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solarPOT/Available_area/ML_panelled_area_allRoofs.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import xarray as xr\n",
"import os\n",
"import time\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.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 sklearn.feature_selection import mutual_info_regression\n",
"# from ELM_ensemble import ELM_Ensemble\n",
"import scipy.spatial as spatial\n",
"from scipy.stats import describe\n",
"\n",
"from pvlib.solarposition import get_solarposition\n",
"from uncertainty import Uncertainty\n",
"\n",
"# import util\n",
"import matplotlib\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def autocorr(x):\n",
" result = np.correlate(x, x, mode='full')\n",
" return result[result.size/2:]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# 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",
" 'MBE' : lambda y, t: np.mean(t - y),\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",
" 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"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class Bias_Correction():\n",
" def __init__(self, column_idx):\n",
" self.params = { 'column_idx' : column_idx }\n",
" \n",
" def get_params(self, deep = False): return self.params\n",
" \n",
" def fit(self, x, t):\n",
" self.MBE = np.mean( t - x[ :, self.params['column_idx'] ] )\n",
" \n",
" def predict(self, x):\n",
" return np.maximum( np.minimum( x[ :, self.params['column_idx'] ] + self.MBE, 1), 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load training data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"DATA_PATH = '/work/hyenergy/output/solar_potential/geographic_potential/available_area' # '/Users/alinawalch/Documents/EPFL/data/rooftops/\n",
"TARGET_PATH = '/scratch/walch/workspace_panelled_area'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fp = os.path.join( DATA_PATH, 'TRN_panelled_area_allRoofs.csv')\n",
"learning_table = pd.read_csv(fp)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DF_UID', 'FLAECHE', 'NEIGUNG', 'AUSRICHTUNG', 'XCOORD', 'YCOORD',\n",
" 'Shape_Length', 'Shape_Area', 'GWR_EGID', 'GBAUP', 'GKAT', 'GAREA',\n",
" 'GASTW', 'GKODX', 'GKODY', 'Shape_Ratio', 'n_neighbors_100',\n",
" 'Estim_build_area', 'n_corners', 'panel_tilt', 'best_align',\n",
" 'panelled_area_ratio_ftr', 'panelled_area_ratio_tgt'],\n",
" dtype='object')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learning_table.columns"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"156686"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(learning_table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correct flat roofs to EW scenario (i.e. double)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"flat_roofs = learning_table.NEIGUNG == 0"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"learning_table.loc[flat_roofs, 'panelled_area_ratio_ftr'] = np.minimum(2*learning_table.loc[flat_roofs, 'panelled_area_ratio_ftr'], 1)\n",
"learning_table.loc[flat_roofs, 'panelled_area_ratio_tgt'] = np.minimum(2*learning_table.loc[flat_roofs, 'panelled_area_ratio_tgt'], 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract sample"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"N_TRAIN = 10000 # len(learning_table) # all data: len(learning_table)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# randomly suffle entries in table\n",
"learning_table = learning_table.sample(N_TRAIN)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# SPLIT DATA INTO TRAINING & TESTING\n",
"learning_table, testing_table = train_test_split(learning_table, test_size = 0.2)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8000"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(learning_table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create table with features and labels\n",
"Possible features for available area are:\n",
"##### Roof data\n",
"- Slope (NEIGUNG)\n",
"- Aspect (AUSRICHTUNG)\n",
"- Roof area (FLAECHE)\n",
"- Roof perimeter (Shape_Length)\n",
"- Roof shape ratio (Shape_Ratio, i.e. Shape_Length / FLAECHE)\n",
"- Roof vertices (n_corners)\n",
"- Panel tilt (different for flat roofs, panel_tilt)\n",
"- ? panel alignment ?\n",
"\n",
"##### Building data\n",
"- Number of floors (GASTW)\n",
"- Period of construction (GBAUP)\n",
"- Building area (GAREA)\n",
"- Building category (GKAT)\n",
"- Number of neighbors (n_neighbors_100, 100m radius)\n",
"\n",
"##### Approximate ratios\n",
"- Approximate shaded area ratio (shaded_area_ratio_ftr, based on 2m res. DOM)\n",
"- Approximate panelled area ratio (panelled_area_ratio_ftr, based on roofs without superstructures)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"label_name = ['panelled_area_ratio_tgt']\n",
"identifier = 'DF_UID'\n",
"\n",
"# choose from: \n",
"# -'shaded_area_ratio_ftr', 'panelled_area_ratio_ftr' (approximate ratios)\n",
"# -'NEIGUNG', 'AUSRICHTUNG', 'FLAECHE', 'Shape_Length', 'Shape_Ratio', 'n_corners', 'panel_tilt' (rooftop info)\n",
"# -'GASTW', 'GBAUP', 'GKAT', 'GAREA', 'n_neighbors_100' (GWR info)\n",
"\n",
"all_features = ['panelled_area_ratio_ftr', \n",
" 'AUSRICHTUNG', 'NEIGUNG', 'FLAECHE', 'Shape_Length', 'Shape_Ratio', 'n_corners', 'panel_tilt',\n",
" 'GASTW', 'GBAUP', 'GKAT', 'GAREA', 'n_neighbors_100'] # \n",
"\n",
"feature_names = all_features # ['panelled_area_ratio_ftr', 'Shape_Ratio', 'NEIGUNG', 'GAREA', 'FLAECHE']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baseline MSE: 0.013312\n",
"Baseline RMSE: 0.115378\n",
"Baseline MAE: 0.043946\n",
"Baseline MBE: -0.043358\n",
"Baseline R2: 0.716164\n"
]
}
],
"source": [
"# Baseline mse:\n",
"baseline_error = get_errors( learning_table.panelled_area_ratio_tgt, learning_table.panelled_area_ratio_ftr, 'Baseline' )"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"err_base = pd.DataFrame( data = baseline_error, columns = ['base'] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FEATURE ANAYSIS"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"X = learning_table.loc[ : , feature_names ].values\n",
"t = learning_table.loc[ : , label_name ].values.reshape((-1,))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Correlation and mutual information"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
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" }</style><table id=\"T_6eee0_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >panelled_area_ratio_ftr</th> <th class=\"col_heading level0 col1\" >AUSRICHTUNG</th> <th class=\"col_heading level0 col2\" >NEIGUNG</th> <th class=\"col_heading level0 col3\" >FLAECHE</th> <th class=\"col_heading level0 col4\" >Shape_Length</th> <th class=\"col_heading level0 col5\" >Shape_Ratio</th> <th class=\"col_heading level0 col6\" >n_corners</th> <th class=\"col_heading level0 col7\" >panel_tilt</th> <th class=\"col_heading level0 col8\" >GASTW</th> <th class=\"col_heading level0 col9\" >GBAUP</th> <th class=\"col_heading level0 col10\" >GKAT</th> <th class=\"col_heading level0 col11\" >GAREA</th> <th class=\"col_heading level0 col12\" >n_neighbors_100</th> <th class=\"col_heading level0 col13\" >panelled_area_ratio_tgt</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row0\" class=\"row_heading level0 row0\" >panelled_area_ratio_ftr</th>\n",
" <td id=\"T_6eee0_row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
" <td id=\"T_6eee0_row0_col1\" class=\"data row0 col1\" >0.005</td>\n",
" <td id=\"T_6eee0_row0_col2\" class=\"data row0 col2\" >-0.084</td>\n",
" <td id=\"T_6eee0_row0_col3\" class=\"data row0 col3\" >0.29</td>\n",
" <td id=\"T_6eee0_row0_col4\" class=\"data row0 col4\" >0.28</td>\n",
" <td id=\"T_6eee0_row0_col5\" class=\"data row0 col5\" >-0.83</td>\n",
" <td id=\"T_6eee0_row0_col6\" class=\"data row0 col6\" >0.089</td>\n",
" <td id=\"T_6eee0_row0_col7\" class=\"data row0 col7\" >-0.2</td>\n",
" <td id=\"T_6eee0_row0_col8\" class=\"data row0 col8\" >-0.039</td>\n",
" <td id=\"T_6eee0_row0_col9\" class=\"data row0 col9\" >-0.0075</td>\n",
" <td id=\"T_6eee0_row0_col10\" class=\"data row0 col10\" >0.063</td>\n",
" <td id=\"T_6eee0_row0_col11\" class=\"data row0 col11\" >0.05</td>\n",
" <td id=\"T_6eee0_row0_col12\" class=\"data row0 col12\" >-0.026</td>\n",
" <td id=\"T_6eee0_row0_col13\" class=\"data row0 col13\" >0.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row1\" class=\"row_heading level0 row1\" >AUSRICHTUNG</th>\n",
" <td id=\"T_6eee0_row1_col0\" class=\"data row1 col0\" >0.005</td>\n",
" <td id=\"T_6eee0_row1_col1\" class=\"data row1 col1\" >1.0</td>\n",
" <td id=\"T_6eee0_row1_col2\" class=\"data row1 col2\" >-0.012</td>\n",
" <td id=\"T_6eee0_row1_col3\" class=\"data row1 col3\" >0.0015</td>\n",
" <td id=\"T_6eee0_row1_col4\" class=\"data row1 col4\" >0.0044</td>\n",
" <td id=\"T_6eee0_row1_col5\" class=\"data row1 col5\" >-0.015</td>\n",
" <td id=\"T_6eee0_row1_col6\" class=\"data row1 col6\" >0.0059</td>\n",
" <td id=\"T_6eee0_row1_col7\" class=\"data row1 col7\" >-0.0034</td>\n",
" <td id=\"T_6eee0_row1_col8\" class=\"data row1 col8\" >-0.0086</td>\n",
" <td id=\"T_6eee0_row1_col9\" class=\"data row1 col9\" >-0.0092</td>\n",
" <td id=\"T_6eee0_row1_col10\" class=\"data row1 col10\" >0.014</td>\n",
" <td id=\"T_6eee0_row1_col11\" class=\"data row1 col11\" >-0.0057</td>\n",
" <td id=\"T_6eee0_row1_col12\" class=\"data row1 col12\" >-0.00052</td>\n",
" <td id=\"T_6eee0_row1_col13\" class=\"data row1 col13\" >0.0022</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row2\" class=\"row_heading level0 row2\" >NEIGUNG</th>\n",
" <td id=\"T_6eee0_row2_col0\" class=\"data row2 col0\" >-0.084</td>\n",
" <td id=\"T_6eee0_row2_col1\" class=\"data row2 col1\" >-0.012</td>\n",
" <td id=\"T_6eee0_row2_col2\" class=\"data row2 col2\" >1.0</td>\n",
" <td id=\"T_6eee0_row2_col3\" class=\"data row2 col3\" >-0.21</td>\n",
" <td id=\"T_6eee0_row2_col4\" class=\"data row2 col4\" >-0.3</td>\n",
" <td id=\"T_6eee0_row2_col5\" class=\"data row2 col5\" >0.086</td>\n",
" <td id=\"T_6eee0_row2_col6\" class=\"data row2 col6\" >-0.19</td>\n",
" <td id=\"T_6eee0_row2_col7\" class=\"data row2 col7\" >0.64</td>\n",
" <td id=\"T_6eee0_row2_col8\" class=\"data row2 col8\" >-0.12</td>\n",
" <td id=\"T_6eee0_row2_col9\" class=\"data row2 col9\" >-0.18</td>\n",
" <td id=\"T_6eee0_row2_col10\" class=\"data row2 col10\" >-0.21</td>\n",
" <td id=\"T_6eee0_row2_col11\" class=\"data row2 col11\" >-0.15</td>\n",
" <td id=\"T_6eee0_row2_col12\" class=\"data row2 col12\" >0.13</td>\n",
" <td id=\"T_6eee0_row2_col13\" class=\"data row2 col13\" >-0.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row3\" class=\"row_heading level0 row3\" >FLAECHE</th>\n",
" <td id=\"T_6eee0_row3_col0\" class=\"data row3 col0\" >0.29</td>\n",
" <td id=\"T_6eee0_row3_col1\" class=\"data row3 col1\" >0.0015</td>\n",
" <td id=\"T_6eee0_row3_col2\" class=\"data row3 col2\" >-0.21</td>\n",
" <td id=\"T_6eee0_row3_col3\" class=\"data row3 col3\" >1.0</td>\n",
" <td id=\"T_6eee0_row3_col4\" class=\"data row3 col4\" >0.74</td>\n",
" <td id=\"T_6eee0_row3_col5\" class=\"data row3 col5\" >-0.34</td>\n",
" <td id=\"T_6eee0_row3_col6\" class=\"data row3 col6\" >0.35</td>\n",
" <td id=\"T_6eee0_row3_col7\" class=\"data row3 col7\" >-0.024</td>\n",
" <td id=\"T_6eee0_row3_col8\" class=\"data row3 col8\" >0.13</td>\n",
" <td id=\"T_6eee0_row3_col9\" class=\"data row3 col9\" >0.049</td>\n",
" <td id=\"T_6eee0_row3_col10\" class=\"data row3 col10\" >0.18</td>\n",
" <td id=\"T_6eee0_row3_col11\" class=\"data row3 col11\" >0.22</td>\n",
" <td id=\"T_6eee0_row3_col12\" class=\"data row3 col12\" >-0.14</td>\n",
" <td id=\"T_6eee0_row3_col13\" class=\"data row3 col13\" >0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row4\" class=\"row_heading level0 row4\" >Shape_Length</th>\n",
" <td id=\"T_6eee0_row4_col0\" class=\"data row4 col0\" >0.28</td>\n",
" <td id=\"T_6eee0_row4_col1\" class=\"data row4 col1\" >0.0044</td>\n",
" <td id=\"T_6eee0_row4_col2\" class=\"data row4 col2\" >-0.3</td>\n",
" <td id=\"T_6eee0_row4_col3\" class=\"data row4 col3\" >0.74</td>\n",
" <td id=\"T_6eee0_row4_col4\" class=\"data row4 col4\" >1.0</td>\n",
" <td id=\"T_6eee0_row4_col5\" class=\"data row4 col5\" >-0.39</td>\n",
" <td id=\"T_6eee0_row4_col6\" class=\"data row4 col6\" >0.53</td>\n",
" <td id=\"T_6eee0_row4_col7\" class=\"data row4 col7\" >-0.03</td>\n",
" <td id=\"T_6eee0_row4_col8\" class=\"data row4 col8\" >0.25</td>\n",
" <td id=\"T_6eee0_row4_col9\" class=\"data row4 col9\" >0.069</td>\n",
" <td id=\"T_6eee0_row4_col10\" class=\"data row4 col10\" >0.24</td>\n",
" <td id=\"T_6eee0_row4_col11\" class=\"data row4 col11\" >0.27</td>\n",
" <td id=\"T_6eee0_row4_col12\" class=\"data row4 col12\" >-0.19</td>\n",
" <td id=\"T_6eee0_row4_col13\" class=\"data row4 col13\" >0.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row5\" class=\"row_heading level0 row5\" >Shape_Ratio</th>\n",
" <td id=\"T_6eee0_row5_col0\" class=\"data row5 col0\" >-0.83</td>\n",
" <td id=\"T_6eee0_row5_col1\" class=\"data row5 col1\" >-0.015</td>\n",
" <td id=\"T_6eee0_row5_col2\" class=\"data row5 col2\" >0.086</td>\n",
" <td id=\"T_6eee0_row5_col3\" class=\"data row5 col3\" >-0.34</td>\n",
" <td id=\"T_6eee0_row5_col4\" class=\"data row5 col4\" >-0.39</td>\n",
" <td id=\"T_6eee0_row5_col5\" class=\"data row5 col5\" >1.0</td>\n",
" <td id=\"T_6eee0_row5_col6\" class=\"data row5 col6\" >-0.18</td>\n",
" <td id=\"T_6eee0_row5_col7\" class=\"data row5 col7\" >-0.032</td>\n",
" <td id=\"T_6eee0_row5_col8\" class=\"data row5 col8\" >-0.07</td>\n",
" <td id=\"T_6eee0_row5_col9\" class=\"data row5 col9\" >0.028</td>\n",
" <td id=\"T_6eee0_row5_col10\" class=\"data row5 col10\" >-0.12</td>\n",
" <td id=\"T_6eee0_row5_col11\" class=\"data row5 col11\" >-0.099</td>\n",
" <td id=\"T_6eee0_row5_col12\" class=\"data row5 col12\" >0.066</td>\n",
" <td id=\"T_6eee0_row5_col13\" class=\"data row5 col13\" >-0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row6\" class=\"row_heading level0 row6\" >n_corners</th>\n",
" <td id=\"T_6eee0_row6_col0\" class=\"data row6 col0\" >0.089</td>\n",
" <td id=\"T_6eee0_row6_col1\" class=\"data row6 col1\" >0.0059</td>\n",
" <td id=\"T_6eee0_row6_col2\" class=\"data row6 col2\" >-0.19</td>\n",
" <td id=\"T_6eee0_row6_col3\" class=\"data row6 col3\" >0.35</td>\n",
" <td id=\"T_6eee0_row6_col4\" class=\"data row6 col4\" >0.53</td>\n",
" <td id=\"T_6eee0_row6_col5\" class=\"data row6 col5\" >-0.18</td>\n",
" <td id=\"T_6eee0_row6_col6\" class=\"data row6 col6\" >1.0</td>\n",
" <td id=\"T_6eee0_row6_col7\" class=\"data row6 col7\" >0.035</td>\n",
" <td id=\"T_6eee0_row6_col8\" class=\"data row6 col8\" >0.2</td>\n",
" <td id=\"T_6eee0_row6_col9\" class=\"data row6 col9\" >0.035</td>\n",
" <td id=\"T_6eee0_row6_col10\" class=\"data row6 col10\" >0.047</td>\n",
" <td id=\"T_6eee0_row6_col11\" class=\"data row6 col11\" >0.057</td>\n",
" <td id=\"T_6eee0_row6_col12\" class=\"data row6 col12\" >-0.036</td>\n",
" <td id=\"T_6eee0_row6_col13\" class=\"data row6 col13\" >0.073</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row7\" class=\"row_heading level0 row7\" >panel_tilt</th>\n",
" <td id=\"T_6eee0_row7_col0\" class=\"data row7 col0\" >-0.2</td>\n",
" <td id=\"T_6eee0_row7_col1\" class=\"data row7 col1\" >-0.0034</td>\n",
" <td id=\"T_6eee0_row7_col2\" class=\"data row7 col2\" >0.64</td>\n",
" <td id=\"T_6eee0_row7_col3\" class=\"data row7 col3\" >-0.024</td>\n",
" <td id=\"T_6eee0_row7_col4\" class=\"data row7 col4\" >-0.03</td>\n",
" <td id=\"T_6eee0_row7_col5\" class=\"data row7 col5\" >-0.032</td>\n",
" <td id=\"T_6eee0_row7_col6\" class=\"data row7 col6\" >0.035</td>\n",
" <td id=\"T_6eee0_row7_col7\" class=\"data row7 col7\" >1.0</td>\n",
" <td id=\"T_6eee0_row7_col8\" class=\"data row7 col8\" >0.13</td>\n",
" <td id=\"T_6eee0_row7_col9\" class=\"data row7 col9\" >-0.11</td>\n",
" <td id=\"T_6eee0_row7_col10\" class=\"data row7 col10\" >-0.064</td>\n",
" <td id=\"T_6eee0_row7_col11\" class=\"data row7 col11\" >-0.036</td>\n",
" <td id=\"T_6eee0_row7_col12\" class=\"data row7 col12\" >0.031</td>\n",
" <td id=\"T_6eee0_row7_col13\" class=\"data row7 col13\" >-0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row8\" class=\"row_heading level0 row8\" >GASTW</th>\n",
" <td id=\"T_6eee0_row8_col0\" class=\"data row8 col0\" >-0.039</td>\n",
" <td id=\"T_6eee0_row8_col1\" class=\"data row8 col1\" >-0.0086</td>\n",
" <td id=\"T_6eee0_row8_col2\" class=\"data row8 col2\" >-0.12</td>\n",
" <td id=\"T_6eee0_row8_col3\" class=\"data row8 col3\" >0.13</td>\n",
" <td id=\"T_6eee0_row8_col4\" class=\"data row8 col4\" >0.25</td>\n",
" <td id=\"T_6eee0_row8_col5\" class=\"data row8 col5\" >-0.07</td>\n",
" <td id=\"T_6eee0_row8_col6\" class=\"data row8 col6\" >0.2</td>\n",
" <td id=\"T_6eee0_row8_col7\" class=\"data row8 col7\" >0.13</td>\n",
" <td id=\"T_6eee0_row8_col8\" class=\"data row8 col8\" >1.0</td>\n",
" <td id=\"T_6eee0_row8_col9\" class=\"data row8 col9\" >-0.056</td>\n",
" <td id=\"T_6eee0_row8_col10\" class=\"data row8 col10\" >0.072</td>\n",
" <td id=\"T_6eee0_row8_col11\" class=\"data row8 col11\" >0.12</td>\n",
" <td id=\"T_6eee0_row8_col12\" class=\"data row8 col12\" >-0.011</td>\n",
" <td id=\"T_6eee0_row8_col13\" class=\"data row8 col13\" >-0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row9\" class=\"row_heading level0 row9\" >GBAUP</th>\n",
" <td id=\"T_6eee0_row9_col0\" class=\"data row9 col0\" >-0.0075</td>\n",
" <td id=\"T_6eee0_row9_col1\" class=\"data row9 col1\" >-0.0092</td>\n",
" <td id=\"T_6eee0_row9_col2\" class=\"data row9 col2\" >-0.18</td>\n",
" <td id=\"T_6eee0_row9_col3\" class=\"data row9 col3\" >0.049</td>\n",
" <td id=\"T_6eee0_row9_col4\" class=\"data row9 col4\" >0.069</td>\n",
" <td id=\"T_6eee0_row9_col5\" class=\"data row9 col5\" >0.028</td>\n",
" <td id=\"T_6eee0_row9_col6\" class=\"data row9 col6\" >0.035</td>\n",
" <td id=\"T_6eee0_row9_col7\" class=\"data row9 col7\" >-0.11</td>\n",
" <td id=\"T_6eee0_row9_col8\" class=\"data row9 col8\" >-0.056</td>\n",
" <td id=\"T_6eee0_row9_col9\" class=\"data row9 col9\" >1.0</td>\n",
" <td id=\"T_6eee0_row9_col10\" class=\"data row9 col10\" >-0.048</td>\n",
" <td id=\"T_6eee0_row9_col11\" class=\"data row9 col11\" >0.068</td>\n",
" <td id=\"T_6eee0_row9_col12\" class=\"data row9 col12\" >0.013</td>\n",
" <td id=\"T_6eee0_row9_col13\" class=\"data row9 col13\" >0.011</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row10\" class=\"row_heading level0 row10\" >GKAT</th>\n",
" <td id=\"T_6eee0_row10_col0\" class=\"data row10 col0\" >0.063</td>\n",
" <td id=\"T_6eee0_row10_col1\" class=\"data row10 col1\" >0.014</td>\n",
" <td id=\"T_6eee0_row10_col2\" class=\"data row10 col2\" >-0.21</td>\n",
" <td id=\"T_6eee0_row10_col3\" class=\"data row10 col3\" >0.18</td>\n",
" <td id=\"T_6eee0_row10_col4\" class=\"data row10 col4\" >0.24</td>\n",
" <td id=\"T_6eee0_row10_col5\" class=\"data row10 col5\" >-0.12</td>\n",
" <td id=\"T_6eee0_row10_col6\" class=\"data row10 col6\" >0.047</td>\n",
