP/zwMNafP9i3b9+U84wbN8723Xff8Jk/o7B///7VKkUAjOckAOqGBEDdjgAYZ0cA1P0IgLodATDOjgCo+xEAdTsCoG6XtAA4fkIDa9K0pg5STSPnzV1tHTssCrPxEpBqQmUaBHIsQADMMXjJ1/kdf8ccc0z446WXXmp33313hUdy4YUXhheF+Jbu7r2y4c73HzBgQLl5V6xYYQ0bNrQffvjBPBR+9tln5QJhyc7+MpKHHnoo/HHChAmlS5UrPMhK7kAArCRUht0IgLohAVC385HcAaj7EQB1OwKgbucjuQNQ9yMA6nYEQN3OR3IHoOZHANTcKhpFAKxIiM8RKHwBAmCezlHZZ/F9+OGH9utf/7rCI/nyyy/NXxriy3PbtWtno0aNss0226x03LJly8JyXX+2oN9NOGXKFNthhx3Wm7fsMuDbb7/d+vTpU24fj4s+j39P165d7b333qvw2Kq6AwGwqmLr708A1A0JgLodATDOjgCo+xEAdTsCYJwdAVD3IwDqdgRA3Y4AqNtlGkkAzI4rsyKQSwECYC61//Nd/vKNJk2a2M8//2y77babTZ48udJH4ctxfemub3vttVdYwuvPBpw+fbrddttt9vHHH4fPMi3b/emnn0JA9KDoW+/eva1nz54hJo4YMSIs9128eHH485gxY2zPPfes9PFVdkcCYGWl0u9HANQNCYC6HQEwzo4AqPsRAHU7AmCcHQFQ9yMA6nYEQN0uaQFwjC8BblIAS4Dnrbb9WAKsX5iMRKAABAiAeTgJ/gKPCy64IHxzqjvwMh3SmjVrrFevXvbYY4+l3e2cc84JLwGpUaNG2n2mTZtm3bt3t6+++irlPltttZU988wz1qNHj6wIEQDjWQmAuiEBULcjAMbZEQB1PwKgbkcAjLMjAOp+BEDdjgCo2xEAdbtMI+cRALMDy6wI5FCAAJhD7JKv6tSpU7izrmbNmjZ79mxr2rRplY9i6NChIfJNnDjRvv76a6tfv354Tp+/AfiII46o1Hz+hl9/RuCgQYPMg6A/H7BFixYhDPpzCVu2bFmpeZSdCICKWvkxBEDdkACo2xEA4+wIgLofAVC3IwDG2REAdT8CoG5HANTtCIC6HQEwO3bMikChCBAAC+VMFNlxEADjTzgBUDckAOp2BMA4OwKg7kcA1O0IgHF2BEDdjwCo2xEAdbukBcD3C2gJ8P4sAdYvTEYiUAACBMACOAnFeAgEwPizTgDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BUPcjAOp2BEDdjgCo22Ua6UuACYDZsWVWBHIlQADMlTTfU06AABh/QRAAdUMCoG5HAIyzIwDqfgRA3Y4AGGdHANT9CIC6HQFQtyMA6nYEwOzYMSsChSJAACyUM1Fkx0EAjD/hBEDdkACo2xEA4+wIgLofAVC3IwDG2REAdT8CoG5HANTtkhYAR01oaI0L4C3A8+etti4dFoYT869//cuaN2+unyRGIoBAXgQIgHlh50sJgPHXAAFQNyQA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1OwKgbpdpJAEwO67MikAuBQiAudTmu0oFCIDxFwMBUDckAOp2BMA4OwKg7kcA1O0IgHF2BEDdjwCo2xEAdTsCoG5HAMyOHbMiUCgCBMBCORNFdhwEwPgTTgDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BUPcjAOp2BEDdLmkB8L3xhbME+ICOLAHWr0xGIvKaPoIAACAASURBVJB/AQJg/s9BUR4BATD+tBMAdUMCoG5HAIyzIwDqfgRA3Y4AGGdHANT9CIC6HQFQtyMA6naZRvoSYAJgdmyZFYFcCRAAcyXN95QTIADGXxAEQN2QAKjbEQDj7AiAuh8BULcjAMbZEQB1PwKgbkcA1O0IgLodATA7dsyKQKEIEAAL5UwU2XEQAONPOAFQNyQA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1u6QFwHfHNyqYtwAf3HFBODG8BVi/PhmJQD4FCID51C/i7yYAxp98AqBuSADU7QiAcXYEQN2PAKjbEQDj7AiAuh8BULcjAOp2BEDdLtNIXwJMAMyOLbMikCsBAmCupPmecgIEwPgLggCoGxIAdTsCYJwdAVD3IwDqdgTAODsCoO5HANTtCIC6HQFQtyMAZseOWREoFAECYKGciSI7DgJg/AknAOqGBEDdjgAYZ0cA1P0IgLodATDOjgCo+xEAdTsCoG6XtAA4bHzjglkCfFjH+eHEsARYvz4ZiUA+BQiA+dQv4u8mAMaffAKgbkgA1O0IgHF2BEDdjwCo2xEA4+wIgLofAVC3IwDqdgRA3S7TSF8CTADMji2zIpArAQJgrqT5nnICBMD4C4IAqBsSAHU7AmCcHQFQ9yMA6nYEwDg7AqDuRwDU7QiAuh0BULcjAGbHjlkRKBQBAmChnIkiO46yAXDmhy2tedNaRSYQ/3Nnr1oSP0mRzvD9mo2L9JdXz8/+ZnWd6pmoCGf518p6Rfirq+cnz1tZt3omKtJZvl65RZH+8vif/fLkPeMnKdYZVtUo1l9eLb+79iz+/xUFcuWP39uMu28MQzfUpapl/13prXGNrVGT/P+70oJ5q+zwfVgCrFyTjEGgUAQIgIVyJorsOAiA8SecAKgbEgB1Ox9JANT9CIC6HQFQt/ORBEDdjwCo2xkBMALPjACo8REANbeKRhEAKxLicwQKX4AAWPjnKJFHSACMP60EQN2QAKjbEQDj7AiAuh8BULcjAMbZEQAj/AiAEXgEQBWPAKjKZR5HAMyOK7MikEsBAmAutfmuUgECYPzFQADUDQmAuh0BMM6OAKj7EQB1OwJgnB0BMMKPABiBRwBU8ZIWAIeOa1IwS4C77zMvnJYNdWm1ek0xDoGkCBAAk3ImN7DfQQCMP2EEQN2QAKjbEQDj7AiAuh8BULcjAMbZEQAj/AiAEXgEQBWPAKjKZR7ndwASALNjy6wI5EqAAJgrab6nnAABMP6CIADqhgRA3Y4AGGdHANT9CIC6HQEwzo4AGOFHAIzAIwCqeEkLgK+Na1YwdwAetc+ccFq4A1C9OhmHQH4FCID59S/abycAxp96AqBuSADU7QiAcXYEQN2PAKjbEQDj7AiAEX4EwAg8AqCKRwBU5TKP8zsACYDZsWVWBHIlQADMlTTfU06AABh/QRAAdUMCoG5HAIyzIwDqfgRA3Y4AGGdHAIzwIwBG4BEAVTwCoCpHAMyOHLMiUDgCBMDCORdFdSQEwPjTTQDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BMMKPABiBRwBU8ZIWAF8d29waNqmlclTbuIXzVtnR+/47zMcS4GpjZSIEcipAAMwpN19WIkAAjL8WCIC6IQFQtyMAxtkRAHU/AqBuRwCMsyMARvgRACPwCIAqHgFQlcs8jgCYHVdmRSCXAgTAXGrzXaUCBMD4i4EAqBsSAHU7AmCcHQFQ9yMA6nYEwDg7AmCEHwEwAo8AqOIRAFU5AmB25JgVgcIRIAAWzrkoqiMhAMafbgKgbkgA1O0IgHF2BEDdjwCo2xEA4+wIgBF+BMAIPAKgipe0APjy2BYFswT4uH3/FU4LS4DVq5NxCORXgACYX/+i/XYCYPypJwDqhgRA3Y4AGGdHANT9CIC6HQEwzo4AGOFHAIzAIwCqeARAVS7zOF8CTADMji2zIpArAQJgrqT5nnICBMD4C4IAqBsSAHU7AmCcHQFQ9yMA6nYEwDg7AmCEHwEwAo8AqOIRAFU5AmB25JgVgcIRIAAWzrkoqiMhAMafbgKgbkgA1O0IgHF2BEDdjwCo2xEA4+wIgBF+BMAIPAKgipe0APjCmJYFswT4xP3+L5wWlgCrVyfjEMivAAEwv/5F++0EwPhTTwDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BMMKPABiBRwBU8QiAqlzmcb4EmACYHVtmRSBXAgTAXEnzPeUECIDxFwQBUDckAOp2BMA4OwKg7kcA1O0IgHF2BMAIPwJgBB4BUMUjAKpyBMDsyDErAoUjQAAsnHNRVEdCAIw/3QRA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRACP8CIAReARAFS9pAfD5MdtZwyYbqxzVNm7hvJV28n6zwnwsAa42ViZCIKcCBMCccvNlJQIEwPhrgQCoGxIAdTsCYJwdAVD3IwDqdgTAODsCYIQfATACjwCo4hEAVbnM4wiA2XFlVgRyKUAAzKU231UqQACMvxgIgLohAVC3IwDG2REAdT8CoG5HAIyzIwBG+BEAI/AIgCoeAVCVy38AXLhwoU2YMCH8Z+LEieE/33zzTTiwM88805544omMBzlr1ixr1apVlQBatmxpPm7d7YADDrCRI0dWaq61a9dWuN9nn31m9913n73zzjs2Z84c22KLLaxNmzZ2+umn2znnnGO1atWqcA7f4bnnnrPHH3/cPvnkE/vuu++scePG1rlzZ7voootsn332qdQcbnrvvffa4MGDw2/343e3Y4891i655BKrV69epeYZO3as3X///TZ69GibP3++bbPNNta2bVs766yzrGfPnpWag51yK0AAzK033/YfAQJg/KVAANQNCYC6HQEwzo4AqPsRAHU7AmCcHQEwwo8AGIFHAFTxkhYAnxvTyhoUwBLgRfNWWs/9ZobTkq0lwBtttFHa056tAHjYYYfZW2+9ldUA+Oijj4ZA9/PPP6f8fR7uhgwZkjG8LV++3E466aSwX6qtRo0adv3119s111yT8R8dj6rHHHOMzZs3L+V+TZs2tVdeecXatWuXcZ4bb7zRbrjhBluzZk3K/Y466ih7/vnnrXbt2uo/yozLggABMAuoTFmxAAGwYqOK9iAAViSU/nMCoG5HAIyzIwDqfgRA3Y4AGGdHAIzwIwBG4BEAVTwCoCqXeVyuA2CLFi3CHXLDhg0LB1aZALhy5UqbOnVqhQC33HKLPfvss2G/Z555xk477bT1xpTcAeghzO+4y7TttttuaT/2uNi9e/cQyho1amT9+vWzjh072rfffmsPP/ywvfTSS2Fsly5dbMSIEeYhL9XmdwqWHPOBBx5ol156qXmsmzx5svXv39+mT58ehvmc5557bso5/M7Dvffe2xYsWBDuOLz88sutR48eYV8Pi3/9619t1apV4Tg//PBDa9asWcp5HnnkEevVq1f4rHXr1nbVVVfZ7rvvbnPnzrV77rkn/A7f/JiffvrpCs8HO+ROgACYO2u+qYwAATD+ciAA6oYEQN3OR36zuk7cBEU8mgCon3wCoG7nI79euUXcBEU8mgAYcfIJgBF4BEAVjwCoymUel4sAeN1111n79u3DfzxClV3SW5kAWJlfvnr1att2221DrNpyyy3D0tXNN998vaElAbBr16723nvvVWbq9fbxmOYRc9q0abbVVlvZRx99FIJZ2c3vDPRltL49+eSTdsYZZ6w3jy9F9uPxze+se/nll61mzZql+3399dch7M2ePTssw50xY4bVrVt3vXl8aa5/h29+d57fUVh2GzRokJ188snhr84++2x77LHH1pvj+++/D8uF/X+6o4fC+vXrl+7nvscdd5y99tpr4e/82D1ushWGAAGwMM5D0R0FATD+lBMAdUMCoG7nIwmAuh8BULcjAOp2PpIAqPsRAHU7IwBG4BEAVbykBcBnPmhdMEuAT+/0y11m2VoCvO45z0YA9DvyunXrljFy+YfVEQDLBjW/6/CPf/zjepf10qVLrXnz5uF5fn4nod/Rt+525JFH2tChQ0P0cxPff93Nnw146qmnhr++88477Yorrii3i9/153f0eaA7/PDD7c0330z5j5jbuJF/l98x6CG27HbHHXfYlVdeGf7q73//e8pn/fm/62+33Xbhu/wOw5IYqP4zzbjqEyAAVp8lM1VBgABYBaw0uxIAdUMCoG7nIwmAuh8BULcjAOp2PpIAqPsRAHU7AmCEnREAVT0CoCqXeZzfAZiEAFh2Ka3f2ed3+KXaqiMAlv0uf+aev7Aj1Xb++efbQw89FD768ssvbYcddijdbfHixeEOO39+oMe5N954I+UcK1assAYNGtiPP/5o++23n33wwQfl9vOlwb179w5/57HwlFNOSTlP2ZA4cODA0qW+JTt36tTJxowZE+5oXLRokW2yySYp5ykJiZtuuqn5HYr+0hO2/AsQAPN/DoryCAiA8aedAKgbEgB1Ox9JANT9CIC6HQFQt/ORBEDdjwCo2xEAI+wIgDIeAVCmyzgwCQHwp59+ChHO77rzt//OnDnT0r14pDoCoC+R9bsld9ppJ/viiy/S+vqddCXPIfRlt778tmQbPny4HXzwweGP6e4iLNnX7+zzZyb68/38N2688cal8/jS4qeeeir8OVOM9M/82YK++ZiSJcP+Z4+MderUCc8JzHQXYcmx+rMBffPf4M8tZMu/AAEw/+egKI+AABh/2gmAuiEBULfzkQRA3Y8AqNsRAHU7H0kA1P0IgLodATDCjgAo4yUtAD71wa8KZgnwbztNC+dlQ10C7C/z+N3vfhd+g78t199km24rCYC+BNZfSOIBz18y4nfj+fP2TjjhhLDktmxkKzuX37nnd8mtXbs2vHV38ODBab/r448/tl//+tfh8z59+tjtt99euu+AAQPs4osvDn/2Z/8de+yxaefxF4Pce++94fPPPvvMdtlll9J9/bmKkyZNsq233jo8vy/T5vv4nYQ+ZsKECaW7+pwlLzzx77r77rvTTuPHevzxx4fP/TdceOGF8j/TDKw+AQJg9VkyUxUECIBVwEqzKwFQNyQA6nY+kgCo+xEAdTsCoG7nIwmAuh8BULcjAEbYEQBlPAKgTJdxoN8BWBIAPQo1adIk4/6pnlNX1SOr7mcA+l1oJS/0+Oqrr+xXv/pV2kMqCYCZjtkD2wsvvBBe9LHu5sGw5O/9RR9/+9vf0k7lS2R9+a5vPXv2DM/WK9n8uYG33XZb+OPEiRPN30qcbvNn/3lA9M2f8ed36ZVsfuejPwdw1113tU8//TTjqfDI57HPx/gdgSWbz3nEEUeEP/qzAP/whz+kncdjowdE3/w3+N2LbPkXIADm/xwU5REQAONPOwFQNyQA6nY+kgCo+xEAdTsCoG7nIwmAuh8BULcjAEbYEQBlPAKgTJdxYNkAWJlv8DvfYrfqDID+hlx/MYUfV6pn5K17rAcddJDVqFHDunfvbm3btrV69eqZLyH2N/n68/o+//zzMMTvEPQg6st9y24e6zp06BD+qm/fvnbrrbem5Vi2bFnpm4jXfWlG2bcE+3fuvPPOaed54IEHSu+08zDpdymWbL5015cFd+zY0caNG5fx1Pg+/pv8uX3+m0u2si818e/yZxem2/xYS+5A9DsY77vvvtjLgfHVIEAArAZEpqi6AAGw6mbrjiAA6oYEQN3ORxIAdT8CoG5HANTtfCQBUPcjAOp2BMAIOwKgjJe0APjkBzta/Sb/fZabDBM58Ot5K+3MTl9WepZCC4D9+/e3fv36heN/8MEH7bzzzsv4W3yZbN26dVPu40uBe/XqVfp8vOOOO85eeumlcvuOHj3aunTpEv6uouXGa9asCW/d9c2f9/fOO++UznXOOeeYPxfQt+nTp9v222+f9rh9P9/fN3/e329+85vSfX1+/57OnTvbqFGjMv52P24/fh/jz/sr2XxOfy6gb48++mjpcupUk82YMcNat24dPvJjeuSRRyp97bBj9gQIgNmzZeYMAgTA+MuDAKgbEgB1Ox9JANT9CIC6HQFQt/ORBEDdjwCo2xEAI+wIgDIeAVCmyziwbADcEJcA+3JcX5brb6WdP39+2rhXWT0PY7vvvnvpyz3832+bNWtWOpw7ALkDsLLXUi73IwDmUpvvKhUgAMZfDARA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRACP8VtWIGMzQ2rPyf9fXhngWkhYAH3t/54K5A/B3+//yJtsN7SUgHix9WatvJ510kj3//PPVcmmXfebeM888U/omX5+cZwDyDMBquciqeRICYDWDMl3lBAiAlXPKtBcBUDckAOp2BMA4OwKg7kcA1O0IgHF2BMAIPwJgBJ4ZAVDjIwBqbhWN8jsAN9QA6M+g8zfR+vbaa6+ZP2evOrahQ4fakUceGabyN/eWvIDD/+xvAd5yyy3DZzFvAfaXh/z+978P88S8BdhfHvLhhx9GvQXYXx7idz36xluAq+MKyv0cBMDcm/ONZkYAjL8MCIC6IQFQtyMAxtkRAHU/AqBuRwCMsyMARvgRACPwCIAqHgFQlcs8bkMNgP68vqZNm5q/abdhw4Y2Z84cq1WrVrUgvf7666Uxcd0A6F/gLwbxuyV32mmn0qXCqb7Y3/p72mmnhY/8OX5nn3126W7Dhw8PzwX0zd+k62/UTbf5W3+HDRsWft+SJUtsk002Kd3Vn93nz/Dzzd/s62/4TbX5Z+7lm4958sknS3dbsWJFeFnJ6tWrwxuG/a3A6TY/1quuuip87L/B38DMln8BAmD+z0FRHgEBMP60EwB1QwKgbucjeQag7kcA1O0IgLqdj+QZgLofAVC34xmAEXY8A1DGS1oAfOT9Nla/yX9DjgwTOfDreSvs3P1/efPthrQEePDgweYv6fDtsssus7vuuitS4r/D77jjDrvyyivDXzz99NN2+umnl5vbo57HPd8yRTd/m66/Wdi3qVOn2o477lg6j7+Ft379+ubxrVu3bvbGG2+kPH7/vEGDBvbjjz/avvvua2PGjCm338CBA0tffPLcc8/ZKaecknIe/+zUU08Nn/kx9e7du9x+/gblsWPH2lZbbWWLFi0qFxnL7ujH+tZbb4VnLvp+JXdDVhs+E0kCBECJjUGxAgTAWEEzAqBuSADU7XwkAVD3IwDqdgRA3c5HEgB1PwKgbkcAjLAjAMp4BECZLuPADTUAHn/88WHprG8ff/yx7bnnntUCtO5LQGbPnm0tWrQoN7c/a7AktKW7e2/p0qXWvHlz++6772yXXXaxzz77bL3j6969ewh/fmffzJkzw/7rbmXDXaq7Ef3FJ/6SEn8TcKa790rCXY0aNcLdkuveKehz9+3bN3y9x82ePXuudyz+7/rbbbdduFPQj93vlGQrDAECYGGch6I7CgJg/CknAOqGBEDdjgAYZ0cA1P0IgLodATDOjgAY4ccS4Ag8lgCreARAVS7zuA0xAH777bfWpEmTcPecP7vuk08+qRTOiBEjbK+99kr7pmBfVtyrV6/S5bFHHXWUvfrqq+vN7fv524enT58e7pj76KOPrHXr1uX2u+iii+z+++8Pf/f444/bWWedtd48ZZcBH3300fbSSy9ZzZo1S/fz5c177723eYSsW7euzZgxw7bZZpv15im7DHjQoEF24oknltvH/+7kk08Of3fmmWfaE088sd4cbrr99tvbDz/8YC1btgzPFaxXr17pfh79/I5Lf9aibyz/rdQll7OdCIA5o+aLygoQAOOvBwKgbkgA1O18JHcA6n4EQN2OAKjb+UjuANT9CIC6HXcARthxB6CMl7QAOHD0LlavAJYAfzNvhfXuPCWcl2wtAX7//fdt2rRp5cJWyYs1OnXqZOeee2656yJVLCu7g4c1D2y++Rt7r7jiikpdVz7viy++aB7bDjjggPAMPw94/mIPD16+NPbzz39ZDu3PFRw3bpy1atUq5dz+ohAPhH7nXaNGjezqq6+2Dh06hDv+Hn744fA9vu2///723nvvlQt7ZSf0Zbl+l59v/jw9X87sz+qbPHmy3XzzzSEy+vbggw+WLvVd94D8vHko9CW5fjehe5S8EGXIkCH2l7/8xfzORl9K7LEy1Z2GPqf/fl+27JsHzX79+oXAOnfuXLv77rvNA6pvfszPPvtspczZKTcCBMDcOPMt6wgQAOMvCQKgbkgA1O18JAFQ9yMA6nYEQN3ORxIAdT8CoG5HAIywIwDKeARAmS7jwFwEQA9vZV86UdEvWbt2bcZd9tlnHxs/fnyIav7vn+lefLHuJJU9Do9eHuV86W6mzUOfv4nY70RMtXkQ9GWy/qy/dNuyZcvCHXseFFNtvmT3mmuuseuvvz7jsbjHsccea74kONXmRv7cxI4dO2ac57rrrrObbrrJ0p0DX/rrcbN27doVnUY+z6EAATCH2HzVfwUIgPFXAwFQNyQA6nY+kgCo+xEAdTsCoG7nIwmAuh8BULcjAEbYEQBlPAKgTJdx4IYWAL/66qvSl2lkenlGqh/td/f5Cyz8ZRdTpkwJd8z50ld/oYXfxdeuXbsQ43ypa9mluJkAP/30U7v33nvt3XffDXfK1alTJywP9heH+J2NlX0zsd9R50tz//nPf9r3338fjqdz584hMPrLPyqz+ZLhe+65J4S+WbNmhSF+B+MxxxwT7iwsu6Q303z+opEBAwbY6NGjbcGCBWH5cdu2bcNbjEteJFKZ42Gf3AkQAHNnzTeVESAAxl8OBEDdkACo2xEA4+wIgLofAVC3IwDG2REAI/x4BmAEHs8AVPGSFgAfGL1bwSwBvqDzp+G0ZGsJsHrOGYcAApUTIABWzom9qlmAABgPSgDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BMMKPABiBRwBU8QiAqlzmcX4HIAEwO7bMikCuBAiAuZLme8oJEADjLwgCoG5IANTtCIBxdgRA3Y8AqNsRAOPsCIARfgTACDwCoIpHAFTlCIDZkWNWBApHgABYOOeiqI6EABh/ugmAuiEBULcjAMbZEQB1PwKgbkcAjLMjAEb4EQAj8AiAKl7SAuCAUXsUzBLgi7p8Ek4LS4DVq5NxCORXgACYX/+i/XYCYPypJwDqhgRA3Y4AGGdHANT9CIC6HQEwzo4AGOFHAIzAIwCqeARAVS7zOF8CTADMji2zIpArAQJgrqT5nnICBMD4C4IAqBsSAHU7AmCcHQFQ9yMA6nYEwDg7AmCEHwEwAo8AqOIRAFU5AmB25JgVgcIRIAAWzrkoqiMhAMafbgKgbkgA1O0IgHF2BEDdjwCo2xEA4+wIgBF+BMAIPAKgipe0AHjvqD0LZgnwJV3+EU4LS4DVq5NxCORXgACYX/+i/XYCYPypJwDqhgRA3Y4AGGdHANT9CIC6HQEwzo4AGOFHAIzAIwCqeARAVS7zOF8CTADMji2zIpArAQJgrqT5nnICBMD4C4IAqBsSAHU7AmCcHQFQ9yMA6nYEwDg7AmCEHwEwAo8AqOIRAFU5AmB25JgVgcIRIAAWzrkoqiMhAMafbgKgbkgA1O0IgHF2BEDdjwCo2xEA4+wIgBF+BMAIPAKgipe0AHj3qD3tfxpvqnJU27hv5/9sl7EEuNo8mQiBfAgQAPOhzncaATD+IiAA6oYEQN2OABhnRwDU/QiAuh0BMM6OABjhRwCMwCMAqngEQFUu8zgCYHZcmRWBXAoQAHOpzXeVChAA4y8GAqBuSADU7QiAcXYEQN2PAKjbEQDj7AiAEX4EwAg8AqCKRwBU5QiA2ZFjVgQKR4AAWDjnoqiOhAAYf7oJgLohAVC3IwDG2REAdT8CoG5HAIyzIwBG+BEAI/AIgCpe0gLgX0f9umCWAF/e5aNwWngLsHp1Mg6B/AoQAPPrX7TfXjYAjp/QwJo0rVm0FuoPX7R6Y3Vo0Y/7ZnWdojeIAZjyc7OY4UU99l8/1yvq3x/z44dM3zVmeNGPXf5D7aI3UAE2m8H/vVXtaqxURzLOBRp+tAIIQWD58h9s3OhbNuhQVfbflQiAwkXAEAQQSClAAOTCyIsAATCenQCoGxIAdTsfSQDU/QiAuh0BULfzkQRA3Y8AqNsRAHU7AqBuRwDU7TKN9GcAcgdgdmyZFYFcCRAAcyXN95QTIADGXxAEQN2QAKjbEQDj7AiAuh8BULcjAMbZEQB1PwKgbkcA1O2SFgDvGNmuYJYA9+k6KZwYlgDr1ycjEcinAAEwn/pF/N0EwPiTTwDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BUPcjAOp2BEDdjgCo22Ua6XcAEgCzY8usCORKgACYK2m+p5wAATD+giAA6oYEQN2OABhnRwDU/QiAuh0BMM6OAKj7EQB1OwKgbpe0AHjbyPYFcwdg364Tw4nhDkD9+mQkAvkUIADmU7+Iv5sAGH/yCYC6IQFQtyMAxtkRAHU/AqBuRwCMsyMA6n4EQN2OAKjbEQB1u0wj/Q5AAmB2bJkVgVwJEABzJc33lBMgAMZfEARA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRAHU/AqBuRwDU7QiAuh0BMDt2zIpAoQgQAAvlTBTZcRAA4084AVA3JADqdgTAODsCoO5HANTtCIBxdgRA3Y8AqNsRAHW7pAXAW97raNs03lQHqaaR383/2f50wPgwG0uAqwmVaRDIsQABMMfgfN0vAgTA+CuBAKgbEgB1OwJgnB0BUPcjAOp2BMA4OwKg7kcA1O0IgLodAVC3yzSSAJgdV2ZFIJcCBMBcavNdpQIEwPiLgQCoGxIAdTsCYJwdAVD3IwDqdgTAODsCoO5HANTtCIC6HQFQtyMAZseOWREoFAECYKGciSI7DgJg/AknAOqGBEDdjgAYZ0cA1P0IgLodATDOjgCo+xEAdTsCoG6XtAD45/f2sW0a19ZBqmnkd/OX29UHjAuzsQS4mlCZBoEcCxAAcwzO1/0iQACMvxIIgLohAVC3IwDG2REAdT8CoG5HAIyzIwDqxbCJpQAAIABJREFUfgRA3Y4AqNsRAHW7TCMJgNlxZVYEcilAAMylNt9VKkAAjL8YCIC6IQFQtyMAxtkRAHU/AqBuRwCMsyMA6n4EQN2OAKjbEQB1OwJgduyYFYFCESAAFsqZKLLjIADGn3ACoG5IANTtCIBxdgRA3Y8AqNsRAOPsCIC6HwFQtyMA6nZJC4A3jti3YJYAX3vg2HBiWAKsX5+MRCCfAgTAfOoX8XcTAONPPgFQNyQA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1OwKgbpdppC8BJgBmx5ZZEciVAAEwV9J8TzkBAmD8BUEA1A0JgLodATDOjgCo+xEAdTsCYJwdAVD3IwDqdgRA3Y4AqNsRALNjx6wIFIoAATBPZ+Lrr7+2xx57zF555RWbPn26fffdd1avXj1r0aKFdenSxY4//njbd999Mx7dm2++aQMHDrQJEybYokWLrEGDBtahQwfr3bu3devWrVK/bOnSpTZgwAAbNGiQTZs2zVasWBGO4cgjj7RLLrnEtt1220rNU9WdCIBVFVt/fwKgbkgA1O0IgHF2BEDdjwCo2xEA4+wIgLofAVC3IwDqdkkLgNeP6GR1C+AtwN/PX27XH/hBODEsAdavT0YikE8BAmAe9D22XXDBBfbNN9+k/fZjjjnGBg8enPLztWvX2vnnnx/iX7rNI+CDDz5oG220Udp9PDx66Js6dWrKfbbeemt79tlnrXv37tWuRACMJyUA6oYEQN2OABhnRwDU/QiAuh0BMM6OAKj7EQB1OwKgbkcA1O0yjSQAZseVWRHIpQABMJfaZva///u/dvbZZ9uaNWusYcOGIQTuv//+9j//8z82f/78cDfga6+9Zh7fPBSm2vr162f9+/cPH+2111525ZVXWuvWrcPY22+/3T7++OPwme/35z//OeUcixcvtvbt29sXX3wRPu/Vq5f17NnTNttsMxsxYoTdcsst5vtsvvnmNnbsWNtjjz2qVYoAGM9JANQNCYC6HQEwzo4AqPsRAHU7AmCcHQFQ9yMA6nYEQN2OAKjbEQCzY8esCBSKAAEwh2fi888/D8Hu559/ts6dO5eGvlSH4EtxN9lkk/U+8mW6bdq0sVWrVlm7du1s1KhRIdqVbL6kt2vXrjZp0iSrVatWCHweB9fdrr/+ervhhhvCX3s07NOnT7ldPPr5UmT/ngMPPNCGDx9erVIEwHhOAqBuSADU7QiAcXYEQN2PAKjbEQDj7AiAuh8BULcjAOp2SQuA1w7vXDBLgG88aHQ4MSwB1q9PRiKQTwECYA71DznkEHv33Xetfv365jHQ/2dVt4suusjuv//+MMwj3T777LPeFOPGjSt9fuDFF19s9913X7l9Vq5cGe4+/P7770NM/PTTT61GjRrrzePLjB966KHw9x4U995776oebtr9CYDxlARA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRAHU/AqBuRwDU7QiAul2mkb4EmACYHVtmRSBXAgTAHEn7nXge23zzu++uu+66Kn+zP/vPX9AxZ84c23nnnUNETLf55/5sv+bNm9vs2bPLPQvw7bfftsMOOywMvfXWW61v374ppykbEq+66iq7+eabq3zM6QYQAOMpCYC6IQFQtyMAxtkRAHU/AqBuRwCMsyMA6n4EQN2OAKjbEQB1OwJgduyYFYFCESAA5uhM3HTTTXbttdeGb/vss89sl112Cf+7v/3X3wjszwD0twBn2mbMmFG6nPe8884LL/lIt/nnJS8J8XGtWrUq3dWPw4/Ht3R3Efpnvvy3bt26tmTJkrAceOTIkdWmRQCMpyQA6oYEQN3OR075uVncBEU8mgCon3wCoG7nI5f/UDtugiIeTQDUTz4BULfzkQ0/WhE3QZGOTloA7De8S8EsAb75oFHhqmIJcJH+w8XP3uAFCIA5OoX+tt2hQ4eGl3t49PO36/qz9z755JPSI/BId+aZZ9oVV1xhW2yxxXpH9vrrr1uPHj3C399111122WWXpT16//zyyy8Pn/u4sm/yPemkk+yFF14In/mxeORLt7Vt2zYcY4MGDWzhwoXVpkUAjKckAOqGBEDdzkcSAHU/AqBuRwDU7XwkAVD3IwDqdgRA3Y4AqNsRAHW7TCN9CTABMDu2zIpArgQIgDmS9rg3a9Ys86Dmb/0dMGBA2m/ebbfd7K233rKmTZuW28fv+PO3Bvvmbwg+8cQT087hgc9Dn28+zu8ILNn8uYHjx4+3OnXqhDf9Zto8OHpADP/ysHy5bbrpppUS88CXaZs3b5516NAh7DJ+QgNr0rRmpeZlp/8KEAD1q4EAqNv5SAKg7kcA1O0IgLpd+L/h3AEoAxIAZTojAOp2PpI7ADU/AqDmVtEoAmBFQnyOQOELEABzdI78zr8ff/wxBDR/C7DfdefP3zv++ONtq622ssmTJ4clwm+88UY4ov32289Gjx5d7uUcd9xxh1155ZXhc9+vW7duaY/ePy+56+/OO+8MdxWWbLvuuqtNmTLFGjVqZPPnz88ocMopp9jzzz8f9vGlyhUtUy6ZbKONNqq0LAGw0lTldiQAam4+igCo2/lIAqDuRwDU7QiAup2PJADqfgRA3Y4AqNv5SAKg5pe0APindw8omCXAtxz8XjgpLAHWrk1GIZBvAQJgjs5ArVq1bPXq1eHbatasae+///56b/Bds2ZNWOJbEgHXvcuv7HME/W3CBx10UNqjHz58uB188MHhcx939dVXl+7bunVr8+cC+gtF/AUhmbYzzjjDnnrqqSr/Fz0BMPsXFgFQNyYA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1OwKgbpdppN8BSADMji2zIpArAQJgjqT9mX7+Mg3fevbsaX//+99TfrO/IMSXAPvmdwe++OKLpfttSHcAsgQ4+xcWAVA3JgDqdgTAODsCoO5HANTtCIBxdgRA3Y8AqNsRAHU7AqBuRwDMjh2zIlAoAgTAHJ2JJk2alC63ffLJJ83vrEu3NW/e3ObMmbPeHXob0jMAK2LlJSAVCVX8OQGwYqN0exAAdTsCYJwdAVD3IwDqdgTAODsCoO5HANTtCIC6XdICYN93D7CtG2+mg1TTyB/mL7PbWAJcTZpMg0B+BAiAOXL3F15MnDgxfFtFy3f33XdfGzduXHheoL94o2QbMmSIHXXUUeGPMW8B9peHlNxZyFuAc3QBZOFrCIA6KgFQtyMAxtkRAHU/AqBuRwCMsyMA6n4EQN2OAKjbEQB1u0wjCYDZcWVWBHIpQADMkfbZZ59tTzzxRPi2YcOG2aGHHpr2m0ti4bpv6fXn9vnz+3zzt/r6HYHpNv984MCB4WMf528hLtn8ZSP+XEDfxo4du96zCEv2W7VqVXhZiS9d7tKli40cObLatLgDMJ6SAKgbEgB1Ox/JS0B0PwKgbkcA1O18JC8B0f0IgLodAVC385G8BETzS1oA7PPOQQVzB+AdhwwPJ4WXgGjXJqMQyLcAATBHZ+Dxxx+33/3ud+HbHnjgATv//PPTfnP9+vXtm2++sR133NGmTp1aut/atWvNlwfPnTvXdt55Z/v888/TztGmTRv74osvrFmzZuG/oMu+lMMD5OGHHx7G+puI+/btm3IevwvR70b07U9/+pP179+/2rQIgPGUBEDdkACo2xEA4+wIgLofAVC3IwDG2REAdT8CoG5HANTtCIC6XaaRfgcgATA7tsyKQK4ECIA5kvag588BXLlyZbj7zyNcqs3vsjvggAPCR+ecc4498sgj5Xa78MILQ0D0Ld3de2XDne8/YMCAcnOsWLHCGjZsaD/88IN5KPQXj6R6a69HyoceeiiMnTBhgrVv377atAiA8ZQEQN2QAKjbEQDj7AiAuh8BULcjAMbZEQB1PwKgbkcA1O0IgLodATA7dsyKQKEIEABzeCbKxjt/C7C/Dbjs9tNPP4Wltv/4xz/SRrcvv/zSdt11V/Plue3atbNRo0bZZpv996Gwy5YtC3NMmjTJatWqZVOmTLEddthhvV9Zdhnw7bffbn369Cm3j8dFn8e/p2vXrvbee+9VqxQBMJ6TAKgbEgB1OwJgnB0BUPcjAOp2BMA4OwKg7kcA1O0IgLpd0gLg5W8fUjBLgP966DvhxLAEWL8+GYlAPgUIgDnUX7RoUYh2s2fPDnHO77A7/vjjbauttrLJkyfbbbfdFpbt+nbBBRfY/fffn/LofDmuL931ba+99gpLeP3ZgNOnTw9zfPzxx+GzTMt2PTb6sXhQ9K13794hSHpMHDFiRFjuu3jx4vDnMWPG2J577lmtUgTAeE4CoG5IANTtCIBxdgRA3Y8AqNsRAOPsCIC6HwFQtyMA6nYEQN0u00hfAkwAzI4tsyKQKwECYK6k//M9/ty+o48+2qZNm5b2m/1Zgf6Cj4033jjlPmvWrLFevXrZY489lnYOXz7sLwGpUaNG2n38GLp3725fffVVyn08TD7zzDPWo0ePalciAMaTEgB1QwKgbkcAjLMjAOp+BEDdjgAYZ0cA1P0IgLodAVC3IwDqdgTA7NgxKwKFIkAAzMOZ8Lfq+nP8XnjhhRDf/E47fyZfp06dwtt9DzzwwEod1dChQ0Pkmzhxon399dfmLw/x5/T5HEcccUSl5vBj8WcEDho0KERJfz5gixYtQhi89NJLrWXLlpWap6o7EQCrKrb+/gRA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRAHU/AqBuRwDU7ZIWAP+/tw+1rRr/95FPukzcyB/nL7O7Dn07TMIS4DhLRiOQLwECYL7ki/x7CYDxFwABUDckAOp2BMA4OwKg7kcA1O0IgHF2BEDdjwCo2xEAdTsCoG6XaSQBMDuuzIpALgUIgLnU5rtKBQiA8RcDAVA3JADqdgTAODsCoO5HANTtCIBxdgRA3Y8AqNsRAHU7AqBuRwDMjh2zIlAoAgTAQjkTRXYcBMD4E04A1A0JgLodATDOjgCo+xEAdTsCYJwdAVD3IwDqdgRA3S5pAfCSYYcXzBLgew97K5wYlgDr1ycjEcinAAEwn/pF/N0EwPiTTwDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BUPcjAOp2BEDdjgCo22Ua6UuACYDZsWVWBHIlQADMlTTfU06AABh/QRAAdUMCoG5HAIyzIwDqfgRA3Y4AGGdHANT9CIC6HQFQtyMA6nYEwOzYMSsChSJAACyUM1Fkx0EAjD/hBEDdkACo2xEA4+wIgLofAVC3IwDG2REAdT8CoG5HANTtkhYALx7WzbZqVABvAV6wzP522JvhxLAEWL8+GYlAPgUIgPnUL+LvJgDGn3wCoG5IANTtCIBxdgRA3Y8AqNsRAOPsCIC6HwFQtyMA6nYEQN0u08gfCYDZgWVWBHIoQADMITZf9V8BAmD81UAA1A0JgLodATDOjgCo+xEAdTsCYJwdAVD3IwDqdgRA3Y4AqNsRALNjx6wIFIoAAbBQzkSRHQcBMP6EEwB1QwKgbkcAjLMjAOp+BEDdjgAYZ0cA1P0IgLodAVC3S1oAvGDYEbZVo811kGoa+eOCpfbAYW+E2VgCXE2oTINAjgUIgDkG5+t+ESAAxl8JBEDdkACo2xEA4+wIgLofAVC3IwDG2REAdT8CoG5HANTtCIC6XaaRBMDsuDIrArkUIADmUpvvKhUgAMZfDARA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRAHU/AqBuRwDU7QiAuh0BMDt2zIpAoQgQAAvlTBTZcRAA4084AVA3JADqdgTAODsCoO5HANTtCIBxdgRA3Y8AqNsRAHW7pAXA89/qblsWwBLgnxYstQcPHxpODEuA9euTkQjkU4AAmE/9Iv5uAmD8yScA6oYEQN2OABhnRwDU/QiAuh0BMM6OAKj7EQB1OwKgbkcA1O0yjSQAZseVWRHIpQABMJfafFepAAEw/mIgAOqGBEDdjgAYZ0cA1P0IgLodATDOjgCo+xEAdTsCoG5HANTtCIDZsWNWBApFgABYKGeiyI6DABh/wgmAuiEBULcjAMbZEQB1PwKgbkcAjLMjAOp+BEDdjgCo2yUtAPZ+q0fBLAEeePiQcGJYAqxfn4xEIJ8CBMB86hfxdxMA408+AVA3JADqdgTAODsCoO5HANTtCIBxdgRA3Y8AqNsRAHU7AqBul2mkLwEmAGbHllkRyJUAATBX0nxPOYGyAfC1cc2sUZNaCFVR4NvVm1dxBLuXCHz1c2MwIgS+WtYoYnRxD33ln3sWN0DEr9/oJ/7vRASfbTmzRszwoh7b4KNlRf37Y378JvN+jBnO2BUrMBAElq/6yd779yNh5IZ6p1rZf1fiDkDhImAIAgikFCAAcmHkRYAAGM9OANQNCYC6nY8kAOp+BEDdjgCo2/lIAqDuRwDU7QiAul0YSQCUAJMWAM9586iCWQL8aLfXNuiwKl1QDEIgQQIEwASdzA3ppxAA488WAVA3JADqdgTAODsCoO5HANTtCIBxdgRA3Y8AqNsRAHU7AqBul2mkLwEmAGbHllkRyJUAATBX0nxPOQECYPwFQQDUDQmAuh0BMM6OAKj7EQB1OwJgnB0BUPcjAOp2BEDdLmkB8Ow3ji6YOwAfP+LVcGI21KXV+lXFSASSIUAATMZ53OB+BQEw/pQRAHVDAqBuRwCMsyMA6n4EQN2OABhnRwDU/QiAuh0BULcjAOp2mUb6HYAEwOzYMisCuRIgAOZKmu8pJ0AAjL8gCIC6IQFQtyMAxtkRAHU/AqBuRwCMsyMA6n4EQN2OAKjbEQB1OwJgduyYFYFCESAAFsqZKLLjIADGn3ACoG5IANTtCIBxdgRA3Y8AqNsRAOPsCIC6HwFQtyMA6nZJC4BnDT3Wtmi0uQ5STSMXL1hqT3QfHGZjCXA1oTINAjkWIADmGJyv+0WAABh/JRAAdUMCoG5HAIyzIwDqfgRA3Y4AGGdHANT9CIC6HQFQtyMA6naZRhIAs+PKrAjkUoAAmEttvqtUgAAYfzEQAHVDAqBuRwCMsyMA6n4EQN2OABhnRwDU/QiAuh0BULcjAOp2BMDs2DErAoUiQAAslDNRZMdBAIw/4QRA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRAHU/AqBuRwDU7ZIWAH879LiCWQL8VPeXw4lhCbB+fTISgXwKEADzqV/E300AjD/5BEDdkACo2xEA4+wIgLofAVC3IwDG2REAdT8CoG5HANTtCIC6XaaRvgSYAJgdW2ZFIFcCBMBcSfM95QQIgPEXBAFQNyQA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1OwKgbpfvALhw4UKbMGFC+M/EiRPDf7755ptwWGeeeaY98cQTFf443+fss8+ucD/f4fHHH7ezzjor475Lly61AQMG2KBBg2zatGm2YsUKa9GihR155JF2ySWX2Lbbblup7/rss8/svvvus3feecfmzJljW2yxhbVp08ZOP/10O+ecc6xWrVqVmue5554Lx/3JJ5/Yd999Z40bN7bOnTvbRRddZPvss0+l5nDTe++91wYPHmyzZs2ytWvXWqtWrezYY48Nv6levXqVmmfs2LF2//332+jRo23+/Pm2zTbbWNu2bYNpz549KzUHO+VWgACYW2++7T8CBMD4S4EAqBsSAHU7AmCcHQFQ9yMA6nYEwDg7AqDuRwDU7QiAul3SAuDpr/sS4Do6SDWNXLxgiT1zZHaXAG+00UZpjzYfAXD69Okh9E2dOjXlcW299db27LPPWvfu3TMqP/rooyHQ/fzzzyn383A3ZMiQjOFt+fLldtJJJ4X9Um01atSw66+/3q655pqMx+JR9ZhjjrF58+al3K9p06b2yiuvWLt27TLOc+ONN9oNN9xga9asSbnfUUcdZc8//7zVrl27mq5ApqkOAQJgdSgyR5UFCIBVJltvAAFQNyQA6nYEwDg7AqDuRwDU7QiAcXYEQN2PAKjbEQB1OwKgbpdpZK4DoN9l53fIDRs2LByWEgDfeust86CVbmvevLnVrVs35ceLFy+29u3b2xdffBE+79WrV7irbbPNNrMRI0bYLbfcYr7P5ptvbn4n3B577JFyHj8GD4Qeyho1amT9+vWzjh072rfffmsPP/ywvfTSS2Fcly5dwrwe8lJtfqegx0bfDjzwQLv00kvDb5s8ebL179/fPFb65nOee+65KefwOw/33ntvW7BgQbjj8PLLL7cePXqEfT0s/vWvf7VVq1aF4/zwww+tWbNmKed55JFHgodvrVu3tquuusp23313mzt3rt1zzz3hd/jmx/z0009n54JkVkmAACixMShWgAAYK2hGANQNCYC6nY/8almjuAmKeDQBUD/5BEDdzkduOTP1v1DEzVocowmA+nkmAOp2YeSKFZETFOdwAmB2znsuAuB1110Xopv/xyOUL0/1pam+KQFw5syZtt1220kgfjed3+Hm2+233259+vQpN49HP492Hsw8yA0fPny97/HPPGL60uGtttrKPvrooxDMym5+Z6Avo/XtySeftDPOOGO9eUaOHGkHHHBA+Hu/s+7ll1+2mjVrlu739ddfh7A3e/bssAx3xowZKcOmL8317/DN787zOwrLbr7M+eSTTw5/5cuoH3vssfWO5fvvvw/nxP+nL3/2UFi/fv3S/VavXm3HHXecvfbaa+Hv/Njdia0wBAiAhXEeiu4oCIDxp5wAqBsSAHU7H0kA1P0IgLodAVC385EEQN2PAKjbEQB1uzCSACgBJi0AnjrkhIJZAvz3Hi+Gc5KrtwDnKwCuXLnSGjZsGCKXB7xPP/005Z15559/vj300EPBZNKkSSHCpQtqfsfgH//4x/WuaX/GoN+J6M/z22233cIdfetuvgx56NChIfq5ie+/7ubPBjz11FPDX9955512xRVXlNvF7/rzO/o80B1++OH25ptvpvznq1u3buZ3Lfp3+R2DHmLLbnfccYddeeWV4a/+/ve/p3zWn/+7vodX/y6/w7AkBkr/QDOoWgUIgNXKyWSVFSAAVlYq/X4EQN2QAKjb+UgCoO5HANTtCIC6nY8kAOp+BEDdjgCo24WRBEAJkAAosVU4yO8ALJYA+Pbbb9thhx0WTG699Vbr27dvSp9x48bZvvvuGz7zZbA333xzuf3KLtv1Z+75CztSbWVD4pdffmk77LBD6W6+zNjvsPPnB3qce+ONN1LO4S8nadCggf3444+233772QcffFBuP18a3Lt37/B3HgtPOeWUlPOUDYkDBw4sXepbsnOnTp1szJgx4Y7GRYsW2SabbJJynpKQuOmmm5rfoegvPWHLvwABMP/noCiPgAAYf9oJgLohAVC385EEQN2PAKjbEQB1Ox9JANT9CIC6HQFQtwsjCYASIAFQYqtwUDEFwGuvvdZuuummYOJLfdO9XdeX+PozBJcsWRKWufpy17KbL5H1uyV32mmn0mcJpoL2O+lOO+208JEvuy37FmNfWnzwwQeHz9LdRVgyp9/Z589M9Of7+Z2FG2+8cenX+dLip556Kvw5U4z0z0qem+hjSpYM//JfSSusTp06YdlzprsIS47Vo6hv/ht8mTRb/gUIgPk/B0V5BATA+NNOANQNCYC6nY8kAOp+BEDdjgCo2/lIAqDuRwDU7QiAul0YSQCUAJMWAHsOOdHqFMBbgJcsWGLP9XghnJMNZQlw165d7fPPPw/La/2OtV/96ld2yCGH2AUXXJD2BRf++/zZeC+88Mtv9bHpXhTin7dt29Y++eSTcPfdwoULS69Zv3PPv3Pt2rXhrbuDBw9Oez1//PHH9utf/zp87s8a9GcOlmwDBgywiy++OPzRn/137LHHpp3HXwxy7733hs8/++wz22WXXUr39ecq+jJlf3OxL23OtPk+fiehj5kwYULprj6nL1P2zb/r7rvvTjuNH+vxxx8fPvffcOGFF0r/PDOoegUIgNXryWyVFCAAVhIqw24EQN2QAKjb+UgCoO5HANTtCIC6nY8kAOp+BEDdjgCo24WRBEAJkAAosVU4qGwA9CjUpEmTjGNSPaeuwi9ZZ4fYZwCm+77atWuHeHXeeeel3MXv+Bs/fny4281DXqbNn3H3+uuvh12WL19uvuTVN397sD8/0Dd/0cff/va3tNP4ElkPiL75m4b9jsCSzZ8beNttt4U/Tpw40dq1a5d2Hn/2X8nLSvwZf36XXsnmy4/9OYC77rpreKZhps0jn8c+H+N3BJZsPucRRxwR/ujPAvzDH/6QdhqPjR4QffPf4HcvsuVfgACY/3NQlEdAAIw/7QRA3ZAAqNv5SAKg7kcA1O0IgLqdjyQA6n4EQN2OAKjbhZEEQAmQACixVTiobACscGezcOdb7KYGQF++63ef+fP5WrRoEQ7D34z74osvhjv7So7NX+BR8ly8ssfqkWzKlCnhBRjz58/P+DP8WXr+Rl3fPOTVq1cv/O8e6zp06BD+d3+GoD9LMN22bNky23zzzcPH6740o+xbgv1uxp133jntPA888EDpnXb+O0844YTSfT1m+rLgjh07mj+7MNPm+3jk9ef2/fTTT6W7ln1LsH+XP7sw3ebHWnIHot/BeN9998VeDoyvBgECYDUgMkXVBQiAVTdbdwQBUDckAOp2PpIAqPsRAHU7AqBu5yMJgLofAVC3IwDqdmEkAVACTFoAPOm1kwpmCfCgowZV+pzkKwD+8MMPYentRhttlPJYhwwZEuKgv+nXo9v06dPXezlH69atQzD0eDh79uyMv7nss/XKLo0ePXp0eC6gb9dcc43deOONaedZs2ZNeOuub/68v3feead033POOSc8F9A3P9btt98+7Ty+n+/vmz/v7ze/+U3pvj6/f0/nzp1t1KhRGX+TH7cfv4/x5/2VbD6n/17fHn30Ufvd736Xdh73c0ff/JgeeeSRSl877Jg9AQJg9myZOYMAATD+8iAA6oYEQN3ORxIAdT8CoG5HANTtfCQBUPcjAOp2BEDdLowkAEqABECJrcJCdF3XAAAgAElEQVRBfgdgSQAs5CXAFf4Qs/C23quvvjrs+uc//9n69etXbhh3AHIHYGWuow1xHwLghnjWEnDMBMD4k0gA1A0JgLodATDOjgCo+xEAdTsCYJwdAVD3IwDqdgRA3Y4AqNtlGlk2ABbyS0Aq8+v9ZR3+fDu/S/HQQw8Nb84tu/EMQJ4BWJnraEPchwC4IZ61BBwzATD+JBIAdUMCoG5HAIyzIwDqfgRA3Y4AGGdHANT9CIC6HQFQt0taADzh1VMKZgnwi0f//+HEbOgB0H9Dw4YNbdGiReE5df7Ci7LbiSeeGJ4X6FvMW4C33HLLMEfMW4D95SG///3vwzwxbwH2l4d8+OGHUW8B9peH7L777uFYeAuw/t9R+RxJAMynfhF/NwEw/uQTAHVDAqBuRwCMsyMA6n4EQN2OABhnRwDU/QiAuh0BULcjAOp2mUb6HYBJCoD+1l1/aUeqAHjttdeav0jEt7Fjx5rfEZhq8+fj1a1b15YsWRKe9zdy5Mhyu2277bYhlu60007hrcDpNn/r72mnnRY+9uf4nX322aW7Dh8+PDwX0Dd/k66/UTfd5m/99bsZa9WqFY5pk002Kd217LMK/c2+fgdkqs0/a9q0afjIxzz55JOlu61YsSI8N3H16tXhDcP+VuB0mx/rVVddFT7233DggQdm58Jk1ioJEACrxMXO1SVAAIyXJADqhgRA3c5H8gxA3Y8AqNsRAHU7H8kzAHU/AqBuRwDU7cJIngEoASYtAB73ak+r07COZFGdg5YsXGIvH/1cmHJDvwOw7BLgQw45xN5+++1yVB7RPHD55m/v9bf4ptr8bbr+pmHf/vSnP1n//v3L7eZRz+Oeb5mim79N199I7NvUqVNtxx13LJ3H38Jbv3598/jWrVs3e+ONN1Iei3/uUfPHH38MxzRmzJhy+w0cONDOO++88HfPPfec+duLU23+2amnnho+SvWW5P322y9EUX/Rit9BWTYylp3Pj/Wtt96yTTfdNOxXcjdkdV6TzFV1AQJg1c0YUQ0CBMB4RAKgbkgA1O18JAFQ9yMA6nYEQN3ORxIAdT8CoG5HANTtwkgCoARIAJTYKhyUpADoL/7wN/P65nf6lbwQpATBY5ovEfY3Crdp0yYsEU71VuGy4c5fjNK+fftyjs8//3xpaEt3997SpUutefPmYalxqrsRfcLu3buH8Od39s2cOTPsv+5WNtzdfvvt1qdPn3K7zJ8/35o1axbeBJzp7r2ScFejRg2bM2fOencK+twlQdTjZs+ePdc7Fv93/e222y7cKejH/vrrr1d4fbFDbgQIgLlx5lvWESAAxl8SBEDdkACo2xEA4+wIgLofAVC3IwDG2REAdT8CoG5HANTtCIC6XaaRG0IAnDVrVghpe+21V9qfMmTIEDvhhBPCHXW1a9e2adOmhTC27lZ2GXCqoOZ3wfmyX18G3LVrV3vvvffWm2PlypUhIE6fPj3cMffRRx9Z69aty+130UUX2f333x/+7vHHH7ezzjprvXnKLgM++uij7aWXXrKaNWuW7udLmffee2+bPXt2WJI8Y8YM22abbdabp+wy4EGDBpk/67Ds5n938sknh78688wz7Yknnlhvjm+//da23377EEdbtmwZnitYr1690v08+h133HH22muvhb9j+W92/nlUZyUAqnKMixIgAEbxhcEEQN2QAKjb+UjuANT9CIC6HQFQt/OR3AGo+xEAdTsCoG4XRnIHoASYtAB47Kun2uYFsAR46cIlNvjoX5azZmsJ8Pvvvx+CXMnmYavkTrZOnTrZueeeW+6aWDeWeYTzZ835EtijjjrK9txzz3Ann7/t16PYCy+8EP7jf/bNX7DhAS7V5ktv/cUZX375Zfi4d+/e4W63zTbbzEaMGBGW+y5evDj82Zfb+nel2oYOHRqOxe+8a9SoUbjbsEOHDiFUPvzww6UvG9l///1DRCwb9srO58ty/S4/3/w3XnbZZeFZfZMnT7abb745REbfHnzwwdKlvusej583D4W+JNfvJrziiiusR48eYTcPo3/5y19C0PSlxB4rU91p6Pv60mC/+9E3D5r9+vULLweZO3eu3X333cHHNz/mZ599VvrnmEHZESAAZseVWSsQIADGXyIEQN2QAKjb+UgCoO5HANTtCIC6nY8kAOp+BEDdjgCo24WRBEAJkAAosVU4KBcB0INe2ZdOVHRQJSGvZL+SAFjROH+RxV133RWiXqbNY6QvYf3qq69S7uZ39T3zzDOlES3dXB76Lr744nDXYarNg6Avk/Vn/aXbli1bFu7Y86CYavMlu76s+frrr8/4m8aPH2/HHnus+ZLgVJu/HGTw4MHWsWPHjPNcd911Yfn0uuegZJC7+ZuU/S5LtsIRIAAWzrkoqiMhAMafbgKgbkgA1O18JAFQ9yMA6nYEQN3ORxIAdT8CoG5HANTtwkgCoARIAJTYKhy0IQRAv2vv1VdfDS+pmDRpUnjxht9F6He1+ZLYXXfdNbxR1+8k9DsDK7P523QHDBhgvjzWg6BHvBYtWoQweOmll4ZlsJXZPv30U7v33nvt3XffDXfK1alTJywPPv3008Px+B15ldn8jjpfmvvPf/7Tvv/++3BXYefOnUNgLHkhSUXzuMk999wTQp8vm/atVatWdswxx4Q7C8su6c00l9/56DajR4+2BQsWhOXHbdu2DW8xLnmRSEXHwue5FSAA5tabb/uPAAEw/lIgAOqGBEDdjgAYZ0cA1P0IgLodATDOjgCo+xEAdTsCoG6XtAB49Cu+BHgLHaSaRi5duNhePSa7S4Cr6VCZBgEE0ggQALk08iJAAIxnJwDqhgRA3Y4AGGdHANT9CIC6HQEwzo4AqPsRAHU7AqBuRwDU7TKNJABmx5VZEcilAAEwl9p8V6kAATD+YiAA6oYEQN2OABhnRwDU/QiAuh0BMM6OAKj7EQB1OwKgbkcA1O0IgNmxY1YECkWAAFgoZ6LIjoMAGH/CCYC6IQFQtyMAxtkRAHU/AqBuRwCMsyMA6n4EQN2OAKjbJS0A9hh8esEsAR5y7DPhxGTrLcD6WWckAghURoAAWBkl9ql2AQJgPCkBUDckAOp2BMA4OwKg7kcA1O0IgHF2BEDdjwCo2xEAdTsCoG6XaaQvASYAZseWWRHIlQABMFfSfE85AQJg/AVBANQNCYC6HQEwzo4AqPsRAHU7AmCcHQFQ9yMA6nYEQN2OAKjbEQCzY8esCBSKAAGwUM5EkR0HATD+hBMAdUMCoG5HAIyzIwDqfgRA3Y4AGGdHANT9CIC6HQFQt0taAOz+8m8KZgnw0OOeDieGJcD69clIBPIpQADMp34RfzcBMP7kEwB1QwKgbkcAjLMjAOp+BEDdjgAYZ0cA1P0IgLodAVC3IwDqdplG+hJgAmB2bJkVgVwJEABzJc33lBMgAMZfEARA3ZAAqNsRAOPsCIC6HwFQtyMAxtkRAHU/AqBuRwDU7QiAuh0BMDt2zIpAoQgQAAvlTBTZcRAA4084AVA3JADqdgTAODsCoO5HANTtCIBxdgRA3Y8AqNsRAHW7pAXAbi//tmCWAL953FPhxLAEWL8+GYlAPgUIgPnUL+LvJgDGn3wCoG5IANTtCIBxdgRA3Y8AqNsRAOPsCIC6HwFQtyMA6nYEQN0u00hfAkwAzI4tsyKQKwECYK6k+Z5yAgTA+AuCAKgbEgB1OwJgnB0BUPcjAOp2BMA4OwKg7kcA1O0IgLrd/2PvTMCtntbH/0okNLilSEW6UZKKhFCGe6mIzOESUiJjpCuSKRrMbhkyZOr6FW5ImVMoCl1p0Gj6RxMiDSqd//Oufvv89jntvc8+77v3Pnvv81nP47k63/Wu7/p+1jru9bnvuxYC0M4OAZgedowKgWwhgADMlpUoZ/NAAPoXHAFoZ4gAtLNDAPrYIQDt/BCAdnYIQB87BKCdHwLQzg4BaGeXbwLw+JfPl8q1drYDSVHkuuW/y5unPhNGowQ4RVAZBgIZJoAAzDBwXreFAALQvxMQgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMA2vkhAO3sEIB2dghAO7tEkQjA9HBlVAhkkgACMJO0eVchAQSgfzMgAO0MEYB2dghAHzsEoJ0fAtDODgHoY4cAtPNDANrZIQDt7BCAdnYIwPSwY1QIZAsBBGC2rEQ5mwcC0L/gCEA7QwSgnR0C0McOAWjnhwC0s0MA+tghAO38EIB2dghAO7t8E4B/f6lr1pQAv33a02FhKAG2708iIVCWBBCAZUm/HL8bAehffASgnSEC0M4OAehjhwC080MA2tkhAH3sEIB2fghAOzsEoJ0dAtDOLlGklgAjANPDllEhkCkCCMBMkeY9RQggAP0bAgFoZ4gAtLNDAPrYIQDt/BCAdnYIQB87BKCdHwLQzg4BaGeHALSzQwCmhx2jQiBbCCAAs2Ulytk8EID+BUcA2hkiAO3sEIA+dghAOz8EoJ0dAtDHDgFo54cAtLNDANrZ5ZsAPPbFC7KmBPjd00eGhaEE2L4/iYRAWRJAAJYl/XL8bgSgf/ERgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMA2vkhAO3sEIB2dghAO7tEkVoCjABMD1tGhUCmCCAAM0Wa9xQhgAD0bwgEoJ0hAtDODgHoY4cAtPNDANrZIQB97BCAdn4IQDs7BKCdXb4JwKPHXJg1GYATz3gqLAwZgPb9SSQEypIAArAs6Zfjd0cLwCc/bCw1d9+uHNOwffr3G2vYAomSCcv3h4KDwJez9nREl+/QKgu3Ld8AHF9f5fvNjmhCq325EghWAmvWWSOJ+/NPGDgIFKxl71nwrd+8Rib99kJOi6rof1dCAFp2ATEQgEAsAghA9kWZEEAA+rEjAO0MEYB2dhqJALTzQwA62CEA7fBEBAHowIcAtMNDANrZiQgC0IYPAWjjVlKUlgCTAVgSJZ5DILsJIACze33ydnYIQP/SIgDtDBGAdnYIQB87BKCdHxmAdnYaiQB08EMA2uEhAO3sEIBmdvkmAI8ac6HsUKuKmUeqAtcvXy3vUwKcKpyMA4EyIYAALBPsvBQB6N8DCEA7QwSgnR0C0McOAWjnhwC0s0MA+tgJAtAOEAFoZ4cANLNDAJrRJQxEAKaHK6NCIJMEEICZpM27CgkgAP2bAQFoZ4gAtLNDAPrYIQDt/BCAdnYIQB87BKCDHwLQAY8SYCs8BKCVXOI4BGB6uDIqBDJJAAGYSdq8CwGYwj2AALTDRADa2SEAfewQgHZ+CEA7OwSgjx0C0MEPAeiAhwC0wss3Adh29EVZUwI8+cwnw7JwC7B1dxIHgbIlgAAsW/7l9u1kAPqXHgFoZ4gAtLNDAPrYIQDt/BCAdnYIQB87BKCDHwLQAQ8BaIWHALSSSxynGYAIwPSwZVQIZIoAAjBTpHlPEQIIQP+GQADaGSIA7ewQgD52CEA7PwSgnR0C0McOAejghwB0wEMAWuEhAK3kEIDpIceoEMgeAgjA7FmLcjUTBKB/uRGAdoYIQDs7BKCPHQLQzg8BaGeHAPSxQwA6+CEAHfAQgFZ4+SYAjxjdTXbYNQtuAV6xWj4884mwLJQAW3cncRAoWwIIwLLlX27fjgD0Lz0C0M4QAWhnhwD0sUMA2vkhAO3sEIA+dghABz8EoAMeAtAKDwFoJZc4bj0CMD1gGRUCGSSAAMwgbF71fwQQgP7dgAC0M0QA2tkhAH3sEIB2fghAOzsEoI8dAtDBDwHogIcAtMJDAFrJIQDTQ45RIZA9BBCA2bMW5WomCED/ciMA7QwRgHZ2CEAfOwSgnR8C0M4OAehjhwB08EMAOuAhAK3w8k0Atvmfi7OmBHjKWY+HZaEE2Lo7iYNA2RJAAJYt/3L7dgSgf+kRgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMAOvghAB3wEIBWeAhAK7nEcVoCjABMD1tGhUCmCCAAM0Wa9xQhgAD0bwgEoJ0hAtDODgHoY4cAtPNDANrZIQB97BCADn4IQAc8BKAVHgLQSg4BmB5yjAqB7CGAAMyetShXM0EA+pcbAWhniAC0s0MA+tghAO38EIB2dghAHzsEoIMfAtABDwFohZdvAvDQF7pnTQnwx11GhGWhBNi6O4mDQNkSQACWLf9y+3YEoH/pEYB2hghAOzsEoI8dAtDODwFoZ4cA9LFDADr4IQAd8BCAVngIQCu5xHFaAowATA9bRoVApgggADNFWkS22WabpN7Wrl07ef/99xP2feONN+Sxxx6TadOmyYoVK2TXXXeV1q1bS48ePaR9+/ZJvWft2rUybNgwGTNmjCxcuFA2bNgg9erVkxNOOEGuvPJKqV+/flLjWDohAC3UisYgAO0MEYB2dghAHzsEoJ0fAtDODgHoY4cAdPBDADrgIQCt8BCAVnIIwPSQY1QIZA8BBGAG1yIVArCgoEB69uwZ5F+8phLwkUceSSgcFy1aFETfvHnzYg5TrVo1GTVqlHTs2DEthBCAfqwIQDtDBKCdHQLQxw4BaOeHALSzQwD62CEAHfwQgA54CEArvLwTgP/uIZV2rWLFkbK4PzQD8Owt/w5KCXDKsDIQBDJKAAGYQdwRAXjppZfKZZddFvfNO+20kzRo0CDm8xtvvFHuvPPO8Kxly5Zy/fXXS8OGDUWF3pAhQ2TGjBnhmfa74447Yo7x+++/y8EHHyxfffVVeN69e3fp0qWLVK5cWSZOnCh33XWXaJ8dd9xRpk6dKgcccEDKKSEA/UgRgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMAOvghAB3wEIBWeAhAK7nEcQjA9HBlVAhkkgACMIO0IwJwwIABcsstt5T6zVqm26RJE9m0aZO0atVKJk+eHKRdpGlJr5YPf/rpp1KxYsUg+FQOFm/67ltvvTX8WKVhnz59inRR6de2bdvwnqOPPlree++9Us+1pAAEYEmESn6OACyZUbweCEA7OwSgjx0C0M4PAWhnhwD0sUMAOvghAB3wEIBWeAhAKzkEYHrIMSoEsocAAjCDa+EVgL169ZLhw4eHGaukO/TQQ7ea/ccffyyHHXZY+Pnll18uDz30UJE+GzdulFq1asmqVauCTJw1a5ZUqFBhq3G0zPjRRx8NP1eheNBBB6WUFALQjxMBaGeIALSzQwD62CEA7fwQgHZ2CEAfOwSggx8C0AEPAWiFl28CsPW/L8maEuBpZ2/590NKgK27kzgIlC0BBGAG+XsEoJ79pxd0LFmyRBo3bixz586NO3N9rmf71a1bV7777rsiZwG+/fbbctxxx4XYQYMGSd++fWOOEy0S+/XrJwMHDkwpKQSgHycC0M4QAWhnhwD0sUMA2vkhAO3sEIA+dghABz8EoAMeAtAKDwFoJZc4TkuAEYDpYcuoEMgUAQRgpkhH3QJsKQFevHhxYTnvJZdcEi75iNf0eeSSEI2LPk/w5ptvlttvvz2Exssi1Gda/lu9enVZs2ZNKAeeNGlSSkkhAP04EYB2hghAOzsEoI8dAtDODwFoZ4cA9LFDADr4IQAd8BCAVngIQCs5BGB6yDEqBLKHAAIwg2sRyQDcb7/9gmDT7Dw9q2+33XaTNm3ayAUXXBDO3IvVXn/9dTnxxBPDo/vuu0+uvvrquDPX57179w7PNS76Jt8zzjhDXnzxxfDsl19+CZIvXmvevLnMnDlTdt11V1m+fHlKSSEA/TgRgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMAOvghAB3wEIBWePkmAFuN6pk1JcCfnrMlCYUSYOvuJA4CZUsAAZhB/hEBmOiVnTt3lpEjR0q1atWKdNOMP709WNuYMWPk9NNPjzuMCj4Vfdo0TjMCI03PDfzkk09EbxrWm34TNRWOKhC1rV+/XipVqpQ0LRV8idqPP/4orVu3Dl2e/LCx1Nx9u6THpuMWAghA+05AANrZaeSXs/b0DVCOoxGA9sVHANrZaWS1L1f6BijP0WvWleev9307AtDFr2Ate88CEAFooVZyjJYAIwBL5kQPCGQzAQRgBldHpdtJJ50kxx57bDjHb+edd5YVK1aE8loVdT/99FOYjd7kq2f1bbfd/0mxoUOHyvXXXx+eT5gwQdq3bx935vo8kvV39913y7XXXlvYt2nTpjJnzhypXbu2LF26NOHXn3XWWTJ69OjQZ+XKlVKjRo2kaSUjOyODIQCTxlqkIwLQxi38Di3f3x5MJALQsQcQgHZ4CEA7O41EADr4IQDt8BCAdnZCBqAVXr4JwIOevzRrMgA/O/fhsCxkAFp3J3EQKFsCCMAM8tebd+OV3C5btkw6dOggM2bMCDN64IEH5MorryycnZ7bp+f3aXv33XflmGOOiTvz9957L0hGbRp30003FfZt2LCh6LmAeqGIliAnaueff748++yzpn/IIwDTv7EQgHbGCEA7O40kA9DODwHoYPf9ZnswkQhAzx5AANrpIQDt7BCAZnYIQDO6hIGaAYgATA9bRoVApgggADNFOon3qJhr0qSJbNiwQf7617/KggULCqNyLQOQEuAkFtzZBQFoB4gAtLNDAPrYIQDt/MgAtLPTSDIAHfwQgHZ4CEA7OwSgmR0C0IwOAZgedIwKgawhgADMmqXYMpHoc/eWLFkiderUCT/PtTMAS8LKJSAlESr5OQKwZEbxeiAA7ewQgD52CEA7PwSgnR0C0MeOS0Ac/BCADniUAFvh5ZsAbPmclgBXteJIWdwfK36TGf+gBDhlQBkIAmVAAAFYBtATvVLP+dNsP23Tpk2Tgw8+OPz9uHHjpFOnTuHvPbcA6+UhL730UhiHW4CzbPFLOR0EYCmBRXVHANrZIQB97BCAdn4IQDs7BKCPHQLQwQ8B6ICHALTCQwBaySWOQwCmhyujQiCTBBCAmaSdxLv69OkjenFHcQGo5cF6fp82vdVXMwLjNX3+2GOPhcca16BBg8Kueo6gnguoberUqaK3AsdqmzZtCucVrlmzRtq2bRsuKkllIwPQTxMBaGeIALSz00jOALTzQwA62HEGoB0eJcAudghABz4EoAMeAtAKDwFoJZc4DgGYHq6MCoFMEkAAZpJ2Eu864YQTZPz48aGnSrI99tgj/H1BQYHUrVtXfvjhh3CD8Ny5c+OOpucIfvXVVyFWb2iKvpDjrbfekuOPPz7EDho0SPr27RtznI8//lgOO+yw8OyGG26QO++8M4nZJ98FAZg8q3g9EYB2hghAOzsEoI8dAtDOjwxAOzuN5AxABz/OALTDQwDa2XEGoJldvgnAFs9eljUlwP89b3hYF24BNm9PAiFQpgQQgGWKv+jLNVtP5d7GjRtl7733lkWLFhXpcNlll8nDD285dyFe9l60uNP+w4YNKzKGXjBSq1Yt+fXXX8OFI7Nnzy4iCCOde/bsKY8++mj4Y3QpcqpwIQD9JBGAdoYIQDs7BKCPHQLQzg8BaGeHAPSxIwPQwQ8B6IBHBqAVHgLQSi5xnGYAIgDTw5ZRIZApAgjADJF+7bXXpEOHDlKxYsWYb1y2bFl4PmPGjPD8nnvukd69exfpO3/+fGnatKloeW6rVq1k8uTJUrly5cI+69atC+W6n376aXjPnDlzpFGjRlu9L7oMeMiQIaJlx9FN5aKOo+9p166dvP/++ymnhAD0I0UA2hkiAO3sEIA+dghAOz8EoJ0dAtDHDgHo4IcAdMBDAFrhIQCt5BCA6SHHqBDIHgIIwAytxV577RUy+0477bRQWqt/Vnm3cuXKINj0TL+ffvopzOaII46Qd955RypVqrTV7LQcV0t3tbVs2TKU8OrZgJotOHjw4EKBmKhsd/Xq1UEgqlDU1qNHD+nSpUuYz8SJE0O57++//x7+PGXKFGnRokXKKSEA/UgRgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMAOvghAB3wEIBWePkmAJs/00u2z4JbgDes+E2+OH9LdRklwNbdSRwEypYAAjBD/FX4ffvttyW+TQXh448/Hi7giNU2b94s3bt3lyeffDLuWN26dQuXgFSoUCFun4ULF0rHjh1lwYIFMftUrVpVnn/+eTnxxBNLnLOlAwLQQq1oDALQzhABaGeHAPSxQwDa+SEA7ewQgD52CEAHPwSgAx4C0AoPAWgllzgOAZgerowKgUwSQABmiLbeoqt/aXmtnvWnmX+//fab7LzzzlKvXj1p06aNdO3atfDijZKmpReFqOSbPn16GKtmzZpy8MEHhxuCtZQ4maY3/OoZgWPGjBEVgno+oM5FxeBVV10le+65ZzLDmPogAE3YigQhAO0MEYB2dghAHzsEoJ0fAtDODgHoY4cAdPBDADrgIQCt8BCAVnIIwPSQY1QIZA8BBGD2rEW5mgkC0L/cCEA7QwSgnR0C0McOAWjnhwC0s0MA+tghAB38EIAOeAhAK7x8E4AHaAlwzapWHCmL27DyN5lJCXDKeDIQBMqCAAKwLKjzTkEA+jcBAtDOEAFoZ4cA9LFDANr5IQDt7BCAPnYIQAc/BKADHgLQCg8BaCWXOA4BmB6ujAqBTBJAAGaSNu8qJIAA9G8GBKCdIQLQzg4B6GOHALTzQwDa2SEAfewQgA5+CEAHPASgFR4C0EoOAZgecowKgewhgADMnrUoVzNBAPqXGwFoZ4gAtLNDAPrYIQDt/BCAdnYIQB87BKCDHwLQAQ8BaIWXbwJw/2cuz5oS4Fnn/yssC7cAW3cncRAoWwIIwLLlX27fjgD0Lz0C0M4QAWhnhwD0sUMA2vkhAO3sEIA+dghABz8EoAMeAtAKDwFoJZc4TkuAEYDpYcuoEMgUAQRgpkjzniIEEID+DYEAtDNEANrZIQB97BCAdn4IQDs7BKCPHQLQwQ8B6ICHALTCQwBaySEA00OOUSGQPQQQgNmzFuVqJghA/3IjAO0MEYB2dghAHzsEoJ0fAtDODgHoY4cAdPBDADrgIQCt8PJNADZ9+oqsKQGe3fWhsCyUAFt3J3EQKFsCCMCy5UIiv1kAACAASURBVF9u344A9C89AtDOEAFoZ4cA9LFDANr5IQDt7BCAPnYIQAc/BKADHgLQCg8BaCWXOE5LgBGA6WHLqBDIFAEEYKZI854iBBCA/g2BALQzRADa2SEAfewQgHZ+CEA7OwSgjx0C0MEPAeiAhwC0wkMAWskhANNDjlEhkD0EEIDZsxblaiYIQP9yIwDtDBGAdnYIQB87BKCdHwLQzg4B6GOHAHTwQwA64CEArfDyTQA2GXll1pQAz73gwbAslABbdydxEChbAgjAsuVfbt+OAPQvPQLQzhABaGeHAPSxQwDa+SEA7ewQgD52CEAHPwSgAx4C0AoPAWgllzhOS4ARgOlhy6gQyBQBBGCmSPOeIgQQgP4NgQC0M0QA2tkhAH3sEIB2fghAOzsEoI8dAtDBDwHogIcAtMJDAFrJIQDTQ45RIZA9BBCA2bMW5WomCED/ciMA7QwRgHZ2CEAfOwSgnR8C0M4OAehjhwB08EMAOuAhAK3w8k0ANn4qe0qAv7qQEmDrviQOAtlAAAGYDatQDueAAPQvOgLQzhABaGeHAPSxQwDa+SEA7ewQgD52CEAHPwSgAx4C0AoPAWgllzhOS4ARgOlhy6gQyBQBBGCmSPOeIgQQgP4NgQC0M0QA2tkhAH3sEIB2fghAOzsEoI8dAtDBDwHogIcAtMLLOwH45FWyXc2qVhwpi9uoAvCiB8J4XAKSMqwMBIGMEkAAZhQ3L4sQQAD69wIC0M4QAWhnhwD0sUMA2vkhAO3sEIA+dghABz8EoAMeAtAKDwFoJZc4DgGYHq6MCoFMEkAAZpI27yokEC0Ab3yvrVTfbQfolJLAjN/qlzKC7hECU6Y1BoaDwC5zKjiiy3dotcUbyjcAx9fvMG+pI5rQgt/XAMFK4M/N1kjiEICuPVAAPxM/FYCT178cYnM1Uy3635UakwFo2gcEQQACWxNAALIryoQAAtCPHQFoZ4gAtLPTSASgnR8C0M4OAWhnp5EIQAc/BKAdHgLLzk5/b+Fn4pdvAnDfJ67OmhLged3uz2mxatpQBEEgjwggAPNoMXPpUxCA/tVCANoZIgDt7BCAPnYIQDs/BKCdHQLQx04QgHaACCw7OwSgmR0C0IwuYaCWAKdbAC5fvlymTZsW/po+fXr466effgrz6tq1q4wcObLEj1u/fr28+eab8s4774RxFixYIKtXr5YqVarIvvvuK8cff7xccsklsvvuuycc66ijjpJJkyaV+L7w37EFBSX2mz17tjz00ENhXkuWLJGdd95ZmjRpIueee65069ZNKlasWOIY2uGFF16Qp556SmbOnCm//PKL7LbbbnLkkUdKr1695NBDD01qDGX64IMPytixY+Wbb74J82/QoIF07txZrrzySqlRo0ZS40ydOlWGDx8uH3zwgSxdulR22WUXad68uVxwwQXSpUuXpMagU2YJIAAzy5u3/S8BBKB/KyAA7QwRgHZ2CEAfOwSgnR8C0M4OAehjhwB08EMAOuCRAWiFhwC0kksclwkBuM0228SdRDICUKXYEUccEYRfoqYy8PHHH5czzzwzbrdUCsAnnngiCLo//vgj5vtU3I0bNy6heFOxecYZZ4R+sVqFChXklltukf79+yf8dpWqJ598svz4448x+9WpU0deeeUVadWqVcJxbrvtNrn11ltl8+bYx2R06tRJRo8eLTvswFFf6fmNtI2KALRxI8pJAAHoBCgiCEA7QwSgnR0C0McOAWjnhwC0s0MA+tghAB38EIAOeAhAK7x8E4CNHr8ma0qAF1x8X1iWdJ2tGC0A69WrFzLk3nrrrfDOZATghx9+GLLhtB1++OFy4oknBpGlGW0rVqyQl19+OYi/P//8U7bddlt57bXXpEOHDjG3WkQAarxm3CVq+++/f9zHmo3YsWPHIMpq164tN954oxxyyCHy888/y4gRI8KctLVt21YmTpwoKvJiNc0UHDVqVHh09NFHy1VXXSUq67788ku58847ZdGiReGZjnnxxRfHHEMzDw866CBZtmxZyDjs3bt3YKRNxeK9994rmzZtCvP87LPPZI899og5jjLs3r17eNawYUPp16+fNGvWTH744Qd54IEHwndo0zk/99xz1l9l4tJAAAGYBqgMWTIBBGDJjErqgQAsiVD85whAOzuN5AxAOz8EoJ0dAtDOTiM5A9DBjxJgOzwEoJ0dJcBmdghAM7qEgZoBmG4BOGDAADn44IPDXyqhtDxVS1O1JSMAp0yZEgSUjrPffvvF/B7NbjvllFNC2avKKy0RjpV5GBGA7dq1k/fff98EVWWaSsyFCxdK1apV5fPPPw/vjG6aGahltNqefvppOf/887d6l5Yi63y0aWbdf/7znyAwI23lypVB7H333XehDHfx4sVSvXr1rcbR0lx9hzbNztOMwug2ZsyYwqzICy+8UJ588smtxli1alVYE/3P+vXrB1FYs2bNwn4qV5WvylVtOneVm7TsIIAAzI51KHezQAD6lxwBaGeIALSz00gEoJ0fAtDODgFoZ6eRCEAHPwSgHR4C0M4OAWhmhwA0o0sYmAkBWHwCpRWAyX756aefLi+99FLorlKuZcuWW4WmQgBGC7W77rpL/vnPf271nrVr10rdunXDeX6aSagZfcXbCSecIOPHjw/ST5lo/+JNzwY8++yzw4/vvvtuufbaa4t00aw/zehTQafnIL7xxhsxcbVv3z6coajv0oxBFbHRbejQoXL99deHH/373/+Oedaf/rv+XnvtFd6lGYYRGZjs+tAvfQQQgOljy8gJCCAA/dsDAWhniAC0s9NIBKCdHwLQzg4BaGenkQhABz8EoB0eAtDODgFoZpdvAvCvI7KnBHhh9/SWABdf9HQJwGHDhsnll18eXqeSToVg8ZYKARhdtqtn7umFHbFaz5495dFHHw2P5s+fL40aNSrs9vvvv4cMOz0/UOXchAkTYo6xYcMG2XXXXeW3336TNm3ayEcffVSkn5YG9+jRI/xMZeFZZ50Vc5xokfjYY48VlvpGOmtptWZaakajllVvv/32MceJiMRKlSqJZijqpSe0sieAACz7NSiXM0AA+pcdAWhniAC0s9NIBKCdHwLQzg4BaGenkQhABz8EoB0eAtDODgFoZocANKNLGKgZgPkiAPWsu0iGnGYCnnrqqVt9eyoEoJbI6nmJevvwV199FZevZtKdc8454bmW3Wr5baS99957cuyxx4Y/xssijPTVzD49M1HP99PMwu22265wHC0tfvbZZ8OfE8lIfaZnC2rTmEjJsP5ZJeNOO+0UzglMlEUYmaueDahNv0HPLaSVPQEEYNmvQbmcAQLQv+wIQDtDBKCdnUYiAO38EIB2dghAOzuNRAA6+CEA7fAQgHZ2CEAzOwSgGV3CwHwSgHoL7quvvhq+d/bs2THPC4wIQC2B1QtJVOBt3LgxZOPpeXunnXZaKLmNlmzRADVzT7Pk9KxBfd/YsWPj8p0xY4YceOCB4XmfPn1kyJAhhX2jsxX17L/OnTvHHUcvBnnwwQdjfpeeq/jpp59KtWrVwvl9iZr20UxCjZk2bVphV2UVufBE33X//ffHHUbnGhGr+g2XXXZZejYmo5aKAAKwVLjonCoCCEA/SQSgnSEC0M5OIxGAdn4IQDs7BKCdnUYiAB38EIB2eAhAOzsEoJldvgnAho9pCXA1M49UBW5c+ass6rGlBFil0O67755w6Fjn1JV2LukoAf7iiy+CwNPz6Zo2bSqzZs2KOa2IAEw0Z71o5MUXXwwXfRRvKgwjP9eLPv71r3/FHUpLZLV8V1uXLl3C2XqRpucGDh48OPxx+vTp4VbjeE3P/lOBqE3P+NMsvUjT8mM9BzDRN0f6quRT2acxmhEYaTpm5NZkPQvwuuuuizsXlY0qELXpN2j2Iq3sCSAAy34NyuUMEID+ZUcA2hkiAO3sNBIBaOeHALSzQwDa2WkkAtDBDwFoh4cAtLNDAJrZIQDN6BIGRgvAZN6gmW/elmoBqOfoHXHEESETTpveCHzSSSfFnOYxxxwjFSpUkI4dO0rz5s2lRo0asnr16nBpiJ7XN3fu3BCnGYIqRLXcN7qprGvdunX4Ud++fWXQoEFxcaxbt0523HHH8Lz4pRnRtwTrOxs3bhx3nIcffrgw007FpGYpRpqW7mpZ8CGHHCIff/xxwqXRPvpNem6ffnOkRV9qou/SswvjNZ1r5CZmPW/xoYce8m4H4lNAAAGYAogMUXoCCMDSMysegQC0M0QA2tlpJALQzg8BaGeHALSz00gEoIMfAtAODwFoZ4cANLNDAJrRJQzMBwHYvXt3efzxx8N3du3aVUaOHBn3m7VMtnr16jGfaymwjhU5H++UU06Rl19+uUjfDz74QNq2bRt+1r9/f7ntttvivmvz5s3h1l1tet7fO++8U9i3W7du4VxAbYsWLZK999477jjaT/tr0/P+/vGPfxT21fH1PUceeaRMnjw54VrrvHX+GqPn/UWajqnnAmp74okn5KKLLoo7zuLFi6Vhw4bhuc4pwj09u5NRkyWAAEyWFP1SSgAB6MeJALQzRADa2WkkAtDODwFoZ4cAtLPTSASggx8C0A4PAWhnhwA0s8s3Abj3Y71luxpZUAL806+yuMe9YV1ysQRYS1Ajl1JoCfCkSZPChRbWpmKsWbNmhZd76L/f7rHHHoXDkQFIBqB1b6UzDgGYTrqMHZcAAtC/ORCAdoYIQDs7BKCPHQLQzg8BaGeHAPSxEwSgHSAC0M4OAWhmhwA0o0sYuDFKAOrNtqk446+kmaaqBFhLdiPlqnojr2a3Rc7cK2kOiZ5Hn7n3/PPPF97kqzGcAcgZgJ69la5YBGC6yDJuQgIIQP8GQQDaGSIA7ewQgD52CEA7PwSgnR0C0McOAejghwB0wBMpgJ+JHwLQhK3EoFwVgHqhhpbCavnrnnvuKR9++GHK5OX48ePlhBNOCOz05t7IBRz6Z70FuEqVKuGZ5xZgvTzkiiuuCON4bgHWy0M+++wz1y3AemGKZj1q4xbgEn9lsrIDAjArlyX/J4UA9K8xAtDOEAFoZ4cA9LFDANr5IQDt7BCAPnYIQAc/BJYDHgLQCi/fBGCDLCoB/vp/S4BzJQPw1VdfDRdhaLmu3lqsmX+Rc+ms+ys67vXXXw+XdsQSgPozvRhEWWnWoWYExmsqKc8555zwWM/xu/DCCwu7vvfee+FcQG1axqw36sZreuvvW2+9JRUrVpQ1a9bI9ttvX9hVz+7TM/y06c2+esNvrKbP6tSpEx5pTOScQ/3zhg0bwmUleoOyvktvBY7Xokuu9RuOPvroVCBnDCcBBKATIOE2AghAG7foKASgnSEC0M5OIzkD0M4PAWhnhwC0s9NIzgB08KME2A4PAWhnRwmwmR0C0IwuYaBmAOaSAHz33XdDdp7e/Ks3+OqZf02bNk0pnKFDh8r1118fxnzuuefk3HPPLTK+Sj2Ve9oSSTctT9YyZW3z5s2TffbZp3AcvYW3Zs2aQb61b99eJkyYEPMb9LmWNf/2229y2GGHyZQpU4r0e+yxx+SSSy4JP3vhhRfkrLPOijmOPjv77LPDM51Tjx49ivRr06aNTJ06VapWrSorVqwoIhmjO+pc33zzTalUqVLoF8mGTOkCMFipCSAAS42MgFQQQAD6KSIA7QwRgHZ2GokAtPNDANrZIQDt7DQSAejghwC0w0MA2tkhAM3sEIBmdAkDc0kAqvw67rjjQhaciirNQNOLP1LZil8C8t1330m9evWKvGL06NGFoi1e9t7atWtDSfIvv/wi++23n8yePXuraXbs2DGIP83s+/rrr2OWMEeLu+LlyDrg0qVLwyUlWgqdKHsvIu4qVKggS5Ys2SpTUMfu27dvmKPKzS5dumw1X/13/b322itkCurcNVOSlh0EEIDZsQ7lbhYIQP+SIwDtDBGAdnYIQB87BKCdHwLQzg4B6GNHCbCDHwLQAY8SYCu8fBOAez16bdbcAvzNJfeEZcnmEuD//ve/odx01apV4ZZfzUI7/PDDS7WdJk6cKC1btpTq1avHjNu4caN07969sDy2U6dOouXGxZv2a9KkiSxatCiIyM8//3yrEuRevXrJ8OHDQ+hTTz0lF1xwwVbjRJcBn3TSSfLyyy/LtttuW9hv5cqVQXCqhNQ5L168WHbZZZetxokuAx4zZoycfvrpRfroz84888zws65du8rIkSO3GuPnn3+WvffeW3799ddwpqKeK6gZlpGm0u+UU06R1157LfyI8t9Sbb20d0YAph0xL4hFAAHo3xcIQDtDBKCdnUaSAWjnhwC0s0MA2tlpJBmADn5kANrhIQDt7MgANLNDAJrRJQzUDMB0C0C9oGPhwoVFxFbkYg2VeBdffHGRORaXZSratER1+fLlod99990nf/vb3xJ+V61atUT/im467ksvvSQq24466qhwhp8KPL3YQ4WXlsbOnTs3hGjsxx9/LA0aNIj5Hr0oRAWhZt7Vrl1bbrrpJmndunXI+BsxYkR4j7YjjjhC3n///SJiL3pALcvVLD9tKjivvvrqcFbfl19+KQMHDgySUdsjjzxSWOpbfEIqblUUakmuZhNee+21hWcYjhs3Tu65555wXqKWEqusjHfTc/Stynqm4o033hguB/nhhx/k/vvvFxWo2nTOo0aNSs+GZFQTAQSgCRtBXgIIQC9BEQSgnSEC0M5OIxGAdn4IQDs7BKCdnUYiAB38EIB2eAhAOzsEoJkdAtCMLmFgJgSgirfoSydK+pKCgoIiXTRjLfoCjZLi9fmAAQPklltuKdI12Xmo9FIpp6W7iZqKvssvvzyc4xerqRDUMlk96y9eW7duXcjYU6EYq2nJbv/+/bf6luJ9P/nkE+ncuXMoCY7V9HKQsWPHyiGHHJLwm5Tb7bffLsXXIBKkpb8qN3fYYYdkloE+GSKAAMwQaF5TlAAC0L8jEIB2hghAOzuNRADa+SEA7ewQgHZ2GokAdPBDANrhIQDt7BCAZnZ5JwAfyaIS4J7pLQFOVrxFNke6BKBm92npsF52MWfOnJAxp6WveqGFZvG1atUqyDgtdY0uxU20aWfNmiUPPvig6OUkmimn5claHqwXh2hmo2bkJdM0o05F5xdffBHKnHU+Rx55ZBCMevlHMk1Lhh944IEg+r755psQohmMJ598csgsjC7pTTSenrU4bNiwcLvysmXLQvlx8+bNg4SNXCSSzHzokzkCCMDMseZNUQQQgP7tgAC0M0QA2tkhAH3sEIB2fghAOzsEoI8dZwA6+CEAHfA4A9AKDwFoJZc4LmQAplkApmfmjAoBCEQIIADZC2VCAAHox44AtDNEANrZIQB97BCAdn4IQDs7BKCPHQLQwQ8B6ICHALTCyzsB+PB1UrFGNSuOlMVtUgF46d1hvExdApKyyTMQBCAQCCAA2QhlQgAB6MeOALQzRADa2SEAfewQgHZ+CEA7OwSgjx0C0MEPAeiAhwC0wkMAWskljkMApocro0IgkwQQgJmkzbsKCSAA/ZsBAWhniAC0s0MA+tghAO38EIB2dghAHzsEoIMfAtABDwFohYcAtJJDAKaHHKNmEwG9jVkvTCmvDQFYXle+jL8bAehfAASgnSEC0M4OAehjhwC080MA2tkhAH3sEIAOfghABzwEoBVevgnAPYdnTwnwt5dRAmzdl8SljkDktuNkL2CJvHnRokVy3nnniV5eUl4bArC8rnwZfzcC0L8ACEA7QwSgnR0C0McOAWjnhwC0s0MA+tghAB38EIAOeAhAKzwEoJVc4jgtAUYApocto5aOgGbwtW7dWp5//nlp2LBhUsFPPPGEXHPNNbJmzRr5sxz/dxMCMKntQqdUE0AA+okiAO0MEYB2dghAHzsEoJ0fAtDODgHoY4cAdPArx/+S5aBWGFoAPxNGBKAJW4lBCMASEdEhQwRUAG6zzTay4447yv333y/dunWL++affvpJunfvLq+88ooUFBRI5cqVgwQsrw0BWF5Xvoy/GwHoXwAEoJ0hAtDODgHoY4cAtPNDANrZIQB97BCADn4ILAc8MgCt8PJNANYf1idrbgH+rtfQsCzcAmzdncSlgsADDzwgN9xwg6xfvz6IwJNPPllGjBghNWrUKDL8m2++KRdeeKEsW7YsyL/mzZuHrMH99tsvFdPIyTEQgDm5bLk/aQSgfw0RgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMAOvghAB3wEIBWeAhAK7nEcZoBiABMD1tGLT2BOXPmyDnnnCMzZ84MEnC33XaTkSNHyt///nf5448/5LrrrpPhw4cH8acZg71795aBAwfKdtttV/qX5VEEAjCPFjOXPgUB6F8tBKCdIQLQzg4B6GOHALTzQwDa2SEAfewQgA5+CEAHPASgFR4C0EoOAZgecoyaLgIbN26Ufv36yb333htEn4rAiy++WD788EP56quvws/q1asnTz/9tBx11FHpmkZOjYsAzKnlyp/JIgD9a4kAtDNEANrZIQB97BCAdn4IQDs7BKCPHQLQwQ8B6ICHALTCyzsB+K/rs6cE+PIhYVkoAbbuTuLSQWDixIlywQUXhH2pEjAiA7t06RKyAKtVq5aO1+bkmAjAnFy23J80AtC/hghAO0MEoJ0dAtDHDgFo54cAtLNDAPrYIQAd/BCADngIQCs8BKCVXOK4UAKMAEwPXEZ1EVC3oOXAH330UeE4VatWlbFjx0q7du1cY+dbMAIw31Y0R74HAehfKASgnSEC0M4OAehjhwC080MA2tkhAH3sEIAOfghABzwEoBUeAtBKDgGYHnKMmk4Co0ePlksvvVRWrVoVMv/22WcfmT9/fsgE1LP/9CzA22+/XSpWrJjOaeTM2AjAnFmq/JooAtC/nghAO0MEoJ0dAtDHDgFo54cAtLNDAPrYIQAd/BCADngIQCu8fBOA9R7KnhLg76+gBNi6L4lLLYHff/9devXqJc8991wQf3q5h4q+Pn36yIQJE6Rbt27h9l8VgS1atAi3/zZu3Di1k8jB0RCAObho+TBlBKB/FRGAdoYIQDs7BKCPHQLQzg8BaGeHAPSxQwA6+CEAHfAQgFZ4CEArucRxWgKMAEwPW0YtHYEpU6bIeeedJ998802Qf/vuu28QfAceeGDhQCtXrpSLLrpIxo0bFyTgDjvsIEOGDAnSsDw3BGB5Xv0y/HYEoB8+AtDOEAFoZ4cA9LFDANr5IQDt7BCAPnYIQAc/BKADHgLQCg8BaCWHAEwPOUZNNQEt51Xxp3/16NFD7rvvPqlcuXLM1zzyyCOhDHjt2rVBBB5//PEyfvz4VE8pZ8ZDAObMUuXXRBGA/vVEANoZIgDt7BCAPnYIQDs/BKCdHQLQxw4B6OCHAHTAQwBa4eWdAHwwi0qAr6QE2LoviUsdAT3br2bNmvLEE09Ip06dShxYzwQ899xz5bPPPgsS8M9y/N9NCMAStwsd0kEAAeinigC0M0QA2tkhAH3sEIB2fghAOzsEoI8dAtDBrxz/S5aDWmFoAfxMGBGAJmwlBoUSYARgiZzokH4CHTp0kJEjR0rt2rWTftmmTZtkwIABMnjwYNG/L68NAVheV76MvxsB6F8ABKCdIQLQzg4B6GOHALTzQwDa2SEAfewQgA5+CCwHPDIArfAQgFZyieMQgOnhyqiZJfDRRx/J4YcfntmXZtHbEIBZtBjlaSoIQP9qIwDtDBGAdnYIQB87BKCdHwLQzg4B6GOHAHTwQwA64CEArfDyTQDWfaCvVKxRzYojZXEqAP/fVYPDeN9//73UrVs3ZWMzEAQgkBkCCMDMcOYtxQggAP1bAgFoZ4gAtLNDAPrYIQDt/BCAdnYIQB87BKCDHwLQAQ8BaIWHALSSSxyHAEwPV0aFQCYJIAAzSZt3FRJAAPo3AwLQzhABaGeHAPSxQwDa+SEA7ewQgD52CEAHPwSgAx4C0AoPAWglhwBMDzlGzQQBvdjjl19+kXXr1oXbgRO1+vXrZ2JKWfkOBGBWLkv+TwoB6F9jBKCdIQLQzg4B6GOHALTzQwDa2SEAfewQgA5+CEAHPASgFV5eCsC/ZEEJ8M+UAFv3JHHpIbBy5Up56KGHZOzYsTJnzhzZvHlziS/SW4C5BKRETHSAQGoJIAD9PBGAdoYIQDs7BKCPHQLQzg8BaGeHAPSxQwA6+CEAHfAQgFZ4CEArucRxmxCA6QHLqCYCU6ZMkVNPPVVWrFhRYsZf9AtUAGq2YHltZACW15Uv4+9GAPoXAAFoZ4gAtLNDAPrYIQDt/BCAdnYIQB87BKCDXzn+lywHtcLQAviZMCIATdhKDEIAloiIDhki8NNPP0njxo1F/3PnnXeWiy++WKpXry633HKLqOB7/PHHQznwp59+Kq+88oqsX78+3PzbrVu3MMOuXbtmaKbZ9xoEYPatSbmYEQLQv8wIQDtDBKCdHQLQxw4BaOeHALSzQwD62CEAHfwQWA54ZABa4eWbANzj/n9KxSwpAV5y9aCwLNwCbN2dxKWCwK233ir6V6VKlYLka9q0qcyePVuaNWsWBGB0ht/SpUvlnHPOkUmTJsl1110ngwdvucm6vDYEYHld+TL+bgSgfwEQgHaGCEA7OwSgjx0C0M4PAWhnhwD0sUMAOvghAB3wEIBWeAhAK7nEcZoBiABMD1tGLR2BQw89VKZPny49e/aUYcOGheB4AlCf6cUgzZs3l0WLFsnbb78txxxzTOlemEe9EYB5tJi59CkIQP9qIQDtDBGAdnYIQB87BKCdHwLQzg4B6GOHAHTwQwA64CEArfDyTgDel0UZgNeQAWjdl8SljkDNmjVDie+LL74op5xyShhYLwHZf//9Qwbghg0bZNttty3ywocfflh69eolp59+uowePTp1k8mxkRCAObZg+TJdBKB/JRGAdoYIQDs7BKCPHQLQzg8BaGeHAPSxQwA6+CEAHfAQgFZ4CEArucRxIQMQAZgeuIxagnqeCwAAIABJREFUKgLbb799KPPVLMADDzwwxC5evFj++te/BgGoZwPqmYDRTfsecsghUq9ePfn2229L9b586owAzKfVzKFviRaAR4zuJjvsWiWHZp8dU/32o/rZMZEcnMX2q3Jw0lk05XrPzM+i2eTWVAo2bsqtCWfTbBEJrtUo2MTeMwNk75nRaeCb6593xRMMgdISiP73jFw9qy76G/YgA7C0W4D+eU7gL3/5i/z6668ydepUad26dfjaVatWif5cBeBnn30mLVq0KELhgw8+kHbt2oVzA7UkuLw2BGB5Xfky/m4EoH8BEIB2hghAOzuNRADa+SEA7ewECeOAJ4IAdOBj7zngIQBd8Ag2Ecg/AXhD9lwCcs1dYU1yVayaNhRBWUdApZ9Kvv/5n/8JJb2RVqdOHVm2bJncfffdcs011xSZt17+ccMNNwRJuHLlyqz7pkxNCAGYKdK8pwgBBKB/QyAA7QwRgHZ2CEAfOwSggx8SxgEPAeiCx95z4SMD0IWPYAMBBKABWhIhW0qAEYBJoKJLmglcccUVMnz48K1u9b3oootk5MiRUrt27XDr7z777BNmMm3aNOnQoUPIEjzuuONkwoQJaZ5h9g6PAMzetcnrmSEA/cuLALQzRADa2SEAfewQgA5+SBgHPASgCx57z4UPAejCR7CBAALQAC2JEARgEpDokhEC48aNk5NOOkkaNmwoCxYsKHznrFmzwpmAej6gXgKiN/+uXbtW5s+fH36m5cGvv/66tG/fPiPzzMaXIACzcVXKwZwQgP5FRgDaGSIA7ewQgD52CEAHPySMAx4C0AWPvefChwB04SPYQCDvBOC9WVQC3JsMQMOWJCTFBDZu3Cjdu3cPUu+2226TBg0aFL7hiSeekEsvvVQ2xTj7+NZbb5X+/funeDa5NRwCMLfWK29miwD0LyUC0M4QAWhnhwD0sUMAOvghYRzwEIAueOw9Fz4EoAsfwQYCCEADtCRCQgYgAjAJUnQpawLz5s0LpcCzZ88OIrBRo0Zy3nnnSatWrcp6amX+fgRgmS9B+ZwAAtC/7ghAO0MEoJ0dAtDHDgHo4IeEccBDALrgsfdc+BCALnwEGwggAA3QkghBACYBiS4QyHICCMAsX6B8nR4C0L+yCEA7QwSgnR0C0McOAejgh4RxwEMAuuCx91z4EIAufAQbCOSdALwni0qAr6UE2LAlCYFA1hBAAGbNUpSviSAA/euNALQzRADa2SEAfewQgA5+SBgHPASgCx57z4UPAejCR7CBAALQAC2JkJABiABMghRdIJC9BBCA2bs2eT0zBKB/eRGAdoYIQDs7BKCPHQLQwQ8J44CHAHTBY++58CEAXfgINhBAABqgJRGCAEwCEl3KhMDq1avl66+/Fv1PvRikpNa2bduSuuTtcwRg3i5tdn8YAtC/PghAO0MEoJ0dAtDHDgHo4JfE/6BzjJ73oQUxbsPL+49O1Qey91wkEYAufAQbCOSdALxbS4CrG0ikNmTTz6tkyXWUAKeWKqNZCRQUFMiIESPk4YcflpkzZyY9zDbbbBPzhuCkB8jxjgjAHF/AXJ0+AtC/cghAO0MEoJ0dAtDHDgHo4IeEccAjA9AFj73nwocAdOEj2EAAAWiAlkQIAjAJSHTJCIGNGzdK586d5Y033gjvUxmYbFMBmEyWYLLj5Vo/BGCurViezBcB6F9IBKCdIQLQzg4B6GOHAHTwQ8I44CEAXfDYey58CEAXPoINBBCABmhJhCAAk4BEl4wQGDRokPTr1y+8a88995SuXbtK8+bNpXr16lKhQoUS59CuXbsS++RrBwRgvq5sln8XAtC/QAhAO0MEoJ0dAtDHDgHo4IeEccBDALrgsfdc+BCALnwEGwjkmwCsMzR7SoB/6EMJsGFLEpJiAk2bNpW5c+fKYYcdJu+8845Urlw5xW/I3+EQgPm7tln9ZQhA//IgAO0MEYB2dghAHzsEoIMfEsYBDwHogsfec+FDALrwEWwggAA0QEsiRDMAEYBJgKJL2gnsuOOO8scff8grr7wiJ554Ytrfl08vQADm02rm0LcgAP2LhQC0M0QA2tkhAH3sEIAOfkgYBzwEoAsee8+FDwHowkewgQAC0AAtiRAEYBKQ6JIRAnvssYcsXbpUPvvsM2nRokVG3pkvL0EAZsFKXn/99TJ06NDCmUycOFGOOuqohDPTAy8fe+wxmTZtmqxYsUJ23XVXad26tfTo0UPat2+f1FetXbtWhg0bJmPGjJGFCxfKhg0bpF69enLCCSfIlVdeKfXr109qHEsnBKCFWtEYBKCdIQLQzg4B6GOHAHTwQ8I44CEAXfDYey58CEAXPoINBPJOAA7JohLg6ykBNmxJQlJMoGPHjvLmm2/KSy+9FC4DoSVPAAGYPKu09Pziiy+kVatWRa6iTiQA9Yabnj17BvkXr6kEfOSRR0RvuInXFi1aFETfvHnzYnapVq2ajBo1SvSXKx0NAeinigC0M0QA2tkhAH3sEIAOfkgYBzwEoAsee8+FDwHowkewgQAC0AAtiZCQAYgATIIUXdJNYPz48aH0V33Ga6+9lu7X5dX4CMAyXM7NmzfLoYceKtOnT5datWrJ8uXLw2wSCcAbb7xR7rzzztCvZcuWotmDDRs2FBV6Q4YMkRkzZoRn2u+OO+6I+XW///67HHzwwfLVV1+F5927d5cuXbqEwzP13XfddZdoH62tnzp1qhxwwAEpp4QA9CNFANoZIgDt7BCAPnYIQAc/JIwDHgLQBY+958KHAHThI9hAAAFogJZECAIwCUh0yRiBq6++Wh588EHp3bu3DB48WLbddtuMvTuXX4QALMPVu//+++Waa66Rxo0byymnnBLEWyIBqGW6TZo0CdmCmjU4efLkIjfeaEmvXmn96aefSsWKFYPgUzlYvN1yyy1y6623hh+rNOzTp0+RLir92rZtG95z9NFHy3vvvZdySghAP1IEoJ0hAtDODgHoY4cAdPBDwjjgIQBd8Nh7LnwIQBc+gg0E8k4ADu4nFf9S3UAitSFBAPbdkojy/fffS926dVP7AkaDQCkJ3HvvvXLTTTeFZKpTTz1V9tlnn5DEVFI7//zzS+qSt88RgGW0tPoPzf322y9k2mnW3fvvv18o5eJlAPbq1UuGDx8eZqySTrMHi7ePP/44XIet7fLLL5eHHnqoSJeNGzeGX5BVq1YFmThr1iypUKHCVuNomfGjjz4afq5C8aCDDkopKQSgHycC0M4QAWhnp5H1npnvG6AcRyMAHYuPhHHAQwC64LH3XPgQgC58BBsIIAAN0JIIQQAmAYkuGSOgyU+aQKVZgOpUkm16TJomOpXXhgAso5Xv1KmTjBs3Trp27SojR46U6Ky8WAJQz/7TCzqWLFkSMgbnzp0bd+b6XM/20/9X5rvvvityFuDbb78txx13XIgdNGiQ9O3bN+Y40SKxX79+MnDgwJSSQgD6cSIA7QwRgHZ2CEAfOwSggx8SxgEPAeiCx95z4UMAuvARbCCAADRASyIEAZgEJLpkhIDKP3UamhSlTV1Jsk0F4J/l+L/XEYDJ7pQU9hs9erScddZZ8pe//CWU6eoNviUJwMWLFxeW815yySXhko94TZ9HLgnRuAYNGhR2vfnmm+X2228Pf46XRajP1IpXr15d1qxZE8qBJ02alEICIghAP04EoJ0hAtDODgHoY4cAdPArx/9jzUGtMLSgHP+/3W5+7D0XQgSgCx/BBgL5JgD30BLgXbKgBPiXVbKEEmDDjiQk1QT0rgP1Gtq0KlL9h95boP4iVnVj8ffvueeeqZ5SzoyHAMzwUkVKb5cuXSojRoyQiy++OMygJAH4+uuvh5tutN13332ih17Ga/pcD8PUpnHRN/meccYZ8uKLL4Znv/zyS/glideaN28uM2fODIIyckFJqnAhAP0kEYB2hghAOzuNpATYzg8BaGcnSBgHPDIAXfDYey58CEAXPoINBBCABmhJhGxCACZBiS6ZIKBHqWnFY4cOHeTVV19NSvplYl658A4EYIZXqUePHkH8tWnTRj788MPC8tySBKBm/F166aVhtmPGjJHTTz897sxV8Kno06ZxasQjTQ35J598IjvttFOJtfIqHFUgalu/fr1UqlQpaVr6X7yJ2o8//iitW7cOXY4Y3U122LVK0mPTcQsBBKB9JyAA7ew0EgFo54cAtLNDADrYaXkMGYB2gAhAOzsRQQC68BFsIJB3AnBQFmUA/pNLQAxbkpAUE9CLPv744w9544035O9//3uKR8/v4RCAGVxfFX5aTqtXVH/++efSrFmzwreXJACHDh0q119/feg/YcIEad++fdyZ6/NI1t/dd98t1157bWHfpk2bypw5c6R27dqiWYiJmpYpa7mytpUrV0qNGjWSpqW19ck2BGCypIr2QwDauGkUAtDODgHoY4cAdPBDwjjgIQBd8Nh7LnwIQBc+gg0EEIAGaEmEhAxABGASpOiSbgJ77LFHcBl6WWnLli3T/bq8Gh8BmKHl3LBhg7Ro0SJc3tGnTx8ZMmRIkTeXJAD13L5Infu7774rxxxzTNyZv/fee3LssceG5xqnV2NHWsOGDUXPBdQLRfSCkERNr8d+9tlnQ5fSXvWOAEz/xkIA2hkjAO3sEIA+dghABz8kjAMeAtAFj73nwocAdOEj2EAAAWiAlkQIAjAJSHTJCIHOnTvLa6+9JqNGjQp3K9CSJ4AATJ6Vq2dE8NWvXz9k4GkJbnQrSQDmWgYgJcCu7ZJUMAIwKUwxOyEA7ewQgD52CEAHPySMAx4C0AWPvefChwB04SPYQCDvBOBdN2bPJSA3DAwrUtrkEMMyEgKBuAQmTpwof/vb38KxapMnTy48Vg1kJRNAAJbMyN1Db/rVCzU0C/CVV16Rk046aasxSxKAuXYGYEnQuASkJEIlP0cAlswoXg8EoJ0dAtDHDgHo4IeEccBDALrgsfdc+BCALnwEGwggAA3QkggJGYAIwCRI0SUTBAYNGiT9+vULdx88+uijCS83zcR8cuUdCMAMrJRewvHYY4/J3nvvLQMHbvl/TYo3vbjjpZdeCj/u37+/6M022jp16hSyBceNGxf+XpvnFmC9PCTyHm4BzsDip/EVCEA7XASgnZ1GcgmInR8C0M6OS0Ac7LgExAcPAejihwB04SPYQAABaICWRAgCMAlIdMkIgdtuuy28Z/z48TJt2rTgS/QykH322Uf0gpCSWuRotZL65eNzBGAGVvWCCy6Qp59+2vSmr7/+Wvbaa69wbp+e36dNhaJmBMZrEeGozzWuQYMGhV11s+u5gNqmTp0qeitwrLZp06Zg0desWRMuLpk0aZJp/vGCyAD040QA2hkiAO3sNBIBaOeHALSzQwA62CEAffAQgC5+CEAXPoINBPJOAN6ZRSXA/SgBNmxJQlJMoEKFCkXKfgsKCkpVBvxnOf7vdQRgijdjrOFSIQB1U9etW1d++OEHady4cbhMJF5r0qSJaNmx3o6j5zNEX8jx1ltvyfHHHx9CNW22b9++MYf5+OOP5bDDDgvPbrjhBrnzzi1XvqeqIQD9JBGAdoYIQDs7BKCPHQLQwa8c/481B7XC0IJNm1IxTPkcg73nWncEoAsfwQYCCEADtCRCQgYgAjAJUnRJNwEVgJ62efNmT3hOx5ZrAai34P7nP/+RhQsXim4izZTTMttIpl0mV7akMwB1Lpdddpk8/PDDYVrxsveixZ32HzZsWJHP0HMIa9WqJb/++quoKJw9e3ZMW96zZ89QS69N02oPPvjglOJAAPpxIgDtDBGAdnYIQB87BKCDHxLGAY8zAF3w2HsufAhAFz6CDQQQgAZoSYQgAJOARBcIZDmBvBKAWrb65JNPBuTNmjUrzGCLtQZaN67n8WlMdFMReOWVV8o999yT0aVLRgDOnz9fmjZtGubcqlWrcONN5cqVC+e5bt26UK776aefSsWKFcNtw40aNdrqO6LLgIcMGSJ9+vQp0kfloo6j72nXrp28//77KWeBAPQjRQDaGSIA7ewQgD52CEAHPySMAx4C0AWPvefChwB04SPYQCDvBODALCoBvpESYMOWJAQCWUMgrwSgZr/pVdBa8qqlrscee2xM0EOHDo1b+qoBGt+7d2/RfplqyQhAnYuW42rprraWLVuG79CMxUWLFsngwYNlxowZ4Vmist3Vq1cHgahCUVuPHj2kS5cuQSbqldpa7vv777+HP0+ZMkVatGiRcgwIQD9SBKCdIQLQzg4B6GOHAHTwQ8I44CEAXfDYey58CEAXPoINBBCABmhJhIQMQARgEqTokmsEvvnmG7nooouCB3r33Xdzbfqlmm9eCUAVYCq+9txzT9HLM2I1PUPvr3/9q6xfvz48Pvzww8Ni16lTR6ZPnx4y/7Q8dtttt42bQVcqwkl2TlYAar169+7dCzMdYw3frVu3cOtwotp4LXvu2LGjLFiwIOYMq1atKs8//7yceOKJSX5B6bohAEvHK1ZvBKCdIQLQzg4B6GOHAHTwQ8I44CEAXfDYey58CEAXPoINBBCABmhJhCAAk4BEl5wkoMeiaQWpCsB8vyAkrwSgyqoJEybIFVdcIffff3/MzXfHHXeIlsDq4p588sny0ksvFTkD74svvgg34+pZef369Su8MTfdOzlZARiZh155rZJPpeXKlSulZs2a4Zw+vQG4Q4cOSU1Xb/jVMwLHjBkTzkHUb65Xr14Qg1dddVUQqelqCEA/WQSgnSEC0M4OAehjhwB08EPCOOAhAF3w2HsufAhAFz6CDQTyTgDekUUlwDdRAmzYkoRkOQEEYJYvULzpHXDAAeFSi1GjRslZZ50Vs9shhxwSpJkKwHnz5oVswOJNJdqIESPC+XdaEktLPQEEoJ8pAtDOEAFoZ4cA9LFDADr4IWEc8BCALnjsPRc+BKALH8EGAghAA7QkQkIGIAIwCVJ0yTUCCMBcW7H/ne9uu+0mK1asCOfWqegr3tauXSvVq1cPaZ2a4vnf//435pfqzcCnnXaa6HhaMkxLPQEEoJ8pAtDOEAFoZ4cA9LFDADr4IWEc8BCALnjsPRc+BKALH8EGAghAA7QkQhCASUCiS04SQADm5LKJVKpUKdxcqxdhaDZg8aa35h511FEh+69nz56h/DVW+/zzz8MlGTvssIOoNKSlngAC0M8UAWhniAC0s0MA+tghAB38kDAOeAhAFzz2ngsfAtCFj2ADgbwTgLffJBV3qW4gkdqQIAD73xEG/f7776Vu3bqpfQGjQaCMCCAAywi897W77LKL/Pbbb3FvAI7c/qsC8JFHHgmXacRqKhAPOugg2W677eSPP/7wTov4GAQQgP5tgQC0M0QA2tkhAH3sEIAOfkgYBzwEoAsee8+FDwHowkewgQAC0AAtiRAEYBKQ6JKTBBCAOblsIi1btpSZM2fKrbfeKjfddNNWX3HCCSeES0JUAOplH/vvv3/ML33rrbekffv24WKN5cuX5yiN7J42AtC/PghAO0MEoJ0dAtDHDgHo4IeEccBDALrgsfdc+BCALnwEGwggAA3QkghBACYBiS45SQABmJPLJuH2Xy3rrVOnjsydO1eqVKlS+CXffvutNGrUKJz/p881bTleGzJkiPzzn/8Mt+p+8sknOUoju6eNAPSvDwLQzhABaGeHAPSxQwA6+CFhHPAQgC547D0XPgSgCx/BBgL5JgDr3pY9JcD/72ZKgA1bkpAsJ4AAzPIFijc9vdRDS3e1tWjRQgYOHCgNGzYMMrBv377h1l/N/rvhhhvkjju2/MMrVjv++OPlnXfekW7dusljjz2WozSye9oIQP/6IADtDBGAdnYIQB87BKCDHxLGAQ8B6ILH3nPhQwC68BFsIIAANEBLIkQzABGASYCiS84RQADm3JL934Qvv/xyGT58eBB9xVtBQYHUrl1b5syZI3peYKz2448/Sv369WXz5s3y9NNPyz/+8Y8cppG9U0cA+tcGAWhniAC0s0MA+tghAB38kDAOeAhAFzz2ngsfAtCFj2ADAQSgAVoSIQjAJCDRJScJIABzctm2TFrFnZYC6yUfKvyi22677SavvPJKKO2N1zRTUC8LqVixoqgMrFGjRg7TyN6pIwD9a4MAtDNEANrZIQB97BCADn5IGAc8BKALHnvPhQ8B6MJHsIFA3gnAW7OoBHgAJcCGLUlIlhNAAGb5AiUzPc3ye/XVV0XP/tt+++3DBSFnnHGG7LTTTgnD9QKR33//XXbffXfp3bt3Mq+ij4EAAtAArVgIAtDOEAFoZ4cA9LFDADr4IWEc8BCALnjsPRc+BKALH8EGAghAA7QkQkIGIAIwCVJ0yTUCCMBcWzHmm3MEEID+JUMA2hkiAO3sEIA+dghABz8kjAMeAtAFj73nwocAdOEj2EAAAWiAlkQIAjAJSHTJSQIIwJxcNiadSwQQgP7VQgDaGSIA7ewQgD52CEAHPySMAx4C0AWPvefChwB04SPYQAABaICWREgmBODy5ctl2rRp4a/p06eHv3766acwu65du8rIkSOTmOn/dXnjjTfCpZ463ooVK2TXXXeV1q1bS48ePaR9+/ZJjbV27VoZNmyYjBkzRhYuXCgbNmyQevXqyQknnCBXXnlluD8gmaaS6aGHHgqXjS5ZskR23nlnadKkiZx77rnh8lE9giyZ9sILL8hTTz0lM2fOlF9++UX0mLMjjzxSevXqJYceemgyQwSmDz74oIwdO1a++eabcHRagwYNpHPnzuGbkj0GberUqeH+hQ8++ECWLl0a7llo3ry5XHDBBdKlS5ek5pINnX744Qfp169fuEdC2eZz26ag+EF5efK1+svw3nvvhRLgP//8U+rUqSPt2rWTunXr5skX5vZnIAD964cAtDNEANrZIQB97BCADn5IGAc8BKALHnvPhQ8B6MJHsIEAAtAALYmQTAjAWBd5RqZWGgGoiqNnz55B/sVrKgH13oBE71y0aFEQffPmzYs5TLVq1WTUqFHSsWPHhASfeOKJIOj++OOPmP1U3I0bNy6heFu/fn040kz7xWoVKlSQW265Rfr3759wLipVTz755HDfQaym3kTvTWjVqlXCcW677TbR49P0DoZYrVOnTjJ69GjZYYcdkthddMkUgbwTgPrLfuONN8r999+/1S+Y/nKridZfdDXutLIjgAD0s0cA2hkiAO3sNLLeM/N9A5TjaASgY/GRMA54CEAXPPaeCx8C0IWPYAOBfBOA9fQSkOrVDSRSG7Jp1Sr5Ps1nAEbLOM2y0wy5t956K3xIaQSg+oA777wzxOldANdff700bNhQVOgNGTJEZsyYEZ5pvzvu2HKxSfGm9wLo5aFfffVVeNS9e/fgEipXriwTJ06Uu+66K9wdsOOOO4pmwh1wwAExx3nzzTeDIFRRVrt27fDOQw45RH7++WcZMWKEvPzyyyGubdu2YVwVebGaZgqqbNR29NFHy1VXXRWSnL788svwrfpt2nTMiy++OOYYmnl40EEHybJly0LGod55cOKJJ4a+Khbvvfde2bRpU5jnZ599JnvssUfMcR5//PHAQ5ty1ey5Zs2aiWbSPfDAA+E7tOmcn3vuudRuRBGZO3dukJSaeTh//vyQCbl69WqpWrWq/OUvf5F9991XjjjiiCA69e9p/0cg7wSgbsQnn3xyqxuAI5+s/1A57LDDZPLkyXF/udgg6SeAAPQzRgDaGSIA7ew0EgFo54cAtLMTJIwDHgLQBY+958KHAHThI9hAAAFogJZESCYE4IABA4J0079UQml5qpamaktWAGqZropDFVmaxab/3q/SLtK0pFcrAz/99NMgwVTwqcQq3jSbTjPctKk07NOnT5EuKv1U2ul7VMhp9WHxps90LjonlVOff/75Vu/SzEAto9X29NNPy/nnn7/VOJMmTZKjjjoq/Fwz6/7zn//ItttuW9hv5cqVQex99913oQx38eLFUj2GNNbSXH2HNs3O04zC6KZlzmeeeWb40YUXXhi8SvG2atWqsCb6n1r+rKKwZs2ahd20+vKUU06R1157LfxM566cUtG+//77ID71stfoQtbov4+WyPr3OhdNDosnM1Mxr1waI68E4JQpU4Lp1YXWX2bdvJpOq3+vNfLPP/+8/Pbbb+G5/pJdcsklubRWeTVXBKB/ORGAdoYIQDs7jUQA2vkhAO3sEIAOdoIAdNFDALrwIQBd+Ag2EEAAGqAlEZIJAVh8GhYBGC3UVNLFOhfv448/DklB2i6//PJwNl9027hxo9SqVStILhV4s2bNipk8pGXGjz76aAhVoagSLrpFCzXNGPznP/+5FWkVknpMmWax7b///iGjr3jTMuTx48cH6adMYh1rpmcDnn322SH07rvvlmuvvbbIMJr1pxJMBd3xxx8vej5irKZnI2rWor5LMwZVxEa3oUOHhoxKbf/+979jnvWnv4N77bVXeJdmGEZkYBLbLG6XTz75JGT06VmO0cJPMyarVKkSKjw1C1D/Ki4E9ZxEncOBBx7omUJexOaVANTsP62v1zTcd999N6TWRjc9D1APyNSNrP+vgv7i08qGQPR/MTe69GbZrmrZp7WXDQn7W3eZ/6c9uJxHVvloS4o8zUagYH3s80tso5WvqIJNm8rXB/O1WUXgzTXPZNV8mAwEIACBdBDIOwF4S//sKQG+5fawZJqJlYmz9UsrAFX8aOmw/vt+48aNQ6lovKbP9Ww//Q7NnIvOHHv77bfluOOOC6GDBg2Svn37xhwmWiRqGezAgQOL9Isu29Uz91RExWrRIlFLWhs1alTYTcuMNcNOzw9UOTdhwoSYY+jlJHrJiSY8tWnTRj766KMi/bQ0WM891Kay8Kyzzoo5TrRI1DMUI6W+kc6HH364aOKVZjSqjNt+++1jjhMRiZUqVRLNUPQcwaaXlmiJsUrMyBpfeuml8re//S1cOrLddtsVzkE5fPHFF6JrqEe/6T8PtOk66881Q7I8t7wSgGrM9Ze7yNKbAAAgAElEQVRczXrxX77IImsW4HnnnRc2qtrh6M1SnjdCpr8dAegnjgC0M0QA2tlpJALQzg8BaGdHpJ8AAtDPkBEgAIHsJ4AATM8ahQzALBeAWvoaKefVaj8VQPGaPo9cEqJxkVJj7X/zzTfL7bdvkZ3xsgj1mZb4aqntmjVrQpmrlrtGNy2RVVmq59BFzhKMNR/NpDvnnHPCIy271fLbSNPS4mOPPTb8MV4WYaSvZvbpmYlaAamZhdGuQ0uLn3322dA1kYzUZ3q2oDaNiZQM659Vru20007huxNlEUbmqlJUm36Dlklb20UXXRRugFZJq+umJb3xxGP0O1SaXn311SFLU2P1tuVEF8NY55dLcXklAPWXT6WeZv9FauSLL4baZ03n1Q2gGYGZ+H8ucmlDZGquCEA/aQSgnSEC0M5OIxGAdn4IQDs7Iv0EEIB+howAAQhkPwEEYHrWKBcE4Ouvv154qcV9990X5E+8ps/1EgxtGhd9k6+ejffiiy+GZ1qaG+s8vci4moGmx41p9t3y5csLX6eZe5olpxlrWro6duzYuHPRS0ki5al61qCeORhpw4YNC2XK2vTsv86dO8cdR8/He/DBB8Pz2bNny3777VfYVysgtUxZby7W0uZETftoJqHGTJs2rbCrjqlJV9r0XSri4jWd66mnnhoe6zdcdtllpo2pl6Wos1GZpxmVzzxT+mqGf/zjH+ECFT0LUrNDE62naZI5FJRXAjByEKb+AjZt2jTuMmiduApA3cCa+kvLPAEEoJ85AtDOEAFoZ6eRCEA7PwSgnR2RfgIIQD9DRoAABLKfQN4JwAFZVAJ865asOJVCu+++e8LNkIpEm9KWAGvGn5aGatPz904//fS4c1TBF7kEQ+Oi7wfQcwP1zDnNdlORl6jpGXcqELWtX79etORVm2b86fmB2vRcwn/9619xh9EkJRWI2vSmYc0IjDStbhw8eHD44/Tp08PFJvGanv0XuaxEz/jTLL1I0/JjLaFVT6JnGiZqKvnUlWiMZgRGmo7ZoUOH8Ec9C/C6666LO4zKRhWI2vQbNHvR0rR0WddGj3nTUm295be0TUuINRtT10czADUTsLy2vBKAEbGnB2dG2+7ii5tsv/K6KTLx3QhAP2UEoJ0hAtDOTiMRgHZ+CEA7OyL9BBCAfoaMAAEIZD8BBGB61ihkAP6vAEzmDdEXMSTTP1af0grA6Asq9Kw8PYcuXtPnkay/4pdmqCSbM2dOuABj6dKlCaevZ+npjbraVOTVqFEj/L3KutatW4e/1zME9SzBeG3dunVBcGkrfmlG9KUmetxZogSmhx9+uDDTTgXnaaedVvhKlZlaFqz3JJR0F4L2UckbuVgjMkj0pSb6Lj27MF7TuUacTKyLVpLdE5F7HjT7L1LCnGxsdD89Bk6zAMt7GTACMCot1rKRiLERQADauEVHIQDtDBGAdnYaiQC080MA2tkR6SeAAPQzZAQIQCD7CSAA07NGuSAA9dw+Pb9Pmx4Ldswxx8SFEX22nsbddNNNhX31HEE9F1AvFNGss0Qt+my96MtRPvjgg3AuoLb+/fvLbbfdFneYzZs3h1t3tel5f++8805hXxVWei6gtkWLFsnee+8ddxztF8luU1mmpa+RpuPre/RS1MmTJyf8Jp23zl9j9Ly/SNMx9Xu16eWrejZfvBZ9HqPO6fHHHzdtTM141BLp4cOHF8nSLO1geg6gZodqqbVmJ5bXlpcCUA++jL45p/ji6pXUWgJcUj+N01RRWuoJIAD9TBGAdoYIQDs7jUQA2vkhAO3siPQTQAD6GTICBCCQ/QTyTgDenEUlwLdldwkwGYD5lwGoPkbP7dNbfRMJ3ZL+yaTCV28N3mOPPcLFLOW15aUATNViqiSMNt6pGpdxJFzHrf+PirZGl94s21WtDpZSEkAAlhJYVHcEoJ0dAtDJLur/RfWNRDQESk8AAVh6ZkRAAAK5RwABmJ41CxmA/ysAozPd0vO2LaOWtgSYMwDz7wxAvZBEz2HUrL2WLVuat9vnn38ezk+sUqWK/Prrr+Zxcj0w7wRgKhdEBeCff/6ZyiEZ638JIAD9WwEBaGeIALSzQwA62SEAfQCJdhFAALrwEQwBCOQIAQRgehYqFwTguHHjpFOnTgGA5xZgvTzkpZdeCuN4bgFW2aTNcwuwXh5yxRVXhHE8twCr/Prss89ctwDr5SHNmjULc8nULcCR0uiS7nkoadfrpSY69/LuePJKAF544YUlrXupnz/11FOljiGgZAIIwJIZldQDAVgSofjPEYB2dghAJzsEoA8g0S4CCEAXPoIhAIEcIZBvArB+/+wpAf7u9i0lwNmaARh97pzeHKsZgfGaPtcbYbVpXIMGDQq76jmCei6gtqlTp4reChyrabVg9erVZc2aNeG8v0mTJhXppuWrymrfffcNtwLHa3rr7znnnBMe6zl+0V4j+qxCvUlXb9SN1/TWXz3mrGLFimFO22+/fWHX6LMK9WZfveE3VtNnderUCY805umnny7stmHDhnBZiSZJ6bv0VuB4Tefar1+/8Fi/4eijjzb9EyRVF7giALfgzysBaNpRBJUJAQSgHzsC0M4QAWhnp5GcAWjnxxmAdnZE+gkgAP0MGQECEMh+AgjA9KyRZgBmuwDUm4fr1q0rP/zwQ7gtV2+ijdeaNGkSpFzkTDjNDIs0lWgquLTp7b16i2+sprfpHnbYYeHRDTfcIHfeeWeRbir1VO5pSyTd9DZdvaRC27x582SfffYpHGf16tVSs2ZNUfmmtxrr7cWxmj7fdddd5bfffgtzmjJlSpFuKjtVemp74YUXRG8vjtX02dlnnx0e6Zx69OhRpFubNm2CFK1ataqsWLGiiGSM7qhzffPNN6VSpUqhXyQbsrS7EwFYWmKJ+yMAU8uT0ZIkgABMElSCbghAO0MEoJ2dRiIA7fwQgHZ2RPoJIAD9DBkBAhDIfgIIwPSsUS4IQP3yyy67TB5++OEAIV72XrS40/7Dhg0rAk1lWq1atcJZcSoKNXssWhBGOkeLu2nTpsnBBx9cZJzRo0cXirZ42Xtr164N0lJLjffbb7/wruKtY8eOQfxpZt/XX38d+hdv0eJuyJAh0qdPnyJdli5dGmSn3gScKHsvIu5UvOnlG8UzBXXsiBBVudmlS5et5qK/g3rxqmYK6txff/1186aMCEAtYd5pp53M42hG5PTp0ykBLlBNToNAhgkgAP3AEYB2hghAOzsEoJMdJcA+gES7CCAAXfgIhgAEcoRA3gnAm7KoBPiO7C4B1i06f/58adq0abjMU6XR5MmTpXLlyoW7d926daFcVy+VUKE2Z84cadSo0Va7O7oMOJZQU7mo4+h72rVrJ++///5WY2zcuDEIxEWLFoWMOb2IomHDhkX69erVS4YPHx5+psePXXDBBVuNE10GfNJJJ8nLL78skbPxtPPKlSvloIMOku+++y6UJGtJ8y677LLVONFlwGPGjBE96zC66c/OPPPM8KOuXbvKyJEjtxrj559/lr333jvI0T333DOcK1ijRo3Cfir9TjnlFHnttdfCzzzlvxofEYCp+MePqi/OAMwjAdi7d++wL7QuXo198aabUS22Nq3Hj9f0F0Z/GXRz6IampZ4AAtDPFAFoZ4gAtLPTSDIA7fzIALSzI9JPAAHoZ8gIEIBA9hNAAKZnjUIGYJoF4IcffigLFy4s/AAVW5FMtsMPP1wuvvjiIh8XS5ZpBy3H1dJdbXpzrGasqXhTETd48GCZMWNGeBarbDfyAi29VYGoQlGblsJqtpvKxIkTJ4ZyX72dVv+s5bYtWrSICX78+PHhYhLNvKtdu7bcdNNN0rp165DxN2LEiMLLRo444oggEaPFXvSAWparWX7a9Dy9q6++OpzVp5djDBw4MHybNj33MFLqW3xCeh6hikItyVX5ee2118qJJ54YuukFKvfcc08QmlpKrLIyVqah9tXSYM1+1KZcb7zxxnDBhpZe33///YGPNp3zqFGjXBtSBWAqGwIwjwRgSfXhkYMftZ9u7HiNAyJT+SsWeywEoJ8xAtDOEAFoZ6eRCEA7PwSgnR2RfgIIQD9DRoAABLKfAAIwPWuUCQGoQi/60omSviSeylDZ1r1793ChRrzWrVu3cAlIIsGkMlJLWBcsWBBzGM3qe/755wslWrx3qei7/PLLwzl+sZoKQS2T1bP+4jXNXNQkJRWKsZp+R//+/eWWW25JiO2TTz6Rzp07i5YEx2pa8jt27Fg55JBDEo4zYMCAcFFKvDVQbnqT8g477FDSMvI8gwTy6gzAZAVgSdYXAZj+HYgA9DNGANoZIgDt7DQSAWjnhwC0syPSTwAB6GfICBCAQPYTyDsBeGMWlQAPTG8JcKoEYGSXqixTyadnv2k2oQo2PadPM+Q6dOiQ1GbWs+P0jEAtj1UhqBKvXr16QQxeddVVoQw2mTZr1ix58MEH5d133w2ZcnqenZYHn3vuuSGzUTPykmmaUaeluV988YWsWrUqZBUeeeSRQTBGLiQpaRxl8cADDwTR980334TuegvyySefHDILo0t6E42lmY/K5oMPPpBly5aF8uPmzZuHW4wjF4mUNBeeZ5YAAjAGbwRg+jchAtDPGAFoZ4gAtLNDADrZcQagDyDRLgIIQBc+giEAgRwhgABMz0KFDMA0C8D0zJxRISCil62o+NSW6Di4fGeFAEQAlskeRwD6sSMA7QwRgHZ2CEAnOwSgDyDRLgIIQBc+giEAgRwhgABMz0IhANPDlVEzQ0BLyy+66KJwz0Oi4+AyM5uyewsCEAFYJrsPAejHjgC0M0QA2tkhAJ3sEIA+gES7CCAAXfgIhgAEcoRAvgnAPftlTwnwt3emtwQ4R7YY08xBAioAtTS5pOPgcvDTSjVlBCACsFQbJlWdEYB+kghAO0MEoJ0dAtDJDgHoA0i0iwAC0IWPYAhAIEcIIADTs1CaAYgATA9bRk0/AQTgFsYIQARg+n/bYrwBAejHjgC0M0QA2tkhAJ3sEIA+gES7CCAAXfgIhgAEcoQAAjA9C4UATA9XRs0MAQQgAjDuTuMSkPT/EiIA/YwRgHaGCEA7OwSgkx0C0AeQaBcBBKALH8EQgECOEMg7AXjDzVKxevUypx8E4F23hXl8//33Urdu3TKfExOAQLIEEIAIQARgsr8taeiHAPRDRQDaGSIA7ewQgE52CEAfQKJdBBCALnwEQwACOUIAAZiehUIApocro2aGAAIwjwXgpZdeKrVq1dpqJy1fvlyGDx8eDn4cMGBA3J0W3e/PP//MzI4sZ29BAPoXHAFoZ4gAtLNDADrZIQB9AIl2EUAAuvARDAEI5AiBvBSA1bIgA/BXMgBz5FeAacYggADMYwGYih1fUFBQ7m+ISQXHeGMgAP10EYB2hghAOzsEoJMdAtAHkGgXAQSgCx/BEIBAjhBAAKZnoTYhANMDllEzQgABmKcCMJW7p7xfEZ1KlsXHQgD66SIA7QwRgHZ2CEAnOwSgDyDRLgIIQBc+giEAgRwhgABMz0IhANPDlVEzQwABmIcCcNKkSSnfPe3atUv5mAwoggD07wIEoJ0hAtDODgHoZIcA9AEk2kUAAejCRzAEIJAjBPJNAO71z5ulYpaUAH8ziEtAcuTXIG+mOXny5JR8y4QJE2Tw4MHlvspzmwKtdaVBIMMEEIB+4AhAO0MEoJ0dAtDJDgHoA0i0iwAC0IWPYAhAIEcIIADTs1CaAYgATA9bRo1PoEKFCkHapaJxzJsIAjAVO4kxSk0AAVhqZFsFIADtDBGAdnYIQCc7BKAPINEuAghAFz6CIQCBHCGAAEzPQiEA08OVURMTUAGYylbej3lDAKZyNzFW0gQQgEmjitsRAWhniAC0s0MAOtkhAH0AiXYRQAC68BEMAQjkCIG8E4B9s6gEeDAlwDnya5A307z11ltT/i0DBgxI+Zi5MiACMFdWKs/miQD0LygC0M4QAWhnhwB0skMA+gAS7SKAAHThIxgCEMgRAgjA9CxUyABEAKYHLqNCIEMEEIAZAs1rihJAAPp3BALQzhABaGeHAHSyQwD6ABLtIoAAdOEjGAIQyBECCMD0LBQCMD1cGRUCmSSAAMwkbd5VSAAB6N8MCEA7QwSgnR0C0MkOAegDSLSLAALQhY9gCEAgRwjknQC8/mbZLgtuAd6oGYBDKAHOkV8DpgmBmAQQgGyMMiGAAPRjRwDaGSIA7ewQgE52CEAfQKJdBBCALnwEQwACOUIAAZiehUIApocro0IgkwQQgJmkzbsKCSAA/ZsBAWhniAC0s0MAOtkhAH0AiXYRQAC68BEMAQjkCAEEYHoWCgGYHq6MmloCCxYskGeeeUa+/PJL2bhxo9SrV086duwoJ510UmpflKOjIQBzdOFyfdoIQP8KIgDtDBGAdnYIQCc7BKAPINEuAghAFz6CIQCBHCGQdwKwTxaVAA+lBDhHfg3ybprz58+XBx54QD755BNZv3691K9fX04++WS56KKLZLvttgvfO3z4cLnmmmtkU4z/vX3kkUfK2LFjpXr16nnHpjQfhAAsDS36powAAtCPEgFoZ4gAtLNDADrZIQB9AIl2EUAAuvARDAEI5AgBBGB6FipkACIA0wOXURMSeO211+TMM8+UDRs2bNXviCOOkLffflsmTJggp556qhQUFMQca5tttpFjjjkm9C3PDQFYnle/DL8dAeiHjwC0M0QA2tkhAJ3sEIA+gES7CCAAXfgIhgAEcoQAAjA9C4UATA9XRk1M4Mcff5TGjRvL6tWrQ0cVeTVq1JCffvopyD7983333SdPPvmkzJw5UzTTr3fv3iFGMwGnTZsmgwYNEi0N1r4qE7UkuLw2BGB5Xfky/m4EoH8BEIB2hghAOzsEoJMdAtAHkGgXAQSgCx/BEIBAjhDIOwF4XRaVAN9NCXCO/BrkzTQHDBggt99+u1SoUEFuuukm6dOnj+y0006yZs0aufvuu8Oz3XffXZYsWSJHHXWUvPPOO6FvdPvll1+kZcuW8v3338s555wjzz77bN7wKe2HIABLS4z+KSGAAPRjRADaGSIA7ewQgE52CEAfQKJdBBCALnwEQwACOUIAAZiehQoZgAjA9MBl1LgE2rRpE879O//88+Wpp57aql+3bt3CzzW7T8uAjzvuuJhj3XPPPUEe7rPPPvLVV1+VW+IIwHK79GX74QhAP38EoJ0hAtDODgHoZIcA9AEk2kUAAejCRzAEIJAjBBCA6VkoBGB6uDJqYgJa7rtq1Sp59dVX5YQTTtiq8+uvvy6dOnUKAlDLguNd8vHhhx9K27ZtpUqVKvLrr7+WW+wIwHK79GX74QhAP38EoJ0hAtDODgHoZIcA9AEk2kUAAejCRzAEIJAjBPJNADa4NntKgL++hxLgHPk1yJtp6g2/mzdvls8//1yaN2++1Xd98cUXobxXBeCff/4Z97tnzZolBxxwQIn98gZcnA9BAOb7Cmfp9yEA/QuDALQzRADa2SEAnewQgD6ARLsIIABd+AiGAARyhAACMD0LpRmACMD0sGXU+AT0PD+Ve19++aXst99+W3WcPXu2NGvWrESxl2y/fF8LBGC+r3CWfh8C0L8wCEA7QwSgnR0C0MkOAegDSLSLAALQhY9gCEAgRwggANOzUAjA9HBl1MQEEICp3SEIwNTyZLQkCSAAkwSVoBsC0M4QAWhnhwB0skMA+gAS7SKAAHThIxgCEMgRAgjA9CwUAjA9XBkVAZjJPYAAzCRt3lVIAAHo3wwIQDtDBKCdHQLQyQ4B6ANItIsAAtCFj2AIQCBHCCAA07NQCMD0cGXU5ATgW2+9JY0aNdqq87x58+T4448PJcDffPONFBQUxBwwul+iswLzfT0QgPm+wln6fQhA/8IgAO0MEYB2dghAJzsEoA8g0S4CCEAXPoIhAIEcIZB3ArB3Fl0Cci+XgOTIr0HeTDNSApyKD1I5WNJlIal4TzaPgQDM5tXJ47khAP2LiwC0M0QA2tkhAJ3sEIA+gES7CCAAXfgIhgAEcoQAAjA9CxUyABGA6YHLqHEJqABMZUMAxsuRTCVlxoJAMQIIQP+WQADaGSIA7ewQgE52CEAfQKJdBBCALnwEQwACOUIAAZiehUIApocroyYmcOGFF6Yc0VNPPZXyMXNlQDIAc2Wl8myeCED/giIA7QwRgHZ2CEAnOwSgDyDRLgIIQBc+giEAgRwhkG8CcO9rsqcEePF9lADnyK8B04RATAIIQDZGmRBAAPqxIwDtDBGAdnYIQCc7BKAPINEuAghAFz6CIQCBHCGAAEzPQmkGIAIwPWwZFQKZIoAAzBRp3lOEAALQvyEQgHaGCEA7OwSgkx0C0AeQaBcBBKALH8EQgECOEEAApmehEIDp4cqoZU9gwYIFhTcJL1q0qOwnlMYZIADTCJeh4xNAAPp3BwLQzhABaGeHAHSyQwD6ABLtIoAAdOEjGAIQyBECeSkAq1Yvc/obfyMDsMwXgQmkhcDs2bOlWbNm5eKGYARgWrYQg5ZEIPq/mI9s3lt22L5aSSE8L0Zg229/hImRQMG69cZIwoIA3LgJEA4Cb6571hFNKAQgAAEIQAACiQggANOzPxCA6eHKqGVPAAFY9mvADPKcAALQv8AIQDtDBKCdHQLQx06jEYB+howAAQhAAAIQiEcAAZievYEATA9XRi17Av+fvXsBu2pM/P9/d9AZ9UvlUNHklHyRTjKSHJIONHLIYdA3JST+NZVTJkNFGERRBvkZjX+RhkopRYaO9B06K0z+kcKETsrhf33uZu/v3j1772c/+15r7bXXft/X5bo8z17rXmu97ns/+9qf7gMBYP7bgDuIuAABoHsDEwDmbkgAmLsdAaCbHQGgux81IIAAAgggkEkgcgHgLXeZ/cIyBfgRdgHm3Rc9AQLA6LUpTxQyAQJA9wYhAMzdkAAwdzsCQDc7AkB3P2pAAAEEEECAADD4PmBHABIABg/PFX0XIAD0nZgLFLsAAaB7DyAAzN2QADB3OwJANzsCQHc/akAAAQQQQIAAMPg+QAAYvDlXDEaAADAYZ65SxAIEgO6NTwCYuyEBYO52BIBudgSA7n7UgAACCCCAQDEFgI1vDs8U4PWPMgWYd1/0BAgAo9emPFHIBAgA3RuEADB3QwLA3O0IAN3sCADd/agBAQQQQAABAsDg+4BGABIABu/OFf0XIAD035grFLkAAaB7ByAAzN2QADB3OwJANzsCQHc/akAAAQQQQIAAMPg+QAAYvDlXDEaAADAYZ65SxAIEgO6NTwCYuyEBYO52BIBudgSA7n7UgAACCCCAQFEFgP1DNAV4NFOAefdFT4AAMHptyhOFTIAA0L1BCABzNyQAzN2OANDNjgDQ3Y8aEEAAAQQQIAAMvg/YEYAEgMHDc0XfBQgAfSfmAsUuQADo3gMIAHM3JADM3Y4A0M2OANDdjxoQQAABBBAgAAy+DxAABm/OFYMRIAAMxpmrFLEAAaB74xMA5m5IAJi7HQGgmx0BoLsfNSCAAAIIIFBMAeCRN4VnCvC6x5gCzLsvegIEgNFrU54oZAIEgO4NQgCYuyEBYO52BIBudgSA7n7UgAACCCCAAAFg8H1AIwAJAIN354r+C2zZssWMHTvWXuiPf/yj/xfM4xXK/frrr7/m8fpcukgFCADdG54AMHdDAsDc7QgA3ewIAN39qAEBBBBAAAECwOD7AAFg8OZcEQGvBQgAvRalvqwECACzYsp4EAFg7oYEgLnbEQC62REAuvtRAwIIIIAAAkUVAPYL0RTgx5kCzLsvWIEKFSp4esFy5cqZn376ydM6C6kyAsBCaq0I3SsBoHtjEgDmbkgAmLsdAaCbHQGgux81IIAAAgggQAAYfB+wIwAJAIOHL/Irli9f3lMBBYA///yzp3UWUmUEgAG11vfff29mzJhhlixZYpYuXWo2btxoNNd8586dpmbNmua4444znTp1Mr169TK1a9cu9a5mzpxpxo8fbxYvXmzrqVOnjmnVqpXp06eP6dixY6nn64AdO3aYMWPGmMmTJ5t169aZ3bt3mwYNGpjOnTub/v37m4YNG2ZVTy4HEQDmopZ8DgFg7oYEgLnbEQC62REAuvtRAwIIIIAAAgSAwfcBAsDgzbmiMT179nRi0Ip3ymG++eYbo/8nAGQNQKcOle3Jc+bMMeecc06phx900EHmr3/9qzn33HNTHqvm6tu3rw3/0hWFgE8++aTt3OnK+vXrbdC3Zs2alIcceOCBZuLEiTaU9KMQALqrEgDmbkgAmLsdAaCbHQGgux81IIAAAgggUFQB4I0hmgI8hinAvPsKR2Dq1Kl2U4/ly5fbm1aWokFOn332WeE8hMd3yghAj0HTVacA8L//+79N+/btTfPmze1Iu0MOOcT88ssvRmHYSy+9ZKZMmWKHo1aqVMmOFDzhhBNKVHfHHXeYESNG2N83a9bMDB482DRu3Ngo0Bs1apRZtmyZfU3H3XvvvSlvZ9u2baZly5Zm9erV9vXevXubHj16mKpVq5p58+aZkSNHGh1TrVo1s2DBgpT34cpGAOgqaAwBYO6GBIC52xEAutkRALr7UQMCCCCAAAIEgMH3ATsCkAAweHiuWGYBjfi766674tmIgj9lL7fffrvNPpS3FGshAAyo5RXslbaApRLq3/3ud/aOLrzwQvPyyy8n3Z2m6TZp0sQuWtmiRQszf/58G9rFiqb0tmvXzk4xrlixog34FA7uW4YNG2buvvtu+2uFhoMGDUo6RKHf6aefbq+jwHLu3LmeKxEAupMSAOZuSACYux0BoJsdAaC7HzUggAACCCBQTAHgUTeEZwTgx2MZAci7L7wCs2fPtsGflkmz31l+/dXUrVvXDBkyxFx//fWmSpUq4b35gO6MADAg6Gwvo4BPwZ2mAmttv8Ry4403mrFjx9pfKaQ75ZRTSlS7cOFC06ZNG/v7fv36mcceeyzpmD179tg3wVV6DHoAACAASURBVNatW22YqOGwqRbW1DTjcePG2XMVKGrUopeFANBdkwAwd0MCwNzt7IfpnuLdOctNbu/Zs3Y+70U11IEAAggggAACKQQSv2d8/vnnpn79+gXnlPgMBIAF13zccMACb7/9thk6dKh599137ZUV/GlfBQ10UiaimY2UvQIEgCHrCRrZ9/7775saNWqYH374IX536sSaNqzNQ4499lizatWqtHeu17W2nz7sNmzYkLQWoFLxDh062HPvu+8+m4anKolBoobKDh8+3FMpAkB3TgLA3A0JAHO3sx+qBIBOgASATnycjAACCCCAQEYBAkB/OoimADMC0B9bas1N4L333rMj/rSMWSz40warAwYMMLfccovNVCjJAgSAIeoRCvX+67/+y64DqCBQ6wDGyieffBKfznvdddfZTT7SFb0e2yRE5zVq1Ch+qN4g99xzj/053ShCvabpv3rzbN++3U4HVqruZSEAdNckAMzdkAAwdzv74UoA6ARIAOjEx8kIIIAAAghkFIhcAHh9iKYAP8EUYN5++RdQTqJc44033ogHf/vvv78N/RT+aUNTSmoBAsA89wyt26dRfa+99ppdj++rr76yd/T888+bK6+8Mn5306dPN126dLE/P/zww7Zzpyt6XR1fRecl7uR78cUX2w1HVP7973/bkC9dOfHEE82HH35o6tSpYzZv3uypFAGgOycBYO6GBIC52+lMAkA3PwJANz/ORgABBBBAIJMAAaA//cOOACQA9AeXWrMS+J//+R+7q++0adPs8ZolWb16dTvNV9N9/8//+T9Z1VPMBxEA5qH1J0yYYHr27Jn2yn/4wx9sGFiuXLn4MRrxp4UrVSZPnmwuuuiitOcr4FPQp6LzNCIwVrRu4KJFi+wbRTv9ZioKHBUgquzatctUrlw5ay198GYqX375pWnVqpU9pO2JA0yVSqT0WeP+50ACwLKK/e/xBIC529kPW0YAOgESADrxcTICCCCAAAIZBQgA/ekgBID+uFJr6QIrVqywwd8rr7xiD1bwp81QlY/ceuutdv8ESnYCBIDZOXl6VLoA8KSTTrKBXevWrUtc74EHHjCDBw+2v3/99ddNx44d096TXo+N+nvwwQfNwIED48c2bdrUrFy50tSrV89s2rQp43NdeumlZtKkSfaYr7/+2i6kmW1JDC9LO4cAsDSh1K8TAObmZj80du7K/WTOJAB07AMEgI6AnI4AAggggEAGgUgGgPunn7UVVGfY8wMjAIOy5jr/K3D55ZfbTEKhn/7ToCQNcFLwd/DBB0NVRgECwDKCeXG4duCNjZDbuXOnWb9+ve3USrQbN25sHnnkkfh039j1tG6f5rmrvPnmm+bMM89Meytz5841Z511ln1d5915553xY1W/1gXUhiLaICRTueqqq+xUZJWy7qBFAOhFT8lcBwFg7sYEgLnb6UxGALr5EQC6+XE2AggggAACmQQIAP3pHwSA/rhSa2aB8uXLxw/Q0mQ333yz887eyjmKtRAAhqjlFbZdffXVdurv008/ba655pr43RXaCECmAPvfsQgAczcmAMzdjgDQzU5nEwC6G1IDAggggAAC6QQIAP3pGwSA/rhSa+kBYFkGF5Xmqbq04WmxFgLAkLV8bNqt1ujTqLtatWrZOyy0NQBLY2UTkNKESn+dALB0o3RHEADmbkcA6GZHAOjuRw0IIIAAAghkEohaAHh037vMfiGZArz2SXYB5t0XrEDiCEAvrqwA8Oeff/aiqoKsgwAwZM02ceJEc8UVV9i7euGFF4zmvKtop5uuXbva/3fZBVibh7z88su2HnYBDlnjl/F2CADLCJZwOAFg7nYEgG52BIDuftSAAAIIIIAAAWDwfUAjAAkAg3cv9iu+/fbbnhO0a9fO8zoLpUICwJC11OzZs02HDh3sXY0YMcLcdttt9v+1bp/W71PRopcaEZiu6PXx48fHz2vUqFH8UK0jqHUBVRYsWGC0K3CqomGxNWvWNNu3bzenn3668fqNxwhA945HAJi7IQFg7nY6kzUA3fyYAuzmx9kIIIAAAghkEmAEoD/9gwDQH1dqRSBIAQLAILWzuFbiDsGjR482N910kz1LO97Ur1/ffPHFF+bYY481q1atSltbkyZNzOrVq81hhx1mpxEnzpl/4403zLnnnmvPve+++8yQIUNS1rNw4ULTpk0b+5pCSIWRXhYCQHdNAsDcDQkAc7cjAHSz09kEgO6G1IAAAggggEA6gcgFgNeFaArwOKYA885DoJAFCABD1nqdO3c2M2bMsHc1b948c8YZZ8Tv8IYbbjBPPPGE/Tnd6L3E4E7HjxkzJukJd+/eberWrWu+++47o6BwxYoVSQFh7OC+ffuacePG2R8XL15sWrZs6akUAaA7JwFg7oYEgLnbEQC62REAuvtRAwIIIIAAApkECAD96R92BCABoD+41IpAQAIEgAFBa2Rfjx49TJUqVdJeUWv7DRgwwL5+xBFHmI8//thUrFgxfvzatWtN06ZN7a41LVq0MPPnzzdVq1aNv75z5047XXfp0qX2vJUrV5qjjjqqxPUSpwGPGjXKDBo0KOkYhYuqR9fR/Pi33nrLcyUCQHdSAsDcDQkAc7cjAHSzIwB096MGBBBAAAEECACD7wMEgMGbc0UEvBYgAPRaNE19CvR++OEH0717d3PaaafZ9fxq1Khhf/fRRx/ZDT/effdde3alSpXM9OnTzdlnn12iNk3H1dRdlWbNmtkpvKpr/fr15v777zfLli2zr2WatqtrKkBUoKjSp08fG04qTNSoQ0333bZtm/35vffeMyeddJLnSgSA7qQEgLkbEgDmbkcA6GZHAOjuRw0IIIAAAggUUwB4TJ/wTAFeM54pwLz7EChkAQLAgFpPAeC//vWvUq+mdf6eeeYZc84556Q89pdffjG9e/e2x6QrvXr1spuAZNoye926daZTp052lGGqcsABB9hQskuXLqXecy4HEADmopZ8DgFg7oYEgLnbEQC62REAuvtRAwIIIIAAAgSAwfcBjQAkAAzenSsi4KUAAaCXmhnq0gi9OXPm2BF22sDjq6++Mt98842dElyvXj07yk5h2yWXXGKqVatW6l1pnUCFfEuWLDFff/21Oeigg+w6fdoB+Lzzziv1fB2gHX61RuDkyZONAkGtD9igQQMbDN58883m8MMPz6qeXA4iAMxFjQDQXW1vDQSAbpLsAuzmxyYgbn6cjQACCCCAAAFg8H2AADB4c66IgNcCBIBei1JfVgIEgFkxZTyIEYC5GxIA5m5nA9Q9P7lVUORnEwAWeQfg8RFAAAEEfBWI2iYgx/QO0RTgp5gC7GvnpXIEfBYgAPQZmOpTCxAAuvcMAsDcDQkAc7cjAHSz09kEgO6G1IAAAggggEA6AQJAf/qGHQFIAOgPLrUiEJAAAWBA0FwmWYAA0L1HEADmbkgAmLsdAaCbHQGgux81IIAAAgggkEmAANCf/kEA6I8rtSIQpAABYJDaXCsuQADo3hkIAHM3JADM3Y4A0M2OANDdjxoQQAABBBAoqgDw2hBNAf6Lf1OAzzjjDPP222+XqXNrfX2dFysTJkwwPXv2zKqOZ5991lxzzTUZj92xY0fKNfM7d+5s+vfvbxo2bJjVtVasWGEee+wxuyfAxo0bTY0aNUyTJk3MFVdcYbSBZ8WKFbOq58UXXzS67w8//ND8+9//NgcffLBp27atufHGG80pp5ySVR3ah2D06NFm6tSp5rPPPjO//vqradSokenWrZt9ptq1a2dVDwcVpgABYGG2W8HfNQGgexMSAOZuSACYux0BoJsdAaC7HzUggAACCCBAABh8H7AjAEMUAJYvX95s2LDBHHbYYb4EgNrEU0HfmjVrUmIfeOCBZuLEiXYDzUzl6aeftgHdjz/+mPIwBXfTpk3LGLzt2rXLXHzxxfa4VEUWw4YNM0OHDs14L9pA9IILLjBffvllyuMOPfRQ8/e//920aNEi+A7GFQMRIAAMhJmL7CtAAOjeJwgAczckAMzdjgDQzY4A0N2PGhBAAAEEECAADL4P+B0Afvrpp2b79u0ZH2zlypXm0ksvtcecc8455o033kg6PnEE4KxZs4wCrXSlfv36pmbNmilf3rZtm2nZsqVZvXq1fb13796mR48epmrVqkajDkeOHGl0TLVq1cyCBQvMCSeckLIe3YMCwl9++cXUq1fP3HHHHaZ169bm22+/NU899ZSZMmWKPe/000+39SrIS1U0UlBho0r79u3NzTffbJ/to48+MiNGjDAKK1VU57XXXpuyDo08bN68ufnqq6/siMMBAwaYLl262GMVLP75z382P/30k73P999/PylYDb63cUW/BAgA/ZKl3owCBIDuHYQAMHdDAsDc7XQmuwC7+bEJiJsfZyOAAAIIIJBJIGprAB4boinAq30cAZhNrx4yZIgZNWqUPfT55583V155ZdJpiQGgAsUjjjgim2pLHKPRdHfffbf9va43aNCgpGMU+im0U2CmQG7u3Lkl6tBrmua7bt06c8ABB5gPPvjANG7cOOk4jQwcO3as/d1zzz1nrrrqqhL1aFp0bJpz165dzSuvvGIqVKgQP+7rr7+2wZ5GQ9aqVct88sknKYNNTXfWNVQmTZpkRxQmlsmTJ5tLLrnE/krTqJ955pmc7Dgp3AIEgOFun8jeHQGge9MSAOZuSACYu53OJAB08yMAdPPjbAQQQAABBDIJEAD60z80AjCfAaBG0WnNvdgaehrJphF4icWLAHDPnj2mbt26ZuvWrTbAW758ecqReX379jXjxo2zl1+6dKkN4dIFahoxeOutt5ZoGK0xqJGIWs/v+OOPtyP69i2ahjxjxgwb+mnNPh2/b9HagJdddpn99YMPPmgGDhyYdIisNFX6559/Nueee66ZOXNmyk7SsWNHo1GLupacNRqQEi0BAsBotWfBPA0BoHtTEQDmbkgAmLudziQAdPMjAHTz42wEEEAAAQQyCUQuAOwVnk1AVj/t3yYgpfXq2bNnmw4dOtjDNJpNm2HsW7wIABOvc9999xmNOkxVFi5caNq0aWNfuv32283w4cOTDkuctqs197RhR6qSGCSuXbvWHHXUUfHDNM34oIMOsusHKpx7/fXXU9axe/duU6dOHfP999+bU0891bz77rtJx2lqcJ8+fezvFBbGplHvW1likDh+/Hg79ZkSLQECwGi1Z8E8DQGge1MRAOZuSACYu53OJAB08yMAdPPjbAQQQAABBDIJEAD60z/sCMA8BoC///3vzV//+lf7cJpyq6m3+xYvAsC77rrL3HPPPbZqTfVNt7uupvhqDUGtW6jpwPvuYKzRip9//rk55phj4msJpmqZv/3tb+byyy+3L2nabeIuxnrOs846y76WbhRhrE6N7NOaiFrfTyML99tvv/jlNLVYU6ZVMoWRei22bqLOiU0Z9qdHUWs+BAgA86HONQ0BoHsnIADM3ZAAMHc7nUkA6OZHAOjmx9kIIIAAAghkEiAA9Kd/5DMA1Eg4jaBT2KZgTVNhy5UrlzEAbNeunVm1apWdXqs1+I488khz9tlnm+uvvz7jBhdaG++ll16ydevcdBuF6PUTTzzRfPjhh3b03ebNm+P3o/vVNX/99Ve76+7UqVPTNsqyZcvMySefbF/XWoOxNQ7185gxY0y/fv3sa1r7r1u3bmnr0cYgo0ePtq+vWLHCHHfccfFjtaGJpilr52JNbc5UdIxGEuqcxYsX+9OZqDVvAgSAeaMv7gsTALq3PwFg7oYEgLnb6UwCQDc/AkA3P85GAAEEEEAgk0DkAsD/vstU2j/1brVB9oTdGgH4zN4pwAqGDjnkkIyXT7VWXa73q5Fomvarop1077333pRVJY4ATHetKlWqmEceecRcd911KQ/RiL9FixaZ6tWr251+MxXtojt9+nR7yK5du0zlypXt/2v3YK0fqKKNPh5//PG01WgTDwWIKtppWCMCY0XrBt5///32xyVLlpgWLVqkrUdr/8U2K9EafxoRGCsKT7UOYNOmTe2ahpmK1iJUgKhzNCKQEi0BAsBotWfBPA0BoHtTEQDmbkgAmLudziQAdPMjAHTz42wEEEAAAQQyCRAA+tM/EgPAbK6g0W9eFU2Dje20u2bNGnP00UenrFoBoKbvXnjhhXZ9vgYNGtjjtDPuyy+/bEf2xe5LG3jE1sVLrEwh2cqVK+0GGJs2bcr4CFpLTzvqqijIq127tv1/hXWtWrWy/681BLWWYLqyc+fO+GYmChRfe+21+KGJuwRrNOOxxx6btp4nnnjC3HDDDfZ1PWf37t3jxyrM1LTg1q1bG61dmKnoGAW8NWrUMD/88INXTUg9IREgAAxJQxTbbRAAurc4AWDuhgSAudvpTAJANz8CQDc/zkYAAQQQQCCTAAGgP/0jXwGg2vPwww832gVYo/O0Ll+68t1339mpt6mmB+ucadOm2XBQO/1qB+H169eX2JyjcePGNjBUeLhhw4aMmIlr62m9v9iox3feeceuC6gydOhQ86c/7R05maroubTrroqCzjlz5sQP69Wrl10XUEX3+pvf/CZtPTpOx6tovb8rr7wyfqzq13Xatm1r5s+fn/GZdN+6f52jdQ4p0RIgAIxWexbM0xAAujcVAWDuhgSAudvpTAJANz8CQDc/zkYAAQQQQCCTQOQCwJ4hmgL8bPBTgDV67rbbbrNNrlFu2jXXpWi33jvvvNNWoanEmlKcWBgByAhAl/4V9nMJAMPeQhG9PwJA94YlAMzdkAAwdzsCQDc7nU0A6G5IDQgggAACCKQTIAD0p2/YEYD/CQATR7v5c7X/rTUWyGl9Pa1JV6tWLadLarMOrW+nqcDnnHOO3Tk3sbAGIGsAOnWwkJ9MABjyBorq7SV+MLc7oIepUr56VB/Vt+f6dfdu3+qOfMU//xz5R/T7AWftesHvS1A/AggggAACCCBQZgECwDKTZXVCPgJA7Vyr3WhVtKZdbHferG44w0F169Y1W7ZssTvlasOLxHLRRRfZ9QJVXHYB3n///W0dLrsAa/OQm266ydbjsguwNg95//332QXYteNE4HwCwAg0YiE+AgGge6sRADoYEgA64O09lQDQmZAKEEAAAQQQQMAHgUgGgDVCsAvwtuBHAPbv39889thjtpe8+uqrpmvXrp70GO26q007UgWAd911l91IREXrDWpEYKqi9fFq1qxptm/fbtf7e/vtt5MOa9iwodFIyWOOOcbuCpyuaNffyy+/3L6sdfx69uwZP1Qbn2hdQJWRI0ca7QqcrmjXX41mrFixor2nSpUqxQ9NXKtQoyg1AjJV0WuHHnqofUnnaPdlSrQECACj1Z4F8zQEgO5NRQDoYEgA6IC391QCQGdCKkAAAQQQQAABHwQIAH1ANcbsDjgA1EYdhx12mB2pp8Duiy++sOGWa0mcAnz22Web2bNnJ1WpEE1hmorWH9QuvqmKdtPVTsMqWqNwxIgRSYcp1FO4p5IpdNOahtqRWGXfHY61C+9BBx1kdu/ebTp27Ghef/31lPei12X0/fff23t67733ko4bP368ue666+zvXnzxRaPdi1MVvXbZZZfZl9Ltkuzqz/n5FSAAzK9/0V6dANC96QkAHQwJAB3w9p5KAOhMSAUIIIAAAggg4IMAAaAPqHkIADXiT9NnVW6++WbzyCOPePJg2vhDO/OqaKRfbEOQWOUK0zRFWDsKN2nSxE4RTrWrcGJwt3jx4vhU5Vg9kyZNigdt6Ubv7dixw+4crKnGqUYjqq5OnTrZ4E/h56effhrfaTgRIzG4GzVqlBk0aFCS1aZNm2yYqp2AFW7OnDkzpaVCxlmzZpny5cubjRs3ph0p6ElDUEleBAgA88LORQkA3fsAAaCDIQGgAx4BoDMeFSCAAAIIIICAbwJRCwCbXHOXqRSSKcCrJuzdBTiITUAS1+LT+nUnn3xyxj7z2Wef2SCtWbNmaY+bNm2aXUtQIV+VKlXMunXrbDC2b0mcBpwqUNPUYE371TTgdu3ambfeeqtEHRrBqABx/fr15oADDjAffPCBady4cdJxN954oxk7dqz93bPPPmuuueaaEvUkTgM+//zzzZQpU0yFChXix2kqc/Pmzc2GDRvslORPPvkk5UYpidOAJ0+ebOSbWPS7Sy65xP7q6quvNhMmTPDtPUrF+RMgAMyffVFfmQDQvfkJAB0MCQAd8PaeyghAZ0IqQAABBBBAAAEfBAgAfUD9zwjAoAJABXmHHHKI+fHHH83xxx9vPvroo1IfSiFc+/bt7RRYrRV40kkn2ZF82u1XoZg2ENF/+llFG2wogEtVNPVWG2esXbvWvtynTx/To0cPU7VqVTNv3jw73Xfbtm32Z0231bVSlRkzZth70ci7evXq2dGGrVq1skHlU089Fd9s5LTTTrMhYmKwl1ifpuVqlJ+KnvGWW26xa/XJZfjw4TZkVHnyySfjU333vR+FtgoKNaVaowkHDhxounTpYg9TMPrQQw/ZQFNTiRVWamQiJXoCBIDRa9OCeCICQPdmIgB0MCQAdMDbeyoBoDMhFSCAAAIIIICADwIEgD6gBhwAKsi6/vrr7YOkGoGX6gljAWBpT1+tWjXz8MMP21AvU9HoQE2//fjjj1MeplF9L7zwQjxES1eXgr5+/frZUYepigLB6dOn27X+0pWdO3faEXsKFFMVTdnVtOZhw4ZlfKZFixaZbt26GU0JTlW0OcjUqVNN69atS2Pk9QIVIAAs0IYr9NsmAHRvQQJAB0MCQAe8vacSADoTUgECCCCAAAII+CAQuQDw6hBNAX4umCnAv/3tb+3IOo2I09TW2M60mbqLRu1p3UBNz126dKndeEPTYzWqrVatWqZp06Z2R91rr73WjgzMpmg33TFjxhhNj1UgqBCvQYMGNhjUuoSHH354NtWY5cuXm9GjR5s333zTbmZSvXp1Oz34iiuusPeT7eYmEydOtFNz//nPf5qtW7faUYVt27a1AWNsQ5LSbkgmjz76qA36NG1apVGjRna9RY0srF27dmlV8HoBCxAAFnDjFfKtEwC6tx4BoIMhAaADHgGgMx4VIIAAAggggIBvAgSA/tBqF+BVAQWA/jwBtSKAAAEgfSAvAgSA7uwEgA6GBIAOeASAznhUgAACCCCAAAK+CRAA+kNLAOiPK7UiEKQAAWCQ2lwrLkAA6N4ZCAAdDAkAHfAIAJ3xqAABBBBAAAEEfBOIWgB43FXhmQK88v8GMwXYt85BxQgUuQABYJF3gHw9PgGguzwBoIMhAaADHgGgMx4VIIAAAggggIBvAgSA/tBqBCABoD+21IpAUAIEgEFJc50kAQJA9w5BAOhgSADogEcA6IxHBQgggAACCCDgmwABoD+0BID+uFIrAkEKEAAGqc214gIEgO6dgQDQwZAA0AGPANAZjwoQQAABBBBAwDeByAWAvw/RFODnmQLsW8elYgQCECAADACZS5QUIAB07xUEgA6GBIAOeASAznhUgAACCCCAAAK+CRAA+kNrRwASAPqDS60IBCRAABgQNJdJFiAAdO8RBIAOhgSADngEgM54VIAAAggggAACvgkQAPpDSwDojyu1IhCkAAFgkNpcKy5AAOjeGQgAHQwJAB3wCACd8agAAQQQQAABBHwTiFoA2PTK8EwBXvFXpgD71nGpGIEABAgAA0DmEiUFCADdewUBoIMhAaADHgGgMx4VIIAAAggggIBvAgSA/tBqBCABoD+21IpAUAIEgEFJc50kAQJA9w5BAOhgSADogEcA6IxHBQgggAACCCDgm0DkAsArQjQC8AVGAPrWcakYgQAECAADQOYSJQUIAN17BQGggyEBoAMeAaAzHhUggAACCCCAgG8CBID+0NoRgASA/uBSKwIBCRAABgTNZZIFCADdewQBoIMhAaADHgGgMx4VIIAAAggggIBvAgSA/tASAPrjSq0IBClAABikNteKCxAAuncGAkAHQwJABzwCQGc8KkAAAQQQQAAB3wQiFwBeHqIpwBOZAuxbx6ViBAIQIAAMAJlLlBQgAHTvFQSADoYEgA54BIDOeFSAAAIIIIAAAr4JEAD6Q2tHABIA+oNLrQgEJEAAGBA0l0kWIAB07xEEgA6GBIAOeASAznhUgAACCCCAAAK+CRAA+kNLAOiPK7UiEKQAAWCQ2lwrLkAA6N4ZCAAdDAkAHfAIAJ3xqAABBBBAAAEEfBOIWgB4/GXhmQK8/G9MAfat41IxAgEIEAAGgMwlSgoQALr3CgJAB0MCQAc8AkBnPCpAAAEEEEAAAd8ECAD9odUIQAJAf2ypFYGgBAgAg5LmOkkCBIDuHYIA0MGQANABjwDQGY8KEEAAAQQQQMA3AQJAf2gJAP1xpVYEghQgAAxSm2vFBQgA3TsDAaCDIQGgAx4BoDMeFSCAAAIIIICAbwKRDACr1/TNK9uKd29nBGC2VhyHQFgFCADD2jIRvy8CQPcGJgB0MCQAdMAjAHTGowIEEEAAAQQQ8E2AANAfWgJAf1ypFYEgBQgAg9TmWnEBAkD3zkAA6GBIAOiARwDojEcFCCCAAAIIIOCbAAGgP7QEgP64UisCQQoQAAapzbUIAD3sAwSADpgEgA54BIDOeFSAAAIIIIAAAr4JRC0A/K8eQ02lkEwB/ujFe2y7ff7556Z+/fq+tSEVI4CAPwIEgP64UmspAowAdO8iBIAOhgSADngEgM54VIAAAggggAACvgkQAPpDqxGABID+2FIrAkEJEAAGJc11kgQIAN07BAGggyEBoAMeAaAzHhUggAACCCCAgG8CBID+0BIA+uNKrQgEKUAAGKQ214oLEAC6dwYCQAdDAkAHPAJAZzwqQAABBBBAAAHfBCIXAF4aoinA/y9TgH3ruFSMQAACBIABIHOJkgIEgO69ggDQwZAA0AGPANAZjwoQQAABBBBAwDcBAkB/aO0IQAJAf3CpFYGABAgAA4LmyCOp5wAAIABJREFUMskCBIDuPYIA0MGQANABjwDQGY8KEEAAAQQQQMA3AQJAf2gJAP1xpVYEghQgAAxSm2vFBQgA3TsDAaCDIQGgAx4BoDMeFSCAAAIIIICAbwJRCwBPuCQ8U4A/nMQUYN86LhUjEIAAAWAAyFyipAABoHuvIAB0MCQAdMAjAHTGowIEEEAAAQQQ8E2AANAfWo0AJAD0x5ZaEQhKgAAwKGmukyRAAOjeIQgAHQwJAB3wCACd8agAAQQQQAABBHwTIAD0h5YA0B9XakUgSAECwCC1uVZcgADQvTMQADoYEgA64BEAOuNRAQIIIIAAAgj4JhC5APDiEE0BnswUYN86LhUjEIAAAWAAyFyipAABoHuvIAB0MCQAdMAjAHTGowIEEEAAAQQQ8E2AANAfWjsCkADQH1xqRSAgAQLAgKC5TLIAAaB7jyAAdDAkAHTAIwB0xqMCBBBAAAEEEPBNgADQH1oCQH9cqRWBIAUIAIPU5lpxAQJA985AAOhgSADogEcA6IxHBQgggAACCCDgm0DUAsATLwrPFOB/vsQUYN86LhUjEIAAAWAAyFyipAABoHuvIAB0MCQAdMAjAHTGowIEEEAAAQQQ8E2AANAfWo0AJAD0x5ZaEQhKgAAwKGmukyRAAOjeIQgAHQwJAB3wCACd8agAAQQQQAABBHwTiFwA2D1EIwBfZgSgbx2XihEIQIAAMABkLlFSgADQvVcQADoYEgA64BEAOuNRAQIIIIAAAgj4JkAA6A+tHQFIAOgPLrUiEJAAAWBA0FwmWYAA0L1HEAA6GBIAOuARADrjUQECCCCAAAII+CZAAOgPLQGgP67UikCQAgSAQWpzrbgAAaB7ZyAAdDAkAHTAIwB0xqMCBBBAAAEEEPBNIGoB4EkXhmcK8P9MYQqwbx2XihEIQIAAMABkLlFSgADQvVcQADoYEgA64BEAOuNRAQIIIIAAAgj4JkAA6A+tRgASAPpjS60IBCVAABiUNNdJEiAAdO8QBIAOhgSADngEgM54VIAAAggggAACvgkQAPpDSwDojyu1IhCkAAFgkNpcKy5AAOjeGQgAHQwJAB3wCACd8agAAQQQQAABBHwTiFwA+LsQTQF+hSnAvnVcKkYgAAECwACQuURJAQJA915BAOhgSADogEcA6IxHBQgggAACCCDgmwABoD+0dgQgAaA/uNSKQEACBIABQXOZZAECQPceQQDoYEgA6IBHAOiMRwUIIIAAAggg4JsAAaA/tASA/rhSKwJBChAABqnNteICiR/MbSteYKqUq4ZOWQV+/aWsZ3B8gsAbe17EAwEEEEAAAQQQQCBiAlEMACtXq5n3VvpxByMA894I3AACjgIEgI6AnJ6bAAFgbm5JZxEAOiESADrxcTICCCCAAAIIIBBKAQJAf5qFANAfV2pFIEgBAsAgtblWXIAA0IPOQADohEgA6MTHyQgggAACCCCAQCgFCAD9aRYCQH9cqRWBIAUIAIPU5loEgF72AQJAJ00CQCc+TkYAAQQQQAABBEIpELkAsNudJjRTgKfea9v8888/N/Xr1w9l+3NTCCCQXoAAkN6RFwFGAHrATgDohEgA6MTHyQgggAACCCCAQCgFCAD9aRY7ApAA0B9cakUgIAECwICguUyyAAGgBz2CANAJkQDQiY+TEUAAAQQQQACBUAoQAPrTLASA/rhSKwJBChAABqnNteICBIAedAYCQCdEAkAnPk5GAAEEEEAAAQRCKRC1ALDZBeGZArzs70wBDmWn56YQyFKAADBLKA7zVoAA0ANPAkAnRAJAJz5ORgABBBBAAAEEQilAAOhPs2gEIAGgP7bUikBQAgSAQUlznSQBAkAPOgQBoBMiAaATHycjgAACCCCAAAKhFCAA9KdZCAD9caVWBIIUIAAMUptrxQUIAD3oDASATogEgE58nIwAAggggAACCIRSIHIB4PkhmgL8KlOAQ9npuSkEshQgAMwSisO8FSAA9MCTANAJkQDQiY+TEUAAAQQQQACBUAoQAPrTLHYEIAGgP7jUikBAAgSAAUFzmWQBAkAPegQBoBMiAaATHycjgAACCCCAAAKhFCAA9KdZCAD9caVWBIIUIAAMUptrxQUIAD3oDASATogEgE58nIwAAggggAACCIRSIGoB4MldwjMF+INpTAEOZafnphDIUoAAMEsoDvNWgADQA08CQCdEAkAnPk5GAAEEEEAAAQRCKUAA6E+zaAQgAaA/ttSKQFACBIBBSXOdJAECQA86BAGgEyIBoBMfJyOAAAIIIIAAAqEUIAD0p1kIAP1xpVYEghQgAAxSm2vFBQgAPegMBIBOiASATnycjAACCCCAAAIIhFIgcgFg5xBNAZ7OFOBQdnpuCoEsBQgAs4Ty4rAPPvjAzJw507zzzjtm+fLlZvPmzWa//fYzhx56qDn11FNNr169TNu2bbO+lOoaP368Wbx4sdmyZYupU6eOadWqlenTp4/p2LFjVvXs2LHDjBkzxkyePNmsW7fO7N692zRo0MB07tzZ9O/f3zRs2DCresp6EAFgWcVSHE8A6IRIAOjEx8kIIIAAAggggEAoBQgA/WkWOwKQANAfXGpFICABAsCAoNu1a2fmz59f6tV+//vfm7/85S+mUqVKaY/99ddfTd++fW34l64oBHzyySdNuXLl0h6zfv16G/StWbMm5TEHHnigmThxounUqVOp913WAwgAyypGAOiBWFIVBIBei1IfAggggAACCCCQf4GoBYDNO4VnBOD7MxgBmP8ezh0gkLsAAWDudmU688gjjzQK3DTa7+KLL7Yj/TS67ueffzYLFiwwDz30kNm4caOt87LLLrPBW7pyxx13mBEjRtiXmzVrZgYPHmwaN25s6x81apRZtmyZfU3H3Xvv3j/S+5Zt27aZli1bmtWrV9uXevfubXr06GGqVq1q5s2bZ0aOHGl0TLVq1ez9nXDCCWV63tIOJgAsTSiL1xkBmAVS+kMIAJ34OBkBBBBAAAEEEAilAAGgP82iEYAEgP7YUisCQQkQAAYk3aVLF3PVVVeZ7t27mwoVKpS46tdff21++9vfmrVr19rXNFow1XRgTdNt0qSJ+emnn0yLFi3scQrtYkVTejXacOnSpaZixYo24FM4uG8ZNmyYufvuu+2vFRoOGjQo6RCFfqeffrq9Tvv27c3cuXM9lSIA9ICTANAJkQDQiY+TEUAAAQQQQACBUAoQAPrTLASA/rhSKwJBChAABqldyrWmTZtmunbtao/S+nuPPvpoiTNuvPFGM3bsWPt7hXSnnHJKiWMWLlxo2rRpY3/fr18/89hjjyUds2fPHlO3bl2zdetWGyZqPcLy5cuXqEfTjMeNG2d/r0CxefPmnmkRAHpASQDohEgA6MTHyQgggAACCCCAQCgFIhcAnneHqVytZt6tbQD4+nB7H59//rmpX79+3u+JG0AAgbIJEACWzcvXozXldv/997fX0Np8CgQTi9b+0wYdmip87LHHmlWrVqW9H72utf30h3nDhg1JawHOnj3bdOjQwZ573333mSFDhqSsJzFIvP32283w4Xv/4HtRCAA9UCQAdEIkAHTi42QEEEAAAQQQQCCUAgSA/jQLAaA/rtSKQJACBIBBapdyrW+//dbUrl3bHqWRgK+++mrSGZ988kl8Ou91111nN/lIV/R6bJMQndeoUaP4oXfddZe555577M/pRhHqNU3/rVmzptm+fbudDvz22297pkUA6AElAaATIgGgEx8nI4AAAggggAACoRQgAPSnWQgA/XGlVgSCFCAADFK7lGu98sor5sILL7RHaU0+rc2XWKZPn260lqDKww8/bG655Za0Ner1AQMG2Nd1XuJOvtqE5KWXXrKv/fvf/7YhX7py4oknmg8//NDUqVPHbN682TMtAkAPKAkAnRAJAJ34OBkBBBBAAAEEEAilQNQCwBYdwzMFeOlMpgCHstNzUwhkKUAAmCWU34f98ssvdt2+xYsX20stWbLEbvKRWDTi7/rrr7e/mjx5srnooovS3pYCPgV9KjpPIwJjResGLlq0yFSvXt3u9JupKHBUgKiya9cuU7ly5awo9MGbqXz55ZemVatW9pC2FS8wVcpVy6peDkoQIAB06g4EgE58nIwAAggggAACCIRSgADQn2bRCEACQH9sqRWBoAQIAIOSLuU6Dz30kPnDH/5gj/rd735npkyZUuKMBx54wAwePNj+/vXXXzcdO3ZMW6tej436e/DBB83AgQPjxzZt2tSsXLnS1KtXz2zatCnjnV166aVm0qRJ9hjtVBybolwaW7ly5Uo7JP46AWDWVMkHEgDmCLf3NAJAJz5ORgABBBBAAAEEQilAAOhPsxAA+uNKrQgEKUAAGKR2mmtpbb2zzz7brrmn3Xk15Vbh3L5F6/Zp/T6VN99805x55plp737u3LnmrLPOsq/rvDvvvDN+bOPGjY3WBdSGItogJFO56qqrzPPPP28PKctuTwSAAXQsAkAnZAJAJz5ORgABBBBAAAEEQikQyQCwagh2Ad7JCMBQdnhuCoEyCBAAlgHLj0NXrFhh2rZta9fi0/TaWbNmmXbt2qW8VCGNAGQKsB+9ZZ86CQCdkAkAnfg4GQEEEEAAAQQQCKUAAaA/zfIjAaA/sNSKQIACBIABYu97qU8//dScdtpp5osvvjAVKlSw6/pp+m+6UkhrAJbGyiYgpQll8ToBYBZI6Q8hAHTi42QEEEAAAQQQQCCUAgSA/jQLAaA/rtSKQJACBIBBaidcS6GfRv5pKq6my06YMMFoum2mMm3aNNO1a1d7iMsuwNo85OWXX7b1sAtwnjqAF5clAHRSJAB04uNkBBBAAAEEEEAglAJRCwBbnnuHqRySKcBLZrELcCg7PTeFQJYCBIBZQnl5mDbT0DRfbcSh8vjjj5sbb7yx1EsoLNT6fSra1VcjAtMVvT5+/Hj7ss5r1KhR/FCtI6h1AVUWLFhgtCtwqqI1CWvWrGm2b99uTj/9dKO1Cr0qjAD0QJIA0AmRANCJj5MRQAABBBBAAIFQChAA+tMsGgFIAOiPLbUiEJQAAWBQ0v+5znfffWc37/jggw/sb+677z4zZMiQrO7i119/NfXr17dTho899lizatWqtOc1adLErF692hx22GF2847ETTneeOMNc+6555Z6/YULF5o2bdrY42677TYzYsSIrO4zm4MIALNRKuUYAkAnRAJAJz5ORgABBBBAAAEEQilAAOhPswQRAGa7kaQG07z11lsZH3TmzJl2QMzixYvNli1bTJ06dUyrVq1Mnz59TMeOHbNC2rFjhxkzZoxdqmvdunVm9+7ddiPNzp07m/79+5uGDRtmVY/W/X/sscfMnDlzzMaNG02NGjWMvq9fccUVplevXqZixYpZ1fPiiy+aZ5991m4aqpl8Bx98sJ1VqMFE6Qb17FvxN998Y0aPHm2mTp1qPvvsM6OMQYOFunXrZp+pdu3aWd0LBxWmAAFggO2mPyAdOnQw7777rr3qHXfcYe69994y3cENN9xgnnjiCXtOutF7icGdjtcfrcSiP1zabVhhpP7w6A9Sqj+2ffv2NePGjbOn6g9ny5Yty3SvmQ4mAPSAkgDQCZEA0ImPkxFAAAEEEEAAgVAKRC4A7HB7eKYAv7F3QIgGmGhgitfFiwBQgZa+x8Zmw6W6R4WAmk2X6Xrr16+3Qd+aNWtSPuaBBx5oJk6caDp16pSR4emnn7YB3Y8//pjyOAV3WuorU/C2a9cuc/HFF9vjUpXy5cubYcOGmaFDh2a8lyVLlpgLLrjAfPnllymPO/TQQ83f//5306JFC6+blvpCIkAAGFBDKHTT+n0afady8803m0ceeaTMV1+7dq1p2rSp0fRcvTHnz59vqlatGq9n586ddrru0qVL7b8kaJrxUUcdVeI6idOAR40aZQYNGpR0jMJF1aPrZPMvLGV9EALAsoqlOJ4A0AmRANCJj5MRQAABBBBAAIFQChAA+tMsdgRgQAHg9ddfbzSQJV2pXr160hJXicdpkE1s5lqzZs3M4MGD7TJaCvT0vXfZsmX28EyDcbZt22YHv2hGnUrv3r1Njx497PfuefPmmZEjRxodU61aNTso54QTTkh5q7NmzbIB4S+//GLq1atnr9m6dWvz7bffmqeeespMmTLFnqfv3apXQV6qopGCChtV2rdvb7MEhXUfffSRfVY9m4rqvPbaa1PWoZGHzZs3N1999ZXNCQYMGGC6dOlij1Ww+Oc//9l+99d9vv/++3YmISV6AgSAAbVp9+7d429wTQFW+JfpXxwqVapkjj766JR3p+m4mjqsoj9qmkIc+6N2//33x/+oZZq2+8MPP9gAUYGiiv4VJPGPmv6Q6I+a/si999575qSTTvJUigDQA04CQCdEAkAnPk5GAAEEEEAAAQRCKUAA6E+zBBkA/vGPf7Qj2spaNE1XM9zSDZbRjDwNbokNllHAF1tjP/Fauvbdd99tf1XaYBkFcnPnzi1xq7oH3Yvu6YADDrBLgO17LY0MHDt2rD33ueeeS7kpqNbhP+OMM+wxGlD0yiuvmAoVKsSvp/0FFOxt2LDB1KpVy67/r3X89y3XXHONvYbKpEmT7IjCxKJpzpdccon9Vc+ePc0zzzxTVn6OLwABAsCAGinb4cyx2zn88MPtnPxURf+CoH+FyPSm1FoCGvac7l8RVK/+GOlfJD7++OOU19EfqhdeeCH+LwNeUhEAeqBJAOiESADoxMfJCCCAAAIIIIBAKAWiFgC2Oic8U4AXzw5mCnCuAWBioJbNcln9+vWza/Mllj179tjlsrZu3WoDvOXLl6f8Tp24XJYCRYVw6QI1jRi89dZbS7xfFEhqKrXW8zv++OPtiL59i6Yhz5gxw4Z+ygdSTb3W2oCXXXaZPfXBBx80AwcOTKpGo/40ou/nn3+2ewFofcRURWsjatSirqURgxoNSImWAAFgQO3pZQAYu2X9IVDIp7n8Sv4POuggO1RZOwCfd955WT2ZdvhNtbCpgkENLVYQ6UchAPRAlQDQCZEA0ImPkxFAAAEEEEAAgVAKEAD60ywaARjmAFBr/2mDDgVXpW2Yqde1tp/CNI2cS/yuPnv2bLtuv0qmDTsT192//fbbzfDhw5PgE6ftas09bdiRqiQGiZqdl7h8l2bk6Tu+1g9UOPf666+nrEPLjWmTk++//96ceuqp8T0HYgdrarBm/KkoLLz00ktT1pMYJCpn0KAjSrQECACj1Z4F8zQEgB40FQGgEyIBoBMfJyOAAAIIIIAAAqEUIAD0p1nCHgBq6mtsiq0GxGiTj3RFr8c2CdF52gU3VhLXyk83ilDHaoqvptpqQI3W8NNU3cSiHYK1WcoxxxwTX0sw1f387W9/M5dffrl9STP8NP02VjS1+KyzzrI/phtFGDtWI/u034DW99PIwv322y9ez1VXXWWef/55+3OmMFKvaW1BFZ0TmzLsT4+i1nwIEADmQ51rGgJADzoBAaATIgGgEx8nI4AAAggggAACoRSIXAB4doimAM8JZgrwcccdZwM2jc5ToKXRcxrZpnXstOZeqjJ9+vT40lUPP/ywueWWW9L2T72uTTBUdF7iTr5aG++ll16yr2lqbqr19GIVn3jiiebDDz+0o+82b94cv55G7mk5LY1K1K67U6dOTXsv2pTk5JNPtq9rY06tORgrmqmnacoqWvuvW7duaevR7L3Ro0fb11esWGFkGCuaJahpytq5WFObMxUdo5GEOmfx4sWhfI9zU7kLEADmbseZDgIEgA54sVMJAJ0QCQCd+DgZAQQQQAABBBAIpQABoD/NYkcA/icAVDB0yCGHZLxQqrXqSruzbJbNUgg2YcIEG2YlFo340+7BKtrQ4qKLLkp7OQV8sU0wdJ5GBMbKKaecYhYtWmS007CCvExFu+gqQFTZtWuXqVy5sv1/bS6i9QNVtC7h448/nrYaLeWlAFFFm3JqRGCsaN1AbfKpomW/tIlnuqK1/xQgqmiNP40IjBUFqFoHsGnTpnZNw0xFaxEqQNQ5GhFIiZYAAWC02rNgnoYA0IOmIgB0QiQAdOLjZAQQQAABBBBAIJQCBID+NEtiAJjNFTT6raxFodv5559vp71qnb4aNWqYLVu22Om1Cuq++eYbW6V28tVafYnTXB944AEzePBg+7rWytOaeemKXo+N+tt30wyFZCtXrrQbYGzatCnjI2gtPe2oq6Igr3bt2vb/Fda1atXK/v+QIUPsWoLpys6dO021atXsywoUX3vttfihiZuarFq1ypqkK0888YS54YYb7MsKOLt37x4/VK6aFty6dWujtQszFR2jgFf2P/zwQ1mbkONDLkAAGPIGiurtEQB60LIEgE6IBIBOfJyMAAIIIIAAAgiEUiBqAWDrs8IzBXjRm3unAGdTcgkANT013ZRbjWDTRpeaMqvy6KOPmv79+8dv5Z577jFav0/lzTffNGeeeWba20xcW0/n3XnnnfFjtY6g1gXUhiKagpypJK6tp/X+YqMe33nnHbsuoMrQoUPNn/70p7TV/PLLL3bXXRUFn3PmzIkf26tXL7suoMr69evNb37zm7T16Dgdr6L1/q688sr4sapf12nbtq2ZP39+xmfSfev+dY6mYVOiJUAAGK32LJinIQD0oKkIAJ0QCQCd+DgZAQQQQAABBBAIpQABoD/NohGAsQDQrynApd25gjlNrdWut0ceeaT5+OOP46cwApARgKX1H143hgCQXpAXAQJAD9gJAJ0QCQCd+DgZAQQQQAABBBAIpUDkAsD2t5nKVWvm3doGgPNG2vtIHO0W9I0lrru3cePG+K61rAHIGoBB98VCvB4BYCG2WgTumQDQg0YkAHRCJAB04uNkBBBAAAEEEEAglAIEgP40S1gCQK3zp9F+KhqJqN1qVaZNm2a6du1q/99lF2BtHvLyyy/belx2Ad5///1tHS67AGvzkJtuusnW47ILsDYPef/999kF2J+3RkHVSgBYUM0VnZslAPSgLQkAnRAJAJ34OBkBBBBAAAEEEAilAAGgP80SlgBQO91q4459A0BND9b6fSra1VcjAtMVvT5+/Hj7ss5r1KhR/FCtI6h1AVUWLFhgtCtwqqL18bRe4fbt2+16f9qoJLE0bNjQjpQ85phj7K7A6Yp2/b388svty1rHr2fPnvFDE9cqHDlypNGuwOmKdv194403TMWKFe09VapUKX5o4lqF2tlXO/ymKnrt0EMPtS/pnOeee86fzkSteRMgAMwbfXFfmADQg/YnAHRCJAB04uNkBBBAAAEEEEAglAKRCwDPCNEU4LfyPwW4c+fOZsaMGbbvqa0PO+ww+//adESbcHzxxRd2t1ztmpuuaB1BhXI6VyFduXLl4ocqRFOYpqLde7WLb6qi3XTbtGljX7rtttvMiBHJG6Qo1FO4p5IpdOvbt68ZN26cPW7NmjXm6KOPjl9Ou/AedNBBds1D7Wqs3YtTFb1ep04d8/3339t7eu+995IOU9ip0FPlxRdfNNq9OFXRa5dddpl9SffUp0+fUL7HuancBQgAc7fjTAcBAkAHvNipBIBOiASATnycjAACCCCAAAIIhFKAANCfZrEjAPMcAGq0nsK9PXv22B1xtTNuYrnhhhvME088YX+VbvReYnCn48eMGZNUh8K0unXrmu+++85uOLJixYqkgDB2cGJwlzgVOfb6pEmT4kFbutF7O3bssKGlphofd9xx9lr7lk6dOtngTyP7Pv300/hOw4nHJQZ3o0aNMholmVg2bdpkw07tBKxwc+bMmSk7iULGWbNmmfLlyxutr5hupKA/PYxagxAgAAxCmWuUECAA9KBTEAA6IRIAOvFxMgIIIIAAAgggEEoBAkB/msXvAPC1114z5513ng26UpWvvvrKvr5s2TL78kMPPWQGDBiQdOjatWtN06ZNjabnat27+fPnm6pVq8aP2blzp52uu3TpUnudlStXmqOOOqrE5RKnAacK1BQuqh5dp127duatt94qUYdCSgWICikPOOAA88EHH8SnKMcOvvHGG83YsWPtj88++6y55pprStSTOA34/PPPN1OmTDEVKlSIH/f111+b5s2bmw0bNtgpyQpJa9WqVaKexGnAkydPNlrrMLHod5dccon91dVXX20mTJjgT0ei1rwKEADmlb94L04A6EHbEwA6IRIAOvFxMgIIIIAAAgggEEqBqAWAp2gKcJUQ7AK8a6tZ6OMIwCOOOMKO7OvevbudxqqfFd4p4FLApjX9vvnmG9vnTjvtNDNnzhxTuXLlEn1Q03E1dVelWbNmdgqv1gZUEHf//ffHA8RU03ZjlWnqrQJEBYoqmgrbo0cPez/z5s2z0323bdtmf9Z025NOOinle0FTlbUxiUbe1atXz9x5552mVatWdsTfU089Fd9sRM+jZ0wM9hIr1LRcjfJTad++vbnlllvsWn0fffSRGT58eHwkpIxiU333vSFNdVZQuGXLFht+Dhw40GhHZRVtoKJAVYGmphIrrNTIREr0BAgAo9emBfFEBIAeNBMBoBMiAaATHycjgAACCCCAAAKhFCAA9KdZfgwgAPzXv/5V6s0rIPzLX/5iR7ulKgrbevfubTfUSFd69eplNwHRVNd0Zd26dUbTbz/++OOUh2hU3wsvvBAP0dLVo6CvX79+dh2/VEWB4PTp0+1af+mKRi5qxF5s7cN9j9NzDB061AwbNiyj36JFi0y3bt2MpgSnKpryO3XqVNO6detS24EDClOAALAw263g75oA0IMmJAB0QiQAdOLjZAQQQAABBBBAIJQCBID+NIvfAaB20dV/ml6raawa+adNLWrUqGEaNGhgTj31VDs1NbbxRmlPqbBMId+SJUtsXQrYWrZsaUfIaSpxNkW76WqNQE2PVSCoEE/3omDw5ptvNocffng21Zjly5eb0aNHmzfffNNuUlK9enU7PfiKK64w1157bdppz/tWPnHiRDvpsoI5AAAgAElEQVQ195///KfZunWrHVXYtm1bGzBm6yKLRx991AZ9n332mb2EdkG+4IIL7MjC2rVrZ/VMHFSYAgSAhdluBX/XiR/Mp5lOpkq5agX/TPl4gNm/TM7HZbkmAggggAACCCCAAAKhFIhcANhOU4APzLv1j7u+Mwvfzv8uwHmH4AYQKGABAsACbrxCvnUCQG9ajwDQG0dqQQABBBBAAAEEEIiGAAGgP+1IAOiPK7UiEKQAAWCQ2lwrLkAA6E1nIAD0xpFaEEAAAQQQQAABBKIhQADoTzsSAPrjSq0IBClAABikNtciAPS4DxAAegxKdQgggAACCCCAAAIFLRC5APD020yVEEwB3qUpwPOZAlzQbw5uvugFCACLvgvkB4ARgN64EwB640gtCCCAAAIIIIAAAtEQIAD0px0JAP1xpVYEghQgAAxSm2vFBQgAvekMBIDeOFILAggggAACCCCAQDQECAD9aUcCQH9cqRWBIAUIAIPU5loEgB73AQJAj0GpDgEEEEAAAQQQQKCgBSIXALYN0RTgd5gCXNBvDm6+6AUIAIu+C+QHgBGA3rgTAHrjSC0IIIAAAggggAAC0RAgAPSnHe0IQAJAf3CpFYGABAgAA4LmMskCBIDe9AgCQG8cqQUBBBBAAAEEEEAgGgIEgP60IwGgP67UikCQAgSAQWpzrbgAAaA3nYEA0BtHakEAAQQQQAABBBCIhkDUAsA2p90aml2AF/zjPttJPv/8c1O/fv1odBieAoEiEiAALKLGDtOjEgB60xoEgN44UgsCCCCAAAIIIIBANAQIAP1pR40AJAD0x5ZaEQhKgAAwKGmukyRAAOhNhyAA9MaRWhBAAAEEEEAAAQSiIUAA6E87EgD640qtCAQpQAAYpDbXigsQAHrTGQgAvXGkFgQQQAABBBBAAIFoCEQuADw1RFOA32MKcDTeJTxFsQoQABZry+f5uQkAvWkAAkBvHKkFAQQQQAABBBBAIBoCBID+tKMdAUgA6A8utSIQkAABYEDQXCZZgADQmx5BAOiNI7UggAACCCCAAAIIREOAANCfdiQA9MeVWhEIUoAAMEhtrhUXIAD0pjMQAHrjSC0IIIAAAggggAAC0RCIWgB4apshodkF+L0F99tOwi7A0Xiv8BTFJ0AAWHxtHoonJgD0phkIAL1xpBYEEEAAAQQQQACBaAgQAPrTjhoBSADojy21IhCUAAFgUNJcJ0mAANCbDkEA6I0jtSCAAAIIIIAAAghEQyByAeApIRoBuJARgNF4l/AUxSpAAFisLZ/n5yYA9KYBCAC9caQWBBBAAAEEEEAAgWgIEAD60452BCABoD+41IpAQAIEgAFBc5lkAQJAb3oEAaA3jtSCAAIIIIAAAgggEA0BAkB/2pEA0B9XakUgSAECwCC1uVZcgADQm85AAOiNI7UggAACCCCAAAIIREMgegHgYFOl8oF5b5xdP2oE4Ch7H2wCkvfm4AYQyEmAADAnNk5yFSAAdBXcez4BoDeO1IIAAggggAACCCAQDQECQH/akQDQH1dqRSBIAQLAILW5VlyAANCbzkAA6I0jtSCAAAIIIIAAAghEQ4AA0J92JAD0x5VaEQhSgAAwSG2uRQDocR8gAPQYlOoQQAABBBBAAAEEClogagHgb1uHZwrwu4uYAlzQbw5uvugFCACLvgvkB4ARgN64EwB640gtCCCAAAIIIIAAAtEQIAD0px01ApAA0B9bakUgKAECwKCkuU6SAAGgNx2CANAbR2pBAAEEEEAAAQQQiIYAAaA/7UgA6I8rtSIQpAABYJDaXCsuQADoTWcgAPTGkVoQQAABBBBAAAEEoiEQuQCw1aDQ7AL87uIHbCdhF+BovFd4iuITIAAsvjYPxRMTAHrTDASA3jhSCwIIIIAAAggggEA0BAgA/WlHOwKQANAfXGpFICABAsCAoLlMsgABoDc9ggDQG0dqQQABBBBAAAEEEIiGAAGgP+1IAOiPK7UiEKQAAWCQ2lwrLkAA6E1nIAD0xpFaEEAAAQQQQAABBKIhELUA8LQW4ZkC/I+lTAGOxruEpyhWAQLAYm35PD83AaA3DUAA6I0jtSCAAAIIIIAAAghEQ4AA0J921AhAAkB/bKkVgaAECACDkuY6SQIEgN50CAJAbxypBQEEEEAAAQQQQCAaAgSA/rQjAaA/rtSKQJACBIBBanOtuAABoDedgQDQG0dqQQABBBBAAAEEEIiGQOQCwOZ/CM0uwP94/0HbSdgFOBrvFZ6i+AQIAIuvzUPxxASA3jQDAaA3jtSCAAIIIIAAAgggEA0BAkB/2tGOACQA9AeXWhEISIAAMCBoLpMsQADoTY8gAPTGkVoQQAABBBBAAAEEoiFAAOhPOxIA+uNKrQgEKUAAGKQ214oLEAB60xkIAL1xpBYEEEAAAQQQQACBaAhELgA8OURTgD9gCnA03iU8RbEKEAAWa8vn+bkJAL1pAAJAbxypBQEEEEAAAQQQQCAaAgSA/rSjHQFIAOgPLrUiEJAAAWBA0FwmWYAA0JseQQDojSO1IIAAAggggAACCERDgADQn3YkAPTHlVoRCFKAADBIba4VFyAA9KYzEAB640gtCCCAAAIIIIAAAtEQiFoA2LbZwNDsAvzOsodsJ2EX4Gi8V3iK4hMgACy+Ng/FExMAetMMBIDeOFILAggggAACCCCAQDQECAD9aUeNACQA9MeWWhEISoAAMChprpMkQADoTYcgAPTGkVoQQAABBBBAAAEEoiFAAOhPOxIA+uNKrQgEKUAAGKQ214oLEAB60xkIAL1xpBYEEEAAAQQQQACBaAhELgA8aUB4pgD/z59tJ2EKcDTeKzxF8QkQABZfm4fiiQkAvWkGAkBvHKkFAQQQQAABBBBAIBoCBID+tKMdAUgA6A8utSIQkAABYEDQXCZZgADQmx5BAOiNI7UggAACCCCAAAIIREMgcgHgiQNMlUoH5r1xdu3+zrzzT0YA5r0huAEEHAQIAB3wODV3AQLA3O0SzyQA9MaRWhBAAAEEEEAAAQSiIUAA6E87EgD640qtCAQpQAAYpDbXigsQAHrTGQgAvXGkFgQQQAABBBBAAIFoCBAA+tOOBID+uFIrAkEKEAAGqc21CAA97gMEgB6DUh0CCCCAAAIIIIBAQQtELQA8/YT/JzRTgOd/+LDtG2wCUtBvEW6+iAUIAIu48fP56IwA9EafANAbR2pBAAEEEEAAAQQQiIYAAaA/7agRgASA/thSKwJBCRAABiXNdZIECAC96RAEgN44UgsCCCCAAAIIIIBANAQIAP1pRwJAf1ypFYEgBQgAg9TmWnEBAkBvOgMBoDeO1IIAAggggAACCCAQDYHIBYD/pSnAB+S9cXbt/t7M/4gpwHlvCG4AAQcBAkAHPE7NXYAAMHe7xDMJAL1xpBYEEEAAAQQQQACBaAgQAPrTjgSA/rhSKwJBChAABqnNteICBIDedAYCQG8cqQUBBBBAAAEEEEAgGgIEgP60IwGgP67UikCQAgSAQWpzLQJAj/sAAaDHoFSHAAIIIIAAAgggUNACkQsAj78lPFOAlz9i+wa7ABf0W4SbL2IBAsAibvx8PnoUPpjz6ce1EUAAAQQQQAABBBBAoKRAFL5nJD7D6QSAdHMEEPBIgADQI0iqKZtAFD6Yy/bEHI0AAggggAACCCCAAAJ+C0ThewYBoN+9hPoRKE4BAsDibPe8P3UUPpjzjsgNIIAAAggggAACCCCAQJJAFL5nJAWAx4VoCvBKpgDzdkOgkAUIAAu59Qr43qPwwVzA/Nw6AggggAACCCCAAAKRFIjC9wwCwEh2TR4KgbwLEADmvQmK8wai8MFcnC3HUyOAAAIIIIAAAgggEF6BKHzPIAAMb//izhAoZAECwEJuvQK+9yh8MBcwP7eOAAIIIIAAAggggEAkBaLwPSPxGdo1uTk0uwC/vepR22fYBTiSbx0eqggECACLoJHD+IhR+GAOoyv3hAACCCCAAAIIIIBAMQtE4XsGAWAx92CeHQH/BAgA/bOl5gwCUfhgpoERQAABBBBAAAEEEEAgXAJR+J5BABiuPsXdIBAVAQLAqLRkgT1HFD6YC4yc20UAAQQQQAABBBBAIPICUfiekRQAHts/PFOAV4+2/YcpwJF/G/GAERUgAIxow4b9saLwwRx2Y+4PAQQQQAABBBBAAIFiE4jC9wwCwGLrtTwvAsEIEAAG48xV9hGIwgczjYoAAggggAACCCCAAALhEojC9wwCwHD1Ke4GgagIEABGpSUL7Dmi8MFcYOTcLgIIIIAAAggggAACkReIwveM5ADwJlNlvwPy3m679nxv3l79mL0PpgDnvTm4AQRyEiAAzImNk1wFovDB7GrA+QgggAACCCCAAAIIIOCtQBS+ZxAAetsnqA0BBPYKEADSE/IiEIUP5rzAcVEEEEAAAQQQQAABBBBIKxCF7xlJAeAx/cIzAnDN49adEYC8AREoTAECwMJst4K/6yh8MBd8I/AACCCAAAIIIIAAAghETCAK3zMIACPWKXkcBEIiQAAYkoYottuIwgdzsbUZz4sAAggggAACCCCAQNgFovA9gwAw7L2M+0OgMAUIAANst82bN5vFixfb/5YsWWL/++abb+wdXH311WbChAllupuZM2ea8ePH2/q2bNli6tSpY1q1amX69OljOnbsmFVdO3bsMGPGjDGTJ08269atM7t37zYNGjQwnTt3Nv379zcNGzbMqp6yHhSFD+ayPjPHI4AAAggggAACCCCAgL8CUfiekRQAHhWiKcAfMwXY395L7Qj4K0AA6K9vUu3lypVLe7WyBIC//vqr6du3rw3/0hWFgE8++aTJdM3169fboG/NmjUpqznwwAPNxIkTTadOnTxXisIHs+coVIgAAggggAACCCCAAAJOAlH4nkEA6NQFOBkBBNIIEAAG2DUSwziNsmvSpIl544037B2UJQC84447zIgRI+x5zZo1M4MHDzaNGzc2CvRGjRplli1bZl/Tcffee2/KJ9y2bZtp2bKlWb16tX29d+/epkePHqZq1apm3rx5ZuTIkUbHVKtWzSxYsMCccMIJnkpF4YPZUxAqQwABBBBAAAEEEEAAAWeBKHzPIAB07gZUgAACKQQIAAPsFn/84x9t6Kb/6tWrZz777DPTqFGjMgWAmqar4PCnn34yLVq0MPPnz7ehXaxoSm+7du3M0qVLTcWKFW3Ap3Bw3zJs2DBz9913218rNBw0aFDSIQr9Tj/9dHud9u3bm7lz53oqFYUPZk9BqAwBBBBAAAEEEEAAAQScBaLwPSPxGc448sbQ7AL81roxtn3YBdi5m1IBAnkRIADMC/vei+YSAN54441m7Nix9nyFdKecckqJJ1i4cKFp06aN/X2/fv3MY489lnTMnj17TN26dc3WrVttmLh8+XJTvnz5EvVomvG4cePs7xUoNm/e3DOtKHwwe4ZBRQgggAACCCCAAAIIIOCJQBS+ZxAAetIVqAQBBPYRIADMY5coawCotf80dXjjxo3m2GOPNatWrUp793pda/vVr1/fbNiwIWktwNmzZ5sOHTrYc++77z4zZMiQlPUkBom33367GT58uGdaUfhg9gyDihBAAAEEEEAAAQQQQMATgSh8zyAA9KQrUAkCCBAAhqcPlDUA/OSTT+LTea+77jq7yUe6otdjm4TovNhUYx1/1113mXvuuceemm4UoV7T9N+aNWua7du32+nAb7/9tmd4Ufhg9gyDihBAAAEEEEAAAQQQQMATgSh8z0gKABvfEJ4pwOv3zkRjCrAnXZVKEAhcgBGAgZP/7wXLGgBOnz7ddOnSxVbw8MMPm1tuuSXt3ev1AQMG2Nd1XuJOvhdffLF56aWX7Gv//ve/bciXrpx44onmww8/NHXq1DGbN2/2TCsKH8yeYVARAggggAACCCCAAAIIeCIQhe8ZBICedAUqQQCBfQQIAPPYJcoaAGrE3/XXX2/vePLkyeaiiy5Ke/cK+BT0qeg8jQiMFa0buGjRIlO9enW702+mosBRAaLKrl27TOXKlbMS04dWpvLll1+aVq1a2UP4F6SsSDkIAQQQQAABBBBAAAEEShEgAPSni+za8715y+cRgB988IGZOXOmeeedd+w69RqAst9++5lDDz3UnHrqqaZXr16mbdu2GR9wwoQJpmfPnlkhPPvss+aaa67JeKw22RwzZoz9/q0NOXfv3m2X5ercubPp37+/adiwYVbXWrFihV2bf86cOXZJrxo1atj1+K+44gr7XNrAM5vy4osvGt23BuloMM/BBx9sTbRXQKr9AVLV+c0335jRo0ebqVOn2n0JtNSYZgx269bNPlPt2rWzuRWOKUABAsA8NlpZA8AHHnjADB482N7x66+/bjp27Jj27vV6bNTfgw8+aAYOHBg/tmnTpmblypV2J+JNmzZlFLj00kvNpEmT7DFff/111n8MypUrl7UsAWDWVByIAAIIIIAAAggggAACGQQiFwA2uj48U4A/fcLK+/H9rV27dmb+/Pml9u3f//735i9/+YupVKlSymO9DADXr19vgz6trZ+qHHjggWbixIlJs+1SHff000/bgO7HH39MWY+Cu2nTpmX8rq3BOBrgo+NSFW3qOWzYMDN06NCMhkuWLDEXXHCB0YCcVEVh69///nfTokWLUtuCAwpPgAAwj21W1gBQ6/Zp/T6VN99805x55plp737u3LnmrLPOsq/rvDvvvDN+bOPGjY3WBdS/XGiDkEzlqquuMs8//3yZ/9ATAOaxY3FpBBBAAAEEEEAAAQSKVIAA0J+GtyMAfQwAjzzySKPATQGUgi6NatPoup9//tmuW//QQw/ZkXMql112mQ3eUpXEAHDWrFm2vnRFG2amWw5LM+VatmxpVq9ebU/v3bu36dGjh6lataqZN2+eGTlypJ1NV61aNXt/J5xwQsrL6B40MOeXX36xA3DuuOMO07p1a/Ptt9+ap556ykyZMsWepzX3Va+CvFRFIwVjz9y+fXtz880322f76KOPzIgRI6ydiuq89tprU9Yhv+bNm5uvvvrKjjjUkmGxJcYULP75z3+2+wDoPt9//31z2GGH+dOZqDVvAgSAeaM3drhtbHOOq6++2uiPVaZSSCMAmQKcx47FpRFAAAEEEEAAAQQQKFIBAkB/Gt7vAFBBlAafdO/e3VSoUKHEQ2g22m9/+1uzdu1a+5pGC6aaDpwYAH766afmiCOOyAlEo+nuvvtue+6oUaPMoEGDkupR6KfQToGZAjkNwNm36DVN89XU4QMOOMBoirMG4yQWjQwcO3bv5irPPfecNdi3aDPOM844w/66a9eu5pVXXkkyko2CPQ3uqVWrlh3skyrY1HRnXUNFs/xiS4bFrqdpzpdccon9UdOon3nmmZzsOCm8AgSAeWybsgaAhbQGYGmsUfhgLu0ZeR0BBBBAAAEEEEAAAQSCFYjC94ykTUCO0BTg/YNFTHG1XXt+MG995t8U4GweUKPUFICpaK26Rx99tMRpXgSAe/bsMXXr1jVbt261AZ7WI0w1Mq9v375m3Lhx9h6WLl1qQ7jEkhioacTgrbfeWuJ+tcagRiJqPb/jjz/ejujbt2ga8owZM2zopwxBx+9btDagRkaq7LsEmH6nUX8a0acRleeee65dazFV0TJjGrWoa2nEoEYDUqIjQACYx7YsawCY+AfPZRdgbR7y8ssv2ydnF+A8dgAujQACCCCAAAIIIIAAAp4KEAB6yhmvLAwBoKbc7r//3jBUoViq9fC8CABnz55tOnToYK9z3333mSFDhqREXbhwoWnTpo197fbbbzfDhw9POi5x2q7W3NOGHalKYpCoEY5HHXVU/DA980EHHWTXD1Q4p7X+UxVtTlKnTh3z/fff2w1T3n333aTDNDW4T58+9ncKC7XWf6qSGCSOHz/eTn2mREeAADCPbVnWAFBDeWNDhrWrr0YEpit6XW9YFZ0Xm2qsn7WOoNYFVNHQ5XS7BWnIsoYOb9++3Q5v1tBjr0oUPpi9sqAeBBBAAAEEEEAAAQQQ8EYgCt8zGAGYui9o3bzYDrUaCfjqq6+WONCLANCr78taw1AbphxzzDHxtQRTPdnf/vY3c/nll9uXNO02cRfjxLX9040ijNWpkX1vvPGGXd9PIwu1g3KsJK7tnymM1GuxdRN1TmzKsDfvTmrJtwABYB5boKwBoLbn1nDfL774whx77LFm1apVae9eQ5W1YKmG+eqPTuKmHPqjoD8OKtn+i8Ztt91mFxf1qkThg9krC+pBAAEEEEAAAQQQQAABbwSi8D0jOQDsa6pUDMEU4J80BXjvABQ/dgHOpvW19t2FF15oD9WafFqbb9+SGABqZ2F9Z9asN63Bp41Gzj77bHP99ddn3OBCa+O99NJLtupcZ8xp5J6uqe/w2nV36tSpaR9x2bJl5uSTT075XGPGjDH9+vWzr+n5u3XrlrYebQwyevRo+/qKFSvMcccdFz9WG5pomrJ2LtbU5kxFx2gkoc5ZvHhxNk3DMQUiQACYx4YqawCoW73hhhvME0/sXXsh3ei9xKHIOl5/NBKLhgdrTYPvvvvOrmmgPw6pdu1NHIqsN77+AHhVovDB7JUF9SCAAAIIIIAAAggggIA3AlH4nhH2AFDfDQ855JCMDZZqnTqXFtYuuppuGwuklixZYlq0aFGiysQAMN31qlSpYh555BGjWXOpimbILVq0yFSvXt3u9JupaPOS6dOn20N27dplKleubP9fg3H0XVtFG308/vjjaavRJh6avquinYY1IjBWtG7g/fffb39M98yxY7X2X2yzEq3xFxv0o9c1/VjrADZt2tSuaZipaC1CZQQ6RyMCKdERIADMY1vmEgBqTQC9aTU9V3/wtPuRtiKPlZ07d9rpukr3NfR35cqVSWsIxI5LHNZc2q5G+peTt956y1OpKHwwewpCZQgggAACCCCAAAIIIOAsEIXvGWEPALNpJI1887I89NBD5g9/+IOt8ne/+52ZMmVKyuoVAGq5K40UVGDYoEEDe5yWxdI6+BrZF7s3beARWxcvsTJ939b3aG2AsWnTpoyPobX0tKOuioK82BRlhXWtWrWyv9cagpp5l67oO3y1atXsywoUX3vttfihibsEazSjZgKmKxoopAFAKnpO7agcKwozNS24devWRgOGMhUdo6C1Ro0a5ocffvCyGakrzwIEgAE2wD/+8Q+7BXis6A9ELKHXlubXXntt0t1om+5URdNxY39AmjVrZv+gaG3A9evX238d0BBilUzTdvVGVoAY20Zdf/j0rw0KE+fNm2en++pfO/Tze++9Z0466SRPpaLwwewpCJUhgAACCCCAAAIIIICAs0AUvmckBYCHXxeeKcD/2rvjbTbFywBQa9Fr6q4GwWgm24cffph2d1rNctPU21Qz3HTf2jhE4aB2+lXopu/Q+27Ooe/WCgwVHm7YsCHj4yaurZc4Nfqdd96xA3NUhg4dav70pz+lrUejG7XrrspZZ51l5syZEz+2V69edl1AFd3rb37zm7T16Dgdr/L888+bK6+8Mn6s6td12rZtawcRZSq6b92/zpE5JToCBIABtqUCvbIsopnuj6beuNqNJ/aHINUj6I2vTUBSbVceO15hZKdOnczHH3+cUkF/OF944QX7rxBelyh8MHttQn0IIIAAAggggAACCCDgJhCF7xlhDwCDnAKsqagKrbQWn6bXzpo1y2iGmkvRbr133nmnreLee+81d9xxR1J1jABkBKBL/wrzuQSAAbaOVwFg7JZnzJhhQz4NL9ZoQm0PrnX6tJbBeeedl9WTaYdfrRE4efJkOzpR6wPqXzoUDGoR0cMPPzyresp6UBQ+mMv6zByPAAIIIIAAAggggAAC/gpE4XtG2APAoDYB+fTTT81pp51mN8HUaDR9Z9X0X9eyefNmO+pPA27OOeccu3NuYmENQNYAdO1jYT2fADCsLRPx+4rCB3PEm4jHQwABBBBAAAEEEECg4ASi8D0jKQBs0Cc8U4A/H2/7QxABoEI/jfzTVFxN59Xafppu61XRVOItW7bYnXI1yjCxXHTRRXa9QBWXXYD333/v7s0uuwBr85CbbrrJ1uOyC7CW/3r//ffZBdirDlSg9RAAFmjDFfptR+GDudDbgPtHAAEEEEAAAQQQQCBqAlH4nlHsAaBmt2marzbiUFEIps0wvCzadVfXSRUAJm6YuWDBAqMRgamK1serWbOm0aw6rZuntQoTS8OGDW1Yeswxx9hdgdMV7fp7+eWX25e1zFfPnj3jh86dO9euC6gycuRIo12B0xXt+qvRjNoMVPdUqVKl+KGJaxVqZ9991z2MHajXDj30UPujzinLEmZetg91+SNAAOiPK7WWIhCFD2YaGQEEEEAAAQQQQAABBMIlEIXvGUkBYP1rwzMC8P/7i21sP0cAahOPM88803zwwQf2Wtr8UpteelkSpwBrc5HZs2cnVa8QTWFaadfXbrraaVgl1QacCvUU7qlkCt369u1rtCOxypo1a8zRRx8dvx9t3qmlvrRUV8eOHc3rr7+ekkKvK9T8/vvv7T1pI8/EoqXDtFSYyosvvmi0e3Gqotcuu+wy+1K6XZK9bAvqClaAADBYb672H4EofDDTmAgggAACCCCAAAIIIBAugSh8zyjWAHDHjh2mQ4cO5t1337WdSptzaJMOr4vq1M68Kvfcc098Q5DYdRSmaYqwwsgmTZrYKcKpdhVODO60MYrW408skyZNigdt6Ubv6Znr169vpxqnGo2o+rQ+v4I/jezTuog6ft+SGNyNGjXKDBo0KOmQTZs2mcMOO8zuBKxwc+bMmSlZFTJqoxVtJrpx48a0IwW9bhPqC0aAADAYZ66yj0AUPphpVAQQQAABBBBAAAEEEAiXQBS+ZxRjAKjQrWvXrvENObQh5SOPPFKmzvXZZ5/ZIK1Zs2Zpz5s2bZrp3r27HVFXpUoVuxGmgrWFu0YAACAASURBVLF9S+I04FSBmqYGa9qvpgFruvJbb71Voo49e/bYAHH9+vXmgAMOsKMaGzdunHScpjaPHTvW/u7ZZ5812jh035I4Dfj88883U6ZMsZuixIqmMjdv3txs2LDBTknWuom1atUqUU/iNGBtqKK1DhOLfnfJJZfYX1199dV23UVKtAQIAKPVngXzNFH4YC4YbG4UAQQQQAABBBBAAIEiEYjC94ykAPCwXuGZArzxaduL/JgCrFBOwZaKpgAr/Es16i7WjbW+XeJUWf1eIVz79u3tFFiFiSeddJIdyafdfhWKvfTSS/Y//aySaW1BTb3Vxhlr1661x/bp08f06NHDVK1a1cybN8+MGDHCbNu2zf6s6ba6VqoyY8YMey8aeVevXj072rBVq1Y2qHzqqafim41ot2Pdf2Kwl1ifpuVqlJ+KnvGWW26xa/V99NFHZvjw4TZkVHnyySfjU333vR+1m4JCbX6i0YQDBw40Xbp0sYcpGH3ooYdsoKmpxAorU400LJI/I5F9TALAyDZtuB8sCh/M4Rbm7hBAAAEEEEAAAQQQKD6BKHzPKMYAMFPYl6oXH3744UYj/hJLLAAsrddXq1bNPPzwwzbUy1Q0OlDTbz/++OOUh2lU3wsvvBAP0dLVpaCvX79+dtRhqqJAcPr06Xatv3Rl586ddsSeAsVURVN2Na152LBhGZ9p0aJFplu3bkZTglMVbQ4ydepU07p169IYeb0ABQgAC7DRonDLUfhgjkI78AwIIIAAAggggAACCERJIArfMwgAS++RqQJAjdp79dVXjabnLl261G68oemxGtWmKbFNmza1O+pee+21dmRgNkW76Y4ZM8ZoeqwCQYV4DRo0sMGgpinrPrIpy5cvN6NHjzZvvvmm+eKLL0z16tXt9OArrrjC3o9G5GVTJk6caKfm/vOf/zRbt261owrbtm1rA8bYhiSl1SOTRx991AZ9sRC1UaNG5oILLrAjC2vXrl1aFbxeoAIEgAXacIV+21H4YC70NuD+EUAAAQQQQAABBBCImkAUvmckBYCH/Hd4pgB/+YztLn5MAY5aP+R5EAijAAFgGFulCO4pCh/MRdBMPCICCCCAAAIIIIAAAgUlEIXvGQSABdXluFkECkaAALBgmipaNxqFD+ZotQhPgwACCCCAAAIIIIBA4QtE4XsGAWDh90OeAIEwChAAhrFViuCeovDBXATNxCMigAACCCCAAAIIIFBQAlH4npEUAB7cMzxTgDc9a/sCU4AL6i3BzSIQFyAApDPkRSAKH8x5geOiCCCAAAIIIIAAAgggkFYgCt8zCADp4Agg4IcAAaAfqtRZqkAUPphLfUgOQAABBBBAAAEEEEAAgUAFovA9gwAw0C7DxRAoGgECwKJp6nA9aBQ+mMMlyt0ggAACCCCAAAIIIIBAFL5nlAgAK9TIe8Pu+nmbeYspwHlvB24AARcBAkAXPc7NWSAKH8w5PzwnIoAAAggggAACCCCAgC8CUfieQQDoS9egUgSKXoAAsOi7QH4AovDBnB85rooAAggggAACCCCAAALpBKLwPYMAkP6NAAJ+CBAA+qFKnaUKROGDudSH5AAEEEAAAQQQQAABBBAIVCAK3zOSAsB615gqYZkC/NUE25bsAhxol+ZiCHgmQADoGSUVlUUgCh/MZXlejkUAAQQQQAABBBBAAAH/BaLwPYMA0P9+whUQKEYBAsBibPUQPHMUPphDwMgtIIAAAggggAACCCCAQIJAFL5nEADSpRFAwA8BAkA/VKmzVIEofDCX+pAcgAACCCCAAAIIIIAAAoEKROF7RlIAWOeq8EwB3vJ/bVsyBTjQLs3FEPBMgADQM0oqKotAFD6Yy/K8HIsAAggggAACCCCAAAL+C0ThewYBoP/9hCsgUIwCBIDF2OoheOYofDCHgJFbQAABBBBAAAEEEEAAgQSBKHzPIACkSyOAgB8CBIB+qFJnqQJR+GAu9SE5AAEEEEAAAQQQQAABBAIViML3jKQA8KDfh2cK8NfP27ZkCnCgXZqLIeCZAAGgZ5RUVBaBKHwwl+V5ORYBBBBAAAEEEEAAAQT8F4jC9wwCQP/7CVdAoBgFCACLsdVD8MxR+GAOASO3gAACCCCAAAIIIIAAAgkCUfieQQBIl0YAAT8ECAD9UKXOUgWi8MFc6kNyAAIIIPD/t3cv8DbV+f/HP+QainJJLhE1g6E8cilyKZRrKWpqzFC5hdIkMY2uU6QwKNKghqnUTC6JlMgtPTKIcSvkGhW55a5y+T8+3357/9c5Z++z9z5nr73X+q7X9/HoMY2z9lrr+/x+2vvst+93fRFAAAEEEEAAgZQK2PA9I0MAePEfvbME+MCbZixZApzSkuZiCCRNgAAwaZScKBEBGz6YE+kvxyKAAAIIIIAAAggggID7AjZ8zyAAdL9OuAICQRQgAAziqHugzzZ8MHuAkVtAAAEEEEAAAQQQQAABh4AN3zMyBIAlOnlnBuCht4w0MwD5Tw4BfwoQAPpz3Hx/1zZ8MPt+EOgAAggggAACCCCAAAKWCdjwPYMA0LKipDsIeESAANAjAxG027DhgzloY0Z/EUAAAQQQQAABBBDwuoAN3zMIAL1eZdwfAv4UIAD057j5/q5t+GD2/SDQAQQQQAABBBBAAAEELBOw4XuGsw9Nit/tmSXAi39821QLS4At+4+G7gRGgAAwMEPtrY7a8MHsLVHuBgEEEEAAAQQQQAABBGz4nkEASB0jgIAbAgSAbqhyzpgCNnwwx+wkByCAAAIIIIAAAggggEBKBWz4nkEAmNKS4WIIBEaAADAwQ+2tjtrwwewtUe4GAQQQQAABBBBAAAEEbPiekSEAvPBuKZS3SNoH9tTZ47L4MEuA0z4Q3AACuRAgAMwFHi/NuYANH8w57z2vRAABBBBAAAEEEEAAATcEbPieQQDoRmVwTgQQIACkBtIiYMMHc1rguCgCCCCAAAIIIIAAAghEFbDhewYBIAWOAAJuCBAAuqHKOWMK2PDBHLOTHIAAAggggAACCCCAAAIpFbDhe0aGAPCCu7yzBPjIO2Ys2QU4pSXNxRBImgABYNIoOVEiAjZ8MCfSX45FAAEEEEAAAQQQQAAB9wVs+J5BAOh+nXAFBIIoQAAYxFH3QJ9t+GD2ACO3gAACCCCAAAIIIIAAAg4BG75nEABS0ggg4IYAAaAbqpwzpoANH8wxO8kBCCCAAAIIIIAAAgggkFIBG75nZAgAi97pnSXAx/5jxpIlwCktaS6GQNIECACTRsmJEhGw4YM5kf5yLAIIIIAAAggggAACCLgvYMP3DAJA9+uEKyAQRAECwCCOugf6bMMHswcYuQUEEEAAAQQQQAABBBBwCNjwPYMAkJJGAAE3BAgA3VDlnDEFbPhgjtlJDkAAAQQQQAABBBBAAIGUCtjwPSNDAFjkDu8sAT7+rhlLlgCntKS5GAJJEyAATBolJ0pEwIYP5kT6y7EIIIAAAggggAACCCDgvoAN3zMIAN2vE66AQBAFCACDOOoe6LMNH8weYOQWEEAAAQQQQAABBBBAwCFgw/cMAkBKGgEE3BAgAHRDlXPGFLDhgzlmJzkAAQQQQAABBBBAAAEEUipgw/cMZx8aF+7gmSXAS05OM2PJEuCUljQXQyBpAgSASaPkRIkI2PDBnEh/ORYBBBBAAAEEEEAAAQTcF7DhewYBoPt1whUQCKIAAWAQR90Dfbbhg9kDjNwCAggggAACCCCAAAIIOARs+J5BAEhJI4CAGwIEgG6ocs6YAjZ8MMfsJAcggAACCCCAAAIIIIBASgVs+J6RIQAsdLt3lgCfmm7GkiXAKS1pLoZA0gQIAJNGyYkSEbDhgzmR/nIsAggggAACCCCAAAIIuC9gw/cMAkD364QrIBBEAQLAII66B/pswwezBxi5BQQQQAABBBBAAAEEEHAI2PA9gwCQkkYAATcECADdUOWcMQVs+GCO2UkOQAABBBBAAAEEEEAAgZQK2PA9I0MAWPA2KZSnSEoNI13s1LnjsuSnGeZHLAFO+3BwAwjkSIAAMEdsvCi3AjZ8MOfWgNcjgAACCCCAAAIIIIBAcgVs+J5BAJjcmuBsCCDwqwABIJWQFgEbPpjTAsdFEUAAAQQQQAABBBBAIKqADd8zMgSABdpLoTznp33ET507IUt+fs/cBzMA0z4c3AACORIgAMwRGy/KrYANH8y5NeD1CCCAAAIIIIAAAgggkFwBG75nEAAmtyY4GwII/CpAAEglpEXAhg/mtMBxUQQQQAABBBBAAAEEEIgqYMP3DAJAChwBBNwQIAB0Q5VzxhSw4YM5Zic5AAEEEEAAAQQQQAABBFIqYMP3DGcfGuW71TNLgD89PdOMJUuAU1rSXAyBpAkQACaNkhMlImDDB3Mi/eVYBBBAAAEEEEAAAQQQcF/Ahu8ZBIDu1wlXQCCIAgSAQRx1D/TZhg9mDzByCwgggAACCCCAAAIIIOAQsOF7BgEgJY0AAm4IEAC6oco5YwrY8MEcs5McgAACCCCAAAIIIIAAAikVsOF7RoYA8Lx23lkCfGaWGUuWAKe0pLkYAkkTIABMGiUnSkTAhg/mRPrLsQgggAACCCCAAAIIIOC+gA3fMwgA3a8TroBAEAUIAIM46h7osw0fzB5g5BYQQAABBBBAAAEEEEDAIWDD9wwCQEoaAQTcECAAdEOVc8YUsOGDOWYnOQABBBBAAAEEEEAAAQRSKmDD9wxnH67P09YzS4CXnpttxpIlwCktaS6GQNIECACTRsmJEhGw4YM5kf5yLAIIIIAAAggggAACCLgvYMP3DAJA9+uEKyAQRAECwCCOugf6bMMHswcYuQUEEEAAAQQQQAABBBBwCNjwPYMAkJJGAAE3BAgA3VDlnDEFduzYIZUrVzbHLV++XMqWLRvzNRyAAAIIIIAAAggggAACCGQn8P3330u9evXMIdu3b5dKlSr5DswZANaVG6WgFEp7H36SU7JCFpj7YAlw2oeDG0AgRwIEgDli40W5FVixYkX4gzm35+L1CCCAAAIIIIAAAggggEBmAZ1oULduXd/BOANAL948AaAXR4V7QiC2AAFgbCOOcEGAANAFVE6JAAIIIIAAAggggAACYQECQHeKgQDQHVfOioDbAgSAbgtz/ogCp06dknXr1pmflSpVSvLly4eUJQLOZRcs77ZkUH3SDWrPJwNl2W1Sd5YNqI+6Q+35aLAsu1Wv197p06dl3759Rr1mzZpSqFD6l88mWgLahz179iT6spQdf8kll/D9LWXaXAiB5AkQACbPkjMhgICI2PDgZQbSnwLUnj/Hze93Td35fQT9e//Unn/Hzu93Tu35fQS5fwQQCKoAAWBQR55+I+CSAL8UugTLaWMKUHsxiTjABQHqzgVUThmXALUXFxMHuSBA7bmAyikRQACBFAgQAKYAmUsgECQBfikM0mh7q6/UnrfGIyh3Q90FZaS9109qz3tjEpQ7ovaCMtL0EwEEbBMgALRtROkPAmkW4JfCNA9AgC9P7QV48NPYdeoujfgBvzS1F/ACSGP3qb004nNpBBBAIBcCBIC5wOOlCCCQVYBfCqmKdAlQe+mSD/Z1qbtgj386e0/tpVM/2Nem9oI9/vQeAQT8K0AA6N+x484R8KQAvxR6clgCcVPUXiCG2XOdpO48NySBuSFqLzBD7bmOUnueGxJuCAEEEIhLgAAwLiYOQgCBeAX4pTBeKY5LtgC1l2xRzhePAHUXjxLHuCFA7bmhyjnjEaD24lHiGAQQQMB7AgSA3hsT7ggBXwvwS6Gvh8/XN0/t+Xr4fHvz1J1vh873N07t+X4IfdsBas+3Q8eNI4BAwAUIAANeAHQfAQQQQAABBBBAAAEEEEAAAQQQQMBuAQJAu8eX3iGAAAIIIIAAAggggAACCCCAAAIIBFyAADDgBUD3EUAAAQQQQAABBBBAAAEEEEAAAQTsFiAAtHt86R0CCCCAAAIIIIAAAggggAACCCCAQMAFCAADXgB0HwEEEEAAAQQQQAABBBBAAAEEEEDAbgECQLvHl94hgAACCCCAAAIIIIAAAggggAACCARcgAAw4AVA9xFAAAEEEEAAAQQQQAABBBBAAAEE7BYgALR7fOkdAggggAACCCCAAAIIIIAAAggggEDABQgAA14AdB8BBBBAAAEEEEAAAQQQQAABBBBAwG4BAkC7x5feIYAAAggggAACCCCAAAIIIIAAAggEXIAAMOAFQPcRQAABBBBAAAEEEEAAAQQQQAABBOwWIAC0e3zpHQK5Evjhhx9k+fLl5p8VK1aYfw4cOGDO2aVLF5k0aVJC5//oo49k/Pjx5nz79u2TUqVKSb169aRHjx7SsmXLhM7FwXYLrFq1SrRePv30U1m/fr1oLebPn18uvfRSadCggXTt2lUaNWoUNwK1FzdVoA88cuSIzJkzx7zXrVy5Ur799lvzXnXy5EkpXry4VK9eXVq3bm3q7+KLL45pRd3FJOKAOAQGDBggw4YNCx+5cOFCadq0abavpPbigOUQI5AnT564JJo0aSKLFi2i7uLS4iAEEEDAmwIEgN4cF+4KAU8IZPdLYSIB4Llz5+T+++834V+0piHgq6++Gvcvop4A4iZcEdAvGUuWLIl57j/96U8yceJEKVCgQNRjqb2YjBzgEJg/f760aNEipknJkiXlzTfflJtvvjnisdRdTEIOiFNgzZo1UqdOHTl9+nT4FdkFgNRenLAcFhZIRgBI3VFQCCCAgD8ECAD9MU7cJQJpEXD+UlihQgWpVq2afPzxx+ZeEgkABw0aJEOGDDGvq127tuhshipVqsjWrVvlxRdflNWrV5uf6XHPPfdcWvrKRb0jULVqVVMbOtvvjjvuMDP9KlasKGfOnJHPP/9cRowYYWZmabv77rtlypQpUW+e2vPOuPrhTjQAvO++++SGG26Qa665RvR9r2zZsnL27FnZvXu3TJ06VaZPn25qUYNnnSlYq1atLF2j7vww2t6/R627a6+91tRZ6dKlzUxobdkFgNSe98fVa3cY+l2vV69e0rt376i3V6RIEalcuXLEn1N3XhtV7gcBBBCILEAASGUggEBUgaeeekrq1q1r/ilTpozs2LEj/MtfvAHgli1bTHCosxd0FoPO7CpcuHD4midOnBCd8aXL7fLlyycbN2404SAtuAJt27aVzp07S4cOHeS8887LArF//35p2LChbN682fxMayrScmBqL7g1lNOea7AXqeac53vvvffktttuM390++23y7Rp0zJcjrrLqT6vyywwatQoefjhh+W3v/2tqbnnn38+2wCQ2qOGciIQCgD1d76nn3464VNQdwmT8QIEEEAgbQIEgGmj58II+E8gJwFgnz595JVXXjGd1dlbOpshc1u2bJlcd9115o8feOABefnll/2Hwx2nVGD27NnSrl07c82+ffvK6NGjs1yf2kvpkATqYvqXGvqXFboUWJ8R6GzUXaBKwbXO7tq1yzxz8tixY2bGnz577Zlnnsk2AKT2XBsOq0+c2wCQurO6POgcAghYJkAAaNmA0h0E3BRINADUZ8LoEjpdrqkzGL766quot6c/37Rpk5QvX16++eYbngXo5kBacG79UlysWDHTkzZt2ogGgs5G7VkwyB7ugs5m/uKLL6Ro0aJy9OjR8J1Sdx4eNJ/dmv4Fh76vhWbb68ys7AJAas9nA+yh281NAEjdeWgguRUEEEAgDgECwDiQOAQBBH4VSDQA3LZtW3g5b8+ePc0mH9Ga/jy0SYi+LtpzZhgLBFTg4MGD4V1Y9Yvy+++/nwGG2qNO3BLQv8ioWbOmeQ6gBoH6fLZQo+7cUg/Wef/zn//I73//e7nooovMTNNSpUqZpZnZBYDUXrBqJJm9zU0ASN0lcyQ4FwIIIOC+AAGg+8ZcAQFrBBINAD/44APR57lpGzlypPz5z3+OaqE/79evn/m5vq5169bWuNGR5AvMmDHDPH9N26OPPmo2k3E2ai/55kE+oz6rVGcyz5o1y9Ta3r17Dccbb7whf/zjH8M01F2QqyQ5ff/xxx/Nc3P37NkjEyZMkG7dupkTxwoAqb3k+AfxLKEAUJec6/OadRWGPpP5kksukQYNGsg999xjNkaK1Ki7IFYMfUYAAT8LEAD6efS4dwRSLJBoAKgz/nRXOW3vvvuudOzYMeod6+6auuOrNn2dzgikIRBJQHfG1GdGLl++3PxYZ2DpTCxno/aondwKTJo0Se69996op+nfv78JA527pVN3uVXn9T169DDBnwYvS5cuDddXrACQ2qN2cirgfA+Ldo727duLvideeOGFfNbmFJrXIYAAAh4QIAD0wCBwCwj4RSDRAHDYsGEyYMAA070PP/xQWrZsGbWr+vPQrL/hw4fLI4884hcW7jPFAiNGjBANX7TpzpjTp0/PcgfUXooHxcLLRQsAr776avOXFPXr16fuLBz3dHZJA7/GjRubnahXrVpllpqHWqwAkPe8dI6cv69dpEgRueWWW6RZs2bmec36bFPd3Gjx4sXmve7AgQOmg02aNJF58+ZJ/vz5wx2m7vw99tw9AggET4AAMHhjTo8RyLFAogHgs88+K08++aS53ieffCI33nhj1GsvWLDA/PKpTV/3+OOP5/g+eaG9AvqFpHnz5maZUunSpWXt2rVSpkyZLB2m9uytgVT1TJdi7t6921zu5MmTsnXrVtFns+ny8ypVqsioUaPCjzgI3RN1l6rRse86P//8s2i4rM+YjPRYg1gBILVnX02kqkf6Xle8ePGIl9PHHbRq1UpWr15tfj569Gjp27dv+FjqLlWjxHUQQACB5AgQACbHkbMgEAiBRANA/mY4EGWRsk5u2LBBGjVqJIcOHZKCBQvK3LlzzYyESI3aS9mwBO5C+tw/3ZlVl8299tpr5vlYoUbdBa4cktbhUMBXsWJF+fLLL0VnZTlbrACQ2kvaUHCiTAK60Yc+l1JD6qpVq8rXX3/Nex5VggACCPhUgADQpwPHbSOQDoFEA0CeSZSOUbLzmtu3b5frr79evvvuO7M8Tp8pqct/ozVqz8468EqvdIdWnQ2oIc2uXbukRIkS5taoO6+MkL/uQ3f6veqqq0zAMnPmTLMcM3OLFQBSe/4ac7/drW7opht+aNMNkS699FLe8/w2iNwvAgggICIEgJQBAgjELZBoADh79mxp166dOT+7AMfNzIGZBDT005l/OgtBZ13ps9k6d+6crRO1Rxm5KTBlyhTp1KmTucRbb70lf/jDH8y/U3duqtt7bt30avz48XL55ZfL4MGDI3ZUN8qaNm2a+dkTTzwhumOrNv2M1SCa2rO3PrzQM32es84y1aYbcNWtW5f3PC8MDPeAAAIIJChAAJggGIcjEGSBRANADWz0WVna9AuOzlCI1kJfgPTn+rrKlSsHmZq+/5/A/v37zTJfXRKnbcyYMdKnT5+YPtReTCIOyIWAPgj/pptuMmcYMmSIPPbYY+bfqbtcoAb4pbqMfPLkyTkS0NnRlSpVovZypMeL4hXQ51LqBm3anAEg73nxCnIcAggg4A0BAkBvjAN3gYAvBBINAM+dOyfly5c3yzZ1Zzl9uHm0ps+X0WVQ5cqVM0vqdKYXLdgChw8fNhvH6G6Y2oYOHSoDBw6MC4Xai4uJg3Io4Nwh+KWXXpIHH3zQnIm6yyFowF+WjACQ2gt4Ebnc/TZt2sicOXPMVXRzJP1djfc8l9E5PQIIIOCCAAGgC6icEgFbBRINANWhd+/eMm7cOEPy+eefy7XXXpuFZ9myZXLdddeZP9fjx44daysh/YpT4MSJE2aG1WeffWZeMWjQIHnuuefifPWvh1F7CXFxcAICzi/DCxculKZNm4ZfTd0lAMmhcQvEegYg73lxU3JgggI6y0//EveXX34xy9R1R3Rn4z0vQVAORwABBNIoQACYRnwujYDfBHISAG7evFlq1Kghp0+fljp16siSJUukcOHC4a6fPHlSGjduLCtXrpR8+fKZpZ5XXHGF32i43yQK6IPw9blWH3/8sTnrQw89JKNGjUr4CtRewmSBf4HO7LvrrrukUKFCUS30eab9+vUzP9ell7ojpr53hRp1F/gycgUgngCQ2nOF3uqTzpo1S1q1apXhPczZ4b1795qfr1692vzxiBEjwu9/vOdZXRp0DgEELBUgALR0YOkWAskQWLp0qWzZsiV8Kn0emz4HRlvDhg2lW7duGS6jy5giNX0+li7f1Fa7dm2zjFOfDah/i/zCCy+Ef7HU4/R5WrRgC3To0EGmT59uEHQJsIZ/2S0JL1CggFx55ZXUXrDLJim910Dv6NGjojWou07r+1TRokXNn61bt85s+BGalap1p7tiNm/ePMu1ec9LynBwEodAPAGgHk7tUTaJCOh7ns7s0/c8XYmh/1//klZ/31u0aJF5dvOBAwfMKfU9cf78+VKwYEHe8xJB5lgEEEDAQwIEgB4aDG4FAa8JJPpcIn0GUaR29uxZ6d69u7z++utRu9i1a1ezC2LevHm9xsD9pFgg0ec/XnbZZaKzU6m9FA+UhZfTL787d+6M2TN9tqm+n7Vo0YK6i6nFAckQiDcA5PM2GdrBOUe873kaEE6cOFGKFy/Oe15wyoOeIoCAhQIEgBYOKl1CIFkCyQoAQ/ejD5DWkG/FihXmb5dLliwpdevWNTsE6xITGgIqkMwAkNqjphIR0FnJOsNFn+unmxbp8jed/aJLgsuUKSNXX321tG3bVu688045//zzY56a97yYRBwQp0C8ASDveXGCcpgRWLx4sflHn9Gsz/rT382OHDliZj5XqFBBGjRoIF26dAk/pzkWG+95sYT4OQIIIJBeAQLA9PpzdQQQQAABBBBAAAEEEEAAAQQQQAABBFwVIAB0lZeTI4AAAggggAACCCCAAAIIIIAAAgggkF4BAsD0+nN1BBBAAAEEEEAAAQQQQAABBBBAAAEEXBUgAHSVl5MjgAAC9B7j1gAAEuBJREFUCCCAAAIIIIAAAggggAACCCCQXgECwPT6c3UEEEAAAQQQQAABBBBAAAEEEEAAAQRcFSAAdJWXkyOAAAIIIIAAAggggAACCCCAAAIIIJBeAQLA9PpzdQQQQAABBBBAAAEEEEAAAQQQQAABBFwVIAB0lZeTI4AAAggggAACCCCAAAIIIIAAAgggkF4BAsD0+nN1BBBAAAEEEEAAAQQQQAABBBBAAAEEXBUgAHSVl5MjgAACCCCAAAIIIIAAAggggAACCCCQXgECwPT6c3UEEEAAAQQQQAABBBBAAAEEEEAAAQRcFSAAdJWXkyOAAAIIIIAAAggggAACCCCAAAIIIJBeAQLA9PpzdQQQQAABBBBAAAEEEEAAAQQQQAABBFwVIAB0lZeTI4AAAggggAACCCCAAAIIIIAAAgggkF4BAsD0+nN1BBBAAAEEEEAAAQQQQAABBBBAAAEEXBUgAHSVl5MjgAACCCCAAAIIIIAAAggggAACCCCQXgECwPT6c3UEEEAAAQQQQAABBBBAAAEEEEAAAQRcFSAAdJWXkyOAAAIIIJBcgUWLFskNN9xgTrpw4UJp2rRpci/A2VIqoOO3ePFiadKkiejY0hBAAAEEEEAAAQQQcEOAANANVc6JAAIIIICASwIEgC7Bpum0BIBpgueyCCCAAAIIIIBAwAQIAAM24HQXAQQQQMDfAgSA/h6/zHdPAGjXeNIbBBBAAAEEEEDAqwIEgF4dGe4LAQQQQAABBKwXIAC0fojpIAIIIIAAAggg4AkBAkBPDAM3gQACCCCAAAJBFCAADOKo02cEEEAAAQQQQCD1AgSAqTfniggggAACCCCAgBEgAKQQEEAAAQQQQAABBFIhQACYCmWugQACCCBgvcDTTz8tzzzzjOnnuXPn5MiRI/L3v/9dpk2bJtu3b5ciRYrINddcI48//rg0aNAg7PHDDz/ISy+9JDNmzJAdO3ZIoUKF5Prrrxc9X+3atbO4xXoG4D333COTJ0+Wyy67zJzvxx9/DN+H/v/8+fNLrVq1pGfPntKpU6ekj8uZM2fkjTfekLffflvWrFkjBw8elPPPP19KlSolFStWlGbNmkn79u2levXqWa69bNkymT17tixdulQ2btxoXqse5cuXN7vkPvjggxFfFzpR5r7v2bNHhg8fLu+//77s3r1bSpQoIQ0bNpSnnnpKatSoEb6+uuhYffTRR7Jr1y654IILpHnz5vK3v/1NqlSpEtEo83ir88iRI2Xq1Kmyc+dOKVCggHHu3r17ts7xBoCHDh2SsWPHygcffCBff/21HD16VC6++GKpW7eudOnSRW6//fZsx1LrS+ti5cqVsm/fPnN/OiblypUzIWS7du2kXr16Sa8HTogAAggggAACCCDgDQECQG+MA3eBAAIIIOBzAWcg9M0335gAafPmzVl6dd5555lw7I477pC1a9dK69at5dtvv81yXMGCBWXOnDly4403ZvhZIgGgBlqtWrUyQWCk1qdPHxkzZkzS5I8dO2b68+mnn2Z7zg4dOpigzNkmTZok9957b7avUzsNS3v37h3xOGcAOHPmTGnZsqVoCJi5aSA5d+5cE7QuWLDAhGeHDx/OcpwGhtoXZ1gYOsg53tu2bZMWLVrI1q1bI95Xx44dzZjny5cvy8/jCQC1DjSs1ZAxWmvTpo288847UrRo0QyHaCB79913y7vvvputrYbTGg7SEEAAAQQQQAABBOwUIAC0c1zpFQIIIIBAigWcgVD9+vVl3bp18tBDD5kQSgMnndWmM890ZmCxYsXM7Did1Xb8+HF55JFHzL/r7DwN7QYPHiw///yzmTGns710tlaoxRsA6uyu4sWLmwBM70MDSQ2HVq9ebWYq6ow4bXq9m2++OSla/fv3lxEjRphztW3b1oRW2gedxaezzrTPOsOvTJkyWQKpiRMnyoABA+SWW24xFldccYWZNfndd9/JqlWrTPC3f/9+yZMnj8yfPz9LMKrXDAWA2nftq4Z6ek96Pg0Pta9DhgwxtpUqVZJ58+aZWZk64+/RRx8VHbfTp0+bWZujRo0yMzn1z3RmYubmHG+dhffFF19Ijx49RMO+Cy+80IS7L7zwQjgE1tmL2ofMLVYAqPeoIa4GeXrPvXr1Mvek96zB8b///W958803zWk1yNR7dzYNePXa2jTw7Natm5nVqD46w3L9+vXy4Ycfmn//73//m5Q64CQIIIAAAggggAAC3hMgAPTemHBHCCCAAAI+FHAGQjp7b/HixSaocTadyaUztbRpSKUBk4ZLmZeZvvLKK6Kz87RNnz5dbrvttvBp4g0A9QUaAGrwmHkG25YtW6RmzZpy6tQpE7jpbLlkNA37dAmthmDZzTjTsOmiiy7KcEkNs3TGnYalkZqGeY0bNzbBmgZZkWYZhgJAfX3JkiVj2oZC0s8++8yMh7NpGDls2DDzRxpAZl6O7RxvPWbKlClmpp2z6TLdRo0ameAzb9688r///c+4O1t2AaCGw1obe/fulZtuusksE4/kM2HCBBM+atNwVJdZh5qaqZXWotZCpFmIemykMUlGTXAOBBBAAAEEEEAAAW8IEAB6Yxy4CwQQQAABnws4A6GBAwfK0KFDI/ZIZ3HpM+K0vfrqq+ZZfJnbyZMnTUCmAd3DDz9snk8XaokEgDrjLDT7K/M1NKzSJaMaumn4k4ymMxV/+eUXGT16tPTt2zcZp8xwDg0q9fmB2nQ2oD4Dz9mcAeC4cePk/vvvz9ZWfxhtBqQ+t/Hyyy83r4/UH+d462zHWbNmRezv8uXLw0GwLl3W5/g5W3YBYGj2ns6g1JopXbp0VFMN+PRaOusyNCNQD77yyivNLNLMdZT0weGECCCAAAIIIIAAAp4WIAD09PBwcwgggAACfhFwBkK6zPbqq6+OeOs6407DIl3KeuDAARPARWq6gYQuI7711lvlvffeCx8SbwCo59dlt5lDstCJdHMMXfaqTTeY0NmCuW268Yg+/1A3OdGlq9Fm88VzHZ39pvev/6szJbXpuUMzKD/55JMsy4BDAaD2XQPCzLMMQ9cN2aq9joEeH6npMludxadLqHVJsLM5x1uX3Wa3Ccfvfvc72bBhgwnjNm3alOE82QWA+lxBndGnS7Q1qMyu6VjqmGpo6XwWoS5/XrJkiVStWlU+//xzMzOShgACCCCAAAIIIBA8AQLA4I05PUYAAQQQcEHAGQhpaBUt/OrcubPZJVeXnOoOwNFaaOmmbgKiYVeoxRsAxjr/66+/Ll27dg0HaxUqVMi1ypNPPinPPvusOY/OVtONTnQ5qi7ZzbzENtLFNLQL7Zyss9ZCwV+kY/XZd3feeWeGHzmfARiPrT67T2fNRWuhJc333XefvPbaaxkOy7zpS3Z++vp//vOfJmjUWZ3OZzpmFwBqQJndxh+R7lvrTusv1JzjrM8m1KBSg0Vdmqy7K9MQQAABBBBAAAEEgiFAABiMcaaXCCCAAAIuCzgDoeyCK+dOtdF259VbjRYMxRsA6my87M7v3HVXl7vq0uTcNl3+q8tuNexyGmjwpc8h1PBJl8HqJiCZm26ioTPddEZePE3vv0uXLhEDwFh9j7XxRuikoeXaeh29nrM5x1tDPX3uY7T22GOPhZeE66Yszv5Huxe1dAaF8ZiEjslcf4MGDZIXX3zRbHDibPp8QV1SrWMSWu6cyHU4FgEEEEAAAQQQQMA/AgSA/hkr7hQBBBBAwMMCBID/f3C+/PJLefvtt2XBggWycuVKs+tuqOnus/qMOl3aHGr682rVqsm2bdvMTsj63EL9uS6Z1VlwoXBNfx7aMEVDRg1TnS234Wrm8oo3APzpp5+yDev+8pe/mB2BtcUbAGqoWLhwYfManen4xBNPxF39uuQ4c9NnCL711ltmNqluPHPixInwIWquz4uM9MzEuC/KgQgggAACCCCAAAKeFiAA9PTwcHMIIIAAAn4RIACMPFK6oYnusqu75P7rX/+SM2fOmGBLn1NXtmxZ8yJ9vl2rVq3Mv48fP166d+8e8WQ6S7BOnTrmZ14KAPXZhG4sAS5SpIgJ6nQH4Llz5ybtPwWdXahLn3Wn5n/84x9mWbLO0lTfzLsdJ+2inAgBBBBAAAEEEEAgrQIEgGnl5+IIIIAAArYIEADGHsmRI0dKv379sgR9I0aMkP79+5s/P3LkiBQrViziyTSsCs1S81IAGGsTkJo1a8r69esT3gREn52o4akGgfpMw9xsqhJtdGbMmBHewOSvf/2rDB48OPZAcgQCCCCAAAIIIICA7wQIAH03ZNwwAggggIAXBQgAY4/K2rVr5aqrrjIHDhkyRPTZeNp0eawuk9WWeYls6Kxnz54V3bRj1apV5o+8FADqzs4zZ86MCKBLoPW+temz9saOHZvhuOyeR6jP7Rs4cKA5Xv89tGtzbOn4j9DAVTcH0dajRw8zI5CGAAIIIIAAAgggYJ8AAaB9Y0qPEEAAAQTSIBD0APDgwYOydOlSadeunVlOGqkNHz48HGLpMwLvuusuc9j06dOlQ4cO5t81DBwwYECWl2sQpiFYqHkpANR7irQr8bFjx0R3c169erXkzZvX/G+tWrUy9C27AFDDOX3moe6OrM9B1Nl6oaXSkXx1tqAusdZrhpo+b1Gd8+XLF3FMpk6danZr1vb888+Hg9g0/CfEJRFAAAEEEEAAAQRcFCAAdBGXUyOAAAIIBEcg6AGg7jhcuXJls5uw7vZbv3590d14NXj6/vvvZdasWTJx4kTRmXzly5eXjRs3mqWt2o4fP252odVlrnq8zkTTWXUlS5aULVu2yIQJE8zmFQ0bNjRLYrV5KQDU5xJquNezZ0/p2LGjXHDBBaKzHTXM3LRpk7lf3dhEN9rI3GLtSDx//nwT+ukOvhoialCq/4Q2Q1FbfXafhoN6zZdfflkeeOCB8GU0jNVdh3VMGjRoYF5XqFAh2bt3r8ybN0/GjRsn+pxG3Zzlq6++MmNDQwABBBBAAAEEELBPgADQvjGlRwgggAACaRAgAPw1AIzVypUrZ8LAzJtN6CYX7du3NxtSRGoalI0ZM0ZCO9x6KQDU3YmbNWsm27dvj3jvGti98847EWfhxQoA9YS6m3KnTp3M8uhYbfLkydK5c+cMAWCs1xQvXtzMYNTNRmgIIIAAAggggAACdgoQANo5rvQKAQQQQCDFAkEPAM+dO2dmoOmsMg2sNBTTWWa6DFYDpho1apjlwTq7L9omHxs2bJChQ4ea1+/bt8+8rnr16ib86tq1q+huu6GQ0UsBoPb90KFDokucdSbezp07JX/+/OZ5h9pfvf9oLZ4AUF+ruwFrn2fPni1r1qyRAwcOmBmBpUqVkmrVqkmTJk3MzMDf/OY3GS6lMxB1THQG5ebNm82YHD582IyBHtuyZUvp1auXlC5dOsX/xXA5BBBAAAEEEEAAgVQKEACmUptrIYAAAggggIAVAvEGvlZ0lk4ggAACCCCAAAII+F6AAND3Q0gHEEAAAQQQQCDVAgSAqRbneggggAACCCCAAAK5ESAAzI0er0UAAQQQQACBQAoQAAZy2Ok0AggggAACCCDgWwECQN8OHTeOAAIIIIAAAukSIABMlzzXRQABBBBAAAEEEMiJAAFgTtR4DQIIIIAAAhYK6C62x48fT7hnJUqUEN3dN0iNADBIo01fEUAAAQQQQAAB/wsQAPp/DOkBAggggAACSREI7Uib6Mm6dOkikyZNSvRlvj6eANDXw8fNI4AAAggggAACgRMgAAzckNNhBBBAAAEEIgsQAFIZCCCAAAIIIIAAAgjYKUAAaOe40isEEEAAAQQQQAABBBBAAAEEEEAAAQSMAAEghYAAAggggAACCCCAAAIIIIAAAggggIDFAgSAFg8uXUMAAQQQQAABBBBAAAEEEEAAAQQQQIAAkBpAAAEEEEAAAQQQQAABBBBAAAEEEEDAYgECQIsHl64hgAACCCCAAAIIIIAAAggggAACCCBAAEgNIIAAAggggAACCCCAAAIIIIAAAgggYLEAAaDFg0vXEEAAAQQQQAABBBBAAAEEEEAAAQQQIACkBhBAAAEEEEAAAQQQQAABBBBAAAEEELBYgADQ4sGlawgggAACCCCAAAIIIIAAAggggAACCBAAUgMIIIAAAggggAACCCCAAAIIIIAAAghYLEAAaPHg0jUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAKkBBBBAAAEEEEAAAQQQQAABBBBAAAEELBYgALR4cOkaAggggAACCCCAAAIIIIAAAggggAACBIDUAAIIIIAAAggggAACCCCAAAIIIIAAAhYLEABaPLh0DQEEEEAAAQQQQAABBBBAAAEEEEAAAQJAagABBBBAAAEEEEAAAQQQQAABBBBAAAGLBQgALR5cuoYAAggggAACCCCAAAIIIIAAAggggAABIDWAAAIIIIAAAggggAACCCCAAAIIIICAxQL/D0JTBrX1g2BeAAAAAElFTkSuQmCC\" 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"source": [
"plot_xr = hyperparams_feas\n",
"\n",
"plt.figure()\n",
"plot_xr.perc_outliers.plot(norm = matplotlib.colors.LogNorm())\n",
"\n",
"plt.figure()\n",
"plot_xr.N_clusters.plot(norm = matplotlib.colors.LogNorm())\n",
"\n",
"plt.figure()\n",
"plot_xr['perc_clust_>1MWp'].plot() #\n",
"\n",
"plt.figure()\n",
"plot_xr.kWp_std.plot() # norm = matplotlib.colors.LogNorm()\n",
"\n",
"plt.figure()\n",
"plot_xr['kWp_50%'].plot() # norm = matplotlib.colors.LogNorm()\n",
"\n",
"plt.figure()\n",
"plot_xr['aprx_add_clst'].plot() #\n",
"\n",
"plt.figure()\n",
"plot_xr['N_BLD_max'].plot() #"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"# Under feasibility criteria: minimise the standard deviation (want as even clusters as possible)\n",
"hyperparam_SELECT = hyperparams_feas.where(hyperparams_feas.kWp_std == hyperparams_feas.kWp_std.min(), drop = True)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (EPS: 1, min_samples: 1)\n",
"Coordinates:\n",
" * EPS (EPS) float64 350.0\n",
" * min_samples (min_samples) float64 15.0\n",
"Data variables:\n",
" exec_time (EPS, min_samples) float64 18.44\n",
" N_clusters (EPS, min_samples) float64 3.687e+03\n",
" N_clust_noDem (EPS, min_samples) float64 117.0\n",
" N_clust_&gt;1MWp (EPS, min_samples) float64 306.0\n",
" perc_outliers (EPS, min_samples) float64 4.228\n",
" aprx_add_clst (EPS, min_samples) float64 5.548e+03\n",
" kWp_mean (EPS, min_samples) float64 1.601e+03\n",
" kWp_std (EPS, min_samples) float64 2.29e+04\n",
" kWp_min (EPS, min_samples) float64 0.0\n",
" kWp_25% (EPS, min_samples) float64 8.855\n",
" kWp_50% (EPS, min_samples) float64 27.56\n",
" kWp_75% (EPS, min_samples) float64 176.3\n",
" kWp_max (EPS, min_samples) float64 1.046e+06\n",
" N_BLD_mean (EPS, min_samples) float64 560.2\n",
" N_BLD_std (EPS, min_samples) float64 5.221e+03\n",
" N_BLD_min (EPS, min_samples) float64 2.0\n",
" N_BLD_25% (EPS, min_samples) float64 22.0\n",
" N_BLD_50% (EPS, min_samples) float64 44.0\n",
" N_BLD_75% (EPS, min_samples) float64 177.2\n",
" N_BLD_max (EPS, min_samples) float64 1.713e+05</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9a07f682-1343-4477-afaa-30c90209ced3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a07f682-1343-4477-afaa-30c90209ced3' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>EPS</span>: 1</li><li><span class='xr-has-index'>min_samples</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c45a4d66-55f5-4f82-9706-0c39ca11d032' class='xr-section-summary-in' type='checkbox' checked><label for='section-c45a4d66-55f5-4f82-9706-0c39ca11d032' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>EPS</span></div><div class='xr-var-dims'>(EPS)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>350.0</div><input id='attrs-8da9b18b-27a0-4ef6-bd16-ef05f45c1e37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8da9b18b-27a0-4ef6-bd16-ef05f45c1e37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-784ba4a9-95e1-4f9b-9e28-475136b0167d' class='xr-var-data-in' type='checkbox'><label for='data-784ba4a9-95e1-4f9b-9e28-475136b0167d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([350.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>min_samples</span></div><div class='xr-var-dims'>(min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0</div><input id='attrs-1bef4d81-417b-45af-82e0-523211278688' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bef4d81-417b-45af-82e0-523211278688' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-906d1889-e639-4b3c-8cd4-81fea597f692' class='xr-var-data-in' type='checkbox'><label for='data-906d1889-e639-4b3c-8cd4-81fea597f692' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e358533d-ac7c-4035-8067-3abd6218fe6d' class='xr-section-summary-in' type='checkbox' ><label for='section-e358533d-ac7c-4035-8067-3abd6218fe6d' class='xr-section-summary' >Data variables: <span>(20)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>exec_time</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.44</div><input id='attrs-d30eaf73-1b3c-42f7-92c9-a32e93acb919' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d30eaf73-1b3c-42f7-92c9-a32e93acb919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c22c6781-949f-4b7d-8f4a-b62bf0bb8224' class='xr-var-data-in' type='checkbox'><label for='data-c22c6781-949f-4b7d-8f4a-b62bf0bb8224' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[18.44421124]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_clusters</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.687e+03</div><input id='attrs-f4585600-e15d-4caa-accf-21927a360d98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4585600-e15d-4caa-accf-21927a360d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d42a01-36bd-44cf-8bd0-2012a7f07c80' class='xr-var-data-in' type='checkbox'><label for='data-b9d42a01-36bd-44cf-8bd0-2012a7f07c80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3687.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_clust_noDem</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>117.0</div><input id='attrs-1ddb3417-5846-40f9-9d0f-1d3a9b94920a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ddb3417-5846-40f9-9d0f-1d3a9b94920a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4db8f8ef-40ec-485b-ac0c-51fc5d79d142' class='xr-var-data-in' type='checkbox'><label for='data-4db8f8ef-40ec-485b-ac0c-51fc5d79d142' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[117.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_clust_&gt;1MWp</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>306.0</div><input id='attrs-f8ae22b6-fa6e-4cf1-91c3-4a9721276f7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8ae22b6-fa6e-4cf1-91c3-4a9721276f7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1113d53-4cbf-479c-aaf7-2a9924f738c9' class='xr-var-data-in' type='checkbox'><label for='data-c1113d53-4cbf-479c-aaf7-2a9924f738c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[306.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perc_outliers</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.228</div><input id='attrs-dcffb9bf-8dce-4c28-87c1-639718a78e73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcffb9bf-8dce-4c28-87c1-639718a78e73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-861ea8c9-7bc2-4b18-b482-6a4e879a9801' class='xr-var-data-in' type='checkbox'><label for='data-861ea8c9-7bc2-4b18-b482-6a4e879a9801' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.22757142]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aprx_add_clst</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.548e+03</div><input id='attrs-5a09dd57-bb04-4d43-98c8-c19282c59dcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a09dd57-bb04-4d43-98c8-c19282c59dcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c41e032-923d-4b31-82fc-cf911bb0fa71' class='xr-var-data-in' type='checkbox'><label for='data-4c41e032-923d-4b31-82fc-cf911bb0fa71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5547.97842082]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_mean</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.601e+03</div><input id='attrs-f7c8afcc-74f8-42d7-bb51-f7561ad71954' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7c8afcc-74f8-42d7-bb51-f7561ad71954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4741662-d0bd-4b18-8e3f-e2b0934e3bde' class='xr-var-data-in' type='checkbox'><label for='data-a4741662-d0bd-4b18-8e3f-e2b0934e3bde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1600.75404227]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_std</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.29e+04</div><input id='attrs-9f817b6d-41f7-4f23-bbcb-379eba5c41c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f817b6d-41f7-4f23-bbcb-379eba5c41c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af4c372a-abd3-4f3d-9b4b-4cc2615b55fb' class='xr-var-data-in' type='checkbox'><label for='data-af4c372a-abd3-4f3d-9b4b-4cc2615b55fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[22898.54356023]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_min</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-1a3ec0c1-8943-40f4-beaa-2fac3e5bb316' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a3ec0c1-8943-40f4-beaa-2fac3e5bb316' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ed00adf-f042-499d-80d1-d82a147002e6' class='xr-var-data-in' type='checkbox'><label for='data-6ed00adf-f042-499d-80d1-d82a147002e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_25%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.855</div><input id='attrs-5cf5bc37-5b4a-460e-9be0-834a91cbb2e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cf5bc37-5b4a-460e-9be0-834a91cbb2e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6204f7ac-963b-4981-b848-9b3359ebcab9' class='xr-var-data-in' type='checkbox'><label for='data-6204f7ac-963b-4981-b848-9b3359ebcab9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[8.85501178]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_50%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.56</div><input id='attrs-24f556d4-bb05-40ef-91d9-966c03e3cb52' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24f556d4-bb05-40ef-91d9-966c03e3cb52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14ad32cd-e082-4378-80db-fe528531e46f' class='xr-var-data-in' type='checkbox'><label for='data-14ad32cd-e082-4378-80db-fe528531e46f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.56183106]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_75%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>176.3</div><input id='attrs-2b118690-a7ba-4628-bd6b-2de2cd904a13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b118690-a7ba-4628-bd6b-2de2cd904a13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-678c0ce2-eba3-45cf-9f8d-80353e4f7424' class='xr-var-data-in' type='checkbox'><label for='data-678c0ce2-eba3-45cf-9f8d-80353e4f7424' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[176.28175541]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kWp_max</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.046e+06</div><input id='attrs-802e30e0-b285-401c-af0d-0704f77e96ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-802e30e0-b285-401c-af0d-0704f77e96ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e98d235-a6a5-4299-b6bf-ca8df6dbf15b' class='xr-var-data-in' type='checkbox'><label for='data-8e98d235-a6a5-4299-b6bf-ca8df6dbf15b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1046375.54058484]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_mean</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>560.2</div><input id='attrs-ad7b61d6-3568-4326-9a45-fde045f4d232' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad7b61d6-3568-4326-9a45-fde045f4d232' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff7783e6-368b-4df2-bd8c-a367461070a0' class='xr-var-data-in' type='checkbox'><label for='data-ff7783e6-368b-4df2-bd8c-a367461070a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[560.17651844]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_std</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.221e+03</div><input id='attrs-a4ea5f37-e7f2-4a95-b533-c3ecac5eb961' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4ea5f37-e7f2-4a95-b533-c3ecac5eb961' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3ebd617-28aa-4341-9fb1-498505b2ebc3' class='xr-var-data-in' type='checkbox'><label for='data-c3ebd617-28aa-4341-9fb1-498505b2ebc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5221.0411113]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_min</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-72b119c6-9dd8-44fe-aa4d-8eda702cc530' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72b119c6-9dd8-44fe-aa4d-8eda702cc530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b9e4145-4972-482e-83bd-f2324d65f34a' class='xr-var-data-in' type='checkbox'><label for='data-9b9e4145-4972-482e-83bd-f2324d65f34a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_25%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.0</div><input id='attrs-7ff77000-dd28-4f93-9a1f-fcefc0ae68b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ff77000-dd28-4f93-9a1f-fcefc0ae68b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90678d3a-8f3a-4d42-b2c2-d85ee7a99ff7' class='xr-var-data-in' type='checkbox'><label for='data-90678d3a-8f3a-4d42-b2c2-d85ee7a99ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[22.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_50%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0</div><input id='attrs-e0d81667-773a-40af-bccb-bc55a7ad6aec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0d81667-773a-40af-bccb-bc55a7ad6aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af28978c-dccd-4a5c-8f1c-9c04fd188557' class='xr-var-data-in' type='checkbox'><label for='data-af28978c-dccd-4a5c-8f1c-9c04fd188557' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[44.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_75%</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>177.2</div><input id='attrs-3d0c0dee-7ddc-4051-9c6b-ecd67432f64f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d0c0dee-7ddc-4051-9c6b-ecd67432f64f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-790767f8-5e56-4b60-9803-fcd9c5c12480' class='xr-var-data-in' type='checkbox'><label for='data-790767f8-5e56-4b60-9803-fcd9c5c12480' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[177.25]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_BLD_max</span></div><div class='xr-var-dims'>(EPS, min_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.713e+05</div><input id='attrs-8c3a4067-67c9-45fd-ac2d-3ecc90e76e86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c3a4067-67c9-45fd-ac2d-3ecc90e76e86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc5433cd-547a-4bd5-8ac8-b32954694069' class='xr-var-data-in' type='checkbox'><label for='data-bc5433cd-547a-4bd5-8ac8-b32954694069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[171331.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-808ce867-07a7-4758-ae7e-1fda4275d0ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-808ce867-07a7-4758-ae7e-1fda4275d0ab' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (EPS: 1, min_samples: 1)\n",
"Coordinates:\n",
" * EPS (EPS) float64 350.0\n",
" * min_samples (min_samples) float64 15.0\n",
"Data variables:\n",
" exec_time (EPS, min_samples) float64 18.44\n",
" N_clusters (EPS, min_samples) float64 3.687e+03\n",
" N_clust_noDem (EPS, min_samples) float64 117.0\n",
" N_clust_>1MWp (EPS, min_samples) float64 306.0\n",
" perc_outliers (EPS, min_samples) float64 4.228\n",
" aprx_add_clst (EPS, min_samples) float64 5.548e+03\n",
" kWp_mean (EPS, min_samples) float64 1.601e+03\n",
" kWp_std (EPS, min_samples) float64 2.29e+04\n",
" kWp_min (EPS, min_samples) float64 0.0\n",
" kWp_25% (EPS, min_samples) float64 8.855\n",
" kWp_50% (EPS, min_samples) float64 27.56\n",
" kWp_75% (EPS, min_samples) float64 176.3\n",
" kWp_max (EPS, min_samples) float64 1.046e+06\n",
" N_BLD_mean (EPS, min_samples) float64 560.2\n",
" N_BLD_std (EPS, min_samples) float64 5.221e+03\n",
" N_BLD_min (EPS, min_samples) float64 2.0\n",
" N_BLD_25% (EPS, min_samples) float64 22.0\n",
" N_BLD_50% (EPS, min_samples) float64 44.0\n",
" N_BLD_75% (EPS, min_samples) float64 177.2\n",
" N_BLD_max (EPS, min_samples) float64 1.713e+05"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyperparam_SELECT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PART 2 : Split large clusters with kMEANS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get number of clusters with larger peak demand"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"PV_clust = cluster_data[cluster_data.clusterID > -1].groupby('clusterID')['pv_potential_kWh_a'].sum() / 1e3"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
