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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"import pandas as pd\n",
"import xarray as xr\n",
"import numpy as np\n",
"import itertools\n",
"\n",
"from rooftop_handling import link_rooftops_with_pixels\n",
"import util\n",
"\n",
"from sklearn import tree\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.neural_network import MLPRegressor\n",
"from sklearn.svm import SVR\n",
"from sklearn.metrics import mean_squared_error as mse\n",
"from sklearn.metrics import mean_absolute_error as mae\n",
"from sklearn.metrics import r2_score as r2\n",
"from sklearn.model_selection import cross_val_predict\n",
"from sklearn.preprocessing import Normalizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.base import clone\n",
"from ELM_ensemble import ELM_Ensemble\n",
"from scipy.stats import describe\n",
"from uncertainty import Uncertainty\n",
"\n",
"import os\n",
"import time\n",
"\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def reindex_dataframe(input_array):\n",
" # function takes xarray and reindexes x and y coordinates to x and y indices\n",
" array_reindexed = input_array\n",
" array_reindexed.coords['x'] = range(len(array_reindexed.x))\n",
" array_reindexed.coords['y'] = range(len(array_reindexed.y))\n",
"\n",
" return array_reindexed.rename({'x' : 'x_idx', 'y' : 'y_idx'})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def notnan( x ):\n",
" return x[~np.isnan(x)]\n",
"\n",
"# ERROR MEASUREMENTS\n",
"error = {\n",
" 'MSE' : lambda y, t: mse(t,y),\n",
" 'RMSE': lambda y, t: np.sqrt(mse(t,y)),\n",
" 'MAE' : lambda y, t: mae(t,y),\n",
" 'MAEP': lambda y, t: mae(np.ones(len(y)), notnan(y/t)),\n",
" 'MBE' : lambda y, t: np.mean(t - y),\n",
" 'MBEP': lambda y, t: np.mean(1 - notnan(y/t)),\n",
" 'R2' : lambda y, t: r2(t,y)\n",
"}\n",
"\n",
"def get_errors(true, pred, label = ''):\n",
" # create a pandas series with errors and print them\n",
" true = true.reshape((-1,1))\n",
" pred = pred.reshape((-1,1))\n",
" \n",
" err_series = pd.Series(data = np.zeros(len(error)), index = error.keys())\n",
" for key, val in error.items():\n",
" print('%s %s: %f' %(label, key, val(pred, true)))\n",
" err_series[ key ] = val(pred, true)\n",
" \n",
" return err_series\n",
"\n",
"def get_errors_perc(true, pred, label = ''):\n",
" # create a pandas series with errors and print them\n",
" pred = pred.reshape((-1,))\n",
" true = true.reshape((-1,))\n",
" pred_ratio = ( pred / true ).reshape((-1,1))\n",
" \n",
" err_series = pd.Series(data = np.zeros(len(error)), index = error.keys())\n",
" for key, val in error.items(): \n",
" if key == 'R2':\n",
" print('%s %s: %f' %(label, key, val(pred, true)))\n",
" err_series[ key ] = val(pred, true)\n",
" else:\n",
" print('%s %s (%%): %f' %(label, key, val( pred_ratio, np.ones( pred_ratio.shape ) )))\n",
" err_series[ key ] = val( pred_ratio, np.ones( pred_ratio.shape ) )\n",
" \n",
" return err_series\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def rescale(vector, training):\n",
" return (vector * training.std()) + training.mean()\n",
"\n",
"def normalize(vector, training):\n",
" return (vector - training.mean()) / training.std()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def shp( vector ):\n",
"# check shape of vector and reshape it to one dimension if it is a one-dimensional vector\n",
" if len( label_name ) == 1:\n",
" return vector.reshape((-1,1))\n",
" else:\n",
" return vector "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def compute_residuals( prediction, target, model_std):\n",
" residuals = ( prediction - target )**2 - model_std**2\n",
" return np.sqrt( np.maximum(residuals, 0) ) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 1: Prepare data table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 1.1: Load roof data and add canton information"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"ROOF_COLS = ['DF_UID', 'NEIGUNG', 'AUSRICHTUNG', 'FLAECHE', 'GWR_EGID', 'shaded_area_ratio_ftr', 'XCOORD', 'YCOORD']\n",
"ROOF_FP = '/work/hyenergy/output/solar_potential/geographic_potential/CH_ROOFS_all_replaced.csv'\n",
"roofs = pd.read_csv( ROOF_FP, usecols = ROOF_COLS )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"GWR_FP = '/work/hyenergy/raw/GWR_Reg-BL/EPFL-150701-GEB.txt'\n",
"GWR_COL_FP = '/work/hyenergy/raw/GWR_Reg-BL/Maske_EINZELD-GEB.csv'\n",
"\n",
"GWR_column_names = pd.read_csv( GWR_COL_FP ).columns\n",
"GWR = pd.read_csv(GWR_FP, sep = '\\t', encoding = 'cp1250', header = None, names = GWR_column_names)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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"\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>GDEKT</th>\n",
" <th>GDENR</th>\n",
" <th>GDENAMK</th>\n",
" <th>EGID</th>\n",
" <th>GPARZ</th>\n",
" <th>GGBKR</th>\n",
" <th>GEBNR</th>\n",
" <th>GBEZ</th>\n",
" <th>ESTRID</th>\n",
" <th>STRNAMK1</th>\n",
" <th>...</th>\n",
" <th>GAREA</th>\n",
" <th>GASTW</th>\n",
" <th>GANZWHG</th>\n",
" <th>GAZZI</th>\n",
" <th>GHEIZ</th>\n",
" <th>GENHZ</th>\n",
" <th>GWWV</th>\n",
" <th>GENWW</th>\n",
" <th>DMUTDAT</th>\n",
" <th>DEXPDAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>TG</td>\n",
" <td>4590</td>\n",
" <td>Hüttlingen</td>\n",
" <td>400064214</td>\n",
" <td>2150</td>\n",
" <td>0.0</td>\n",
" <td>3/29</td>\n",
" <td>NaN</td>\n",
" <td>1125563</td>\n",
" <td>Geeregass</td>\n",
" <td>...</td>\n",
" <td>25.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14.11.2014</td>\n",
" <td>01.07.2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>TG</td>\n",
" <td>4721</td>\n",
" <td>Bichelsee-Balterswil</td>\n",
" <td>400063706</td>\n",
" <td>192</td>\n",
" <td>0.0</td>\n",
" <td>1/.052</td>\n",
" <td>NaN</td>\n",
" <td>1126980</td>\n",
" <td>Riesenbergstr.</td>\n",
" <td>...</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>27.11.2014</td>\n",
" <td>01.07.2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>TG</td>\n",
" <td>4946</td>\n",
" <td>Weinfelden</td>\n",
" <td>400064183</td>\n",
" <td>2796</td>\n",
" <td>0.0</td>\n",
" <td>2259</td>\n",
" <td>NaN</td>\n",
" <td>1129096</td>\n",
" <td>Florastr.</td>\n",
" <td>...</td>\n",
" <td>16.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>27.11.2014</td>\n",
" <td>01.07.2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>TG</td>\n",
" <td>4566</td>\n",
" <td>Frauenfeld</td>\n",
" <td>400064684</td>\n",
" <td>1097</td>\n",
" <td>0.0</td>\n",
" <td>1/720</td>\n",
" <td>NaN</td>\n",
" <td>1125237</td>\n",
" <td>Blumenstr.</td>\n",
" <td>...</td>\n",
" <td>16.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14.11.2014</td>\n",
" <td>01.07.2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>TG</td>\n",
" <td>4921</td>\n",
" <td>Bussnang</td>\n",
" <td>400064700</td>\n",
" <td>7038</td>\n",
" <td>0.0</td>\n",
" <td>9/.024</td>\n",
" <td>NaN</td>\n",
" <td>1128909</td>\n",
" <td>Laagasse</td>\n",
" <td>...</td>\n",
" <td>32.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>27.11.2014</td>\n",
" <td>01.07.2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" GDEKT GDENR GDENAMK EGID GPARZ GGBKR GEBNR GBEZ \\\n",
"0 TG 4590 Hüttlingen 400064214 2150 0.0 3/29 NaN \n",
"1 TG 4721 Bichelsee-Balterswil 400063706 192 0.0 1/.052 NaN \n",
"2 TG 4946 Weinfelden 400064183 2796 0.0 2259 NaN \n",
"3 TG 4566 Frauenfeld 400064684 1097 0.0 1/720 NaN \n",
"4 TG 4921 Bussnang 400064700 7038 0.0 9/.024 NaN \n",
"\n",
" ESTRID STRNAMK1 ... GAREA GASTW GANZWHG GAZZI GHEIZ \\\n",
"0 1125563 Geeregass ... 25.0 NaN 0 NaN NaN \n",
"1 1126980 Riesenbergstr. ... 10.0 NaN 0 NaN NaN \n",
"2 1129096 Florastr. ... 16.0 NaN 0 NaN NaN \n",
"3 1125237 Blumenstr. ... 16.0 NaN 0 NaN NaN \n",
"4 1128909 Laagasse ... 32.0 NaN 0 NaN NaN \n",
"\n",
" GENHZ GWWV GENWW DMUTDAT DEXPDAT \n",
"0 NaN NaN NaN 14.11.2014 01.07.2015 \n",
"1 NaN NaN NaN 27.11.2014 01.07.2015 \n",
"2 NaN NaN NaN 27.11.2014 01.07.2015 \n",
"3 NaN NaN NaN 14.11.2014 01.07.2015 \n",
"4 NaN NaN NaN 27.11.2014 01.07.2015 \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GWR.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"roofs_with_communes = roofs.merge( GWR[['EGID', 'GDEKT', 'GDENR']], \n",
" left_on = 'GWR_EGID', right_on = 'EGID', how = 'left' )\n",
"\n",
"roofs_with_communes.drop('EGID', axis = 1, inplace = True)\n",
"roofs_with_communes.rename(columns = {'GDEKT' : 'Kanton', 'GDENR' : 'Commune_ID'}, inplace = True)\n",
"roofs_with_communes = roofs_with_communes.drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>DF_UID</th>\n",
" <th>FLAECHE</th>\n",
" <th>NEIGUNG</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>GWR_EGID</th>\n",
" <th>shaded_area_ratio_ftr</th>\n",
" <th>Kanton</th>\n",
" <th>Commune_ID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>15.948816</td>\n",
" <td>37</td>\n",
" <td>621674.951286</td>\n",
" <td>254203.972784</td>\n",
" <td>10.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>13.730555</td>\n",
" <td>44</td>\n",
" <td>621675.374179</td>\n",
" <td>254206.313551</td>\n",
" <td>-170.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>63.437476</td>\n",
" <td>12</td>\n",
" <td>621673.977050</td>\n",
" <td>254186.956803</td>\n",
" <td>-80.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>38.368910</td>\n",
" <td>33</td>\n",
" <td>621696.695450</td>\n",
" <td>254249.767806</td>\n",
" <td>-80.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>36.403273</td>\n",
" <td>35</td>\n",
" <td>621692.928934</td>\n",
" <td>254250.448974</td>\n",
" <td>100.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.285714</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"0 1 15.948816 37 621674.951286 254203.972784 10.0 \n",
"1 2 13.730555 44 621675.374179 254206.313551 -170.0 \n",
"2 3 63.437476 12 621673.977050 254186.956803 -80.0 \n",
"3 4 38.368910 33 621696.695450 254249.767806 -80.0 \n",
"4 5 36.403273 35 621692.928934 254250.448974 100.0 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID \n",
"0 245033426.0 0.000000 BL 2823.0 \n",
"1 245033426.0 0.000000 BL 2823.0 \n",
"2 245033426.0 0.000000 BL 2823.0 \n",
"3 245033426.0 0.000000 BL 2823.0 \n",
"4 245033426.0 0.285714 BL 2823.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_with_communes.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9639231"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(roofs_with_communes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 1.2: Add SON predicted values (or later our own) and get PV potential (label)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"SON_COLS = ['DF_UID', 'MSTRAHLUNG']\n",
"SON_FP = '/work/hyenergy/raw/Sonnendach/Sonnendach_all_roofs/SOLKAT_CH_DACH_LV03.txt'\n",
"son_rad = pd.read_csv( SON_FP, usecols = SON_COLS )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9639231"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(son_rad)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"roofs_with_SON_pot = roofs_with_communes.merge( son_rad, on = 'DF_UID', how = 'left')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# NOTE: NO SPECIAL TREATMENT OF FLAT ROOFS!!\n",
