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diff --git a/Available_area/ML_panelled_area_onlySP.ipynb b/Available_area/ML_panelled_area_onlySP.ipynb
index 8e28949..78e4b8a 100644
--- a/Available_area/ML_panelled_area_onlySP.ipynb
+++ b/Available_area/ML_panelled_area_onlySP.ipynb
@@ -1,9847 +1,10169 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+ " from ._conv import register_converters as _register_converters\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import xarray as xr\n",
"import os\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 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.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": false
},
"outputs": [
{
"data": {
"text/plain": [
"37729"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(learning_table)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# randomly suffle entries in table\n",
"learning_table = learning_table.sample(len(learning_table))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# SPLIT DATA INTO TRAINING & TESTING\n",
"learning_table, testing_table = train_test_split(learning_table, test_size = 0.2)"
]
},
{
"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": 60,
+ "execution_count": 11,
"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",
" 'NEIGUNG', 'AUSRICHTUNG', 'FLAECHE', 'Shape_Length', 'Shape_Ratio', 'n_corners', 'panel_tilt',\n",
" 'GASTW', 'GBAUP', 'GKAT', 'GAREA', 'n_neighbors_100'] # \n",
"\n",
"feature_names = all_features # ['FLAECHE', 'panelled_area_ratio_ftr', 'Shape_Ratio', 'NEIGUNG', 'panel_tilt']"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Baseline MSE: 0.052319\n",
- "Baseline RMSE: 0.228734\n",
- "Baseline MAE: 0.170976\n",
- "Baseline MBE: -0.170122\n",
- "Baseline R2: -0.092586\n"
+ "Baseline MSE: 0.052152\n",
+ "Baseline RMSE: 0.228368\n",
+ "Baseline MAE: 0.170817\n",
+ "Baseline MBE: -0.169870\n",
+ "Baseline R2: -0.090969\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": 62,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"err_base = pd.DataFrame( data = baseline_error, columns = ['base'] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TRAINING & VALIDATION"
]
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"X = learning_table.loc[ : , feature_names ].values\n",
"t = learning_table.loc[ : , label_name ].values.reshape((-1,))"
]
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 15,
"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": 43,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Bias correction MSE: 0.022659\n",
- "Bias correction RMSE: 0.150531\n",
- "Bias correction MAE: 0.122135\n",
- "Bias correction MBE: -0.004245\n",
- "Bias correction R2: 0.526801\n"
+ "Bias correction MSE: 0.022563\n",
+ "Bias correction RMSE: 0.150209\n",
+ "Bias correction MAE: 0.121761\n",
+ "Bias correction MBE: -0.004281\n",
+ "Bias correction R2: 0.528009\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
+ " warnings.warn(CV_WARNING, FutureWarning)\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": 44,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"err_base['MBE+'] = mbe_error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine Learning models"
]
},
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# ML MODEL PARAMETERS\n",
"n_est = 1000\n",
"tree_depth = 10\n",
"n_samples_leaf = 3\n",
"ELM_nodes = 100 # 100\n",
"ELM_est = 30\n",
"MLP_layers = (500, 400, 200)\n",
"m_RF = 3\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_estimators = n_est, min_samples_leaf = n_samples_leaf, max_features = m_RF, n_jobs = -1 ),\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": [
"# UNCERTAINTY\n",
"Assuming that model selection has been performed and primary model was accepted"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 302.95 seconds\n",
"Wall clock time: 303.42 seconds\n"
]
}
],
"source": [
"# select and fit model estimator\n",
"estimator = set_estimators()\n",
"est_unc = estimator[ 'RF_unc' ]\n",
"\n",
"tt = util.Timer()\n",
"est_unc.fit(x, t)\n",
"tt.stop()\n",
"\n",
"prediction, model_std = est_unc.predict_model_uncertainty(x)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=3, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n",
" oob_score=False, random_state=None, verbose=0, warm_start=False)"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"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 = util.Timer()\n",
"residuals, data_std = est_unc.crossval_residuals(x, t, cv = 5, model_std = model_std)\n",
"tt.stop()\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 = util.Timer()\n",
"residuals = est_unc.fit_residuals( x, t, model_std )\n",
"tt.stop()"
]
},
{
"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": {
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" 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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\" 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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF SINGLE ESTIMATOR - simple validation"
]
},
{
"cell_type": "code",
- "execution_count": 86,
+ "execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU time: 31.10 seconds\n",
- "Wall clock time: 31.34 seconds\n",
+ "CPU time: 66.36 seconds\n",
+ "Wall clock time: 263.30 seconds\n",
"\n",
- "Training MSE: 0.005157\n",
+ "Training MSE: 0.005065\n",
"\n",
- "Validation MSE: 0.014736\n",
- "Validation RMSE: 0.121393\n",
- "Validation MAE: 0.091981\n",
- "Validation MBE: 0.001263\n",
- "Validation R2: 0.687267\n"
+ "Validation MSE: 0.015243\n",
+ "Validation RMSE: 0.123464\n",
+ "Validation MAE: 0.093713\n",
+ "Validation MBE: 0.001723\n",
+ "Validation R2: 0.681449\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['RF']\n",
"\n",
"tt = util.Timer()\n",
"RF.fit(x_tr,t_tr)\n",
"tt.stop()\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": 36,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ERROR:root:Internal Python error in the inspect module.\n",
"Below is the traceback from this internal error.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n",
" File \"<ipython-input-36-0a0a518aaad2>\", line 5, in <module>\n",
" y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\", line 680, in cross_val_predict\n",
" for train, test in cv.split(X, y, groups))\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\", line 779, in __call__\n",
" while self.dispatch_one_batch(iterator):\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\", line 625, in dispatch_one_batch\n",
" self._dispatch(tasks)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\", line 588, in _dispatch\n",
" job = self._backend.apply_async(batch, callback=cb)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\", line 111, in apply_async\n",
" result = ImmediateResult(func)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\", line 332, in __init__\n",
" self.results = batch()\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\", line 131, in __call__\n",
" return [func(*args, **kwargs) for func, args, kwargs in self.items]\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\", line 131, in <listcomp>\n",
" return [func(*args, **kwargs) for func, args, kwargs in self.items]\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\", line 755, in _fit_and_predict\n",
" predictions = func(X_test)\n",
" File \"/home/walch/code/energy-potential/libs/ELM_ensemble.py\", line 115, in predict\n",
" y_pred_tmp = model.predict(x)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/hpelm/elm.py\", line 382, in predict\n",
" Y = self.nnet._predict(X)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/hpelm/nnets/slfn.py\", line 133, in _predict\n",
" H = self._project(X)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/hpelm/nnets/slfn.py\", line 119, in _project\n",
" return np.hstack([self.func[ftype](X, W, B) for _, ftype, W, B in self.neurons])\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/hpelm/nnets/slfn.py\", line 119, in <listcomp>\n",
" return np.hstack([self.func[ftype](X, W, B) for _, ftype, W, B in self.neurons])\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/hpelm/nnets/slfn.py\", line 62, in <lambda>\n",
" self.func[\"sigm\"] = lambda X, W, B: 1 / (1 + np.exp(np.dot(X, W) + B))\n",
"KeyboardInterrupt\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2018, in showtraceback\n",
" stb = value._render_traceback_()\n",
"AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/IPython/core/ultratb.py\", line 1095, in get_records\n",
" return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/IPython/core/ultratb.py\", line 313, in wrapped\n",
" return f(*args, **kwargs)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/IPython/core/ultratb.py\", line 347, in _fixed_getinnerframes\n",
" records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/inspect.py\", line 1453, in getinnerframes\n",
" frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/inspect.py\", line 1411, in getframeinfo\n",
" filename = getsourcefile(frame) or getfile(frame)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/inspect.py\", line 666, in getsourcefile\n",
" if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/inspect.py\", line 703, in getmodule\n",
" if ismodule(module) and hasattr(module, '__file__'):\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/py/_vendored_packages/apipkg.py\", line 195, in __getattribute__\n",
" return getattr(getmod(), name)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/py/_vendored_packages/apipkg.py\", line 179, in getmod\n",
" x = importobj(modpath, None)\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/py/_vendored_packages/apipkg.py\", line 69, in importobj\n",
" module = __import__(modpath, None, None, ['__doc__'])\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/pytest.py\", line 28, in <module>\n",
" from _pytest.python_api import approx, raises\n",
" File \"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/_pytest/python_api.py\", line 7, in <module>\n",
" from more_itertools.more import always_iterable\n",
" File \"<frozen importlib._bootstrap>\", line 961, in _find_and_load\n",
" File \"<frozen importlib._bootstrap>\", line 946, in _find_and_load_unlocked\n",
" File \"<frozen importlib._bootstrap>\", line 885, in _find_spec\n",
" File \"<frozen importlib._bootstrap_external>\", line 1157, in find_spec\n",
" File \"<frozen importlib._bootstrap_external>\", line 1129, in _get_spec\n",
" File \"<frozen importlib._bootstrap_external>\", line 1260, in find_spec\n",
" File \"<frozen importlib._bootstrap_external>\", line 96, in _path_isfile\n",
" File \"<frozen importlib._bootstrap_external>\", line 88, in _path_is_mode_type\n",
" File \"<frozen importlib._bootstrap_external>\", line 82, in _path_stat\n",
"KeyboardInterrupt\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------\u001b[0m"
]
}
],
"source": [
"estimator = set_estimators()\n",
"est_CV = estimator['ELM']\n",
"\n",
"tt = util.Timer()\n",
"y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
"tt.stop()\n",
"\n",
"CV_err = get_errors(t, y.reshape((-1,)), label = 'Cross-validation')"
]
},
{
"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",
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"\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 = util.Timer()\n",
" y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" tt.stop()\n",
"\n",
" 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 = util.Timer()\n",
" RF.fit(x_tr,t_tr)\n",
" tt.stop()\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": [
"### Feature importance (all features)"
]
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 34,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU time: 66.93 seconds\n",
+ "Wall clock time: 67.79 seconds\n",
+ "\n",
+ "Training MSE: 0.005151\n",
+ "\n",
+ "Validation MSE: 0.014490\n",
+ "Validation RMSE: 0.120375\n",
+ "Validation MAE: 0.091125\n",
+ "Validation MBE: 0.000914\n",
+ "Validation R2: 0.695224\n",
+ "CPU time: 66.89 seconds\n",
+ "Wall clock time: 68.10 seconds\n",
+ "\n",
+ "Training MSE: 0.005107\n",
+ "\n",
+ "Validation MSE: 0.014884\n",
+ "Validation RMSE: 0.122001\n",
+ "Validation MAE: 0.092786\n",
+ "Validation MBE: 0.000814\n",
+ "Validation R2: 0.686291\n",
+ "CPU time: 67.13 seconds\n",
+ "Wall clock time: 67.82 seconds\n",
+ "\n",
+ "Training MSE: 0.005084\n",
+ "\n",
+ "Validation MSE: 0.015148\n",
+ "Validation RMSE: 0.123075\n",
+ "Validation MAE: 0.093172\n",
+ "Validation MBE: 0.000173\n",
+ "Validation R2: 0.683058\n",
+ "CPU time: 66.98 seconds\n",
+ "Wall clock time: 70.27 seconds\n",
+ "\n",
+ "Training MSE: 0.005176\n",
+ "\n",
+ "Validation MSE: 0.014222\n",
+ "Validation RMSE: 0.119254\n",
+ "Validation MAE: 0.090867\n",
+ "Validation MBE: -0.000092\n",
+ "Validation R2: 0.703652\n",
+ "CPU time: 67.23 seconds\n",
+ "Wall clock time: 69.77 seconds\n",
+ "\n",
+ "Training MSE: 0.005110\n",
+ "\n",
+ "Validation MSE: 0.014839\n",
+ "Validation RMSE: 0.121814\n",
+ "Validation MAE: 0.092525\n",
+ "Validation MBE: 0.003970\n",
+ "Validation R2: 0.691759\n",
+ "CPU time: 67.30 seconds\n",
+ "Wall clock time: 67.67 seconds\n",
+ "\n",
+ "Training MSE: 0.005134\n",
+ "\n",
+ "Validation MSE: 0.014581\n",
+ "Validation RMSE: 0.120751\n",
+ "Validation MAE: 0.091447\n",
+ "Validation MBE: 0.003203\n",
+ "Validation R2: 0.692142\n",
+ "CPU time: 67.00 seconds\n",
+ "Wall clock time: 67.11 seconds\n",
+ "\n",
+ "Training MSE: 0.005102\n",
+ "\n",
+ "Validation MSE: 0.014994\n",
+ "Validation RMSE: 0.122449\n",
+ "Validation MAE: 0.092382\n",
+ "Validation MBE: 0.001996\n",
+ "Validation R2: 0.679936\n",
+ "CPU time: 67.02 seconds\n",
+ "Wall clock time: 67.07 seconds\n",
+ "\n",
+ "Training MSE: 0.005090\n",
+ "\n",
+ "Validation MSE: 0.015228\n",
+ "Validation RMSE: 0.123401\n",
+ "Validation MAE: 0.093246\n",
+ "Validation MBE: -0.001322\n",
+ "Validation R2: 0.678359\n",
+ "CPU time: 67.07 seconds\n",
+ "Wall clock time: 67.15 seconds\n",
+ "\n",
+ "Training MSE: 0.005027\n",
+ "\n",
+ "Validation MSE: 0.015737\n",
+ "Validation RMSE: 0.125449\n",
+ "Validation MAE: 0.094835\n",
+ "Validation MBE: -0.002426\n",
+ "Validation R2: 0.666357\n",
+ "CPU time: 67.22 seconds\n",
+ "Wall clock time: 67.26 seconds\n",
+ "\n",
+ "Training MSE: 0.005116\n",
+ "\n",
+ "Validation MSE: 0.014810\n",
+ "Validation RMSE: 0.121697\n",
+ "Validation MAE: 0.092225\n",
+ "Validation MBE: 0.000776\n",
+ "Validation R2: 0.687679\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:40: RuntimeWarning: invalid value encountered in true_divide\n"
+ ]
+ }
+ ],
"source": [
- "# for uncertainty class\n",
+ "N_TESTS = 10\n",
"n_features = x.shape[1]\n",
- "importance = np.mean([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
- "std = np.std([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
- "ind = np.argsort(importance)[-n_features:]"
+ "\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 = util.Timer()\n",
+ " RF.fit(x_tr,t_tr)\n",
+ " tt.stop()\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": 59,
+ "execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
- "# for standard estimators\n",
"n_features = x.shape[1]\n",
"importance = np.mean([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"std = np.std([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"ind = np.argsort(importance)[-n_features:]"
]
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "importance = importance_sum/N_TESTS\n",
+ "std = std_sum / N_TESTS\n",
+ "ind = np.argsort(importance)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 0.2443196 0.06991045 0.02858856 0.32131008 0.03394023 0.07713111\n",
- " 0.02102188 0.07043492 0.01793954 0.02330312 0.0340633 0.02735653\n",
- " 0.03068067]\n",
- "[ 0.06281097 0.05549367 0.00235276 0.07968281 0.00421037 0.07804187\n",