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" <th>BLD_ID</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>peak_demand_W</th>\n",
" <th>annual_demand_kWh</th>\n",
" <th>pv_potential_kWh_a</th>\n",
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],
"text/plain": [
" BLD_ID x y peak_demand_W \\\n",
"0 3.000000e+07 2.574685e+06 1.083026e+06 0.000000 \n",
"1 3.000000e+07 2.574188e+06 1.084778e+06 966.458332 \n",
"2 3.000000e+07 2.574218e+06 1.085029e+06 966.458332 \n",
"3 3.000000e+07 2.573684e+06 1.085899e+06 966.458332 \n",
"4 3.000000e+07 2.573552e+06 1.086617e+06 966.458332 \n",
"... ... ... ... ... \n",
"2157120 1.491879e+09 2.567039e+06 1.249476e+06 966.458332 \n",
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"2157122 1.491732e+09 2.579994e+06 1.253240e+06 966.458332 \n",
"2157123 1.491805e+09 2.580004e+06 1.253290e+06 966.458332 \n",
"2157124 1.491805e+09 2.582641e+06 1.250163e+06 966.458332 \n",
"\n",
" annual_demand_kWh pv_potential_kWh_a available_area clusterID \n",
"0 0.000000 62557.60270 315.894507 -1 \n",
"1 3698.619653 5087.15430 29.367821 0 \n",
"2 3698.619653 11609.81575 66.977718 0 \n",
"3 3698.619653 5447.79390 32.125742 -1 \n",
"4 3698.619653 25382.15077 171.522968 1 \n",
"... ... ... ... ... \n",
"2157120 3698.619653 0.00000 0.000000 2518 \n",
"2157121 3698.619653 0.00000 0.000000 3061 \n",
"2157122 3698.619653 0.00000 0.000000 3061 \n",
"2157123 3698.619653 0.00000 0.000000 3061 \n",
"2157124 3698.619653 0.00000 0.000000 2622 \n",
"\n",
"[2156946 rows x 8 columns]"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cluster_data"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"large_clusters = peak_demand_kW[(peak_demand_kW > 1000) & (peak_demand_kW.index >= 0)]\n",
"demand_in_large_clusters = large_clusters.sum() / 1e3"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Approximate number of additional clusters: 5216\n"
]
}
],
"source": [
"print('Approximate number of additional clusters: %d' %(demand_in_large_clusters))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"small_clusters = peak_demand_kW[(peak_demand_kW <= 1000) & (peak_demand_kW > 0)]\n",
"data_small_clusters = cluster_data[cluster_data.clusterID.isin(small_clusters.loc[small_clusters.index > 0].index.values)]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"GWR_large_clusters = GWR[GWR.clusterID.isin(large_clusters.loc[large_clusters.index > 0].index.values)]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>peak_demand_kW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4934.000000</td>\n",
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" <tr>\n",
" <th>mean</th>\n",
" <td>136.868414</td>\n",
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" <tr>\n",
" <th>std</th>\n",
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" <tr>\n",
" <th>min</th>\n",
" <td>1.932917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>18.327823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>46.460536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>166.785203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>999.644013</td>\n",
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],
"text/plain": [
" peak_demand_kW\n",
"count 4934.000000\n",
"mean 136.868414\n",
"std 195.638906\n",
"min 1.932917\n",
"25% 18.327823\n",
"50% 46.460536\n",
"75% 166.785203\n",
"max 999.644013"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"small_clusters.describe()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
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" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"639.9999861283738\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(2476823.95, 2835315.05, 1065207.09325, 1300947.6697500001)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GWR_plot = GWR_large_clusters.sample(200000)\n",
"cmap = categorical_cmap( GWR_plot, 'clusterID', cmap = 'tab10', N = 10 )\n",
"\n",
"plt.figure()\n",
"plt.scatter(GWR_plot[coords[0]], GWR_plot[coords[1]], c = GWR_plot['clusterID'], s = 2, cmap = cmap)\n",
"plt.colorbar()\n",
"plt.axis('equal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Perform kmeans on the remaining clusters"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"GWR_dbscan = GWR.copy()\n",
"peak_demand_dbscan = peak_demand.copy()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"GWR = GWR_dbscan.copy()\n",
"peak_demand = peak_demand_dbscan.copy()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 4748.771876183521\n",
"Fit and predict in 0.08s\n",
"Number of clusters: 5\n",
"Number of clusters with peak demand > 1MW: 1\n",
"7 3142.7866865421934\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 0\n",
"8 1052.6945918788936\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"27 1461.7578438475377\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"28 1973.6873006075984\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 1\n",
"53 19045.901336680265\n",
"Fit and predict in 0.42s\n",
"Number of clusters: 20\n",
"Number of clusters with peak demand > 1MW: 11\n",
"54 1260.2153033928041\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"74 8771.04338957972\n",
"Fit and predict in 0.16s\n",
"Number of clusters: 9\n",
"Number of clusters with peak demand > 1MW: 4\n",
"75 274809.9639263198\n",
"Fit and predict in 45.53s\n",
"Number of clusters: 275\n",
"Number of clusters with peak demand > 1MW: 109\n",
"76 3371.492828386601\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 2\n",
"81 1673.8457497431873\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"83 4560.498050015758\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 5\n",
"Number of clusters with peak demand > 1MW: 2\n",
"86 1381.6941802879114\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"100 1529.7096532265289\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"108 2009.8140279070558\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 0\n",
"109 5489.541329984773\n",
"Fit and predict in 0.08s\n",
"Number of clusters: 6\n",
"Number of clusters with peak demand > 1MW: 2\n",
"110 1254.2167024615835\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"113 3134.449203203304\n",
"Fit and predict in 0.06s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 2\n",
"118 3604.771704347395\n",
"Fit and predict in 0.11s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 2\n",
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]
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"Fit and predict in 0.06s\n",
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"Fit and predict in 0.04s\n",
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"2250 1998.704332618536\n"
]
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"Fit and predict in 0.04s\n",
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"Number of clusters: 3\n",
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]
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"Number of clusters: 2\n",
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"5110 26250.30068908244\n",
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"5122 1234.2068266494807\n",
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"5128 1581.3587788505406\n",
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"5134 13320.309866684463\n",
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"5157 3769.8274148987366\n",
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"5163 1199.73223642796\n",
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"5179 2065.1839173927033\n",
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"5208 1048.5789657838873\n",
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"5210 1597.9103834163127\n",
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"5277 1160.2798410627022\n",
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"5280 6204.2125126223345\n",
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"5298 1278.9976707228316\n",
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"5355 3352.9165975432156\n",
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"\n",
"Number of remaining large clusters: 949\n",
"\n",
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"5406 1362.3096726334893\n",
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"5407 1190.9237964912425\n",
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"5408 1117.1622355692066\n",
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"5409 1413.2149509865963\n",
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"5416 1012.701442161231\n",
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"5417 1788.5193632285684\n",
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"5424 1052.2050034980614\n",
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"5426 1266.1163227945663\n",
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"5427 1409.5167167848447\n",
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"5433 1555.9161282491218\n",
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"5435 1313.032491000977\n",
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"5443 1445.333348594823\n",
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"5445 1030.3969241192083\n",
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"5447 1493.8688898157122\n",
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"5453 1378.4700533235698\n",
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"5460 1047.3067901822785\n",
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"5522 1489.5363023153197\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
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"5698 1173.9166289151442\n",
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"5706 1161.6927299286265\n"
]
},
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"Fit and predict in 0.02s\n",
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"5907 1361.8717545123325\n"
]
},
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"Fit and predict in 0.03s\n",
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"6108 1042.571904844182\n"
]
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"Fit and predict in 0.03s\n",
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"6339 2256.21417030915\n"
]
},
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"Fit and predict in 0.04s\n",
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"6534 1206.3457319476045\n",
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]
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"Number of clusters: 2\n",
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]
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"6754 1283.1993567758752\n",
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"6939 1606.729556094346\n",
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]
},
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"Number of clusters: 2\n",
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"7132 1172.7615733390637\n",
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"7143 1182.164433453839\n",
"Fit and predict in 0.02s\n"
]
},
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"text": [
"Number of clusters: 2\n",
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"7145 1253.7769927885395\n",
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"7384 1257.9564939940337\n"
]
},
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"text": [
"Fit and predict in 0.03s\n",
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"7602 2323.6140950306158\n"
]
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"text": [
"Fit and predict in 0.04s\n",
"Number of clusters: 3\n",
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"7815 1048.7853113157646\n"
]
},
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"Fit and predict in 0.09s\n",
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"8044 1041.564826520613\n"
]
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"output_type": "stream",
"text": [
"Fit and predict in 0.05s\n",
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"\n",
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"\n",
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"8612 1015.6325985051509\n"
]
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"Fit and predict in 0.02s\n",
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"9474 1261.6776700622797\n"
]
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"Fit and predict in 0.02s\n",
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"Number of clusters with peak demand > 1MW: 0\n",
"9770 1284.5331910866907\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9776 1051.9200437559898\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9786 1008.2719563148337\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9787 1271.3764683632774\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9790 1305.8695281383937\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9792 1111.2350084727161\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9795 1307.8783343146752\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9796 1089.208428280641\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9799 1055.7715747079458\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9801 1070.7486413073361\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9807 1207.2806208116026\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9816 1339.4648139512713\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9847 1277.3895335302843\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9850 1211.9864727511901\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9853 1027.324006176383\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9854 1302.5324423325856\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9857 1193.673192822673\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9859 1006.2246870842158\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9865 1351.7985385392012\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9870 1043.3514454045087\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9871 1124.0974388707218\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9873 1118.174402255461\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9876 1064.7809785763257\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9879 1139.8081536831426\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9880 1104.9873529341112\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9882 1369.0986729646936\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9883 1327.744252106613\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9884 1025.2685377451871\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9892 1004.4621212352763\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9893 1027.0067386503597\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9895 1498.9462892264019\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 1\n",
"9898 1662.9685974141646\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9904 1196.9768104706955\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9907 1450.3217715656253\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9922 1000.523451080871\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9925 1103.690473829751\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9931 1178.1806535288936\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9938 1027.8622959539077\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9940 1125.7248684722956\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9942 1083.8008520230574\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9943 1027.045759348013\n",
"Fit and predict in 0.02s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9963 1135.845636039307\n",
"Fit and predict in 0.01s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9964 1103.8353192669967\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9969 1136.7658491430434\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9972 1374.8637108963267\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9977 1114.065511010893\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"9978 1183.243729386918\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10000 1019.3874516951997\n",
"Fit and predict in 0.05s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10005 1296.6237713112716\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10008 1133.2388693939738\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10014 1244.3803649785955\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10018 1250.516905744837\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10022 1276.6989403438479\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10028 1437.6642179683442\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10029 1023.1281504315697\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10031 1060.926639436165\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10033 1507.469015236962\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10036 1246.0307169458613\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10061 1161.187222827817\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10069 1441.0910239685802\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10075 1124.6546145868815\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10078 1145.218016172216\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10090 1245.3948357929642\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10092 1063.7225014585458\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10103 1109.1901619403677\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10106 1065.868697218634\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10107 1333.8881924148552\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10108 1191.294843225733\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10115 1005.5757345077976\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10155 1063.9136301167048\n",
"Fit and predict in 0.01s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"\n",
"Number of remaining large clusters: 4\n",
"\n",
"10362 1010.1898113211469\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10420 1191.9406384058802\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10432 1154.4930522305244\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"10523 1083.3315145292227\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"\n",
"Number of remaining large clusters: 0\n",
"\n"
]
}
],
"source": [
"large_clusters = peak_demand[(peak_demand.peak_demand_kW > 1000) & (peak_demand.index >= 0)]\n",
"\n",
"while(len(large_clusters) > 0):\n",
" \n",
" for cID, d in large_clusters.iterrows():\n",
" if cID == -1: continue\n",
" print(cID, d.peak_demand_kW)\n",
"\n",
" GWR_sel = GWR[GWR.clusterID == cID].copy()\n",
" N_clust = int(np.ceil(d.peak_demand_kW / 1e3))\n",
"\n",
" if len(GWR_sel) == 0:\n",
" continue\n",
"\n",
" model = KMeans(n_clusters = N_clust)\n",
"\n",
" tt = time.time()\n",
" GWR_sel['clusterID'] = model.fit_predict( GWR_sel[coords] )\n",
" print('Fit and predict in %.2fs' %(time.time()-tt))\n",
"\n",
" clusterDemand,_ = get_cluster_demand(GWR_sel, residential_demand, 'clusterID')\n",
" cluster_peaks = clusterDemand.max(dim = ['timestamp', 'daytype']).rename('peak_demand_kW').to_dataframe() / 1e3\n",
"\n",
" # Add new cluster IDs (to replace old)\n",
" clusterIdMax = GWR.clusterID.max() + 1\n",
" GWR.loc[GWR.clusterID == cID, 'clusterID'] = clusterIdMax + GWR_sel['clusterID']\n",
"\n",
" # Remove old cluster peak and append new ones\n",
" cluster_peaks.index = cluster_peaks.index + clusterIdMax\n",
"\n",
" peak_demand = peak_demand[peak_demand.index != cID]\n",
" peak_demand = pd.concat([peak_demand, cluster_peaks])\n",
"\n",
" print('Number of clusters: %d' %(len(cluster_peaks)))\n",
" print('Number of clusters with peak demand > 1MW: %d' %(len(cluster_peaks[cluster_peaks.peak_demand_kW > 1000])))\n",
" \n",
" large_clusters = peak_demand[(peak_demand.peak_demand_kW > 1000) & (peak_demand.index >= 0)]\n",
" print('\\nNumber of remaining large clusters: %d\\n' %(len(large_clusters)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate the results"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
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"window.mpl = {};\n",
"\n",
"\n",
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
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"\n",
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"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
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"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
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"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
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"\n",
" this.imageObj.onload = function() {\n",
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" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
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"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
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" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
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" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
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"\n",
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"\n",
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"\n",
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"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
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"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"peak_demand[peak_demand.index >= 0].peak_demand_kW.plot.hist(bins = 50, range = (0,1000))\n",
"plt.xlim(0,1000)\n",
"plt.xlabel('Peak power demand (kWp)')\n",
"\n",
"ax = plt.gca()\n",
"ax.set_axisbelow(True)\n",