" <td id=\"T_6eee0_row10_col7\" class=\"data row10 col7\" >-0.064</td>\n",
" <td id=\"T_6eee0_row10_col8\" class=\"data row10 col8\" >0.072</td>\n",
" <td id=\"T_6eee0_row10_col9\" class=\"data row10 col9\" >-0.048</td>\n",
" <td id=\"T_6eee0_row10_col10\" class=\"data row10 col10\" >1.0</td>\n",
" <td id=\"T_6eee0_row10_col11\" class=\"data row10 col11\" >0.35</td>\n",
" <td id=\"T_6eee0_row10_col12\" class=\"data row10 col12\" >-0.27</td>\n",
" <td id=\"T_6eee0_row10_col13\" class=\"data row10 col13\" >0.073</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row11\" class=\"row_heading level0 row11\" >GAREA</th>\n",
" <td id=\"T_6eee0_row11_col0\" class=\"data row11 col0\" >0.05</td>\n",
" <td id=\"T_6eee0_row11_col1\" class=\"data row11 col1\" >-0.0057</td>\n",
" <td id=\"T_6eee0_row11_col2\" class=\"data row11 col2\" >-0.15</td>\n",
" <td id=\"T_6eee0_row11_col3\" class=\"data row11 col3\" >0.22</td>\n",
" <td id=\"T_6eee0_row11_col4\" class=\"data row11 col4\" >0.27</td>\n",
" <td id=\"T_6eee0_row11_col5\" class=\"data row11 col5\" >-0.099</td>\n",
" <td id=\"T_6eee0_row11_col6\" class=\"data row11 col6\" >0.057</td>\n",
" <td id=\"T_6eee0_row11_col7\" class=\"data row11 col7\" >-0.036</td>\n",
" <td id=\"T_6eee0_row11_col8\" class=\"data row11 col8\" >0.12</td>\n",
" <td id=\"T_6eee0_row11_col9\" class=\"data row11 col9\" >0.068</td>\n",
" <td id=\"T_6eee0_row11_col10\" class=\"data row11 col10\" >0.35</td>\n",
" <td id=\"T_6eee0_row11_col11\" class=\"data row11 col11\" >1.0</td>\n",
" <td id=\"T_6eee0_row11_col12\" class=\"data row11 col12\" >-0.21</td>\n",
" <td id=\"T_6eee0_row11_col13\" class=\"data row11 col13\" >0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row12\" class=\"row_heading level0 row12\" >n_neighbors_100</th>\n",
" <td id=\"T_6eee0_row12_col0\" class=\"data row12 col0\" >-0.026</td>\n",
" <td id=\"T_6eee0_row12_col1\" class=\"data row12 col1\" >-0.00052</td>\n",
" <td id=\"T_6eee0_row12_col2\" class=\"data row12 col2\" >0.13</td>\n",
" <td id=\"T_6eee0_row12_col3\" class=\"data row12 col3\" >-0.14</td>\n",
" <td id=\"T_6eee0_row12_col4\" class=\"data row12 col4\" >-0.19</td>\n",
" <td id=\"T_6eee0_row12_col5\" class=\"data row12 col5\" >0.066</td>\n",
" <td id=\"T_6eee0_row12_col6\" class=\"data row12 col6\" >-0.036</td>\n",
" <td id=\"T_6eee0_row12_col7\" class=\"data row12 col7\" >0.031</td>\n",
" <td id=\"T_6eee0_row12_col8\" class=\"data row12 col8\" >-0.011</td>\n",
" <td id=\"T_6eee0_row12_col9\" class=\"data row12 col9\" >0.013</td>\n",
" <td id=\"T_6eee0_row12_col10\" class=\"data row12 col10\" >-0.27</td>\n",
" <td id=\"T_6eee0_row12_col11\" class=\"data row12 col11\" >-0.21</td>\n",
" <td id=\"T_6eee0_row12_col12\" class=\"data row12 col12\" >1.0</td>\n",
" <td id=\"T_6eee0_row12_col13\" class=\"data row12 col13\" >-0.043</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6eee0_level0_row13\" class=\"row_heading level0 row13\" >panelled_area_ratio_tgt</th>\n",
" <td id=\"T_6eee0_row13_col0\" class=\"data row13 col0\" >0.87</td>\n",
" <td id=\"T_6eee0_row13_col1\" class=\"data row13 col1\" >0.0022</td>\n",
" <td id=\"T_6eee0_row13_col2\" class=\"data row13 col2\" >-0.17</td>\n",
" <td id=\"T_6eee0_row13_col3\" class=\"data row13 col3\" >0.25</td>\n",
" <td id=\"T_6eee0_row13_col4\" class=\"data row13 col4\" >0.24</td>\n",
" <td id=\"T_6eee0_row13_col5\" class=\"data row13 col5\" >-0.7</td>\n",
" <td id=\"T_6eee0_row13_col6\" class=\"data row13 col6\" >0.073</td>\n",
" <td id=\"T_6eee0_row13_col7\" class=\"data row13 col7\" >-0.29</td>\n",
" <td id=\"T_6eee0_row13_col8\" class=\"data row13 col8\" >-0.1</td>\n",
" <td id=\"T_6eee0_row13_col9\" class=\"data row13 col9\" >0.011</td>\n",
" <td id=\"T_6eee0_row13_col10\" class=\"data row13 col10\" >0.073</td>\n",
" <td id=\"T_6eee0_row13_col11\" class=\"data row13 col11\" >0.05</td>\n",
" <td id=\"T_6eee0_row13_col12\" class=\"data row13 col12\" >-0.043</td>\n",
" <td id=\"T_6eee0_row13_col13\" class=\"data row13 col13\" >1.0</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x2b95e0c6fc90>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Compute the correlation between variables\n",
"correlation_matrix = learning_table.loc[ : , feature_names + label_name ].corr()\n",
"correlation_matrix.style.background_gradient(cmap='coolwarm', vmin = -1, vmax = 1).format(\"{:.2}\") # Formatting"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>Mutual_info</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>panelled_area_ratio_ftr</th>\n",
" <td>5.549206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLAECHE</th>\n",
" <td>0.510141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Shape_Ratio</th>\n",
" <td>0.488063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Shape_Length</th>\n",
" <td>0.290752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>panel_tilt</th>\n",
" <td>0.080864</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NEIGUNG</th>\n",
" <td>0.073247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GAREA</th>\n",
" <td>0.054576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n_corners</th>\n",
" <td>0.047445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AUSRICHTUNG</th>\n",
" <td>0.021868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n_neighbors_100</th>\n",
" <td>0.016082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GBAUP</th>\n",
" <td>0.015565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GKAT</th>\n",
" <td>0.015555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GASTW</th>\n",
" <td>0.005155</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Mutual_info\n",
"panelled_area_ratio_ftr 5.549206\n",
"FLAECHE 0.510141\n",
"Shape_Ratio 0.488063\n",
"Shape_Length 0.290752\n",
"panel_tilt 0.080864\n",
"NEIGUNG 0.073247\n",
"GAREA 0.054576\n",
"n_corners 0.047445\n",
"AUSRICHTUNG 0.021868\n",
"n_neighbors_100 0.016082\n",
"GBAUP 0.015565\n",
"GKAT 0.015555\n",
"GASTW 0.005155"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(pd.DataFrame(mutual_info_regression(X, t), index = feature_names, columns = ['Mutual_info'])\n",
" .sort_values('Mutual_info', ascending = False))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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"/* global mpl */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function () {\n",
" if (typeof WebSocket !== 'undefined') {\n",
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" } else if (typeof MozWebSocket !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert(\n",
" '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",
"\n",
"mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
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"\n",
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" if (!this.supports_binary) {\n",
" var warnings = document.getElementById('mpl-warnings');\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent =\n",
" 'This browser does not support binary websocket messages. ' +\n",
" 'Performance may be slow.';\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
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"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = document.createElement('div');\n",
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" this._init_header(this);\n",
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"};\n",
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" 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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" this.header = titletext;\n",
"};\n",
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"\n",
"mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
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" var fig = this;\n",
"\n",
" var canvas_div = (this.canvas_div = document.createElement('div'));\n",
" canvas_div.setAttribute(\n",
" 'style',\n",
" 'border: 1px solid #ddd;' +\n",
" 'box-sizing: content-box;' +\n",
" 'clear: both;' +\n",
" 'min-height: 1px;' +\n",
" 'min-width: 1px;' +\n",
" 'outline: 0;' +\n",
" 'overflow: hidden;' +\n",
" 'position: relative;' +\n",
" 'resize: both;'\n",
" );\n",
"\n",
" function on_keyboard_event_closure(name) {\n",
" return function (event) {\n",
" return fig.key_event(event, name);\n",
" };\n",
" }\n",
"\n",
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" 'keydown',\n",
" on_keyboard_event_closure('key_press')\n",
" );\n",
" canvas_div.addEventListener(\n",
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" on_keyboard_event_closure('key_release')\n",
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"\n",
" this._canvas_extra_style(canvas_div);\n",
" this.root.appendChild(canvas_div);\n",
"\n",
" var canvas = (this.canvas = document.createElement('canvas'));\n",
" canvas.classList.add('mpl-canvas');\n",
" canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
" this.context = canvas.getContext('2d');\n",
"\n",
" var backingStore =\n",
" this.context.backingStorePixelRatio ||\n",
" this.context.webkitBackingStorePixelRatio ||\n",
" this.context.mozBackingStorePixelRatio ||\n",
" this.context.msBackingStorePixelRatio ||\n",
" this.context.oBackingStorePixelRatio ||\n",
" this.context.backingStorePixelRatio ||\n",
" 1;\n",
"\n",
" this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
" 'canvas'\n",
" ));\n",
" rubberband_canvas.setAttribute(\n",
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" 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
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" if (window.ResizeObserver !== undefined) {\n",
" this.ResizeObserver = window.ResizeObserver;\n",
" } else {\n",
" var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
" this.ResizeObserver = obs.ResizeObserver;\n",
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" }\n",
"\n",
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" var width, height;\n",
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" // Chrome 84 implements new version of spec.\n",
" width = entry.contentBoxSize[0].inlineSize;\n",
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" } else {\n",
" // Firefox implements old version of spec.\n",
" width = entry.contentBoxSize.inlineSize;\n",
" height = entry.contentBoxSize.blockSize;\n",
" }\n",
" } else {\n",
" // Chrome <84 implements even older version of spec.\n",
" width = entry.contentRect.width;\n",
" height = entry.contentRect.height;\n",
" }\n",
"\n",
" // Keep the size of the canvas and rubber band canvas in sync with\n",
" // the canvas container.\n",
" if (entry.devicePixelContentBoxSize) {\n",
" // Chrome 84 implements new version of spec.\n",
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" 'width',\n",
" entry.devicePixelContentBoxSize[0].inlineSize\n",
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" canvas.setAttribute(\n",
" 'height',\n",
" entry.devicePixelContentBoxSize[0].blockSize\n",
" );\n",
" } else {\n",
" canvas.setAttribute('width', width * fig.ratio);\n",
" canvas.setAttribute('height', height * fig.ratio);\n",
" }\n",
" canvas.setAttribute(\n",
" 'style',\n",
" 'width: ' + width + 'px; height: ' + height + 'px;'\n",
" );\n",
"\n",
" rubberband_canvas.setAttribute('width', width);\n",
" rubberband_canvas.setAttribute('height', height);\n",
"\n",
" // And update the size in Python. We ignore the initial 0/0 size\n",
" // that occurs as the element is placed into the DOM, which should\n",
" // otherwise not happen due to the minimum size styling.\n",
" if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
" fig.request_resize(width, height);\n",
" }\n",
" }\n",
" });\n",
" this.resizeObserverInstance.observe(canvas_div);\n",
"\n",
" function on_mouse_event_closure(name) {\n",
" return function (event) {\n",
" return fig.mouse_event(event, name);\n",
" };\n",
" }\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mousedown',\n",
" on_mouse_event_closure('button_press')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseup',\n",
" on_mouse_event_closure('button_release')\n",
" );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband_canvas.addEventListener(\n",
" 'mousemove',\n",
" on_mouse_event_closure('motion_notify')\n",
" );\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseenter',\n",
" on_mouse_event_closure('figure_enter')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseleave',\n",
" on_mouse_event_closure('figure_leave')\n",
" );\n",
"\n",
" canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
" canvas_div.appendChild(canvas);\n",
" canvas_div.appendChild(rubberband_canvas);\n",
"\n",
" this.rubberband_context = rubberband_canvas.getContext('2d');\n",
" this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
" this._resize_canvas = function (width, height, forward) {\n",
" if (forward) {\n",
" canvas_div.style.width = width + 'px';\n",
" canvas_div.style.height = height + 'px';\n",
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" this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
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" canvas_div.focus();\n",
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"\n",
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"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'mpl-toolbar';\n",
" this.root.appendChild(toolbar);\n",
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" };\n",
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"\n",
" function on_mouseover_closure(tooltip) {\n",
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" if (!event.currentTarget.disabled) {\n",
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" }\n",
"\n",
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" for (var toolbar_ind in mpl.toolbar_items) {\n",
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" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
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" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
" button.classList = 'mpl-widget';\n",
" button.setAttribute('role', 'button');\n",
" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
" this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\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",
"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",
"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], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / fig.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
" var x1 = msg['x1'] / fig.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
" 0,\n",
" fig.canvas.width / fig.ratio,\n",
" fig.canvas.height / fig.ratio\n",
" );\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",
" 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.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\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",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\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(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\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(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\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",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
" }\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",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\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",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\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, 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",
"\n",
"///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
"// prettier-ignore\n",
"var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function () {\n",
" comm.close();\n",
" };\n",
" ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
"};\n",
"\n",
"mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
" fig.cell_info[0].output_area.element.on(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
" fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
" '<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 / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<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 () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\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",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" if (event.target !== this) {\n",
" // Ignore bubbled events from children.\n",
" return;\n",
" }\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('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",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\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",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
"};\n",
"\n",
"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(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x2b95e12d6fd0>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(np.log(learning_table.FLAECHE), learning_table.panelled_area_ratio_tgt, s =1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TRAINING & VALIDATION"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"norm = Normalizer()\n",
"x = norm.fit_transform(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MBE Approximation"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bias correction MSE: 0.011437\n",
"Bias correction RMSE: 0.106942\n",
"Bias correction MAE: 0.069833\n",
"Bias correction MBE: -0.000015\n",
"Bias correction R2: 0.756156\n"
]
}
],
"source": [
"ftr_idx = learning_table.loc[ : , feature_names ].columns.get_loc('panelled_area_ratio_ftr')\n",
"mbe_est = Bias_Correction( ftr_idx )\n",
"\n",
"y = cross_val_predict( mbe_est, X, t)\n",
"\n",
"mbe_error = get_errors( t, y, 'Bias correction' )"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"err_base['MBE+'] = mbe_error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine Learning models"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"# ML MODEL PARAMETERS\n",
"n_est = 200\n",
"tree_depth = None\n",
"n_samples_leaf = 3\n",
"ELM_nodes = 100 # 100\n",
"ELM_est = 30\n",
"MLP_layers = (500, 400, 200)\n",
"m_RF = None\n",
"\n",
"k_CV = 5\n",
"\n",
"# DEFINITION OF ESTIMATORS\n",
"def set_estimators():\n",
" estimator = {\n",
" 'KNN': KNeighborsRegressor( n_jobs = -1 ),\n",
" 'LIN': LinearRegression( n_jobs = -1 ),\n",
" 'RF' : RandomForestRegressor( n_jobs = -1 ), # n_estimators = n_est, min_samples_leaf = n_samples_leaf, max_features = m_RF, \n",
" # 'XGB': XGBRegressor( n_estimators = n_est, max_depth = tree_depth, n_jobs = -1 ),\n",
" # 'ELM': ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes),\n",
" # 'MLP': MLPRegressor( hidden_layer_sizes = (500, 400, 100) ),\n",
" \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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training without uncertainty (Part 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF SINGLE ESTIMATOR - simple validation"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"max_depth: 5\n",