"EFF = 0.17\n",
"PERF = 0.8\n",
"roofs_with_SON_pot['PV_kWh_m2'] = roofs_with_SON_pot.MSTRAHLUNG * EFF * PERF"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>FLAECHE</th>\n",
" <th>NEIGUNG</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>GWR_EGID</th>\n",
" <th>shaded_area_ratio_ftr</th>\n",
" <th>Kanton</th>\n",
" <th>Commune_ID</th>\n",
" <th>MSTRAHLUNG</th>\n",
" <th>PV_kWh_m2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>15.948816</td>\n",
" <td>37</td>\n",
" <td>621674.951286</td>\n",
" <td>254203.972784</td>\n",
" <td>10.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" <td>1320</td>\n",
" <td>179.520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>13.730555</td>\n",
" <td>44</td>\n",
" <td>621675.374179</td>\n",
" <td>254206.313551</td>\n",
" <td>-170.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" <td>593</td>\n",
" <td>80.648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>63.437476</td>\n",
" <td>12</td>\n",
" <td>621673.977050</td>\n",
" <td>254186.956803</td>\n",
" <td>-80.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" <td>1146</td>\n",
" <td>155.856</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>38.368910</td>\n",
" <td>33</td>\n",
" <td>621696.695450</td>\n",
" <td>254249.767806</td>\n",
" <td>-80.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.000000</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" <td>1128</td>\n",
" <td>153.408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>36.403273</td>\n",
" <td>35</td>\n",
" <td>621692.928934</td>\n",
" <td>254250.448974</td>\n",
" <td>100.0</td>\n",
" <td>245033426.0</td>\n",
" <td>0.285714</td>\n",
" <td>BL</td>\n",
" <td>2823.0</td>\n",
" <td>721</td>\n",
" <td>98.056</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"0 1 15.948816 37 621674.951286 254203.972784 10.0 \n",
"1 2 13.730555 44 621675.374179 254206.313551 -170.0 \n",
"2 3 63.437476 12 621673.977050 254186.956803 -80.0 \n",
"3 4 38.368910 33 621696.695450 254249.767806 -80.0 \n",
"4 5 36.403273 35 621692.928934 254250.448974 100.0 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID MSTRAHLUNG \\\n",
"0 245033426.0 0.000000 BL 2823.0 1320 \n",
"1 245033426.0 0.000000 BL 2823.0 593 \n",
"2 245033426.0 0.000000 BL 2823.0 1146 \n",
"3 245033426.0 0.000000 BL 2823.0 1128 \n",
"4 245033426.0 0.285714 BL 2823.0 721 \n",
"\n",
" PV_kWh_m2 \n",
"0 179.520 \n",
"1 80.648 \n",
"2 155.856 \n",
"3 153.408 \n",
"4 98.056 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_with_SON_pot.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 1.3: Add GHI information"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"GHI_FP = '/work/hyenergy/raw/MeteoSwiss_satellite/raw_data/global_daily/MSG_SIS_D_*_LV03_nearest.nc'\n",
"GHI = xr.open_mfdataset( GHI_FP ).drop('crs')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"GHI_range = GHI.sel( time = slice('2004-01-01', '2015-12-31'))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"yearly_GHI_kWh = GHI_range.groupby('time.year').sum('time') / 1000\n",
"yearly_GHI_kWh.SIS_CH.attrs['units'] = '$kWh/m^2$'"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"mean_GHI = yearly_GHI_kWh.mean('year')\n",
"mean_GHI = mean_GHI.where( mean_GHI.SIS_CH > 0 )\n",
"mean_GHI = mean_GHI.rename({'SIS_CH' : 'SIS'}) # need to do to be able to link roofs with pixels"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (x: 370, y: 240)\n",
"Coordinates:\n",
" * x (x) float32 474500.0 475500.0 476500.0 ... 842500.0 843500.0\n",
" * y (y) float32 303500.0 302500.0 301500.0 ... 66500.0 65500.0 64500.0\n",
"Data variables:\n",
" SIS (y, x) float64 dask.array<shape=(240, 370), chunksize=(240, 370)>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_GHI"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(474000.0, 844000.0, 64000.0, 304000.0)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# VERIFY\n",
"plt.figure()\n",
"mean_GHI.SIS.plot()\n",
"plt.axis('equal')\n",
"plt.axis('off')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# Link rooftops with pixels:\n",
"roofs_with_SON_pot['XCOORD'] = roofs_with_SON_pot['XCOORD'].astype('float32')\n",
"roofs_with_SON_pot['YCOORD'] = roofs_with_SON_pot['YCOORD'].astype('float32')\n",
"\n",
"roofs_linked = link_rooftops_with_pixels( roofs_with_SON_pot, mean_GHI.expand_dims('timestamp') )"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>FLAECHE</th>\n",
" <th>NEIGUNG</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>GWR_EGID</th>\n",
" <th>shaded_area_ratio_ftr</th>\n",
" <th>Kanton</th>\n",
" <th>Commune_ID</th>\n",
" <th>MSTRAHLUNG</th>\n",
" <th>PV_kWh_m2</th>\n",
" <th>x_idx</th>\n",
" <th>y_idx</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5343176</td>\n",
" <td>108.129956</td>\n",
" <td>16</td>\n",
" <td>722110.3750</td>\n",
" <td>75509.609375</td>\n",
" <td>-17.0</td>\n",
" <td>11157515.0</td>\n",
" <td>0.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>1548</td>\n",
" <td>210.528</td>\n",
" <td>248.0</td>\n",
" <td>228.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5343175</td>\n",
" <td>110.595071</td>\n",
" <td>17</td>\n",
" <td>722108.1875</td>\n",
" <td>75516.570312</td>\n",
" <td>163.0</td>\n",
" <td>11157515.0</td>\n",
" <td>0.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>1114</td>\n",
" <td>151.504</td>\n",
" <td>248.0</td>\n",
" <td>228.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5343151</td>\n",
" <td>116.397070</td>\n",
" <td>11</td>\n",
" <td>721725.8125</td>\n",
" <td>75517.546875</td>\n",
" <td>38.0</td>\n",
" <td>11157516.0</td>\n",
" <td>0.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>1449</td>\n",
" <td>197.064</td>\n",
" <td>247.0</td>\n",
" <td>228.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5343169</td>\n",
" <td>43.013045</td>\n",
" <td>22</td>\n",
" <td>721796.8125</td>\n",
" <td>75527.015625</td>\n",
" <td>11.0</td>\n",
" <td>11157516.0</td>\n",
" <td>0.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>1602</td>\n",
" <td>217.872</td>\n",
" <td>247.0</td>\n",
" <td>228.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5343167</td>\n",
" <td>133.080202</td>\n",
" <td>5</td>\n",
" <td>721826.8125</td>\n",
" <td>75529.304688</td>\n",
" <td>171.0</td>\n",
" <td>11157516.0</td>\n",
" <td>0.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>1289</td>\n",
" <td>175.304</td>\n",
" <td>247.0</td>\n",
" <td>228.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"0 5343176 108.129956 16 722110.3750 75509.609375 -17.0 \n",
"1 5343175 110.595071 17 722108.1875 75516.570312 163.0 \n",
"2 5343151 116.397070 11 721725.8125 75517.546875 38.0 \n",
"3 5343169 43.013045 22 721796.8125 75527.015625 11.0 \n",
"4 5343167 133.080202 5 721826.8125 75529.304688 171.0 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID MSTRAHLUNG \\\n",
"0 11157515.0 0.0 TI 5250.0 1548 \n",
"1 11157515.0 0.0 TI 5250.0 1114 \n",
"2 11157516.0 0.0 TI 5250.0 1449 \n",
"3 11157516.0 0.0 TI 5250.0 1602 \n",
"4 11157516.0 0.0 TI 5250.0 1289 \n",
"\n",
" PV_kWh_m2 x_idx y_idx \n",
"0 210.528 248.0 228.0 \n",
"1 151.504 248.0 228.0 \n",
"2 197.064 247.0 228.0 \n",
"3 217.872 247.0 228.0 \n",
"4 175.304 247.0 228.0 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_linked.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# reindex meteo data from coordinates to indices\n",
"GHI_reindexed = reindex_dataframe(mean_GHI)\n",
"\n",
"# convert xarray to dataframe\n",
"GHI_df = util.to_pandas(GHI_reindexed)\n",
"\n",
"# merge meteo data with roofs\n",
"roofs_with_meteo = roofs_linked.merge(GHI_df, on = ['x_idx', 'y_idx']).drop( columns = ['x_idx', 'y_idx'] )"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
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" <td>163.0</td>\n",
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" <td>0.000000</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>1114</td>\n",
" <td>151.504</td>\n",
" <td>1367.466223</td>\n",
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" <td>25</td>\n",
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" <td>-59.0</td>\n",
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" <td>5250.0</td>\n",
" <td>545</td>\n",
" <td>74.120</td>\n",
" <td>1367.466223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5343178</td>\n",
" <td>20.850745</td>\n",
" <td>24</td>\n",
" <td>722491.9375</td>\n",
" <td>75593.523438</td>\n",
" <td>121.0</td>\n",
" <td>11103162.0</td>\n",
" <td>0.000000</td>\n",
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" <td>5250.0</td>\n",
" <td>598</td>\n",
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" <td>11157513.0</td>\n",
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" <td>5250.0</td>\n",
" <td>1032</td>\n",
" <td>140.352</td>\n",
" <td>1367.466223</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"0 5343176 108.129956 16 722110.3750 75509.609375 -17.0 \n",
"1 5343175 110.595071 17 722108.1875 75516.570312 163.0 \n",
"2 5343177 20.512037 25 722494.3125 75592.125000 -59.0 \n",
"3 5343178 20.850745 24 722491.9375 75593.523438 121.0 \n",
"4 5343214 168.164462 11 722125.8125 75788.093750 168.0 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID MSTRAHLUNG \\\n",
"0 11157515.0 0.000000 TI 5250.0 1548 \n",
"1 11157515.0 0.000000 TI 5250.0 1114 \n",
"2 11103162.0 0.000000 TI 5250.0 545 \n",
"3 11103162.0 0.000000 TI 5250.0 598 \n",
"4 11157513.0 0.261905 TI 5250.0 1032 \n",
"\n",
" PV_kWh_m2 SIS \n",
"0 210.528 1367.466223 \n",
"1 151.504 1367.466223 \n",
"2 74.120 1367.466223 \n",
"3 81.328 1367.466223 \n",
"4 140.352 1367.466223 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_with_meteo.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 1.4: Add shading information\n",
"Use only rooftops that have shading information - drop all others (approx. 10%)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>non_null_cells</th>\n",
" <th>null_cells</th>\n",
" <th>unshaded_illumination</th>\n",
" </tr>\n",
" </thead>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
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],
"text/plain": [
" DF_UID non_null_cells null_cells unshaded_illumination\n",
"0 1 2 0 0.684932\n",
"1 2 4 0 0.625000\n",
"2 3 15 0 0.747945\n",
"3 4 9 0 0.700913\n",
"4 5 5 2 0.535616"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SHADE_FP = '/work/hyenergy/output/solar_potential/geographic_potential/available_area/shading_stats/CH_LV03/summary_stats_mean.csv'\n",
"mean_illumination = pd.read_csv( SHADE_FP )\n",
"mean_illumination.rename({'zone' : 'DF_UID', 'mean' : 'unshaded_illumination'}, axis = 1, inplace = True)\n",