"ax.yaxis.grid(linestyle = '--')\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('histogram_peak_demand_clusters.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>peak_demand_kW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8993.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>353.478076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>310.701534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.932917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>38.125278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>303.047962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>620.193814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>999.821369</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" peak_demand_kW\n",
"count 8993.000000\n",
"mean 353.478076\n",
"std 310.701534\n",
"min 1.932917\n",
"25% 38.125278\n",
"50% 303.047962\n",
"75% 620.193814\n",
"max 999.821369"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"peak_demand[peak_demand.index >= 0].describe()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"clusterDemandAll,clusterCountsAll = get_cluster_demand(GWR, residential_demand, 'clusterID')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"electricity_demand_out = xr.merge([(clusterDemandAll/ 1e3).rename('cluster_demand_kW'), clusterCountsAll, \n",
" residential_demand ])"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (clusterID: 8994, daytype: 2, timestamp: 288)\n",
"Coordinates:\n",
" * clusterID (clusterID) int64 -1 0 1 2 3 ... 10607 10608 10609 10610\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:0...\n",
" * daytype (daytype) object &#x27;weekday&#x27; &#x27;weekend&#x27;\n",
"Data variables:\n",
" cluster_demand (clusterID, timestamp, daytype) float64 2.833e+07 ... 2.1...\n",
" N_SFH (clusterID) float64 6.067e+04 31.0 24.0 21.0 ... 6.0 4.0 1.0\n",
" N_DWELL (clusterID) float64 5.226e+04 99.0 30.0 ... 854.0 881.0\n",
" N_MFH (clusterID) float64 2.167e+04 20.0 11.0 ... 35.0 0.0 0.0\n",
" N_MFH_GE (clusterID) float64 124.0 0.0 0.0 0.0 ... 0.0 0.0 61.0 40.0\n",
" appart (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n",
" maison (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n",
" communal_GVA (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
" communal_rest (timestamp, daytype) float64 79.66 183.9 ... 39.96 99.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8c9c28b6-5c92-4ada-b980-4d50f932a574' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8c9c28b6-5c92-4ada-b980-4d50f932a574' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>clusterID</span>: 8994</li><li><span class='xr-has-index'>daytype</span>: 2</li><li><span class='xr-has-index'>timestamp</span>: 288</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-60c32815-8ed4-45ce-8b8d-ddf6b4af5b30' class='xr-section-summary-in' type='checkbox' checked><label for='section-60c32815-8ed4-45ce-8b8d-ddf6b4af5b30' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>clusterID</span></div><div class='xr-var-dims'>(clusterID)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-1 0 1 2 ... 10608 10609 10610</div><input id='attrs-52478f1e-a8f3-42c5-a6e0-7becf8158942' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52478f1e-a8f3-42c5-a6e0-7becf8158942' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f448c9-6ee3-4e1c-a3b2-d073ee6cbadc' class='xr-var-data-in' type='checkbox'><label for='data-59f448c9-6ee3-4e1c-a3b2-d073ee6cbadc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ -1, 0, 1, ..., 10608, 10609, 10610], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>timestamp</span></div><div class='xr-var-dims'>(timestamp)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-01-15 ... 2001-12-15T23:00:00</div><input id='attrs-0b6c3d71-4b7e-469c-b6e8-b54c8cf236a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b6c3d71-4b7e-469c-b6e8-b54c8cf236a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66498184-8af7-4d8e-9c09-d3287fb6c3d6' class='xr-var-data-in' type='checkbox'><label for='data-66498184-8af7-4d8e-9c09-d3287fb6c3d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2001-01-15T00:00:00.000000000&#x27;, &#x27;2001-01-15T01:00:00.000000000&#x27;,\n",
" &#x27;2001-01-15T02:00:00.000000000&#x27;, ..., &#x27;2001-12-15T21:00:00.000000000&#x27;,\n",
" &#x27;2001-12-15T22:00:00.000000000&#x27;, &#x27;2001-12-15T23:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>daytype</span></div><div class='xr-var-dims'>(daytype)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;weekday&#x27; &#x27;weekend&#x27;</div><input id='attrs-e5d61569-41c5-4f93-b9ba-b55868cc2df3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5d61569-41c5-4f93-b9ba-b55868cc2df3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ca59548-0a90-4cfe-8030-01d7c810a36d' class='xr-var-data-in' type='checkbox'><label for='data-8ca59548-0a90-4cfe-8030-01d7c810a36d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;weekday&#x27;, &#x27;weekend&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c55268d7-2ece-48dd-af12-99a02ef02334' class='xr-section-summary-in' type='checkbox' checked><label for='section-c55268d7-2ece-48dd-af12-99a02ef02334' class='xr-section-summary' >Data variables: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cluster_demand</span></div><div class='xr-var-dims'>(clusterID, timestamp, daytype)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.833e+07 3.447e+07 ... 2.132e+05</div><input id='attrs-40aec093-d166-43f4-bfa4-2e039d90dd61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40aec093-d166-43f4-bfa4-2e039d90dd61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ba8071c-11e7-45f3-9609-18a873cfa7bd' class='xr-var-data-in' type='checkbox'><label for='data-7ba8071c-11e7-45f3-9609-18a873cfa7bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[2.83297669e+07, 3.44653988e+07],\n",
" [2.48960317e+07, 2.84079392e+07],\n",
" [2.36665333e+07, 2.65808589e+07],\n",
" ...,\n",
" [5.84337629e+07, 5.94702451e+07],\n",
" [4.59421758e+07, 4.86974139e+07],\n",
" [3.59934512e+07, 3.90572056e+07]],\n",
"\n",
" [[2.69030118e+04, 3.23557011e+04],\n",
" [2.36084156e+04, 2.67255114e+04],\n",
" [2.20475407e+04, 2.47680135e+04],\n",
" ...,\n",
" [6.05847764e+04, 6.00330072e+04],\n",
" [4.73575332e+04, 4.72684933e+04],\n",
" [3.56337528e+04, 3.72993994e+04]],\n",
"\n",
" [[1.29019702e+04, 1.57482571e+04],\n",
" [1.13397323e+04, 1.30129726e+04],\n",
" [1.07330274e+04, 1.21580995e+04],\n",
" ...,\n",
"...\n",
" ...,\n",
" [2.39927663e+05, 2.29838903e+05],\n",
" [1.86533780e+05, 1.73384476e+05],\n",
" [1.34513673e+05, 1.33534672e+05]],\n",
"\n",
" [[1.54485948e+05, 1.90551159e+05],\n",
" [1.35777366e+05, 1.59490709e+05],\n",
" [1.24724298e+05, 1.47324322e+05],\n",
" ...,\n",
" [3.69241903e+05, 3.61042989e+05],\n",
" [2.87032369e+05, 2.74158286e+05],\n",
" [2.08438661e+05, 2.14645867e+05]],\n",
"\n",
" [[1.52905988e+05, 1.82726122e+05],\n",
" [1.34035656e+05, 1.51615180e+05],\n",
" [1.22661756e+05, 1.39120157e+05],\n",
" ...,\n",
" [3.75725162e+05, 3.63376920e+05],\n",
" [2.92041878e+05, 2.74682033e+05],\n",
" [2.11073631e+05, 2.13209315e+05]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_SFH</span></div><div class='xr-var-dims'>(clusterID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.067e+04 31.0 24.0 ... 6.0 4.0 1.0</div><input id='attrs-b57486ef-8af5-4383-a013-90deb7b36b05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b57486ef-8af5-4383-a013-90deb7b36b05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0142961c-2db5-4653-b4eb-cc05a8e76902' class='xr-var-data-in' type='checkbox'><label for='data-0142961c-2db5-4653-b4eb-cc05a8e76902' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6.0671e+04, 3.1000e+01, 2.4000e+01, ..., 6.0000e+00, 4.0000e+00,\n",
" 1.0000e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_DWELL</span></div><div class='xr-var-dims'>(clusterID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.226e+04 99.0 30.0 ... 854.0 881.0</div><input id='attrs-f1fa112a-c880-46f2-8300-535a4b175699' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1fa112a-c880-46f2-8300-535a4b175699' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99a41f99-f438-4407-a794-79d1f952ef7c' class='xr-var-data-in' type='checkbox'><label for='data-99a41f99-f438-4407-a794-79d1f952ef7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5.2255e+04, 9.9000e+01, 3.0000e+01, ..., 5.6000e+02, 8.5400e+02,\n",
" 8.8100e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_MFH</span></div><div class='xr-var-dims'>(clusterID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.167e+04 20.0 11.0 ... 0.0 0.0</div><input id='attrs-287009d7-6469-4161-8e89-2dbbada02551' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-287009d7-6469-4161-8e89-2dbbada02551' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e24b23b2-6925-46bb-8211-53fcc72b2a23' class='xr-var-data-in' type='checkbox'><label for='data-e24b23b2-6925-46bb-8211-53fcc72b2a23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2.167e+04, 2.000e+01, 1.100e+01, ..., 3.500e+01, 0.000e+00,\n",
" 0.000e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_MFH_GE</span></div><div class='xr-var-dims'>(clusterID)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>124.0 0.0 0.0 0.0 ... 0.0 61.0 40.0</div><input id='attrs-e355f789-c193-47c6-9814-f8e50818eb04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e355f789-c193-47c6-9814-f8e50818eb04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fd4265e-dd66-48bf-b42e-99b63d4ea71d' class='xr-var-data-in' type='checkbox'><label for='data-5fd4265e-dd66-48bf-b42e-99b63d4ea71d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([124., 0., 0., ..., 0., 61., 40.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>appart</span></div><div class='xr-var-dims'>(timestamp, daytype)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>162.3 181.7 141.5 ... 233.7 227.8</div><input id='attrs-f2d7ab16-b3db-44d1-8d1d-79740e4326d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2d7ab16-b3db-44d1-8d1d-79740e4326d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7a45448-4ae0-4488-9a06-81761cf993b2' class='xr-var-data-in' type='checkbox'><label for='data-e7a45448-4ae0-4488-9a06-81761cf993b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[162.267134, 181.725886],\n",
" [141.511422, 147.918781],\n",
" [128.611598, 133.756344],\n",
" ...,\n",
" [419.152541, 397.512196],\n",
" [325.779659, 298.243901],\n",
" [233.661016, 227.804876]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>maison</span></div><div class='xr-var-dims'>(timestamp, daytype)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>298.2 344.7 261.2 ... 377.5 411.3</div><input id='attrs-220865c6-beb8-497d-a315-ff4a51e3a382' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-220865c6-beb8-497d-a315-ff4a51e3a382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55690832-9b92-4cd8-b7db-2db683391b70' class='xr-var-data-in' type='checkbox'><label for='data-55690832-9b92-4cd8-b7db-2db683391b70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[298.236713, 344.72222 ],\n",
" [261.159419, 277.777777],\n",
" [252.00483 , 259.861109],\n",
" ...,\n",
" [584.516907, 600.138891],\n",
" [462.94686 , 511.527781],\n",
" [377.487921, 411.250001]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>communal_GVA</span></div><div class='xr-var-dims'>(timestamp, daytype)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>241.3 557.0 227.6 ... 121.0 302.5</div><input id='attrs-2af32d6d-86cc-4415-a3cf-c31c011cf3ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2af32d6d-86cc-4415-a3cf-c31c011cf3ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7b713bc-2f2e-4168-94e4-a2812ee6a8bc' class='xr-var-data-in' type='checkbox'><label for='data-e7b713bc-2f2e-4168-94e4-a2812ee6a8bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[241.260153, 557.022354],\n",
" [227.573341, 525.523923],\n",
" [227.573341, 525.523923],\n",
" ...,\n",
" [146.6814 , 314.21341 ],\n",
" [114.176285, 285.440716],\n",
" [121.019693, 302.549232]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>communal_rest</span></div><div class='xr-var-dims'>(timestamp, daytype)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>79.66 183.9 75.14 ... 39.96 99.9</div><input id='attrs-09f36ddb-569d-46da-af39-f6d76504f662' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09f36ddb-569d-46da-af39-f6d76504f662' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da0e722a-d89e-44cc-b316-b4712bebbb10' class='xr-var-data-in' type='checkbox'><label for='data-da0e722a-d89e-44cc-b316-b4712bebbb10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 79.661371, 183.922475],\n",
" [ 75.142141, 173.52205 ],\n",
" [ 75.142141, 173.52205 ],\n",
" ...,\n",
" [ 48.432538, 103.749711],\n",
" [ 37.699717, 94.249293],\n",
" [ 39.959333, 99.898331]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15b88c1b-c1d3-43a0-a0c7-80bed8f53b58' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15b88c1b-c1d3-43a0-a0c7-80bed8f53b58' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (clusterID: 8994, daytype: 2, timestamp: 288)\n",
"Coordinates:\n",
" * clusterID (clusterID) int64 -1 0 1 2 3 ... 10607 10608 10609 10610\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:0...\n",
" * daytype (daytype) object 'weekday' 'weekend'\n",
"Data variables:\n",
" cluster_demand (clusterID, timestamp, daytype) float64 2.833e+07 ... 2.1...\n",
" N_SFH (clusterID) float64 6.067e+04 31.0 24.0 21.0 ... 6.0 4.0 1.0\n",
" N_DWELL (clusterID) float64 5.226e+04 99.0 30.0 ... 854.0 881.0\n",
" N_MFH (clusterID) float64 2.167e+04 20.0 11.0 ... 35.0 0.0 0.0\n",
" N_MFH_GE (clusterID) float64 124.0 0.0 0.0 0.0 ... 0.0 0.0 61.0 40.0\n",
" appart (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n",
" maison (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n",
" communal_GVA (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
" communal_rest (timestamp, daytype) float64 79.66 183.9 ... 39.96 99.9"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"electricity_demand_out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save results"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"electricity_demand_out.to_netcdf('demand_clusters.nc')\n",
"GWR.to_csv('GWR_clusters.csv', index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Single iteration"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 4748.771876183521\n",
"Fit and predict in 0.08s\n",
"Number of clusters: 5\n",
"Number of clusters with peak demand > 1MW: 2\n",
"7 3142.7866865421934\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 0\n",
"8 1052.6945918788936\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"27 1461.7578438475377\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"28 1973.6873006075984\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 1\n",
"53 19045.901336680265\n",
"Fit and predict in 0.40s\n",
"Number of clusters: 20\n",
"Number of clusters with peak demand > 1MW: 11\n",
"54 1260.2153033928041\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"74 8771.04338957972\n",
"Fit and predict in 0.14s\n",
"Number of clusters: 9\n",
"Number of clusters with peak demand > 1MW: 4\n",
"75 274809.9639263198\n",
"Fit and predict in 43.23s\n",
"Number of clusters: 275\n",
"Number of clusters with peak demand > 1MW: 109\n",
"76 3371.492828386601\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 2\n",
"81 1673.8457497431873\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"83 4560.498050015758\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 5\n",
"Number of clusters with peak demand > 1MW: 2\n",
"86 1381.6941802879114\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"100 1529.7096532265289\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"108 2009.8140279070558\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 0\n",
"109 5489.541329984773\n",
"Fit and predict in 0.08s\n",
"Number of clusters: 6\n",
"Number of clusters with peak demand > 1MW: 2\n",
"110 1254.2167024615835\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"113 3134.449203203304\n",
"Fit and predict in 0.06s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 2\n",
"118 3604.771704347395\n",
"Fit and predict in 0.09s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 2\n",
"119 2935.382938427621\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 1\n",
"122 1550.4228698305792\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 1\n",
"123 1376.9202783239434\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"128 2418.0720920091253\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 0\n",
"143 1422.8774877606388\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"146 1711.33518766883\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 1\n",
"151 8780.44462405959\n",
"Fit and predict in 0.19s\n",
"Number of clusters: 9\n",
"Number of clusters with peak demand > 1MW: 4\n",
"154 6605.561533793985\n",
"Fit and predict in 0.11s\n",
"Number of clusters: 7\n",
"Number of clusters with peak demand > 1MW: 4\n",
"164 1701.8838848702367\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"166 9332.012200543095\n",
"Fit and predict in 0.17s\n",
"Number of clusters: 10\n",
"Number of clusters with peak demand > 1MW: 4\n",
"167 1147.3422412276361\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"171 2143.1147364126573\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 0\n",
"174 2936.3451563331487\n",
"Fit and predict in 0.05s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 2\n",
"184 3034.1824784991095\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 1\n",
"198 13376.316738818705\n",
"Fit and predict in 0.25s\n",
"Number of clusters: 14\n",
"Number of clusters with peak demand > 1MW: 6\n",
"215 1935.8330079883397\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"217 1010.5669010007416\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"225 3697.379288550638\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 2\n",
"230 3446.5634485123264\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 0\n",
"247 1995.984242908293\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 1\n",
"255 2218.2639016930248\n",
"Fit and predict in 0.05s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 0\n",
"258 4516.175595040673\n",
"Fit and predict in 0.08s\n",
"Number of clusters: 5\n",
"Number of clusters with peak demand > 1MW: 1\n",
"259 2363.8787447737054\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 1\n",
"264 1220.1116806750472\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"268 6019.287836509292\n",
"Fit and predict in 0.10s\n",
"Number of clusters: 7\n",
"Number of clusters with peak demand > 1MW: 2\n",
"269 9668.78027048671\n",
"Fit and predict in 0.16s\n",
"Number of clusters: 10\n",
"Number of clusters with peak demand > 1MW: 5\n",
"288 1307.3535992165357\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"295 39159.02645775045\n",
"Fit and predict in 1.18s\n",
"Number of clusters: 40\n",
"Number of clusters with peak demand > 1MW: 18\n",
"301 1015.9634988141743\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"314 2154.0456507036556\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 0\n",
"322 2168.8032244836245\n",
"Fit and predict in 0.05s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 1\n",
"335 1665.6397746856176\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"339 3719.221690233578\n",
"Fit and predict in 0.07s\n",
"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 1\n",
"362 1497.2308842043726\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"363 2879.7914687262896\n",
"Fit and predict in 0.05s\n",
"Number of clusters: 3\n",
"Number of clusters with peak demand > 1MW: 1\n",
"379 1440.8270854517207\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"407 1243.583834010653\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"412 1941.8124254605532\n",
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 1\n",
"416 1045.769115224811\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"427 4509.955382920604\n",
"Fit and predict in 0.11s\n",
"Number of clusters: 5\n",
"Number of clusters with peak demand > 1MW: 2\n",
"431 1036.880588462829\n",
"Fit and predict in 0.02s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"456 1066.0238387630502\n",
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"469 1897.9216295070605\n",
"Fit and predict in 0.04s\n",
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"Fit and predict in 0.04s\n",
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"3149 1041.2713042241887\n"
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"Number of clusters with peak demand > 1MW: 0\n",
"4056 1535.0133719494977\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit and predict in 0.03s\n",
"Number of clusters: 2\n",
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"5024 1026.4775075052253\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit and predict in 0.04s\n",
"Number of clusters: 2\n",
"Number of clusters with peak demand > 1MW: 0\n",
"5025 1356.2071558129655\n",
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"5355 3352.9165975432156\n",
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"Number of clusters: 4\n",
"Number of clusters with peak demand > 1MW: 1\n"
]
}
],
"source": [
"for cID, d in large_clusters.iterrows():\n",
" if cID == -1: continue\n",
" print(cID, d.peak_demand_kW)\n",
" \n",
" GWR_sel = GWR[GWR.clusterID == cID].copy()\n",
" N_clust = int(np.ceil(d.peak_demand_kW / 1e3))\n",
" \n",
" if len(GWR_sel) == 0:\n",
" continue\n",
" \n",
" model = KMeans(n_clusters = N_clust)\n",
"\n",
" tt = time.time()\n",
" GWR_sel['clusterID'] = model.fit_predict( GWR_sel[coords] )\n",
" print('Fit and predict in %.2fs' %(time.time()-tt))\n",
" \n",
" clusterDemand,_ = get_cluster_demand(GWR_sel, residential_demand, 'clusterID')\n",
" cluster_peaks = clusterDemand.max(dim = ['timestamp', 'daytype']).rename('peak_demand_kW').to_dataframe() / 1e3\n",
" \n",
" # Add new cluster IDs (to replace old)\n",
" clusterIdMax = GWR.clusterID.max() + 1\n",
" GWR.loc[GWR.clusterID == cID, 'clusterID'] = clusterIdMax + GWR_sel['clusterID']\n",
" \n",
" # Remove old cluster peak and append new ones\n",
" cluster_peaks.index = cluster_peaks.index + clusterIdMax\n",
" \n",
" peak_demand = peak_demand[peak_demand.index != cID]\n",
" peak_demand = pd.concat([peak_demand, cluster_peaks])\n",
"\n",
" print('Number of clusters: %d' %(len(cluster_peaks)))\n",
" print('Number of clusters with peak demand > 1MW: %d' %(len(cluster_peaks[cluster_peaks.peak_demand_kW > 1000])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
c4science · Help