- " 0.00180922 0.03090784 0.0017357 0.00239004 0.0026466 0.00277835\n",
- " 0.00241116]\n",
- "[ 8 6 9 11 2 12 4 10 1 7 5 0 3]\n"
+ "[0.1959617 0.08937452 0.03061263 0.17047249 0.11040428 0.16103018\n",
+ " 0.0255799 0.08836116 0.01780337 0.02783123 0.02940941 0.02834111\n",
+ " 0.02481802]\n",
+ "[0.07816953 0.05086127 0.01646425 0.12353627 0.09821654 0.11427837\n",
+ " 0.01320574 0.03744614 0.00490672 0.0049492 0.003387 0.00425592\n",
+ " 0.00228393]\n",
+ "[ 8 12 6 9 11 10 2 7 1 4 5 3 0]\n"
]
}
],
"source": [
"print(importance)\n",
"print(std)\n",
"print(ind)"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
- "label_dict = {'panelled_area_ratio_ftr' : '$C_{pv}^{raw}$',\n",
+ "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": 62,
+ "execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"ftr_names = [label_dict[feature] for feature in feature_names]"
]
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame(data = importance, index = ftr_names, columns = ['Importance'])\n",
+ "df['Std'] = std"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "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>0.195962</td>\n",
+ " <td>0.078170</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof area</th>\n",
+ " <td>0.170472</td>\n",
+ " <td>0.123536</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof shape</th>\n",
+ " <td>0.161030</td>\n",
+ " <td>0.114278</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof length</th>\n",
+ " <td>0.110404</td>\n",
+ " <td>0.098217</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof tilt</th>\n",
+ " <td>0.089375</td>\n",
+ " <td>0.050861</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Panel tilt</th>\n",
+ " <td>0.088361</td>\n",
+ " <td>0.037446</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof aspect</th>\n",
+ " <td>0.030613</td>\n",
+ " <td>0.016464</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. type</th>\n",
+ " <td>0.029409</td>\n",
+ " <td>0.003387</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. area</th>\n",
+ " <td>0.028341</td>\n",
+ " <td>0.004256</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. period</th>\n",
+ " <td>0.027831</td>\n",
+ " <td>0.004949</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof corners</th>\n",
+ " <td>0.025580</td>\n",
+ " <td>0.013206</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. density</th>\n",
+ " <td>0.024818</td>\n",
+ " <td>0.002284</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. floors</th>\n",
+ " <td>0.017803</td>\n",
+ " <td>0.004907</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Importance Std\n",
+ "$C_{pv}^{F}$ 0.195962 0.078170\n",
+ "Roof area 0.170472 0.123536\n",
+ "Roof shape 0.161030 0.114278\n",
+ "Roof length 0.110404 0.098217\n",
+ "Roof tilt 0.089375 0.050861\n",
+ "Panel tilt 0.088361 0.037446\n",
+ "Roof aspect 0.030613 0.016464\n",
+ "Build. type 0.029409 0.003387\n",
+ "Build. area 0.028341 0.004256\n",
+ "Build. period 0.027831 0.004949\n",
+ "Roof corners 0.025580 0.013206\n",
+ "Build. density 0.024818 0.002284\n",
+ "Build. floors 0.017803 0.004907"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sorted = df.sort_values('Importance', ascending = False)\n",
+ "df_sorted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_sorted.to_csv('ftr_importance_Cpv_normFtr')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
"metadata": {
"scrolled": false
},
"outputs": [
{
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"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",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" 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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],\n",
- " color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\")\n",
- "plt.yticks(range(n_features), np.asarray(ftr_names)[ind])\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.xlim([0, 0.42])\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)"
+ "# 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>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# raw data\n",
"plt.figure()\n",
"bins=25\n",
"plt.hist(av_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": [
"### 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 = util.Timer()\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",
"tt.stop()"
]
},
{
"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 = util.Timer()\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"
],
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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": "3.6.4"
+ "version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
diff --git a/Available_area/ML_shaded_area_only.ipynb b/Available_area/ML_shaded_area_only.ipynb
index c375e6b..90e968a 100644
--- a/Available_area/ML_shaded_area_only.ipynb
+++ b/Available_area/ML_shaded_area_only.ipynb
@@ -1,9569 +1,9753 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+ " from ._conv import register_converters as _register_converters\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import xarray as xr\n",
"import os\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 xgboost import XGBRegressor\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"
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib import rc"
]
},
{
"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' # '/work/hyenergy/output/solar_potential/geographic_potential/available_area' # \n",
"TARGET_PATH = '/scratch/walch/workspace_available_area'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fp = os.path.join( DATA_PATH, 'TRN_shaded_area.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', 'shaded_area_ratio_tgt',\n",
" 'shaded_area_ratio_ftr', 'tilt_cat', 'orientation_cat'],\n",
" dtype='object')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learning_table.columns"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"185794"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(learning_table)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# randomly suffle entries in table\n",
"learning_table = learning_table.sample(len(learning_table))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# SPLIT DATA INTO TRAINING & TESTING\n",
"learning_table, testing_table = train_test_split(learning_table, test_size = 0.2)"
]
},
{
"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": 11,
"metadata": {},
"outputs": [],
"source": [
"label_name = ['shaded_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 = ['shaded_area_ratio_ftr', \n",
" 'NEIGUNG', 'AUSRICHTUNG', 'FLAECHE', 'Shape_Length', 'Shape_Ratio', \n",
- " 'GBAUP', 'GKAT', 'GAREA', 'n_neighbors_100'] # 'GASTW', 'n_corners',\n",
+ " 'GBAUP', 'GKAT', 'GAREA', 'n_neighbors_100', 'GASTW', 'n_corners',] # \n",
"\n",
"feature_names = all_features"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Baseline MSE: 0.052377\n",
- "Baseline RMSE: 0.228860\n",
- "Baseline MAE: 0.125782\n",
- "Baseline MBE: 0.089304\n",
- "Baseline R2: 0.125102\n"
+ "Baseline MSE: 0.051936\n",
+ "Baseline RMSE: 0.227894\n",
+ "Baseline MAE: 0.125210\n",
+ "Baseline MBE: 0.088791\n",
+ "Baseline R2: 0.125177\n"
]
}
],
"source": [
"# Baseline mse:\n",
"baseline_error = get_errors( learning_table.shaded_area_ratio_tgt, learning_table.shaded_area_ratio_ftr, 'Baseline' )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"err_base = pd.DataFrame( data = baseline_error, columns = ['base'] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TRAINING & VALIDATION"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"X = learning_table.loc[ : , feature_names ].values\n",
"t = learning_table.loc[ : , label_name ].values.reshape((-1,))"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 13,
"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": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Bias correction MSE: 0.044378\n",
- "Bias correction RMSE: 0.210661\n",
- "Bias correction MAE: 0.139047\n",
- "Bias correction MBE: 0.000075\n",
- "Bias correction R2: 0.258716\n"
+ "Bias correction MSE: 0.044074\n",
+ "Bias correction RMSE: 0.209938\n",
+ "Bias correction MAE: 0.138377\n",
+ "Bias correction MBE: 0.000072\n",
+ "Bias correction R2: 0.259610\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
+ " warnings.warn(CV_WARNING, FutureWarning)\n"
]
}
],
"source": [
"ftr_idx = learning_table.loc[ : , feature_names ].columns.get_loc('shaded_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": 17,
"metadata": {},
"outputs": [],
"source": [
"err_base['MBE+'] = mbe_error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine Learning models"
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# ML MODEL PARAMETERS\n",
"n_est = 500\n",
"tree_depth = 10\n",
"n_samples_leaf = 5\n",
"ELM_nodes = 100\n",
"ELM_est = 200\n",
"MLP_layers = (400, 200, 100)\n",
"RF_m = 3\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",
" 'ELM': ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes),\n",
" 'RF' : RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, n_jobs = -1, max_features = RF_m ),\n",
"# 'XGB': XGBRegressor( n_estimators = n_est, max_depth = tree_depth, n_jobs = -1 ),\n",
" 'MLP': MLPRegressor( hidden_layer_sizes = MLP_layers ),\n",
" \n",
" 'RF_unc' : Uncertainty( RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, n_jobs = -1, max_features = RF_m )),\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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF SINGLE ESTIMATOR - simple validation"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU time: 210.62 seconds\n",
- "Wall clock time: 211.28 seconds\n",
+ "CPU time: 223.30 seconds\n",
+ "Wall clock time: 224.86 seconds\n",
"\n",
- "Training MSE: 0.017800\n",
+ "Training MSE: 0.017607\n",
"\n",
- "Validation MSE: 0.032766\n",
- "Validation RMSE: 0.181013\n",
- "Validation MAE: 0.119423\n",
- "Validation MBE: -0.001710\n",
- "Validation R2: 0.454352\n"
+ "Validation MSE: 0.033002\n",
+ "Validation RMSE: 0.181666\n",
+ "Validation MAE: 0.119883\n",
+ "Validation MBE: -0.000579\n",
+ "Validation R2: 0.448880\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['RF']\n",
"\n",
"tt = util.Timer()\n",
"RF.fit(x_tr,t_tr)\n",
"tt.stop()\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": null,
"metadata": {},
"outputs": [],
"source": [
"estimator = set_estimators()\n",
"est_CV = estimator['ELM_unc']\n",
"\n",
"tt = util.Timer()\n",
"y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
"tt.stop()\n",
"\n",
"CV_err = get_errors(t, y.reshape((-1,)), label = 'Cross-validation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TRAINING OF ALL ESTIMATORS - using cross-validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training KNN\n",
"\n",
"CPU time: 73.76 seconds\n",
"Wall clock time: 74.43 seconds\n",
"Cross-validation MSE: 0.058383\n",
"Cross-validation RMSE: 0.241626\n",
"Cross-validation MAE: 0.158547\n",
"Cross-validation MBE: 0.008060\n",
"Cross-validation R2: 0.020748\n",
"\n",
"Training LIN\n",
"\n",
"CPU time: 1.94 seconds\n",
"Wall clock time: 2.69 seconds\n",
"Cross-validation MSE: 0.041815\n",
"Cross-validation RMSE: 0.204487\n",
"Cross-validation MAE: 0.138688\n",
"Cross-validation MBE: 0.000001\n",
"Cross-validation R2: 0.298639\n",
"\n",
"Training ELM\n",
"\n"
]
}
],
"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, est_CV in estimator.items():\n",
" print('\\nTraining %s\\n' %name)\n",
"\n",
"# est_CV = estimator[name]\n",
" \n",
" tt = util.Timer()\n",
" y = cross_val_predict(est_CV, x, t, cv = 5) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" tt.stop()\n",
"\n",
" 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": null,
"metadata": {},
"outputs": [],
"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 = util.Timer()\n",
" RF.fit(x_tr,t_tr)\n",
" tt.stop()\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": [
"# UNCERTAINTY\n",
"Assuming that model selection has been performed and primary model was accepted"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 305.95 seconds\n",
"Wall clock time: 307.99 seconds\n"
]
}
],
"source": [
"# select and fit model estimator\n",
"estimator = set_estimators()\n",
"est_unc = estimator[ 'RF_unc' ]\n",
"\n",
"tt = util.Timer()\n",
"est_unc.fit(x, t)\n",
"tt.stop()\n",
"\n",
"prediction, model_std = est_unc.predict_model_uncertainty(x)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=3, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n",
" oob_score=False, random_state=None, verbose=0, warm_start=False)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set residual estimator\n",
"n_est_residuals = 500\n",
"\n",
"est_unc.residuals.set_params( n_estimators = n_est_residuals )\n",
"# est_unc.set_residual_model( RandomForestRegressor( n_estimators = n_est_residuals, min_samples_leaf = n_samples_leaf, n_jobs = -1 ))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 147.90 seconds\n",
"Wall clock time: 148.60 seconds\n",
"Cross-validation MSE: 0.005077\n",
"Cross-validation RMSE: 0.071255\n",
"Cross-validation MAE: 0.053826\n",
"Cross-validation MBE: -0.001566\n",
"Cross-validation R2: 0.102729\n"
]
}
],
"source": [
"# model selection for residual estimator using cross-validation (FOR NOW ONLY CROSS-VALIDATION POSSIBLE)\n",
"tt = util.Timer()\n",
"residuals, data_std = est_unc.crossval_residuals(x, t, cv = 5, model_std = model_std)\n",
"tt.stop()\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 = util.Timer()\n",
"residuals = est_unc.fit_residuals( x, t, model_std )\n",
"tt.stop()"
]
},
{
"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": [
"### 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"
],
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\" 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": [
"### Feature importance (all features)"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# for uncertainty class\n",
"n_features = x.shape[1]\n",
"importance = np.mean([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"std = np.std([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"ind = np.argsort(importance)[-n_features:]"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# for standard estimators\n",
"n_features = x.shape[1]\n",
"importance = np.mean([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"std = np.std([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"ind = np.argsort(importance)[-n_features:]"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 0.41168311 0.08791411 0.0756924 0.05832483 0.05060207 0.0535861\n",
- " 0.026635 0.02147976 0.04204805 0.06718435 0.05484289 0.05000733]\n",
- "[ 0.0054518 0.00308719 0.00305331 0.00354426 0.00321403 0.00266213\n",
- " 0.00182413 0.00165687 0.00316821 0.00300424 0.00338655 0.00250139]\n",
- "[ 7 6 8 11 4 5 10 3 9 2 1 0]\n"
+ "[0.40938561 0.09988432 0.06182773 0.06130039 0.05959331 0.05412155\n",
+ " 0.04620998 0.06043008 0.05102459 0.04552899 0.02370643 0.02698702]\n",
+ "[0.02425077 0.01585789 0.00803136 0.01053544 0.00947926 0.0055208\n",
+ " 0.00354058 0.00378755 0.00371956 0.00304008 0.00382516 0.00276747]\n",
+ "[10 11 9 6 8 5 4 7 3 2 1 0]\n"
]
}
],
"source": [
"print(importance)\n",
"print(std)\n",
"print(ind)"
]
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
- "label_dict = {'shaded_area_ratio_ftr' : '$C_{sh}^{raw}$',\n",
+ "label_dict = {'shaded_area_ratio_ftr' : '$C_{sh}^{F}$',\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'}"
]
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"ftr_names = [label_dict[feature] for feature in feature_names]"
]
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame(data = importance, index = ftr_names, columns = ['Importance'])\n",
+ "df['Std'] = std"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "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>$C_{sh}^{F}$</th>\n",
+ " <td>0.409386</td>\n",