"mean_illumination.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8589502"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(mean_illumination)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"roofs_with_shade = roofs_with_meteo.merge( mean_illumination[['DF_UID', 'unshaded_illumination']],\n",
" on = 'DF_UID', how = 'inner')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"8589502"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(roofs_with_shade)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"MEAN_SHADED = 0.2 # mean shading value for fully shaded parts of the roof\n",
"roofs_with_shade['illumination'] = roofs_with_shade.shaded_area_ratio_ftr * MEAN_SHADED \\\n",
" + (1 - roofs_with_shade.shaded_area_ratio_ftr) * \\\n",
" roofs_with_shade.unshaded_illumination"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# Division of shading classes:\n",
"# Class 0: 0-0.5 --> VERY HIGH shading\n",
"# Class 1: 0.5-0.6 --> HIGH shading\n",
"# Class 2: 0.6-0.7 --> MEDIUM shading\n",
"# Class 3: 0.7-0.8 --> LOW shading\n",
"# Class 4: 0.8-0.9 --> VERY LOW shading\n",
"# Class 5: > 0.9 --> NO shading\n",
"\n",
"roofs_with_shade['illum_class'] = np.maximum(np.ceil((roofs_with_shade.illumination - 0.5) * 10), 0).astype('int')\n",
"# Divide in only 4 classes\n",
"# roofs_with_shade['illum_class'] = np.maximum(np.ceil((roofs_with_shade.illumination - 0.1) * 5) - 2, 0).astype('int')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <td>110.595071</td>\n",
" <td>17</td>\n",
" <td>722108.1875</td>\n",
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" <td>163.0</td>\n",
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" <td>151.504</td>\n",
" <td>1367.466223</td>\n",
" <td>0.804061</td>\n",
" <td>0.804061</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5343177</td>\n",
" <td>20.512037</td>\n",
" <td>25</td>\n",
" <td>722494.3125</td>\n",
" <td>75592.125000</td>\n",
" <td>-59.0</td>\n",
" <td>11103162.0</td>\n",
" <td>0.000000</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>545</td>\n",
" <td>74.120</td>\n",
" <td>1367.466223</td>\n",
" <td>0.738014</td>\n",
" <td>0.738014</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>20.850745</td>\n",
" <td>24</td>\n",
" <td>722491.9375</td>\n",
" <td>75593.523438</td>\n",
" <td>121.0</td>\n",
" <td>11103162.0</td>\n",
" <td>0.000000</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>598</td>\n",
" <td>81.328</td>\n",
" <td>1367.466223</td>\n",
" <td>0.654795</td>\n",
" <td>0.654795</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5343214</td>\n",
" <td>168.164462</td>\n",
" <td>11</td>\n",
" <td>722125.8125</td>\n",
" <td>75788.093750</td>\n",
" <td>168.0</td>\n",
" <td>11157513.0</td>\n",
" <td>0.261905</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>1032</td>\n",
" <td>140.352</td>\n",
" <td>1367.466223</td>\n",
" <td>0.537340</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"0 5343176 108.129956 16 722110.3750 75509.609375 -17.0 \n",
"1 5343175 110.595071 17 722108.1875 75516.570312 163.0 \n",
"2 5343177 20.512037 25 722494.3125 75592.125000 -59.0 \n",
"3 5343178 20.850745 24 722491.9375 75593.523438 121.0 \n",
"4 5343214 168.164462 11 722125.8125 75788.093750 168.0 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID MSTRAHLUNG \\\n",
"0 11157515.0 0.000000 TI 5250.0 1548 \n",
"1 11157515.0 0.000000 TI 5250.0 1114 \n",
"2 11103162.0 0.000000 TI 5250.0 545 \n",
"3 11103162.0 0.000000 TI 5250.0 598 \n",
"4 11157513.0 0.261905 TI 5250.0 1032 \n",
"\n",
" PV_kWh_m2 SIS unshaded_illumination illumination illum_class \n",
"0 210.528 1367.466223 0.811781 0.811781 4 \n",
"1 151.504 1367.466223 0.804061 0.804061 4 \n",
"2 74.120 1367.466223 0.738014 0.738014 3 \n",
"3 81.328 1367.466223 0.654795 0.654795 2 \n",
"4 140.352 1367.466223 0.537340 0.448989 0 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_with_shade.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Cross-check distribution of classes"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
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" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"for illum in range(roofs_with_shade.illum_class.max() + 1):\n",
" \n",
" roofs_with_shade[roofs_with_shade.illum_class == illum].illumination.hist(bins = 50, range = (0,1), label = ('Class = %d' %illum), facecolor = ('C%d' %illum))\n",
"\n",
"roofs_with_shade.illumination.hist(bins = 50, range = (0,1), facecolor = 'None', edgecolor = 'gray')\n",
"# roofs_with_shade.unshaded_illumination.hist(bins = 50, range = (0,1), facecolor = 'None', edgecolor = 'C1', label = 'unshaded')\n",
"plt.legend()\n",
"plt.title('Distribution of mean rooftop illumination per class')\n",
"plt.xlabel('Rooftop illumination')\n",
"plt.ylabel('Frequency')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.25 0.660959\n",
"0.50 0.756849\n",
"0.75 0.832192\n",
"Name: illumination, dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_with_shade.illumination.quantile([0.25, 0.5, 0.75])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>FLAECHE</th>\n",
" <th>NEIGUNG</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>GWR_EGID</th>\n",
" <th>shaded_area_ratio_ftr</th>\n",
" <th>Kanton</th>\n",
" <th>Commune_ID</th>\n",
" <th>MSTRAHLUNG</th>\n",
" <th>PV_kWh_m2</th>\n",
" <th>SIS</th>\n",
" <th>unshaded_illumination</th>\n",
" <th>illumination</th>\n",
" </tr>\n",
" <tr>\n",
" <th>illum_class</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>545868</td>\n",
" <td>545868</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" <td>546355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>756525</td>\n",
" <td>756525</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" <td>757217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1596322</td>\n",
" <td>1596322</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" <td>1597844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2608661</td>\n",
" <td>2608661</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" <td>2610923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2508989</td>\n",
" <td>2508989</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" <td>2511183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565523</td>\n",
" <td>565523</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" <td>565980</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"illum_class \n",
"0 546355 546355 546355 546355 546355 546355 \n",
"1 757217 757217 757217 757217 757217 757217 \n",
"2 1597844 1597844 1597844 1597844 1597844 1597844 \n",
"3 2610923 2610923 2610923 2610923 2610923 2610923 \n",
"4 2511183 2511183 2511183 2511183 2511183 2511183 \n",
"5 565980 565980 565980 565980 565980 565980 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID MSTRAHLUNG \\\n",
"illum_class \n",
"0 546355 546355 545868 545868 546355 \n",
"1 757217 757217 756525 756525 757217 \n",
"2 1597844 1597844 1596322 1596322 1597844 \n",
"3 2610923 2610923 2608661 2608661 2610923 \n",
"4 2511183 2511183 2508989 2508989 2511183 \n",
"5 565980 565980 565523 565523 565980 \n",
"\n",
" PV_kWh_m2 SIS unshaded_illumination illumination \n",
"illum_class \n",
"0 546355 546355 546355 546355 \n",
"1 757217 757217 757217 757217 \n",
"2 1597844 1597844 1597844 1597844 \n",
"3 2610923 2610923 2610923 2610923 \n",
"4 2511183 2511183 2511183 2511183 \n",
"5 565980 565980 565980 565980 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_with_shade.groupby('illum_class').count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 1.5: Add mean horizon values (to be tested instead of mean illuminance)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"fp = '/work/hyenergy/output/solar_potential/geographic_potential/available_area/shading_stats/CH_LV03'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"south = pd.read_csv( os.path.join(fp, 'horizon_stats_275.csv'), usecols = ['zone', 'mean'] )\n",
"south.rename({'zone' : 'DF_UID', 'mean' : 'horizon_S'}, axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"east = pd.read_csv( os.path.join(fp, 'horizon_stats_000.csv'), usecols = ['zone', 'mean'] )\n",
"east.rename({'zone' : 'DF_UID', 'mean' : 'horizon_E'}, axis = 1, inplace = True)\n",
"\n",
"west = pd.read_csv( os.path.join(fp, 'horizon_stats_180.csv'), usecols = ['zone', 'mean'] )\n",
"west.rename({'zone' : 'DF_UID', 'mean' : 'horizon_W'}, axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"SE = pd.read_csv( os.path.join(fp, 'horizon_stats_315.csv'), usecols = ['zone', 'mean'] )\n",
"SE.rename({'zone' : 'DF_UID', 'mean' : 'horizon_SE'}, axis = 1, inplace = True)\n",
"\n",
"SW = pd.read_csv( os.path.join(fp, 'horizon_stats_225.csv'), usecols = ['zone', 'mean'] )\n",
"SW.rename({'zone' : 'DF_UID', 'mean' : 'horizon_SW'}, axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"horizons = pd.concat([south, east.horizon_E, west.horizon_W, SE.horizon_SE, SW.horizon_SW], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"roofs_with_horizon = roofs_with_shade.merge( horizons, on = 'DF_UID', how = 'inner' )"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
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" .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>DF_UID</th>\n",
" <th>FLAECHE</th>\n",
" <th>NEIGUNG</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>GWR_EGID</th>\n",
" <th>shaded_area_ratio_ftr</th>\n",
" <th>Kanton</th>\n",
" <th>Commune_ID</th>\n",
" <th>...</th>\n",
" <th>PV_kWh_m2</th>\n",
" <th>SIS</th>\n",
" <th>unshaded_illumination</th>\n",
" <th>illumination</th>\n",
" <th>illum_class</th>\n",
" <th>horizon_S</th>\n",
" <th>horizon_E</th>\n",
" <th>horizon_W</th>\n",
" <th>horizon_SE</th>\n",
" <th>horizon_SW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5343176</td>\n",
" <td>108.129956</td>\n",
" <td>16</td>\n",
" <td>722110.3750</td>\n",
" <td>75509.609375</td>\n",
" <td>-17.0</td>\n",
" <td>11157515.0</td>\n",
" <td>0.000000</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>...</td>\n",
" <td>210.528</td>\n",
" <td>1367.466223</td>\n",
" <td>0.811781</td>\n",
" <td>0.811781</td>\n",
" <td>4</td>\n",
" <td>6.250884</td>\n",
" <td>14.689941</td>\n",
" <td>9.421774</td>\n",
" <td>8.257477</td>\n",
" <td>4.558886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5343175</td>\n",
" <td>110.595071</td>\n",
" <td>17</td>\n",
" <td>722108.1875</td>\n",
" <td>75516.570312</td>\n",
" <td>163.0</td>\n",
" <td>11157515.0</td>\n",
" <td>0.000000</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>...</td>\n",
" <td>151.504</td>\n",
" <td>1367.466223</td>\n",
" <td>0.804061</td>\n",
" <td>0.804061</td>\n",
" <td>4</td>\n",
" <td>11.759160</td>\n",
" <td>16.118021</td>\n",
" <td>2.435227</td>\n",
" <td>12.480068</td>\n",
" <td>7.400148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5343177</td>\n",
" <td>20.512037</td>\n",
" <td>25</td>\n",
" <td>722494.3125</td>\n",
" <td>75592.125000</td>\n",
" <td>-59.0</td>\n",