"Fitted model in 2.64s\n",
"\n",
"Training MSE: 0.009833\n",
"\n",
"Validation MSE: 0.011301\n",
"Validation RMSE: 0.106307\n",
"Validation MAE: 0.065663\n",
"Validation MBE: -0.001879\n",
"Validation R2: 0.763163\n",
"max_depth: 7\n",
"Fitted model in 3.37s\n",
"\n",
"Training MSE: 0.008209\n",
"\n",
"Validation MSE: 0.010830\n",
"Validation RMSE: 0.104069\n",
"Validation MAE: 0.062554\n",
"Validation MBE: -0.001793\n",
"Validation R2: 0.773032\n",
"max_depth: 10\n",
"Fitted model in 4.37s\n",
"\n",
"Training MSE: 0.006091\n",
"\n",
"Validation MSE: 0.010968\n",
"Validation RMSE: 0.104728\n",
"Validation MAE: 0.061939\n",
"Validation MBE: -0.001085\n",
"Validation R2: 0.770147\n",
"max_depth: 12\n",
"Fitted model in 4.77s\n",
"\n",
"Training MSE: 0.005333\n",
"\n",
"Validation MSE: 0.010968\n",
"Validation RMSE: 0.104726\n",
"Validation MAE: 0.062327\n",
"Validation MBE: 0.000167\n",
"Validation R2: 0.770155\n",
"max_depth: 15\n",
"Fitted model in 5.17s\n",
"\n",
"Training MSE: 0.005054\n",
"\n",
"Validation MSE: 0.010949\n",
"Validation RMSE: 0.104639\n",
"Validation MAE: 0.062517\n",
"Validation MBE: 0.000706\n",
"Validation R2: 0.770540\n",
"max_depth: None\n",
"Fitted model in 5.27s\n",
"\n",
"Training MSE: 0.005010\n",
"\n",
"Validation MSE: 0.010959\n",
"Validation RMSE: 0.104687\n",
"Validation MAE: 0.062616\n",
"Validation MBE: 0.001063\n",
"Validation R2: 0.770327\n"
]
}
],
"source": [
"# for training one estimator\n",
"training, validation = train_test_split(learning_table, test_size = float(1 / k_CV))\n",
"x_tr = norm.transform( training.loc[ : , feature_names ].values )\n",
"t_tr = training.loc[ : , label_name ].values.reshape((-1,))\n",
"\n",
"estimator = set_estimators()\n",
"RF = estimator['KNN']\n",
"\n",
"for max_depth in [5,7,10,12,15,None]:\n",
" print('max_depth:', max_depth)\n",
" RF = RandomForestRegressor( min_samples_leaf = 5, max_depth = max_depth, n_jobs = -1 )\n",
" \n",
" tt = time.time()\n",
" RF.fit(x_tr,t_tr)\n",
" print('Fitted model in %.2fs' %(time.time()-tt))\n",
"\n",
" y_tr = RF.predict(x_tr)\n",
" print( '\\nTraining MSE: %f\\n' %mse(y_tr,t_tr))\n",
"\n",
" x_val = norm.transform( validation.loc[ : , feature_names ] )\n",
" t_val = validation.loc[ : , label_name]\n",
"\n",
" y_val = RF.predict(x_val)\n",
" val_err = get_errors(t_val, y_val.reshape((-1,1)), label = 'Validation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF SINGLE ESTIMATOR - cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bias correction MSE: 0.011437\n",
"Bias correction RMSE: 0.106942\n",
"Bias correction MAE: 0.069833\n",
"Bias correction MBE: -0.000015\n",
"Bias correction R2: 0.756156\n",
"\n",
"max_depth: 5\n",
"Cross-validated model in 77.32s\n",
"Cross-validation MSE: 0.010785\n",
"Cross-validation RMSE: 0.103849\n",
"Cross-validation MAE: 0.063792\n",
"Cross-validation MBE: 0.006178\n",
"Cross-validation R2: 0.770055\n",
"\n",
"max_depth: 7\n",
"Cross-validated model in 38.30s\n",
"Cross-validation MSE: 0.010841\n",
"Cross-validation RMSE: 0.104121\n",
"Cross-validation MAE: 0.064110\n",
"Cross-validation MBE: 0.006222\n",
"Cross-validation R2: 0.768848\n",
"\n",
"max_depth: 10\n",
"Cross-validated model in 38.00s\n",
"Cross-validation MSE: 0.010838\n",
"Cross-validation RMSE: 0.104105\n",
"Cross-validation MAE: 0.063861\n",
"Cross-validation MBE: 0.006101\n",
"Cross-validation R2: 0.768919\n",
"\n",
"max_depth: 12\n",
"Cross-validated model in 38.19s\n",
"Cross-validation MSE: 0.010863\n",
"Cross-validation RMSE: 0.104224\n",
"Cross-validation MAE: 0.063898\n",
"Cross-validation MBE: 0.006112\n",
"Cross-validation R2: 0.768392\n",
"\n",
"max_depth: 15\n",
"Cross-validated model in 38.30s\n",
"Cross-validation MSE: 0.010810\n",
"Cross-validation RMSE: 0.103971\n",
"Cross-validation MAE: 0.063911\n",
"Cross-validation MBE: 0.006303\n",
"Cross-validation R2: 0.769516\n",
"\n",
"max_depth: None\n",
"Cross-validated model in 37.97s\n",
"Cross-validation MSE: 0.010788\n",
"Cross-validation RMSE: 0.103865\n",
"Cross-validation MAE: 0.063769\n",
"Cross-validation MBE: 0.005968\n",
"Cross-validation R2: 0.769985\n"
]
}
],
"source": [
"estimator = set_estimators()\n",
"est_CV = estimator['RF']\n",
"\n",
"y = cross_val_predict( mbe_est, X, t)\n",
"mbe_error = get_errors( t, y, 'Bias correction' )\n",
"\n",
"for max_depth in [5,7,10,12,15,None]:\n",
" print('\\nmax_depth:', max_depth)\n",
" RF = RandomForestRegressor( min_samples_leaf = 5, max_depth = max_depth, n_jobs = -1 )\n",
"\n",
" tt = time.time()\n",
" y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" print('Cross-validated model in %.2fs' %(time.time()-tt))\n",
"\n",
" CV_err = get_errors(t, y.reshape((-1,)), label = 'Cross-validation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feature importance (all features)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished in 4.38s\n",
"\n",
"Training MSE: 0.001472\n",
"\n",
"Validation MSE: 0.011101\n",
"Validation RMSE: 0.105362\n",
"Validation MAE: 0.063961\n",
"Validation MBE: 0.005022\n",
"Validation R2: 0.776115\n",
"Finished in 4.34s\n",
"\n",
"Training MSE: 0.001395\n",
"\n",
"Validation MSE: 0.012638\n",
"Validation RMSE: 0.112421\n",
"Validation MAE: 0.064480\n",
"Validation MBE: -0.001409\n",
"Validation R2: 0.744929\n",
"Finished in 4.33s\n",
"\n",
"Training MSE: 0.001447\n",
"\n",
"Validation MSE: 0.010816\n",
"Validation RMSE: 0.104001\n",
"Validation MAE: 0.062070\n",
"Validation MBE: 0.002834\n",
"Validation R2: 0.784105\n",
"Finished in 4.34s\n",
"\n",
"Training MSE: 0.001459\n",
"\n",
"Validation MSE: 0.010790\n",
"Validation RMSE: 0.103874\n",
"Validation MAE: 0.060773\n",
"Validation MBE: 0.001826\n",
"Validation R2: 0.784318\n",
"Finished in 4.33s\n",
"\n",
"Training MSE: 0.001510\n",
"\n",
"Validation MSE: 0.009278\n",
"Validation RMSE: 0.096324\n",
"Validation MAE: 0.056768\n",
"Validation MBE: 0.008768\n",
"Validation R2: 0.803994\n",
"Finished in 4.42s\n",
"\n",
"Training MSE: 0.001428\n",
"\n",
"Validation MSE: 0.011962\n",
"Validation RMSE: 0.109370\n",
"Validation MAE: 0.064357\n",
"Validation MBE: 0.003015\n",
"Validation R2: 0.755044\n",
"Finished in 4.29s\n",
"\n",
"Training MSE: 0.001404\n",
"\n",
"Validation MSE: 0.011697\n",
"Validation RMSE: 0.108151\n",
"Validation MAE: 0.064184\n",
"Validation MBE: 0.000719\n",
"Validation R2: 0.761592\n",
"Finished in 4.34s\n",
"\n",
"Training MSE: 0.001479\n",
"\n",
"Validation MSE: 0.010662\n",
"Validation RMSE: 0.103255\n",
"Validation MAE: 0.060857\n",
"Validation MBE: 0.005692\n",
"Validation R2: 0.773907\n",
"Finished in 4.34s\n",
"\n",
"Training MSE: 0.001453\n",
"\n",
"Validation MSE: 0.010868\n",
"Validation RMSE: 0.104249\n",
"Validation MAE: 0.061765\n",
"Validation MBE: 0.002179\n",
"Validation R2: 0.778732\n",
"Finished in 4.33s\n",
"\n",
"Training MSE: 0.001471\n",
"\n",
"Validation MSE: 0.010998\n",
"Validation RMSE: 0.104873\n",
"Validation MAE: 0.062811\n",
"Validation MBE: 0.002695\n",
"Validation R2: 0.774425\n"
]
}
],
"source": [
"N_TESTS = 10\n",
"n_features = x.shape[1]\n",
"\n",
"importance_sum = np.zeros(n_features)\n",
"std_sum = np.zeros(n_features)\n",
"ind_sum = np.zeros(n_features)\n",
"\n",
"for i in range(N_TESTS):\n",
" # for training one estimator\n",
" training, validation = train_test_split(learning_table, test_size = float(1 / k_CV))\n",
" x_tr = norm.transform( training.loc[ : , feature_names ].values )\n",
" t_tr = training.loc[ : , label_name ].values.reshape((-1,))\n",
"\n",
" estimator = set_estimators()\n",
" RF = estimator['RF']\n",
"\n",
" tt = time.time()\n",
" RF.fit(x_tr,t_tr)\n",
" print('Finished in %.2fs' %(time.time()-tt))\n",
"\n",
" y_tr = RF.predict(x_tr)\n",
" print( '\\nTraining MSE: %f\\n' %mse(y_tr,t_tr))\n",
"\n",
" x_val = norm.transform( validation.loc[ : , feature_names ] )\n",
" t_val = validation.loc[ : , label_name]\n",
"\n",
" y_val = RF.predict(x_val)\n",
" val_err = get_errors(t_val, y_val.reshape((-1,1)), label = 'Validation')\n",
"\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:]\n",
" \n",
" importance_sum += importance\n",
" std_sum += std\n",
" ind_sum += ind\n",
" \n",
"importance = importance_sum/N_TESTS\n",
"std = std_sum / N_TESTS\n",
"ind = ind_sum / N_TESTS\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"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": 86,
"metadata": {},
"outputs": [],
"source": [
"importance = importance_sum/N_TESTS\n",
"std = std_sum / N_TESTS\n",
"ind = np.argsort(importance)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.70864074 0.07854705 0.06859403 0.05090573 0.03264378 0.0290803\n",
" 0.03158836]\n",
"[0.01089989 0.00742697 0.00833909 0.01352991 0.01010017 0.00426053\n",
" 0.00439542]\n",
"[5 6 4 3 2 1 0]\n"
]
}
],
"source": [
"print(importance)\n",
"print(std)\n",
"print(ind)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"label_dict = {'panelled_area_ratio_ftr' : '$C_{pv}^{F}$',\n",
" 'NEIGUNG' : 'Roof tilt',\n",
" 'AUSRICHTUNG' : 'Roof aspect',\n",
" 'GKAT' : 'Build. type',\n",
" 'FLAECHE' : 'Roof area',\n",
" 'GAREA' : 'Build. area',\n",
" 'Shape_Ratio' : 'Roof shape',\n",
" 'n_neighbors_100' : 'Build. density',\n",
" 'Shape_Length' : 'Roof length',\n",
" 'GBAUP' : 'Build. period',\n",
" 'n_corners' : 'Roof corners',\n",
" 'GASTW' : 'Build. floors',\n",
" 'panel_tilt' : 'Panel tilt'}"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"ftr_names = [label_dict[feature] for feature in feature_names]"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(data = importance, index = ftr_names, columns = ['Importance'])\n",
"df['Std'] = std"
]
},
{
"cell_type": "code",
"execution_count": 91,
"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>Importance</th>\n",
" <th>Std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>$C_{pv}^{F}$</th>\n",
" <td>0.708641</td>\n",
" <td>0.010900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Roof shape</th>\n",
" <td>0.078547</td>\n",
" <td>0.007427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Roof tilt</th>\n",
" <td>0.068594</td>\n",
" <td>0.008339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Build. type</th>\n",
" <td>0.050906</td>\n",
" <td>0.013530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Build. area</th>\n",
" <td>0.032644</td>\n",
" <td>0.010100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Roof area</th>\n",
" <td>0.031588</td>\n",
" <td>0.004395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Build. density</th>\n",
" <td>0.029080</td>\n",
" <td>0.004261</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Importance Std\n",
"$C_{pv}^{F}$ 0.708641 0.010900\n",
"Roof shape 0.078547 0.007427\n",
"Roof tilt 0.068594 0.008339\n",
"Build. type 0.050906 0.013530\n",
"Build. area 0.032644 0.010100\n",
"Roof area 0.031588 0.004395\n",
"Build. density 0.029080 0.004261"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sorted = df.sort_values('Importance', ascending = False)\n",
"df_sorted"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" canvas.setAttribute(\n",
" 'height',\n",
" entry.devicePixelContentBoxSize[0].blockSize\n",
" );\n",
" } else {\n",
" canvas.setAttribute('width', width * fig.ratio);\n",
" canvas.setAttribute('height', height * fig.ratio);\n",
" }\n",
" canvas.setAttribute(\n",
" 'style',\n",
" 'width: ' + width + 'px; height: ' + height + 'px;'\n",
" );\n",
"\n",
" rubberband_canvas.setAttribute('width', width);\n",
" rubberband_canvas.setAttribute('height', height);\n",
"\n",
" // And update the size in Python. We ignore the initial 0/0 size\n",
" // that occurs as the element is placed into the DOM, which should\n",
" // otherwise not happen due to the minimum size styling.\n",
" if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
" fig.request_resize(width, height);\n",
" }\n",
" }\n",
" });\n",
" this.resizeObserverInstance.observe(canvas_div);\n",
"\n",
" function on_mouse_event_closure(name) {\n",
" return function (event) {\n",
" return fig.mouse_event(event, name);\n",
" };\n",
" }\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mousedown',\n",
" on_mouse_event_closure('button_press')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseup',\n",
" on_mouse_event_closure('button_release')\n",
" );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband_canvas.addEventListener(\n",
" 'mousemove',\n",
" on_mouse_event_closure('motion_notify')\n",
" );\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseenter',\n",
" on_mouse_event_closure('figure_enter')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseleave',\n",
" on_mouse_event_closure('figure_leave')\n",
" );\n",
"\n",
" canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
" canvas_div.appendChild(canvas);\n",
" canvas_div.appendChild(rubberband_canvas);\n",
"\n",
" this.rubberband_context = rubberband_canvas.getContext('2d');\n",
" this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
" this._resize_canvas = function (width, height, forward) {\n",
" if (forward) {\n",
" canvas_div.style.width = width + 'px';\n",
" canvas_div.style.height = height + 'px';\n",
" }\n",
" };\n",
"\n",
" // Disable right mouse context menu.\n",
" this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
" event.preventDefault();\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 toolbar = document.createElement('div');\n",
" toolbar.classList = 'mpl-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\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",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
" button.classList = 'mpl-widget';\n",
" button.setAttribute('role', 'button');\n",
" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
" this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\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",
"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",
"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], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / fig.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
" var x1 = msg['x1'] / fig.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
" 0,\n",
" fig.canvas.width / fig.ratio,\n",
" fig.canvas.height / fig.ratio\n",
" );\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",
" 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.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\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",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\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(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\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(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\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",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
" }\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",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\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",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\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, 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",
"\n",
"///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
"// prettier-ignore\n",
"var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function () {\n",
" comm.close();\n",
" };\n",
" ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
"};\n",
"\n",
"mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
" fig.cell_info[0].output_area.element.on(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
" fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
" '<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 / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<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 () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\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",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" if (event.target !== this) {\n",
" // Ignore bubbled events from children.\n",
" return;\n",
" }\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('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",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\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",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
"};\n",
"\n",
"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(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"ax = plt.axes()\n",
"# plt.title(\"Feature Importances for Random Forest (%d trees)\\n Validation MSE: %f\" %(n_est, mse(y_val,t_val)))\n",
"plt.barh(range(n_features),importance[ind], # [ind]\n",
" color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\") # \n",
"plt.yticks(range(n_features), np.asarray(ftr_names)[ind]) # [ind]\n",
"plt.ylim([-1, len(ind)])\n",
"plt.show()\n",
"\n",
"# pos : [left, bottom, width, height]\n",
"# ax.set_position( [0.23, 0.11, 0.7, 0.77] )\n",
"plt.tight_layout()\n",
"plt.subplots_adjust(left=0.2)\n",
"# plt.savefig('Feature_importance_panels.pdf', dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" 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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"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# raw data\n",
"plt.figure()\n",
"bins=25\n",
"plt.hist(learning_table.panelled_area_ratio_ftr, range=(0,1), bins=bins, facecolor='w',edgecolor='r', alpha=0.5, density=True, label = 'Approx. ratio')\n",
"plt.hist(learning_table.available_area_ratio, range=(0,1), bins=bins, density=True, facecolor='g', alpha=0.2, edgecolor='g', label = '\"Ground truth\"')\n",
"plt.hist(y_val, bins=bins, range=(0,1), density=True, facecolor='b', alpha=0.2, edgecolor='b', label = 'RF - validation')\n",
"plt.legend()\n",
"plt.title('Distribution of available area ratio')\n",
"plt.show()\n",
"plt.savefig('availabe_area_hist.png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# UNCERTAINTY\n",
"Assuming that model selection has been performed and primary model was accepted"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# select and fit model estimator\n",
"estimator = set_estimators()\n",
"est_unc = estimator[ 'RF_unc' ]\n",
"\n",
"tt = time.time()\n",
"est_unc.fit(x, t)\n",
"print('Fitted model in %.2fs' %(time.time()-tt))\n",
"\n",
"prediction, model_std = est_unc.predict_model_uncertainty(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# set residual estimator\n",
"n_est_residuals = 500\n",
"\n",
"est_unc.residuals.set_params( n_estimators = n_est_residuals )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# model selection for residual estimator using cross-validation (FOR NOW ONLY CROSS-VALIDATION POSSIBLE)\n",
"tt = time.time()\n",
"residuals, data_std = est_unc.crossval_residuals(x, t, cv = 5, model_std = model_std)\n",
"print('Cross-validated residuals in %.2fs' %(time.time()-tt))\n",
"\n",
"CV_err = get_errors(residuals, data_std.reshape((-1,)), label = 'Cross-validation')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model uncertainty: \n",