+ " <td>0.024251</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof tilt</th>\n",
+ " <td>0.099884</td>\n",
+ " <td>0.015858</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof aspect</th>\n",
+ " <td>0.061828</td>\n",
+ " <td>0.008031</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof area</th>\n",
+ " <td>0.061300</td>\n",
+ " <td>0.010535</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. type</th>\n",
+ " <td>0.060430</td>\n",
+ " <td>0.003788</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof length</th>\n",
+ " <td>0.059593</td>\n",
+ " <td>0.009479</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof shape</th>\n",
+ " <td>0.054122</td>\n",
+ " <td>0.005521</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. area</th>\n",
+ " <td>0.051025</td>\n",
+ " <td>0.003720</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. period</th>\n",
+ " <td>0.046210</td>\n",
+ " <td>0.003541</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. density</th>\n",
+ " <td>0.045529</td>\n",
+ " <td>0.003040</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Roof corners</th>\n",
+ " <td>0.026987</td>\n",
+ " <td>0.002767</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Build. floors</th>\n",
+ " <td>0.023706</td>\n",
+ " <td>0.003825</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Importance Std\n",
+ "$C_{sh}^{F}$ 0.409386 0.024251\n",
+ "Roof tilt 0.099884 0.015858\n",
+ "Roof aspect 0.061828 0.008031\n",
+ "Roof area 0.061300 0.010535\n",
+ "Build. type 0.060430 0.003788\n",
+ "Roof length 0.059593 0.009479\n",
+ "Roof shape 0.054122 0.005521\n",
+ "Build. area 0.051025 0.003720\n",
+ "Build. period 0.046210 0.003541\n",
+ "Build. density 0.045529 0.003040\n",
+ "Roof corners 0.026987 0.002767\n",
+ "Build. floors 0.023706 0.003825"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sorted = df.sort_values('Importance', ascending = False)\n",
+ "df_sorted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_sorted.to_csv('ftr_importance_Csh_normFtrs.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
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"\n",
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"\n",
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" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
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" nav_element.append(status_bar);\n",
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"}\n",
"\n",
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"\n",
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"}\n",
"\n",
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" }\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",
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" {\n",
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" break;\n",
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"}\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",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" 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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n",
+ " (prop.get_family(), self.defaultFamily[fontext]))\n"
+ ]
}
],
"source": [
+ "plt.rcParams.update({'font.size': 16, 'font.family':'serif', 'font.serif' : 'Times New Roman'}) # , \n",
+ "# rc('font',**{'family':'serif','serif':['Times New Roman']})\n",
+ "\n",
"plt.figure()\n",
"ax = plt.axes()\n",
+ "\n",
"# plt.title(\"Feature Importance (Random Forest)\")\n",
"plt.barh(range(n_features),importance[ind],\n",
" color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\")\n",
"plt.yticks(range(n_features), np.asarray(ftr_names)[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_importances_shade.pdf', dpi=300)"
+ "# plt.savefig('Feature_importances_shade.pdf', dpi=300)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"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>"
]
},
"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": [
"# raw data\n",
"plt.figure()\n",
"bins=25\n",
"plt.hist(av_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": [
"### 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 = util.Timer()\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",
"tt.stop()"
]
},
{
"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 = util.Timer()\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"
],
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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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OL700kvGZpHQFkQsoW/VU8A4bYw2uh0C8dpHRGxxaAla4A8hzBPohPHDH1he7BFtxjU70BxLwNgjiWiu80caATCClyCLBF4BAmDgh5AdaAoFan4KDk7Q+RQcljFxTYjbvjxE2gA+uFcOzhQnVnFfHBw+rrdwEiI5gCM45oqKCgMQVe8BbAwAon6cnAS4wHkiEofoCvZWVU34He59QxtxOhXL0QASwGpVh4uIGhyqc18cwMTtHsAbb7wxfA8grsHBPXJV9/TZAiD64dwDiFO1iP4BILCfDgDkpEgAEBFQRACxRA94AFzhehTAr5MaAkDkASBh3HGaGWAFCER0EuWq7nkEbGGJEgCEyBcOhQDkvN4DCDgHuCNyBojHFTz33HNPeFm/Prty4BiRRPwhgj9sYNsAQhwCqbovEVfyYHkWf/ggejx69GhzByD2kroBIJ7jtY/4fCG0wMEWLD8jmozoJvauerVH/GEGiMa444oeQDy0xxVLBMCmeIvymU2tAAGwqUeAz6cCVCDmFagPSmO+4WwgFaACVKAeBQiANA0qQAWogIsCBECaCBWgAvGmAAEw3kaU/aECVMB3BQiAvkvKCqkAFWhiBQiATTwAfDwVoAJUgApQASpABaKtAAEw2orzeVSAClABKkAFqAAVaGIFCIBNPAB8PBWgAlSAClABKkAFoq0AATDaivN5VIAKUAEqQAWoABVoYgUIgE08AHw8FaACVIAKUAEqQAWirQAB0EJxXDCLz1fhm5bOp6EsqmNRKkAFqAAVoAJUIAoK4MJ6XMSOy/DxBaNETARAi1Ffv369+bYnExWgAlSAClABKhA8BfDZwr333jt4DfehxQRACxHxLVB8IgkGhA+rM1EBKkAFqAAVoAKxrwA+5YgATmFhobRt2zb2G6zQQgKghagwIBgOQJAAaCEki1IBKkAFqAAViKIC9N8iBEALg6MBWYjHolSAClABKkAFmkgB+m8CoJXp0YCs5GNhKkAFqAAVoAJNogD9NwHQyvBoQFbysTAVoAJUgApQgSZRgP6bAGhleF4MCEfNKysrZdeuXVbPYmEqEIsKtGjRQvbaa69YbBrbRAWoABWoVwEv/jve5eMeQIsRdjOgUCgk+fn5UlZWZvEUFqUCsasA7r/EFQqpqamx20i2jApQASpQQwE3/50IghEALUa5IQPCJdFfffWViY506NBBkpOTeVm0hdYsGnsKILq9ZcsW8wfO/vvvz0hg7A0RW0QFqEA9ChAAuQRsNTkaMqDy8nL59ttvpUePHpKSkmL1HBamArGqwI4dO2Tt2rXyi1/8Qlq1ahWrzWS7qAAVoALVFCAABgQAFy9eLFOmTJHly5ebJdX58+fLqFGj6jXnsWPHypw5c2r9vm/fvrJy5Urz81tvvVUmTJhQLU+nTp1k48aNnqeJFwCkY/QsJzMGUAHnDx3aeQAHj02mAgmsAAEwIAD42muvyXvvvScDBw6U0047zRUAcTEzIhNOwiGMgw8+WP7v//7PgJ8DgM8++6y88cYb4XzOcq3XOUEA9KoU88WrAgTAeB1Z9osKxLcCBMCAAGBVM8Smc7cIYE2zfeGFF+TUU08NL8k6AIifr1ixImIrjxgAQyGRysqIn9uogklJIsnJjSoSjcyzZ8+WK664wnyGJ9LxcLMFZ2ny448/lkMOOSQa3QrcMxAtxxhgLkSSCICRqMYyVIAKNLUCBMAEAcCTTz5ZKioqZOHChWGbQyQQy8r4lFvLli1l6NChMnHiRNlnn30822VEAAj4W7pUpLTU83OsMuJ05pAhjYbA999/X0aMGCHHHnusLFiwwKoJdRVGhLakpEQ6duxIAPRd3doV1gfDiJbjMAe+aR1JIgBGohrLUAEq0NQKEAATAACxZxAffH7qqafkzDPPDNsclpVxerFXr16yadMmueOOO+SLL74wewTbt29fp20CIvHPSc7HpOv6FnC9jhFXwixe/BOQtWypOwfQVgDnyJEijTyIMm7cOHO1x6xZs2TVqlXSvXt31bYCyBsbkY3VCCCu/8Gp72iknTt3Cu7ic0ta0VACoJvy/D0VoAKxqAABMAEAcNKkSXLPPffIhg0bGnTK27dvl3333Veuu+46ueqqq+q017oOjiBjRACYliaifWqyvFykpKTRAAgtsrOz5cMPP5S//OUvgsMzt9xyi9EE19sABm+++WYZP358WKePPvpIBg0aJF9//bWJot57773y2GOPyTfffCOZmZmCKOzkyZPD98W5LQHj2TfeeKNg+RaQgyXc++67z+wDdRIAcNq0afLiiy/K22+/LZ07dzbPOOOMM0yWuqAHMHvNNdcIDha1adNGjjvuOFNvVlZWnWO+bds2ueyyy+Sdd96RgoICYyNo129+85tw/iOOOEL69+9v7Ovxxx+Xfv36yaJFi4xdXHvttQZsAUqDBw82z8J+VCRoBVtbsmSJQPM+ffoI7PWYY46p933pgPIf//hH80cL+ohLxl9//XXz/z/77DNzHUtOTo7cf//9pr1I0Kpq+uUvf2k0q7kEjD9w0OZnnnlG8IJ02nzooYfW2SYCYCy6NraJClABNwUIgHEOgFjaQoTvV7/6lXG8bgnLnfvtt59Mnz69zqy+RgBjGAAfffRRowEg7OWXXzaHZwByDkQAoD744AMDRU7Cz7BsjH9IU6dONaDTs2dPs/fykksukaOOOsoAG5IbAL711lsG2gGVSIB4tAV3K6ZBu/9BDaK1d911l4wcOVKeeOIJA1CffvqpgamaAIho8EEHHSQXXnihjBkzxhwUuv76682XWvC8utIPP/wgTz/9tIGy9PR0eeWVV+TKK680h5KwbQAJAIgT6hdffLH8/ve/N0uqvXv3NkvogF/AM7YaPPzww6bfX375pfn5J598YuBv2LBh5goVnFxHP1evXl1vxBUAePfdd8vhhx9u+grYO/DAA+X5558344P/DZjEM9F/7HFt3ry5GcshQ4aYQ08AVMAq2lATAC+//HLB4ShEfnGFEYAagL1mzRqTv2YiALq9Ver/fWhXSCp3+78XOKl5kiTvFZ0IdOS9Z0kq0LQKEADjHAAR4TjyyCMNECBC01AC3CFactFFF4WjXW7mGdEeQGcJOIYBcPjw4Wa5HDAAOEI00IEgaIKoHMDMuefQiQoiMgbQqyvNmzfPANLWrVs9AWDNOhDlateunVnKB9A7AIgoZFVgP+yww0yUEKBZEwABRQBXRMuctH79erNFANCFPxa8pJNOOskAJkDMAUBE+6CLkwCUo0ePls2bN5s9pk7CHxiIMsPO6kqAM+iEqGNdCQCIvaoAU1wwXl/CBc3YX+nYfn1LwFUBEOAIjQGpv/3tb03ViL4C4nFgB5HBmokA6MViaucB/C1dv1RKd/q/Fzi1RaoM2XsIITCyoWGpBFGAABgQACwtLTURCKQBAwaY5UWAHSISWI684YYbjEPE8lvVdO6555qIEaIsNRMiVliWRHk4aSyfYdkODhORDy8pHgEQIARYBhjhXkQkwAiWPwFfTsKyMKJof/rTn+Q///mPHH/88SZi5yyl4mcAFSy5QieAJGABY4mlV7cIIMYEwAaQwh5NACD2bD700ENhyETEC1EztMNJiM4h6oXn14QegNu///3vWlsBAD6vvvqqnHDCCbWGHc9FhHHu3LnGxpwoMODun//8p8mPCCC+hDFz5sxweRwwgjatW7euVieijrC9v/71ryZSh7soEdmEdtAIv7/66qtN5K0+AHzyySeNXVdNWE7+85//bGwdkA0oR/2IWJ544ol1LoejfFUA/O9//2uittCt6hxAXwGGiAwTAL28GdzzlO0sk8XfLZbk5snSMsm/vcAVlRUS2h2SkT1GSkoLXkDvPhLMkagKEAADAoBOJK+moZ533nkGJODE4LSQz0mIyCByhX1QWPKrmc4++2yzDwzOEpEURI5uv/12s9/Na4pHAER0CvCCpUUnYUkTBw2whAoQQLrzzjsNFAEaoC8u0H7ppZfM77777js54IADzB7Bs846y4D6u+++a5ZHf/zxR3Pi1A0AAS2IYgGQACOIomFf20033WSiUUj1ASCWVgGONQEQgIevsgC+aibYCsC0ZgKI4R+WtLG8ijx4flJSUvjqFAAg9igij5PwjAcffLCaTTq/Q/8ByoiWIhqJSCIig4DF008/3QBl1bqqtqm+wzKwW0QyMX5dunQxAAiQd65M8hIBhG7oB8av6qEfXLqOpfa///3vBECvLweXfA4ApiWnSask/76gUl5ZLiWhEgKgT+PEauJXAQJgQAAwVk0w3gAQEai9997bQAQOR1RNuIAbewGdpUks/+Kwx7Jly8z+OCzDAqqRnnvuOfO/ES3D/jMkRFgRofIKgNjnh2VcRHGR1q1bZ6AEezmrAiCWS519hcgHSESUuK4lYMAj2oaDEgA4LwlRYiylOvADsMLyL/45d+fVBYCINAI4EbnGEmpdCUCJpXbogoToKPTHHzSNAUAcVAFQ4g8a7DtEAnDjfzsAiAhj165dzXg5+yqRr+YSMGAdh3eqLgHjKx/QHJHLmolLwF6sqHYeAmBkurEUFfBLAQIgAdDKluINAAE0iNhh+RWHFqomwBOWSavuc8NeQSwzYvkRZZzlTizBAsIAMQAoHJhwlum9AiDKIzKLCC50xv4zwAuWlasCIMAH0TYcisDSKEATy/iIiNWMegGCEOHCCVjUh7IANJx4xfJt1ain03csKQMakQfRT2w/wNIvtiA0BICImuJgCu46RPtwKATPh4aIqOF0LZZW0UYAF6KZAEFEsS+44IJGASCgFJAK4MSp7e+//94sP+PghwOAgHscYsE44oofHDrBGNc8BAJtsV8TwAvgdg6BYIyd6G9VuyAARvYKIQBGphtLUQG/FCAAEgCtbMkKAGPwHkDAGmAC+8ZqJueaF5x2da5iQZTt0ksvNXvwan57GZE6LCXjKxMAoXPOOcfk8wqAAE0clADMAUQAfohAAVCqAuDf/vY3A2KIfuEaGOzXcyKRdS17Yu8cTv5ijyAilFhezs3NNWBX86oUaIC9jwCyN9980ywfo00ALGwxaAgAURbw50QdsZyN9kELnN7Fci3ah7qxbw8winYBvmouJ1cdi/qWgHG6F1fD4LQ2YPOBBx4wS8lVv5qDk7233Xab2cuI6GBd18AA6BABxqEftJ/XwFi9IuotTADU0ZW1UgGvChAACYBebaXOfBEBYEC+BGIlDAsnjAKMAEY21ATAyHRjKSrglwIEQAKglS1FBIB4Ir8FbKU7C8eOAgTAyMaCABiZbixFBfxSgABIALSypYgB0OqpLEwFYkcBAmBkY0EAjEw3lqICfilAACQAWtkSAdBKPhaOAwUIgJENIgEwMt1Yigr4pQABkABoZUsEQCv5WDgOFCAARjaIBMDIdGMpKuCXAgRAAqCVLREAreRj4ThQgAAY2SASACPTjaWogF8KEAAJgFa2RAC0ko+F40ABAmBkg0gAjEw3lqICfilAACQAWtkSAdBKPhaOAwUIgJENIgEwMt1Yigr4pQABkABoZUsEQCv5WDgOFCAARjaIBMDIdGMpKuCXAgRAAqCVLREAreRj4ThQgAAY2SASACPTjaWogF8KEAAJgFa2FCkAJtI90D179qz2+TY3wfF5Mnxn1/lknFt+/r5pFSAARqY/ATAy3ViKCvilAAGQAGhlS5EAYBC+BIdvyDb0PdrGiIZv4LZp08Z8R9dLCoVC5vu7nTp1qvPbvF7qYJ7oKRDvABjaFZLK3ZW+CwoAzFuXJ5mtM6VVUivf6i+vLJeSUImM7DFSUlp4m3O+PZwVUYEAKUAAJABamWskAFhWJrJ4sUhyskjLllaPdy1cUfHTV+dGjhTxyF+mTjcA3LNnj+zatUuSkpJc28AM8a1APAMg4G/p+qVSurPU90EEqH297WsZ1HWQpCWn+VY/AdA3KVlRnCtAACQAWpm4DQCmpYm08u8P/zr7UV4uUlLSOAAcO3aszJkzp1p9jz32mJx//vmyYB6YaYIAACAASURBVMECuemmm+S///2vvP7669K9e3e56qqrZMmSJbJ9+3bp06ePTJo0SY455phw+ZpLwM2aNZOZM2fKK6+8Yuro2rWr3HPPPfLrX//alKm5BDx79myzhDx37lzz33Xr1snhhx8uaFN2drYpU1lZadrx+OOPy1577SXjxo2TjRs3SlFRkbzwwgtWY8zCDSsQzwDoLNMmN0+Wlkn+/rVWVF4kH+V/JDndciSjVYZvZkYA9E1KVhTnChAACYBWJh6PAAhoOuGEE6R///5y2223GX1WrlxpoO6ggw6Su+++W/bZZx/JyMiQ9evXG/gbNmyYtGrVyoAjYG716tUGDpHqAsC9995bJk+eLIceeqg8+OCD8uijj8p3330nmZmZdQLgRRddJL/85S8NXDZv3lx+97vfyYABA+TJJ580z7jzzjvl3nvvlVmzZhkIvf/+++Wpp54yewkJgFYm7lo4EQAQETo/l2khamF5oVkCJgC6mhgzUAEVBQiABEArw4pHAIQgNZeAnagcYOqUU05pULN+/frJxRdfLJdddlm9AHjzzTfL7bffbn6PyGFaWpq8+uqrkpubWycAIvq4Zs0a2XfffU2ZadOmGThFlA+pc+fOcs0115h/SFieBqQCEgmAVibuWpgA6CpRnRkIgJHpxlJUwC8FCIAEQCtbSjQARMQPS7ZOArxNmDBBXn75ZdmwYYNZit2xY4dcffXVJsKHVFcE8J///KecccYZ4Xratm1rIoFjxoypEwAvvfRSA4pOmj9/vpx22mmye/dus8yLaOSiRYtkJDY7/i+deuqp5vcEQCsTdy1MAHSViAAYmUQsRQVUFSAAEgCtDCzRALDm1SyXXHKJ2ceHZeH99ttPWrduLaeffrqJIE6dOrVeAATAjRo1Kqw9AA75sf+wvj2AhYWF4fyAutGjRwsOozgAuHjxYhkxYkQ4j/N7AqCVibsWJgC6SkQAjEwilqICqgoQAAmAVgYWrwB43HHHSe/evU1UDqm+u/kOPPBAOfPMM+XPf/6zyVdaWirY3weQixYA4rlYAr722mtN5BEJS8BYLsZVNgTAxpn47j27DVh7TQDAtWvXSpduXaRlq4YPSiQ1T5LkvZK9Vt3k+bTu6kPHuATc5MPLBiS4AgRAAqDVFIhXAMShixUrVgiWalNTU82p36OPPrrW5cyIssH540QuTvcCBAGLF1xwQVQBEIdA7rvvPvn73/8uBxxwgAHXJ554Qo466ihBtJHJmwKAv+2h7YL/ek2hipCs+26dbGu5TXbttavBYqktUmXI3kMCA4EEQK9WwHxUIHgKEAAJgFZWawOAsXwP4JdffinnnXeefPLJJ2ZPn3MNTM0lYMAfYA8ngbOysuT666+XefPmVbtEuq49gH4uAWMAsffwyiuvDF8DA4D95ptvzJUwTz/9tNUYJ1LhXbt3SWmo1MB8c2nuqesVFRUGAMvalMmepPojhxWVFRLaHQrUBcUEQE8mwExUIJAKEAAJgFaGGwkABuFLIFaixEBhHP7AdTBYnnZOG8dAs2K+CQ4A7tVsL89fYakor5B136+TyvRKkQbuBQ/i/XRBBcBNxQUyuHOOypdAWiUnSWrr4Czjx/ykYwObTAECIAHQyvgiAUA8MJG+BWwlsMfCuENw4cKF5q5ARKQeeughE7VEBBMgyORNAQJgdZ2CCIAFpSXywofLpL3sL8l7+Xt5NdTJbJMqY44ZQgj0NqWYK4YVIAASAK3MM1IAtHooC9dSAF8HOfvss+Wzzz4zBxhwifVdd91V7VoYyuauAAEw+AC4sbBQ5ublyQHpgySjdbr7oDcix46KCtmxMyTjTxgpWW35neFGSMesMagAAZAAaGWWBEAr+Vg4xhQgAMYPAA7qkCPt2/j3iTkoU7yjXBBhJADG2MRlcyJSgABIAIzIcJxCBEAr+Vg4xhQgABIAGzJJAmCMTVg2x0oBAiABUM2A4vmCXCvRWDhmFSAAEgAJgDE7PdkwnxUgABIArUyKEUAr+Vg4xhQgABIACYAxNinZHDUFCIAEQCvjIgBaycfCMaYAAZAASACMsUnJ5qgpQAAkAFoZFwHQSj4WjjEFCIAEQAJgjE1KNkdNAQIgAdDKuAiAVvKxcIwpQAAkABIAY2xSsjlqChAACYBWxhUpAIZ2haRyd6XVs70WTmqeFJhvr3rtU0P5br31VnnhhRfMt4yRxo4dK4WFheZn9aUjjjii2ufrIm2HX/VE+nzbcgRAAiAB0HYWsXxQFCAAEgCtbDUSAAT8LV2/VEp3llo922vh1BapMmTvIZ4hEMA0Z84cUz2+pdulSxc56aSTZOLEidKuXbvwY/GNX3yBo2rq2rWrrF+/3mvTVPLVBMCioiJzOXRGRv13ojUW3N5++2058sgjpea3kQsKCqRFixaSlpam0jftSgmABEACoPYsY/2xogABkABoZYuRAKDzeank5snSMsn/TzVV7VBFZYWEdodkZI+Rnr8LCgDctGmT+ZRaZWWlrFq1Si644AIZMWKEPP3009UA8Pe//71ceOGF4Z8BGDt06GClqW3hmgDopT6/ANDLs2I5DwEwugD4zto8ObRzjmS08u/CZnwJZP6HeXJoNi+CjuW5xrY1vQIEQAKglRXaAGBacpq0Smpl9Xy3wuWV5VISKmk0ANZcMr366qtl9uzZsm3btmoAeMUVVwj++ZEQqevcubPMnz9fcnNzw1U+//zzcu655xooTU1Nleuvv97kQaQR+c855xy55ZZbTOQNyW0JePv27XLxxRcL6kWk7pprrpGXXnqp2hLwP/7xD5k6daqsXr1a2rRpI0cddZT5/x07dpS1a9fKL37xi2pdPu+884w+NUESEcLLL7/c1I9vFONbxQ888IDsv//+pjzKQL+5c+ea/+KTdocffriB7+zsbD9kbVQdBMDoAeCWkkJ5bmme7NMiR1Jb+AeABdsLJW99nhzbJ0c6pftXL5ThRdCNmk7MHOMKEAAJgFYmmggA+M0338jJJ59s4G/jxo1qAIiKTz/9dGndurU88cQT4efgZ8nJyfLUU0+Zn91xxx0GyLA0/emnn5oI5FVXXSXXXXedJwC85JJLDJA9+uijBiBvvPFGwZIuopmAPCT8DgDWu3dv2bx5s1x55ZVm+fvVV1+VXbt2yb/+9S857bTTDCCmp6ebNrdt27YWAJ5yyiny1VdfycMPP2zyAV6//vprE1UFsAIAL7roIgOGkyZNkubNm8vvfvc7GTBggDz55JNWthlJ4aACoNaeWkTr89blSWbrTN//WHO+2XtgRo5kpvgHaqj3rdXvSm6vwZLdzr96DQCWl0vB9h0y/uSj+C3gSCYYy8SUAgRAAqCVQcYrACIC1qpVKwM7+KIJ0r333mtAyEnYA5ifnx+OvOHn2Cf4xz/+MWJNEdkbM2aMifalpKQI9O3UqZM899xzcuKJJ9ZZ75QpU0wEbdmyZa4AWFpaKu3bt5fHH39czjrrLJMf+/b23ntvA2IOANZ80IcffihDhgyRkpISE4Wsbw9g1QggwK9Xr17y3nvvybBhw0yVgOhu3bqZPZZnnHGGAcDzzz9f1qxZI/vuu6/JM23aNLntttuqwXbEgjayYBABUHNPLSLoX2/7WgZ1HSSI2PuZHAD0+5u9+Vu3yoIlr0tu+30ku02Kn02W4tBOKZA9Mv6CsyWrg79w6WtDWRkV8KAAAZAA6MFM6s8SrwD4ww8/yPTp06WsrExmzZolX375pbz88suSlJRUDQARrcKeQSdlZWXVedjinXfekRNOOCGcDxExLN3WTKFQyAAfnn322WebpVBEzTZs2BB+9rPPPmtADdAEoMM+RUTXEKlDamgJ+JNPPjFLvTi80r179/DjEXFDFM4BwI8//tjUg5PEAMTdu3cbLVauXCl9+/b1BIAvvviiiRICoLE30kl41ujRo82yNQDw0ksvFSxLOwkQjHJ4ZkNp957d5nCLnwl1bt+5XZKaJUmzZs08VV1RXiHrvl8nlemVIj+bR62ykWxH8NIAzT21ReVF8lH+R5LTzd99euiXGgBu2igL8hZKbpfekt3AwScv2tbMU1y2Qwq2l8r4C8+RrOysSKpgGSoQMwoQAAMCgIsXLxZEepYvX26iTnCSo0aNqteQnAhNzQyff/65HHDAAeEfI7L05z//2SzLIQJz5513GufsNcUrANbcA4gTr9ibdvvtt1cDQK97AHfs2CGASicB8uo7KYslXUQAAVDHHnusGa8HH3zQFF2yZIlpx4QJE+T44483y67PPPOM3HPPPeaqFzcABNABwBoCQMAYopvHHXecjB8/3hxq+f77783zAIYASC8RQCwTY/m6JgCiPAAPdufsAXTajvbjuhrYYENwZ0AttF3wXz8T6qvYVSFtWrSR5s2ae6o6VgBQY09tYXmhWQIOJAD26CfZ7TI9jaHXTMUlpVJQXEQA9CoY88W0AgTAgADga6+9ZpbSBg4caJynVwB09mg5Vghn7kRj8vLyzMlWQA0cLupEVObdd9+VoUOHejLcRAFAAA8ieABl7L1DAiR5BUBPYv4vE54F+AJsHXzwwWY8DjvsMPNbgB6WSNEOJ40bN04QFfQCgIgYZmZmCpa4zzzzTFMFDmpgCRjgiQgg/sgYPHiwgT4s1yIhPw6iOAD4/vvvy/Dhw2Xr1q1mSdlJXpeAsQQNOIwUAJ2lWkTpmos3UPMyBrv27DIRwNTkVNmr2c9Ry4bKEgC9KFs7j3oEkAAY2cCwVMIoQAAMCABWtUg4Pa8AWPOetqr1YA8YDABw6SScPsVm/6rXnTQ0GxIFAKEBoAgg9tBDD6kCICJfWJ4FWAHYsNTrJCeqhkMihx56qLzyyismGoi9il4AEPXgBDAOc+CgByKRN910k7z11lvhQyBbtmwxQIjTu4gAfvbZZ3LttdeaZXAHABHNBBxiiRp7E3EIBHsDa54CRpTaOQSCiOef/vQn05+qh0AA0Y2NAEayV8/LWx0AWBoqJQD+TyxGAKtbDSOAXmYR8wRFAQJgnAMgolRYgsO+rZtvvtlc3uskQAYONVQ92HDfffeZKFDNC46dMrjKA/+cBAMCCOAKE+xDq5rw3G+//dZcGYIDFU7S3LNUc+JFeg9gXV/OwClc58AC+qwVAUQfcKIXS/6IyALwqib8DvCGccAF1YBS7NfzCoCAyqrXwOCKG4AklmadPYD4AwCng7HdAFHnG264QX7961+HARDtQeQY0UgsV+PgSkPXwGA5G/sbR44caZaza14DQwCM3GU484lLwD9pmO/sAWQEMHKjYsmEUIAAGKcAiKVf7BscNGiQAQVEjGbMmGH2bsEJI+FqETjt3/72t2FjdyCnKuRVnQkAjZpAgt83BgA1Ty3WNWsb+yWQhJj5Ae8kI4A/DyABsLoxEwADPrnZ/KgpQACMUwCsy4Jwlx2WjxGNcQAQ13H85je/CWfH3Wu4D865+qRWRM2HCCDq1Lq3rK5+J9q3gKP29mjCBwUVAAt2FJgDFSkt/LueRPOuPi4BVzdyswRcWCDjx5wuWZ19PgWMGwaSk5twVvHRiaYAATCBABAnfLGZHyeBkSJZAq45QSLZA5hok4z99V8BVQCsKJWUFqmS1NzjIZCKCln33Topa1UpexooUlJRIiu2LJM+Hff39ROImnf1EQBrAGBRsRR8vUbGjxgoWRmp/hp2aqrIkCGEQH9VZW0NKEAATCAAxKlL3OmGDf9IOASCi31xIMBJOOmakZGhegiEM5IK2CqgBYCVu3fJj9tLpWWzVGnu8RTwzlCF/LB+nXz6Q6Xs2Fl/z0orC+WbUJ6cPGiQtE+tvl/WRg/Nu/oIgDUA8MdCKfjqKxl/3DB/I4DYVx0KiWB7Top/0WEbu2LZ+FeAABgQAKx6GhT3uOGrFDjQgSs9EMnDJn2czMT1GkjYzI9DCv369TOb7xH5u+uuu8wXJU499VSTB1d5YD8gIoP4ZBdOmOKgiPY1MPE/rdhDbQW0AHDnrl3yY2mptE5Klb08RgANAK5bJ6u3VkrFrvp7/mNZofy3ME/OysmRzj5eUKwJaZp1B/IaGAcATzpCsrp29M/M8bWhkhICoH+KsiYPChAAAwKA9V3sfN5555mDHPgaxdq1a80hD6TJkyfLI488YqAQV3QABAGJNT8nhvvjAH343q1zEbQDiB7sx1wjg8uIG3MIxEu9zEMFGlJAGwAbswQcClXI+nXr5NvCSgk1cC/11u2F8tEWAqAzrgTAKhZOAOQLrwkUIAAGBACbwDY8PdILACISCQhlogJ+KRBTAFgBAPxevi3aRQBsxAATAAmAjTAXZlVQgABIALQyq4YMCJcT4/Lgjh07VvtahNUDWZgKiEgsAeCO7aWyYUO+fF20S3Y18GliRACX5efJ6EP9XwL+cGOejOjp//d6uQRcfboVcwmY7584UoAASAC0Mmc3A8JFwrjkFxCYkpJirqFhogK2CgAAcf0JvtfbTPyzqcrdu6W4rExaJaXIXs3dPzGHr7Zs27xZNheXSX5Zw73aVFwoC1flyfDuOdIuJcNWgnB553DJaUNypEOaf/XiAQRAAqBvhsqKYk4BN/8dcw1WaFCzPQ19dV7hgfFUpZsBQdqNGzdW+9RXPPWffWkaBXbv2S34yosBQB//qNi1e7dsr6iQ5OYtpbkHAJQ9eyS0a7esK9kjlQ1E/6BSfmGhLPgsT47ulSOdfDwEonW4hABY27YZAWya+c6n6ijg5r91nhpbtRIALcbDqwFhOXjnzgbuyLBoA4smngI7du6QZRuWSZsWbXy9U28Lvo29YoX0yzxEMlq7X9UC5tvZwMGPqiPjAGBuvxzJbudfpE7rcIkDgO+szZNDO/u/vIw9gPM/zJNDs3OkfRv/9FD9EgiXgBPvZRPHPfbqv+NYAiEAWowuDchCPBaNWAGtz59pHUyoGgEMEgBuKSmU55bmyT4tciS1hX+QBj0KthdK3vo8ObZPjnRK969uAmDE04oFE0wB+m/uAbQyeRqQlXwsHKECBMCfhdOMADpAfGBGjmT6uG8RrUfdb32ZJ34DMQEwwknFYgmnAP03AdDK6GlAVvKxcIQKEACjC4CDOvi7TKsZESUARjipWCzhFKD/JgBaGT0NyEo+Fo5QAQIgAbA+0yEARjipWCzhFKD/JgBaGT0NyEo+Fo5QAQIgAZAAGOHkYTEq8D8F6L8JgFaTQd2A8IH0ykqrNtZZOClJJDnZ/3pZY1QUAAC+uWaxpOyVJq2SWvn2TK2TqZpLntHYA8gl4J9MjNfA+DbVWFEMKKDuv2Ogj25N4ClgN4Ua+L2qAQH+li4VKS21aGE9RVNTRYYMIQT6r2xUaizcXiZ/f2OxSChNkpv7B4BaJ1MJgLXNQutaHC4BR2UK8iFxoICq/w6IPgRAi4FSNaCyMpHFi3+CtJYtLVpZo2hFhQjgcuRIkZQU/+plTVFTYGtRmcx4bbGkt0yTNq38A0Ctk6kEQAJgg5OjvFykpITvpKi9QfggE9EuLpa2bdtKUVGRpKe733saj6oRAC1GVdWAHABMSxPx0ckLX7YWIx4bRR0AzExNk/TW/gGgVlSKAEgAJADGxruDrfhZAVX/HRChCYAWA6VqQARAi5GJ76IEwJ/Hl3sAq9s6l4Dje+6zd/4poOq//Wumak0EQAt5VQ2IAGgxMvFdlABIAKzPwgmA8T332Tv/FFD13/41U7UmAqCFvKoGRAC0GJn4LkoArA6Ay/LzZPShOdI5w79PquEJQTwVTQCM77nP3vmngKr/9q+ZqjURAC3kVTUgAqDFyMR3UQLgz+O7qbhQFq7Kk+Hdc6Sdz59rC+KpaAJgfM999s4/BVT9t3/NVK2JAGghr6oBEQAtRia+ixIAfx5f5+DK0b1ypJNCBFDje71ovdaBGwJgfM999s4/BVT9t3/NVK2JAGghr6oBEQAtRia+ixIAawNgbr8cyW7n7xKwFqSFAfDTdyW312Bf252/abMsWPaW5PY8ULLbZfo6EXgRtK9ysrImVkDVfzdx37w+ngDoVak68qkaEAHQYmTiuygBMA4AcOtWWbDkdcltv49kt/HvPs784iJZ8N1Kye01SLI7dPB1IhAAfZWTlTWxAqr+u4n75vXxBECvShEALZRiUT8VIADGAQBu2igL8hZKbpfeku3j0nX+lm2y4JuPJbf3YMnu2NFPs+On4HxVk5U1tQIEQH4L2MoGVQ2IEUCrsYnnwgTAOALAHv18XarN37xFFqz+kAAYzy8A9s0XBVT9ty8t1K+EEUALjVUNiABoMTLxXTSwAKix5+3HQlnw5TLJ7X+4r3vpYEGqewCdCCABUPh1ovh+X8Vq71T9d6x2uka7CIAWA6VqQARAi5GJ76KqAKgAaQamsDT50X8kN2tff/e8lZbJgoJvJHfo8ZLdIcvXgScAVpeTewB9NS9W1sQKqPrvJu6b18cTAL0qVUc+VQMiAFqMTHwXVQNApYMJBgCdwwn7HCLZ7f07nZpfWCQLNnwhuTnHSXanzr4OPAGQAOirQbGymFJA1X/HVE/rbwwB0GKgVA2IAGgxMvFdVA0AlQ4mhCOACocT8gsKZMH3KwmA/zN57T2Am79YIxccf6RkdfHxhHF5hSSVFUvy0SNEUvw7ER3fbwH2zlYBVf9t27golScAWgitakAEQIuRie+i6gDo8740A4BKhxMIgNVtXUtnPOXHrUXyyUcb5bi+R0l6po/L7aGQpO4pkSHjDpLkDAJgfL+9Yqd3qv47drrZYEsIgBYDpWpABECLkYnvogTAn8eXABg9ANy2pViWL9siowccLh07t/dtklWU7pRQSbmMHN9PUrIIgL4Jy4oaVEDVfwdEewKgxUCpGhAB0GJk4rsoAZAAWJ+Fa0YAHQA8a8jh0rmLf/s4y4tDUlIQIgDG92sr5nqn6r9jrrd1N4gAaDFQqgZEALQYmfguSgAkABIA43uOs3f6Cqj6b/3m+/IEAqCFjKoGRAC0GJn4LkoAJAASAON7jrN3+gqo+m/95vvyBAKghYyqBkQAtBiZ+C5KACQAEgDje46zd/oKqPpv/eb78gQCoIWMqgZEALQYmfguSgCsAYDffSq5g4+S7E7+fvs2H18Z+Xyp5PYZ4v9XRjZtlgXL3pLcngcG5lNw3AMY3++VROudqv8OiJgEQIuBUjUgAqDFyMR3UQJgFQDcgm/fLpPcngdJdnqarwOfX1wiC9Z9JrndDpTs9FSf6y6SBd+tlNxegyS7g3936vEQiK/DxMriWAFV/x0Q3QiAFgOlakAEQIuRie+iBMAqAOjcL7jvQMnO8u9kKp5gPl+Hy6s16+49WLI7+he5JADG99xn7/xTQNV/+9dM1ZoIgBbyqhoQAdBiZOK7KAGwDgD0GaQMACpdXq1Zt2abuQQc3++VROudqv8OiJgEQIuBUjUgAqDFyMR3UQIgAbA+CycAxvfcZ+/8U0DVf/vXTNWaCIAW8qoaEAHQYmTiuygBkABIAIzvOc7e6Sug6r/1m+/LEwiAFjKqGhAB0GJk4ruoAcCX3pLMNq0lvVUr3zqbr3QyNahLnprRNK26terFGHIJ2LepxopiQAFV/x0D/fPShEAA4OLFi2XKlCmyfPlyyc/Pl/nz58uoUaPq7d/zzz8v06dPlxUrVkhFRYX069dPbr31Vjn++OPDZfD/J0yYUK2OTp06ycaNG73oZvKoGhAB0PM4JFrGrVsKZcajz0imNJP05Ba+dT+/WOdkKgGw9hBpgZpWvQRA36YZK4oRBVT9d4z00a0ZgQDA1157Td577z0ZOHCgnHbaaa4AeMUVV0iXLl3kyCOPlIyMDHnsscfk7rvvlg8++EAGDBhgNAEAPvvss/LGG2+ENdprr72kQyOuZFA1IAKgm+0m7O+35m+VGTOflMw2qZKe0to3HcKnXgN0oEITeIJYt2abGQH0baqxohhQQNV/x0D/vDQhEABYtSPNmjVzBcC6Oo4o4FlnnSW33HJLGABfeOEFEyWMNKkaEAEw0mGJ+3JhAExvK+lp/t1PpwkPWnVr1asZtdSsW1MPAmDcv1oSqoOq/jsgSiYEAO7evVt69uwp1113nVx22WVhAMSyctu2baVly5YydOhQmThxouyzzz6eh07VgAIKgKGQSGWlZwk9Z0xKEklO9pw9JjKW7ghJech/MbZt2ir/eOpZyc7IJAAG8KoWAmD16VleHJKSgpCMHN9PUrJSYmLushHxr4Cq/w6IfAkBgAC9u+66Sz7//HPp+L9LV7GsXFZWJr169ZJNmzbJHXfcIV988YWsXLlS2rdvX+fwYT8h/jkJBtStWzcpKiqS9PR0f4c8gAAI+Fu6VKS01F8pUFtqqsiQIcGBQMDf428slYLt/ouxvaRYPlu5TI7q1ls6ZLT1TWzN6JFW3Vr1akKaZt2aejAC6NtUY0UxoAABUCTuAfDpp5+WcePGyb/+9S855phj6jW77du3y7777muihFdddVWd+eo6OIKMBMCf5HKYFZG6li39m+FgbsDlyJEiKQEJEDhXtbRukSyt/RRDRDZv3SzvLH9bcnv0C8x3ZLXARKteTUjTrFtTDwKgf+801tT0ChAA4xwA586dK+eff77MmzdPTjrpJFeLO/bYY2W//fYzJ4jrSowANixhAIOWrjYRaQatu/oMPGzaKAvyFhIAA/q1DgJg9VnFJeBI3zIsZ6MAATCOARCRvwsuuEDw34aujHEMCHCHCOBFF10UPijiZlyqBhRAmgpgk92GOOLfEwCrS6cVmdKqVxPSNOvW1IMRwIhfBywYgwqo+u8Y7G9dTQrEEnBpaamsWbPGtB/XuNx7773mipfMzEzp3r273HDDDfLDDz/I448/bvIA+saMGSP333+/nHrqqeF+t27d2hz6QLrmmmvk5JNPNuU3b95s9gAuWrRIPv30U+nRo4en4VM1oADSVACb7GmcI8lEACQANmQ3WqCmVS/6QgCM5E3AMrGq1NCKNwAAIABJREFUgKr/jtVO12hXIADw7bffNsBXM5133nkye/ZsGTt2rKxdu1aQD+mII44wMFdffvz87LPPFlwwvXXrVnP332GHHSa333679O3b1/PQqRpQAGkqgE32PNaNzUgAJAASAL3NGi4Be9OJufxVQNV/+9tUtdoCAYBqvbesWNWAAkhTAWyypQXUX5wASAAkAHqbXgRAbzoxl78KqPpvf5uqVhsB0EJaVQMKIE2hyW++HZKU1Erx8RO1Ul4uUlaaJEcfkaxyCji0KySVu/29rw8A+OjCPOmYninprf37Xi/MlYdAfp60mkueQaxbs81cArZwFiwacwqo+u+Y623dDSIAWgyUqgEFEAALS0Iya8FSaZZc6uulzbgCZk8oVcblDpGMNH9vgwb8LV2/VEp3+ntfX/H2clm4/Gs5OGuQtGuTZmFltYsSAAmA9RkUAdDXqcbK4lgBVf8dEN0IgBYDpWpAAQRAZ9kzrU2ypPp4911pRYWUbA/J+BNGSlZbfy8CLNtZJou/WyzJzZOlZZJ/lxduLiqS+Us/kkEdcqR9mwwLKyMANsVSqom0BvArI5ptZgTQ12nMyppYAVX/3cR98/p4AqBXperIp2pAAQbAzNQ0X5c9i3eUS0FpiSoApiWnSask/5ZqNxYWyty8PALg/+aNFpho1UsArP3CIwBaOAsWjTkFVP13zPWWS8C+D4mqAREAw+NFAKxuulwC5hIwl4B9f52zwgRTQNV/B0RLRgAtBkrVgAiA1QBwc0mBXHBsjsoScN66PMlsnckIIJc8q70Nghhd1GwzI4AWzoJFY04BVf8dc71lBND3IVE1IAJgeLwKtpfIp1uXyTGD9pe2bfzbp4cHlFeWy9fbvpZBXQcJloH9SlwCrhG1VIJLTeAJYt2abSYA+vV2YD2xoICq/46FDnpoAyOAHkSqL4uqAREAw7Jv3V4oH23Jk9FDBknHtukWI1a7aFF5kXyU/5HkdMuRjFb+HdYgABIAGzJULVDTqhd9IQD6+uphZU2sgKr/buK+eX08AdCrUnXkUzUgAmAtADwrJ0c6Z/gHaXhAYXmhYAmYAMhTrzWnuCZMadWtVS8B0MJRsGhMKqDqv2Oyx7UbRQC0GChVAyIARg0A31mbJ4d29j8COP/DPDk0m9fAYCC1wESrXs02a9atqQcjgBbOgkVjTgFV/x1zva27QQRAi4FSNSACYFQAcEtJoTy3NE/2aZEjqS38iy4WbC+UvO/flWP3Gyyd2vpXr4GHTZtlwbK3JLfngZLdLtPCgqsX1YQHrbq16tWENM26NfUgAPo21VhRDCig6r9joH9emkAA9KJSPXlUDYgAGBUAdPbqHZiRI5kp/oHaxq1b5a2PXpfc9vtIdht/L6/OLy6SBd+tlNxegyS7QwcLCyYANiSeJkxp1a1VL3QiAPo21VhRDCig6r9joH9emkAA9KISAdCTSuZLIC+9JZltWku6jx8D3lpWKB9tXSZnjTjc9z2AWoc1wnf1dekt2T7vW8zfsk0WfPOx5PYeLNkdO3oaGy+ZNOFBq26tejWjdJp1a+pBAPQyi5gnKAoQAEUIgBbWqmpAQYwAbimUGY8+I5nSTNKTW1goW73o1ort8tHuL+Wsk06Szj5GvPAUdQDs0c/XZdqgwoMWmGjVq6mzZt2aegAAly7Pl1MHHyads9v5Nr/LS0JStiUkR1/QV1Ky/I2Wm0YmJYmvHyf3reesqCkVUPXfTdmxRjybANgIsWpmVTWgIAJg/laZMfNJyWyTKukprS2UrQGAuAZm+3/lrJNPls6dO/tWLwGwupSa8KBVt1a9mpCmWbemHps2F8i/P/9YcvrsJ5mZ/t2ZGSoNyZ6vf5Rxv+wvGW39e2+ErTs1VWTIEEKgr2/O4Fem6r8DIg8B0GKgVA0oyACY3lbS01ItlK0BgMXb5KPijwmA/5NFy8lr1RtU4KEedf+BcHTvg6VTVpZv87u0qFhKvlor448bJlmd/avXNLCiQiQUEhk5UiRFIbromwqsKNoKqPrvaHcmwucRACMUDsVUDYgAGB6ZrQTAalaqBSZa9RIAa79ktLTWqldzDIt/LJSCr76S8ScdIVld/dvTalQvLxcpKSEAWvi5eC2q6r8DIhoB0GKgVA1IEQBDRQVSOTzH97+It27cKo8+Ok86tm3PCOCmjbIgb6Hkcg+gmWFaYKJVr2abNesOoh4EQAsnxKIRK6DqvyNuVXQLEgAt9FY1ICUADJWVyNKNy6S07/4iLf39rm5xQbEsfO09ObjtgdIuzb8rVRgBrHspjqeA9cBSE9I06yYA1nihMwJo4eHiu6iq/w6IdARAi4FSNSAlACzbXiiLN+RJ8sGDpGUbf7+ru3nzZpn/yusyqO0AaZ/W3kLZ6kUJgATA+owpiMBDAKw+mowA+vaqZEWNUEDVfzeiHU2ZlQBoob6qASkDYNrAHGmV6l+UDjJu3LhR5r70EgHQfK2DS8BVp5YWqGnVqwlpmnUHUQ8CoIUTYtGIFVD13xG3KroFCYAWeqsaEAEwPDKIAC4rWiGjc38lnTv6fw2Mxjd7CYCMWjb0atECNa16NaGVAGjhhFg0YgVU/XfErYpuQQKghd6qBkQADI/Mph8LZOEPn8rw/idIu4xOFiNWu6j5Zu/6PDm2T450SvcvIkoAJAASAL1NVQKgN52Yy18FVP23v01Vq40AaCGtqgERAMMjk19QIAu+XylHH3qcdOrgfwTwrS/zJLdfjmS3IwAuWP2h75+Y04weBTHiRT2qv3QJgBZOiEUjVkDVf0fcqugWJABa6K1qQATAWgCYm3OcZHfyFwDzCwtlwWcEQE0o0aybABidSKvmGBIALZwQi0asgKr/jrhV0S1IALTQW9WACIDRA8BP35XcXoN9jgBulgXL3pLcngfyW8C8B7DWW0YLXLXqJQBaOAoWjUkFVP13TPa4dqMIgBYDpWpABMDoAODWrbJgyeuS234fyW7j36ei8ouLZMF3KyW31yDJ7tDBwspqF9Vy8lr1asJDENtMPbgE7OsLgZVFpICq/46oRdEvRAC00FzVgAiA0QFA57qWLr0lO8PHPYBbtsmCbz7mfrr/jaIWqGnVqwlpmnUHUQ8uAVs4IRaNWAFV/x1xq6JbkABoobeqAREAowuAPn+yLYiOmG2u/jKgHtHRgwBo4YRYNGIFVP13xK2KbkECoIXeqgZEACQA1mObWmCiVS8jXlzGb+g1SwC0cEIsGrECqv474lZFtyAB0EJvVQMiABIACYCuszOI0Eogrj6sBEBXM2cGBQVU/bdCezWqJABaqKpqQIoA+Ob3SyTloMP8/xTcpo0y//WX5dCMQ3z9FrBzD6DKNTBKn2wLIpiwzdFZ8iQAEgAt3A6L+qSAqv/2qY3a1RAALRRWNSAlACwsLJK/f/iJSLeDJblVW4ve1y5aULhJ8j5dIMd26y+dMjJ9q5sAGB0wIQBGR2cCIAHQt5cjK4pYAVX/HXGroluQAGiht6oBKQHg1m3FMmPJfyX9gP7SJs2/U6+QcePmjfLWsoWS6/eBiv99CYQRwJ+MVQvUtOplm2u/ZLS01qpXcwy5BGzhhFg0YgVU/XfErYpuQQKghd6qBqQMgJkH9pd0H799axyE1nIqAbCalWo5ea16NeEhiG2mHowAWrgdFvVJAVX/7VMbtashAFoorGpABMDwyJgl4O8+ldzBR0l2p44WI1ZHJGaTzhc7gggmbHN1+6Ae0dGDEUBfX2mszKMCqv7bYxuaOhsB0GIEVA2IAPgzAG7ZIgtWL5PcngdJdnqaxYjVAYBKX+wgPEQHHoKoMyOAjAD6+hJjZREpoOq/I2pR9AsRAC00VzUgAuDPALgZAPih5O47ULKz/DtcYhyx0hc7gggmbHN0oJUASAC0cDss6pMCqv7bpzZqV0MAtFBY1YAIgLUBsPdgye7o8xKwA5c+102Yig5MBVFnAiAB0MLtsKhPCqj6b5/aqF0NAdBCYVUDIgASAOuxTS3o0aqXwFN7ILW01qpXcwy5B9DCCbFoxAqo+u+IWxXdgoEAwMWLF8uUKVNk+fLlkp+fL/Pnz5dRo0Y1qNSiRYvkqquukpUrV0qXLl3kuuuuk/Hjx1crM23aNFMv6uzXr59MnTpVRowY4XkEVA2IAEgAJAC6zsUgAo8mTAVRDwKgq5kzg4ICqv5bob0aVQYCAF977TV57733ZODAgXLaaae5AuC3334r/fv3lwsvvFD+8Ic/mLKXXHKJPP3006Y80ty5c+Xcc88VQODw4cPl4YcfllmzZsmqVauke/funrRWNSACIAGQAOg6D4MIPATA6sNKAHQ1c2ZQUEDVfyu0V6PKQABg1Y43a9bMFQCvv/56efHFF+Xzzz8PF0X075NPPpG8vDzzs6FDhxqgnD59ejhPnz59TGRx0qRJnrRWNSACIAGQAOg6DwmA1SUKoh4EQFczZwYFBVT9t0J7NaqMSwAcOXKkDBgwQO6///6wZlg2PvPMM6WsrEz27NkjKSkpMm/ePBk9enQ4z+WXXy4rVqwQLB97SaoGRAAkABIAXadhEIGHEUBGAF0NmxnUFVD13+qt9+cBcQmAvXr1krFjx8qNN94YVun99983S70bNmwwANi1a1ezNDxs2LBwnokTJ8qcOXNk9erVdapbUVEh+OckGFC3bt2kqKhI0tPT/RkRpxYCIAGQAOg6pwiAjAA2aCTl5SIlJSIjR4qkpLjaEzMkjgIEQJG4BcDzzz9fbrjhhrA1A/YOP/xwc+Bj9+7dBgABhTk5OeE8d955pzzxxBPyxRdf1DkLbr31VpkwYUKt3wUOAN//WDIP6CPpbf2F1nx+VYPQSmj15EG1wFWrXs2oJZeAPZkMM/msAAEwTgFQawk4LiKAG7fJjFfflszsLpLext+/iPP5VQ0CIAHQk5vSAjWtegmAnoaVmQKkAAEwTgEQh0Beeuklc6LXSRdffLHZ31f1EMigQYPMKWAn9e3bV0455ZT4PgTyw2aZ8crbkvmLX/gfAeRXNQiABEBPLlAL1LTqJQB6GlZmCpACBMCAAGBpaamsWbPGmBYOd9x7771y5JFHSmZmprmyBUu9P/zwgzz++OMmj3MNDK6AwVUwgD6cAq7rGpgZM2aYZeBHHnlEZs6cae4N7NGjhyczVjUgrT2ADgDuv7+kt8vw1E+vmbScj1a9mk6Nba5uNVp6aNWraRuadQdRDy4Be33DMp+fCqj6bz8bqlhXIPYAvv322wb4aqbzzjtPZs+ebQ58rF27VpDPSTjJe+WVV4YvgkZUsK6LoCdPnmz2BeLewPvuu0+wfOw1qRoQAZDRNEbTXKdiEIGHAFh9WAmArmbODAoKqPpvhfZqVBkIANTouB91qhoQAZAASAB0naYEwOhEWjWhlQDoaubMoKCAqv9WaK9GlQRAC1VVDYgASAAkALrOTgIgAbBBI+E1MK5zKFEzqPrvgIhKALQYKFUDIgASAAmArrOTAEgAJAC6ThNmqEMBVf8dEMUJgBYDpWpABEACIAHQdXYSAAmABEDXacIMBMA6bYAAaDE1CIDRcT5BdPJsM22joVeLln1o1Yu+aNXNPYAWTohFI1ZA1X9H3KroFiQAWuitakCMADICyAig6+zUghJN4NGsO4h6EABdzZwZFBRQ9d8K7dWokgBooaqqAREACYAEQNfZGUTgIQBWH1YCoKuZM4OCAqr+W6G9GlUSAC1UVTUgAiABkADoOjsJgNUlCqIeAMDNX6yRC44/UrK6dHAd80ZlKK+QpLJiST56hEiKv5++bFQ7mDnmFFD13zHX27obRAC0GChVAyIAEgAJgK6zM4jAwwhg9WH9cWuRfPLRRjmu71GSnpnlOuaNyhAKSeqeEhky7iBJziAANkq7OM+s6r8Doh0B0GKgVA2IAEgAJAC6zk4CYPAjgNu2FMvyZVtk9IDDpWPn9q5j3pgMFaU7JVRSLiPH95OULAJgY7SL97yq/jsg4hEALQZK1YAIgARAAqDr7CQAxg8AnjXkcOncJdN1zBuTobw4JCUFIQJgY0RLkLyq/jsgGhIALQZK1YAIgARAAqDr7CQAEgAbMhICoOsUStgMqv47IKoSAC0GStWACIAEQAKg6+wkABIACYCu04QZ6lBA1X8HRHECoMVAqRoQAZAASAB0nZ0EQAIgAdB1mjADAbBOGyAAWkwNAmB0nE8QnTzbTNto6NWiZR9a9aIvWnU7h0C4B9DCGbFooxVQ9d+Nbk3TFCAAWuiuakCMADICyAig6+zUghJN4NGsO4h6EABdzZwZFBRQ9d8K7dWokgBooaqqAREACYAEQNfZGUTgIQBWH1YCoKuZM4OCAqr+W6G9GlUSAC1UVTUgAiABkADoOjsJgNUlCqIeBEBXM2cGBQVU/bdCezWqJABaqKpqQARAAiAB0HV2BhF4GAFkBNDVsJlBXQFV/63een8eQAC00FHVgAiABEACoOvsJAAyAtiQkfAeQNcplLAZVP13QFQlAFoMlKoBEQAJgARA19lJACQAEgBdpwkz1KGAqv8OiOIEQIuBUjUgAiABkADoOjsJgARAAqDrNGEGAmCdNkAAtJgaBMDoOJ8gOnm2mbbR0KtFyz606kVftOrmIRALJ8SiESug6r8jblV0CxIALfRWNSBGABkBZATQdXZqQYkm8GjWHUQ9CICuZs4MCgqo+m+F9mpUSQC0UFXVgAiABEACoOvsDCLwEACrDysB0NXMmUFBAVX/rdBejSoJgBaqqhoQAZAASAB0nZ0EwOoSBVEPAqCrmTODggKq/luhvRpVEgAtVFU1IAIgAZAA6Do7gwg8jAAyAuhq2MygroCq/1ZvvT8PIABa6KhqQARAAiAB0HV2EgAZAWzISHgPoOsUStgMqv47IKoSAC0GStWAysok9OY7UpmSLtKqpUUrqxfdumGLPLrwP9Kx936S3i7Dt3oZ1Qi+Iw4iTAWxzZwrjAD6+uJlZREpoOq/I2pR9AsRAC001zSgUGGZLJ31XyltliaSnGzRyupFiwq2yhur3pKDBnaWdlltfauXTo0A2JAxaYGaVr2a9qxZdxD14B5AX1/DrMyjApr+22MTmjwbAdBiCDQNqGxrmSyesVKS01pJy9QWFq2sXnTTxm3ywsfvyqDBWdK+AwFQy2Fq1Ut4CD5ocwwZAfTthc6KIlZA039H3KgoFyQAWgiuaUAOAKZlJkurdP8igBs3FMjcpQRAZ9i1QE2rXsIDAbApIq2adscIoIUTYtGIFdD03xE3KsoFCYAWgmsaEAHw54EhTEUHeqhzdHTWhKkgjiEB0MIJsWjECmj674gbFeWCBEALwTUNiABIAKzPNLWcvFa9BJ7aI6mltVa9mmNIALRwQiwasQKa/jviRkW5IAHQQnBNAyIAEgAJgO6TM4jAowlTQdSDAOhu58zhvwKa/tv/1urUSAC00FXTgAiABEACoPvkDCLwEACrjysB0N3OmcN/BTT9t/+t1amRAGihq6YBEQAJgARA98lJAKyuURD1IAC62zlz+K+Apv/2v7U6NRIALXTVNCACIAGQAOg+OYMIPIwAMgLobtnMoa2Apv/Wbrtf9RMALZTUNCACIAGQAOg+OQmAjAA2ZCX8FJz7HErUHJr+OyiaEgAtRkrTgAiABEACoPvkJAASAAmA7vOEOWoroOm/g6I3AdBipDQNiABIACQAuk9OAiABkADoPk+YgwBYlw0EBgCnTZsmU6ZMkfz8fOnXr59MnTpVRowYUaddH3HEEbJo0aJavzvxxBPllVdeMT8fO3aszJkzp1qeoUOHypIlSzzPFQJgdJxPEJ0820zbaOhFomUfWvWiL1p18xCIZ5fDjD4qoOm/fWymalWBAMC5c+fKueeeK4DA4cOHy8MPPyyzZs2SVatWSffu3WsJVFBQIKFQKPzzbdu2ycEHH2zKAPwcANy0aZM89thj4XzJycmSmZnpWXBNA2IEkBFARgDdp6IWlGgCj2bdQdSDAOhu58zhvwKa/tv/1urUGAgARGRu4MCBMn369LAKffr0kVGjRsmkSZNclUG08JZbbjHRwzZt2oQBsLCwUF544QXX8vVl0DQgAiABkADoPjWDCDwEwOrjSgB0t3Pm8F8BTf/tf2t1aox5AEQkLyUlRebNmyejR48Oq3D55ZfLihUr6lzqrSnVgQceKDk5OfLII4+Ef4VIIOAPUb+MjAz55S9/KXfeead07NixXqUrKioE/5wEA+rWrZsUFRVJenq6ryNEACQAEgDdpxQBsLpGQdSDAOhu58zhvwIEQJGYB8ANGzZI165d5b333pNhw4aFrWDixIlmD9/q1asbtIylS5cKIogffPCBDBkyJJwXy8qpqanSo0cP+fbbb+XPf/6zVFZWyvLly6Vly5Z11nnrrbfKhAkTav2OAPiTJFrOR6tetjn48BBE26DdMQLoP86wxsYqQAAMEAC+//77JornJETrnnjiCfniiy8aHPc//OEPgrKffvppg/mwPAwYfOaZZ+TUU0+tMy8jgA1PMS1nrFUvHTEBsCGLpt1Fxz4YAWwsujC/HwoQAAMAgDZLwGVlZZKdnS233XabYMnYLe2///4ybtw4uf76692ymt9rGhCXgH8eAjri6Dhi6hwdnfmHByOAnhwMM6kqoOm/VRvuY+UxvwSMvmIJd9CgQeYUsJP69u0rp5xySoOHQGbPni3jx4+XH374Qdq3b9+gbDgpjKVm7BMcM2aMJ4k1DYgASACszwi1QE2rXgJP7ZHU0lqrXs0xZATQk7thJp8V0PTfPjdVrbpAAKBzDcyMGTPChzlmzpwpK1euNMu2ADbAW80TwbgnED/Hsm7VVFpaKtjPd9ppp5kI4dq1a+XGG2+U77//Xj7//HNJS0vzJLimAREACYAEQPdpGETg0YSpIOpBAHS3c+bwXwFN/+1/a3VqDAQAouuI/k2ePNlc5dK/f3+57777ZOTIkUYVXPzcs2dPQcTPSV9++aX07t1bFi5cKMcee2w19Xbs2GGukPn4448FV8EAAo888ki5/fbbzaler0nTgAiABEACoPtMDCLwEACrjysB0N3OmcN/BTT9t/+t1akxMACo0327WjUNiABIACQAus9PAmB1jYKoBwHQ3c6Zw38FNP23/63VqZEAaKGrpgERAAmABED3yRlE4GEEkBFAd8tmDm0FNP23dtv9qp8AaKGkpgERAAmABED3yUkAZASwISspLw5JSUFIRo7vJylZKe4GxRwJo4Cm/w6KiARAi5HSNCACIAGQAOg+OQmABEACoPs8YY7aCmj676DoTQC0GClNAyIAEgAJgO6TkwAYHwC4dHm+nDr4MOmc3c590BuRo7wkJGUFu+Xoiw9hBLARuiVCVk3/HRT9CIAWI6VpQARAAiAB0H1yEgCDD4CbNhfIvz//WHL67CeZmd6u4HK3jJ9yhMp2yp6SZBn3hzMko1OG12LMlwAKaPrvoMhHALQYKU0DIgASAAmA7pOTABh8AHTG8OjeB0unrCz3QW9EjtLSMikpKZPxF54jWdn+1t2IZjBrDCqg6b9jsLt1NokAaDFSmgZEACQAEgDdJycBMH4AMLf3YMnu2NF90BuRo7ikVAqKiwiAjdAsUbJq+u+gaEgAtBgpTQMiABIACYDuk5MASABsyEoMABYWyPgxp0tWZ58jgElJIsnJ7kbKHDGpgKb/jskO19EoAqDFSGkaEAGQAEgAdJ+cBEACYIMAWFQsBV+vkfEjBkpWRqq7QTUmR2qqyJAhhMDGaBZDeTX9dwx1s8GmEAAtRkrTgAiABEACoPvkJAASABsEwB8LpeCrr2T8ccP8jQBWVIiEQiL4HGkK7xd0n6mxl0PTf8deb+tuEQHQYqQ0DYgASAAkALpPTgIgAdATAJ50hGR19XF/YXm5SEkJAdB9isZsDk3/HbOdrtEwAqDFSGkaEAGQAEgAdJ+cBEACIAHQfZ4wR20FNP13UPQmAFqMlKYBEQAJgARA98lJACQAEgDd5wlzEADrsgECoMXMIABGx/kE0cmzzbSNhl4tWvahVS/6olW3Vr1oc7GzB5BLwBaeLj6LavrvoChGALQYKU0DYgSQEUBGAN0npyY8BLFutrm6zRAA3edQoubQ9N9B0ZQAaDFSmgZEACQAEgDdJ2cQgSeo0TQtrbXqZQTQff4kcg5N/x0UXQmAFiOlaUAEQAIgAdB9cmrCQxDrZpsZAXSfNcxh/jgoLpa2bdtKUVGRpKenJ6QoBECLYdc0IAIgAZAA6D45gwg8jABWH1fNMeQSsPscStQcmv47KJoSAC1GStOACIAEQAKg++TUhIcg1s02MwLoPmuYgxHAn2yAAGgxFwiA0flLnk6NOkcbhjWjdJp1c64QAC1cWkIV1fTfQRGSAGgxUpoGxAggI4DRhh7CQ3RAmwAYPZ25BGzh4OK8qKb/Dop0BECLkdI0IAIgAZAA6D45gwitBEACoLtlM4e2Apr+W7vtftVPALRQUtOACIAEQAKg++QkAEYPprS01qoXyjAC6D6HEjWHpv8OiqYEQIuR0jQgAiABkADoPjk14SGIdbPN1W2GAOg+hxI1h6b/DoqmBECLkdI0IAIgAZAA6D45gwg8XAKOXtSSAOg+hxI1h6b/DoqmBECLkdI0IAIgAZAA6D45CYDRgyktrbXq5RKw+/xJ5Bya/jsouhIALUZK04AIgARAAqD75NSEhyDWzTZzCdh91jCH+eOAXwLhPYA2U0HTgAiABEACoPvsDCLwcAk4elFLLgG7z6FEzaHpv4OiKSOAFiOlaUAEQAIgAdB9chIAowdTWlpr1cslYPf5k8g5NP13UHQlAFqMlKYBEQAJgARA98mpCQ9BrJtt5hKw+6xhDi4B/2QDBECLuUAAjE70gU6NOkcbhjWXaTXr5lwhAFq4tIQqqum/gyIkAdBipDQNiBFARgCjDT2Eh+iANgEwejpzD6CFg4vzopr+OyjSEQAtRkrTgAhytswHAAAgAElEQVSABEACoPvkDCK0EgAJgO6WzRzaCmj6b+22+1U/AdBCSU0DIgASAAmA7pOTABg9mNLSWqteKMMIoPscStQcmv47KJoSAC1GStOACIAEQAKg++TUhIcg1s02V7cZAqD7HErUHJr+OyiaEgAtRkrTgAiABEACoPvkDCLwcAk4elFLAqD7HErUHJr+OyiaEgAtRkrTgAiABEACoPvkJABGD6a0tNaql0vA7vMnkXNo+u+g6EoAtBgpTQMiABIACYDuk1MTHoJYN9vMJWD3WcMc5o8DfgqO9wDaTAVNAyIAEgAJgO6zM4jAwyXg6EUtuQTsPocSNYem/w6KpoGJAE6bNk2mTJki+fn50q9fP5k6daqMGDGiTp1nz54t559/fq3f7dixQ1q1ahX+eWPqrOtBmgZEACQAEgDdX6MEwOjBlJbWWvVyCdh9/iRyDk3/HRRdAwGAc+fOlXPPPVcAbMOHD5eHH35YZs2aJatWrZLu3bvX0hoAePnll8vq1aur/a5z587h/9/YOgmA7iat9SLXqpeRGMJDQ1ZNu4uOfWjqzAig+3s7UXMQAAPyKbihQ4fKwIEDZfr06WFb7dOnj4waNUomTZpUJwBeccUVUlhYWK9tN7ZOAqD7a0LrRa5VLwEwOg6eOteeO1o2rVVvUMeQAOj+3k7UHATAAABgKBSSlJQUmTdvnowePTpsq4jwrVixQhYtWlQnAI4bN066du0qu3btkkMOOURuv/12GTBggMkbSZ0EQPfXhJbz0ao3qE5NSw+teqkzAbCpIq0EQPf3dqLmIAAGAAA3bNhgQO69996TYcOGhW114sSJMmfOnFrLvMiwZMkSWbNmjRx44IHmpM/9998vr776qnzyySey//77SyR1ot6Kigrzz0mou1u3blJUVCTp6em+ziPsAXxz+gpJyWwurdKSfat7Y/6PMv/DJXLo4M7SvkNb3+qlk2c0rSmcfBChlXMlenOFAOjrKz6uKiMABggA33//fcnJyQkb4J133ilPPPGEfPHFF65GuXv3brOEPHLkSHnggQfCANjYOm+99VaZMGFCredpAGDhpkKZ9fA8aZYWkuSUFq599JqhYFup5H3+lRzb9xDp1LG912Ke8mk5Y6166Yij54i1xlCrXk3b0Kw7iHpotpkA6OnVnZCZCIABAEC/lmsvvPBCWb9+vbz22msRLwFHMwK4NX+rzJj5pKSlpUhqaopvE3TT1gJ5c/XHktt7sGR37OhbvXRqhClGAL1PJy3o0ao3qPObAOjdJhMtJwEwAAAIo8SBjUGDBplTwE7q27evnHLKKXUeAqlpyHv27JEhQ4aYJeFHH33U/Nq2TtShaUAOAGamt5X0tFTf5iYdRHRAjTpTZwKxt9eW5lwhAHobg0TMpem/g6JnoK6BmTFjhlkGfuSRR2TmzJmycuVK6dGjh4wZM8bsE3ROBGOZ9rDDDjP7/TDIWPbFcjH2EQIEkZxrYOqr08sAahoQAfDnEdB0EFp1a9Ub1EiMlh5a9WrqrFl3EPXQbDMAcPMXa+SC44+UrC4dvLzWveUpr5CksmJJPnqESIp/KzTeHs5cfiig6b/9aF806ggEAEIIRP8mT55sLoLu37+/3HfffWZPH9IRRxwhPXv2FNz/h3TllVfK888/Lxs3bpS2bdua07/Yv1d1D6FbnV7E1zQgAiABsD4b1HKYWvUSeGqPpJbWWvUGdQx/3Fokn3y0UY7re5SkZ2Z5ea17yxMKSeqeEhky7iBJziAAehMttnJp+u/Y6mn9rQkMAMaioJoGRAAkABIA3Wd9EIEnqDClpbVWvdB525ZiWb5si4wecLh07OzfobeK0p0SKimXkeP7SUoWAdB9psZeDk3/HXu9rbtFBECLkdI0IAIgAZAA6D45NeEhiHWzzdVtxgHAs4YcLp27ZLoblMcc5cUhKSkIEQA96hWL2TT9dyz2t642EQAtRkrTgAiABEACoPvkDCLwMAJYfVw1x5AA6D6HEjWHpv8OiqYEQIuR0jQgAiABkADoPjk14SGIdbPNjAC6zxrmgAKa/jsoChMALUZK04AIgARAAqD75Awi8DACyAigu2Uzh7YCmv5bu+1+1U8AtFBS04AIgARAAqD75CQARg+mtLTWqhfKcAnYfQ4lag5N/x0UTQmAFiOlaUAEQAIgAdB9cmrCQxDrZpu5BOw+a5iDS8A/2QAB0GIuEACjE32gU6PO0YZhzWVazbo5VwiAFi4toYpq+u+gCEkAtBgpTQNiBJARwGhDD+EhOqBNAIyezlwCtnBwcV5U038HRToCoMVIaRoQAZAASAB0n5xBhFYCIAHQ3bKZQ1sBTf+t3Xa/6icAWiipaUAEQAIgAdB9chIAowdTWlpr1QtlGAF0n0OJmkPTfwdFUwKgxUhpGhABkABIAHSfnJrwEMS62ebqNkMAdJ9DiZpD038HRVMCoMVIaRoQAZAASAB0n5xBBB4uAUcvakkAdJ9DiZpD038HRVMCoMVIaRoQAZAASAB0n5wEwOjBlJbWWvVyCdh9/iRyDk3/HRRdCYAWI6VpQARAAiAB0H1yasJDEOtmm7kE7D5rmAMKaPrvoChMALQYKU0DIgASAAmA7pMziMDDJeDoRS25BOw+hxI1h6b/DoqmBECLkdI0IAIgAZAA6D45CYDRgyktrbXq5RKw+/xJ5Bya/jsouhIALUZK04AIgARAAqD75NSEhyDWzTZzCdh91jAHl4B/sgECoMVcIABGJ/pAp0adow3Dmsu0mnVzrhAALVxaQhXV9N9BEZIAaDFSmgbECCAjgNGGHsJDdECbABg9nbkH0MLBxXlRTf8dFOkIgBYjpWlABEACIAHQfXIGEVoJgARAd8tmDm0FNP23dtv9qp8AaKGkpgERAAmABED3yUkAjB5MaWmtVS+UYQTQfQ4lag5N/x0UTQmAFiOlaUAEQAIgAdB9cmrCQxDrZpur2wwB0H0OJWoOTf8dFE0JgBYjpWlABEACIAHQfXIGEXi4BBy9qCUB0H0OJWoOTf8dFE0JgBYjpWlABEACIAHQfXISAKMHU1paa9XLJWD3+ZPIOTT9d1B0JQBajJSmAREACYAEQPfJqQkPQaybbeYSsPusYQ4ooOm/g6IwAdBipDQNiABIACQAuk/OIAIPl4CjF7XkErD7HErUHJr+OyiaEgAtRkrTgAiABEACoPvkJABGD6a0tNaql0vA7vMnkXNo+u+g6EoAtBgpTQMiABIACYDuk1MTHoJYN9tcewl46fJ8OXXwYdI5u527QXnMUV4SkrKC3XL0xYdISlaKx1LMFksKaPrvWOpnQ20hAFqMlKYBEQAJgARA98kZRODhEnD0opabNhfIvz//WHL67CeZmWnuBuUxR6hsp+wpSZZxfzhDMjpleCzFbLGkgKb/jqV+EgCVRkPTgAiABEACoPvEJQBGD6a0tNaqtypoH937YOmUleVuUB5zlJaWSUlJmYy/8BzJyvavXo+PZzYfFND03z40LypVMAJoIbOmAREACYAEQPfJGQ14yO09WLI7dnRvTCNyaLVbq15GLasPbnFJqRQUFxEAG2HzsZZV03/HWl/raw8B0GKkNA2IAEgAJAC6T84gAg9hKvhRSwKg+9yM9Rya/jvW++60jwBoMVKaBkQAJAASAN0nJwEw+DAVxDEkALrPzVjPoem/Y73vBEAfRkjTgAiABEACoPskDSI8MAIYfGglALrPzVjPoem/Y73vBEAfRkjTgAiABEACoPskJQAGH6aCOIYEQPe5Ges5NP13rPedAOjDCGkaEAGQAEgAdJ+kQYQHRgCDD60EQPe5Ges5NP13rPedAOjDCGkaEAGQAEgAdJ+kBMDgw1QQx5AA6D43Yz2Hpv+O9b4TAH0YIU0DIgASAAmA7pM0iPDACGDwoZUA6D43Yz2Hpv+O9b4TAH0YIU0DIgASAAmA7pOUABh8mAriGBIA3edmrOfQ9N+x3ncCoA8jpGlABEACIAHQfZIGER4YAQw+tBIA3edmrOfQ9N+x3vfAAeC0adNkypQpkp+fL/369ZOpU6fKiBEj6tR55syZ8vjjj8tnn31mfj9o0CCZOHGiDBkyJJx/7NixMmfOnGrlhw4dKkuWLPE8dpoGRAAkABIA3aciATD4MBXEMSQAus/NWM+h6b9jve+BAsC5c+fKueeeK4DA4cOHy8MPPyyzZs2SVatWSffu3Wtpfc4555h8w4YNk1atWsnkyZPl+eefl5UrV0rXrl1NfgDgpk2b5LHHHguXT05OlszMTM9jp2lABEACIAHQfSoGER4YAQw+tBIA3edmrOfQ9N+x3vdAASAicwMHDpTp06eHde3Tp4+MGjVKJk2a5Kr1rl27pF27dvLQQw/JmDFjwgBYWFgoL7zwgmv5+jJoGhABkABIAHSfmgTA4MNUEMeQAOg+N2M9h6b/jvW+BwYAQ6GQpKSkyLx582T06NFhXS+//HJZsWKFLFq0yFXrkpIS+f/2zgTGqiJdwD+IjfZ0Q4Mt0DgC6oiyPByQhyCyiXFQMSw6ohgXEKQVFdFE3FAIoLhr3JGnKIr43DURcTQRZEnIgEsUUEAWH+ioNPSStmmRfvnrvdPD0t11z7mnTt+CrxKSN886det89depr6vq1GnRooUpY/DgwdUCqPKns355eXnSr18/mTFjhsmXanIZQAggAogA2nuij/LADKD/0ooA2vtmpudwOX5n+r17I4Dbtm0zy7ZLly41S7pB0j19uofv22+/tbIeP368LFy40OwJ1CVhTbqsnJOTI23btpWNGzfK5MmTZffu3bJy5Upp3LhxjWXu2rVL9F+QNICOPfZYKS4uliZNmljrESYDAogAIoD2HoMA+i9TPrahEcCdRVJ4+YWS3yrfHqhhczRqJJKVFfYq8ocggACKNKiqqqoKwSzxrIEALlu2THr16lX9+zpbN3fuXFm7dm2dddL9fzNnzpRPP/1UunTpUmtefblEZXD+/PkyfPjwGvNNmTJFpk6desB/QwD/D4mrB7mrcqkz8lDXw4O4SyY+fORcUlwiRRvWS2GfbpKflxP/uJiTI6IvLSKB8bP9/xIRQA8EMJ0l4AcffFCmT58uH3/8sXTv3t0aSCeeeKKMGTNGJk2axAygldaBGVw9yF2ViwAmM8DDmb5ysIl2yY6dUrRunRSefXr8M4C6ylRZKdK3r0h2doQnMZekQgAB9EAAtSH1JRA9ykXfAg5Sx44dZciQIbW+BKJHxqj86dJvz549rfGwfft2s9Q8a9as6hdFbBe5DCCWgP9NHwFMRtTgnAxnhNh/ztUCeF5/yT8m9X3jtjHF/PeKCpHSUgQwJVjRM7kcv6PXKtkrM34JWHEEx8A888wzZhlYJU3P+tNjXXTZVt/sVXkL3gjWZV/d0zdv3jxzHEyQdM+f/isrKxNdzr3gggukoKBANm3aJLfffrts2bJF1qxZI7m5uSm1gssAQgARwNqC0JWouSoX4WEG8KCdAUQAUxorMzGTy/E7E++3pjp5IYBacZ39U7HTvXqdO3eWRx55RPrqFLmI9O/fX9q1aydz5swx/1v/782bNx9wv3fffbcRv99++80cIfP555+LHgWjEjhgwACZNm2aeakj1eQygBBABBABtPdEH6UVId63XX1sQ2YA7X0z03O4HL8z/d6D+nkjgJkI1GUAIYAIIAJo7/U+ygMCiADWGdksAds7fgw5XI7fMVQvkSIQwDQwuwwgBBABRADtnRMB9F+mfGxDZgDtfTPTc7gcvzP93pkBjKGFXAYQAogAIoD2TuqjPDAD6L+0IoD2vpnpOVyO35l+7whgDC3kMoAQQAQQAbR3UgTQf5nysQ0RQHvfzPQcLsfvTL93BDCGFnIZQAggAogA2jupj/LADKD/0ooA2vtmpudwOX5n+r0jgDG0kMsAQgARQATQ3kkRQP9lysc2RADtfTPTc7gcvzP93hHAGFrIZQAhgAggAmjvpD7KAzOA/ksrAmjvm5mew+X4nen3jgDG0EIuAwgBRAARQHsnRQD9lykf2xABtPfNTM/hcvzO9HtHAGNoIZcBhAAigAigvZP6KA/MAPovrQigvW9meg6X43em3zsCGEMLuQwgBBABRADtnRQB9F+mfGxDBNDeNzM9h8vxO9PvHQGMoYVcBhACiAAigPZO6qM8MAPov7QigPa+mek5XI7fmX7vCGAMLeQygBBABBABtHdSBNB/mfKxDRFAe9/M9Bwux+9Mv3cEMIYWchlACCACiADaO6mP8sAMoP/SigDa+2am53A5fmf6vSOAMbSQywBCABFABNDeSRFA/2XKxzZUAfx57XoZ/bcBkt/6aHughslRsUsalZdI1sA+ItnZYa4kbwgCLsfvENWo16wNqqqqquq1Bh7/uMsAQgARQATQ/nDwUR6YAfRfWnf8WixfrvpJzu54pjRpnm8P1DA5Kislp6pUeozpIll5CGAYdGHyuhy/w9SjPvMigGnQdxlACCACiADaOycC6L9M+diG238pkZX//EWGdT1DWrQ6yh6oIXLsKvtdKksrpG9hJ8nORwBDoAuV1eX4Haoi9ZgZAUwDvssAQgARQATQ3jl9lAdmAP2X1kAAR/Q4Q1q1bm4P1BA5KkoqpbSoEgEMwSxKVpfjd5T61Mc1CGAa1F0GEAKIACKA9s6JAPovUz62IQJo75uZnsPl+J3p9x7UDwFMo6VcBhACiAAigPbO6aM8MAPov7QigPa+mek5XI7fmX7vCGAMLeQygBBABBABtHdSBNB/mfKxDVUAV6z8UYZ37ymtCprZAzVEjorSSikv2iMDr/krewBDcAub1eX4HbYu9ZWfGcA0yLsMIAQQAUQA7Z3TR3lgBtB/af3Xz0XyjzWfS68Of5HmzXPtgRoiR2X571JVmiVjxv1d8lrmhbiSrGEIuBy/w9SjPvMigGnQdxlACCACiADaOycC6L9M+diGQZ0HnnSKtMyP9xiYsrJyKS0tl8Kxl0p+Qbxl23vUoZPD5fjtC0UEMI2WchlACCACiADaO6eP8sAMINJaV2SXlJZJUUkxAmjv/mnlcDl+p1WxBC9GANOA7TKAEEAEEAG0d04EEJmqK0pcxYercvVeEEB7v48jh8vxO476JVEGApgGZZcBhAAigAigvXO6HIh9LJs6JyPELjkjgPZ+H0cOl+N3HPVLogwEMA3KLgMIAUQAEUB753Q5EPtYNnVGAO29hhxmprWkRJo2bSrFxcXSpEmTQxIKAphGs7sMIAQQAUQA7Z3TR+HRu3JVb1flUudkxJIlYHufjyuHy/E7rjq6LgcBTIOwywBCABFABNDeOX0UHmQqOZlyFR+uykUA7X0+rhwux++46ui6HAQwDcIuAwgBRAARQHvndDkQ+1g2dU5GLl1yZg+gvd/HkcPl+B1H/ZIoAwFMg7LLAEIAEUAE0N45XQ7EPpZNnRFAe68hh5lpZQ+gIIBp9AWXAYQAIoAIoL1z+ig8LAEnI2m+cjYzgDuLpPDyCyW/VcwHQTdqJJKVZe9Yh0AOl+O3L/gQwDRaymUAIYAIIAJo75wIIDJVV5S4ig9X5ZqZqeISKdqwXgr7dJP8vBx7JwiTIydHpEcPJJAZQBM1CGCYzrNfXgQwmcHH5cPWVdmuyvV1VsMVD1fluuTssmwfeVDnfZ+jJTt2StG6dVJ49unxzgDu2iVSWSnSt69IdnYaI9/BcanL8dsXQghgGi3lMoCYAWQGkBlAe+f0UR4QwGT+cPSVswrgz2vXy+i/DZD81kfbO0GqOSp2SaPyEska2AcBZAaQGcBU+01t+RDAZB7kPg7y1JnYONiWJl3FtKtyfRXAHb8Wy5erfpKzO54pTZrHuAewslJyqkqlx5gukpXHDKDL8Ttdt0jqemYA0yDtMoCYAWQGkBlAe+f0UR58FRNXrF2V6yvn7b+UyMp//iLDup4hLVodZe8EKebYVfa7VJZWSN/CTpKdjwC6HL9TbJJ6z4YAptEELgMIAUQAEUB75/RRHnwVE1esXZXrK2cVwBUrf5Th3XtKq4Jm9k6QYo6K0kopL9ojA6/5KwLIEjBLwCn2m1qzIYAs8yUtab4Oaq4GeVfluuTssmwfeVDnfZ8i//q5SP6x5nPp1eEv0rx5brrDVPX1leW/S1VplowZ93fJa5kXW7m+FuRy/PaFCTOAabSUywBiBpAZwKTlkoE4mT9oEEA41zXsBP1w4EmnSMv8+PYAlpWVS2lpuRSOvVTyC+IrN40htF4vdTl+1+uNhfhxBDAErP2zugwgBBABRADtndNHaUUAEcBUBHDQSd2loEULeydIMQefmNsXlMvxO8UmqfdsCGAaTeAygBBABBABtHdOBBCZqg+Z8jHuEEAEcP++4pUAPvXUU/LAAw/Ijz/+KJ06dZJHH31U+vTpU2v/f/PNN2Xy5MmyYcMGOeGEE2TGjBkybNiw6vxVVVUydepUmTVrluzYsUNOO+00efLJJ03ZqSQEMJnBx8eHLXUmNhCTVJ6iIvSVZPqK00/M6S149pk5l+N3apFf/7m8EcDXXntNLrvsMlEJ7N27tzz77LMye/ZsWb16tbRp0+YAksuXLzdyOG3aNCN9b7/9ttx1112yZMkSI3qa7rvvPiOFc+bMkfbt28v06dNl8eLF8u2330purn3zrcsAYgaQGUBmAO0PSB/lQe/KVb1dlUudk5E0l5z1E3M/r9sgo3ueKvlNY/7EnPpf0z9JVu//9OYzcy7Hb/uTKzNyeCOAKm3dunWTp59+uppchw4dZOjQoXLvvfceQHPEiBGiDbxgwYLq/zZo0CBp1qyZvPrqq6Kzf61bt5Ybb7xRJk2aZPLs2rVLWrZsacRw3Lhx1hZyGUAIIAKIAFq7oDORcjkQuywbAUxG1HzkrAdMr1r1P3Jm+96S2zTmt4B/r5Smjf6Q3mNP9eaQaZfjt/3JlRk5vBDAyspKyc7Oltdff32fJdwJEybIF198IYsWLTqAps4KTpw40fwL0iOPPGKWjTdv3izff/+9WRZetWqVdO3atTrPkCFDJC8vT1588cUDylRB1H9BKi4uNrOPP/zwgzRp0iTWFt3+03b5rzn/LUc2bixHNG4cW9m/Fu+UZZu/kd5t/0OOahpvnV2V7apcheqqbFflUud9u4KPnGlD/9vQx7jbXlwiKzZ+J13aHC+5uUfGNqZoQX9U7pGGlQ1k+JCB0jS/aaxlH/mnIyU7N/6Dq1UAjz32WNm5c6c0bRpvnWMF4LAwLwRw27Ztcswxx8jSpUvl9NNPr8Zxzz33GFHTJdv9U1ZWllnaHTlyZPV/mjdvnowaNcpI3LJly8xS8tatW81MYJCuvvpqI4gLFy48oMwpU6aYPYMkCEAAAhCAAAT8J6ATOH/+85/9v5EId+CVAKq09erVq/o2df/e3LlzZe3atTUKoMrhJZdcUv3fXnnlFbnqqqukoqKiWgBVLgsKCqrzjB071szoffjhhweUuf8M4J49e6SoqEiOOuooadCgQQT8tV8S/HXiYnYx1op6Xhick2lAOMM5GQLJ/Arx7D9n3QZWWlpqJoAaNmyYzA1l2K94IYCZsgScZNuxPyEZ2nCGczIEkvkV4hnOyRBI5leIZ7ecvRBARaAvgZx66qnmLeAgdezYUXTPXm0vgajdf/DBB9X5zznnHLO/b++XQHSP4C233GLyqGi2aNEi5ZdAXDYNge+S7r/LhjOckyGQzK8Qz3BOhkAyv0I8u+XsjQAGx8A888wzZhlYz+577rnn5JtvvpG2bdvK5ZdfbvYJBjKoy8V9+/Y1x7yoJL777rty5513HnAMjOZ/4YUX5MQTTxTdU/jpp5+mfAyMy6Yh8F3SRQCToQtnOCdNIJnf4/kM52QIuP0VbwRQMejs3/33328Ogu7cubPoW70qeZr69+8v7dq1My9+BOmNN94w0he88asyOHz48Or/HhwErWcK7n0QtJZd30n3G6qc3nbbbdI4xreA6/u+Mu334ZxMi8AZzskQSOZXiGc4J0PA7a94JYBuUVA6BCAAAQhAAAIQODQIIICHRjtzlxCAAAQgAAEIQKCaAAJIMEAAAhCAAAQgAIFDjAACeIg1OLcLAQhAAAIQgAAEEEBiAAIQgAAEIAABCBxiBBDAempwfaP5gQceMG80d+rUyXyjuE+fPrXW5s0335TJkyfLhg0bzDeM9Y3mYcOG1VPt/frZMKz1aKGXXnpJvv76a3OTevakHg/Uo0cPv266HmobhvPe1Zs/f775Yo8e1/TOO+/UQ839+smwnPVbp3fccYe89dZb5rSD4447Th566CE599xz/brxhGsblrM+w59++mnZsmWL5Ofny4UXXmhOcjjiiCMSrrk/P7d48WIzDq5cudKMhW+//bYMHTq0zhtYtGiR3HTTTeYIOP2Kh57jW1hY6M9NZ1BNEcB6aIzgTEN9wOj3iPUYmtmzZ8vq1aulTZs2B9Ro+fLlRg6nTZtmpE87yV133bXPmYb1cBte/GRY1pdeeqlpE/3mtD649dghHTj1YaPnTJJqJhCWc1CKfndbeR9//PHSvHlzBNASYGE56+H2ylcPuL/99tvNN0/185K5ublyyimnEM61EAjLOfjM6PPPP2+eHd99951ceeWVMmLECHNcGalmAgsWLJClS5dKt27d5IILLrAK4MaNG80RcPrJ1nHjxplrr732WvNxB72eFI4AAhiOVyy59asmGvD612KQOnToYP7yqe2rJnrwqHaWIA0aNEiaNWtmAp9UO4GwrPcv6Y8//jCcn3jiCXPYOKlmAlE4K9t+/frJqFGj5LPPPhOdqWIGsO4IC8tZD87XGRb9Xvrhhx9O+KZIICzn6667TtasWSOffPJJ9S/cfPPNsmLFChPbJDuBBg0aWAVw0qRJ8t577xnWQdLZvy+//FJ0ooQUjgACGI5X2rldfNc47UodpAVEYb0/Cv2coM6evP766zJ48OCDlFR6txWV89133y1ffRKCaz8AAAaASURBVPWVeejrbAkCWHc7ROGsy7w6s5qdnW2+hnT00UfLyJEjRQfSww47LL2GP0ivjsJZtzGoiHz00Udmu4h+fOC8886TK664Qm699daDlFS8t5WKAOqHH7p27SqPPfZY9Y/r8+Oiiy6S8vJy/sgJ2SQIYEhg6Wbftm2bWUrUqWtdKgiS7jN78cUXzWfo9k9ZWVnmCyf64A7SvHnzzMyJnkhPqplAFNb7lzR+/HhZuHCh2RPIXp74OGv86/LYF198YfZLIYD2Xhwlnk8++WTZtGmT6NYGXSpbt26daExPmDDBbCMhHUggCmct5fHHHxed9dMvTO3evVuuueaafb5dD+u6CaQigO3btzfPCt3OECT97Ktuc9B2KygoAHMIAghgCFhxZA0eLhq0+k3jIOlLHXPnzjVLNTUJoMqhbpQPUrDnpKKiIo5qHZRlRGG9Nwjd/zdz5kzzfeguXboclIziuKmwnHVWVXnqHthzzjnHVAEBtLdEWM5aog6Y+ozQvVPBjN/DDz9c/QKa/VcPvRxROOsz4uKLL5bp06eLLh+vX7/eSLbuVdOX90h2AqkKoE586CdSg6R/TJ5xxhnmJZJWrVrZf4gc1QQQwISDIcrygr4YMnHiRPMvSLqxWN860030pJoJRGEdlPTggw+ah/nHH38s3bt3B3EdBMJy1lk/XcbZewlyz5495hcaNmxoZsH1TXfSvgTCctardY+l7v3TOA6S7iXWpWFdPdDVBVL6nPUlvZ49exqxDtLLL78sV199tZSVlZm4JtVNIBUBZAk43ihCAOPlmVJp+heiHi+iMyBB6tixozkGo7aXQHTW5IMPPqjOrzMneXl5vARiIR6WtRanD3GVP1361Yc6yU4gDGedkdIZkr3TnXfeKRrjurdHZ60Qk5qZh+GsJehSmW4X0T1pgYQo4/vuu88smZHi4azP87POOstwDZK+oDd69GgjgOy3tEdaKgKoe1fff/99c2JGkHSpXf+o5CUQO+P9cyCA4ZmlfUVwxIC+oafLwLNmzRI9f06PGmnbtq1521T3CQYyqMvF+pePLhOrJOpmbh0wlyxZYpYbSLUTCMtal311yUYHTd1XEqScnBzRf6SaCYTlvH8pLAGnFllhOeuRL/rHpfK9/vrrzR5AlZIbbrjBnA1Iiieep0yZIrq0rs/yYAlYxUTFUNuMVDMBlePgj0FdFVCGAwYMMC8u6cqXLvVu3brVnM2qKTgGRo+A0eV1lT59+YZjYKJFGAIYjVvaV+nsn8qG7lvQc410SVclT1P//v2lXbt25sWPIL3xxhtG+vQv+eAg6OHDh6ddj0OhgDCslXtNy+r6xqo+5Em1EwjDGQGMHklhOesgqdtHdJZE/7C86qqreAs4BfxhOOtLH8E+bhUWfdv6/PPPN/8/Xakh1UxA906q8O2f9O1pHf/0Dxd9iUnzBUkPgtZ4Dg6C1llBDoKOFmEIYDRuXAUBCEAAAhCAAAS8JYAAett0VBwCEIAABCAAAQhEI4AARuPGVRCAAAQgAAEIQMBbAgigt01HxSEAAQhAAAIQgEA0AghgNG5cBQEIQAACEIAABLwlgAB623RUHAIQgAAEIAABCEQjgABG48ZVEIAABCAAAQhAwFsCCKC3TUfFIQABCEAAAhCAQDQCCGA0blwFAQhAAAIQgAAEvCWAAHrbdFQcAhCAAAQgAAEIRCOAAEbjxlUQgAAEIAABCEDAWwIIoLdNR8UhAAEIQAACEIBANAIIYDRuXAUBCEAAAhCAAAS8JYAAett0VBwCEIAABCAAAQhEI4AARuPGVRCAAAQgAAEIQMBbAgigt01HxSEAAQhAAAIQgEA0AghgNG5cBQEIQAACEIAABLwlgAB623RUHAIQgAAEIAABCEQjgABG48ZVEIAABCAAAQhAwFsCCKC3TUfFIQABCEAAAhCAQDQCCGA0blwFAQhAAAIQgAAEvCWAAHrbdFQcAhCAAAQgAAEIRCOAAEbjxlUQgAAEIAABCEDAWwIIoLdNR8UhAAEIQAACEIBANAIIYDRuXAUBCEAAAhCAAAS8JYAAett0VBwCEIAABCAAAQhEI4AARuPGVRCAAAQgAAEIQMBbAgigt01HxSEAAQhAAAIQgEA0AghgNG5cBQEIQAACEIAABLwlgAB623RUHAIQgAAEIAABCEQjgABG48ZVEIAABCAAAQhAwFsCCKC3TUfFIQABCEAAAhCAQDQC/wvTmRHP/TbC7QAAAABJRU5ErkJggg==\" 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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
diff --git a/PV_output/prepare_pv_for_sharing.ipynb b/PV_output/prepare_pv_for_sharing.ipynb
index e3e8d22..7c7c4d9 100644
--- a/PV_output/prepare_pv_for_sharing.ipynb
+++ b/PV_output/prepare_pv_for_sharing.ipynb
@@ -1,986 +1,993 @@
{
"cells": [
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"import pandas as pd\n",
"import xarray as xr\n",
"import os\n",
"import util"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load PV potential"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/technical_potential'\n",
"pv_pot = xr.open_mfdataset( os.path.join(path, 'pv_potential_ELM50_1000_dhm25_200_SVF_eff_nobias.nc'), \n",
" chunks = {'DF_UID' : 100000})"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 143)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" pv_potential (DF_UID, timestamp) float32 dask.array<shape=(9639231, 143), chunksize=(100000, 143)>\n",
" yearly_PV_kWh (DF_UID) float32 dask.array<shape=(9639231,), chunksize=(100000,)>"
]
},
- "execution_count": 8,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_pot"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pv_annual = pv_pot.yearly_PV_kWh.to_dataframe()"
]
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" }\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>yearly_PV_kWh</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DF_UID</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9890019</th>\n",
" <td>17457.642578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890020</th>\n",
" <td>1079.603882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890021</th>\n",
" <td>204.146652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890022</th>\n",
" <td>22877.886719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890023</th>\n",