" <td>11103162.0</td>\n",
" <td>0.000000</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>...</td>\n",
" <td>74.120</td>\n",
" <td>1367.466223</td>\n",
" <td>0.738014</td>\n",
" <td>0.738014</td>\n",
" <td>3</td>\n",
" <td>12.829329</td>\n",
" <td>21.675634</td>\n",
" <td>12.766143</td>\n",
" <td>14.530394</td>\n",
" <td>1.346442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5343178</td>\n",
" <td>20.850745</td>\n",
" <td>24</td>\n",
" <td>722491.9375</td>\n",
" <td>75593.523438</td>\n",
" <td>121.0</td>\n",
" <td>11103162.0</td>\n",
" <td>0.000000</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>...</td>\n",
" <td>81.328</td>\n",
" <td>1367.466223</td>\n",
" <td>0.654795</td>\n",
" <td>0.654795</td>\n",
" <td>2</td>\n",
" <td>16.843894</td>\n",
" <td>18.360150</td>\n",
" <td>20.702796</td>\n",
" <td>13.453623</td>\n",
" <td>15.055898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5343214</td>\n",
" <td>168.164462</td>\n",
" <td>11</td>\n",
" <td>722125.8125</td>\n",
" <td>75788.093750</td>\n",
" <td>168.0</td>\n",
" <td>11157513.0</td>\n",
" <td>0.261905</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" <td>...</td>\n",
" <td>140.352</td>\n",
" <td>1367.466223</td>\n",
" <td>0.537340</td>\n",
" <td>0.448989</td>\n",
" <td>0</td>\n",
" <td>44.287322</td>\n",
" <td>15.740415</td>\n",
" <td>16.178937</td>\n",
" <td>41.987424</td>\n",
" <td>26.342700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"0 5343176 108.129956 16 722110.3750 75509.609375 -17.0 \n",
"1 5343175 110.595071 17 722108.1875 75516.570312 163.0 \n",
"2 5343177 20.512037 25 722494.3125 75592.125000 -59.0 \n",
"3 5343178 20.850745 24 722491.9375 75593.523438 121.0 \n",
"4 5343214 168.164462 11 722125.8125 75788.093750 168.0 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID ... \\\n",
"0 11157515.0 0.000000 TI 5250.0 ... \n",
"1 11157515.0 0.000000 TI 5250.0 ... \n",
"2 11103162.0 0.000000 TI 5250.0 ... \n",
"3 11103162.0 0.000000 TI 5250.0 ... \n",
"4 11157513.0 0.261905 TI 5250.0 ... \n",
"\n",
" PV_kWh_m2 SIS unshaded_illumination illumination illum_class \\\n",
"0 210.528 1367.466223 0.811781 0.811781 4 \n",
"1 151.504 1367.466223 0.804061 0.804061 4 \n",
"2 74.120 1367.466223 0.738014 0.738014 3 \n",
"3 81.328 1367.466223 0.654795 0.654795 2 \n",
"4 140.352 1367.466223 0.537340 0.448989 0 \n",
"\n",
" horizon_S horizon_E horizon_W horizon_SE horizon_SW \n",
"0 6.250884 14.689941 9.421774 8.257477 4.558886 \n",
"1 11.759160 16.118021 2.435227 12.480068 7.400148 \n",
"2 12.829329 21.675634 12.766143 14.530394 1.346442 \n",
"3 16.843894 18.360150 20.702796 13.453623 15.055898 \n",
"4 44.287322 15.740415 16.178937 41.987424 26.342700 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs_with_horizon.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 1.6: Save data to file"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'roofs_with_horizon' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-8ab17d7442f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroofs_with_horizon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'roofs_with_horizon' is not defined"
]
}
],
"source": [
"data = roofs_with_horizon.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-304fa4ce4ebd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8581888"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"data.to_csv('/work/hyenergy/output/solar_potential/technical_potential/predict_pv/data_all.csv', index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 2: Prepare features"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"path = '/Volumes/Seagate Backup Plus Drive/DOKUMENTE/EPFL/data/Results/PV_w_uncertainty/pv_prediction/'# '/work/hyenergy/output/solar_potential/technical_potential/predict_pv/'\n",
"data = pd.read_csv( os.path.join(path, 'data_all.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8581888"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 2.1: Choose training areas and testing areas (based on Cantons)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"COMMUNE_FP = '/Users/alinawalch/Documents/EPFL/data/GWR/GDEListe_20150101.txt' # '/work/hyenergy/raw/GWR_Reg-BL/GDEListe_20150101.txt'\n",
"commune_info = pd.read_csv( COMMUNE_FP, sep = '\\t', encoding = 'cp1250' )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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>GDEKT</th>\n",
" <th>GDEBZNR</th>\n",
" <th>GDENR</th>\n",
" <th>GDENAME</th>\n",
" <th>GDENAMK</th>\n",
" <th>GDEBZNA</th>\n",
" <th>GDEKTNA</th>\n",
" <th>GDEMUTDAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2058</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6021.0</td>\n",
" <td>Ardon</td>\n",
" <td>Ardon</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6022.0</td>\n",
" <td>Chamoson</td>\n",
" <td>Chamoson</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6023.0</td>\n",
" <td>Conthey</td>\n",
" <td>Conthey</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6024.0</td>\n",
" <td>Nendaz</td>\n",
" <td>Nendaz</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6025.0</td>\n",
" <td>Vétroz</td>\n",
" <td>Vétroz</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GDEKT GDEBZNR GDENR GDENAME GDENAMK GDEBZNA \\\n",
"2058 VS 2302.0 6021.0 Ardon Ardon District de Conthey \n",
"2059 VS 2302.0 6022.0 Chamoson Chamoson District de Conthey \n",
"2060 VS 2302.0 6023.0 Conthey Conthey District de Conthey \n",
"2061 VS 2302.0 6024.0 Nendaz Nendaz District de Conthey \n",
"2062 VS 2302.0 6025.0 Vétroz Vétroz District de Conthey \n",
"\n",
" GDEKTNA GDEMUTDAT \n",
"2058 Valais / Wallis 01.01.1960 \n",
"2059 Valais / Wallis 01.01.1960 \n",
"2060 Valais / Wallis 01.01.1960 \n",
"2061 Valais / Wallis 01.01.1960 \n",
"2062 Valais / Wallis 01.01.1960 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# find french-speaking communes in Valais based on the start of their district name ('District' and not 'Bezirk')\n",
"districts_valais = commune_info[ commune_info.GDEKT == 'VS' ]\n",
"french_communes = [bzg[0:8] == 'District' for bzg in districts_valais.GDEBZNA.values]\n",
"districts_valais[french_communes].head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"french_cantons = ['NE', 'JU', 'VD', 'GE', 'FR'] # plus french communes in Valais; considering FR even though bilangue"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"training_data = data[(data.Kanton.isin( french_cantons )) \\\n",
" | (data.Commune_ID.isin( districts_valais[french_communes].GDENR.values )) ]\n",
"training_data = training_data.reset_index().drop('index', axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>DF_UID</th>\n",
" <th>FLAECHE</th>\n",
" <th>NEIGUNG</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>GWR_EGID</th>\n",
" <th>shaded_area_ratio_ftr</th>\n",
" <th>Kanton</th>\n",
" <th>Commune_ID</th>\n",
" <th>...</th>\n",
" <th>PV_kWh_m2</th>\n",
" <th>SIS</th>\n",
" <th>unshaded_illumination</th>\n",
" <th>illumination</th>\n",
" <th>illum_class</th>\n",
" <th>horizon_S</th>\n",
" <th>horizon_E</th>\n",
" <th>horizon_W</th>\n",
" <th>horizon_SE</th>\n",
" <th>horizon_SW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9449166</td>\n",
" <td>113.600622</td>\n",
" <td>0</td>\n",
" <td>579172.9375</td>\n",
" <td>79689.500000</td>\n",
" <td>0.0</td>\n",
" <td>902671.0</td>\n",
" <td>0.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
" <td>...</td>\n",
" <td>192.848</td>\n",
" <td>1576.254293</td>\n",
" <td>0.816486</td>\n",
" <td>0.816486</td>\n",
" <td>4</td>\n",
" <td>21.288716</td>\n",
" <td>8.315230</td>\n",
" <td>0.0</td>\n",
" <td>24.376141</td>\n",
" <td>1.387305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9449168</td>\n",
" <td>32.135801</td>\n",
" <td>27</td>\n",
" <td>579235.2500</td>\n",
" <td>79689.906250</td>\n",
" <td>7.0</td>\n",
" <td>902671.0</td>\n",
" <td>0.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
" <td>...</td>\n",
" <td>214.472</td>\n",
" <td>1576.254293</td>\n",
" <td>0.728311</td>\n",
" <td>0.728311</td>\n",
" <td>3</td>\n",
" <td>31.419407</td>\n",
" <td>4.143541</td>\n",
" <td>0.0</td>\n",
" <td>20.988251</td>\n",
" <td>15.724294</td>\n",
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" <td>579235.6875</td>\n",
" <td>79693.085938</td>\n",
" <td>-173.0</td>\n",
" <td>902671.0</td>\n",
" <td>0.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
" <td>...</td>\n",
" <td>147.696</td>\n",
" <td>1576.254293</td>\n",
" <td>0.753425</td>\n",
" <td>0.753425</td>\n",
" <td>3</td>\n",
" <td>30.855970</td>\n",
" <td>3.495298</td>\n",
" <td>0.0</td>\n",
" <td>19.400254</td>\n",
" <td>14.879618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9449170</td>\n",
" <td>162.651604</td>\n",
" <td>34</td>\n",
" <td>579209.9375</td>\n",
" <td>79712.953125</td>\n",
" <td>22.0</td>\n",
" <td>902671.0</td>\n",
" <td>0.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
" <td>...</td>\n",
" <td>213.792</td>\n",
" <td>1576.254293</td>\n",
" <td>0.886675</td>\n",
" <td>0.886675</td>\n",
" <td>4</td>\n",
" <td>22.641191</td>\n",
" <td>2.523374</td>\n",
" <td>0.0</td>\n",
" <td>14.788742</td>\n",
" <td>2.355230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9449157</td>\n",
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" <td>33</td>\n",
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" <td>22.0</td>\n",
" <td>902671.0</td>\n",
" <td>0.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
" <td>...</td>\n",
" <td>221.680</td>\n",
" <td>1576.254293</td>\n",
" <td>0.873858</td>\n",
" <td>0.873858</td>\n",
" <td>4</td>\n",
" <td>19.500396</td>\n",
" <td>8.517506</td>\n",
" <td>0.0</td>\n",
" <td>18.515527</td>\n",
" <td>0.794733</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE NEIGUNG XCOORD YCOORD AUSRICHTUNG \\\n",
"0 9449166 113.600622 0 579172.9375 79689.500000 0.0 \n",
"1 9449168 32.135801 27 579235.2500 79689.906250 7.0 \n",
"2 9449167 33.816775 25 579235.6875 79693.085938 -173.0 \n",
"3 9449170 162.651604 34 579209.9375 79712.953125 22.0 \n",
"4 9449157 175.680874 33 579190.1875 79714.609375 22.0 \n",
"\n",
" GWR_EGID shaded_area_ratio_ftr Kanton Commune_ID ... PV_kWh_m2 \\\n",
"0 902671.0 0.0 VS 6032.0 ... 192.848 \n",
"1 902671.0 0.0 VS 6032.0 ... 214.472 \n",
"2 902671.0 0.0 VS 6032.0 ... 147.696 \n",
"3 902671.0 0.0 VS 6032.0 ... 213.792 \n",
"4 902671.0 0.0 VS 6032.0 ... 221.680 \n",
"\n",
" SIS unshaded_illumination illumination illum_class horizon_S \\\n",
"0 1576.254293 0.816486 0.816486 4 21.288716 \n",
"1 1576.254293 0.728311 0.728311 3 31.419407 \n",
"2 1576.254293 0.753425 0.753425 3 30.855970 \n",
"3 1576.254293 0.886675 0.886675 4 22.641191 \n",
"4 1576.254293 0.873858 0.873858 4 19.500396 \n",
"\n",
" horizon_E horizon_W horizon_SE horizon_SW \n",
"0 8.315230 0.0 24.376141 1.387305 \n",
"1 4.143541 0.0 20.988251 15.724294 \n",
"2 3.495298 0.0 19.400254 14.879618 \n",
"3 2.523374 0.0 14.788742 2.355230 \n",