"DescribeResult(nobs=30183, minmax=(array([ 0.]), array([ 0.23043749])), mean=array([ 0.08277616]), variance=array([ 0.00140049]), skewness=array([-0.08401687]), kurtosis=array([-0.63315846]))\n",
"\n",
"Data uncertainty: \n",
"DescribeResult(nobs=30183, minmax=(0.0, 0.20006856475588075), mean=0.054637946188143126, variance=0.00086324774513733631, skewness=0.27955546666166076, kurtosis=-0.17704802524120433)\n"
]
}
],
"source": [
"# analyse model and data uncertainty\n",
"print('Model uncertainty: ')\n",
"print(describe(model_std))\n",
"\n",
"print('\\nData uncertainty: ')\n",
"print(describe(data_std))\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 191.04 seconds\n",
"Wall clock time: 191.47 seconds\n"
]
}
],
"source": [
"# train final residual model\n",
"tt = time.time()\n",
"residuals = est_unc.fit_residuals( x, t, model_std )\n",
"print('Finished in %.2fs' %(time.time()-tt))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training MSE: 0.005077\n",
"\n"
]
}
],
"source": [
"# just to check: predict for training data and get training mse\n",
"data_std_tr = est_unc.predict_residuals(x)\n",
"print( '\\nTraining MSE: %f\\n' %mse(residuals, data_std))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PREDICT ON DATA FOR ALL OF SWITZERLAND"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"fp = os.path.join( DATA_PATH, 'QRY_available_area_data_all.csv' )\n",
"query_data = pd.read_csv(fp)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DF_UID', 'FLAECHE', 'NEIGUNG', 'AUSRICHTUNG', 'XCOORD', 'YCOORD',\n",
" 'Shape_Length', 'Shape_Area', 'GWR_EGID', 'GBAUP', 'GKAT', 'GAREA',\n",
" 'GASTW', 'GKODX', 'GKODY', 'Shape_Ratio', 'n_neighbors_100',\n",
" 'Estim_build_area', 'n_corners', 'panel_tilt', 'shaded_area_ratio_ftr',\n",
" 'best_align', 'panelled_area_ratio_ftr'],\n",
" dtype='object')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_data.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assess test data and plot"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"x_test = norm.transform( testing_table.loc[ : , feature_names ] )\n",
"t_test = testing_table.loc[ : , label_name]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"y_test, model_std_test = est_unc.predict_model_uncertainty(x_test)\n",
"data_std_test = est_unc.predict_residuals(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"identifiers = testing_table.DF_UID.values.reshape((-1,1))\n",
"test_results = pd.DataFrame( np.hstack([identifiers, t_test, y_test, model_std_test, data_std_test.reshape((-1,1)) ]), \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": 36,
"metadata": {},
"outputs": [],
"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()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, 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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"output_type": "display_data"
},
{
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I6VwAGjbR7gPhHAAGEnncNekF552Q9ulB422Ri3pOeO4OLfGoR1xz0QCH73zHA/aLecM2sm0PhMA5P6hnP7AOZOEO97HnHrB9jKa4HxnsM3hAJD3Ke9X+0Mg1M7djmmohl6fBayPH8bsY+jiBvta9MYSvN3oEW6sGQLmHN1TQ36MBIButYll724BkNNRuPjMmUJXiscDgNSTofbQ+axRbkv9lGgFBICJVji59Ye+yPji5wuDQMFVsQzJMpmGgPm1Rk+K2xAwIYghOL7M+NKIlkwBkCFRQp/t6bPr5Dw+Tpa3Yc0UAL2GgG04ZrjOuZkyPWSc6xIPAJqEgO0V3gSccCHA0Ad3JDBhGJHzIUNPgCD48e+cj+gM73CBBb1VzvxeAJAwMz3MPoChsOCmv6F2xlA47YbARrCNlNyGgOnVIVg6Q8BuAJBedcK109PHOVSEYi5acgIgP8x4PU5V4Ecc+xLqYTGtzwQAacv0QjPUyRXodrLn+YYCYKQQMBeQ8R4JFwL2MqZsj9dnAevgamWCd2j4OtRG3OgRzr74Ac5Vxlx57Ez8Gz9QQ8fSizbh7J3TRDgHO/RjNtoq4FgA6DYEzEUgvN+5spjPOKU0UUAAmCYD4VMzwr3IGJ7hS5tfuvYk6EiLQOhpoHfJTvRIcEsAexEIQ0L0pJgsAqGnjvDHL197NaBdLz1EXBXMZAqAnGTO8CYfmHayH5bOBRumAMjQJufm8F88i0DYBvaBX/j2ZHf+jSFIho/iAUCTRSB8qXJyOseB84pCT6twamtvAxPOA8ixJUQ653Sx/YQyzoNq4FiEQn34oqdHxC8ApPeSHxehW6WEAqBpf+lloUfYXqzBvjBUyPAbx4i2EilFWwTCdnLekg1DXMA0O8wiEDcAyBcy56HSq2YnTrHgPercgsb+jd5yeo4JgLRvgrgzpG9anwkA0n4Jm7QjezUyx5zARI1DAZBtdC4C4bwwens4rcSelxvvmEYaL6/PAtZrr7ZnOJ0REGfivFfCOL3pbvQI115CPudv0lPHucp2srfyoUbO03D8tnfeY/y44LPbmbwAIKeHMCpkugiEO0RwziWhlx8+SmmigAAwTQbCp2aEe5GFqzoUAJ3bwDCcxC9szjniggl+ATu3gWH4ieExegS4VQlDZASXcNvAcN4HXyT8CqYXg1/BnEjM+TD2F7EpABKouE0HoYbXpZeK89vo/XBu2WIKgNSFng6Gtend4nxBznHhS9lkGxiWJywzZMMHPFePcvNtghOhNx4AZJ32/Cn2kQ9LggcX2RBk7JW8BGuGaakpQY4gyvA9wyvsi302azQAtIGD85y4MIUvPHoF7X3LCChc9UtNCAQEZYKkXwDIuhmi5Ziyb5xfSHsJt2WISX/5guOCIHq5qQtf3ny52tu8EGwjpUj3jf13tovzRe1tYOhV52IY5zYwbgCQkEfb5cIqtpXeaN4T9NSHhoDZZi6kol0QbmlrXMzlTKb1mQAgP7A4J5bQzHud83m54p8gzHs8FADDbQNDnfjRQHBlindMI42X12cB6yXMso/sE+8TQiW3N+FzhWNJOyd0u9EjXHupBaMU1NDe8oj5GB7n33nvceoH73PqTtvy097pdeN0Ad5njJSwfl7HCwDyOUDPIvvGnRU4TYD2zBXDvA9ZNz/K7URgpP2yz85Fhj699lRNvAoIAONVLj3LxQuA7A1vat6kDBUT2giJfGCFbgTNr0kuQuANz//Nh0mkjaD58uCDh5ODefPTU0FPAmGLUMhkCoCcd8gtavhw5Bc6F2ownMAtTpjchoDtEeTDkTBkbwRN753JRtAszxc299niC4PeJi66IXzxIRsvALJeQhjD7lyYQy8MX6TcCJqrE+3E/hI+uVKb2hDQ+EIjVNhbckQDQL7sOL7cloF62htB85nAernHIseMgMIJ/fSg8Zp+ASA3Geb1CbfUMdZG0LH6y/N8qTk/SOiFIgByAQXtl1vmREvR7ht6wJ0bQXOMuW9juI2gw3law12XenP1N8OkvIcI7VzgRE8U5286Q8Aszw8se6sSe/W8s17T+kwAkPXSa8U+86OCYM77neNOr1koANobQfM35qe98B6y56GyvnAAaN+zsWw4nH5enwV2nRwvRj1o22w7Iwz0XvLDi3sB2t51Uz0i2RgXRvB+C10IwvnU/KjjfGJOm3GGg/2yd0IaPxr4ccrVvSYbQccKAbOf/DDks49TcvjxyLmg/GjlhxafSfaiD+alF5L2G7riOj3folnUKgFgFg22uioFpIAUkAJJV4ALSQjEXFXv3HMw6Q1J8AW5KpkRBW6ubX8g0SPIj3VGfiJtbZXgZqn6SAoIAGUbUkAKSAEpIAUSqwA9xwz5chutTEj0XDvn29obQdOTyWkwduIUFUZH6ElXSjMFBIBpNiBqjhSQAlJACkiBNFeAC6E495pePe47ylX2PIaRU4M4d1SpGCggACwGg6QmSgEpIAWkgBRIIwUY7uUcRs4NpdePcz+5it25+X4aNVdNCaeAAFB2IQWkgBSQAlJACkiBLFNAAJhlA67uSgEpIAWkgBSQAlJAACgbkAJSQApIASkgBaRAlikgAMyyAVd3pYAUkAJSQApIASkgAPRmA9SPGwhrd3NvOqq0FJACUkAKSIFkK8DNsXkSkfPc82S3IWXXEwB6k55ncnJvJyUpIAWkgBSQAlKg+Cng3Li6+LXeQ4sFgB7EA8CDu3etX78eFSvyfypJASkgBaSAFJAC6a7Ajz/+iFq1eGqktY8hjwHMuiQA9DbkFgDu2rVLAOhNR5WWAlJACkgBKZA0BQiARx5J9hMAJk30DLuQADDDBlTdkQJSQApIgcxXQAAIyAPozc4FgN70U2kpIAWkgBSQAklXQAAoAPRqdAJArwqqvBSQAlJACkiBJCsgABQAejU5AaBXBVVeCkiBtFfgt99+w8GDB/Hrr7+mfVvVQClABUqVKoXDDjsMJUqED3QKAAWAXu8UAaBXBVVeCkiBtFbgwIED2LRpE/bs2ZPW7VTjpECoAuXKlUONGjVQunTpIuIIAAWAXu8YAaBXBVVeCkiBtFXg0KFD+OabbyxvSrVq1awXaSSPStp2Qg3LOgXoseaHy9atWy2vdf369VGyZMlCOggABYBebwwBoFcFVV4KSIG0VWDfvn1Ys2YNjj/+eNCboiQFipMC9Fp/++23qFu3LsqUKSMADBk8rQL2Zs0CQG/6qbQUkAJprIANgOFeoGncbDVNClgKRLNfeQDlAfR6mwgAvSqo8lJACqStAgLAtB0aNcxAAQFgdJHkATQwoihZBIDe9FNpKSAF0lgBAWAaD46aFlMBAaAAMKaReMggAPQgnopKASmQ3gr4CYCHfjuEdbvWYff+3aiQUwG1j6yNkiUKT8z3U40zzzwTJ598MiZMmOBntXHXNXz4cMycORNffvll3HWooDsFBIACQHcW4y63ANCdXsotBaRAMVLALwAs2FqAGStmYMW2Fdh3cB/KHFYGjao2Ql6jPORWy02IIn4AIFeShttCJJ4GCwDjUc1bGQGgANCbBUUvHRYAuWcW/9mJ+xDxn5IUkAJSoDgp4AcAEv4mLZyEbXu2odaRtXDE4Ufg519+xvpd61G1XFX0bdXXdwjs3bs3nnvuuUJSr1q1Cvfeey/mzJmD77//HrVr18bf/vY39OvXL5iP5X744Qe0bNkSDz/8MHJycqxV0HyeX3fddVbZY445BqNHj8Zdd92FW2+91frHxHJ33HEH/v3vf2P//v047bTTMH78eDRv3hzPPvssrrnmmkLteeaZZ8DrKSVOAQGgADBx1gWEBUB+6d1zzz3B6w4bNgz8m5IUkAJSoDgp4BUAGfYdM28MlmxagsbVGhfaQ5B7tS3fuhwtarTAoPaDfA0H79q1C+effz5OOukkjBgxwpK8cuXKFgBecMEFqFq1KhYsWIDrr78eBLHLLrvMykMgmz59OvLy8jBo0CDrb02aNME555yDbdu24bHHHsPhhx+O/v3747PPPrPqswGQecqWLYuhQ4fiyCOPxOOPP26B38qVK62/DxkyBG+//Tbef/99q17m4d+VEqeAAFAAmDjrigCA/Frkl2KvXr0wZcoUdO7cWR7ARI6C6pYCUiAhCngFwLU/rMXQD4danr6KOfxeLpx27duF7Xu3Y0SnEahTqY6vfTAJAd9yyy2WN/DVV18NAiAhbd26dcHQ74oVK5Cbm4tFixZZXj0mehO5uTA9fATAefPmoVu3btiyZYvlNbTTiSeeiIEDB1qgqRCwr8NrVJkAUABoZChxZoo4B/CLL77AqaeeisWLF6NFixZxVq9iUkAKSIHUKeAVAPM352Pk3JFoeFRDlCpZqkhHDh46iJXbV2JIhyFoWr2prx0NB4AM6z799NMW4O3du9c6LYILRejNY6IHcMOGDXjvvfeCbWFI95JLLrHCus7TJKpUqWJ5+wiArLdv375FPHq8BsPC999/vwDQ19E1q0wAKAA0s5T4cgkA49NNpaSAFCgGCngFwHTyAL744ovWPLwHH3wQbdq0QYUKFTBu3DgsXLgwuDLXngPI1bp24v++9NJLiwAgQ8qc3kMAJOBNnjwZH330UZFRrVSpkhVylgcw+QYvABQAJtLqBICJVFd1SwEpkFIFvAJgquYAUrQuXbqgYcOGFpgx9enTB8uXL8cHH3wQ1PTss8+25vbZW7OEA0A7BPz5559bUR2m0BAwPYacc8i/16kTPpTN+YIvvPAC8vPzUzqm2XRxAaAAMJH2LgBMpLqqWwpIgZQq4BUA2XjnKuDjKh6HI0ofgZ8P/IzvfvwuYauAeV3OuyPYvfzyyyhfvjymTZtmLcTgf/Nou3/961+YNGmS9b+jASDr4gKPHTt24NFHH7UWgdx+++349NNPcd9991mriLmgpUOHDti9ezfGjh2LBg0aYOPGjZg1a5a1oIRzB3l9tonzBY877jjLA+mcL5jSgc7QiwsABYCJNG0BYCLVVd1SQAqkVAE/ANCGwNB9AHOr5qJ7o+6+bwFjC8bVt1dffTWWLl1qzfejJ2/MmDGYMWOGtRr5iiuusFbivvXWWzEBkAv7rr322uA2MAQ/hn65wviGG26wLkn4Gzx4sLWKeOvWrdZ2MYRC5q1Vq5YVQu7Zs6flgeSWMdoGJvGmLQAUACbSygSAiVRXdUsBKZBSBfwCQHYi2SeBJFK47777zoI6buly1llnJfJSqtuDAgJAAaAH84lZVAAYUyJlkAJSoLgq4CcAFlcN2G5u6/XTTz+hadOm1qbQ3NqFq4XpZWRIWCk9FRAACgATaZkCwESqq7qlgBRIqQICwN/lf+edd6x5f6tXr7bm7rVt29Y6Y/j4449P6fjo4tEVEAAKABN5jwgAE6mu6pYCUiClCggAUyq/Lu5RAQGgANCjCUUt7gkAdWZwIodGdUsBKeBVAQGgVwVVPpUKCAAFgIm0P08AqDODEzk0qlsKSAGvCggAvSqo8qlUQAAoAEyk/XkCQJ0ZnMihUd1SQAp4VUAA6FVBlU+lAgJAAWAi7c8TALJhOjM4kcOjuqWAFPCigADQi3oqm2oFBIACwETaYFgA5H5Xs+fPxoU9LsQb099A13ZdUbJEybDtEAAmcnhUtxSQAl4UEAB6UU9lU62AAFAAmEgbLAKAPPaIO94v+HoBZr07C926dEPbhm2R1ygv7I73AsBEDo/qlgJSwIsCAkAv6qlsqhUQAAoAE2mDhQDQeeZl6X2lMe3Zabiy95U4UOZAxDMvBYCJHB7VLQWkgBcFBIBm6vXu3ds63m3mzJlWgTPPPBMnn3yytVdgvMmPOuK9dqaUEwBmFwDeDGAAgGMALAXQB8BnUSS4FcBNAGoD2AbgVQB3AthneAMEAbB8hfIYM28MlmxagsbVGuP777/HE088YR3+zTMhl29djhY1WmBQ+0GFwsECQEOllU0KSIGkKxDtBfra15uS1p6LG9ZI2rXiuVAoAO7YscM6IYSbRsdKH330ETp16oSdO3eiUqVKwexu6oajiv8AACAASURBVIh1jWz9XQCYPQB4OYDnAdwIYCEAwt2lABoC2BJGhisBPA3gLwAWAGgA4FkALwLob3jDBAFwx6EdGPrhUMvTVzGnonVckA2ANWrUwK59u7B973aM6DQCdSrVCVYvADRUWtmkgBRIugLZBIAHDhxA6dKl49I4FADdVBIJAN3UobzhFRAAZg8AEvoWAbgl0GWuulgPYDKAMWFk+AeAXADOk7wfBNAKQHvDGyoIgN/u/RYj545Ew6MaolTJUkUA8OChg1i5fSWGdBiCptWbCgANBVY2KSAFUqdAcQZAhlBPOukkS7x//etflkfupptuwogRI1CiRAnUqVMH1157Lb755hv8+9//Rl5eHp599lmsX7/eOvbt3XffRalSpdC+fXtMnDjRys/066+/YsCAAXj66aet31nH5s2bsWvXrogh4P3792Po0KGYNm0atmzZgtq1a+Pvf/87zjrrLNStW7fQAF999dVWO0JDwPQQ9uvXD2+88QZYX8eOHTFp0iTUr1/fKs8yt956K1566SXr/7MfbPszzzwDOiGyMQkAswMA+dm2B8AlAH6fhPF7eg4Afep/CiMDPYCPAOgSCBPXAzCLzwoA90aQLQcA/9mJ/v3veOPLA5iNjxf1WQpktgLFHQAXL15sARrB7/PPP7em5HBe3l//+lcL6AhVBLPu3btbA0kwa968Odq0aWNB1GGHHYZRo0aB9SxbtszyEI4dOxZjxozBk08+idzcXDz44IN4+eWX0blz54gAePnll+OTTz6xQJL1r1mzBtu2bcMll1xiwWePHj3w9ddfo2LFiihbtiyOPPLIIgD4pz/9yYLVxx9/3Mo3aNAg/O9//8Py5cstuCUAsn8Ew/vuuw8lS5ZEr169cMopp2Dq1KmZbagReicAzA4ArAlgA4C2AD5xdHksgI4Br144JfoCeABACQCHAXgsMCcwkmrDAQwL/ZEAqDmAWfl8UaelQEYrUNwBkN62r776yvL4MdHr9vrrr1vQRAAkHM2YMSM4hlOmTLGAr6CgIFiGoWHOzeMCjy5duqBmzZq47bbbLC8g08GDBy0v3qmnnhoWAFeuXImGDRvivffew9lnn13EXiKFgJ0eQIJfgwYNMH/+fLRty9ccsH37dtSqVQvPPfccLr30UgsAr7nmGqxatQonnHCCleeRRx6xPJ6ck56NSQCY3QA4DsAZAFqHkeHMwHy/uwNzBk8EMBHAPwGMjCBbRA8gv8i0CjgbHzHqsxTIXAWKOwDWq1fPCtXaid42et3YL0ISPYGDBw8O/k6oGz9+PMqUKVNoUPfs2YOHH34YV155pQWDH3/8MTp06BDMw/Dxb7/9FhYA6R1kub1791qeutBkAoCEVnoJ2W6Gne1EgOW16cUkAN588834+eefg78Tblnu0KFDmWukUXomAMwOAIwnBPwfAJ8GVg3bKvUC8ASA8gBM7pjo+wC+Mwvdzu2Gdg3boXuj7toHMCsfQeq0FCi+CmQ6ADLMy392YqiYC/PChUyrVatmZSMAzp07F2ecQd/C7ykaAHLOHn/3AoBOcHUCILeaIeANGTIkOAeQ29HYiV5Lu23F1wrjb7kAMDsAkL3kIhBu+cKtX5i4CGQdAC72CLcIZDGA9wEMckh0RWBlMAHwVwOzC38SyMYNmPPuK+gz6DZMvn88One5FCVrHhu2Oq0CNlBZWaSAFEiJAsUdABkCZrjXTnfeeac1584OAYcC4D//+U9rbt3atWuteXbhUrgQMD2NLVq0COsBZF38nYtKwoWAFyxYgHbt2llzAo866qjgJU1DwM8//7zl1bQXgQgA/xg1AWD2AKC9Dcz1ARDkZ91lABoB2BzYIobzBLnPHxPn83G7F+YnPDIE/CgAgiHrMknhzwIePhy4554/yg8bBvBvYZIA0ERm5ZECUiAVChR3AOTiDYZ5b7jhBsuzx//NRRv8b84BDAVAhnrpVTv22GOtuXPHHXccvv32W7z22msYOHCg9d/333+/tRDkqaeeQqNGjfDQQw/hxRdfjLoIhHPzPvjgA2vVLheBsE7C6WWXXYYNGzZYc/m4Wrdr167WIpDy5csXWQTChSr2IhDuL8j5jJzv51wEwv4IAAWAps+K32fGZk7iFjD2RtBfAuAiD8Id00cA1gLoHfhvLvrg5I8/A6B7biuANwJ/+8OHHl2b8AC4aRPWPPUU6g4ZgjUjR6LutdcCEZbhCwAzx/jUEymQaQoUdwBs0qSJNf+N268wdMoQLxd52NvAhAIgx48LJugFnD17Nnbv3m3BILdreeCBByyvIBd93HHHHRawcaXtX/7yF8t7F20bGOp41113WaDIxRtcbcz/JhgyjRw50lqwwe1krrrqqqjbwHA+IBemcA7i5MmTi2wDIwAUAJo+hzINAE377Ve+8AAIoGDqVOT26oWCKVOQ27NnxOsJAP0aCtUjBaSA3woU56PgdJSa39ZQ/OpTCDj6mAkAvdm0ANCbfiotBaRAGisgAEzjwVHTYiogABQAxjQSDxnSBgB59Bz/2Yk7v2fr7u8exlNFpYAUcCggAJQ5FGcFBIACwETab9oA4PDhw3GPY+HJsGHDwL8pSQEpIAXiVaA4A2C8fVa5zFFAACgATKQ1ewLAQ78dwuz5s3FhjwvxxvQ30LVdV5Qswd1r3Cd6/+bMmWMd/cPd7HkskTyA7nVUCSkgBf5QQAAoayjOCggABYCJtN+4AZCnhsxYMQMLvl6AWe/OQrcu3dC2YVvkNcoLu2G0SSe0oMREJeWRAlLAVAEBoKlSypeOCggABYCJtMu4ADCeI+NMOiEANFFJeaSAFDBVQABoqpTypaMCAkABYCLt0jUAMuw7Zt4YLNm0BI3L1MK2tWvx2owZuDgvD1Xr1MHyfevRokYLDGo/yHU4WACYyKFW3VIg+xQQAGbfmGdSjwWAAsBE2rNrAFz7w1oM/XAoqparioqffAF8/PEf7evYEbtan4Lte7djRKcRqFOpjqu2CwBdyaXMUkAKxFDATwDUTgUyt2QrIAAUACbS5lwDYP7mfIycOxINj2qIUj/vwc4lS1D5ww+xs1MnVD7lFBw8oixWbl+JIR2GoGn1pq7aLgB0JZcySwEpkEQA1E4FMrdkKyAAFAAm0uZcA2AhD2BORWxdtgzVZszA1rw8VGvWDLv27ZIHMJEjprqlgBQwVsBvD6B2KjCWvlhlfPbZZ61zlZ3H0KVDBwSAAsBE2qFrACw0B7BaY2zLzw8CYNWmTbF863LNAUzkiKluKSAFjBXwEwB50WRGKXr37o3nnnvO6uthhx2GKlWqoFmzZrjiiivA33iOr2lKV8Axbb9pPnppZ86ciS+//NK0iJVv79691rnJRx99tHG5ZBzVJwAUABobZBwZXQMgr+FcBVx2427Ufv0drLvoXOytWcGaG9i3Vd+4toIxebhqHk4co6wiUiBLFSjuALh582Y888wz+PXXX8H//fbbb+O+++7DGWecgddff90CQ5OU6QD422+/WRqNGjUqLgA00TA0jwAwHtX8LaOzgL3pGRcA2hDIfQAXfjYbOZ/Ox/7W7dD69G7o3qh7XPBn+nWteTjeBlylpUA2KVDcAZAhSXq0nIlh6LPOOgv//Oc/cd1111k/PfTQQxYorl692vIUXnjhhRg7dizKly+Pjz76CJ06dSpUh33SEjfdnzBhAr7++mscccQR1gb8/O9onrASJUpgxowZ6N69e7DOSpUqWeXomVy7di3q1q2L6dOnY/LkyVi4cCHq16+Pxx57DG3atAmWmT9/PgYPHozPPvsMOTk5OP300/Hiiy+icuXKOHToEO6//3488cQT+P7779GgQQMMGTIEl1xyiVXe7tPs2bNx9913Iz8/38p7zTXXFOonNWGbounDAqGAbHsSb7/9duu6O3fuxPnnn29pXqFCBatO2ztrX5Dan3POObjxxhtxxx13BNtBb+Qpp5yCVatW4YQTTnB1+8kDGF0uAaArcyqSOW4AZE0MB895bhKqD7oNm+8fj85X93W99YuzRaYeQM3D8TboKi0FskWBTARAjt3JJ5+MmjVrggDERPhq3rw56tSpgzVr1uBvf/ubBXOPPPIIDhw4gEcffRRDhw61QI+JYMh/Tz/9tHXiUsOGDbFlyxb0798fhDm73nB2YgqAjRo1wgMPPGDBH0Fv0aJFFgTRa0koat26Nf7yl7/ghhtusP724Ycf4v/+7/9QtWpVjB492joRiv1i+blz51pg9c4776Bjx45BAGRInNeoV68eypQpg/Hjx1te0vfff99q+pFHHomyZctG1ScSAD744IPo0qWLdUQpAfCyyy6z2su27dq1ywLCk046CSNGjLCuVa1aNQtap06diq+++iooXb9+/az+fuzcMcPwBhQACgANTSWubJ4AkFcsmDoVub16oWDKFOT27BlXI+xCJgDIvKb5PDVGhaWAFCj2CvgNgASQ8847z4KMc889N6H60MsUzgPIixKUli1bhuXLl4dtw6uvvmoB07Zt26zfTUPAn3/+OVq2bGnNhyMgegHAJ598Etdee61VBdvZpEkTFBQUgGB45ZVXYt26dZg3b16RS+zfv9/yYhLinB5Dejv37NmDadOmBQGQ3tE//elPwTpM5wDG0of1jBs3zvI+0uPHNHDgQAtEP/30U+u/w4WAOUWpVq1aWLBggeXR/OWXXyxQJ6ReffXVru1FACgAdG00LgoIAF2IpaxSQAoULwX8BkBC1eOPP255rRjSTGSKBoCXX345/vvf/wY9TYQlzg1csWIFfvzxRxw8eBDs+08//WSFdiMB4OLFi0HYWbp0qeXlYuiVkEUPVuPGjT0BIEO7hEkm1k2ooxesQ4cOVt2XXnqp5V0LTbw2PWtstzPRk8lQKkPKdgj4u+++w7HHHhsTAN3qQ01eeeWVQp48ehcZ0maoNxIA8u8EUnpVaR+vvfaaFS4mSJYrV861uQgABYCujcZFAQGgC7GUVQpIgeKlgN8AmC4eQIY+a9eujTfffNOac0ev2k033QSCIUGLnjV63wheDOmGA8Cff/4Zxx9/vOXJJNgyhEmvHP97yZIlVpg5XOLqY87vy8vLC/5MWHv44YcLzQF01kFPJuf2McxLz9mpp56KCy64ICwAEvAYHibkOeGOF+NcQXrYbAC0+2c3JJwHMB59wtXDcDT/sb5oAPjGG2/gz3/+M+gNZNj4mGOOseYOxpMEgALAeOzGtIwA0FQp5ZMCUqDYKeA3ACZz+kkkD6C9CITz97jogTDGkDBDp/bWMFwNay9eIAAybEqvJUO7dqL377TTTrOgj1DFxHl3hJdoAFi9enVwEQnnGTJ988031iINe8GFvQgkGgCy3SwXLgTMNhJGCU1sS7gUCQDvvfdevPDCC9aiEDuZ6BNpEYhzO5lQAOT8QM6dpFfQmbgamXDOBSSDBg2ywsbOULabm0gAKAB0Yy9u8yYFAE23bjF9uJrmcyuG8ksBKZBZChR3AIy0DQy9aJz/VqpUKSt8S28dAYWrf7m69s4778SGDRuCHkDOSWvXrp01r46LRRiOpAfwuOOOAxcp0APIkPKAAQOwcuXKqADIfQh5TS52IOwQcv7zn/9Yq3Cdq4CjASCv0bRpU8tLyWuXLl3a8g4yLMxFIFzZyxAqF2K0b9/eWnTBflWsWNGaSxcJAAm6119/vQWW7Bvn7zEsHkufeACQ1yEgvvzyy9Z8SXpebQDnohd7cQrnPcabBIACwHhtx6RcUgDQdOsWU7AzzWcigPJIASmQuQoUdwB0bgTNECrhjQsoCEHOjaA5P42LFhhq5Ry7nj174qqrrgoCIEeYIWLOa9u+fbvlweNzmd6yu+66ywpXtmjRwgLHiy66KCoAbty40fI8Esi4wGHixInW5tSh28BEA0C2h/MBeW16IrlSt1WrVtY2MPRYcl8/eta4iplz7vg3to/52b9IAEgvKPv+wQcfWFrYXslY+sQDgIRYjgNhmBtJc/U1V2Ezsc3c8oVb8RCq400CQAFgvLZjUi4pAMiHi8nWLaZgZ5rPRADlkQJSIHMVKM4AmLmjkvk9o0eU2/BwkQpD5vEmAaAAMF7bMSkXEQA/GT0a++++GzmjRqHN4MER6zLdBsYE2kzysCGm+UwEUB4pIAUyVwE/AdD0QzZz1VTPYilAD+T69eutMDRXAjNM7iUJAAWAXuwnVtkgAL6/6edCefd37YDmq1dhab0TkTN7rvXbxQ1rFKlPABhLYv0uBaRAqhTwEwBNp7Kkqq+6buoVYCiZ8xo555BH9YWuYnbbQgGgANCtzbjJHxEAdzw6ASdOHItV/Qaiyk23CgDdqKq8UkAKpIUCfgKg6WK2tOi4GpERCggABYCJNOSIAPjTG9Nx1YA+eH7cZJS/sIcAMJGjoLqlgBRIiAJ+AmBCGqhKpUAUBQSAAsBE3iBpA4A8V3j2/Nm4sMeFeGP6G+jarmvEc4U1BzCRJqG6pUDmKCAAzJyxzMaeCAAFgIm0+7QAwIKtBZixYgYWfL0As96dhW5duqFtw7bIa5SH3Gq5RfovAEykSahuKZA5CggAM2css7EnAkABYCLtPuUASPibtHAStu3ZhtL7SmPas9NwZe8rcaDMAVQtVxV9W/UtBIFuPIWJFE51SwEpkP4KCADTf4zUwsgKCAAFgIm8P5IDgJs2oWDOHPTs1QtTp0xBbufOQI0aIMyNmTcGSzYtQeMytbBt7Vq8NmMGLs7LQ9U6dbB833q0qNECg9oPssLBbj2FJsJpYreJSsojBYqnAr4C4KZNAP/ZqUYN6zmmJAUSpYAAUACYKNtivUkBwEPDh2HdxBHYXRqocACo3W8oSg6/B2t/WIuhHw61PH0VP/mCW8P/0deOHbGr9SnYvnc7RnQagb2/7HXlKTQVTVs7mCqlfFKg+CngKwAOHw7cc88fIgwbBvBvSlIgQQoIAAWACTItq9qEA6DltVv0PD5f9BYO+2opDjZpjtNano+8llfh4KGDGDl3JBoe1RClft6DnUuWoPKHH2Jnp06ofMopOHhEWazcvhKDzxiMWd/M+t1TWK0xvv/+e+vcSW62ecwxx2D51uWFPIVuBNPmrm7UUl4pULwU8BUA6f2bMwfo1QuYMgUIRDKKlyKFWxvpSLVofeJxZ7feeqv1L1vS2rVrUbdu3ahH5CVCCwGgADARdmXXmVAAdM7vK7fxJ9R+/W2su+g87KlZ3vL69cjtgeeXPf+7BzCnIrYuW4ZqM2Zga14eqjVrhl37dlkewOtPvR5PLH4imI/QZgMgd1u389FTWKfS72cxuknpuqhE4Wk3o6i8UqCoAr4CIKv/4gvg1FOBxYuBFi0SKnnv3r3Bs4BvuOEGPPbYY4Wu9be//Q2PPvqodRYtNx+ON2UbAMbTX2r766+/YuvWrahatSoOO+wwI7kZXZo5cya+/PJLo/zhMgkABYBxG49BwYQBYKH5fdUaY1t+fhDuqjZtanntTj7mZKuJX37/peXZC5eHcwC71u+K0f8Z/bunsGQp6+ByJwDSk0hP4ZAOQ9C0elODbhfOkq4AqPC066FUASlQSIHiDoA8Q/3HH3+0nnlly5a1+sY+8cO3YsWK6NSpkwDQ0OZ/+eUXzJ8/39Js586dqFSpkmHJ+LIJAOPTzU2pEm4yK28RBRIGgIXm90Xx7l3V/CpMXz7dWgVcduNu1H79Hay76FzsrVkhuAq47OFl/5grmFOxCABmsgeQL4BevXphypQp1uHifPArSQEpYKZAcQfAH374AatXr8agQYPQs2dPq9PTpk3DmDFjUK9ePQtibA8gz6EdMGAAXnzxRQsaTzvtNIwfPx4tW7YMijV79mwrdMvzalu3bm15EK+55ppCQDRv3jzceeed+Pzzzy2PV15eHu677z4cccQRVj2xQsBnnnmmdRTahAkTgtft3r17obayDk7hWbVqFV555RVUrlwZd999t/U3O3333XdWf9555x2wb7m5uXj44YfRqlUrK8u///1v3HPPPVi+fDlq1qxp9WXw4MFBD12JEiXwyCOP4K233sIHH3yASy+91PKoOpPtQX377bcxatQo/Pe//0WpUqXQpk0bTJw4ESeccIKVPTQEbHsS33//fWts2Ab2+ZlnnkHDhg2tMaGuzsTf5s6diy1btuDNN98M/kQwZfvvv/9+/OUvfylURh7A6Pe5ANDsORgpV8IAMH9z/h/z+0qWKhLedXrtDit5mLUP4MLPZiPn0/nY37odWp/eDd0bdbe2gAn1Jvo5B5DCpKsHMN3b5s30VFoKJF6BTADAjh07YtasWSBsMJ199tm44IILQAhxAmC/fv3w6quv4sknn8Txxx+PsWPHWufRErKqVKliQV/9+vVx8803W6BFwLv99tuxefPmIAD+73//Q/PmzS0Y6tatmxX2vOWWW6y/EWD8BMDdu3dj5MiR6NKli9VuwttXX32FRo0a4aeffrKuybN07733XmuuN5/TtWrVsuCMkMr2TZo0CWeccQbYbvaJYfNhXJwDgAB49NFHW7BMDQl2ixcvRo8ePfD1119bHlR6VY888khMnz7dyt+0aVP8/PPPGDp0qAV9DN+WLFkyIgASRglu1apVw4033miFiull3Lt3L4YMGQKCpT1uvM6SJUvQoUMHayzsj/kZM2bgz3/+szW3vXz58gJAF48FAaALscJkTRgAmnoA7Xl7hLw5z01C9UG3YfP949H56r6FTgJxu1+gG1kEgG7UUl4pUHwU8B0A33kHOO884O23gXPPTagQhBl6AAl0xx13HFasWGFBCgGJAHHdddcFAZDQQi8aPU9XXnml1S56lmxvHT1pd911l+U1I2TZ6e9//7sFMHZIlHUSlB5//PFgHsIWAYrXKFOmjG8eQILbv/71L+s6v/32mwV59OgRpDjF54477rDAi/AamgjBZ511luWptBOjJAMHDsTGjRuDAEhvJ72gdjKdA0jwJTzm5+fjpJNOiuoBZDuY6F0llBL+qFOkEHCTJk0sbyXbynTRRRfhqKOOCgK2s6/yAEa/xQSA3h5BCQNAkzmAzj3+2I2CqVOR26sXCrhXYCDc4exeoX0A35mFbud2Q7uG7YKewnilEADGq5zKSYH0VsB3ALzxRoBwdMMNQMjCDL+VsAGQCwnotWrWrJkFSgxT0mPmDKsuW7bM8pgRmOj9sxPDtwTDp59+2grl2v/b/p1AyHpsAGS4mHUdfvjhwTp4zT179lhhToZh/QoB0xNJMLUT289+0vvGRS4E1Y+dW4M5BKbHjV5Cwqqd6H3jeBNUy5UrZ8EyodAOnTNfJAD85ptvrOsuXLgQ27Ztw6FDh6x66Hnt2rVrRABkOJdtYaJ3r0WLFvj2229Ru3btiABIICXgFhQUWN5Xwj2n+hCIQ5MAUADo93PFWV/CANACOscpH5Hm9zmPeosFgKwzESeBmABgqlbkmrQtkQaiuqVAcVbAdwBMgQeQAEgQYSiWifPgCCVOAFy6dKk1B82GD3vMmIfepaeeesrKb//vSABIwDvnnHPQt2/fIsNOqCldunRMAORcZYZSOYfOTvSMEZTs+YrhIJLtZxvpOWNomiHqSADI0C29hRdffHGRdnJuJMO2BECGV1mnnSIBIL2qBGd65TgfjwBIz59dPtIcQOdiEoaLTznlFKxZs8bSKJIHcPv27dY12JYFCxZY3taVK1eGvc0EgALARD5/PQPgJ6NHY//ddyNn1Ci0GTy4SFttr12k+X3OAiYAyPwmUOQG2EzqS9WKXJO2JdJAVLcUKM4K+A6ASd4GhiFgAiC9WwQwpnXr1lmeLycA0lvFUCnn6TlDwNy7jmFQhlMZAuacQHoQ7cQQKufI2SBDbxnnonHRRKQUywN4+eWXW57Kl19+2aqCbSeUOVcsxwJALtYghBKmwoWA27VrZ4XCCbaRUjgAJHCxLL18hGEmAhkXu3CBhu2FY9ib/9sLAHLu4gsvvGCFkUMTNeKcwE8++cQaL2co25lXACgATOTz1zUAvva14ygkAPu7dkDz1auwtN6JyJk