" <td>10378.332031</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" yearly_PV_kWh\n",
"DF_UID \n",
"9890019 17457.642578\n",
"9890020 1079.603882\n",
"9890021 204.146652\n",
"9890022 22877.886719\n",
"9890023 10378.332031"
]
},
- "execution_count": 43,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_annual.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load uncertainty"
]
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/uncertainty'\n",
"pv_pot_unc = xr.open_mfdataset( os.path.join(path, 'unc_pv_potential/*.nc'), \n",
" chunks = {'DF_UID' : 100000})"
]
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 143)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
"Data variables:\n",
" sigma_PV (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(100000, 143)>\n",
" var_PV (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(100000, 143)>\n",
" yearly_std_PV (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(100000,)>"
]
},
- "execution_count": 39,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_pot_unc"
]
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"pv_pot_unc_annual = pv_pot_unc.yearly_std_PV.to_dataframe()"
]
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\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>yearly_std_PV</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DF_UID</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9890019</th>\n",
" <td>4949.553131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890020</th>\n",
" <td>724.849397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890021</th>\n",
" <td>193.304750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890022</th>\n",
" <td>6842.172803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9890023</th>\n",
" <td>3284.971896</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" yearly_std_PV\n",
"DF_UID \n",
"9890019 4949.553131\n",
"9890020 724.849397\n",
"9890021 193.304750\n",
"9890022 6842.172803\n",
"9890023 3284.971896"
]
},
- "execution_count": 44,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_pot_unc_annual.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load solar radiation"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/geographic_potential/tilted_irradiance'\n",
"irrad = xr.open_mfdataset( os.path.join(path, 'tilted_irradiance_ELM50_1000_dhm25_200_interp_SVF_nobias.nc'), \n",
" chunks = {'DF_UID' : 100000})"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 146)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
"Data variables:\n",
" tilted_irradiance (DF_UID, timestamp) float64 dask.array<shape=(9639231, 146), chunksize=(100000, 146)>\n",
" yearly_kWh_m2 (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(100000,)>"
]
},
- "execution_count": 27,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irrad"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"irrad_annual = irrad.yearly_kWh_m2.to_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load uncertainty"
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/uncertainty'\n",
"irrad_unc = xr.open_mfdataset( os.path.join(path, 'var_Gt_all.nc'), \n",
" chunks = {'DF_UID' : 100000})"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 143)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
"Data variables:\n",
" var_Gt_indep (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(100000, 143)>\n",
" var_Gt_dep (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(100000, 143)>\n",
" sigma_Gt_dep (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(100000, 143)>"
]
},
- "execution_count": 29,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irrad_unc"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"irrad_unc_annual = (util.get_yearly_sum(irrad_unc, 'sigma_Gt_dep')/1e3).rename('yearly_kWh_m2_std').to_dataframe()"
]
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 16,
"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>yearly_kWh_m2_std</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DF_UID</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>309.039122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>85.539804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>239.814424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>259.121816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>188.438262</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" yearly_kWh_m2_std\n",
"DF_UID \n",
"1 309.039122\n",
"2 85.539804\n",
"3 239.814424\n",
"4 259.121816\n",
"5 188.438262"
]
},
- "execution_count": 37,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irrad_unc_annual.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load roof area"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/geographic_potential/available_area'\n",
"area = pd.read_csv( os.path.join(path, 'PRED_available_area_unc_filled.csv'), \n",
" usecols = ['DF_UID','tilted_area', 'available_area', 'available_area_std', 'predicted_area_ratio'] )"
]
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>predicted_area_ratio</th>\n",
" <th>tilted_area</th>\n",
" <th>available_area</th>\n",
" <th>available_area_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.090127</td>\n",
" <td>15.948816</td>\n",
" <td>1.437423</td>\n",
" <td>1.850753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.000000</td>\n",
" <td>13.730555</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.463675</td>\n",
" <td>63.437476</td>\n",
" <td>29.414390</td>\n",
" <td>13.349188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.370741</td>\n",
" <td>38.368910</td>\n",
" <td>14.224941</td>\n",
" <td>6.081246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.287692</td>\n",
" <td>36.403273</td>\n",
" <td>10.472945</td>\n",
" <td>6.134511</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID predicted_area_ratio tilted_area available_area \\\n",
"0 1 0.090127 15.948816 1.437423 \n",
"1 2 0.000000 13.730555 0.000000 \n",
"2 3 0.463675 63.437476 29.414390 \n",
"3 4 0.370741 38.368910 14.224941 \n",
"4 5 0.287692 36.403273 10.472945 \n",
"\n",
" available_area_std \n",
"0 1.850753 \n",
"1 0.000000 \n",
"2 13.349188 \n",
"3 6.081246 \n",
"4 6.134511 "
]
},
- "execution_count": 31,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"area.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare shared files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hourly"
]
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"pv_shared = xr.merge([pv_pot.pv_potential, pv_pot_unc.sigma_PV, \n",
" irrad.tilted_irradiance, irrad_unc.sigma_Gt_dep])"
]
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"pv_shared = pv_shared.astype('float32')"
]
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"pv_shared = pv_shared.rename({'pv_potential' : 'pv_potential_W', \n",
" 'sigma_PV' : 'pv_potential_W_std', \n",
" 'tilted_irradiance' : 'radiation_W_m2', \n",
" 'sigma_Gt_dep' : 'radiation_W_m2_std'})"
]
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 146)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" pv_potential_W (DF_UID, timestamp) float32 dask.array<shape=(9639231, 146), chunksize=(100000, 146)>\n",
" pv_potential_W_std (DF_UID, timestamp) float32 dask.array<shape=(9639231, 146), chunksize=(100000, 146)>\n",
" radiation_W_m2 (DF_UID, timestamp) float32 dask.array<shape=(9639231, 146), chunksize=(100000, 146)>\n",
" radiation_W_m2_std (DF_UID, timestamp) float32 dask.array<shape=(9639231, 146), chunksize=(100000, 146)>"
]
},
- "execution_count": 55,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_shared"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/technical_potential'\n",
"pv_shared.to_netcdf( os.path.join(path, 'pv_potential_hourly_shared_v2.nc') )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Annual"
]
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"annual_shared = pv_annual.merge(pv_pot_unc_annual, left_index = True, right_index = True)\n",
"annual_shared = annual_shared.merge(irrad_annual, left_index = True, right_index = True)\n",
"annual_shared = annual_shared.merge(irrad_unc_annual, left_index = True, right_index = True)\n",
"annual_shared = annual_shared.merge(area[['DF_UID','available_area']], left_index = True, right_on = 'DF_UID')"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"annual_shared = annual_shared.set_index('DF_UID')"
]
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"cols = ['pv_potential_kWh_a', 'pv_potential_kWh_a_std', \n",
" 'radiation_kWh_m2_a', 'radiation_kWh_m2_a_std', \n",
" 'available_area']\n",
"annual_shared.columns = cols"
]
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pv_potential_kWh_a</th>\n",
" <th>pv_potential_kWh_a_std</th>\n",
" <th>radiation_kWh_m2_a</th>\n",
" <th>radiation_kWh_m2_a_std</th>\n",
" <th>available_area</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DF_UID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>239.800095</td>\n",
" <td>324.268393</td>\n",
" <td>1210.184846</td>\n",
" <td>309.039122</td>\n",
" <td>1.437423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>474.558362</td>\n",
" <td>85.539804</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3891.605469</td>\n",
" <td>2071.079104</td>\n",
" <td>951.934814</td>\n",
" <td>239.814424</td>\n",
" <td>29.414390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2022.193970</td>\n",
" <td>1044.604313</td>\n",
" <td>1029.797436</td>\n",
" <td>259.121816</td>\n",
" <td>14.224941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1030.456787</td>\n",
" <td>702.281552</td>\n",
" <td>711.799309</td>\n",
" <td>188.438262</td>\n",
" <td>10.472945</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pv_potential_kWh_a pv_potential_kWh_a_std radiation_kWh_m2_a \\\n",
"DF_UID \n",
"1 239.800095 324.268393 1210.184846 \n",
"2 0.000000 0.000000 474.558362 \n",
"3 3891.605469 2071.079104 951.934814 \n",
"4 2022.193970 1044.604313 1029.797436 \n",
"5 1030.456787 702.281552 711.799309 \n",
"\n",
" radiation_kWh_m2_a_std available_area \n",
"DF_UID \n",
"1 309.039122 1.437423 \n",
"2 85.539804 0.000000 \n",
"3 239.814424 29.414390 \n",
"4 259.121816 14.224941 \n",
"5 188.438262 10.472945 "
]
},
- "execution_count": 65,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"annual_shared.head()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/technical_potential'\n",
"annual_shared.to_csv( os.path.join(path, 'pv_potential_annual_shared_v2.csv') )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Area"
]
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 28,
"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>predicted_area_ratio</th>\n",
" <th>tilted_area</th>\n",
" <th>available_area</th>\n",
" <th>available_area_std</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DF_UID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.090127</td>\n",
" <td>15.948816</td>\n",
" <td>1.437423</td>\n",
" <td>1.850753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.000000</td>\n",
" <td>13.730555</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.463675</td>\n",
" <td>63.437476</td>\n",
" <td>29.414390</td>\n",
" <td>13.349188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.370741</td>\n",
" <td>38.368910</td>\n",
" <td>14.224941</td>\n",
" <td>6.081246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.287692</td>\n",
" <td>36.403273</td>\n",
" <td>10.472945</td>\n",
" <td>6.134511</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" predicted_area_ratio tilted_area available_area available_area_std\n",
"DF_UID \n",
"1 0.090127 15.948816 1.437423 1.850753\n",
"2 0.000000 13.730555 0.000000 0.000000\n",
"3 0.463675 63.437476 29.414390 13.349188\n",
"4 0.370741 38.368910 14.224941 6.081246\n",
"5 0.287692 36.403273 10.472945 6.134511"
]
},
- "execution_count": 68,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"area = area.rename({'predicted_area_ratio' : 'available_area_ratio'}).set_index('DF_UID')\n",
"area.head()"
]
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/technical_potential'\n",
- "annual_shared.to_csv( os.path.join(path, 'available_area_shared_v2.nc') )"
+ "area.to_csv( os.path.join(path, 'available_area_shared_v2.csv') )"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
diff --git a/Solar/Covariance.ipynb b/Solar/Covariance.ipynb
index 99cd4fd..5358517 100644
--- a/Solar/Covariance.ipynb
+++ b/Solar/Covariance.ipynb
@@ -1,9724 +1,9724 @@
{
"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",
"\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"import util\n",
"import pyproj\n",
"\n",
"import os\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fp = '/work/hyenergy/raw/MeteoSwiss_satellite/raw_data/2001.nc'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"meteo = xr.open_dataset(fp)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/core/nanops.py:161: RuntimeWarning: Mean of empty slice\n",
" return np.nanmean(a, axis=axis, dtype=dtype)\n"
]
}
],
"source": [
"meteo['SIS'] = meteo.SIS.where( meteo.SIS.mean(['lon', 'lat']) > 0)\n",
"meteo['SISDIR'] = meteo.SISDIR.where(meteo.SIS.mean(['lon', 'lat']) > 0)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 365, hour: 17, lat: 95, lon: 217)\n",
"Coordinates:\n",
" * lon (lon) float64 5.979 6.0 6.021 6.042 ... 10.44 10.46 10.48\n",
" * lat (lat) float64 45.83 45.85 45.88 45.9 ... 47.75 47.77 47.79\n",
" * hour (hour) int64 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n",
" * date (date) datetime64[ns] 2001-01-01 2001-01-02 ... 2001-12-31\n",
"Data variables:\n",
" SIS (date, lon, lat, hour) float64 nan nan nan ... nan nan nan\n",
" SISDIR (date, lon, lat, hour) float64 nan nan nan ... nan nan nan\n",
" SISCF (date, lon, lat, hour) float64 ...\n",
" SISDIRCF (date, lon, lat, hour) float64 ...\n",
" KI (date, lon, lat, hour) float64 ...\n",
" ALB (date, lon, lat, hour) float64 ...\n",
" year (date, lon, lat, hour) float64 ...\n",
" KI_SIS (date, lon, lat, hour) float64 ...\n",
" KI_SISDIR (date, lon, lat, hour) float64 ...\n",
" SIS_mean_t (lon, lat) float64 nan nan nan nan nan ... nan nan nan nan\n",
" SISDIR_mean_t (lon, lat) float64 nan nan nan nan nan ... nan nan nan nan\n",
" SIS_std_t (lon, lat) float64 nan nan nan nan nan ... nan nan nan nan\n",
" SISDIR_std_t (lon, lat) float64 nan nan nan nan nan ... nan nan nan nan\n",
" n_t int64 4773\n",
" cov_Gh_GB_t (lon, lat) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
" corr_Gh_GB_t (lon, lat) float64 nan nan nan nan nan ... nan nan nan nan\n",
" SIS_mean_s (date, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" SISDIR_mean_s (date, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" SIS_std_s (date, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" SISDIR_std_s (date, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" n_s int64 11243\n",
" cov_Gh_GB_s (date, hour) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
" corr_Gh_GB_s (date, hour) float64 nan nan nan nan ... nan nan nan nan"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meteo"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"meteo['SIS_mean_t'] = meteo.SIS.mean( ['date', 'hour'])\n",
"meteo['SISDIR_mean_t'] = meteo.SISDIR.mean(['date', 'hour'])\n",
"meteo['SIS_std_t'] = meteo.SIS.std( ['date', 'hour'])\n",
"meteo['SISDIR_std_t'] = meteo.SISDIR.std( ['date', 'hour'])\n",
"meteo['n_t'] = len(meteo.SIS.mean(['lon', 'lat']).to_dataframe().dropna())\n",
"\n",
"meteo['cov_Gh_GB_t'] = ((meteo.SIS - meteo.SIS_mean_t)*(meteo.SISDIR - meteo.SISDIR_mean_t)).sum(['date', 'hour'])/meteo.n_t\n",
"meteo['corr_Gh_GB_t'] = meteo.cov_Gh_GB_t / (meteo.SIS_std_t * meteo.SISDIR_std_t)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"meteo['SIS_mean_s'] = meteo.SIS.mean( ['lon', 'lat'])\n",
"meteo['SISDIR_mean_s'] = meteo.SISDIR.mean(['lon', 'lat'])\n",
"meteo['SIS_std_s'] = meteo.SIS.std( ['lon', 'lat'])\n",
"meteo['SISDIR_std_s'] = meteo.SISDIR.std( ['lon', 'lat'])\n",