"4 8.517506 0.0 18.515527 0.794733 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"training_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1909876"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(training_data)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"testing_data = data[ np.logical_not(data.DF_UID.isin( training_data.DF_UID.values )) ]\n",
"testing_data = testing_data.reset_index().drop('index', axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6672012"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(testing_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 2.2: Define features & normalize"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"label_name = ['MSTRAHLUNG']\n",
"identifier = 'DF_UID'\n",
"\n",
"all_features = ['SIS', 'illum_class', 'AUSRICHTUNG', 'NEIGUNG', 'horizon_S', 'horizon_E', 'horizon_W', 'horizon_SW', 'horizon_SE']\n",
"feature_labels = ['Roof tilt', 'Roof aspect', 'Annual GHI', 'Horizon (S)', 'Horizon (E)', 'Horizon (W)']\n",
"\n",
"feature_names = ['NEIGUNG', 'AUSRICHTUNG', 'SIS', 'horizon_S', 'horizon_E', 'horizon_W' ]\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"training = training_data.sample(10000)\n",
"testing = testing_data.sample(10000)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"X = training.loc[ : , feature_names ].values\n",
"T = training.loc[ : , label_name ].values.reshape((-1,))\n",
"\n",
"norm = Normalizer()\n",
"x = norm.fit_transform(X)\n",
"t = normalize(T, T)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"X_te = testing.loc[ : , feature_names ].values\n",
"T_te = testing.loc[ : , label_name ].values.reshape((-1,))\n",
"\n",
"x_te = norm.transform(X_te)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DF_UID', 'FLAECHE', 'NEIGUNG', 'XCOORD', 'YCOORD', 'AUSRICHTUNG',\n",
" 'GWR_EGID', 'shaded_area_ratio_ftr', 'Kanton', 'Commune_ID',\n",
" 'MSTRAHLUNG', 'PV_kWh_m2', 'SIS', 'unshaded_illumination',\n",
" 'illumination', 'illum_class', 'horizon_S', 'horizon_E', 'horizon_W',\n",
" 'horizon_SE', 'horizon_SW'],\n",
" dtype='object')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"training.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 2.3: Feature importances"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
" min_impurity_split=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=500, n_jobs=-1, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_est = 500\n",
"max_features = 3\n",
"RF = RandomForestRegressor( n_estimators = n_est, max_features = max_features, n_jobs = -1 )\n",
"RF.fit(X, T)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 1.62 seconds\n",
"Wall clock time: 1.60 seconds\n"
]
}
],
"source": [
"tt = util.Timer()\n",
"Y_te = RF.predict(X_te)\n",
"tt.stop()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing MSE: 10476.348176\n",
"Testing RMSE: 102.354034\n",
"Testing MAE: 65.554081\n",
"Testing MAEP: 0.080385\n",
"Testing MBE: -19.699505\n",
"Testing MBEP: -0.042020\n",
"Testing R2: 0.813077\n"
]
}
],
"source": [
"err = get_errors(T_te, Y_te, label = 'Testing' )"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# for standard estimators\n",
"n_features = X.shape[1]\n",
"importance = np.mean([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"std = np.std([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"ind = np.argsort(importance)[-n_features:]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.12319459 0.37850716 0.06558748 0.29990844 0.06181301 0.07098932]\n",
"[ 0.01317223 0.09046872 0.0055153 0.10021499 0.00664499 0.0093173 ]\n",
"[4 2 5 0 3 1]\n"
]
}
],
"source": [
"print(importance)\n",
"print(std)\n",
"print(ind)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
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" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"ax = plt.axes()\n",
"plt.title(\"Feature Importance for Random Forest (%d trees)\" %(n_est)) # \\n Testing RMSE: %f ; , np.sqrt(mse(Y_te,T_te))\n",
"plt.barh(range(n_features),importance[ind],\n",
" color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\", capsize = 2)\n",
"plt.yticks(range(n_features), np.asarray(feature_labels)[ind])\n",
"plt.ylim([-1, len(ind)])\n",
"plt.xlabel('Feature importance')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"# plt.savefig('Feature_importance_test.png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Part 2.3.1 Permutated feature importances\n",
"Idea: feature importance can vary for multiple reasons:\n",
" 1. Random forests are random (surprise!), so forests should be trained multiple times to assess errors and importances.\n",
" **NOTE:** effect decreases with sample size\n",
" 2. Feature importance measure can be dependent of the order of the features, particularly if there are correlations between features\n",
" \n",
"Hence, we perform permutations of the feature space and perform multiple runs, in order to get a representative measure of feature importances"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# FOR-LOOP with permutation of features:\n",
"n_features = X.shape[1]\n",
"all_permutations = np.asarray(list(itertools.permutations( range(n_features) )))\n",
"\n",
"# pick randomly N_PERM.. permutations of the features\n",
"N_PERMUTATIONS = 20\n",
"permutations = all_permutations[ np.random.randint(0, len(all_permutations), N_PERMUTATIONS) ]\n",
"\n",
"N_SAMPLES = 10000"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3 1 0 4 5 2]\n",
"Completed iteration 0 in 14.496 seconds\n",
"[4 2 3 1 0 5]\n",
"Completed iteration 1 in 14.302 seconds\n",
"[1 2 0 3 4 5]\n",
"Completed iteration 2 in 14.363 seconds\n",
"[1 4 2 5 3 0]\n",
"Completed iteration 3 in 14.290 seconds\n",
"[5 1 2 4 0 3]\n",
"Completed iteration 4 in 14.130 seconds\n",
"[1 3 2 0 4 5]\n",
"Completed iteration 5 in 14.617 seconds\n",
"[2 4 0 3 1 5]\n",
"Completed iteration 6 in 14.286 seconds\n",
"[4 5 1 3 2 0]\n",
"Completed iteration 7 in 14.808 seconds\n",
"[3 2 4 0 1 5]\n",
"Completed iteration 8 in 14.540 seconds\n",
"[5 4 0 2 1 3]\n",
"Completed iteration 9 in 14.444 seconds\n",
"[1 5 3 2 0 4]\n",
"Completed iteration 10 in 14.722 seconds\n",
"[1 3 4 2 0 5]\n",
"Completed iteration 11 in 14.175 seconds\n",
"[1 0 3 2 4 5]\n",
"Completed iteration 12 in 14.379 seconds\n",
"[0 3 2 5 4 1]\n",
"Completed iteration 13 in 14.530 seconds\n",
"[3 1 0 2 4 5]\n",
"Completed iteration 14 in 14.248 seconds\n",
"[5 4 0 2 1 3]\n",
"Completed iteration 15 in 14.452 seconds\n",
"[4 0 1 2 3 5]\n",
"Completed iteration 16 in 14.389 seconds\n",
"[2 1 5 3 4 0]\n",
"Completed iteration 17 in 14.105 seconds\n",
"[4 2 0 5 3 1]\n",
"Completed iteration 18 in 14.156 seconds\n",
"[1 4 2 0 5 3]\n",
"Completed iteration 19 in 14.364 seconds\n"
]
}
],
"source": [
"stds = np.zeros((N_PERMUTATIONS, n_features))\n",
"importances = np.zeros((N_PERMUTATIONS, n_features))\n",
"\n",
"for i, perm in enumerate(permutations):\n",
" tt = time.time()\n",
" print(perm)\n",
" \n",
" training = training_data.sample(N_SAMPLES)\n",
" testing = testing_data.sample( N_SAMPLES)\n",
" \n",
" X = training.loc[ : , feature_names ].values\n",
" T = training.loc[ : , label_name ].values.reshape((-1,))\n",
" \n",
" X_tr = X[:,perm]\n",
" \n",
" RF = RandomForestRegressor( n_estimators = n_est, max_features = max_features, n_jobs = -1 )\n",
" RF.fit(X_tr, T)\n",
" \n",
" importances[i,perm] = np.mean([tree.feature_importances_ for tree in RF.estimators_], axis=0)\n",
" stds[i,perm] = np.std( [tree.feature_importances_ for tree in RF.estimators_], axis=0)\n",
" \n",
" print('Completed iteration %d in %.3f seconds' %(i, time.time() - tt))\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# assuming independence between all runs of the process, stds are summed in quadrature and means are averaged\n",
"importance = np.mean( importances, axis = 0 )\n",
"std = 1/N_PERMUTATIONS * np.sqrt(np.sum(stds**2, axis = 0))\n",
"ind = np.argsort(importance)[-n_features:]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
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"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"ax = plt.axes()\n",
"plt.title(\"Feature Importance for Random Forest (%d trees)\" %(n_est)) # \\n Testing RMSE: %f ; , np.sqrt(mse(Y_te,T_te))\n",
"plt.barh(range(n_features),importance[ind],\n",
" color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\", capsize = 2)\n",
"plt.yticks(range(n_features), np.asarray(feature_labels)[ind])\n",
"plt.ylim([-1, len(ind)])\n",
"plt.xlabel('Feature importance')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"plt.savefig('Feature_importance_perm_v2.png', dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16.978872165285232"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sqrt(mse(Y_te,T_te))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 3: Machine Learning"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# ML MODEL PARAMETERS\n",
"n_est = 500\n",
"n_samples_leaf = 3\n",
"RF_features = 3\n",
"ELM_nodes = 25\n",
"ELM_est = 50\n",
"MLP_layers = (100, 20,) # WORKS RESONABLY WELL\n",
"SVM_paramC = 1\n",
"SVM_gamma = 500\n",
"knn_neighbors = 10\n",
"\n",
"k_CV = 5\n",
"\n",
"# DEFINITION OF ESTIMATORS\n",
"def set_estimators():\n",
" estimator = {\n",
" 'KNN': KNeighborsRegressor( n_neighbors = knn_neighbors, n_jobs = -1 ),\n",
" 'LIN': LinearRegression( n_jobs = -1 ),\n",
" 'RF' : RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, max_features = RF_features, n_jobs = -1 ),\n",
" 'ELM' : ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes),\n",
" 'MLP': MLPRegressor( hidden_layer_sizes = MLP_layers ),\n",
" 'SVM': SVR( C = SVM_paramC, kernel = 'rbf', gamma = SVM_gamma ),\n",
"# 'RF_unc' : Uncertainty( RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, n_jobs = -1 ) ),\n",
"# 'ELM_unc': Uncertainty( ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes) )\n",
" }\n",
" return estimator\n",
"\n",
"# 'XGB': XGBRegressor( n_estimators = n_est, max_depth = tree_depth, n_jobs = -1 ),"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF SINGLE ESTIMATOR - simple validation and cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"SAMPLE_SIZE = 10000\n",
"TR = training_data.sample(SAMPLE_SIZE)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"CPU time: 0.88 seconds\n",
"Wall clock time: 0.92 seconds\n",
"Cross-validation (normalized prediction) MSE: 21255.165300\n",
"Cross-validation (normalized prediction) RMSE: 145.791513\n",
"Cross-validation (normalized prediction) MAE: 94.073900\n",
"Cross-validation (normalized prediction) MAEP: 0.122322\n",
"Cross-validation (normalized prediction) MBE: -7.046100\n",
"Cross-validation (normalized prediction) MBEP: -0.050130\n",
"Cross-validation (normalized prediction) R2: 0.657248\n",
"2\n",
"CPU time: 1.50 seconds\n",
"Wall clock time: 1.77 seconds\n",
"Cross-validation (normalized prediction) MSE: 16519.797575\n",