912rrxQ0Lr1SNNb/P7qARAEY4Vi5UJDfAZgJZqdow2qRtiTQQ1S0FirMCmQKAHAOu7GXi4gWm0JW1BD2uqCUUERbtRSBcIMHQL8GRi0AIVpzrxwUR9LQR+GwAZPiXq4O5GpV5uPKXod/33nsP//jHP6zrxgJAerT69+9vASBX0T700EN46aWXrBC0qQfwwIEDlhexevXq1gpkLphgiJWeMy4C4cpgLoThyuFLLrnE8vjRC0q45QIWpnAAuGHDBmshCUGZXlR6Ehku5ny/888/31pAQp04N3LRokWeAJCrtbkwhTDJMG+FChWQk5NjtY16sv2EY16P/QqXBIACwEQ+fz0D4I5HJ+DEiWOxqt9AVLnp953hQwGQfzOBO5M81tFLBscxuQE2U8gyzefngKXimn62X3VJgVQqkEkAGKpjKACyrwxh0uvEFbbhtoHh9iO33XabtYjk9NNPt7YqIew5Q5kEH67IpXeKnjxCHD1W9CCaACAXn3BFMqGPmybzep9++mmRbWBCTxNxhoB5HYazCaiEpYMHD6Jx48ZW+JvtZiIEjhgxwgJDzlmkR5DQ+te//jUiAPIHrjzm9jCcf3fVVVdZUMqVugRjbrnDbVy4upjb2XjxAHLrGnpU6U2lJ5fQyXmdTNSV3lkuCGF4P1ISAAoAE/n89AyAP70xHVcN6IPnx01G+Qt7JB4AN23CmqeeQt0hQ7Bm5EjUvfbaiAeym8KT3/n8HDDTtvl5TdUlBTJFgeIMgJkyBupHUQUYsqfXj1AYbh6jXUIAKABM5P1T/ADQ0JtI0Uzhye98fg6Yadv8vKbqkgKZooCvAJiBZwFnyjgXl35wcQlD7g888IC19yDD89GOlhMACgATadsCwASAop8DJgD0U03VlW0K+AqAhtNPsk1j9ddcAXs1MecEMvRs7yGoELC5hs6c2gcwCQaOKgAAIABJREFUPt3sUkkDwFirhdkgozmApvkMF4u42VYmFTCWimt6MymVlgLpo4CvAEgPIP/Ziccy6mjG9BnsDGyJPIDyACbSrBMGgPGsFvYVAA2+1gttLP3uLHTr0g1tG7ZFXqM86wi60JQKGEvFNRNpcKpbCiRTAV8BMJkN17WkAGBtbM2tcLhghKeLOBNXhXMrGQD8P78vEc+yJA+gtwEPC4BltmzGoekv4IKJY/Fmv4Eo2eMK7Du6urW6NxTsIi0CCc1nslrYVwCMsVgknqPlUgFjqbimN5NSaSmQPgoIANNnLNQS9woIAOUBdG815iXCAmDu5AeQ+/BDwVoKbu6Pgj53eAJAk9XCvgJglFBx6DF1nJTLo3m4ZxPPo1y+dTlCj6mjGKmAsVRc09x8lFMKpLcC9guUpzxwvzclKVCcFOARfNwORx7A8KMmD6A3a47oAdy2cB4mDOiDW8dNRtVW7T17ANMJANf+sBZDPxyKquWqomJORXDPQBsAueHorn27sH3vdozoNAJ1KtUJKpwKGEvFNb2ZlEpLgfRRgKsuec4rT87gUWQ8yowbBCtJgXRWgPsEcjPsrVu3WptFcwNvbnbtTAoBA7qTvVlxxDmAq79ahgE9zsO46W+jXpNm1lW8hIDTCQDzN+dj5NyRaHhUQ5QqWaoIAB48dBArt6/EkA5D0LR6UwFgBBtzc9yeNzNVaSkQvwJ8kdJW6U1RkgLFSQF6remU4IdLaBIACgC92nJWAmAhD+CBEti6Zg1emzEDF+floVrduth1+CF5AA0sy81xe7GqE0zGUki/e1GAHhWeJkFvipIUKA4K0GvNPQIjeawFgAJAr3aclQBYaA7g8i0o8fHvZxgz/daxA5Y3PlpzAA0sy81xe7Gq8xMmY11Lv0sBKSAFirsCAkABoFcbzkoApGjBVcA71qPCmh2o+fEn2NixDXbXrYKqVWqhb6u+RbaCScV8vFRc041R+dU+P2HSTfuVVwpIASlQHBUQAAoAvdptxgIgvXxznpuE6oNuw+b7x6Pz1X1RskThSbT2PoALP5uNnE/nY3/rdmh9ejd0b9Rd+wAaWpZfAMjL+VmXYfOVTQpIASlQLBUQAAoAvRpuRgJgIbBbOB/7W7VDq9O7ht3g2QQUKbKbE0O8DoqzfLpDkZ/t87MuP8dAdUkBKSAF0k0BAaAA0KtNZhwAOjd4LrfxJ9R+/W2su+g87KlZ3tr2JVxoN9b+g25PDPE6KALAxWjRooWfMqouKSAFpEBGKSAAFAB6NeiMAsDQDZ635eej2owZ2JqXh6pNm0bc4DkaAMZzYojXQREACgD9tCHVJQWkQOYpIAAUAHq16owCwNANnrcuWxYEwGrNmkXc4DkSAMZ7YojXQREACgD9tCHVJQWkQOYpIAAUAHq16owCwNANnkMBMNIGz5EAMN4TQ7wOigBQAOinDakuKSAFMk8BAaAA0KtVJwUAy2zZjEPTX8AFE8fizX4DUbLHFcGj5Zwd+GT0aOy/+27kjBqFNoMHR+ybKbB59QDGe2KI10ERAAoA/bQh1SUFpEDmKSAAFAB6teqkAGDu5AeQ+/BDwbYW3NwfBX3uKHK03P6uHdB89SosrXcicmb/sTkzj6BzJtOQrdc5gPGeGBJrUNycepHuK2P9bJ+fdcUaA/0uBaSAFCjOCggABYBe7TcpAEgP4LaF8zBhQB/cOm4yqrZqH/QAvvb1pmAfdjw6ASdOHItV/Qaiyk23Bv9uCoAs4Fy0UXbjbtR+/R2su+hc7K1ZwfUq4HhPDIk1KG5OvUh3KPKzfX7WFWsM9LsUkAJSoDgrIADMPAC8GcAAAMcAWAqgD4DPohhpJQCjAVwMoDKAdQBITrMNDTspAMi2rP5qGQb0OA/jpr+Nek2aWc0j2DkB8Kc3puOqAX3w/LjJKH9hj7gA0IbAGStmwHSDZ6NVwC5ODImlvZtTL9Idivxsn591xRoD/S4FpIAUKM4KCAAzCwAvB/A8gBsBLAyA3KUAGgLYEsZQSwOYH/jtXgAbABwP4IcAPJrYdkYCIDtuusGzBYxTpyK3Vy8UTJmC3J49i+jm9sQQE+FNYcc0n8k1E5HHz/b5WVci+qo6pYAUkALpooAAMLMAkNC3CMAtAQPjuWXrAUwGMCaM0REU6S1sBOCXOI0yYwHQBOxszWIBoBugNJ3fZwo7pvniHH/Pxfxsn591ee6YKpACUkAKpLECAsDMAUB68/YAuATATIfNPQeAYd4/hbFDhnl3BMrx960ApgG4H8CvEew2BwD/2akCgO927dqF9zf9XKiISciWBcKFbUNDu8xnUp9fIWA3YOc3KJrO7zOFHdN8qXpO+dk+P+tKlR66rhSQAlIgGQoIADMHAGsGQrhtAXziMJ6xADoCaBXGoFYAqANgKoBHANQH8DCAiQBGRDDA4QCGhf4mAIweAnYDlKbz+0xhxzRfMh444a7hZ/v8rCtVeui6UkAKSIFkKCAAzHwAHAfgDACtwxjUSgBlANR1ePz6B8LChfdN+aNwRngAve4XGKqlSQjYjafQBGRM8vCapvmS8cARAKZKZV1XCkgBKVBYAQFg5gBgPCHgjwNz/852mMX5gRXABL0DBjdMsZkD6Fwt7HW/QAGggWUYZvETUP2sy7D5yiYFpIAUKJYKCAAzBwBpgFwEwi1fuPULExeBcFuXf0RYBMKVv1cCqMdFr4Ey/QAMAsCQsklKCgDu3LIZ+QvnYeKAPug3bjKatmqPykdXd7UNjN/7BTrFkQfQxFTC5/ET2vysK/4eqaQUkAJSIP0VEABmFgDa28BcHwBB7ud3WWCV7+bAFjHc6uXOgGnWArAcwLOBlcKcA/g0gEmBvQFNLNgTAEY74s0JbC9NfgAvO04Cuezm/rg8zEkg0RaBxLNfYKpCxSYgY5KHA2iaz2SwE5HHz/b5WVci+qo6pYAUkALpooAAMLMAkHbFLWDsjaC/BNA34Bnkbx8BWAugt8MA2wAYD+DkwCKSp2KsAg613bAAaOqxMz3ijfXt3EqG/T1VrlbdkwfQFBRTFSo2ARmTPOkOgNxrcfb82biwx4V4Y/ob6NquK0qWoOM6vmSqSXy1q5QUkAJSIHMUEABmHgAm2zrDAqCpx870iLdInXJzEoipBzDloeJNm1AwZw569uqFqdxYunNnoEbRNTmmsGOaL9mGY2+OveDrBZj17ix069INbRu2RV6jPORWy42rOena17g6o0JSQApIgQQqIAAUAHo1r4geQBOPHS9usr9fqgDQdF9BX0PFw4cD99zzR5eHDQP4t5BkBDuGMOnVCNyWd563XHpfaUx7dhqu7H0lDpQ5EPG8ZZNrGGliUpHySAEpIAUyXAEBoADQq4lHnAMYruJ4N3hORwCMtaqYfQ1NRotFNm3CmqeeQt0hQ7Bm5EjUvfba+D2AhjDp1QjclGfYd8y8MViyaQkaV2uM77//Hk888QSuv/56HHPMMVi+dTla1GiBQe0HuQ4HCwDdjITySgEpkM0KCAAFgF7tXwDI41QenYATJ47Fqn4DUeUmrr2BtUI5LgA0OFuY9RrBjiFMejUCN+XX/rAWQz8cann6KuZUBDe+tgGwRo0a2LVvF7bv3Y4RnUagTiXuU26ejDQxr045pYAUkAIZq4AAUADo1bgFgFGOs0s5ABrCpFcjcFM+f3M+Rs4diYZHNUSpkqWKAODBQwexcvtKDOkwBE2rN3VTtRkUu6pRmaWAFJACmamAAFAA6NWyBYACQFc2FI8HkF5C/rMTPYX8F5rkAXQ1FMosBaRAFisgABQAejV/AaAA0JUNxTMHcPjw4bjHsTBm2LBh4N8EgK6kV2YpIAWkQFABAaAA0OvtIABMAQC62T/PaOGJVytwWd7tKmB6/+bMmYNevXphypQp6Ny5szyALjVXdikgBaSAUwEBoADQ6x0hAEwyALrdPy8dAZBGV6gf78xCt3O7oV3DdujeqHvYfQBNwrsmebwavMpLASkgBTJBAQGgANCrHQsAkwiAbj1nFmhNnYrcXr1QwE2le/b0Ot6+lnfjyTSBO5M8vnZAlUkBKSAFiqkCAkABoFfTFQAmCQDjmTuX7gAIFxtVm8CdSR6vBq/yUkAKSIFMUEAAKAD0ascCwCQBYDyrZ9MeAF1sVG0CdyZ5vBp8vOVNVzLHW7/KSQEpIAXcKCAAFAC6sZdweQWASQLAePfPS+cQMD2AJqee0PBM4M4kj1eDj7e86UrmeOtXOSkgBaSAGwUEgAJAN/YiAAwoEHqkXbgzg/0+CSQjPYAu5iiawJ1JHq8GH29505XM8davclJACkgBNwoIAAWAbuxFAJhCACw0B7BMLWxbuxavzZiBi/PyULVOHSzftz7sGbpp7QHMIgA09WJ6vSFVXgpIASlgooAAUABoYifR8igEnKQQMAchuAr4849x3KcFOOIA8HNp4LvWuah6Wkf0bdW3yBYqJgDo9/w0N/WZtM8UntLZA2jaB683pMpLASkgBUwUEAAKAE3sRAA4bjLKX9gjqEMqQsD2xa398xY9j88XvYXD/rsUB09qjpYtu6J7yz+H3T/PBLD8np/mpj6T9pnCkwDQ6+2s8lJACmSLAgJAAaBXW5cH0IUH8JPRo7H/7ruRM2oU2gweHFH7WFDEcPCc5yah+qDbsPn+8eh8dV+ULFEybH2x6mIhv+enuanPpH0CQK+3qcpLASkgBQorIAAUAHq9JwSAUQDwta83FdJ3f9cOaL56FZbWOxE5s+dav8W7WMQUnEzzmXjP3IR2Tepj//1sn+k1vRp9vOXTvX3x9kvlpIAUKH4KCAAFgF6tNm0AsMyWzTg0/QVcMHEs3uw3ECV7XIF9R1cPQpYTxsKt2rVhzK98oWFi1r/j0Qk4ceJYrOo3EFVuujV9ANBwQ2Y3oV1T2BEAer0FVV4KSAEp4F4BAaAA0L3VFC6RNgCYO/kB5D78ULB1BTf3R0GfO9IKAP3cLsYUnIzyGW7I7Ca0KwAsemuZauL1plR5KSAFpEAsBQSAAsBYNhLrd08AuHPLZuQvnIeJA/qg37jJaNqqPSofXd0Ki4aGT8M1xJmPHsBtC+dhwoA+uHXcZFRt1T7tPIBpC4A+b8js5og3I0DNgI2gab8CwFiPE/0uBaRAshQQAAoAvdqaJwB8afIDeNnhtbvs5v64vM8dcQEgO7L6q2UY0OM8jJv+Nuo1aRbsm8mq3WSEgNMWAH2eiwdDjyI1FwB6vQVVXgpIASngXgEBoADQvdX4GAKmB3Dn1s3BGitXqx63B9AUAJM1VzCcFzNrANCFR1EA6PUWVHkpIAWkgHsFBIACQPdW4yMARrp4PCFgUwBM1lzBrAZAF549AaDXW1DlpYAUkALuFRAACgDdW00xB0DTuYKmnsJI+QSAU5HbqxcKpkxBbs+eEe1MAOj1FlR5KSAFpIB7BQSAAkD3VlPMAdBvT2Ekj2JGAqDhdjHU2BTsTPOZLKAwyePV4L2UT/f2eembykoBKVC8FBAACgC9WqynRSCpCAGbAqAbT2G41cfxAqDpKR+m4ORrvgQs7jBqnwl4muTxau0eywsAPQqo4lJACvimgABQAOjVmDIWAE1BMVK+eADQOud3xQws/Gw2chbOx/5W7dDq9K7Ia5RX5JxfI3Dy2xOXgMUdRv0wAU+TPF6t3WN5AaBHAVVcCkgB3xQQAAoAvRqTADDC9jNuAZDwN2nhJGzbsw3lNv6E2q+/jXUXnYc9Ncujarmq6NuqbyEINAInvwEwVfWZgKdJHq/W7rG8ANCjgCouBaSAbwoIAAWAXo0pYwEw0ibVFCwU7sLtP+gGABn2HTNvDJZsWoLG1RpjW34+qs2Yga15eajatCmWb12OFjVaYFD7QShZoqQ1ZtkEgH6Hxb0afbzlBYDxKqdyUkAK+K2AAFAA6NWmMhYAI21SnQgAXPvDWgz9cKjl6auYUxE/zPoYzZ/8CEuvOxOVunXErn27sH3vdozoNAJ1KtXJKgBMRFjcq9HHW14AGK9yKicFpIDfCggABYBebSpjATDSJtWJAMDxC+fgla/Go2b5E1CqZCm0H/gsOny7A3OPr4J5Y3vj10MHUbLEJgzpMARNqzfNGgBMVFjcq9HHW14AGK9yKicFpIDfCggABYBebSpjATCaMH6HgB9b/BlezB+HCjmVUe7wCij14ac4Z9oCvHdlW/zaqTX2/LIblcvsyyoPYCLD4iZGv2nTJvCfnWrUqAH+85IEgF7UU1kpIAX8VEAAKAD0ak8CQB8Wgby6YgNmFPwDq3fko1bFBvjlmxVo/t5bWHrO+Ti8fiOs/3ElLmrQLqvmAIaGxbcuWxacF1mtWTNPYXETox8+fDjuueeeYNZhw4aBf/OSTAAwEeDppc0qKwWkQGYqIAAUAHq1bAGgDwD42teb8N2ubzBr5ZP4cf8OVPz+Z7T84CMsOutM/HjMEaiYcxQmnPv3rFoFnL85HyPnjkTDoxpaYfFQADx46CBWbl8ZV1jcxOgJYnPmzEGvXr0wZcoUdO7cOSkewESAp0l/lUcKSIHsUkAAKAD0avECQJ8AkANBCFy44S2s/eoDHLt0ETY0b4m6Tc7G6ceeh76ndyg0Vpm+CjjVHkCKbeKxc3MDmdSXCPB000bllQJSIDsUEAAKAL1augDQRwDkYHDu27evP4kuDw7Hu7cPx/EXXWdt/cJ5h86U6QCY6jmAqQLARFzX602u8lJACmSeAgJAAaBXqxYA+gyAHJCf3piOqwb0wfPjJqP8hT2sMQoFwE9Gj8b+u+9GzqhRaDN4cMRxLM6g6FwFXHbjbtR+/R2su+hc7K1ZwdPm2KZGb+KxM63LDdj5fV03bVReKSAFskMBAaAA0KulCwCTCICcK2in/V07oPnqVVha70TkzJ4b/HumeQoL7QP46Xzsb90OrU/vhu6Nusd9PJ6p0fsNYqb1meYz7YfySQEpIAVCFRAACgC93hUCwBQB4I5HJ+DEiWOxqt9AVLnp1ogAaOopNM2XCo9iSk4C2bQJBXPmoGevXpg6ZQpyO3cGkrQNjADQ62NJ5aWAFIilgABQABjLRmL9LgBMEQCGCxPboWJTT6FpPqcRpAIAeX2T65rkiWXQwd+55YtjGxgMGwYkYRsYN6Fi475kQUZtn5MFg6wu+qqAAFAA6NWgBIBpDoDRPIU2ANLDtubREch99AkU3HQ96t40NHjmcHEKKfsKgJs2Yc1TT6HukCFYM3Ik6l57rTyAXp8WCSyv7XMSKK6qzkgFBIACQK+GLQBMcwCM5SkstPXMskXY0Kwl6jQ5C62OPR/HHVm/WC0+8RUADb2Obm4g09CuaT431870vNo+J9NHWP3zWwEBoADQq00JAIsxAE76bG5w8+kjHZtP77I2n66Cbg2us/YfjBUqDvUS0qhSMadQAOj1di7e5QXOxXv81PrkKiAAFAB6tTgBYDEFwO4NquPPMwdHPX7uhCrN8Hz3UZi5cnPQTsKFlEPPRmbmcKuUw4GiKbSZ5DPJ48bg/a7PFFBM87npSzbklW7ZMMrqo18KCAAFgF5tKW0AcOeWzchfOA8TB/RBv3GT0bRVe1Q+urrVv1BAWf3VMgzocR7GTX8b9Zo0C2oQDmTCCWRSX2ieMls249D0F3DBxLF4s99AlOxxBfYdXb1I23i9SPsAOj1xsUK7drsj5WtRfT96zxyACjmVUe7wCjiwsiB4/nDpBrnY88tu7N6/E892H4cvNucEZTBpGzNHAsVQPf30FPoNbH7XZwoopvm83ryZVl66ZdqIqj+JVEAAKAD0al9pA4AvTX4ALz/8ULA/l93cH5f3uSPhABgJPEMBMHfyA8h1tK/g5v4o6HNHygCwfqVtuGHWXahZ/gTrrN1QAPz10EFs/GkNHu82Gt/8UNU1AJqCop+eQr+Bze/6TAHFNJ/XmzfTyku3TBtR9SeRCggABYBe7SttAJAgtnPrH6HKytWqJ8UDGAk8w3kAty2chwkD+uDWcZNRtVX7jPYAmgKgqafQBMZM8rgxeL/rMwUU03xu+pINeaVbNoyy+uiXAgJAAaBXW0obAIzWEZOQbbhQcaQ6nfVFAs9w4eRwoedw+UzgyWsION45gCZti3aesTOMHS3cHaq9CYyZ5HFj8L7W52JjaYGMm1H6I690i083lcpOBQSAAkCvlp/1AGgCiXaedAJAgqdzFXBFxyrgH61VwEehW4Nri6wCjgWA0baVCV1RnFUA6GJjaYFMfI8l6RafbiqVnQoIAAWAXi2/2AGgm8UibuAuNG+6ewDt9hUCtqWLsKF5S9RtcjZOP/a84D6AsRafOOuatfJJ/Lh/B8JtKzPh3DtRsKNSIakiAWUmegBNN5YWyMT3WEpn3XRSSXxjqlKJU0AAKAD0al3FDgDdLBbJBgBkHyOFbMOFxSMB26srNmBGwT+ibitzUYN2qF/tquApI1nlAXSxsXQ6g4zXB0Yiy6ezbjqpJJEjr7rjUUAAKACMx26cZYodALpZLJItABgJxNwA4GOLP8OL+eOibitTucw+tK19C44uXysobdZ4AAWAXp81McunMwDqpJKYw6cMSVZAACgA9GpyxQ4Ao3U43n0Aw9VZXELAdtu9LCphX8cvnINXvhofdVuZkiU2oU3tG3F8pVwBYM+eEU0xnUHG6wMjkeXTXbd0b18ix0Z1p58CAkABoFerFABGUDDbAFAewNi3kumqYoFCbC3D5UiFbm7m9qWiffEpqVLZoIAAUADo1c4FgAJAazNrzQGMfSsJAGNr5CVHKgDLzdy+VLTPi54qm9kKCAAFgF4tXACYRQAYa38/rii2VwGH21Zmwrl/z95VwJoD6PVZE7N8KgDLzdy+VLQvpmjKkLUKCAAFgF6NXwCYJQBour9ftG1lsnofQAGg12dNzPJ+AlYiQrt+ti+mGMogBWIoIAAUAHq9SQSAWQCAzg2jTfb3i+UpdEqmVcBFDUigEN9jyU/dEhHa9bN98SmkUlLgDwUEgAJAr/eDADDDAdB5ZFzdUscCK/6LBgsXYGWrtkCjk7Dm1w1ws79fvEfBfTJ6NPbffTdyRo1Cm8GDw6puOsfO1OhTVZ9AwXSECufzU7dEhHb9bF98CqmUFBAAOm2ghAzCkwICwAwHwBbV96P3zAHW/n61l+Sj2qJPgz3e2rI11p3SFG729zMFwNB8+7t2QPPVq7C03onImT3XagMXnzhTqoDN9A4yap+LM4NNr5vsfG7Cp362zW/AMq3P73x+aqK6pEAkBeQBlAfQ690hAEwBAJbZshmHpr+ACyaOxZv9BqJkjyuw7+jqQSiKdXSbDU92vmj11a+0DTfMusva3y9n7z7s2bAOn773Flqfcz7KHVsb+8vmwM3+fvEC4I5HJ+DEiWOxqt9AVLnp1swFQBdnBnu9eRNV3k341M82mIKY6TVN6/M7n2n7lE8KeFFAAJh5AHgzgAEAjgGwFEAfAJ8ZGMn/AXgBwL8BdDfIb2cRAPoMgJFgzLmvYO7kB5D78EPBKxfc3B8Ffe6IGwCj1ef0AJY7vAJ2bt2M916einMu64nK1apjzy+7k+IBNJkraOJhc+OdMqnPxb0Co/o2bYLpmcFurp3MvG7Cp362yxTETK9pWp/f+Uzbp3xSwIsCAsDMAsDLATwP4EYACwHQTXIpgIYAtkQxlOMBzAewGsAOAeCmmPeUyYkh8W4EHQnGnPURErctnIcJA/rg1nGTUbVVe88ewEj1OecA1qrYAD9s2xIEwEpVj8b6H1cmZA6gqafQOVgmgOXGO2VSX0xjcWQwrc80n5trJzuvKRT52S6/r2lan9/5/NREdUmBSAoIADMLAAl9iwDcEhjwkgDWA5gMYEwEIygF4GMAzwA4A0AlAWBqATAS3IUC5eqvlmFAj/MwbvrbqNekWXB4Q/OZHvEWrT7nKuDD9x6O/7w2E2dc3B2/lP0FFXOOgpv9/UzBzjSf065NFoq48U75DWKm9ZnmS+dXmykU+dkHv69pWp/f+fzURHVJAQFgZBvIlEUgpQHsAXAJgJmO7j4XgLo/RZDgHgCkhzwAzxoAYA4A/rNTBQDf7dq1C+9v+jnmnWbiOWMlmZAvXg8g+x8OxhIJgDu3bEb+wnmYOKAP+o2bjKat2qNyyJxCe3+/FesW4Yt5H6BF+7OQW/t0nH7seXCzv58p2MWTL9xCEduenMZp+sL2G8RM6zPNF/OGS2EGU439bKLf1zStz+98fmqiuqSAADDzAbAmgA0A2gL4xNHdsQA6AmgVRoJ2AF4CcDKAbYYAOBzAsNC6BIBF1S1OAPjS5AfwsmNO4WU398flYeYUcn+/L758H/f16407Jz6LFiefjZIlSoYF9khz9uIBO6prUl+4hSICwNS8AE2hyM/W+X1N0/r8zuenJqpLCggAsxcAxwVCu61DJKDnbhmAvwF4K/CbPIANayAUUMKZjomHsjgBID2AXNxhJy7uCPUA8rdInsLQvposZLGvZQJ2pgAYLdwd7JyLbVb89sSZ1meaL51fbaZQ5Gcf/L6maX1+5/NTE9UlBQSAmQ+AbkPA9PotAfCrQxrOGWQ6FFg48j+DW0ergCOIFApFpvDE6pIdAo42zs5+RPIUhvbVZCFLygDQxTYrfoOYaX2m+Qzuz5RlMYUiPxvo9zVN6/M7n5+aqC4pIADMfABkD7kIhFu+cOsXJgLdOgD/CLMIpAyAE0NkGQWAnsF+AFYCOGBw6wgADQHQFJ5MQdHvRSCRxtoJd5E8heE8gOFWFYfziqbCA2i6zYoJiCViWxmT6xrcmynNYgpFfjbS72ua1ud3Pj81UV1SQACYHQBobwNzfQAEuQ3MZQAaAWB8j1vEcJ7gnRHkMAkBhxYVABoCoCk8mYJiKgDQBBLtPCZeTOZNOgACZvvxGeZLxLYyAsD4XtqmIGZau2l9fuczbZ/ySQEvCmgbmMzaBoa2wC1g7I2gvwTQN+AZ5G8fAVgLoLcAMLwCJnP7WNIkn0nc4A2nAAAgAElEQVSecHWZgqIAsOg4GM0BNAQ7jo0JiG3+8kssfvNN3D1kCEaNHIlTL7gA1U/mDIuiyaQ+0+t6efAno6wpFPnZFr+vaVqf3/n81ER1SQF5ALPDA5gKS5cH0NAD6MZ7Fi5vIreBiWY4JiDrZb5jcfcAIgFzCk1BMRU3vOk1TaHItD6TfH5f07Q+v/OZ9DWd87iZFpHO/cj0tskDmHkewGTbrABQAFjEI2oaxqZ0xR4AXRzdZgp2pvmSfbMbX8/FSmvjOg0ymoKYQVVWFtP6/M5n2r50zedmWkS69iEb2iUAFAB6tXMBoACwCACahrEzAgANQ8XsqynYmebzevMmrLwLr6ifbTAFMdNrmtbndz7T9qVrPjen7aRrH7KhXQJAAaBXOxcACgCN5kRSpnRYBczNrF+/ZwAOu/chHLyrPy4aNs7azDpcMgWxVOXzevMmrLwLr2isNrgJJ5qCWKxr2r+b1ud3PtP2pXM+U03SuQ+Z3jYBoADQq40LAAWAxQIAucG3fZzdqf0fRqf1u/FhrQpY/NDNaHXs+TjuyPpWP5zJ5GzhRHj2TIHS682byPJ+9cE4nJiAsLMpxPidL5Hjkqy6TTVJVnt0naIKCAAFgF7vCwGgALBYAOCkz+Zi1son8eP+Hai5+Ht0e2ExZl1xKjaeegwq5lRBtwbXFTnTONzZwqGQSI/inOcmofqg27D5/vHofHXfpHkUvd68iSzvFwAahxMTEHY2hRi/8yVyXJJVt6kmyWqPriMADGcDJWQYnhQQAKYxAEY6ko1NNlnda5rPS13JWATSvUF1/HnmYKzekY9aFRvgl29WoPl7b2HpOefj8PqNsP7HlTihSjM8330UZq7840i8cGcLOwGwYGsBZqyYgYWfzUbOwvnY36odWp3eFXmN8pBbLbeIZZhCkWk+T3duggv72QcjmPAx7GxLY3TdBCwWSfDQJKV6U+2S0hhdJKwC8gDKA+j11hAApjEARjqSzRTsTPOlOwC2qL4fvWcOQIWcyih3eAUcWFkQBMDSDXKx55fd2L1/J57tPg5fbM4JjmgkOGUGwt+khZOwbc82lNv4E2q//jbWXXQe9tQsj6rlqqJvq75FINAUikzzxbp53cyfi1WX29/96gOvawoTfl7TzXVN22eaz63W6Zg/m/qajvqbtEkAKAA0sZNoeQSAaQyA9ACGO5LNFOxM86U7ANavtA03zLoLNcufgFIlSxUBwF8PHcTGn9bg8W6j8c0PVWMCIMO+Y+aNwZJNS9C4WmNsy89HtRkzsDUvD1WbNsXyrcvRokYLDGo/qFA42BRQTPPFunmN58/FqiiO3/3qgxsQ8/Oabq5rCjum+eKQO+2KZFNf0058wwYJAAWAhqYSMZsAMI0BkE0zPTEk0gibwJ1JnnAwGSlEHe9q4UgngfjtAVz7w1oM/XCo5emrmFMRW5ctCwJgtWbNsGvfLmzfux0jOo1AnUp1gtKaAoppvlg3r/H8uVgVxfG7X31wA2J+XtPNdU1hxzRfHHKnXZFs6mvaiW/YIAGgANDQVASATgVMgMckj6mHLVw+U7AzzZcKAIwUovYbAOOZA0gv37evP4kuDw7Hu7cPx/EXXWd589i2/M35GDl3JBoe1dDyKP4w62M0f/IjLL3uTFTq1hEHDx3Eyu0rMaTDEDSt3tR3ADQO7SZgZazpA8NPGDOFCT+vKQA0Henw+UzHzNtVVNqLAgJAAaAX+2FZeQDlAYx7QUmkELXfAMj67FXAP/+4EUet/wFNFy5GfqtTsb1WJRxR8Vh0a3BtcBWwvV3M2q8+wLHLFmFDs5ao0+Qsa7uYvqd3wGOLP8OL+eOCcwrbD3wWHb7dgbnHV8G8sb2tOYWVy+xLmAfQOLSbgJWxpg8MP2HMFCZMrmkMzy7mHpq2zzSfqcbpnC+b+prO4xCtbQJAAaBX2xUACgDjBkBKF85DmQgAtPcBXP3KXdixaj72HQaUOQgcdWI71L303uA+gM7tYo78/me0/OAjLDrrTOw65ghru5gJ596Jr7ZXxIyCfwRXFR/20UKcM20B3ruyLQ6e2cpaVXxRg3YJmwNoHNpNwMpY0weGCYwZ1eXCi2lyTWN49hsAXfTDSJc0zyQATPMBAiAAFAB6tVIBoACw2AAgh6r05k34+tM38OSDw3Hd7cPRsPWFOFD99w2gTULFBLv61a7Cxh//F9xXsKIDFH+0QPEoTDj37wlbBXxo4wbMefcV9Bl0GyZz78Eul6JkzWPDWqIJFHl9CIQr79t1XXgxTa5pDM9+A6CLfiRiPJJdpwAw2Yq7v54AUADo3moKlxAACgCLFQBG8jry7yaLRRjabVv7FhxdvlbwZBErVLx0ETY0b4m6Tc7G6ceeZ4WKQ5MfJ4tYew8+fAtWLJ2DfYcDZX4BGjXvjLyb/+Fp70GvD4LQ8iYwZnRNF15M02uawomv+Vz0w0iXNM9kql2adyOjmycAFAB6NXABYAoAcOeWzchfOA8TB/RBv3GT0bRVe1Q+urrVEmf4lAsZvvjyfdzXrzfunPgsWpx8dnBbEj8XqXipK5khYHuoIi2MMdkupmSJTWhT+0YcX+n3jZ6jLRZh2NmZTE4WYf5IIBPce3DHelRYswPHfvwJNnRsg911q6BqlVpx7z1oOi/ONF+0PsTzwDEFO9N8pnBilM9FaNe0ffFolG5ljLRLt0ZnWXsEgAJAryYvAEwBAL40+QG8/PBDwStfdnN/XN7njkIAaC9kWLFuEb6Y/wFatDsLjWq3LHTubSighOuKCdyZ5AmF02gwlqg5gLxmNHh26wG0+2B6mkmsk0Xs+sJ5ChO59+ADt9+OaQ/9YU9X9u+POx78//bOBbyK6tz7f0EIt4SACeEiAVQuEQELhYAB5CIHAaWgrbZC1VM9nloVFIrUKhJATxWqAtrjOX7aoz6g1lbDQQF7EEQuSkAQiBhEKwpCRCK3yB3p96zN3tudycyed2bNzp7s+c/z8H097netmfVf76z55V1rveuxKu7gZP2cl7AjrUtqJ4UTkZ2DqV3p8+kOyn4oL9LODw8a4GcgABIAdd2fAJgEAFQQc2DfD0eWNcnOqRQBjN3IUOdYXax6vQj9rhmNU/VPWp57a+UIEriT2PgFAOPBs5M1gColjFMAlIKiWaRQwWmicg9WTJyI9BgArJgwAekmAOhk/ZyXsCOtS2onhRORnYOpXenz6Q7Kfigv0s4PDxrgZyAAEgB13Z8AmAQAjNdpRog5WP4Nlr46H0OuG4PMrGaW594GBQCdwLPV5o7S/ZmV5JKCndTOLFKopqdjcw8ak09r5R50ADHSD7uXsCOtS2QnnbKV2sWZsje+U6Ln0x2RfVJe6ic+edxAPgYBkACo6/gEQJ8BoHEaU0UKIwCoIoVW5976GQClJ4ZYnQTiNEJZKQ+gyeYO49S5FOx07BIZAVTTy8tfmIucyfdgr9pVfNO4SkfYxfqG9MPuJexI6xLZSadspXYEQNOhQ+onuh8glnevAAGQAOjee86WJAD6DACNGxmMAGh17q2fAVB6YohXAKi0cLK5QwfspOsdVWQ39vzhQ4tXRk8faTy8v+vzh0O7ircVoXjdYqQVr8GJ/ALk9xqO0Z1Gm+4qln7YRTAmHH2kdYnspNFOqR0BkAAo9GO/mREACYC6PkkA9BkApmoEsLx4NWZPugt3z3oSWfl9cbxZTpX0M14CoOpWCdhJo5PS+uLZxa7t/PnDazDgy4NY0SYTr9xfEMo9GDnNJOKSCmIXTpuEc//jcZz+/QSMnDqrUmQvuqv4aDka7PkOuQvfws6RV+Joy0ahc47H5Y+rDIEJmBaVDEAisEsAiCXrvhJN/G4j/UPB7+1I5ecjABIAdf2bAOgzAEzVNYCSdDHJAEBpdNILAIycZlK8ewn2L12IEX/7GIt+ejHOG/KTUO7B8xu3j0JxZBq7x4Q/YeCuCrzTOh0bHr8jugvcGFEsLylBdlER9o0ejawuXcwjigmYFpUMQMkCsWTdV6KJ320IgH7vIZ4EonroHP93k6+fkADoMwCMPff28In9qHOsDla9vgD9rhmFU/VPVYoU1aQ0MH4FQK/PM5aAotX0tCpr7P+WG77GiJc3YNEvemBPj+bRXeAjO+Q631Xs8bSoNK9gskAsWff19YgvfDgCoFCoJJoxAkgA1HU/AqAPATA2UhTKA7h6Gbr3HYy83F5VIkV2DiDZQCGxicCJETolYKfKSuySEQG0yisoXdvn1s6qrZJUNhc27YpJfa7Hw6seRsfzOqJ2rdrwdFexcDpWmlcwWSCWrPvavZM14XcCoP97iQBIANT1UgKgTwFQPVYqnQTiVwC0yivoFuwkEUArG/XfJcmsK04cwEODxuOZDc+E1vplpGXg4KJ3o5tKMkdcjkPHD+HbY99i+sDpaJvZNurlXkKRNK+gl/dUDfF7fbqDsh/KEwD90Avxn4EASADU9VICoI8B0CpyZhWNM2uKJLonsUnlCKBZUu5EAqDVxhOlseQ4uz3f7cDTw2dg5vuv4PP9JWid0QH9Jr+A/l/ux8o2TbHq0Zsq5YuMTXrtNTxJQMHre/q9Pt1B2Q/lJf3qh+cM8jMQAAmAuv5PACQAVtmNa+VUZlAkiez5eQrYSVslu4olEUCrjSdOIoDPj5qFhdt3YtH2Z6HWirbY8DWuenkD3vxFD5SF1gpW3VWciOiZBBT8DmxeP5/uoOyH8pJ+9cNzBvkZCIAEQF3/JwASAAmAJj6Q6AigWVoc9RjSNYAvjnoIC7bvRbyk15FdxbHN8xR2hGllPL0np4B1x3xReQKgSKakGhEACYC6DkgAJAASAKsZAK0iopFp9th8gWbH2UXyBUY25NjtKk4UAJ4pnIqdc6ajoi6QfhLIHf8gahVOq6ImAVB3mK7+8gTA6tfc6R0JgARApz5jtCcAEgAJgNUMgFY7jyMAGLsL/Iuty9DKcJxdbL7AyKPH20Ed27z3H34YJx54AGkPPYQ+999vOX7YQVvoBJL1L+KD9Utw7tbNON25G37ccxhG97yxygkkdnVFHiJV7HQHZaflpel4nNRLAHSiVnJsCYAEQF3PIwASAH0BgPE2RrjdpCJds2fmAomcArbaeRwLgOp/20X2YlPyWAGgsW0nhvdHt88/w+YLLkLa4pWhn1VbjVc8GHN6AkmqgJ20HbqDstPy0nQ8TuolADpRKzm2BEACoK7nEQAJgL4AwHgbI1INAFUE0GznsREAnUCxFAD3Pz0bF82Zic/G34umt9/tGAAVlMaeaSw5gUQKTqlipzsoOy0vTcfjpF4CoBO1kmNLACQA6noeAZAA6AsAtDqRwwhF8RzeCIp+jQBK2yCF4nigaLyXlSbSCOAXB79wfAJJqoCdtB26g7Kb8l4Dm9f1uWkTy8RXgABIANR9RwiABEBfAKDqBrOUMkEGQCkUxwNFrwGwZG8JZqyc4egEEik4pYqd7qDsprzXwOZ1fW7axDIEQDsf4FnAdgrF/50ASAAkAJr4QCLXAEojgJLNIqqu4x9/hD3vvo2X5szEDePvRcvLr0C9iy8xvY0kAqimeZe/MBc5k+/B3kefwKCbxiGSTJoRwLEonTcPeWPG6I28Hpf2Gti8rs/j5rI6AIwAMgKo+yIQAAmABECfAqB0s0g8u9imWW0qid0EEtrdu60IJa//BQXzt2DNmK7ocs31GN1pdGh3L9cAEgB1Pzos740CBEACoK4nEQAJgARAnwKgdLNIPLtI0yoljN6yHru79kTbzoOR32oYxvXqj0jqmcjJIj9/eA0GfHkQK9pk4pX7C5CR1hSzh94XgsDYXcD191Qgd+HfsXPkUBxrmR46m3hc/rhKqWBSZWpX2g7dQdlNea8jdl7X56ZNLMMpYDsf4BSwnUKcAq6igGRXqcRGVZxoO6/XxZm5g04bvDwKzuu21vRNINKpYrshQMFfBOwaf30EPZetwPrBA3CoecMo2G39NgNFpU9FzxY+d0Uxhrz0HpbecBlOD8gPnS08skMBJvedHJoONosUdr3m5xjVaRTzANp1SAJ+9xrYvK4vAU0OfJWMADICqPsSMALICKAWxBIAW4SiZ7GX1+CpA+1qyjYCdu1qtwK2fYQOxe9he/5lQKdLsOP73SGwa9xgIF4teQzpaU3QoE46Tm4vRbelS7B5yDDU7ZCHo6cq0KTecVyWeyeaNWodeiRV99ERfdH98y+w8YK2aLBodQgOjXkFvUo+HdFBGolLlp3uoOymvNfA5nV9btrEMowA2vkAI4B2CjECyAigAVCMgqRiBNAqNYpOW6VgJ7XTATtpO775bhdeKZkVArvcD0uQvX5t9Lb7evbGzh91CYFdm8wrsWzHy2jZ6ELUrlW7CgB+f+Y0ap1Thj65v0abzLxoHVZ5BWOh2Cz5dCR6HqtBsoDN6/vqDck/lHZywofXwOZ1fV5pwnp+UIARQEYAdd8HRgAZAUx4BNBqN6sRYrycArZKjSIFJz/sAo73ckvb8eXBUvx16xMhsEs7dhxHd+/E2qVL0HvIMDRolYsT9dNCYNcxaxRWfVnkKAKonk8Cu2aQSAC0H7qdnPDhNbB5XZ99a2nhVAECIAHQqc8Y7QmABEDXACgFO6tdqokEQKscelJwShUAjI0AqqlddQLJ0lfnY8h1Y9AkOyc6tdu79W+wZtf/RtcAnvp0W3QKuE77TtE1gO2zb4ymhJECYHWfUxx5pb2O7Enr0x2UI+WdnPDhNbBJ6nMSofRKE9bDCGCsD3AKWO+NIAASAF0DoBTsrHapJhIAVbdK1yeauUCqAGDsGsDWGR1wsPybKABmZjWrBHZ7Dv8julkkI2azyOHQZpHzMHvo71C6P7OSXJIIYDwATKWp4kQAkQTEVIdI7USfi7IylC5fjjFjx2K+ynk4aBDQoup50U4ilKL70siRAowAMgLoyGFMjAmABEDXACgFOysnTUUATMTaQymgWukcuwu4zrE6WPX6AvS7ZhRO1T9VBewqpYvZvB67u/VEu85XoFerK6PpYmLv4yUASqeK/bqpJBFAJAU7qZ3og1FYCEyb9oPp1KmA+m+Gy0mEUnRfGjlSgABIAHTkMATAswpIpgElNtK6dOy8XBcnBbFk2Xnd1mREABOx9lAXAFX5CNht27keG1cvQ/e+g5GX28sU7OIljHaz41kaAZTa+XVTiRSInEQKpWAntRN9MMrKsOO559BuyhTsmDED7W65xTQC6HnkUfRwNIooQAAkAOq+DYwAMgIoAmIdiJUCZSoAYCLWHnoBgKqOb/eW4f21b+B/HivEv04sRJ/eV+O8nBam/S+J7Kk67eysYNLMnyQAqOrb8fR05D39DEpvvw3tbn8wuibRD+lnJCAmjhQKp2ITAWLS9Y6S9up+pFjeXAECIAFQ990gABIAUxIApRtUpHAqgZ1IXV5HHr0CQOmaTSdtjQeA8U4fOb9x+yp+ZweATuszixQaIVG1VQo7EjsJEEkjhaFpV8FUrILixWsW4+prr8Ybr72B4QXDK23UcfORkLQ1EeDp5lmDWoYASADU9X0CoI8B0ApivI7GpeJ0txPYkQKWXbTL7wDoZM2mpK3x1jvOXbcy7ukjIzrcWmVNYTwAdFOfVY5CY397tqZQGrFzYGc3FRs6kWX9iyguXYV3i9fg8vwC5Of1w+ieN1Y5kcXJx4IA6ESt5NgSAAmAup5HAPQxAFpBDAHQvNNiQdYJ7AQFAL2OdlqtdxzVIQe/XHB/3JQyFzbtihdHPYQF2/dGH8sKAL2sz+yPHc8ihcKInTSyZxedjJ7J/MG7aL22FA1PAkfqArt65yHrx5dXOZPZyceCAOhEreTYEgAJgLqeRwD0MQBaQQwB0B4AncAOAdDdcXZW6x2755zAzQsmxU0qXXHiAJ4fNQsb96bZAqCX9ZkBoGeRQunmCaldnOlpNe37yOpH8GHZh7i4Xmsc3LQJTd55BwcGDkTmpZfi4+O70L1F9+jZzU4/FARAp4pVvz0BkACo63UEQB8DYLzO9XLa1su63MKp+qBt3PQ2/jD+Ztw353l0v/SKSov7jbtPpdBmtNNpq2RaVN3Pr2sAnUCxTlvbZ5bj3xf9Pu6xcnu+24H/HvEwPj2YZQuAXtank99REimUgpOu3RcHv8CD7zyIrAZZyEjLwL4tW5BdVIR9o0cju2tXHDp+CN8e+xbTB05H28y2jr8T0ueTrHl0fHMWEClAACQAihwljhEBkACY9E0glVKUrFmG7gWD0Sm3J/JbDYPZZgEnIOMVADrJ7xd0APQyYqf6z8v6dABQEimUgJP6Y2f5C3ORM/ke7H30CQy6aZzlpg2r+kr2lmDGyhnoeF7H0NnNRgA8feY0tn+7HVP6T0GXnC6h18BJ+hlJO1SdBEDdT7D78gRAAqB77zlbkgBIAEwqAMYu7q9zrC5WvV6EfteMxqn6J5GR1hRmmwWSAYBO8vsFBQCtNil5uWZP9bWX9ekAoFVUNNYf7cAptGljWxGK1y1GWvEanFCbNnoNx+hOo003bVjV5yYCKE4/42BnNAFQ9xPsvjwBkADo3nsIgHG105kqNKvYz/Ul69mMH3azY8rMNgskAwCd5PcLCgDG22kdC/Zmx8qN6HCL613AOvUlEwCjmzaOlqPBnu+Qu/At7Bx5JY62bBSayh2XP64KBFoBYKU1gNkXo7ykJDoFnNWlCz7e93GVNYDi9DMEQN3varWUJwASAHUdjRFARgCTFgE0Tu0d2Lc3ek5tk+wcHD1VAbPNAskAQHVPKdhJ7arjDwXJAOEWiux2Wsc7Vk47D6DhmDppfW7bqnS0iwDGm9p1A2x2U8WxQFl/TwVyF/4dO0cOxbGW6ZZAKY3Y2UUyI34lrU/ih7RxpgABkADozGOqWhMACYBJA0Dj4n4jAH5/5jTMNgsQAGXHGUoHBx0osoPYRJwE8uXCZ/EvjxXi/yYWos3IWy03C0k3sujaKQ3spnadTtna1RfRvZLd2jU40bsAvXuNwKhOo0ynlKXAJgJAaT5DqSPSzpECBEACoCOHMTEmABIAkwaAjABWdT6vp+MlA0QiAdAqcqb+u/G+dieBRNqiY6fTVitQjI3ENSnejd5/fh9rf9UHB/JbRSNxalOGdNPGubXOxdziuSgXThXbRQpjfcBTAJTmPZQ4IW0cK0AAJAA6dhpDAQIgATBpAFiT1gAqN5FO7Urt7KJn8V5uKShKBggdKJK0QQfYvAZFnbaatcPow/0mv4D+X+7HyjZNserRm7Dr8HaodazTB9yKwhWFtmlbCgcU4pWPXjmb30+4ti8UgZw/H3ljx6J03jzkjRlj2e2eAqCDfIYSP6SNMwUIgARAZx5T1ZoASABMGgCqj3HlXcB1sOr1Beh3zSicqn8KGWnnwWyzgJXTS6BIYmMGHQRA+bRzMiJ7UlD0GgCNUeza76zFkJfew9IbLsP3A3tH17H++SePisDu+kuuF4GiMb9fUgDQAXjqfqhYvqoCBEACoO57QQAkACYVAFWC50p5AFcvQ/e+g5GX2wu9Wl3pmzyAUgC0SmitA56SKJvOQKADRZJnS+UIoJMk1bFTu5nFX6HPn9fi/V/1xsH8811NFUfy+yUtAkgA1HnttMsSAAmAuk5EACQAJh0AVRfUhJNA7KZ24yW0HterP7w6zcQqQul2MCAAvoYbJ92FF2c9iUZXXxuSUaqJ0yTVER/pMeFPGLizAu/kpmPj43dG/9hR9bk54YMRQLfeX3PLEQAJgLreSwAkAPoCAK0ibE5gRxJlk9i4mQJWH/ZF25/F4RP7YZbQevbQ+1C6P9P2fdV5PtvKHfi6dGds0COAbpJUqz92djw9HXlPP4PS229Du9sfjO5kVvVFz/j1+xpARgDdvnKelCMAph4A3gFgEoDmADYDuAvAOgtv+TcANwK4JPz7BgC/j2NvVg0B0MFHUfKxs3qzvf6we1mfl3XpAJtZhE2nPq/7yyoC+Ldtu1FU+hQ+31+C1hkdYJbQemSHArTPvtHyyK/Is3rdF5IvjTTa5fbZUnkK2LiOVSdJdcTX3eT3YwRQ4umpZUMATC0AvB7AiwB+DaAYwN0AfgagI4BvTFx3PoA1AN4DcBzAZACjAXQGsFvo6gRAAiAjgCY+YAY7VgD4XxvW4ZWSWUhPa4IGddJhltC6Sb3juCz3TjRr1Druq+kWsoTvu6kZAdD9FHBEOy+TXqtOcprfTwSADvL2iepjBFDntdMuSwBMLQBU0LcewJ1hz6gFYBeAJwE8IvCW2gAOhMsrkJRcBEACIAFQEwCfKF6Ov259Ai0bXYjatWpXAUCV0LrWOWXok/trtMnMCxQAqiP0zrz2Mq6aMxNvjr8Xta79BY43ywlpkIzdwjqwazct7lXS64iDOMnvJwI2B3n7RPURACXf2ITZEABTBwDrAjgK4KcAFsR4zAsA1MKhnwi8KD0cKVRRwzct7NMAqH+RS5X56tChQ3i77IjtLbyOTvi5Pj8/m9nH06rzJO2Q2Hh9T7P6/DwFHG93LyOAlb0v1p/ynvwj8v70eNSg9I4JKL3rtykJgKpRXkx3x6rpKYg5yNvn6X1tvyw0cKMAATB1ALBleNr2MgDvxzjDTACXA8gXOMh/AhgangJWU8JmVyGAqcYfCIBVpfILFOmAnRTa/NJWvwKg3e5ergG0BkAVASwvXo3Zk+7C3bOeRFZ+X19FAK2idm4ihV5FOxMGgA4idgRAwRc3ySYEwNQHwFkA+gHobeNrvwNwL4ABALbEsWUE0CLFg1Ezv0ARAdB9AmIz7Zz2q3R3b2W7qgmtZw/9XWB3AUvB3ovoWWyqHbsp20rr9rasx+6uPdG282DktxoGs7Q9dvV5Fe0kACaZrGrI7QmAqQOAOlPAaj7lAQBXAPjAoe9yDaCFYE5BwU53P9fnl2eTgoIOFDtpq9PIXryE1kHOAyjtVwkA6kbZYjdtRNL2NP76CHouW4H1gwfgUPOGyEhrCrO0PXYA6FW0k5SdI54AACAASURBVABoN5ryd6UAATB1AFD1p9oEolK+qNQv6lKbQHYCeCrOJhCVMkbBn5r6XevitSAAEgC5CcTEBxQouFnblwongVhBlhN4jo3EeQmAulE21QYj2J/6dBu6LV2CzUOGoU77TqHze83S9tgBoHIjL9oa647vP/wwTjzwANIeegh97r/fcoj3esrW6/pcfJtYxEYBAmBqAWAkDcxtYRBUaWCuA9AJwN5wihiV3uW+sF+oKd8ZAG4Ip4OJuMt3ai2y8O0hABIACYAWAOhmd++Bb/aipHg15ky6C+NnPYku+X3RpFlO0jSWjANGsLOCLD8AoG6UzQzsT24vjQJg3Q55ofN7zdL22AGgVd+rPojVzkkU88Tw/uj2+WfYfMFFSFu8Mtqdqr6aBIoSP6SNMwUIgKkFgKr3VQqYSCLoTQDGhSOD6rcVAL4AcHPYTdT/bmPiMtMAqM0ekosASABMGpwYgUIaPbFybAmgSGwiH2w3EcC/PPlHvBqz6/W6Oybg+rt+mzSNJYOAURMryHKiXaIigLpRNtUGI9gbAdAqbY8dAFr1vREAnUQx9z89GxfNmYnPxt+LprermMDZy9gXZqBohERVzuvInrQ+iR/SxpkCBMDUA0BnHqBvTQAkACYNTvwOgE7XACpXUlEglQg6cjXJzqlxEUAryEoVADSCvZcRQLO+NwKbbhTTDADNQJEAqP+B9HMNBEACoK5/EgAJgARAEx8w2yxQ55h/dvdKYUwyQJjVZXXqSWxkTxKJlU6Lqrokm0CkEcB46xgTuQZQoom0DU40sYpOGp9HGrHz2k7ih7RxpgABkADozGOqWhMACYAEwDgAqH7y4+7emgKA0mlRJ7AjWSpgt44xNm2P2fm9Zml77KaA4w3Gsf2VCCgmAOp+CmteeQIgAVDXawmABEACoA0Aqp/9tru3pgCg1ZS42TSm1xFAswTUsbrFO7/XTR5AKQAmAooJgLqfwppXngBIANT1WgIgAZAAKABAq2k7KYgly04yQCRyClgKRdIIoJPomWQa28uTQKRtTQQUEwAlnp5aNgRAAqCuRxMACYAEQAIgjGv7JPBkNfi4hV1JBNBJ9EzaBunUrtTOTJdEamIFz2abQJKVV1D3Q8XyVRUgABIAdd8LAiAB0BcA6CSyowMebj/EjAB6eySfcQrYKhJntHMSPSMAtqgC9slKF6P7oWJ5AqCZD5xDx9BSgABIAPQFADqJ7BAA5TAmGR2SPQWs1uJt+OSv2FX6DrI+24ryizqjdd5A9Oj4M5zfuL1r/yQAVgXAZKWLkfghbZwpwAggI4DOPKaqNQGQAOj6A6sDYjqRHZ37MgJYVb1kAuDcdSuhzuTF1rXo/OGnaHgSOFIX2Pqj9kDn3hjR4Va4PUeZAFgVAKVrBb2eKtb9UEXKl5WVQf2LXC1atID6F8SLAEgA1PV7AiAB0BcAGM+RdaDNWK9OXVKgMGuLzn116pMMEMkCwFEdcvDLBffj8/0laFe7FY7t2YW1S5eg95BhqN+yNXZ8vxsXNu2KF0c9hAXbf0iuLf0DQNJfTs49rolrAI1rO6Vt8HqqWOKHEpvCwkJMm6YOuzp7TZ06Feq/BfEiABIAdf2eAEgAJACa+ECyoMhrUJQMEMZ7Vtd5xt1zTuDmBZOQntYEDeqkh05QWfrqfAy5bgzUCSrqTN6KEwfw/KhZ2Lg3zbYpkpNljDZ2+QJjbyqFJx1gNz6fZGOMup/02aR2Xk8V23ae0EBF/5YvX46xY8di3rx5GDRoECOAQGMAh4USppQZ1wDqdScBkABIACQAVtosUF3nGbfPLMe/L/o9Wja6ELVr1a4CgOpM3j3f7cB/j3gYnx7Msh3p3ACgk3OPpfDkVwD0IuWNsW3SqWLbznNgsHHjRvTo0QMbNmxA9+7dHZRMLVNGABkB1PVoAiABkABIAKwEgNV1nrEfIoCq6yVTxU6ibH4EwEpJr7esx+6uPdG282DktxpmusZSCrt2U8XSNXtSO6UtAfCshxEACYAEwBgFvJw+87Iu9Yh+rs/Pz+a1djptlYKClwBg9YJL2yEZIKR1eW0XuwawdUYHHCz/JjoFnJnVDLsOb3e9BtDJyS2SfnUSPfOy/72YAo499q7x10fQc9kKrB88AIeaN0RGWlPMHnofSvdnVnpsKQDaTRVL1+xJ7QiAP3QTAZAAKBnf49kwAsgIoK/hlABo7qBSGJMMENK6EmEX2QV8+MR+1DlWB6teX4B+14zCqfqnkJF2HkZ0uMXxLuBKZzevWYbuBYPRKbenZbTLDgDjpalxu0NZCva6APi3bbtRVPpUaKONguxTn25Dt6VLsHnIMNRp3ykE2SM7FKB99o2odU6t6GNJAdBuV7F0zZ7UjgBIAIx9d7gGUDLCW9sQAAmABEATHwjyJhApnHhlVwnYVi9D976DkZfbC71aXek4D2BloKyLVa8Xod81o3Gq/knLaFc8AIxEz6zS1JhFz/wUAfyvDevwSsms6Eabk9tLowBYt0NeaKNNk3rHcVnunWjWqLXnAOgE2KRTu1I7vU+j/0szAsgIoK6XEgAJgARAAmCVEyO8hBgJKFpN2TqJAEumlM2iXVYAGBs9s0pTY1afl9rpRgCfKF6Ov259IrrRxgiAaqNNrXPK0Cf312iTmec5AKp+XbxmMa6+9mq88dobGF4wvFKkMVYrKdhJ7XQ/jn4vTwAkAOr6KAGQAEgAFABgdaVG8XqaVTJAeH1Pt/WZgZgTAJRsKjGLdlkBoDF6Zpamxqw+PwFgMiOApftKUbT+RRSXrsK7xWtweX4B8vP6YXTPG5GX/QNsRvSSgp3UTuL7NdmGAEgA1PVfAiABkAAoAMDqSo3iFp50BgKv7+m2Pl0AlKSVMYt2WQGgMXpmBECr6JmfADBZawAV/M0tnovyD95F67Wl0RNedvXOQ9aPL8e4/HFVIFAKdlI7nXeiJpQlABIAdf2UAEgAJAAKALC6UqO4hSedgcDre7qtTxcAGQG8Cy/OehKNrr425A6RfojdBZwRswv4cGgX8HmYPfR3rnYBW+2MVlPxj6x+BB+WfYiL67XGwU2b0OSdd3Bg4EBkXnopPj6+C91bdMfkvpMrTQdLwU5qp/NO1ISyBEACoK6fEgAJgARAAQBavWhuYae66pMMEH5pgy4AJnINoFWaGr+vAYwcBVdpJ/OnW1HevjNa5w1Cj44/dZUHMF5ewZEdcvHgOw8iq0EWMtIysG/LFmQXFWHf6NHI7toVh44fwrfHvsX0gdPRNrNt1EWlYCe1k/h+TbYhABIAdf2XAEgAJAASAJO+CUR1gS4AKpC1SytjFu2S7AK2SlNjVp9XU8BW5xTHRvci95Kkben45Cw0nP8EKuoC6SeBI2PuwSd3TTJ9/+PVZ5dX8Dc/vhbzS+aj43kdQye8GAHw9JnT2P7tdkzpPwVdcrqcbUJZGUqXL8eYsWMxf9485A0aBLRoYTo6EwDPykIAJAASAGMU8DKS4WVdZgN2dUWAJO2Q2Pi9DdLnC1JbpYODXzTxAgBVxCteWhmzvH2SPIDFu5dg28712GhIU5PIPIBW5xQbfd0KFI396sWxd5I1hf1aX4yy78qQ3TBbHAE8UzgVO+dMj8Jp7vgHUatwmmsAdHKyiPQ98ZsdAZAAqOuTjAAyAsgIICOAKRMBjEx5JuIkkI2b3sYfxt+M++Y8j+6XXhFav5ZIeLYCNiMAWoGiTi5LqwigZFdxZtoxNG/UHF8c/AIXZ1+M8pKS6BRwVpcu+Hjfx5XWAEZ2C3+wfgnO3boZpzt3w497DtPaLezkZBHdj2iyyhMACYC6vkcAJAAm9CMWz0ET+fH0U4RVGp1MhJ1kgEhGP5i11asIYKTNdpG9RNmZae5WY4kmTiJ7Uk2sAFCaV3BMlzF467O3UH60HPX3VCB34d+xc+RQHGuZHlobGNkFHN0tfLQcDfZ8h9yFb2HnyCtxtGWjSnaxmkqmgJ2cLCJ5R/xoQwAkAOr6JQGQAEgAZAQw5SKAqkulsOO1XXUDYCLaqhMBVLkR1QaPY6eOoWhbEYrXLUba2jU40bsAvXuNwKhOo0IpYFSUNrpbWBApjOgqAUBlK7XT/YgmqzwBkACo63sEQAIgAZAASAC89krMeu0tXNC5a8gbdKZPUxkAJWsA1c7oSIoXBXnLX5iLnMn3YO+jT2DQTeOiqV/UFDF3C7v/hBMACYDuvedsSQIgAZAASAAkAPoQAK1On5FOn+tArHQXsFVewdiTPt5/+GGceOABpD30EPrcf3/0bSvZW4IZK2dwt7DLrzgBkADo0nWixQiABEACIAGQAOhDALQ6fSbZAKjuXykP4Ob12N2tJ9p1vgK9Wl1ZJa/gieH90e3zz7D5gouQtnhl9G1TibsdRwALC4FpMbuDp04F1H8zuTgFrIsH/i9/jv8f0ddPSAAkABIACYAEQB8CoNXpM34AQPUMVieBGCOP+5+ejYvmzMRn4+9F09vvjr5tlU4Mka4BLCvDjueeQ7spU7Bjxgy0u+WWwOYLZASQEUBduiQAEgAJgARAAqAPATDe4G6ELK83skgSS6vnk9iZ2UQgNnYXcLzdwrFalM6fj7yxY1GqEkaPGWMpEyOAunjg//KMAOr1EQGQAEgAJAASAAmAld4CCdh5AYCqjlAewDi7hY2vJwHwrCKMADICqId/3ARiqZ/bvF1WFfq5Pj8/m9l0l47GQWqrdHDwiyaSnHfJjIpJo2xmz5hojSXPZrWpxOzZqhMAI9PJVruFCYDmXk8AJABKx3grO0YAGQFkBJARQEYAAxABtNpU4gcADEUChVO7UjtOAevigf/LcwpYr48IgARAAiABMOUA0Em0SxI9Uy4itfNzBPDAvr3Rx2uSnYMmzXJM3//qjgASAJ1/yBkBZATQuddULkEAJAASAAmAKQeATqJdUrCT2vkVAJ0snajxAFhWhtLlyzFm7FjMV5tFBg2y3C2s+xFNVnkCIAFQ1/cIgARAAiABMOUA0CqFik5yZAJgiyp+IgHFeLuAY1896dSuyM5BvkDdj2iyyhMACYC6vkcAJAASAAmAKQeATqJdUrCT2jEC+AMo1vtmL8689jKumjMTb46/F7Wu/QWON8sJSaRgPGEA6CBfoO5HNFnlCYAEQF3fIwASAAmABEACYAA2gTiBYklkT9VnZ5f35B+R96fHo7cuvWMCSu/6rSkAWh0ZZ3xuUQTQwaYS3Y9ossoTAAmAur5HACQAEgAJgARAAmClt8AO7JSxVXQvdppd2ZQXr8bsSXfh7llPIiu/b6UI4OuflEXva3VkXEIjhbpf0CSWJwASAHXdjwBIACQAEgAJgDUYAJ3seJZOT0sA0Cq6JzmlJDIFHAuAVkfGEQDNP1IEQAIgATBGAS+TrXpZl9lg52Q6Rjpou7VjW6sqlyqaSAYIv7TV60TQkvfBCTz5dQ2gkx3PEk0kU7uRCKBZdM8tACZls4jkBfGpDQGQAKjrmowAMgLICCAjgIGNADqBJ78CoJMdz14CoKrLThMrwDb7o5gA6OxzTgAkADrzmKrWBEACIAGQABhYAHQCT3awE28wTkaUVeeekilgCQBaAbYOAHq9WUT3I5qs8gRAAqCu7xEACYAEQAJg0gHQSaRIZ/mEDhQRAKvmAbTTxAqwnQKg3WYR4zpBVb9kt3BZWRnUv8jVokULqH814SIAEgB1/ZQASAAkABIAkw6ATiJFBMCzOfRioUg6tSu18yoC6CQqGm8K2G6ziFsALCwsxLRp06KPOXXqVKj/VhMuAiABUNdPCYAEQNHHxOwvdp0PcbLqk3w4k/VsibivZIDwgyZOIkU6fqfTVrtolxPY0WmD1E/ctlWS3iXy/F5qIgVAKzg1aiqNAC5fvhxjx47FvHnzMGjQIEYAJYOGT2zO8clz1NTHIAASAAmAjADaRpOk0OF3O7dQpNrlJez4GQCl6V281iQZAIgafGYwI4CMAOqCJwGQAEgAJAASAA0+YARFJ+lizIYUHfB0W5/be1olb9Y5R1nShqQAYA0+M5gASAAkAMYo4HbAkwxO1fEXe7KiJ17qlqw2SO8bpLZKB4cgaeK2rU7SxfhlPHHbVieRPS+jokkBwBp8ZjABkAAoHeOt7BgBZASQEUBGABkBFEQAD+zbG7Vqkp2DJs1yfP3uBB0AUz1dDAGQAEgAZAQwrg9IPgISG2mEze92QWqrdHAIkiZsa1Wv0Jna9VME0Lgr2uxsYbe7haXvUnXaEQAJgLr+xgggI4C+jmJ4DZRBAgDp4BAkTdjW4ACg2dnCBEDpqFAz7LgLWK+fCIAEQAIgp4A5BWwzBWw1zPoZKHWeTRrZk9pJ1kXqrgE0RgC9TBej95lNTGlGABkB1PUsAiABkABIACQAEgArKSABOy93RlvlHjSbAZAmqSYA6uKB/8szAqjXRwRAAiABkABIACQAOgZAL3dGW+Ue9AsA+vHIOEYAGQHUwz+AAEgAJAASAAmABEDHAGh1eoubqWer3IN+AcA/TpyIlx5/PKrRDRMm4LePPab7/dUqTwAkAGo5EAiAlvq5GcTidYaf6/Pzs5l9AHTWYwWprdLBIUiasK1VvUJnF7CZj7nV2Gza2S8AWDFxItJjALBiwgSkGwBQGiWU2tm9vwRAAqCdj9j9zgggI4CMADICyAggI4COI4A6f4iZgZ2fAVAdGbfjuefQbsoU7JgxA+1uuQVo0aKSBNIoodTO7uNNACQA2vmI3e8EQAIgAZAASAAkAAYWAL1KGC2JEiqRpXZ2H28CIAHQzkfsficAEgAJgARAAiABsEYAoNVuYbNpZ+luYc8SRguihCGRpXY2X28CIAHQDvDsficAEgAJgARAAiABsEYAoNVuYR0A9DJhdOn8+cgbOxal8+Yhb8wYy++v1C7eB5wASAC0Azy73wmABEACIAGQAEgAjCpw5p9nsHHT2/jD+Jtx35zn0f3SK1DrnFoJHyckawDr7i3DJ2vfwLOPFeLWiYXo2PtqnMxpUeXZVBu+XPgs/uWxQvzfxEK0GXmraRvi2cW6hLJb/sJc5Ey+B3sffQKDbhoXqs+pjbKX1BWx23loJypOVCA9LR25jXMr3ZMAmHoAeAeASQCaA9gM4C4A6+JQ3M8AzADQFsCnACYDWGxHfTG/EwAJgAkf2K380e1uQZ36knFP9bzJuq9kLEjWsyXjvsm4Z7L6301bvzr0KYp3L8G2neuxcc0ydC8YjE65PZHfahjG9eqf0D8U7ABQ+mwRuy+2LkOrLeuxu2tPtO08uEob7Owi707pvlIUbStC8brFSCtegxP5BcjvNRyjO41GXnZeyExi48ZuW/k2HD99HPXOrYdOWZ0q3ZMAmFoAeD2AFwH8GkAxgLsBKMDrCOAbk4G8D4BVAO4D8CaAXwD4HYDuAD6SDPxMA2OtkpvBM57mfq7Pz8/m9cczSG0VjgFJg9Nk9EUy7um1D0vrc9pWBUSLtj+Lwyf2o86xulj1ehH6XTMap+qfREZaU8weeh9K92faupXT+6oKrU4VibR17rqVomeLtOHI4T3I2nkQXdZtQEmvHijPzUTDjJbRNsS2tfHXR9Bz2QqsHzwAh5o3jLZVwZ0Cu7nFc1F+tBxNinej95/fx9pf9cGB/FbIapCFcfnjQnrY2RjrarDnO+QufAs7R16Joy0bResy2rVu3BoN6zTEkVNHsOvQrkp2BMDUAkAFfesB3Bl+w1R8eReAJwE8YvLW/QVAQwBXxfy2FsCmMETavqgEQAKg9GOSKnZuPk41FewlA0Cq9Ku0HUHqfydt/du23SgqfQqf7y9B64wOOFj+DZa+Oh9DrhuDzKxm2HV4O0Z2KED77BurTH0a/czJfSPn91qdKqLqHtUhB79ccL/ts12YNRb/u+0/Q3Y/+qQczdarT+rZ65ue+fiwY1aoDbF2qq2nPt2GbkuXYPOQYajTvlO0rUa7fpNfQP8v92Nlm6ZY9ehNIbur2vfB1vIK7DjwUUg3M5sLm3bF8z+ZjplrZuLDsg9xcfbFOLR4Jbo9uwKbbx2AxsP74+N9H6N7i+6YVDApapf7fVMc37cv2oZ62dnYWXt/yG5y38n4ruI7NG7cWP2u/p/D0vc9lexS5Si4ugCOAvgpgAUxHfQCAPUn109MOm0nAJWafHbMb9PU+wKgm0UnpwFQ/yJXOoCvDh06hLfLjtj6hZsXu6Z+PNnWqj2XCpqkQhuksGP7QocNgqQJ22r+Xv/XhnV4pWQW0tOaoEGddBzYtzcKgE2yc3D0VAWa1DuOy3LvRLNGreO6lhuNrU4VUTfqnnMCNy+YZPtsHbOuwdJ/zA/ZZZyshaO7d2Lt0iXoPWQYGrTKxeG6Z0JtiLVTbT25vTQKgHU75EXbarSr/c5aDHnpPSy94TJ8P7B3yK5OrXLsqTiO7Ibnh3Qzs6k4cQAPDRqPB5bPibah773PR2Fy9cybQ3VF7J7Z8Ewo0rf/5dfR7qMvkfsPYOeFwI5L2qDJz0fj22PfYvrA6WhaqykBUDrI+dyuJYDdAC4D8H7Ms84EcDmAfJPnPwngJgAvx/z2GwBTAeRYtLcw/HulnxUAZmSo5YC8qAAVoAJUIGgKlOwtwYyVM9DxvI6oXas21GkVzzzzDG677Ta0aNECp8+cxvZvt2NK/ynoktOlWuWRPtv1na/HX7b+xbYNRrt9W7Ygu6gI+0aPRnbXrtG2SuzW716Pf+KfyG+VH9JNWtfBRe9GI4CZIy43veeBj0uR9/AiFHxzHGua1UPp/SOQeXGnaD+0qd+GAFitnpi4m1kB4CwA/QD0FgKg2kQyJbyJxOxpLSOABMDEdS5rpgJUgAr4WYEvDn6BB995MBR5ykjLqAKAh44fikae2maqPYfVd0mf7bYetyESPYvXBqOdEdoibZXYfXnoy5AQShN1T526VGQv9p6MANr7GKeAnU0BGxWN7gImANo7Gy2oABWgAqmogEpN8sjqR6Jr1L7++utoBLB58+bRNWpq7Zkx/Umi9ZA+W+z6ObXOzqoNRrvykpJoBDCrSxfT9XiqPjO7S5tfGmr+pq83hdb26dTFNYDOPSlVAFC1XK1YVSlfVOoXdalNIGqd31NxNoE0AHB1jGzvAdjiZhMIAdC587EEFaACVCBVFIjd8Vr3eF289PxLuOHmG3Cy3slKu0+T0V7ps7mxq7+nArkL/46dI4fiWMt0yx25VnZKj9Au4P27kL5jP1q++z72XN4HFe2aIqtp69BOYePuXuk9z884Hw3rNsSRk0fw1eGvuAvY4HypBICRNDC3hUFQpYG5DkAnAHvDKWLUOkGV9kVdar3gynDuv0UAfg7g927TwBAAkzGs8Z5UgApQAf8oEMln994n72HR3xdhxNARKOhYgFGdRkVz3iXraaXP5tQulN9v7Rqc6F2A3r1GVGlrqL71L+KD9Utw7kebcfqSbujZczhG9fxlpTyArzz+b9ixbQ2OnwvUOw1c0KkA10/4f5V0q5Qv0O6e24oQmwcwLyuv0rMxDUxqpYFR75VKARNJBK3SuagkQ5G97CsAfAHg5pgXUOUJfCgmEfS9bhNBEwCTNazxvlSAClAB/yigplwXr1mMq6+9Gm+89gaGFwyv9mlfKzWkzya227Mba557HJlPPY6Dd05AwS0TUKtlqyq3PzRxAg78+QlU1AXSTwJNfnUPGj+mknD8cM2cOAEv/PkJnKkL1DoJ3PSre3CvwUZZ8yQQ73w9lSKA3qkir4lrAOVa0ZIKUAEqEAgFNm7ciB49emDDhg3o3l2dLeCfS/psIrvCQmCayp4WvqZOBdR/M1x/nDgRLz3+A/DdMGECfvvYY5Ws1M5p9S9yqd3T6p/x2rtpEz565hm0fPpp7Ln9dlxy223IufTsWkInFyOAqRcBdNL/XtgSAL1QkXVQASpABVJIARE8Jam90meT2CkY2/Dmm3hgyhQ8NGMGelx1lSmMSeFOIsmKAQMw4N13o6YrLr8cA1aoCT5nFwGQAOjMY6paEwB1FWR5KkAFqECKKSCBp2Q1WfpsErvCwkJMi4kATp06Feq/JfJS0Ll/69boLZp27swIoEvBOQXsUrhwMQKgnn4sTQWoABVIOQUk8JSsRkufTWLnZWSvuvVgBJARQF2fIwDqKsjyVIAKUIEUU0ACT8lqsuTZFNgtX74cY8eOxbx58zBo0CDT9XjJaoMX9yUAEgB1/YgAqKsgy1MBKkAFUkwBCWQlq8mSZ0vG1G5160EAJADq+hwBUFdBlqcCVIAKpJgCEshKRpOlkb2aPLUr1ZUASACU+oqVHQFQV0GWpwJUgAqkmAJ+BcAgRPakrkQAJABKfYUAqKsUy1MBKkAFAqKAXwEwCJE9qYsRAAmAUl8hAOoqxfJUgApQgYAo4FcADIj8omYSAAmAIkeJY8QpYF0FWZ4KUAEqkGIKEAD936EEQAKgrpcSAHUVZHkqQAWoQIopQAD0f4cSAAmAul5KANRVkOWpABWgAimmAAHQ/x1KACQA6nopAVBXQZanAlSACqSYAgRA/3coAZAAqOulBEBdBVmeClABKpBiChAA/d+hBEACoK6XEgB1FWR5KkAFqEAKKSBNtpxCTa6RTSEAEgB1HZcAqKsgy1MBKkAFUkgBJluuGZ1JACQA6noqAVBXQZanAlSACqSQAky2XDM6kwBIANT1VAKgroIsTwWoABWgAlSgmhUgABIAdV2OAKirIMtTASpABagAFahmBQiABEBdlyMA6irI8lSAClABKkAFqlkBAiABUNflCIC6CrI8FaACVIAKUIFqVoAASADUdTkCoK6CLE8FqAAVoAJUoJoVIAASAHVdjgCoqyDLUwEqQAWoABWoZgUIgARAXZcjAOoqyPJUgApQASpABapZAQIgAVDX5QiAugqyPBWgAlSAClCBalaAAEgA1HU5AqCugixPBagAFaACVKCaFSAAEgB1XY4AqKsgy1MBKkAFqAAVqGYFCIAEQF2XIwDqKsjytQMteAAACN9JREFUVIAKUAEqQAWqWQECIAFQ1+UIgLoKsjwVoAJUgApQgWpWgABIANR1OQKgroIsTwWoABWgAlSgmhUgABIAdV2OAKirIMtTASpABagAFahmBQiABEBdlwsB4K5du5CRof4nLypABagAFaACVMDvCigAbN26tXrMxgAO+/15E/F85ySi0gDV2QrAVwFqL5tKBagAFaACVCCVFDgfwO5UapC0LQRAqVLmdkq/lgAqTH5OD8Ohci6z3/XuzNJSBdgPUqUSb8e+SLzGkjuwHyQqVY8N+6J6dLa6i9J/D4B/JvcxknN3AmDidA9NDwc5vJw4aR3VzH5wJFdCjdkXCZVXXDn7QSxVwg3ZFwmXmDewUoAAmDjf4IudOG2d1Mx+cKJWYm3ZF4nVV1o7+0GqVOLt2BeJ15h3sFCAAJg41+CLnThtndTMfnCiVmJt2ReJ1VdaO/tBqlTi7dgXideYdyAAVrsPpAG4D8AfAJyo9rvzhhEF2A/+8QX2hT/6gv3gj35QT8G+8E9fBO5JGAEMXJezwVSAClABKkAFqEDQFSAABt0D2H4qQAWoABWgAlQgcAoQAAPX5WwwFaACVIAKUAEqEHQFCIBB9wC2nwpQASpABagAFQicAgTAwHU5G0wFqAAVoAJUgAoEXQECYGI84A4AkwA0B7AZwF0A1iXmVqw1rED/sOY9ALQAMBrAghh1lK9PA/BvADIBrAFwO4BPqaCnCqid79cA6ATgGID3AEwG8EnMXeoBeAzAz8O7IP8O4DcA9nr6JMGuTPm2+tc2LMNWANMBLAn/3+yD5PiHej/+A8AcAHezL5LTCbzrWQUIgN57wvUAXgTwawDF4Zf8ZwA6AvjG+9uxxrACwwAUANgI4DUTAFQQogbfmwF8DmAGgC4ALgZwnCp6psBbAF4BsB7AueGP3SVhnY+E7/I0gBHhvlCn5TwF4Ey4/zx7kIBXdDWA7wF8FtbhpvAfSD8CoGCQfVD9DtITwKsADgN4JwYA2RfV3xe8IwEwIT6goE99/O4M114LwC4ATwJ4JCF3ZKVGBdS5jrERQPWHjjrvUUWd/hg2bhyOOCkgVMDCKzEKZIf/8LkcwMrw0Yj7ANwA4G/hW6poYSmAPgDWJuYxWCuA/WEIVLqzD6rXJRqF/zhVke4HAGwKA6Aah9gX1dsXvFtYAUYAvXWFugCOAvipYfrxhfC040+8vR1rs1DACIAXAPgHABX9UANv5Ho3/H+Pp5IJU+Ci8DS7irZ+BGAQgGUAmgA4GHPXLwHMBvBEwp4kuBXXBqBmIdQ4pN4BtTSFfVC9/qC0VwB+D4AVMQDI96F6+4F3i1GAAOitO7QEsBvAZQDej6l6JgAVAcn39nasTQiAqj/Umj/VP2UxZdR0jIJFNW3Py3sFVPR7YfiPn77h6lXk73/Ca/9i76jWyKppMTVVz8sbBRR0q3FIrff7Lhx1XRz+/9kH3mgsqUWtdb0fgJoCVstNYgGQ74NEQdokRAECoLeyWgHgLAD9APT29nasTRMA/xpeJ6UGaF7eK6DWNqm1mQr+vrIBQLVsQkWlfuf9YwS2RjUjkRsG8GsB3Br+Q/RSCwhnH3jvKq0BfADgX8IbAtUdJADIvvC+L1ijQQECoLcuwSlgb/V0WxungN0q5105tbFDLXlQu7N3xFTLKS/vNHZa09vhpRB/4RSwU+lc248CUBT+QzNSiZqSV2OU2vg0FIDqFy6JcC0xC7pVgADoVjnrcmoTiJrOUqlf1KWmwXaGdzpyE4j3epvVaLUJRG0AURtB1JUR3pzATSDe9okaU9SGJ7UJZ4BJmp3IovdfhHdrq7t3CKeJ4SYQb/vCWNvy8Fik1ryqjQfsg8TqrWpPB9DGcBs1/b4NwKPhDYLsi8T3A+9gogAB0Hu3iKSBuS0MgirX03XhvGjMc+a93pEa1S47teFAXR8CmBBeU6YWXisAV2vL1PSiSoehIlIqDUxXpoHxvEP+M7zGTEX/YnP/qXQvKi+gutTU8PBwGhiVEkMBo7rUWk1e3iigcs2pnH8qA4GCELXWTL0DKuK0lH3gjcgua4mdAub74FJEFtNXgACor6FZDSoFTCQRtNp1Oi6cEzAxd2OtSgEVbVKbCIyX2n2nonyRRNAKzFUi6NXh5MPbKZ+nCqjoq9n1rwCeD/8QSUKsIlBpACKJoL/29EmCXdlzAAaHk6Ir+N4Sjjgp+FMX+yB5/mEEQPZF8voi0HcmAAa6+9l4KkAFqAAVoAJUIIgKEACD2OtsMxWgAlSAClABKhBoBQiAge5+Np4KUAEqQAWoABUIogIEwCD2OttMBagAFaACVIAKBFoBAmCgu5+NpwJUgApQASpABYKoAAEwiL3ONlMBKkAFqAAVoAKBVoAAGOjuZ+OpABWgAlSAClCBICpAAAxir7PNVIAKUAEqQAWoQKAVIAAGuvvZeCpABagAFaACVCCIChAAg9jrbDMVoAJUgApQASoQaAUIgIHufjaeClABKkAFqAAVCKICBMAg9jrbTAWoABWgAlSACgRaAQJgoLufjacCVIAKUAEqQAWCqAABMIi9zjZTASpABagAFaACgVaAABjo7mfjqQAVoAJUgApQgSAqQAAMYq+zzVSAClABKkAFqECgFSAABrr72XgqQAWoABWgAlQgiAoQAIPY62wzFaACVIAKUAEqEGgFCICB7n42ngpQASpABagAFQiiAgTAIPY620wFqAAVoAJUgAoEWgECYKC7n42nAlSAClABKkAFgqgAATCIvc42UwEqQAWoABWgAoFWgAAY6O5n46kAFaACVIAKUIEgKkAADGKvs81UgApQASpABahAoBUgAAa6+9l4KkAFqAAVoAJUIIgKEACD2OtsMxWgAlSAClABKhBoBQiAge5+Np4KUAEqQAWoABUIogIEwCD2OttMBagAFaACVIAKBFoBAmCgu5+NpwJUgApQASpABYKoAAEwiL3ONlMBKkAFqAAVoAKBVoAAGOjuZ+OpABWgAlSAClCBICpAAAxir7PNVIAKUAEqQAWoQKAV+P/yA69rrQ68TQAAAABJRU5ErkJggg==\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot results\n",
"\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('Model and uncertainties for available area (testing)')\n",
"plt.legend()\n",
"\n",
"# plt.savefig( os.path.join( TARGET_PATH, 'uncertainty_available_area_te.png'), dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": true
},
"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>index</th>\n",
" <th>target</th>\n",
" <th>prediction</th>\n",
" <th>model_std</th>\n",
" <th>data_std</th>\n",
" <th>total_std</th>\n",
" <th>data_std_rel_to_total</th>\n",
" <th>model_std_rel_to_total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5112</td>\n",
" <td>0.666185</td>\n",
" <td>0.733069</td>\n",
" <td>0.073780</td>\n",
" <td>0.044861</td>\n",
" <td>0.086348</td>\n",
" <td>0.032650</td>\n",
" <td>0.053698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3800</td>\n",
" <td>0.781004</td>\n",
" <td>0.721603</td>\n",
" <td>0.057908</td>\n",
" <td>0.019578</td>\n",
" <td>0.061128</td>\n",
" <td>0.015445</td>\n",