"Cross-validation (normalized prediction) RMSE: 128.529365\n",
"Cross-validation (normalized prediction) MAE: 82.762950\n",
"Cross-validation (normalized prediction) MAEP: 0.110534\n",
"Cross-validation (normalized prediction) MBE: -7.974450\n",
"Cross-validation (normalized prediction) MBEP: -0.049539\n",
"Cross-validation (normalized prediction) R2: 0.733608\n",
"5\n",
"CPU time: 2.42 seconds\n",
"Wall clock time: 2.70 seconds\n",
"Cross-validation (normalized prediction) MSE: 12898.988300\n",
"Cross-validation (normalized prediction) RMSE: 113.573713\n",
"Cross-validation (normalized prediction) MAE: 73.032020\n",
"Cross-validation (normalized prediction) MAEP: 0.101874\n",
"Cross-validation (normalized prediction) MBE: -10.184700\n",
"Cross-validation (normalized prediction) MBEP: -0.051636\n",
"Cross-validation (normalized prediction) R2: 0.791996\n",
"10\n",
"CPU time: 3.22 seconds\n",
"Wall clock time: 3.39 seconds\n",
"Cross-validation (normalized prediction) MSE: 12220.697672\n",
"Cross-validation (normalized prediction) RMSE: 110.547264\n",
"Cross-validation (normalized prediction) MAE: 70.512620\n",
"Cross-validation (normalized prediction) MAEP: 0.099718\n",
"Cross-validation (normalized prediction) MBE: -12.910220\n",
"Cross-validation (normalized prediction) MBEP: -0.054317\n",
"Cross-validation (normalized prediction) R2: 0.802934\n",
"15\n",
"CPU time: 3.13 seconds\n",
"Wall clock time: 3.29 seconds\n",
"Cross-validation (normalized prediction) MSE: 12196.403433\n",
"Cross-validation (normalized prediction) RMSE: 110.437328\n",
"Cross-validation (normalized prediction) MAE: 70.200867\n",
"Cross-validation (normalized prediction) MAEP: 0.100365\n",
"Cross-validation (normalized prediction) MBE: -14.759200\n",
"Cross-validation (normalized prediction) MBEP: -0.056852\n",
"Cross-validation (normalized prediction) R2: 0.803326\n",
"20\n",
"CPU time: 2.62 seconds\n",
"Wall clock time: 2.70 seconds\n",
"Cross-validation (normalized prediction) MSE: 12232.623785\n",
"Cross-validation (normalized prediction) RMSE: 110.601193\n",
"Cross-validation (normalized prediction) MAE: 70.499210\n",
"Cross-validation (normalized prediction) MAEP: 0.101121\n",
"Cross-validation (normalized prediction) MBE: -16.023280\n",
"Cross-validation (normalized prediction) MBEP: -0.058483\n",
"Cross-validation (normalized prediction) R2: 0.802742\n"
]
}
],
"source": [
"# for training one estimator\n",
"X_cv = TR.loc[ : , feature_names ].values\n",
"x_cv = norm.transform( X_cv )\n",
"T_cv = TR.loc[ : , label_name ].values.reshape((-1,))\n",
"t_cv = normalize(T_cv, T_cv)\n",
"\n",
"for knn_neighbors in [1,2,5,10,15,20]:\n",
" print(knn_neighbors)\n",
"\n",
" estimator = set_estimators()\n",
" est = estimator[ 'KNN' ]\n",
"\n",
" # normalized cross-validation\n",
" tt = util.Timer()\n",
" y_cv = cross_val_predict(est, x_cv, t_cv, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" tt.stop()\n",
"\n",
" Y_cv = rescale(y_cv, T_cv)\n",
" err_cv = get_errors(T_cv, Y_cv, label = 'Cross-validation (normalized prediction)')\n",
" \n",
" '''\n",
" # non-normalized cross-validation\n",
" tt = util.Timer()\n",
" Y_cv = cross_val_predict(est, x_cv, T_cv, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" tt.stop()\n",
" \n",
" err_cv = get_errors(T_cv, Y_cv, label = 'Cross-validation (unscaled prediction)') \n",
"\n",
" # non-normalized cross-validation and features\n",
" tt = util.Timer()\n",
" Y_cv = cross_val_predict(est, X_cv, T_cv, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" tt.stop()\n",
" \n",
" err_cv = get_errors(T_cv, Y_cv, label = 'Cross-validation (unscaled features)') \n",
" '''\n"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 0.03 seconds\n",
"Wall clock time: 0.03 seconds\n",
"\n",
"Training MSE: 8894.870951\n",
"\n",
"Validation MSE: 17531.800144\n",
"Validation RMSE: 132.407704\n",
"Validation MAE: 72.204435\n",
"Validation MAEP: 0.105121\n",
"Validation MBE: -13.080503\n",
"Validation MBEP: -0.060401\n",
"Validation R2: 0.750575\n"
]
}
],
"source": [
"# for training one estimator\n",
"SAMPLE_SIZE = 1000\n",
"\n",
"TR, VAL = train_test_split(training_data.sample(SAMPLE_SIZE), test_size = float(1 / k_CV))\n",
"x_tr = norm.transform( TR.loc[ : , feature_names ].values )\n",
"T_tr = TR.loc[ : , label_name ].values.reshape((-1,))\n",
"t_tr = normalize(T_tr, T_tr)\n",
"\n",
"estimator = set_estimators()\n",
"est = estimator['SVM']\n",
"\n",
"tt = util.Timer()\n",
"est.fit(x_tr,t_tr)\n",
"tt.stop()\n",
"\n",
"y_tr = est.predict(x_tr)\n",
"Y_tr = rescale(y_tr, T_tr)\n",
"print('\\nTraining MSE: %f\\n' %mse(Y_tr,T_tr))\n",
"\n",
"x_val = norm.transform( VAL.loc[ : , feature_names ] )\n",
"T_val = VAL.loc[ : , label_name].values\n",
"\n",
"y_val = est.predict(x_val)\n",
"Y_val = rescale(y_val, T_tr)\n",
"val_err = get_errors(T_val, Y_val, label = 'Validation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF ALL ESTIMATORS - using cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# cross-validation\n",
"estimator = set_estimators()\n",
"err_cv = pd.DataFrame(data = np.zeros((len(error), len(estimator))), index = error.keys(), columns = estimator.keys())\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['MSE', 'RMSE', 'MAE', 'MBE', 'R2', 'time_wall', 'time_cpu'], dtype=object)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.hstack([err_cv.index.values, ['time_wall', 'time_cpu']])"
]
},
{
"cell_type": "code",
"execution_count": 454,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training KNN\n",
"\n",
"CPU time: 0.05 seconds\n",
"Wall clock time: 0.52 seconds\n",
"Cross-validation MSE: 390.601561\n",
"Cross-validation RMSE: 19.763642\n",
"Cross-validation MAE: 12.628552\n",
"Cross-validation MBE: -0.920856\n",
"Cross-validation R2: 0.662392\n",
"\n",
"Training LIN\n",
"\n",
"CPU time: 0.18 seconds\n",
"Wall clock time: 0.08 seconds\n",
"Cross-validation MSE: 426.018097\n",
"Cross-validation RMSE: 20.640206\n",
"Cross-validation MAE: 15.137084\n",
"Cross-validation MBE: 0.015428\n",
"Cross-validation R2: 0.631781\n",
"\n",
"Training RF\n",
"\n",
"CPU time: 9.28 seconds\n",
"Wall clock time: 9.90 seconds\n",
"Cross-validation MSE: 236.552011\n",
"Cross-validation RMSE: 15.380247\n",
"Cross-validation MAE: 9.878819\n",
"Cross-validation MBE: 0.176590\n",
"Cross-validation R2: 0.795542\n",
"\n",
"Training ELM\n",
"\n",
"CPU time: 7.11 seconds\n",
"Wall clock time: 7.09 seconds\n",
"Cross-validation MSE: 302.561609\n",
"Cross-validation RMSE: 17.394298\n",
"Cross-validation MAE: 12.113127\n",
"Cross-validation MBE: -0.012290\n",
"Cross-validation R2: 0.738488\n",
"\n",
"Training MLP\n",
"\n",
"CPU time: 55.49 seconds\n",
"Wall clock time: 55.69 seconds\n",
"Cross-validation MSE: 684.869669\n",
"Cross-validation RMSE: 26.170015\n",
"Cross-validation MAE: 19.296994\n",
"Cross-validation MBE: 0.215949\n",
"Cross-validation R2: 0.408048\n",
"\n",
"Training SVM\n",
"\n",
"CPU time: 0.25 seconds\n",
"Wall clock time: 0.26 seconds\n",
"Cross-validation MSE: 357.127920\n",
"Cross-validation RMSE: 18.897828\n",
"Cross-validation MAE: 10.933959\n",
"Cross-validation MBE: -3.912714\n",
"Cross-validation R2: 0.691324\n"
]
}
],
"source": [
"# cross-validation\n",
"err_cv = pd.DataFrame(data = np.zeros((len(error), len(estimator))), index = error.keys(), columns = estimator.keys())\n",
"t_cv_wall = []\n",
"t_cv_cpu = []\n",
"estimator = set_estimators()\n",
" \n",
"for name, est in estimator.items():\n",
" print('\\nTraining %s\\n' %name)\n",
"\n",
" est_CV = estimator[name]\n",
" \n",
" tt = util.Timer()\n",
" y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" tt.stop()\n",
"\n",
" Y = rescale(y, T)\n",
" err_cv[name] = get_errors(T, Y.reshape((-1,)), label = 'Cross-validation')\n",
" t_cv_wall.append(tt.walltime)\n",
" t_cv_cpu.append(tt.cputime)\n",
" \n",
"err_cv.loc['t_cv_wall', :] = t_cv_wall\n",
"err_cv.loc['t_cv_cpu' , :] = t_cv_cpu"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# err_cv.to_csv(\"error_stats_panels_cv.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 495,
"metadata": {},
"outputs": [],
"source": [
"err_cv.to_csv(\"error_stats_pv_cv_subset1000.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 3.2: Apply final model to the test data"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"estimator = set_estimators()\n",
"RF_unc = Uncertainty( estimator['RF'] )\n",
"RF_unc.fit(x, T)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model variance not given\n",
"Estimating model variance ... \n",
"Fitting model ...\n",
"CPU time: 16.46 seconds\n",
"Wall clock time: 16.51 seconds\n"
]
}
],
"source": [
"# set residual estimator and fit \n",
"RF_unc.residuals.set_params( n_estimators = n_est )\n",
"\n",
"tt = util.Timer()\n",
"residuals = RF_unc.fit_residuals( x, T )\n",
"tt.stop()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 1.76 seconds\n",
"Wall clock time: 1.75 seconds\n"
]
}
],
"source": [
"# get prediction, model uncertainty and data uncertainty\n",
"tt = util.Timer()\n",
"Y_te, mUnc_te = RF_unc.predict_model_uncertainty(x_te)\n",
"dUnc_te = RF_unc.predict_residuals(x_te)\n",
"tt.stop()\n",
"\n",
"Res_te = compute_residuals( shp(Y_te), shp(T_te), shp(mUnc_te) )"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction MSE: 13063.334483\n",
"Prediction RMSE: 114.294945\n",
"Prediction MAE: 77.536506\n",
"Prediction MAEP: 0.088313\n",
"Prediction MBE: -40.160145\n",
"Prediction MBEP: -0.052858\n",
"Prediction R2: 0.766919\n",
"\n",
"Residuals MSE: 7024.267192\n",
"Residuals RMSE: 83.810901\n",
"Residuals MAE: 53.798174\n",
"Residuals MAEP: 1.527003\n",
"Residuals MBE: -0.699149\n",
"Residuals MBEP: -0.211541\n",
"Residuals R2: -5.372928\n"
]
}
],
"source": [
"pred_err = get_errors( shp(T_te), shp(Y_te), 'Prediction' )\n",
"print('')\n",
"res_err = get_errors( shp(dUnc_te), shp(Res_te), 'Residuals' )"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# merge all test results into one dataframe\n",
"identifier = 'DF_UID'\n",
"identifiers = shp(testing[ identifier ].values)\n",
"test_results = pd.DataFrame( np.hstack([identifiers, shp(T_te), shp(Y_te), shp(mUnc_te), shp(dUnc_te) ]), \n",
" columns = ['DF_UID', 'target', 'prediction', 'model_std', 'data_std'])\n",
"test_results['total_std'] = np.sqrt(test_results.model_std ** 2 + test_results.data_std ** 2)\n",
"test_results['data_std_rel_to_total'] = test_results['total_std'] * test_results['data_std'] / ( test_results['data_std'] + test_results['model_std'] )\n",
"test_results['model_std_rel_to_total'] = test_results['total_std'] * test_results['model_std'] / ( test_results['data_std'] + test_results['model_std'] )"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\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",
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" 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",
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"\n",
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"\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",
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"\n",
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"\n",
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"\n",
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"\n",
"mpl.figure.prototype._init_header = function() {\n",
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" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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"\tthis.context.webkitBackingStorePixelRatio ||\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",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# prepare sample for plotting\n",
"SAMPLE_LENGTH = 50\n",
"test_sample = test_results.sample( SAMPLE_LENGTH ).sort_values(by = 'prediction', ascending = False).reset_index()\n",
"\n",
"# plot results\n",
"plt.figure()\n",
"test_sample.prediction.plot( kind = 'bar', color = 'lightblue', width = 0.9)\n",
"ticks, labels = plt.xticks( range(0, SAMPLE_LENGTH, 10), range(0, SAMPLE_LENGTH, 10))\n",
"\n",
"plt.errorbar( range(SAMPLE_LENGTH), test_sample.prediction, yerr = test_sample.total_std, \n",
" color = 'k', elinewidth = 1, capsize=1.5, \n",
" fmt = '.', markersize = 1,\n",
" label = 'Data uncertainty')\n",
"\n",
"plt.errorbar( range(SAMPLE_LENGTH), test_sample.prediction, yerr = test_sample.model_std_rel_to_total, \n",
" color = 'r', elinewidth = 1, capsize=1.5, \n",
" fmt = '.', markersize = 1,\n",
" label = 'Model uncertainty')\n",
"\n",
"\n",
"test_sample.target.plot( style = 'go', alpha = 0.5 )\n",
"plt.title('Prediction and uncertainty of solar irradiation')\n",
"plt.legend(loc = 'lower left')\n",
"plt.xlabel('Sample (randomly selected)')\n",
"plt.ylabel('Annual solar irradiation (in $kWh / m^2_{roof})$')\n",
"plt.tight_layout()\n",
"\n",
"# plt.savefig( os.path.join( 'pred_uncertainty_irrad_te_6D_v5.png'), dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 4: Plotting"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AggggAACCCCAAAFAngEEEEAAAQQQQAABBBBAAAEEEEAAAQRKLEAAsMSLy9QQQAABBBBAAAEEEEAAAQQQQAABBBAgAMgzgAACCCCAAAIIIIAAAggggAACCCCAQIkFCACWeHGZGgIIIIAAAggggAACCCCAAAIIIIAAAgQAeQYQQAABBBBAAAEEEEAAAQQQQAABBBAosQABwBIvLlNDAAEEEEAAAQQQQAABBBBAAAEEEECAACDPAAIIIIAAAggggAACCCCAAAIIIIAAAiUW+P8BM+exxpZb9V4AAAAASUVORK5CYII=\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(training.XCOORD, training.YCOORD, s = 0.5, label = 'training data')\n",
"plt.scatter(testing.XCOORD, testing.YCOORD, s = 0.5, label = 'test data')\n",
"\n",
"plt.axis('equal')\n",
"plt.axis('off')\n",
"plt.legend(markerscale=6)\n",
"plt.tight_layout()\n",
"plt.savefig('train_test_areas_sample.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SPIELWIESE"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2001-01-15 08:00:00</th>\n",
" <th>2001-01-15 09:00:00</th>\n",
" <th>2001-01-15 10:00:00</th>\n",
" <th>2001-01-15 11:00:00</th>\n",
" <th>2001-01-15 12:00:00</th>\n",
" <th>2001-01-15 13:00:00</th>\n",
" <th>2001-01-15 14:00:00</th>\n",
" <th>2001-01-15 15:00:00</th>\n",
" <th>2001-01-15 16:00:00</th>\n",
" <th>2001-02-15 07:00:00</th>\n",
" <th>...</th>\n",
" <th>2001-11-15 14:00:00</th>\n",
" <th>2001-11-15 15:00:00</th>\n",
" <th>2001-12-15 08:00:00</th>\n",
" <th>2001-12-15 09:00:00</th>\n",
" <th>2001-12-15 10:00:00</th>\n",
" <th>2001-12-15 11:00:00</th>\n",
" <th>2001-12-15 12:00:00</th>\n",
" <th>2001-12-15 13:00:00</th>\n",
" <th>2001-12-15 14:00:00</th>\n",
" <th>2001-12-15 15:00:00</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DF_UID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.866667</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.800000</td>\n",
" <td>...</td>\n",
" <td>0.800000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.600000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>0.888889</td>\n",
" <td>0.888889</td>\n",
" <td>0.555556</td>\n",
" <td>0.555556</td>\n",
" <td>0.555556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.888889</td>\n",
" <td>0.888889</td>\n",
" <td>0.555556</td>\n",
" <td>0.555556</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.200000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>0.500000</td>\n",
" <td>0.700000</td>\n",
" <td>0.900000</td>\n",
" <td>0.900000</td>\n",
" <td>0.900000</td>\n",
" <td>0.900000</td>\n",
" <td>0.300000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.300000</td>\n",
" <td>...</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.600000</td>\n",
" <td>0.800000</td>\n",
" <td>0.900000</td>\n",
" <td>0.900000</td>\n",
" <td>0.900000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.750000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.750000</td>\n",
" <td>0.750000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.166667</td>\n",
" <td>0.333333</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.333333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.878788</td>\n",
" <td>0.181818</td>\n",
" <td>0.151515</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.181818</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.969697</td>\n",
" <td>0.757576</td>\n",
" <td>0.151515</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11.0</th>\n",
" <td>0.269231</td>\n",
" <td>0.307692</td>\n",
" <td>0.500000</td>\n",
" <td>0.769231</td>\n",
" <td>0.807692</td>\n",
" <td>0.884615</td>\n",
" <td>0.884615</td>\n",
" <td>0.384615</td>\n",
" <td>0.0</td>\n",
" <td>0.192308</td>\n",
" <td>...</td>\n",
" <td>0.884615</td>\n",
" <td>0.000000</td>\n",
" <td>0.269231</td>\n",
" <td>0.307692</td>\n",
" <td>0.500000</td>\n",
" <td>0.807692</td>\n",
" <td>0.807692</td>\n",
" <td>0.807692</td>\n",
" <td>0.846154</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.750000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.750000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.750000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.333333</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" <td>1.000000</td>\n",
" <td>0.666667</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.925000</td>\n",
" <td>0.275000</td>\n",
" <td>0.150000</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.275000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.800000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17.0</th>\n",
" <td>0.223301</td>\n",
" <td>0.300971</td>\n",
" <td>0.864078</td>\n",
" <td>0.941748</td>\n",
" <td>0.961165</td>\n",
" <td>0.961165</td>\n",
" <td>0.970874</td>\n",
" <td>0.213592</td>\n",
" <td>0.0</td>\n",
" <td>0.135922</td>\n",
" <td>...</td>\n",
" <td>0.970874</td>\n",
" <td>0.000000</td>\n",
" <td>0.233010</td>\n",
" <td>0.300971</td>\n",
" <td>0.757282</td>\n",
" <td>0.941748</td>\n",
" <td>0.961165</td>\n",
" <td>0.941748</td>\n",
" <td>0.941748</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18.0</th>\n",
" <td>0.440000</td>\n",
" <td>0.520000</td>\n",
" <td>0.640000</td>\n",
" <td>0.800000</td>\n",
" <td>0.840000</td>\n",
" <td>0.840000</td>\n",
" <td>1.000000</td>\n",
" <td>0.920000</td>\n",
" <td>0.0</td>\n",
" <td>0.320000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.720000</td>\n",
" <td>0.480000</td>\n",
" <td>0.520000</td>\n",
" <td>0.600000</td>\n",
" <td>0.840000</td>\n",
" <td>0.840000</td>\n",
" <td>0.760000</td>\n",
" <td>0.960000</td>\n",
" <td>0.640000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.437500</td>\n",
" <td>0.312500</td>\n",
" <td>0.0</td>\n",
" <td>0.968750</td>\n",
" <td>...</td>\n",
" <td>0.437500</td>\n",
" <td>0.312500</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.906250</td>\n",
" <td>0.343750</td>\n",
" <td>0.312500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.222222</td>\n",
" <td>0.777778</td>\n",
" <td>0.888889</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.888889</td>\n",
" <td>0.444444</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.888889</td>\n",
" <td>0.222222</td>\n",
" <td>0.000000</td>\n",
" <td>0.222222</td>\n",
" <td>0.777778</td>\n",
" <td>0.888889</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.888889</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23.0</th>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.833333</td>\n",
" <td>0.833333</td>\n",
" <td>0.833333</td>\n",
" <td>0.833333</td>\n",
" <td>0.833333</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.833333</td>\n",
" <td>0.333333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.833333</td>\n",
" <td>0.833333</td>\n",
" <td>0.833333</td>\n",
" <td>0.666667</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24.0</th>\n",
" <td>0.833333</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.666667</td>\n",
" <td>0.0</td>\n",
" <td>0.166667</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25.0</th>\n",
" <td>0.166667</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.833333</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.833333</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.833333</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.833333</td>\n",
" <td>0.666667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.333333</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.833333</td>\n",
" <td>0.333333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27.0</th>\n",
" <td>0.333333</td>\n",
" <td>0.444444</td>\n",
" <td>0.333333</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.333333</td>\n",
" <td>0.333333</td>\n",
" <td>0.222222</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.818182</td>\n",
" <td>0.848485</td>\n",
" <td>0.848485</td>\n",
" <td>0.636364</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.818182</td>\n",
" <td>0.848485</td>\n",
" <td>0.757576</td>\n",
" <td>0.545455</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29.0</th>\n",
" <td>0.461538</td>\n",
" <td>0.461538</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.269231</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.461538</td>\n",
" <td>0.461538</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.884615</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30.0</th>\n",
" <td>0.272727</td>\n",
" <td>0.272727</td>\n",
" <td>0.454545</td>\n",
" <td>0.636364</td>\n",
" <td>0.636364</td>\n",
" <td>0.727273</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.363636</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.272727</td>\n",
" <td>0.272727</td>\n",
" <td>0.454545</td>\n",
" <td>0.545455</td>\n",
" <td>0.636364</td>\n",
" <td>0.545455</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31.0</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.166667</td>\n",
" <td>...</td>\n",
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" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>30 rows × 146 columns</p>\n",
"</div>"
],
"text/plain": [