" <td>0.045683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2826</td>\n",
" <td>0.719743</td>\n",
" <td>0.643931</td>\n",
" <td>0.103353</td>\n",
" <td>0.083139</td>\n",
" <td>0.132643</td>\n",
" <td>0.059133</td>\n",
" <td>0.073510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6087</td>\n",
" <td>0.669936</td>\n",
" <td>0.634076</td>\n",
" <td>0.117555</td>\n",
" <td>0.048672</td>\n",
" <td>0.127233</td>\n",
" <td>0.037254</td>\n",
" <td>0.089979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6505</td>\n",
" <td>0.679335</td>\n",
" <td>0.631681</td>\n",
" <td>0.087011</td>\n",
" <td>0.013520</td>\n",
" <td>0.088055</td>\n",
" <td>0.011842</td>\n",
" <td>0.076213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7515</td>\n",
" <td>0.597130</td>\n",
" <td>0.577413</td>\n",
" <td>0.104469</td>\n",
" <td>0.061322</td>\n",
" <td>0.121137</td>\n",
" <td>0.044805</td>\n",
" <td>0.076332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>634</td>\n",
" <td>0.573574</td>\n",
" <td>0.566711</td>\n",
" <td>0.137995</td>\n",
" <td>0.075377</td>\n",
" <td>0.157240</td>\n",
" <td>0.055547</td>\n",
" <td>0.101693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3385</td>\n",
" <td>0.442386</td>\n",
" <td>0.561231</td>\n",
" <td>0.154282</td>\n",
" <td>0.059789</td>\n",
" <td>0.165462</td>\n",
" <td>0.046213</td>\n",
" <td>0.119249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>6711</td>\n",
" <td>0.711896</td>\n",
" <td>0.549734</td>\n",
" <td>0.149378</td>\n",
" <td>0.113508</td>\n",
" <td>0.187611</td>\n",
" <td>0.081006</td>\n",
" <td>0.106605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3367</td>\n",
" <td>0.645016</td>\n",
" <td>0.547473</td>\n",
" <td>0.104896</td>\n",
" <td>0.053263</td>\n",
" <td>0.117644</td>\n",
" <td>0.039619</td>\n",
" <td>0.078026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>844</td>\n",
" <td>0.533425</td>\n",
" <td>0.487654</td>\n",
" <td>0.145759</td>\n",
" <td>0.096344</td>\n",
" <td>0.174722</td>\n",
" <td>0.069530</td>\n",
" <td>0.105192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2930</td>\n",
" <td>0.424949</td>\n",
" <td>0.473441</td>\n",
" <td>0.139380</td>\n",
" <td>0.059062</td>\n",
" <td>0.151377</td>\n",
" <td>0.045054</td>\n",
" <td>0.106323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2897</td>\n",
" <td>0.386575</td>\n",
" <td>0.473012</td>\n",
" <td>0.112342</td>\n",
" <td>0.066291</td>\n",
" <td>0.130442</td>\n",
" <td>0.048407</td>\n",
" <td>0.082035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1080</td>\n",
" <td>0.583378</td>\n",
" <td>0.461423</td>\n",
" <td>0.139658</td>\n",
" <td>0.082853</td>\n",
" <td>0.162385</td>\n",
" <td>0.060465</td>\n",
" <td>0.101920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2287</td>\n",
" <td>0.299093</td>\n",
" <td>0.457730</td>\n",
" <td>0.107004</td>\n",
" <td>0.035487</td>\n",
" <td>0.112735</td>\n",
" <td>0.028076</td>\n",
" <td>0.084659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>6117</td>\n",
" <td>0.508792</td>\n",
" <td>0.454679</td>\n",
" <td>0.127980</td>\n",
" <td>0.063675</td>\n",
" <td>0.142945</td>\n",
" <td>0.047492</td>\n",
" <td>0.095454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>4188</td>\n",
" <td>0.242021</td>\n",
" <td>0.445127</td>\n",
" <td>0.138892</td>\n",
" <td>0.059460</td>\n",
" <td>0.151085</td>\n",
" <td>0.045291</td>\n",
" <td>0.105794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>928</td>\n",
" <td>0.456606</td>\n",
" <td>0.434970</td>\n",
" <td>0.156071</td>\n",
" <td>0.053742</td>\n",
" <td>0.165065</td>\n",
" <td>0.042280</td>\n",
" <td>0.122785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>4011</td>\n",
" <td>0.530514</td>\n",
" <td>0.430458</td>\n",
" <td>0.147803</td>\n",
" <td>0.067104</td>\n",
" <td>0.162322</td>\n",
" <td>0.050685</td>\n",
" <td>0.111638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5638</td>\n",
" <td>0.640336</td>\n",
" <td>0.384122</td>\n",
" <td>0.142335</td>\n",
" <td>0.066355</td>\n",
" <td>0.157042</td>\n",
" <td>0.049933</td>\n",
" <td>0.107109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>3582</td>\n",
" <td>0.239098</td>\n",
" <td>0.379863</td>\n",
" <td>0.151871</td>\n",
" <td>0.072181</td>\n",
" <td>0.168151</td>\n",
" <td>0.054172</td>\n",
" <td>0.113979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3035</td>\n",
" <td>0.324393</td>\n",
" <td>0.370557</td>\n",
" <td>0.143239</td>\n",
" <td>0.073532</td>\n",
" <td>0.161011</td>\n",
" <td>0.054617</td>\n",
" <td>0.106393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1793</td>\n",
" <td>0.407622</td>\n",
" <td>0.362650</td>\n",
" <td>0.211149</td>\n",
" <td>0.103955</td>\n",
" <td>0.235352</td>\n",
" <td>0.077644</td>\n",
" <td>0.157708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>6634</td>\n",
" <td>0.381022</td>\n",
" <td>0.352373</td>\n",
" <td>0.040915</td>\n",
" <td>0.019129</td>\n",
" <td>0.045166</td>\n",
" <td>0.014389</td>\n",
" <td>0.030777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>6028</td>\n",
" <td>0.367499</td>\n",
" <td>0.350027</td>\n",
" <td>0.110596</td>\n",
" <td>0.058262</td>\n",
" <td>0.125003</td>\n",
" <td>0.043131</td>\n",
" <td>0.081873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>4152</td>\n",
" <td>0.344037</td>\n",
" <td>0.341424</td>\n",
" <td>0.163512</td>\n",
" <td>0.082708</td>\n",
" <td>0.183239</td>\n",
" <td>0.061552</td>\n",
" <td>0.121687</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>275</td>\n",
" <td>0.269779</td>\n",
" <td>0.329494</td>\n",
" <td>0.135059</td>\n",
" <td>0.070116</td>\n",
" <td>0.152175</td>\n",
" <td>0.052004</td>\n",
" <td>0.100171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>6767</td>\n",
" <td>0.313654</td>\n",
" <td>0.327830</td>\n",
" <td>0.037650</td>\n",
" <td>0.021560</td>\n",
" <td>0.043386</td>\n",
" <td>0.015798</td>\n",
" <td>0.027588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>7290</td>\n",
" <td>0.196313</td>\n",
" <td>0.322979</td>\n",
" <td>0.126772</td>\n",
" <td>0.101140</td>\n",
" <td>0.162174</td>\n",
" <td>0.071968</td>\n",
" <td>0.090206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>6394</td>\n",
" <td>0.409478</td>\n",
" <td>0.293125</td>\n",
" <td>0.104044</td>\n",
" <td>0.042471</td>\n",
" <td>0.112378</td>\n",
" <td>0.032576</td>\n",
" <td>0.079802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>6142</td>\n",
" <td>0.318830</td>\n",
" <td>0.290986</td>\n",
" <td>0.105137</td>\n",
" <td>0.069006</td>\n",
" <td>0.125760</td>\n",
" <td>0.049834</td>\n",
" <td>0.075927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>4084</td>\n",
" <td>0.250145</td>\n",
" <td>0.290401</td>\n",
" <td>0.115978</td>\n",
" <td>0.044767</td>\n",
" <td>0.124318</td>\n",
" <td>0.034622</td>\n",
" <td>0.089696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>54</td>\n",
" <td>0.276082</td>\n",
" <td>0.276332</td>\n",
" <td>0.108295</td>\n",
" <td>0.079900</td>\n",
" <td>0.134580</td>\n",
" <td>0.057137</td>\n",
" <td>0.077443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>6278</td>\n",
" <td>0.351978</td>\n",
" <td>0.273389</td>\n",
" <td>0.116013</td>\n",
" <td>0.056655</td>\n",
" <td>0.129108</td>\n",
" <td>0.042362</td>\n",
" <td>0.086746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>2831</td>\n",
" <td>0.351116</td>\n",
" <td>0.272747</td>\n",
" <td>0.114130</td>\n",
" <td>0.063083</td>\n",
" <td>0.130404</td>\n",
" <td>0.046420</td>\n",
" <td>0.083984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>5861</td>\n",
" <td>0.039808</td>\n",
" <td>0.250852</td>\n",
" <td>0.148063</td>\n",
" <td>0.071341</td>\n",
" <td>0.164353</td>\n",
" <td>0.053441</td>\n",
" <td>0.110913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>3727</td>\n",
" <td>0.458208</td>\n",
" <td>0.228975</td>\n",
" <td>0.149321</td>\n",
" <td>0.076029</td>\n",
" <td>0.167563</td>\n",
" <td>0.056532</td>\n",
" <td>0.111030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>1699</td>\n",
" <td>0.484834</td>\n",
" <td>0.215771</td>\n",
" <td>0.126167</td>\n",
" <td>0.072383</td>\n",
" <td>0.145456</td>\n",
" <td>0.053027</td>\n",
" <td>0.092429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2087</td>\n",
" <td>0.265639</td>\n",
" <td>0.214880</td>\n",
" <td>0.118282</td>\n",
" <td>0.055026</td>\n",
" <td>0.130455</td>\n",
" <td>0.041420</td>\n",
" <td>0.089035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>4873</td>\n",
" <td>0.220159</td>\n",
" <td>0.203922</td>\n",
" <td>0.049835</td>\n",
" <td>0.029388</td>\n",
" <td>0.057855</td>\n",
" <td>0.021462</td>\n",
" <td>0.036393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>1091</td>\n",
" <td>0.318426</td>\n",
" <td>0.190137</td>\n",
" <td>0.090190</td>\n",
" <td>0.050932</td>\n",
" <td>0.103577</td>\n",
" <td>0.037382</td>\n",
" <td>0.066195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2402</td>\n",
" <td>0.198468</td>\n",
" <td>0.184025</td>\n",
" <td>0.041188</td>\n",
" <td>0.019645</td>\n",
" <td>0.045633</td>\n",
" <td>0.014737</td>\n",
" <td>0.030897</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2377</td>\n",
" <td>0.000000</td>\n",
" <td>0.160469</td>\n",
" <td>0.111370</td>\n",
" <td>0.053972</td>\n",
" <td>0.123759</td>\n",
" <td>0.040398</td>\n",
" <td>0.083361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>4525</td>\n",
" <td>0.222469</td>\n",
" <td>0.146840</td>\n",
" <td>0.097871</td>\n",
" <td>0.059508</td>\n",
" <td>0.114542</td>\n",
" <td>0.043311</td>\n",
" <td>0.071232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>2178</td>\n",
" <td>0.000000</td>\n",
" <td>0.096112</td>\n",
" <td>0.048693</td>\n",
" <td>0.035618</td>\n",
" <td>0.060329</td>\n",
" <td>0.025487</td>\n",
" <td>0.034842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>7400</td>\n",
" <td>0.094097</td>\n",
" <td>0.055160</td>\n",
" <td>0.047187</td>\n",
" <td>0.023597</td>\n",
" <td>0.052758</td>\n",
" <td>0.017588</td>\n",
" <td>0.035170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>6251</td>\n",
" <td>0.000000</td>\n",
" <td>0.035054</td>\n",
" <td>0.052793</td>\n",
" <td>0.019293</td>\n",
" <td>0.056207</td>\n",
" <td>0.015043</td>\n",
" <td>0.041164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>5656</td>\n",
" <td>0.000000</td>\n",
" <td>0.019140</td>\n",
" <td>0.062883</td>\n",
" <td>0.010455</td>\n",
" <td>0.063746</td>\n",
" <td>0.009087</td>\n",
" <td>0.054659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>229</td>\n",
" <td>0.000000</td>\n",
" <td>0.006318</td>\n",
" <td>0.022148</td>\n",
" <td>0.008202</td>\n",
" <td>0.023618</td>\n",
" <td>0.006383</td>\n",
" <td>0.017235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>318</td>\n",
" <td>0.000000</td>\n",
" <td>0.000954</td>\n",
" <td>0.009216</td>\n",
" <td>0.000000</td>\n",
" <td>0.009216</td>\n",
" <td>0.000000</td>\n",
" <td>0.009216</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index target prediction model_std data_std total_std \\\n",
"0 5112 0.666185 0.733069 0.073780 0.044861 0.086348 \n",
"1 3800 0.781004 0.721603 0.057908 0.019578 0.061128 \n",
"2 2826 0.719743 0.643931 0.103353 0.083139 0.132643 \n",
"3 6087 0.669936 0.634076 0.117555 0.048672 0.127233 \n",
"4 6505 0.679335 0.631681 0.087011 0.013520 0.088055 \n",
"5 7515 0.597130 0.577413 0.104469 0.061322 0.121137 \n",
"6 634 0.573574 0.566711 0.137995 0.075377 0.157240 \n",
"7 3385 0.442386 0.561231 0.154282 0.059789 0.165462 \n",
"8 6711 0.711896 0.549734 0.149378 0.113508 0.187611 \n",
"9 3367 0.645016 0.547473 0.104896 0.053263 0.117644 \n",
"10 844 0.533425 0.487654 0.145759 0.096344 0.174722 \n",
"11 2930 0.424949 0.473441 0.139380 0.059062 0.151377 \n",
"12 2897 0.386575 0.473012 0.112342 0.066291 0.130442 \n",
"13 1080 0.583378 0.461423 0.139658 0.082853 0.162385 \n",
"14 2287 0.299093 0.457730 0.107004 0.035487 0.112735 \n",
"15 6117 0.508792 0.454679 0.127980 0.063675 0.142945 \n",
"16 4188 0.242021 0.445127 0.138892 0.059460 0.151085 \n",
"17 928 0.456606 0.434970 0.156071 0.053742 0.165065 \n",
"18 4011 0.530514 0.430458 0.147803 0.067104 0.162322 \n",
"19 5638 0.640336 0.384122 0.142335 0.066355 0.157042 \n",
"20 3582 0.239098 0.379863 0.151871 0.072181 0.168151 \n",
"21 3035 0.324393 0.370557 0.143239 0.073532 0.161011 \n",
"22 1793 0.407622 0.362650 0.211149 0.103955 0.235352 \n",
"23 6634 0.381022 0.352373 0.040915 0.019129 0.045166 \n",
"24 6028 0.367499 0.350027 0.110596 0.058262 0.125003 \n",
"25 4152 0.344037 0.341424 0.163512 0.082708 0.183239 \n",
"26 275 0.269779 0.329494 0.135059 0.070116 0.152175 \n",
"27 6767 0.313654 0.327830 0.037650 0.021560 0.043386 \n",
"28 7290 0.196313 0.322979 0.126772 0.101140 0.162174 \n",
"29 6394 0.409478 0.293125 0.104044 0.042471 0.112378 \n",
"30 6142 0.318830 0.290986 0.105137 0.069006 0.125760 \n",
"31 4084 0.250145 0.290401 0.115978 0.044767 0.124318 \n",
"32 54 0.276082 0.276332 0.108295 0.079900 0.134580 \n",
"33 6278 0.351978 0.273389 0.116013 0.056655 0.129108 \n",
"34 2831 0.351116 0.272747 0.114130 0.063083 0.130404 \n",
"35 5861 0.039808 0.250852 0.148063 0.071341 0.164353 \n",
"36 3727 0.458208 0.228975 0.149321 0.076029 0.167563 \n",
"37 1699 0.484834 0.215771 0.126167 0.072383 0.145456 \n",
"38 2087 0.265639 0.214880 0.118282 0.055026 0.130455 \n",
"39 4873 0.220159 0.203922 0.049835 0.029388 0.057855 \n",
"40 1091 0.318426 0.190137 0.090190 0.050932 0.103577 \n",
"41 2402 0.198468 0.184025 0.041188 0.019645 0.045633 \n",
"42 2377 0.000000 0.160469 0.111370 0.053972 0.123759 \n",
"43 4525 0.222469 0.146840 0.097871 0.059508 0.114542 \n",
"44 2178 0.000000 0.096112 0.048693 0.035618 0.060329 \n",
"45 7400 0.094097 0.055160 0.047187 0.023597 0.052758 \n",
"46 6251 0.000000 0.035054 0.052793 0.019293 0.056207 \n",
"47 5656 0.000000 0.019140 0.062883 0.010455 0.063746 \n",
"48 229 0.000000 0.006318 0.022148 0.008202 0.023618 \n",
"49 318 0.000000 0.000954 0.009216 0.000000 0.009216 \n",
"\n",
" data_std_rel_to_total model_std_rel_to_total \n",
"0 0.032650 0.053698 \n",
"1 0.015445 0.045683 \n",
"2 0.059133 0.073510 \n",
"3 0.037254 0.089979 \n",
"4 0.011842 0.076213 \n",
"5 0.044805 0.076332 \n",
"6 0.055547 0.101693 \n",
"7 0.046213 0.119249 \n",
"8 0.081006 0.106605 \n",
"9 0.039619 0.078026 \n",
"10 0.069530 0.105192 \n",
"11 0.045054 0.106323 \n",
"12 0.048407 0.082035 \n",
"13 0.060465 0.101920 \n",
"14 0.028076 0.084659 \n",
"15 0.047492 0.095454 \n",
"16 0.045291 0.105794 \n",
"17 0.042280 0.122785 \n",
"18 0.050685 0.111638 \n",
"19 0.049933 0.107109 \n",
"20 0.054172 0.113979 \n",
"21 0.054617 0.106393 \n",
"22 0.077644 0.157708 \n",
"23 0.014389 0.030777 \n",
"24 0.043131 0.081873 \n",
"25 0.061552 0.121687 \n",
"26 0.052004 0.100171 \n",
"27 0.015798 0.027588 \n",
"28 0.071968 0.090206 \n",
"29 0.032576 0.079802 \n",
"30 0.049834 0.075927 \n",
"31 0.034622 0.089696 \n",
"32 0.057137 0.077443 \n",
"33 0.042362 0.086746 \n",
"34 0.046420 0.083984 \n",
"35 0.053441 0.110913 \n",
"36 0.056532 0.111030 \n",
"37 0.053027 0.092429 \n",
"38 0.041420 0.089035 \n",
"39 0.021462 0.036393 \n",
"40 0.037382 0.066195 \n",
"41 0.014737 0.030897 \n",
"42 0.040398 0.083361 \n",
"43 0.043311 0.071232 \n",
"44 0.025487 0.034842 \n",
"45 0.017588 0.035170 \n",
"46 0.015043 0.041164 \n",
"47 0.009087 0.054659 \n",
"48 0.006383 0.017235 \n",
"49 0.000000 0.009216 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_sample"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training without uncertainty (Part 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF ALL ESTIMATORS - using cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training KNN\n",
"\n",
"CPU time: 1.77 seconds\n",
"Wall clock time: 69.90 seconds\n",
"Cross-validation MSE: 0.023915\n",
"Cross-validation RMSE: 0.154645\n",
"Cross-validation MAE: 0.118106\n",
"Cross-validation MBE: -0.000674\n",
"Cross-validation R2: 0.499095\n",
"\n",
"Training LIN\n",
"\n",
"CPU time: 3.04 seconds\n",
"Wall clock time: 14.20 seconds\n",
"Cross-validation MSE: 0.018058\n",
"Cross-validation RMSE: 0.134382\n",
"Cross-validation MAE: 0.105491\n",
"Cross-validation MBE: 0.000008\n",
"Cross-validation R2: 0.621762\n",
"\n",
"Training RF\n",
"\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-1ec8c3e3db2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mtt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mest_CV\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# cannot use n_jobs = -1 as not all are scikit-learn estimators\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mtt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_predict\u001b[0;34m(estimator, X, y, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method)\u001b[0m\n\u001b[1;32m 678\u001b[0m prediction_blocks = parallel(delayed(_fit_and_predict)(\n\u001b[1;32m 679\u001b[0m clone(estimator), X, y, train, test, verbose, fit_params, method)\n\u001b[0;32m--> 680\u001b[0;31m for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m 681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 682\u001b[0m \u001b[0;31m# Concatenate the predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 780\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 626\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 586\u001b[0m \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 589\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 112\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_predict\u001b[0;34m(estimator, X, y, train, test, verbose, fit_params, method)\u001b[0m\n\u001b[1;32m 751\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 752\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 753\u001b[0;31m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 754\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 327\u001b[0m verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 328\u001b[0;31m for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m 329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 787\u001b[0m \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 790\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 697\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 601\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 603\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 604\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 598\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 601\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"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",
" \n",
"for name, RF in estimator.items():\n",
" print('\\nTraining %s\\n' %name)\n",
"\n",
" est_CV = estimator[name]\n",
" \n",
" tt = time.time()\n",
" y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" print('Finished in %.2fs' %(time.time()-tt))\n",
"\n",
" err_cv[name] = get_errors(t, y.reshape((-1,)), label = 'Cross-validation')\n",
" \n",
"err_cv = pd.concat([err_base, err_cv], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>base</th>\n",
" <th>MBE+</th>\n",
" <th>KNN</th>\n",
" <th>LIN</th>\n",
" <th>RF</th>\n",
" <th>XGB</th>\n",
" <th>ELM</th>\n",
" <th>MLP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MSE</th>\n",
" <td>0.052496</td>\n",
" <td>0.022751</td>\n",
" <td>0.024183</td>\n",
" <td>1.805268e-02</td>\n",
" <td>0.014735</td>\n",
" <td>0.015186</td>\n",
" <td>0.017579</td>\n",
" <td>0.022509</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RMSE</th>\n",
" <td>0.229120</td>\n",
" <td>0.150834</td>\n",
" <td>0.155509</td>\n",
" <td>1.343602e-01</td>\n",
" <td>0.121387</td>\n",
" <td>0.123233</td>\n",
" <td>0.132585</td>\n",
" <td>0.150029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAE</th>\n",
" <td>0.171266</td>\n",
" <td>0.122387</td>\n",
" <td>0.118705</td>\n",
" <td>1.054718e-01</td>\n",
" <td>0.091532</td>\n",
" <td>0.092057</td>\n",
" <td>0.103028</td>\n",
" <td>0.117700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MBE</th>\n",
" <td>-0.170315</td>\n",
" <td>-0.004303</td>\n",
" <td>-0.000424</td>\n",
" <td>-9.113220e-07</td>\n",
" <td>0.002513</td>\n",
" <td>0.000883</td>\n",
" <td>0.000073</td>\n",
" <td>-0.003906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>R2</th>\n",
" <td>-0.099542</td>\n",
" <td>0.523476</td>\n",
" <td>0.493477</td>\n",
" <td>6.218820e-01</td>\n",
" <td>0.691376</td>\n",
" <td>0.681918</td>\n",
" <td>0.631807</td>\n",
" <td>0.528548</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" base MBE+ KNN LIN RF XGB \\\n",
"MSE 0.052496 0.022751 0.024183 1.805268e-02 0.014735 0.015186 \n",
"RMSE 0.229120 0.150834 0.155509 1.343602e-01 0.121387 0.123233 \n",
"MAE 0.171266 0.122387 0.118705 1.054718e-01 0.091532 0.092057 \n",
"MBE -0.170315 -0.004303 -0.000424 -9.113220e-07 0.002513 0.000883 \n",
"R2 -0.099542 0.523476 0.493477 6.218820e-01 0.691376 0.681918 \n",
"\n",
" ELM MLP \n",
"MSE 0.017579 0.022509 \n",
"RMSE 0.132585 0.150029 \n",
"MAE 0.103028 0.117700 \n",
"MBE 0.000073 -0.003906 \n",
"R2 0.631807 0.528548 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"err_cv"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# err_cv.to_csv(\"error_stats_panels_cv.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF ALL ESTIMATORS: multiple times, simple validation"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Current iteration: 0\n",
"\n",
"Training KNN\n",
"\n",
"CPU time: 0.03 seconds\n",
"Wall clock time: 0.03 seconds\n",
"\n",
"Training MSE: 0.015845\n",
"\n",
"Validation MSE: 0.024554\n",
"Validation RMSE: 0.156698\n",
"Validation MAE: 0.119903\n",
"Validation MBE: -0.000967\n",
"Validation R2: 0.486188\n",
"\n",
"Training LIN\n",
"\n",
"CPU time: 0.01 seconds\n",
"Wall clock time: 0.01 seconds\n",
"\n",
"Training MSE: 0.018004\n",
"\n",
"Validation MSE: 0.018218\n",
"Validation RMSE: 0.134974\n",
"Validation MAE: 0.106054\n",
"Validation MBE: -0.001470\n",
"Validation R2: 0.618778\n",
"\n",
"Training RF\n",
"\n",
"CPU time: 80.86 seconds\n",
"Wall clock time: 21.62 seconds\n",
"\n",
"Training MSE: 0.002038\n",
"\n",
"Validation MSE: 0.015032\n",
"Validation RMSE: 0.122605\n",
"Validation MAE: 0.092920\n",
"Validation MBE: 0.001716\n",
"Validation R2: 0.685448\n",
"\n",
"Training XGB\n",
"\n",
"CPU time: 12.91 seconds\n",
"Wall clock time: 12.95 seconds\n",
"\n",
"Training MSE: 0.003628\n",
"\n",
"Validation MSE: 0.015367\n",
"Validation RMSE: 0.123964\n",
"Validation MAE: 0.093454\n",
"Validation MBE: -0.000113\n",
"Validation R2: 0.678435\n",
"\n",
"Training ELM\n",
"\n",
"CPU time: 5.02 seconds\n",
"Wall clock time: 2.40 seconds\n",
"\n",
"Training MSE: 0.017343\n",
"\n",
"Validation MSE: 0.017628\n",
"Validation RMSE: 0.132772\n",
"Validation MAE: 0.103703\n",
"Validation MBE: -0.001046\n",
"Validation R2: 0.631111\n",
"\n",
"Training MLP\n",
"\n",
"CPU time: 88.62 seconds\n",
"Wall clock time: 35.06 seconds\n",
"\n",
"Training MSE: 0.019094\n",
"\n",
"Validation MSE: 0.019689\n",
"Validation RMSE: 0.140319\n",
"Validation MAE: 0.109282\n",
"Validation MBE: 0.000616\n",
"Validation R2: 0.587987\n",
"Current iteration: 1\n",
"\n",
"Training KNN\n",
"\n",
"CPU time: 0.03 seconds\n",
"Wall clock time: 0.02 seconds\n",
"\n",
"Training MSE: 0.015787\n",
"\n",
"Validation MSE: 0.024071\n",
"Validation RMSE: 0.155149\n",
"Validation MAE: 0.118085\n",
"Validation MBE: 0.000395\n",
"Validation R2: 0.490574\n",
"\n",
"Training LIN\n",
"\n",
"CPU time: 0.01 seconds\n",
"Wall clock time: 0.01 seconds\n",
"\n",
"Training MSE: 0.018061\n",
"\n",
"Validation MSE: 0.017984\n",
"Validation RMSE: 0.134105\n",
"Validation MAE: 0.105458\n",
"Validation MBE: 0.000720\n",
"Validation R2: 0.619396\n",
"\n",
"Training RF\n",
"\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-29-3e7c883816e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mtt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mRF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_tr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt_tr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0mtt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 327\u001b[0m verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 328\u001b[0;31m for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m 329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 787\u001b[0m \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 790\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 697\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# testing all estimators multiple times\n",
"err_df = pd.DataFrame(data = np.zeros((len(error), len(estimator))), index = error.keys(), columns = estimator.keys())\n",
"N_ITER = 10\n",
"\n",
"for i in range(N_ITER):\n",
" print(\"Current iteration: %d\" %i)\n",
" \n",
" # create a new random split and test it\n",
" training, validation = train_test_split( learning_table, test_size = float(1 / k_CV) )\n",
" x_tr = norm.transform( training.loc[ : , feature_names ].values )\n",
" t_tr = training.loc[ : , label_name ].values.reshape((-1,))\n",
" \n",
" estimator = set_estimators()\n",
" \n",
" for name, RF in estimator.items():\n",
" print('\\nTraining %s\\n' %name)\n",
"\n",
" tt = time.time()\n",
" RF.fit(x_tr,t_tr)\n",
" print('Finished in %.2fs' %(time.time()-tt))\n",
"\n",
" y_tr = RF.predict(x_tr)\n",
" print( '\\nTraining MSE: %f\\n' %mse(y_tr,t_tr))\n",
"\n",
" x_val = norm.transform( validation.loc[ : , feature_names ] )\n",
" t_val = validation.loc[ : , label_name].values\n",
"\n",
" y_val = RF.predict(x_val)\n",
"\n",
" err_df[name] = err_df[name] + get_errors(t_val, y_val, label = 'Validation')\n",
" \n",
"err_df = err_df / N_ITER\n",
"err_df = pd.concat([err_base, err_df])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'err_df' 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-18-22218743ae02>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0merr_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'err_df' is not defined"
]
}
],
"source": [
"err_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cross-validation (alternative to fitting and prediction):"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [],
"source": [
"x_crossval = learning_table.loc[ ind , feature_names ]\n",
"t_crossval = learning_table.loc[ ind , label_name ]"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 1.59 seconds\n",
"Wall clock time: 1390.94 seconds\n"
]
}
],
"source": [
"tt = time.time()\n",
"RF_crossval = RandomForestRegressor(n_estimators = n_est, n_jobs = -1)\n",
" \n",
"y_crossval = cross_val_predict(RF_crossval, x_crossval, t_crossval, cv=5, n_jobs=-1)\n",
"mse_crossval = mse(y_crossval, t_crossval)\n",
"\n",
"print('Finished in %.2fs' %(time.time()-tt))"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0190154455691502"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mse_crossval"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation MSE (shading): 0.031725\n",
"Validation MSE (panelling): 0.006306\n",
"Validation MSE (combined): 0.011516\n"
]
}
],
"source": [
"# FOR MULTIPLE TARGETS (shaded_area_ratio_tgt, panelled_area_ratio_tgt)\n",
"print( 'Validation MSE (shading): %f' %mse(y_crossval[:,0],t_crossval.iloc[:,0]))\n",
"print( 'Validation MSE (panelling): %f' %mse(y_crossval[:,1],t_crossval.iloc[:,1]))\n",
"print( 'Validation MSE (combined): %f' %mse((1 - y_crossval[:,0]) * y_crossval[:,1],( 1 - t_crossval.iloc[:,0]) * t_crossval.iloc[:,1]))"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation RMSE (shading): 0.178115\n",
"Validation RMSE (panelling): 0.079411\n",
"Validation RMSE (combined): 0.107312\n"
]
}
],
"source": [
"# FOR MULTIPLE TARGETS (shaded_area_ratio_tgt, panelled_area_ratio_tgt)\n",
"print( 'Validation RMSE (shading): %f' %np.sqrt(mse(y_crossval[:,0],t_crossval.iloc[:,0])))\n",
"print( 'Validation RMSE (panelling): %f' %np.sqrt(mse(y_crossval[:,1],t_crossval.iloc[:,1])))\n",
"print( 'Validation RMSE (combined): %f' %np.sqrt(mse((1 - y_crossval[:,0]) * y_crossval[:,1],( 1 - t_crossval.iloc[:,0]) * t_crossval.iloc[:,1])))"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation MAE (shading): 0.118970\n",
"Validation MAE (panelling): 0.046132\n",
"Validation MAE (combined): 0.077618\n"
]
}
],
"source": [
"# FOR MULTIPLE TARGETS (shaded_area_ratio_tgt, panelled_area_ratio_tgt)\n",
"print( 'Validation MAE (shading): %f' %mae(y_crossval[:,0],t_crossval.iloc[:,0]))\n",
"print( 'Validation MAE (panelling): %f' %mae(y_crossval[:,1],t_crossval.iloc[:,1]))\n",
"print( 'Validation MAE (combined): %f' %mae((1 - y_crossval[:,0]) * y_crossval[:,1],( 1 - t_crossval.iloc[:,0]) * t_crossval.iloc[:,1]))"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation MBE (shading): 0.006775\n",
"Validation MBE (panelling): -0.002807\n",
"Validation MBE (combined): -0.005909\n"
]
}
],
"source": [
"# FOR MULTIPLE TARGETS (shaded_area_ratio_tgt, panelled_area_ratio_tgt)\n",
"print( 'Validation MBE (shading): %f' %np.mean(y_crossval[:,0]-t_crossval.iloc[:,0]))\n",
"print( 'Validation MBE (panelling): %f' %np.mean(y_crossval[:,1]-t_crossval.iloc[:,1]))\n",
"print( 'Validation MBE (combined): %f' %np.mean((1 - y_crossval[:,0]) * y_crossval[:,1]-( 1 - t_crossval.iloc[:,0]) * t_crossval.iloc[:,1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Validation with leaving features out"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"shuffle = np.random.permutation(len(learning_table))\n",
"shuffled_table = learning_table.loc[ shuffle , :]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"fixed_features = ['fully_shaded_ratio_2m', 'NEIGUNG', 'perim_ratio', 'AUSRICHTUN']\n",
"maybe_features = ['GAREA', 'SHAPE_Leng','GBAUP', 'GASTW'] # TAKEN OUT FLAECHE (lowest marginal improvement)\n",
"test_features = np.hstack([fixed_features, maybe_features])\n",
"\n",
"T = shuffled_table.loc[ : , label_name ]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Leaving out GAREA: 0.026506\n",
"Leaving out SHAPE_Leng: 0.026597\n",
"Leaving out GBAUP: 0.026666\n",
"Leaving out GASTW: 0.026386\n"
]
}
],
"source": [
"MSE_leaveout = np.zeros(len(maybe_features))\n",
"n_est = 500\n",
"tree_depth = 100\n",
"\n",
"for drop_feature in maybe_features:\n",
" # leave one test feature out\n",
" X = shuffled_table.loc[ : , test_features ].drop(drop_feature, axis=1)\n",
" \n",
" RF_test = RandomForestRegressor(n_jobs = -1) # n_estimators = n_est, max_depth = tree_depth, \n",
" Y = cross_val_predict(RF_test, X, T, cv=5, n_jobs=-1)\n",
" \n",
" print(\"Leaving out %s: %f\" %(drop_feature, mse(Y, T)))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cross-validation and feature selection"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"n_crossval = 10\n",
"all_features_sorted = np.asarray(all_features)[ind]\n",
"MSE = np.zeros(n_features)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"RF_test = RandomForestRegressor(n_estimators = n_est, max_depth = tree_depth, n_jobs = -1)\n",
"Y = cross_val_predict(RF_test, X1, T, cv=5, n_jobs=-1)\n",
"print(mse(Y, T))"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration 1 out of 14\n",
"CPU time: 0.37 seconds\n",
"Wall clock time: 134.33 seconds\n",
"Iteration 2 out of 14\n",
"CPU time: 0.43 seconds\n",
"Wall clock time: 142.81 seconds\n",
"Iteration 3 out of 14\n",
"CPU time: 0.42 seconds\n",
"Wall clock time: 128.43 seconds\n",
"Iteration 4 out of 14\n",
"CPU time: 0.36 seconds\n",
"Wall clock time: 115.09 seconds\n",
"Iteration 5 out of 14\n",
"CPU time: 0.34 seconds\n",
"Wall clock time: 110.06 seconds\n",
"Iteration 6 out of 14\n",
"CPU time: 0.25 seconds\n",
"Wall clock time: 104.81 seconds\n",
"Iteration 7 out of 14\n",
"CPU time: 0.42 seconds\n",
"Wall clock time: 95.86 seconds\n",
"Iteration 8 out of 14\n",
"CPU time: 0.23 seconds\n",
"Wall clock time: 88.49 seconds\n",
"Iteration 9 out of 14\n",
"CPU time: 0.20 seconds\n",
"Wall clock time: 72.49 seconds\n",
"Iteration 10 out of 14\n",
"CPU time: 0.19 seconds\n",
"Wall clock time: 56.79 seconds\n",
"Iteration 11 out of 14\n",
"CPU time: 0.18 seconds\n",
"Wall clock time: 42.77 seconds\n",
"Iteration 12 out of 14\n",
"CPU time: 0.17 seconds\n",
"Wall clock time: 36.34 seconds\n",
"Iteration 13 out of 14\n",
"CPU time: 0.15 seconds\n",
"Wall clock time: 10.90 seconds\n",
"Iteration 14 out of 14\n",
"CPU time: 0.15 seconds\n",
"Wall clock time: 5.84 seconds\n"
]
}
],
"source": [
"shuffle = np.random.permutation(len(learning_table))\n",
"shuffled_table = learning_table.loc[ shuffle , :]\n",
"\n",
"tt = time.time()\n",
"for i in range(n_features):\n",
" \n",
" print('Iteration %d out of %d' %(i+1, n_features))\n",
"\n",
" X = shuffled_table.loc[ : , all_features_sorted[ i : ] ]\n",
" t_cv = shuffled_table.loc[ : , label_name ]\n",
"\n",
" RF_CV = RandomForestRegressor(n_estimators = n_est, max_depth = tree_depth, n_jobs = -1)\n",
" y_CV = cross_val_predict(RF_CV, X, t_cv, cv=5, n_jobs=-1)\n",
"\n",
" MSE[i] = mse(y_CV, t_cv)\n",
" \n",
" tt.restart()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualisation"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" 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"
}
],
"source": [
"plt.figure()\n",
"plt.bar(range(n_features,0,-1), MSE)\n",
"plt.title('Mean-squared error for different numbers of features')\n",
"plt.xlabel('Number of features')\n",
"plt.ylabel('Mean-squared error')\n",
"plt.show()\n",
"plt.savefig('MSE_varying_features.png', dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.02356106 0.02355203 0.02354303 0.0235818 0.02361018 0.02373792\n",
" 0.02427768 0.02517083 0.02510396 0.02596514 0.02861912 0.03549382\n",
" 0.03489173 0.03438738]\n"
]
}
],
"source": [
"print(MSE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(min(MSE)) # --> 10 features minimum, nearly the same error with 8 features"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" 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",
"bins=20\n",
"plt.hist(learning_table.available_area_ratio,bins=bins, range=(0,1), density=True, facecolor='r', alpha=0.2, edgecolor='r', label = 'Available area ratio')\n",
"plt.hist(y, bins=bins, range=(0,1), density=True, facecolor='b', alpha=0.2, edgecolor='b', label = 'training')\n",
"plt.hist(y_val, bins=bins, range=(0,1), density=True, facecolor='g', alpha=0.2, edgecolor='g', label = 'RF - validation')\n",
"plt.legend()\n",
"plt.title('Distribution of predicted data')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot random sample\n",
"Plot original value and corrected value for different roof IDs (random)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"validation['prediction'] = ( 1 - y_val[:,0] ) * y_val[:,1]\n",
"validation_sample = validation.sample(30)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DF_UID', 'FLAECHE', 'NEIGUNG', 'AUSRICHTUNG', 'XCOORD', 'YCOORD',\n",
" 'Shape_Length', 'Shape_Area', 'GWR_EGID', 'GBAUP', 'GKAT', 'GAREA',\n",
" 'GASTW', 'GKODX', 'GKODY', 'Shape_Ratio', 'n_neighbors_100',\n",
" 'Estim_build_area', 'n_corners', 'panel_tilt', 'best_align',\n",
" 'panelled_area_ratio_ftr', 'panelled_area_ratio_tgt',\n",
" 'shaded_area_ratio_ftr', 'shaded_area_ratio_tgt', 'tilt_cat',\n",
" 'orientation_cat', 'available_area_ratio', 'prediction'],\n",
" dtype='object')"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validation_sample.columns"
]
},
{
"cell_type": "code",
"execution_count": 62,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" 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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"output_type": "display_data"
},
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Text(0.5,1,'Performance of available area ratio estimation')"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(range(len(validation_sample)), (1-validation_sample.shaded_area_ratio_ftr) * validation_sample.panelled_area_ratio_ftr, facecolor='w',edgecolor='r', alpha=0.7, label = 'Approx. ratio')\n",
"plt.scatter(range(len(validation_sample)), validation_sample.available_area_ratio,facecolor='g', alpha=0.5, edgecolor='g', label = 'Target ratio')\n",
"plt.scatter(range(len(validation_sample)), validation_sample.prediction,facecolor='b', alpha=0.5, edgecolor='b', label = 'Corrected ratio')\n",
"plt.legend()\n",
"plt.xlabel('Rooftop sample (random)')\n",
"plt.ylabel('Available area ratio')\n",
"plt.title('Performance of available area ratio estimation')\n",
"# plt.savefig('random_sample_available_area.png', dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" 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": [
"<div id='d61e12df-2c70-4f85-8686-5501feddc104'></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Text(0,0.5,'orientation')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure(figsize=(10,3))\n",
"\n",
"plt.subplot(131)\n",
"var = horizon_50cm\n",
"plt.scatter(var.NEIGUNG, var.AUSRICHTUN, c=(var.fully_shaded_ratio), s=2)\n",
"plt.xlabel('tilt')\n",
"plt.ylabel('orientation')\n",
"\n",
"plt.subplot(132)\n",
"var = x_crossval\n",
"plt.scatter(var.NEIGUNG, var.AUSRICHTUN, c=(y_crossval), s=3)\n",
"plt.xlabel('tilt')\n",
"plt.ylabel('orientation')\n",
"\n",
"plt.subplot(133)\n",
"var = horizon_2m\n",
"plt.scatter(var.NEIGUNG, var.AUSRICHTUN, c=(var.fully_shaded_ratio), s=2)\n",
"# plt.colorbar()\n",
"plt.xlabel('tilt')\n",
"plt.ylabel('orientation')"
]
},
{
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Text(0,0.5,'orientation')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var = horizon_2m\n",
"plt.figure()\n",
"plt.scatter(var.NEIGUNG, var.AUSRICHTUN, c=(var.fully_shaded_ratio), s=3)\n",
"#var.mean_06_15h.hist(bins=100, range=(0,1))\n",
"plt.colorbar()\n",
"plt.xlabel('tilt')\n",
"plt.ylabel('orientation')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Text(0,0.5,'orientation')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var = x_crossval\n",
"plt.figure()\n",
"plt.scatter(var.NEIGUNG, var.AUSRICHTUN, c=(y_crossval), s=3)\n",
"#var.mean_06_15h.hist(bins=100, range=(0,1))\n",
"plt.colorbar()\n",
"plt.xlabel('tilt')\n",
"plt.ylabel('orientation')"
]
},
{
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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