" 2001-01-15 08:00:00 2001-01-15 09:00:00 2001-01-15 10:00:00 \\\n",
"DF_UID \n",
"1.0 0.000000 0.000000 1.000000 \n",
"2.0 0.000000 0.000000 0.750000 \n",
"3.0 1.000000 1.000000 1.000000 \n",
"4.0 0.888889 0.888889 0.555556 \n",
"5.0 0.000000 0.000000 0.000000 \n",
"6.0 0.500000 0.700000 0.900000 \n",
"7.0 1.000000 1.000000 1.000000 \n",
"8.0 0.000000 0.000000 0.000000 \n",
"10.0 1.000000 1.000000 1.000000 \n",
"11.0 0.269231 0.307692 0.500000 \n",
"12.0 0.000000 0.000000 0.500000 \n",
"13.0 0.000000 0.250000 0.500000 \n",
"14.0 0.000000 0.000000 1.000000 \n",
"15.0 0.000000 0.333333 0.666667 \n",
"16.0 1.000000 1.000000 1.000000 \n",
"17.0 0.223301 0.300971 0.864078 \n",
"18.0 0.440000 0.520000 0.640000 \n",
"19.0 1.000000 1.000000 1.000000 \n",
"20.0 1.000000 1.000000 0.000000 \n",
"21.0 1.000000 1.000000 1.000000 \n",
"22.0 0.000000 0.222222 0.777778 \n",
"23.0 0.000000 0.166667 0.833333 \n",
"24.0 0.833333 1.000000 1.000000 \n",
"25.0 0.166667 0.500000 0.500000 \n",
"26.0 1.000000 1.000000 0.833333 \n",
"27.0 0.333333 0.444444 0.333333 \n",
"28.0 1.000000 1.000000 0.818182 \n",
"29.0 0.461538 0.461538 1.000000 \n",
"30.0 0.272727 0.272727 0.454545 \n",
"31.0 1.000000 1.000000 0.666667 \n",
"\n",
" 2001-01-15 11:00:00 2001-01-15 12:00:00 2001-01-15 13:00:00 \\\n",
"DF_UID \n",
"1.0 1.000000 1.000000 1.000000 \n",
"2.0 0.750000 0.750000 0.750000 \n",
"3.0 1.000000 1.000000 1.000000 \n",
"4.0 0.555556 0.555556 0.000000 \n",
"5.0 0.200000 0.000000 0.000000 \n",
"6.0 0.900000 0.900000 0.900000 \n",
"7.0 1.000000 1.000000 0.750000 \n",
"8.0 0.000000 0.166667 0.166667 \n",
"10.0 1.000000 1.000000 0.878788 \n",
"11.0 0.769231 0.807692 0.884615 \n",
"12.0 1.000000 1.000000 1.000000 \n",
"13.0 0.500000 0.500000 0.500000 \n",
"14.0 1.000000 1.000000 1.000000 \n",
"15.0 0.666667 1.000000 0.666667 \n",
"16.0 1.000000 1.000000 0.925000 \n",
"17.0 0.941748 0.961165 0.961165 \n",
"18.0 0.800000 0.840000 0.840000 \n",
"19.0 1.000000 1.000000 1.000000 \n",
"20.0 0.000000 0.000000 0.000000 \n",
"21.0 1.000000 1.000000 1.000000 \n",
"22.0 0.888889 1.000000 1.000000 \n",
"23.0 0.833333 0.833333 0.833333 \n",
"24.0 1.000000 1.000000 1.000000 \n",
"25.0 0.500000 0.500000 0.500000 \n",
"26.0 0.666667 0.000000 0.000000 \n",
"27.0 0.111111 0.000000 0.000000 \n",
"28.0 0.848485 0.848485 0.636364 \n",
"29.0 1.000000 1.000000 1.000000 \n",
"30.0 0.636364 0.636364 0.727273 \n",
"31.0 0.666667 0.666667 0.666667 \n",
"\n",
" 2001-01-15 14:00:00 2001-01-15 15:00:00 2001-01-15 16:00:00 \\\n",
"DF_UID \n",
"1.0 1.000000 0.000000 0.0 \n",
"2.0 1.000000 0.000000 0.0 \n",
"3.0 0.866667 0.000000 0.0 \n",
"4.0 0.000000 0.000000 0.0 \n",
"5.0 0.000000 0.000000 0.0 \n",
"6.0 0.300000 0.000000 0.0 \n",
"7.0 0.750000 0.000000 0.0 \n",
"8.0 0.333333 0.000000 0.0 \n",
"10.0 0.181818 0.151515 0.0 \n",
"11.0 0.884615 0.384615 0.0 \n",
"12.0 1.000000 0.000000 0.0 \n",
"13.0 0.750000 0.000000 0.0 \n",
"14.0 1.000000 0.000000 0.0 \n",
"15.0 1.000000 0.000000 0.0 \n",
"16.0 0.275000 0.150000 0.0 \n",
"17.0 0.970874 0.213592 0.0 \n",
"18.0 1.000000 0.920000 0.0 \n",
"19.0 0.437500 0.312500 0.0 \n",
"20.0 0.000000 0.000000 0.0 \n",
"21.0 1.000000 1.000000 0.0 \n",
"22.0 0.888889 0.444444 0.0 \n",
"23.0 0.833333 0.500000 0.0 \n",
"24.0 1.000000 0.666667 0.0 \n",
"25.0 0.833333 0.500000 0.0 \n",
"26.0 0.000000 0.000000 0.0 \n",
"27.0 0.000000 0.000000 0.0 \n",
"28.0 0.000000 0.000000 0.0 \n",
"29.0 0.000000 0.000000 0.0 \n",
"30.0 0.000000 0.000000 0.0 \n",
"31.0 0.000000 0.000000 0.0 \n",
"\n",
" 2001-02-15 07:00:00 ... 2001-11-15 14:00:00 \\\n",
"DF_UID ... \n",
"1.0 0.000000 ... 1.000000 \n",
"2.0 0.000000 ... 1.000000 \n",
"3.0 0.800000 ... 0.800000 \n",
"4.0 1.000000 ... 0.000000 \n",
"5.0 0.200000 ... 0.000000 \n",
"6.0 0.300000 ... 0.100000 \n",
"7.0 1.000000 ... 0.750000 \n",
"8.0 0.000000 ... 0.333333 \n",
"10.0 1.000000 ... 0.181818 \n",
"11.0 0.192308 ... 0.884615 \n",
"12.0 0.000000 ... 1.000000 \n",
"13.0 0.000000 ... 0.750000 \n",
"14.0 0.000000 ... 1.000000 \n",
"15.0 0.000000 ... 1.000000 \n",
"16.0 1.000000 ... 0.275000 \n",
"17.0 0.135922 ... 0.970874 \n",
"18.0 0.320000 ... 1.000000 \n",
"19.0 0.968750 ... 0.437500 \n",
"20.0 1.000000 ... 0.000000 \n",
"21.0 0.000000 ... 1.000000 \n",
"22.0 0.000000 ... 0.888889 \n",
"23.0 0.000000 ... 0.833333 \n",
"24.0 0.166667 ... 1.000000 \n",
"25.0 0.000000 ... 0.833333 \n",
"26.0 0.333333 ... 0.000000 \n",
"27.0 0.000000 ... 0.000000 \n",
"28.0 1.000000 ... 0.000000 \n",
"29.0 0.269231 ... 0.000000 \n",
"30.0 0.363636 ... 0.000000 \n",
"31.0 0.166667 ... 0.000000 \n",
"\n",
" 2001-11-15 15:00:00 2001-12-15 08:00:00 2001-12-15 09:00:00 \\\n",
"DF_UID \n",
"1.0 0.000000 0.000000 0.000000 \n",
"2.0 0.000000 0.000000 0.000000 \n",
"3.0 0.000000 1.000000 1.000000 \n",
"4.0 0.000000 0.888889 0.888889 \n",
"5.0 0.000000 0.000000 0.000000 \n",
"6.0 0.000000 0.500000 0.600000 \n",
"7.0 0.000000 1.000000 1.000000 \n",
"8.0 0.000000 0.000000 0.000000 \n",
"10.0 0.000000 1.000000 1.000000 \n",
"11.0 0.000000 0.269231 0.307692 \n",
"12.0 0.000000 0.000000 0.000000 \n",
"13.0 0.000000 0.000000 0.250000 \n",
"14.0 0.000000 0.000000 0.000000 \n",
"15.0 0.000000 0.000000 0.000000 \n",
"16.0 0.000000 1.000000 1.000000 \n",
"17.0 0.000000 0.233010 0.300971 \n",
"18.0 0.720000 0.480000 0.520000 \n",
"19.0 0.312500 1.000000 1.000000 \n",
"20.0 0.000000 1.000000 1.000000 \n",
"21.0 1.000000 1.000000 1.000000 \n",
"22.0 0.222222 0.000000 0.222222 \n",
"23.0 0.333333 0.000000 0.000000 \n",
"24.0 0.000000 1.000000 1.000000 \n",
"25.0 0.000000 0.166667 0.500000 \n",
"26.0 0.000000 1.000000 1.000000 \n",
"27.0 0.000000 0.333333 0.333333 \n",
"28.0 0.000000 1.000000 1.000000 \n",
"29.0 0.000000 0.461538 0.461538 \n",
"30.0 0.000000 0.272727 0.272727 \n",
"31.0 0.000000 1.000000 1.000000 \n",
"\n",
" 2001-12-15 10:00:00 2001-12-15 11:00:00 2001-12-15 12:00:00 \\\n",
"DF_UID \n",
"1.0 1.000000 1.000000 1.000000 \n",
"2.0 0.750000 0.750000 0.750000 \n",
"3.0 1.000000 1.000000 1.000000 \n",
"4.0 0.555556 0.555556 0.111111 \n",
"5.0 0.000000 0.000000 0.000000 \n",
"6.0 0.800000 0.900000 0.900000 \n",
"7.0 1.000000 1.000000 1.000000 \n",
"8.0 0.000000 0.166667 0.166667 \n",
"10.0 1.000000 1.000000 0.969697 \n",
"11.0 0.500000 0.807692 0.807692 \n",
"12.0 0.250000 1.000000 1.000000 \n",
"13.0 0.500000 0.500000 0.500000 \n",
"14.0 1.000000 1.000000 1.000000 \n",
"15.0 0.666667 0.666667 0.666667 \n",
"16.0 1.000000 1.000000 1.000000 \n",
"17.0 0.757282 0.941748 0.961165 \n",
"18.0 0.600000 0.840000 0.840000 \n",
"19.0 1.000000 1.000000 1.000000 \n",
"20.0 0.000000 0.000000 0.000000 \n",
"21.0 1.000000 1.000000 1.000000 \n",
"22.0 0.777778 0.888889 1.000000 \n",
"23.0 0.500000 0.833333 0.833333 \n",
"24.0 1.000000 1.000000 1.000000 \n",
"25.0 0.500000 0.500000 0.500000 \n",
"26.0 0.833333 0.333333 0.000000 \n",
"27.0 0.222222 0.111111 0.000000 \n",
"28.0 0.818182 0.848485 0.757576 \n",
"29.0 1.000000 1.000000 1.000000 \n",
"30.0 0.454545 0.545455 0.636364 \n",
"31.0 0.666667 0.666667 0.666667 \n",
"\n",
" 2001-12-15 13:00:00 2001-12-15 14:00:00 2001-12-15 15:00:00 \n",
"DF_UID \n",
"1.0 1.000000 1.000000 0.000000 \n",
"2.0 0.750000 1.000000 0.000000 \n",
"3.0 1.000000 0.600000 0.000000 \n",
"4.0 0.000000 0.000000 0.000000 \n",
"5.0 0.000000 0.000000 0.000000 \n",
"6.0 0.900000 0.200000 0.000000 \n",
"7.0 0.750000 0.750000 0.000000 \n",
"8.0 0.000000 0.166667 0.000000 \n",
"10.0 0.757576 0.151515 0.000000 \n",
"11.0 0.807692 0.846154 0.000000 \n",
"12.0 1.000000 1.000000 0.000000 \n",
"13.0 0.500000 0.750000 0.000000 \n",
"14.0 1.000000 1.000000 0.000000 \n",
"15.0 0.666667 1.000000 0.000000 \n",
"16.0 0.800000 0.250000 0.000000 \n",
"17.0 0.941748 0.941748 0.000000 \n",
"18.0 0.760000 0.960000 0.640000 \n",
"19.0 0.906250 0.343750 0.312500 \n",
"20.0 0.000000 0.000000 0.000000 \n",
"21.0 1.000000 1.000000 1.000000 \n",
"22.0 1.000000 0.888889 0.000000 \n",
"23.0 0.833333 0.666667 0.000000 \n",
"24.0 1.000000 1.000000 0.000000 \n",
"25.0 0.500000 0.833333 0.166667 \n",
"26.0 0.000000 0.000000 0.000000 \n",
"27.0 0.000000 0.000000 0.000000 \n",
"28.0 0.545455 0.000000 0.000000 \n",
"29.0 0.884615 0.000000 0.000000 \n",
"30.0 0.545455 0.000000 0.000000 \n",
"31.0 0.666667 0.000000 0.000000 \n",
"\n",
"[30 rows x 146 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv('/scratch/walch/scratch/CH_partly_shaded_final.csv', nrows = 30, index_col = 0)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 146)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) float64 1.0 2.0 3.0 ... 9.89e+06 9.89e+06 9.89e+06\n",
" * timestamp (timestamp) object '2001-01-15 08:00:00' ... '2001-12-15 15:00:00'\n",
"Data variables:\n",
" partly_shading (DF_UID, timestamp) float64 ..."
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xr.open_dataset('/scratch/walch/scratch/CH_partly_shaded_final.nc')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"solpos = pd.read_csv('/scratch/walch/grassgis/scripts/executables/workspace_CH_shading_stats/SOLPOS_rnd.txt', sep = ' ', header = None)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"horizons = solpos[4].sort_values().unique()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
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" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>305</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>310</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>315</th>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>320</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>325</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>330</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>335</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>350</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3\n",
"4 \n",
"0 6 6 6 6\n",
"10 4 4 4 4\n",
"20 4 4 4 4\n",
"30 3 3 3 3\n",
"150 2 2 2 2\n",
"160 4 4 4 4\n",
"170 4 4 4 4\n",
"180 6 6 6 6\n",
"190 7 7 7 7\n",
"200 4 4 4 4\n",
"205 5 5 5 5\n",
"210 3 3 3 3\n",
"215 3 3 3 3\n",
"220 3 3 3 3\n",
"225 6 6 6 6\n",
"230 2 2 2 2\n",
"235 5 5 5 5\n",
"240 2 2 2 2\n",
"245 2 2 2 2\n",
"250 4 4 4 4\n",
"255 6 6 6 6\n",
"260 2 2 2 2\n",
"265 2 2 2 2\n",
"275 4 4 4 4\n",
"280 5 5 5 5\n",
"285 3 3 3 3\n",
"290 3 3 3 3\n",
"295 2 2 2 2\n",
"300 3 3 3 3\n",
"305 5 5 5 5\n",
"310 2 2 2 2\n",
"315 6 6 6 6\n",
"320 3 3 3 3\n",
"325 3 3 3 3\n",
"330 3 3 3 3\n",
"335 5 5 5 5\n",
"340 5 5 5 5\n",
"350 5 5 5 5"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"solpos.groupby(4).count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"pd.DataFrame(horizons).astype('float').to_csv('/scratch/walch/workspace_CH_shading_stats_vis0/horizons.csv', float_format = '%03d', header = False, index = False)\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(horizons)"
]
},
{
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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