"meteo['n_s'] = len(meteo.SIS_mean_t).to_dataframe().dropna())\n",
"\n",
"meteo['cov_Gh_GB_s'] = ((meteo.SIS - meteo.SIS_mean_s)*(meteo.SISDIR - meteo.SISDIR_mean_s)).sum(['lon', 'lat'])/meteo.n_s\n",
"meteo['corr_Gh_GB_s'] = meteo.cov_Gh_GB_s / (meteo.SIS_std_s * meteo.SISDIR_std_s)\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# seperate all months\n",
"G = []\n",
"for month in range(1,13):\n",
" data = meteo.sel(date = ('2001-%02d' %month))[['SIS', 'SISDIR']]\n",
" data.coords['month'] = month\n",
" G.append( data )"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"for data in G:\n",
" data['SIS_mean'] = data.SIS.mean('date')\n",
" data['SISDIR_mean'] = data.SISDIR.mean('date')\n",
" data['SIS_std'] = data.SIS.std('date')\n",
" data['SISDIR_std'] = data.SISDIR.std('date')\n",
" data['n'] = len(data.date)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"for data in G: \n",
" data['cov_Gh_GB'] = ((data.SIS - data.SIS_mean)*(data.SISDIR - data.SISDIR_mean)).sum('date') / data.n\n",
" data['corr_Gh_GB'] = data.cov_Gh_GB / (data.SIS_std * data.SISDIR_std)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 31, hour: 17, lat: 95, lon: 217)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2001-01-01 2001-01-02 ... 2001-01-31\n",
" * lat (lat) float64 45.83 45.85 45.88 45.9 ... 47.75 47.77 47.79\n",
" * hour (hour) int64 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n",
" * lon (lon) float64 5.979 6.0 6.021 6.042 ... 10.42 10.44 10.46 10.48\n",
" month int64 1\n",
"Data variables:\n",
" SIS (date, lon, lat, hour) float64 nan nan nan nan ... nan nan nan\n",
" SISDIR (date, lon, lat, hour) float64 nan nan nan nan ... nan nan nan\n",
" SIS_mean (lon, lat, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" SISDIR_mean (lon, lat, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" SIS_std (lon, lat, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" SISDIR_std (lon, lat, hour) float64 nan nan nan nan ... nan nan nan nan\n",
" n int64 31\n",
" cov_Gh_GB (lon, lat, hour) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
" corr_Gh_GB (lon, lat, hour) float64 nan nan nan nan ... nan nan nan nan"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G[0]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7fa727996e10>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G[5].corr_Gh_GB.sel(hour = 11).plot(x = 'lon', y = 'lat')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
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"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
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" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
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" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fa7278b7eb8>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"for i in range(12):\n",
" G[i].corr_Gh_GB.isel(lon = 100, lat =50).plot(label = '%d' %(i+1))\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fa72788e240>]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"G[1].corr_Gh_GB.isel(lon = 100, lat =50).plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TO DO:\n",
"Merge individual months back together; can create average over all MMH timestamps, and attribute per pixel to nearest 200x200m pixel"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {},
"outputs": [],
"source": [
"# get average correlation\n",
"covrr = []\n",
"for data in G:\n",
" covrr.append( data.drop(['SIS', 'SISDIR']).drop('date') )\n",
"covrr = xr.concat( covrr, dim = 'month' )"
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (hour: 17, lat: 95, lon: 217, month: 12)\n",
"Coordinates:\n",
" * lat (lat) float64 45.83 45.85 45.88 45.9 ... 47.75 47.77 47.79\n",
" * hour (hour) int64 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n",
" * lon (lon) float64 5.979 6.0 6.021 6.042 ... 10.42 10.44 10.46 10.48\n",
" * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
"Data variables:\n",
" SIS_mean (month, lon, lat, hour) float64 nan nan nan nan ... nan nan nan\n",
" SISDIR_mean (month, lon, lat, hour) float64 nan nan nan nan ... nan nan nan\n",
" SIS_std (month, lon, lat, hour) float64 nan nan nan nan ... nan nan nan\n",
" SISDIR_std (month, lon, lat, hour) float64 nan nan nan nan ... nan nan nan\n",
" n (month) int64 31 28 31 30 31 30 31 31 30 31 30 31\n",
" cov_Gh_GB (month, lon, lat, hour) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
" corr_Gh_GB (month, lon, lat, hour) float64 nan nan nan nan ... nan nan nan"
]
},
"execution_count": 253,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"covrr"
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {},
"outputs": [],
"source": [
"covrr['cov_Gh_GB_mean_t'] = covrr.cov_Gh_GB.mean( ['hour', 'month'])\n",
"covrr['corr_Gh_GB_mean_t'] = covrr.corr_Gh_GB.mean(['hour', 'month'])\n",
"\n",
"covrr['cov_Gh_GB_mean_s'] = covrr.cov_Gh_GB.mean( ['lon', 'lat'])\n",
"covrr['corr_Gh_GB_mean_s'] = covrr.corr_Gh_GB.mean(['lon', 'lat'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compare mean to total in terms of space"
]
},
{
"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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AWTYVmFdnmRLh4CbUhWYUNGQvv6mDk6AdGUfGVO2IXpNDWCuIeDRZU3QamWdoDVkNVEmE+oUFZKuKulapXzurP/6qCpQBPJJAB98cXJsAnjIbpyQy4kAyrLlzEAEaQNzIoBZACgopEUMdYL0/mOhLpD+f8+GLbWFMwDsDaAuHLO5Ngu0twv/spwAxusjJ4Dx8PPajsCAICD7FjZ+ye434+ynPobykDZuWMI0hNL1SVMoGxgSOREr6Q6lF1T6OJGxTRFAegqyjCtrRLLTUtLpnNlbkkmW0eUbsSEsDHECOCBDY6s2WgQEkPjSAkZG0CR2nw0zg9x3L4BFAD4eOoKLQ64CsD1lvQD+CeDLIRSc2VebanOr9ulgPrkTwHi95wQwHn5e2xEYEAScAA4IrN5oTATySQDve3FK7BnAw3bjZF1OM4AxEfLq+UTACWA8tJ0AxsPPazsCA4KAE8ABgdUbjYlAPgngf1+cGpsAHrEbPZydAMbs9oKt7gQwXtc4AYyHn9d2BPqNwEUvHgeFb9nIBbPvSLeVSQA3pCrRnCovmhAwMRmV2BhhkSihswZQVWJh73fNmNNvvL1ifATySQDvfmEaqmNkAtnY2IEjd+f6DSeA8Xu+MFtwAhivX5wAxsPPazsC/UZgUwSQ+1iUCo6avURkM9ZVSoMmUFq7ceW2aIMawPVJM2quDb590gSWhzRyek2ypYUm69pHRHVWt9VE2+oyS5ZA30FpB1UvrT8MfoNVJZaeju9rX0sHF0Qi3f7woF3MNKmWp6EWnJSGe3QC2O9hNaAVnQAOKLzeeI4IOAHMEbCsw50AxsPPazsC/UbACWBl2tTaCWC/h1FeK+aTAN71wvTYM4Dv3H0x8cnFBiavePrJ4iHgBDAefk4A4+HntR2BfiPgBNAJYL8Hz1aqmE8CeMfzM2ITwKP3WOgEcCuNlXyc1glgPJSdAMbDz2sPAQQum3N0dBcKTSp9mm4thZJuKdsunE2nh9zLpXOOiSopP2+ipKObBlAhU6aA8xBwzxpAhYiF/jtm0HPXS74QcAKYL6T9PH1BwAlgX1Dq/RgngPHw89pDAIGtRQCp65MuLpPYkAB+5umPRsiWlaaibaYhdDbk0gB2hFzBTalyjAjavK48xKbnk4aP5JOF2j55Aqqd9e2mH5SvIOtIA1gT8hmrXZJjFukTZSbN95IdiWhfaVjIoXNq4cvw0tZ0CLgyGEE3dgyL6sgYuibRHL2uLm1FOQyLzOIEML8fwHwSwH88vz2qa2wM9adsbEzhPXssiIYygC7H8/405nUKEgEngPG6xQlgPPy89hBAwAmgmTw7ARwCg3mAbyGfBPC252fGJoDv2+M1J4ADPCa2ZvNOAOOh7wQwHn5eewgg4ATQCeAQGMZ5uQUngHmB2U/SRwScAPYRqF4OcwIYD7+irX3rgr3S937c9s/izoWzo9eyAzlsu1dywub2BXtExytMqMo1JS3pdg7dbn5ObfZ0sHR42jcy0QylScu3BrC3EDBDsO0hfOoh4E2HgJOwEOExM16MPTa8gc0jkE8CePNzO8aeAXz/nq/6DODmu3XQHuEEMF7XOQGMh1/R1h5KBFD6ulTQ0LUG/7rMXLuNqap0X1OjF6d856Vjo+ry+OP/OpfalQFyqtMecdThKUTbEHLtZuv7tJ/vS+snfV/29Up7SJ2e7ru908hUWYlp7eTRR2IsTNJ+guEYXYPIc33QD7K+cNW50l6CGV6E2d6G5Wk/QZuVnFheb9dS0pa+zopwbh3rBDDOaMytbj4J4E3P7RSbAJ6wZ/RD1DWAuXXzoDnaCWC8rnICGA+/oq3tBLD/Xe8E0EyjSSadAPZ/HG2Nmk4Atwbqfs7eEHACGG9sOAGMh1/R1nYC2P+udwLoBLD/o2fr1swnAbzxuVkYHmMVcFNjCh/ac67PAG7dITOgZ3cCGA9eJ4Dx8Cua2v9cuFt0r8lOC81lFobiEjBbkVw1gNe+emBUb1SiKdpuKQ2gFnawTebY/eHL7wrX391WghrAXEPAtEJR6FPp2IQHZ7RkjSK7E1mayCKlPmU2Kx4C3nIzgByXpWEMEtv3bv980Xw283mj+SSA1z87OzYBPGmvKHe0h4DzOUjyeC4ngPHAdgIYD7+iqZ1NADP1VwQh06MtF282EUDpzUQkWzorImynlK1PY5zLwpLeCKDIWEun5amtKW1Ja8t0IhE1GTbz2qR90zFxCKBy7mZ65jWn7Hq2H7Y62q5vr462mfl0dQ0bUpXdyKw8/+S3R0IrPaAItTz5GtrNZ08klbmCe/MYzMzdK3PsppT1y+hyM2qW5m9Mmb3mPemaMzWE3KfFNsKQmkCdW9crn0FpCseXmX0bCZ7azc4TrPacAA784yifBPC6Z3eLTQA/sle0OMgJ4MAPja1yBieA8WB3AhgPv6Kp7QSwuwmxE0AngEXz4c+4USeAxdjrhXvPTgDj9Y0TwHj4FU1tJ4BOAKMZPp8BLJrPfE83mk8CeO0zu8eeAfzYW17wGcAhPGKdAMbrXCeA8fAb0rUzF3pk23PkEgK+8bV9IpwU3pXtCNN7rW0fEe3rSwh4VaqmG97p8Giwb9Gs3Or2mm6rS5lyTCFfDwEXRwhYA0X9zde5SBOG9Ac7xs3lkwD+7pk9YxPAj7/lOSeAMfq70Ks6AYzXQ04A4+E3pGtnEkDpq5RvtqbUDHq7csKWprHINuXdFAGUN1y2Dx61eSwL2iZE28y8sTrRpgigFqOIqIp0ioSqDb6vBSgkjixrkradWG76M+nl+L+0cJvSBOqYESFvrs61KsmPG5CZ97c8eOJJbziuvDE6Rr57ukceJ71dXdJInDR/al/m0R0oQXXIBVxeaotzlGtXukMtUCkv7crzq3akyxtRZn2g++H/Wrgi3eHwcB4dQ+1eprYx8wOie5Q3IceSxpU0gGpvfJnhoOumBpD3FfVHp4016QWzf4zUljajLSxWcgKY2QPx/3cCGB9Db2HLIeAEMB6WTgDj4TekazsBdALoBHBIf8Rzvrl8EsDfPP2W2DOAp+/9DO/RF4Hk3NODo4ITwHj95AQwHn5DurYTQCeATgCH9Ec855vLJwG85ul9YhPAT+79lBPAnHt58FRwAhivr5wAxsNvSNfuLwFc1DY+wmVKuVm4tIUUYz1pAD0EbKbIHgI2HDwEXNiPFCeAhd0/xXZ1TgDj9bgTwHj4FUVtrgDe2GHec8rDmk3qtIiDx61uN62bCKDy2UqHpzao/6oIuV/XhgUe0nOJLDZ2mN6NWriu+kYWpPPqOtby9VI/SM0gi65LGjW10aUfLElryho7rL786kYHbzu+l32OTI9A7pcmMNsYOnOASFso7RrJ78gyM8Duuj57pEnvl+njJ/8/HZsMWrhMjR7rUmM3vnJD1E5mLmG+zvYHpBZQeY7V/rhyq5upYZRGc1Wb9a18AKUl5IIe9Yk0gNnXlW2dQ52j9JDSBcp7UHgqF3Bm/0vXp/aEUfa1WL/YWGHJ1qZm9o3/3zcE8kkAr356Hwwb8Wbj+b5dKdC8oR1n+QxgX+EalMcVIgG8EMB3AVwO4DwA2wFY2Au6JwK4sZd9vwNwata+OwG8O+O9MQCuoPE9v0MA3ATgc/wO62NvOgHsI1DFfJgTQCeATgCL+QnQde/5JIBXPb1fbAJ4zt5P8OJdAzhEh2+hEcD9APwFAMVD9wQCyNxTFhPrKmcyQxWASZsgaySAEwGcllGPP7O7UiMAdwDYBsBZUTIG4LcAOOI/0sf+dgLYR6CK+TAngE4AnQAW8xPACaD3fmEiUEgEkIZmTwP4FICvAXg2EMCekOPSJB57xiZgJQEcBeD4Xo7ZBQATHZJ0PhmO4ezgPwFsC2B5H7rMCWAfQNpSh9y+YI90U73lKr1v0U7pY3JJfbalrjGzHWkAGUbzELDZqShM7CFgywTiIeCB+OQVbpv5nAG88qkDYs8AnrvPYwTTZwALd0jFurJCIoC/B7AOwPkA7t0EAaQrLgnbwQAe3gwBJPlrC7N+/w3Ecm2oczqAH1GOk9EGBRM07/oQgJt7aJtCLhNzWaHh2dL6+nrU1pq2x8vAITCYCaA0c4mSzgigRW3joq20WvLS40zR4lbbt9uwJdFWOrwlbWO71aFuTBotaf2k76pJmM/gyiR/AwHUeYmESlsoE+l1KcubK+9AHittmbRp0gSuS5nxtPznmjoqwT+WluAdl9bjBY0dFyYkYPctT8Bs30L5DmaOHt2bTKil69Mx1AIKV2kBpTVcEzwJtUiGdXTOtI9eZ/fHX0O7aRiHJZKQT19zyNwxLMHHCDCt0h4fuufMe9oQ6sv/T/dEbz/pIqVxzPT9i/onUqAQn9L0dYocal82Zhw7ug7t03jKxo77J4a8wMJT+lFpQYXr2MQGKF/w8BK7b5ZDt5uf2T3+fz8QyCcB/NlTB8YmgJ/d51HepRPAfvT1YKhSKATwJABfDbNxJGCbIoA/B3A4gNmbAZhtUiFO/eDMoCuktu+t0XMW+ErQCO6c1c4qAN8EcFUP7V8U9nXb5QQwP0PdCaATwMyR5gTQCWB+njxb7iz5JIA/efKg2ATw/H2jORYngFtuCBRUS4VAAKeGGb13AojyzmyCAHJJ4woAl4TZu1zA3B7AawCOAnD3JgjgagBfB/CLHhr3GcBcEN9CxyoTRmZWAs5SZc7uaJ9WyPLUnKXSbAdfD1QqK11f5upcnk9ZPzgb4zOAPgMYjckSm+kbjDOAr7ZNSs8M8h4+MDMyCfaSAwJOAHMAyw8dcAQKgQAyTMtwa2a2eC784DcGn5YkXdr3MQC/pkMGABK1XAvrUF94NYD+hICzz+cawFx7oB/Hi2Ap3DU+hLJ6IoAKWWrf1iSACssxDZdsX1o7udYIWN5myoNn6vn7Bzhp4uNpZNaGMOuksrroPVmvTErUR6/rOoZH2+5hSEvvVRVCdgoJq1GGcEVQFQqWfYlsQNJWLOhIk1eFelVXeNal7BpIZBT6TXaY5YTsUDLTvSntms7FsCiLwpwiRCRI2enm6sO5VEf6Qbal97KHlUKu2jIlmq5LVi4KBaufmlPWN+UlHRhfYanUhL1CzEqx9kaSkyK0hUm+yYImO5zNe1L4ekxZsJcJ+ZezrV7YT8JI95Q5zg0zs4zJDL+LWCqMn9ZadlRExyoEr2u2sWJ9IImBxgP7TVY76VSFIZyv0DDrOQHMHnWbf51PAvjDJw+JPQP4hX0f5E35DODmu3ZQHlEIBJA6uulZ6HE17lwA3wfwYsY+hobXAPhgP9Dmwo7Xw6KQ2wBoEci+ACK7cwCchfyXLwLpB7oDWMUJoJE7J4BGWJwAOgEcwMfNgDadTwJ42ROHxiaAF+z3gBPAAR0RW7fxQiCAPSHQkwZwBwCv0I80kLTseiSM9BDkbCKV6tTx0ddvZdAAXhYWbexOr9dQmTYwtIo5O8MGhgtM3AZm647Lbmd3AugEkAPCZwDtY+EzgAX0cMrxUpwA5giYHz6gCAwmAkhzaIaAOVtoQpruhSFjev7R/oVawVsAvCVYwdDS5a6g7XsjoxqNoK/MMoL+rBtBD+iYy7nxQiaA/164Szokm60B9BBwl6rDQ8AWMh9KIeB5rdvgwtl0zfLSVwTySQC/98RhqIqRCaRlQzu+vN99vDUPAfe1gwfZcYVKAAcLjK4BzENP/e018nh6yFlas5pSszihdUq2PqoDNqRlX9HYadYeLAORyooEsEsfZxo4ad+ktWNqty4ySHkr8O81toj91G0eirZ1wYqFC0a0eETXLYuYtOVLynCg7ks6LtVpSNn96rX2UxOWfV0dwQZlRZtZxew0jJPlTH9G5Z/hKDuY7LRhC1vMm50aObUr7Zr6RLYqPE66O92T7FSkVZN+MNMyRinR1J60al0LaqitMyzUvq5FdaUnpPayNdW3tFi0qpENjLbSxElrxzRsLLyW8tKQh7fULFOEufSS1KXqHnR9wlP3rzrcrxk+YZU9HnQsdaXSR47JSLtnfWj9N7d5crRlerpsq5hsSytHW94AACAASURBVB7pG2kLI9wyNbRsJzulnxNA9VLftvkkgN99/O2xCeBX9mc+BieAfevdwXeUE8B4feYEMB5+fartBNDy3ToBNA2gE0DDwQlgnx4fBXWQE8CC6o6ivxgngPGGgBPAePj1qbYTQCeA0cxaWDnsBNAJYJ8eHAV4UD4J4CWPHxF7BvDr+zN/Qs4zgJ8G8MWQqpXWbp8B0GVz8OZ+OQ/AOfRZD4s8/xr0/PQEtolngB68p4Q2Kemi1OvbwS2kAHt6cFySE8B4/eQEMB5+m6x950ILk6ZtS2KGgNnOh2Y+BRFKtt2TlYVSuOnijtueWQm7F+b3ZSlHykPAJab18xBw8YWAX2uaEPX9r/bl97GXzSGQTwL4rceOik0Av3nAf3IlgB8G8IewsJJ55EjumFmLCReYZCG7cMHlb4ItG12nmcuTg+l6AJ8PBzNpA/8/FcBLAOjcQacQJo/42eYw9/29I+AEMN7ocAIYD78+EcBV7d3T7E2QDyBK03or+ZzJk07+bSKPOtFAEMCmzszsgF1efNL18RoUvpVPobZrU3RB6gprcpZLXn6a8ZLmS/6A0hxS5yVdpPz6dJ/Znn/UxGkhhlKA0RuPRX6C0p6taa99U1o3adekG1ve0pVBsbrMFtVn+v7xdabPnlLgjc7SqmUbI7Oe9HLSISY7TDc5PKRjy/Smy06PJl/BbM3i2rYRSAYvO2EkDz6ledP70v1F91Rq5FbXLW2hjpXvXnR9QQOYnWpNOPOY7FlM+UJqVpNtaGzoHMKIfpIsmXrSTO9G29dd5yiNoMYfj+ltHGSOmWzt35qkjVNqNFnka7ii1fwQnQBu8lGW3lkEBJCk7wkA54abpoUBc1peAeB7PaDERZi0ZDsyYx9TtB4A4JDw3t8BcPHmGRnH0OGDYnDOCnrpJwJOAPsJXKjmBDAefpusrRlAJ4BG1JwA2gybE0AjxMTBCeAAPoAGoOl8EsBvRDOAtlipP6VlQxIX5zYDSNdx6lXo00sXDpXfBzeO43q4Ds4AMr0rPXgZJmbGrn8AuDakb2UVzgCeGY6hFdyewdWDs4J/6s+9eR1DwAlgvJHgBDAefk4AfQYwGgOZGTF8BlA2pfbx8BnAAXzI5LnpfBLArz36ztgE8NsH0j0NTKJgqXGscIB2H6T2PpecLwNwEIBHMo6nB+9hYVavJ8RpvfbDwEc4hc00rNQEqnAWkTZwF4SsYPwFxPDvpXnuviF3OieA8brUCWA8/Hqs/fN5b4/en1lhlo1bcgaQ4dTMsCHDbW+yRwlpunRxCslmXqxCzNQAegjYQ8AcG8UcAj5i1MvRx+MjOzAC6KU3BPJJAC985N2xCeClb2VirDeVb4VFGdk7eiOAPwBwKIADe2jr8KD3Y4pWDh4mfLgcwDUALgnHnwSAbXBhCTWAewH4adAFcnbRSz8RcALYT+BCNSeA8fDbJAEcm7C8qdIjKTduppda2oMuSh1t1hgsImXSPtWUtKQNm7VYQZ5m2QSQBLGl03Kojk1k/vDtulzle2Ve3skhZ6/0YWuDp580gNRhTSy3PL66nuxzy+ttVGkTVD87r6v0XWqD16hzZvv1yTtQPnOcVZPGT7rB+uAnKE+7EQlbdLeqrbvmku+NLt8Y7dOxyrG7tm04RpWbL6O0c/IXFNGmdk8aNyEoXz2dU/exsnUkasvsOrJz4vakBdR70rXp3Hotzd2a5Ago169mG+UdqBk2Xdvoclt1zTIyYfdWE7aNATNdL69f/aR+UT91aTjbIA2kcNCYlnYzU/enY4eXhJRvQWMqPas8/jI/PKqjfL46t9rnftUbG/IR6zrXtjNxUhfevEb9wFE/S/u5NmgBG5LmN7nD8FXYtmJd+lKcAGb2ypv/H6QEsK8zgP0JATPX3KOB3Akw6vp+GTJ6MekDNYTUD/5fBqIkjDxu1qYR972bQsAJYLzx4QQwHn491tYMoBNAI0Jps+Qg8HcC2KUFdAJoHyEngAPwIBqAJvNJAL/8yNGojKEBbN2QxPfeymypOdnAcBaPWj5av7AwfPt6yLjV0yKQpwBwqfGXMuA+OawM5i8TrsJaC4CE76qMY5j2lZm/uGrYSz8RcALYT+BCNSeA8fBzAhgsVHwG0GZcfQawi8z5DOAAPFy2cpP5JIBffPg9sQngDw7ieoycCKBsYLhog0SQNjAnhpk6anpoEUOdIAkcC/39uJiDxysETKJHYsi2WGgLcxSAs0IImKmhOENI+5hM4riVe3fwnd4JYLw+cwIYD7907Rvm7xf9zxCuQpQ+A+gzgJnDy0PAZjfkIeAt9NDZCs0UAQEkqrSAkRE0TVS5yEPi0HsBLALw8QA/F31wQcfHAEwBsBrA7eG9unAMPYioB3w/ABpP0gj6zwAu5lfGVujGIXNKJ4DxutIJYDz8eiSA8kabXL6+W+vyw6susc88dUny/5NGSeFS5QJuClo+6qekeRoffATrUsOjdqT3ky6PmjBptaSl0r5sLRePlX+aFoto0YqILHVp6VyyoKQFqA16O32ZS4c4qawBDzdRBw3sWGm5eaX9WthqprvSXDEUPL91YvTexHBPun9drwDkfaxuNy+3ppQRCWnsmlI2+yafPeoFtS87xKr20j576MCG0N6whNnVSIenWc1hpW3p+5dGTX5y0tjJm/Dxhu0xucqe+1oNrGOl3ZNmj5hl6xelzUvnBA7X1pAaliZO8ieUZrE53L/eH1XelF4YNKGiIboWjb01AcORCdMJSmvI/3UP0mOOSphuMsoFHBYW6Z40NnS9ao/HUweaWaQXVB1hSJ2n9qk/dG5pCnVejodML0C2n51bW+OMmtV1QRf4VB2TMwDrWy3n8szaNdF2n5rF0ZY4S2/K1x/egRZwXnpDIJ8E8H8fOjb2DOCPDqYFX04zgN75gwgBJ4DxOssJYDz80rUzZwCdADoBdALY9cFyAriFHjIF0Ew+CeB5D70vNgH86cG3OQEsgHEzUJfgBDAesk4A4+HnBLDTPoI+A9ge4eAzgD4DuIUeKQXZjBPAguyWor0oJ4Dxut4JYDz8cO2rZg2lMCo1gD4D6DOAPgM4OGcA76k3V45f7MNEDl6yEcgnAfzsg8fFngH82SG3+gzgEB7GTgDjda4TwHj4pQmgtHXUKYkMSrM0JWEeesrzKw++qpJUpAO0feb/V15iGrvlIX+wcu1mauKk65P+Tq91K9QKZucQrik1Pzjp2uSvRu1VV3t0POjyLdR93LF+dxw5ak43pGpDe21ZuVsnZPgOKvSne7yrYfeojX2qF9p5OsrTukZpyVRnScuY6JgJFeZjSB88+d7pQmRcrNfKvUsdmTSA0qxJY5Z9r9TNKV+uPP2ky8v0pMvWVDI3L8v0YaYpU1Fd3R+30gD2dN1dPpDW77pO5TeWXnB9sjqtcdR9CzNdZ7s8JFPlGF1mOryRZdbvamdM0PVpbHI8aGzo+qQ1VZ3s3L48Tn6COkb4cMzIrzE7z7H0fLq3MYkN6TzO2eNV1/Jai+lGp1WuTfdp9rml4dN5qf+79P5jo3rVE0zH+NYp1O0DOw03c/YxwUuQY0Xj5snG7dL96ASw27BOv8gnATz3wffHJoBXHnJz9DEAYGJYL0MKASeA8brTCWA8/JwAOgHsNoKcADoBjPlIKejqTgALunuK7uKcAMbrcieA8fBzAugE0AlgmM0lED4DGPOBUuDV80kAz3ngA7FnAK869G8+A1jgYyrO5TkBjIMe4AQwHn5OAJ0AOgF0AhjzKTJ4queTAJ51/wmxCeDVb7vJCeDgGV45X6kTwJwh61bBCWA8/GLVvm/RTml/tmy93NqUacykx6JOKzsHsHK4SufGvL5WJ5H2bdMFypRar8cHLdi61PB07t4Fbaa3emT9zGh7/Pinoy01YPIalO5QujDpr5Ykx0bH0qNQesXX2szjb68q81yTx5+OzdRfvdRED9UuzdrSltHR64pSW11Lzzvp2aSbk7Yw2UEv1u65YOVbKF2YdGdpv7mgtWTIVu2NLjO9mMqLG+2ahieSqCwxj0DVl+felEp5vVqtytIklH+WukWWceWWE7ornzKzQwHzWyZiXLlpHHUN2bmAM3V069urAxZmsK19un9dN/VtwlrtTi6365TXo/qTr6WLlA5PnoHyBWQ/zqqkdy0g78n5rZO6XYvyXNObT2NCOlPpTpXnWtdJX0SN+4Wt46O3t9F1lhreS9tMC0qdY3b+5bSuM/SN2mWbVz56ZPRyxxkrou0+Y5iOFZhRSZ/eLuzOnfVfVfNtHxDIJwE8874PoSJGKri2DUn88rAbeVeuAexD3w7GQ5wAxus1J4Dx8ItV2wlglwDfCaCRQieAgag6AYz1bBmoyk4ABwpZb7c/CDgB7A9qXXWcAMbDL1ZtJ4BOAH0GsDz6DPkMYKxHSd4q55MAnnHfibFnAH992F98BjBvoyP/J3ICGA9zJ4Dx8ItV2wmgE0AngE4AYz1E8lw5nwTwtHtJAC3NY39K24Y2/PZwJ4D9wW6w1HECGK+nnADGwy9W7XlLJqM0jGDlBF6Zspyl0vV15eltx8qU5cKVVmtSmfkLqkj3RW2d9IDSvr3QNDU67NCaedF2fMJssZ5tmY4pIWfxnJbJ0Xu6lukV5nFH/zZpx6QXk05MOqy6lOnTeKx86d5IUnoDTK6wnMjKjbwg5ARmzlbp2uqSdt/bVNo9qd1Mr7/snLq9gU8dnuxYlKNWWjPl41X79K+TJ5x86nRvr7aYzo25gHXfK1vtnuR/Jw2gXvO88sTTOUYGTz7p8aSRo75tXJlpAHWf8qTL9i/kMboHXZ/y+C5rNb2ktIb0+Hu91TSZUyoNe2k2dR5d49iyDVgb8uZOLV8bHZt9Ho4r1W/osH5a2mbtL2uzcx804pVoy3Egrary8cpzsCffRulYlyRN69elH+yMXqstaQH53uJmO/cOw1d16wu1Tzz+9OBB0b7aqTbOj9vuhWi7U5VpAlk+tuOj6f/9n74h4ASwbzj5UflBwAlgPJydAMbDL1ZtJ4BOAJ0A2oIWJ4CxHiV5q5xPAnjqPSfFngH8/duvJza+CCRvIyS/J3ICGA9vJ4Dx8ItV2wmgE0AngE4AYz1E8lw5nwTwY/ecHJsAXvv2PzsBzPMYyefpnADGQ9sJYDz8cqr92OIZ0fE1pW3RlqFWDwFb6NhDwDYmPARsKewoY4g+I8Gux0PAOT1qBuxgJ4ADBq033A8EnAD2A7SMKk4A4+GXU+1sArisvRZTykyjVF5iX3ijAiNMddrrxe2V0XZiogWNIderTiqt3sZOE9InOy2XL/VjXfmIzV5kSbtprK5748Bo+5Up/4i2zE+8KmgLpf2SvmvbCtOERT6AHXaO5UnTfD3RYGT20FGm/ZL+ivq/Ne2mVZSOTRo4Xfe8JtPW7Va9DMpnq3uRflB6P+Wu5XHyREz73wU8dG69T60ddYAsslXRvcmjTwSD55FeUDo++eptSBn29B+Ufm9B87jovWEJa1/ehDpfpgZQ963rkz+i/OxIapR/WNpEaRZFBIU7tZzbVqyLzplJhvi6vt10ecqVXF7anr6nppSJ6EeXm8fhxDDeMnMjP7txWrTvoJpXo+2oUiNhGkPUk04K+awbOqqifc80W97c+9bsGG3PmPJAtF3dXpvWb0ZvZFzvDlWWh1fY8/85zea1KA9GeSVK57pj5cpo/5K2sdiuwjz87m+cFW1fbDDNalXCvCIPGj0/2m5fsQqfffjk6P9EhY3/6eMNO5a73/7j9P/+T24I5JMAfuS/H4k9A3jdEdfxBj0EnFs3D5qjnQDG6yongPHwy6m2E0CDywmghT2dADoBzOkBUgAH55MAnnT3KbEJ4PVH/tEJYAGMm4G6BCeA8ZB1AhgPvx5rP7Boh+h9ZUBY2W4rR7crt1W1CgH7DKDPAPoMoM8ADsAjaMCazCcBPPHuj6GiOoYNzMY2/OXIa50ADtho2PoNOwGM1wdOAOPhlxMBPKBqaSCAiWib7OxAeYmFbbNLR9BA1XVYKJibjSHv7tpguTI2pHOrCenSGkNKtMy2VqbYxcC9DbtE25NGPxZtX2q18BlJqsKbsuuQ5cfK5KjoGNqDrAup6Z5pmh69t1OVheZGhWvIPOfiNkvr9eB6I8Izqi2ULLKjUCZDxAqHKsWYwrFqTyFhpl4bUWYzZwoXK+yq0KpCxDxG+xRibgrhXLUrbRmPU4h2QYulwtN1ZoZ1Ryaao32vt1ooXangJlR0T+XGMLLOJesYtSPLE1mdMCwtqxwtBtH1ZYes2UfpEHUIfQsrhXMV7mXavPXtlhZQfatQ9fwm65v9aheF+yjHgmZ7b2qVhUm3CbZA+gHDlG6zQih2dRgHGht3r7dxtf1w+3EzvXJNOsSr8fRS87bRPoWAlVYvUdKJKxa8Pdr3lR3+GW0VfhYOi5IWcuf4Y2iXRakE//T6/tHrCcMt1d7HJj0cbRk+vuSGD0f/t02xUD3LotMuSP/v//QPASeA/cPNaw0MAk4A4+HqBDAefj3W7m0G0AmgETgngKaxcwLoBHAAHj8D2mQ+CeCH7v4flMeYAUxubMONR/6BeLgGcEBHxdZr3AlgPOydAMbDzwmgzwBGY0AzbT4D6DOAA/BIKZgm80kAT/jPqbEJ4E1H/d4JYMGMni1/IU4A42HqBDAefk4AnQA6AfQQ8AA8RQqzSSeAhdkvxXpVTgDj9bwTwHj49Vj7C8+Z/ujcsfdH20TWKE2arC8QB9smYAetTpk+cHip2VeML7XX6zra0RJsXlqCBkypy8aWtkbHJDutjVWp4WjqNPF0i7bBxkXnnd86Mfp3WsWatL5rXcp0Y7LpeLXN7FrGlzWkU4FJbzeq1DRxbeFadOwb7bVY1Wa6Q6Umy7Rn4fvblNeFaytPt7u41bRe0tbJ0iTztTR1St0mqxClRpNujteoFHi6X2kBdx1mOkzalRhmCciCZU1yRPq96DorLC3duvbqdCo42bKsCCnhdhpu1iZqn/rB9UnzNpTuTppCaSt1veq/zPrCXvcyNmH6Nh4rvaC0gNnaR1r6GIblaWsY6uxYaoOGUZrL4WHMcJ/OpXsQZtpSI5nWLWaNI73/97V7RofTvuadY16yaw7jVf1WE8ZMRYnZtqxN1UD38NdV+0bvVQV7nbo2s7Y5ZRtL17Y2NQLZKe90vdJwSnP44zuORUdVR/o2Fp3zhexb8tf9RCCfBPD9/z4t9gzgze/4bfRRBGB+W16GFAKFSAAvBPBdAJcDOI+LPwEs7AX1EwHc2MM+mq59G8Ax/HFNqy8A/wHwZVqxZRxPJbep8rsKz/+9PvayE8A+ApXLYU4AnQBG3zplRpKdANpCDCeAuTxFCvPYfBLA4+46PTYBvPWdv3ECWJhDaYtcVaERwP0A/CX82rgnEEBO4dgyu65yJgAuSeMUi/3E7174i+WvAK4B8Bx9UgOhZFv2U9kKCeCvw3F6j8sSzfV188UJ4OYxyvkIJ4BOAKNvHSeA0WfHZwBzfoQUbAUngAXbNUV5YYVEABk/ehrApwB8DcCzgQD21DHPhGPPyKHXSC4fDzN+r2cQwJ8C4F9/ihPA/qC2mTpOAJ0AOgEsnBDwPnsuiD6xNx308wH4tBdXk/kkgO+964zYM4C3v5PzIx4CHqqjtJAIIJcb0UjrfNqubYIA7gPgSQAHAzDjqr6VowDcRausDD0DZwCZm4khY5JC5r35CQAT2Wy+OAHcPEY5H3H43aY5+t3Of4q29cGfLxV0fqNCmjI6ALYG3V5NsANcFlJ3jQr5gquChmtdiinbTKPWlebNurk85Etd3m6+ffRSG5Mwq5HbGvaKtkfXPB9tpTtT2jh650mTVtdhuquGsK0PmkDq817ZYJrBfUYujrbZOrz64DvH9Gfyp9MxmWni7HpN30j9nfRn0uEJI73O1HkpTZo0haUBG70eE/RyrZ3lWN5mKes0+6ROlMed7o146FzSkMlPcFloY7vK1en0dvL0k85ROkFdJ3Vv0i0q/ZruV2nt5EFIPWH29dFzL7M0hr4gptnaP92/zq37YLvZOYV1DdIA1gRNII9VO0qbl51yj9cj7WNDysbIASMs7Zp8+6QBvfLlw/DRHfl4A56sM3XKkWNfjrY6t9qiplFY3b54Nxt7daZD/e5bb46281ts3L3QMAW71y7rNvbmNJmX5b/u2zvaJlrt6yA5pRXbTDSd6eQRXdIvJ4Ddhla/XuSTAL7nzk/EJoD/eNeveJ+uAexXbxd+pUIhgCcB+Cr9Vfms3AwB5M/QwwHMzgFekryHAMwF8NGMep8PM4kkngcBuBQAVa98v6fC5KaW4NQKk7Yura+vR22tzdp4iY+AE0DLG+wE0JQYTgCdAMZ/qhRGC04AC6Mf/CoMgUIggFPDjN47g16P19XbDCB/Pq8AcAmAH/WxE/ltehP9cwNx3NRqptMBXE3dOSeXemj/IgDfzH7fCWAfe6KPhzkBdALIoeIzgD4D2MdHxqA5LJ8E8Oh/fTL2DOAd76aM3mcAB80Ay/FCC4EAHg+A8QqLa1nhYg36LzCewxk37ftYWLTBBJir+3Cv/CblohKuBD6CbgibqbMrgBcBzAIwr4djfQawD6DHOeTrL7wfD6yaGTXhIWBb3+QhYPv4ewjYMsF4CDjOE2br1s0nAXzXHWfGJoB3Hv1LJ4Bbd8gM6NkLgQAyjJptxcIwLMO13w+ETCBwZpBJMz/YB1RE/nYEwISZfSGMDA8z9w1N1db34RyuAewDSLkcQgL4PyHfrnz7VF8+bRXh98CYRDvqOsznb2ypab+WtFuEfkzCJnDVxqtt4zE+YXlnpSGr6zC91JSE+dVJP8dcufq/sYPqAaR1fitTlMMAyr2b+b+0dBPLrD156XGrcK40asoNrHuT/qw+NQyjy7qHPuX1lp1zmPo26dd0zjfa7fqy26VvnTSAui7p57L9ANmm9s1tNp2Yrqkrf7D547FPkkGjKY2atHaZWkV50DHPLsu4cuuLDSnDNzNPr3IBC0/qIlnUnnIZs4+6MDGSKKuU2oSRJfksZvaH2hWx1mttG4NOLxNH4az71z5eS6YnYDRWgkZV7a1ur8GcRsOxJWWzuzOqLffvrGEMaAC7Vpo+79mWael7ks7ythW7R/sWvrpNtN1nd1uU8T+THsbKpPW3/AR5LpYfP03JM/DJvR6MttSYqn9++chh0XvDlpjvYbI2GGtua7Y7U8bVYdZIyxu8bVXXY/Abu90Wveel/wjkkwC+459nxSaA/z6GATGfAex/jxd2zUIggD0h1FMIeAcArwRvv3/1UImEkR5+nE3kk41hX6qbjwVgbrNWqPdrA/BWarEB0G6G30Z8zQUgdwA4tY/d5gSwj0D19TAngE4AnQAamXUC2NenxuA5zgng4OmrYrjSwUQAaQ7NEDBnC7sv9bOe4s/Y0xg53Ix5NGcDSTBJDrmghOFeThvRbPpa/njuRf/X03hwAriFPyVOAJ0AOgF0AriFHysF01w+CeBR/zwLZdWZaxZzg6F9Yyv+4zOAuYE2yI4uVAI4WGB0Ahizpy6bc3TUwrHBZuXPdft7CNhDwNGY8BCwSRQKJQR8w2tmF/PScVwL56U/COSTAB7xj7NjE8D/vucXvE23gelPZw+COk4A43WSE8B4+CGbAA4v6cDqDvvVKq1fTan59dWFPKrjQ77Tls4SyOevqaP7UN4Y8rpWh7ypj7ZsF+XkZZFvX03I5/pCCxeIU0dl2jtquOpSlo82FXwGX2+zXLuLm8dG24mV1taYso1pDVhmLl3uk35wfFkjKkssnZc84rLzxnZpDdshP71sfZvaV05cLgbI9vSTF122xx/vWdpHdVlbwEiv1VamZ15NqWnptE/Hrms3T0Xeh65Tur5sX0Du1zHZmkqFObWfGshk0HUqE4h0d7qGTM/Dbcup6ACkqdT16jqFFXV60j7Ky1E6Tmks9f6qZG06n7O0m9qn61Q+3kwvQmEiraVyD69vH477V9jCprUrg0azzIIYM7c1rd33Zv4t2tKLcnzCxtZrbROirbwMf7uAKhVg7XrD/tv736rbTOc5fnA9lTLA3iPN615p9NhP8pq8aymDHkD9nDHRNjXexqY8IYbXNqN2mGk1E0FbW9dk/oUsTgDTUOT8jxPAnCHzCgOIgBPAeOA6AYyHnxPAgJ8TQIU9nQA6AYz5UCng6vkkgIf//ZzYM4D3HnsV0fQZwAIeU3EuzQlgHPQAJ4Dx8HMC6AQwQsBnAH0GMOajZFBUzycBfNvtn4pNAO9/b5T+L1cC+GkAXwQwKXj7fiakYe2tj84DcA6AacHl469hQaeFH6zQ+o2uINQMURvBVDrU/FvaHC/9QsAJYL9gS1dyAhgPPyeATgCdAHoIOOZTZPBULwIC+OFgpXY2gMcAkNx9CMDOAEzv0L18BMBvADAJA1O77hQWcl6fkZGLeSmfCY4dnJKkpRvt3V4Lf4NnABTYlToBjNchTgDj4YfnXmciGGBlyvzLIr1Y8PmbEhZDyJOvKWjDqkIu3GWpmrQ+Sua48rSTXm5W5fKoXer9lOt3uzLzA/97457R9q3Vr0ZbacKWJMdiRZvlBW5oN5+6seWmD5QnnXzXSqPF51ayc+FKc8f8tpn/Zx4rXZq85BIlnel2pHXTdWXr5diO/PN0PTpWWjhpDaUJZB1p3rJ1fV0auTf7600tN8ykjVwXNJLUC2Zr9OSvp/OwnrzxiAWLNHXU/LGoju7Z3jMdWuZ1WV8GQ+TO8vS9qL40f2pfvoCsp/vtOrctslDJvO4lSdPH6XqWtNjrqVWmOdQ9U1up65NPZZdu0nz2GlNVuH6+LaBoesP0e6Uh7+64nQzXi3Y2jz2OfeUhfnADvzOBHarMxWr3yqXR9onmGbZttC3LNpXmPamZVOEtPSLff6Pd0lX+6g7zCKxcZ4//9iDvaxttusSSUW3obDC/wpJ2O+aXx0Y5YaPyjhmWR0DtbwAAIABJREFUms5L7gjkkwAectunY88APvi+/+NN5jIDSNL3BIBzAzr8wC8BcAV/5/SA2JUAdmHK64x9zPJFi7ZDwnusdzCAQ3NH3GtsCgEngPHGhxPAePg5AQwLXpwA0pqzi3DxfyeATgBjPl4Krno+CeDBt54bmwA+dBz5WZRG1ZzbrfCXWU+pUiv4Gyckargl4/jfc40dgON66BDOADLOzFSwj4esXf8Ilmy0fmOZA+DOcB10MadrOutEeeq89B8BJ4D9x441nQDGw88JoBPAaAT5DKDPAMZ8lAyK6oOUAGZj+y1OWvcAONPdkJwdBOCRjP2XASBx46xeT+WzAH4Y1qFz2pzeM9QEqkgLSI/eGwHsD+CnAM4K4eZB0feFeJFOAOP1ihPAfuJ33yJKPYBRpZZ+ykPAwXbDQ8DpEeUzgIU5A3j2Y/TjB1476Sv9/PQXb7V8EsC33vqZ2DOAjxzHyG2fZwB7I4A/COHbA3vo+cMBUO/3taAZpI/R5WF275JwPMMDXOxBYqnyMwD7hQxexTugYt65E8B4ADoB7Cd+IoDSzUmzR52WNFRTy4wcLg86POXhfbHZfPuowzpmxIvR/5MTpi1r7DTPwEQwNasL/oATEyVYGvRMy0I+X+kF5d8mjRj1U/NbJkbtyNtO+WdFSqSxosaMuj0WeQbK60/QMB9tB+z6VLry2pruqiZh98q6ui4dKx2b9H2tnabPYpEGMVt/qNfKDcwZNt2fzi3PQGGvDBzlpe2oCv6J0l9m5zdWG9QY6n6lR8z2AZTFjfWLYSUNnXIB6zX7PtvbUMdQS8ei656/cQKOGfd89N6YxIZu+C5PUjfeFUbm/9IHqu+yc/52jcWKdF+qzrxWy8MrLz1pQedu2AYzhlte3wnl5t8nveQfVlK2BLR3luK517mIEehcEzIzhCfv9e+NQmxY1m6aU2o51f/SjdaU2ARIbdBE/ncjJVPAAcPnp70mVUfXK4/H2qCXPPWB03HULsyWCTy8dLto27zEdLeJZhubVXYb6EwAzROtnzrGWmj+xsMiQ2Cc9PCZdpATwDQOufyTTwJ4wC2fjU0AHzuePKvPGsD+hIAfAPBoWDUsKE+h7JQ2liHr12IA/wbwiQysOUNI0mgfLC/9QsAJYL9gS1dyAthP/JwAmu+dFko4ATQzYieATgD7+UgZFNXySQD3v/lzsQng4+/nZFyfCSCP5SIQavlo/cLCXxd0JecvnZ4WgTwF4D8AvpTRgSeHlcEkgHxQXsffVVmLQH4SQsqZs4KDYgwU0kU6AYzXG04A+4mfE0AngBw6PgPoM4D9fIQMympFQABlA8OpYhJB2sCcCIDpZ7ic/Q9BJ3hh6EBqCT8PgMeTPDIETKsXEkO2xcJQLy1ivgngL0EDyAUgrPOnQTkQCuSinQDG6wgngH3E7+HF24cZHgsprUuZBYeHgD0EzHHgIeDBFwK+ZN+uVHQf2YHf3V42h0A+CeC+fzsv9gzgkx/gWoucZgB5PC1gZAT9LAAu8tAAuRfAIgAfD1hx0cdXAVBYynAuPf5uD+/VZeB5LIBLg//fQgBcEOKrgDc34Daz3wlgPACdAPYRv2wCKB1XGxJRC41hNSz9+hQWXZky37J/1+8WbVs7zFftnPF8hpiejLmDWSpKbCingsZsZcq0VtUhB+8TLdMhr8A17aZ9ktZMK1Cla6POL9tPTbfZVafLo045hjN97+xaTFvF864OHmw7Vq6M3pOeL9u3jm1Iz5XtnaecsLqGTP2h7o16wwiboEuUbs4wNg2dSnaeX2kBuT99/wE/7ZO+b3L5+qgZEnjp7bI9DTPPldl2JvY6JrMP6C3IIk2h7veZBiYKAF5ea/rM3cetwLFj+f3SZR+jOmlvvtBWTaIlnetZ2j/1T7oPQt5ntrcyQ5PH1zpG17ssaAwvu/8YnHXwPdHbew/j91rXwqaGgPeCtgm49NFjon1VCyiTAmYeye8w4IwplEABv19hesFTtnkEv3idCyaBHWv5XQhMCnmnTx5JezWgLYyrle01ac/AHy5+l9WfYt+z0o2qHzneJoRc2Deu5SJK4N/Pz462tS8a3lV1QfeXKEF7sEicddK8aN9Z29g9nv7PT0bb776TizG7ihPAbnD0+iKfBHCfm86PTQCfOoGR1pwJYN/A8KO2OgJOAON1gRPAPuLnBNDIrBNAGzDZJtROAJ0A9vFRMqgPcwI4qLtvyF28E8B4XeoEsI/4OQF0Apg5VJwA+gxgHx8dQ+qwfBLAvf/6eSSqw6rzfqCY2tiKpz/ISOugnwFkWIQhZ2YbmRAWpmQickQ/4BkSVZwAxutGJ4CbwO/2BXtEe7crX4fGDgt91ZQq44MNPQ8BWyhZ4UgPAafgIWAPAcd7LBdu7XwSwLeQAA6PQQCbWvHM0CCAXGlFAsgMIyvodJQ1Qs4v3BEzsFfmBDAevk4AcySAygVbEfL5qnpdR8gJW9KWzps7KXjjzU2OjQ6bWWZ5WFuCzq28pAPyqasO7dWUmu5utS2yRTLknn2oeQfcuMzysR410XRNIxLmryYtnK6F2ru0Fx1MY6hcu115ae081IZJfycvuqbO7g/dte0j0lqt6eVmtiY9njz0pN3jeaSXG1XKrEq836DRCno8tc86uh5dr64hO98ttWBpf7lwT/KZk2ec2uBWfn3CJDvnsPwQF7ZOwLgyyxK1ImkLGXSscGUOY+WkVXu6bmnUZINDrZqwFq6q80j9zOjfRxZYDtyrDvwjWjrth4XwU7vM0cvy1EbzvNunelEac3knTim38bQ25KGWbo5tKBfw5HLToWufMJvXQs9b4GdPvR27Tbd80+taTDh3xc5/jrbSsPJanm626/jVX94dbZMjgnfkCBtf++3OvPbA0eNewAtN5nO5rq3a2mk2zap0s8rrvLRuFDqCz2UqZeOxba19jkqb7PV7DuNiSmBp8ygcPGa+tRfyXD+40hZmNf+DkyLAqIXBQ7MlhfoZNobXHGA4Dhttn5Xr9rWcwC+02jWqj87a+b7otZdNI+AEcKuMED50/wfAP7fK2Qv4pE4A43WOE8BN4NfTDKATQCeAHDJOAJ0Axnv0Ds7a+SSAe/71f2PPAD73wR8R6JEAzOV8cBb+QmPGkVcG5+UP3FU7AYyHrRPADPw+8/RHo1cXTrQVg0+1jou2mSFgJ4BOAJ0A+gxgvMfu4K2dTwK4x41fiE0An/8QU/QOegL4vwA43U17muzw7+AdTFvgyp0AxgPRCeAmCGCjRbeikK20fkwdxqLQrUKNmWFYhnZZKkKosjSM0pqwXdxu4alVqRoo1KdtY6dZndSlLHymENW6VPWbQouyWenJ8kREVSE/hSUVapQdCkOuCskqFZwgUXhX4Vi+X1ViGkhdX0dIWZeZai3bnqQ04KBQsMLnDJdqX20IZ+saslOD0UImO/2cjlHoV+3zOPWTVufK4kaWN6rLFHevNE+K7mlcuaVjk/UMrVdYGBIWXsJG/a9rku5vm/L16dRybwTrnJFBCrAihC5vem3PqJnmjZX40YH0hQXUl2p/bOnG6N8fLrOQ6wGjF6RD/bKwUd/esd7ae9/op6PtnNYpeLphevT/rBGUDNGCyMLw+1dbqLY69OOzLdPwk/vsHLvuwoQHXdYusuLJlAA83mBh1wWft1zYS4+0sPFvPh7lXMX4RAsWhFD6z5YcFb03ptKkAK/WjY+202vNguexZ3fAjJ3t+rarsXD2PS/Rbxeonmefs43TTAtRObEJe09eGv0/dZjVn1Jp21/+9j3RdsRy+26saEyhYZpZLtXPtvrfPcpsX+5cZ5ZMu9dYW+q/z81iMgcvm0PACeDmEBqQ/TcDeDtVFQBeogIj6ywfGJCzDoJGnQDG6yQngBn4Zc8AOgG0L1EngE4ARfSdAMZ74A722vkkgLv95YuxZwBfPPEHhHywh4B/u5lxc9pgH1f9vX4ngP1Fzuo5AXQC6DOAPgMYfQp8BjDew7QYaueTAO56Q3wC+NKHhwQBLIah1a97dALYL9jSlYqeAF4+9yjsXWUZEP6y7oBoKw2gzwD6DCDHg4eAu1aB+wxgvAfuYK+dTwI4+/oLYs8AzjnpsqEwAzjYh82AXb8TwHjQOgHMIIATEqZVqgtp3dZ2mA5vfGJDOr2b4K4uMcsJlcqQumx1RyVqg77q9LlMDwncOPvaaLsyZYRKusGWzjLIPmZUaXO0b27bNtFW2jjp8Khvk43GmDLTh6XzEAd9V2uwW7F9pn3SdlQi1IG9L92f7EGi+w75jTPPqTakocsMB3NfZlo3vmbKuGxrlEzNH4+RBQvfl+Yv205Gr2WvQiuWymCNIm2a6mpFrjRxtEmRHlDXrWtSSjTZ2FALKP2l7k33lOnnpz5Tzl/V0bG6BpJFaf2WNZutzGGjzbbnjSQjUcA1D3JBH1C+LoFRe1m6tKt3/WO0VZFNycVPMYUoMG3CWpww+Zno/9lVy6KtdJ4/XvLO6PWXp90RbanzTIYUhV97+fjovX0nLIm2h4+aG22nlq2NtnUdw3HJxZbWdMPxtlDy+3v8Ldrq3j7/8Ifxhf3uit57YL1p/xrebzYtC8/aMdpOOdzanzZiPc6baHq6hvA5WtcxInr9jZ+dGm0/dc4t0fb11rHYaZilFtQ4vGjOe6PXTXNGR9syk2Gich1Qt6dJn750sN2ndJ6//rNpGMe+FMb2uASaLdsemnc0n8rfHGpRtNP//Qm7hoP/G20vmG1teekbAk4A+4bTABz1QQAn8iMWycu7F/MHK8LiBDBepzsBdAKYHkFOAI3UOAF0AhjvsTp0a+eTAO7y5y/FngF8+eTvszMGuwbwswC+A+B3AM4EwF8zNBXdD8D/Afjq0B1xm74zJ4Dxet4JoBNAJ4BhZtFnAH0GMN7jdOjXzicB3Pm6L8cmgPM+8r2hQAA5bf8tAHRop2s9l/0vAHAxF9kHe5ihP/h6uEMngPG63QmgE0AngE4AozHgIeB4D9NiqO0EcKv0MrVJuwBYTPcwAO8A8BwA6i8epfphq1xVAZzUCWC8Tih6Avjw4u0xqtR0QmtTloZKRTqySWWNoF6PpSpo/8aXmtffvKTpmzaGlF6NqWH42ULLzf3jnczjbfty8857otV0TfRVY6E3H33oup0z6Pmk75PfHl9LF5fW3QWfOr2Wt11FSXtah6fUatK+ZYd5OeslfaA0ZWpPWjteX5cmkXnJu0q2VyA99NJ+fcEPUe1Jw6dr0ZatrQqeedIxKt2ZdF48Rp5+wkb1V7ab1k6veR/y1ZMGUOnddOV6n/6C0p/xfxZhpGPZru5J70nHKF2fdH9P101FXYu188YafryAXbY1ndvHpzwUbb/02AnRdtjzw7BxuunWLjzi9mgrrG5dad5+i9fyBz5w6qzHcEi1JQKQB+F1a94avT59/P3RVv6TTzdPx4N1ps1rSJqv5OmTH4y2n7+FGaWAWz7042i7KDkG599kGsCrPnhNtF2X0vg03Sj1qfosXHyaOU4kNpoeb/5H7NivHEOrMmBq+VooNZ/Gtrwsv/bMcdExn9vd9HfUTT60ztLjzb3Htlee8stoO6/VtLBXv3JotN3QWIXEcvPPTMw0v8ZP7mJ4XvNX0wCWmYQXG7ftQGeV4Tp9O9NYjq2ynTuO4PcnsKLF9JjXHmCp4bz0DYF8EsCd/hR/BvCVjw6JGUDO9lEDSKPPJznk+dEAQPHv9WEWsG8dOMSOcgIYr0OdADoBdAIYFuA4AXQCGO9xOvRr55MA7vhHEkD7AdOfkmpqwaunDAkCyF8pXGXFMPCnAdDbhr9+9gVAwe4Z/cFnKNRxAhivF50AOgF0AugEMHqK+AxgvIdpMdR2ArhVeplhIv7JeuIkAAcxuU6YCbQQUxEWJ4DxOt0JoBNAJ4BOAIuWAM5bPwEjq8yCaVmdhYVLwrfK3pMsXVxLytLo3XjQVfGetkOgdj4J4A7XXhh7BnD+xy4l6oN9FfAQGDkDcwtOAOPhWvQE8NWlk7E6ZWEG6ftqSu2HVlOH6d1eS47DzPI10f9jErbvF2tNf7XHcPM/ky6N2rNZ5eax1thhusFfrXlbtB1bYdqlGZWmS7L27D35ysmfT+1Jl0ddmvR2b8pzm6W1U5uZQ0N6vgbda6lpuJjjVl571C9m3kvXqtiytL5Oei5p6kaVmrZKqcKWJcek/dnkjZedN3d8GReyWZ5j5dvVtcrbL1sLSA2aMBI28heU9lHvsy3t03u6/8xjeBzxle5ucZvlqq1P42A6MmoPdb8bAn7SBK5Kms5vSZPpO3nckg2mSVyxxrY/3t+0oJMS9eEYy1l74gNnobM9aECV4l1PtFJ7ozRhWtOyihSSLTaefnnwH6Ltk80zou1h1ebtJ2/D36x8G+rabEwvXG368J/uc0O0vWyB6eXOn2FefcztTJ9LlteSdv/q4wlltir4nEdOwciHbGxMeMzuoaTNPgcrD7P2mw63Nr66xx2Y32ImfBPL7Vj6/bGMSJjWdkPKtHzcPvPDt0T/V55hOYGPnfxCtL1lmWkgZ40yzd5jy6ejNWn3n1xu+YcTTYZdarhhlGi216Xbb0hjVbLaznXc2x+Pts2BzC1rsr55Y2ONE8AIib6VfBLAmVuAAL42dAggxbBnBfsX6gFpBkqj2YUATOBbhMUJYLxOdwLoBNAJYKcRfSeATgA5DnwGsPcvFSeA8b5w+1mbq8aYTeBPgfTNDjYw5wI4Jvz1s+nBXc0JYLz+cwLoBNAJoBPA6CniM4AeAt7c10k+CeD2f/hK7BDwgv/5Lm9psIeAmQLoJwA49Z/pA8jpc6aymbS5fhuq+50AxuvZoiOAN8yneTowq8KsOYaXpjwEnDANlIeAPQTMceAhYHuoZmsAh5e14dE7d4v21e5rMo7dxlnomuW3+1m6uS889+H0ez/c00LvQ6XklQD+fgsQwFOHBAGkzoazfkxan0kAtwcwh0qOoTK+cr0PJ4C5Itb9eCeApSm0dJp2aFRpyCXaYcPq8RamXQT2r3odyvV7c+Pu0Xt7DqMnJ3OYmq6pLujGdiyvw4LgaSeftkNHmn+bvOOk5ZtStj7tHyitn7qnSz9n10btmt7TF7TaUx3ZmIwva0jnLpan28iQ51gkT7o/agDTOYWDl+H4hGm/pEe0+zPdlXR48tmTP2BtqSVtXZuqhjR5zN/LUhryJKuuPOW4T3582dhIl6fzZHr76XrXBT9F3i+L7pUaSIVzt60wPabOIy2grpueifIRVDtL2kyzJk0dNYB/en3/6L29x9rCgEmV0vOZ/mxkINHLWkdjaYvpAVc210TbjUlL3blspb3/60OY0YlYmYdeZlncNi56ObHM2r9+hZ13zhLzxWM5Ybdno+2hNZZjWHhoXFHXVxP645SHLPftt/a7LdrWBu9I5guO2n/jALx0l3kGpnY137/aEfaDYGOz6ecmjGzEW8bYfdclrU8XXTIr2pa22f2v+bRpQS/Y5U6sSNp9vtJkWsBdq5dH2/+u2Tnable9LtoOS7Thjj9wMSNw1ifNB1G+itIP/mOVfd6W3bwdLjrXtI/MTcxSUmeLM8o2dtdRcrgla+26OsuClrLFjjnxiIejrbSA69qq8fIv+d0KrKfVLq9vH7tX5jVmWdlciwlVpnF0AtiAkSOjmdKBnFWLvpe2/91XURrDBqajqQULPs4MagN6rTZoBrbQB5Ap4CjczSSANPX8ciCHA3sFBdp6IRLACwHwZ8flAM7j8yQINXuCkMmdb+wFW94bfX8+SW4SfH/OCUu/VYUusVcAYAZ1PvFuos8q9dV97C8ngE4AnQAGI2kngE4AnQBu+psjrzOATgDVGeQUp9DPHcC/g+ZveggLMx3clX38vh9yhxUaAWR8kUv+OCVxTyCA/KltsaWuQjZ/QYjd90bWvsQEAUwgEASflwDgz2H+XLXpFov/c2qAq4P4c5gxiCcAfKSPPe0E0AmgE0AngNHjwmcA7anpM4C9f3vkkwDO+G38GcCFpw2JGUDynK8EPmBT8ABDTz8E8PU+ftcPycMKiQAynsNULZ8C8DUAjNNwBrCnQlEnj+3NwZv3xbjJj0Insw1Oub8RCCHTvzBgwfg/SSfTw7DQ4+GfALYN9TfX6UVDAG9dsFeEhcKnmRpADwGbpUeLh4AjHDwE7CFgjoNcQ8BN7ZV46W87492nMD2rFdcAbu4rqMf90ffSdr/5WuwQ8KLTv63vTtOJDO5CLckOdFQK3/19jfQN7rvexNUXEgH8PWVItNgCcO8mCOA+gbAdDMAEKW8uFHe+BoCrfEzwY+W+8JphXk4HkyCa4MYKjbI4O/ghAJacs3uhqMeEPVYoUlpaX1+P2lrzMxuqRQRwapnpj8YEH7xECbVepg9a0m6rACclzKdOnn/8/+lW0zNJOzalrC56XRfyx5bD9IPUt0mLJizly5eE6a6khaPObW3KdGLyv1Md5bXN1LWJoGXnrlVdacASJR1pvaG0b9KEZer6dF7Vy/b2YzvRPaaGp3V90uTpOtX++IDZ8vbR6WNVf3KZaamk/VuSNI0d75G5gyNcg05wbPCkW91uuOjaqFmULk7nliZS1009Iwv1hE81mUfeHsNe1+HhGkyP91LTlGi7NlmNyUHPt+dwO/a/9SYGe9co86S7s2533PqC+dIlKq2f3z6TJvxd+rbJ5XaP7GthvKB1QvTeijYbV4+vYdQG+MZM07stS45O3598FK945fBon6xIRlSaxpQeeNKJyrj47GmWA/i2tfbj5oRx9juQ40RjQv2VztncaY/Mny+083xm+3uwtM3yDV9zG1OLAjV7mG7y1O2NyDDP8Qt1k6P/599ruE65z66rkx8gagLfax59JeNa8cHZ/H0L/PUe88rc7wDTwE4fbu3e9SvT/R31iUeh/NDyBvzXQsM+2WrtzWDGUz4MP1qOC97G37bAZf99j705zPqivNo8LdsarW8r3ihHie1CkDqifZoFTWpHmlbxHVPtmppS5bjz3r2j/4fvYLrLw7edb5VDuf2l3VEz0kjxhgYbr50pG2uH7WLtyCyaBHD3E15O1/3zgZbbeKiUfM4AOgEcKqNm4O6jUAggU7N8NczG8UmzKQL4cz5jNiPc5BOSuf741O1aZmbhZaqaqYLmlPCpAExZ3VXonPpNZnbqAfaLwr5uu5wAOgHkgHAC6ASQ48AJoBPA3r6y80oAf/31+DOAZ1A5NegXgfBXx2f4u5NrskJauMwusl8wRVgKgQBODTN6/Pn8XOiD3ggg7fRJ6DgqOXvXW+mNAHLBCH/bknD2RgDpT0BdwC96aNxnAH0GsNuw4EyRzwDazI/PAPoMIMeBzwD2/sWUTwI4/VfxCeDiTwwJAkgDaH44/xpkYMobpI7iYtGiLIVAAI8P4dYQdIj6gbE+dhJjaCRd2sfULb9mBAVAVz6wN3fdQIWAs880pDSAdy40OweGGBVaUghMqcE8BJypALDQsxNAJ4D83HgIuPhCwEfe83mkggRmWLmF0l9+mXMaVhad9YVu3xlOALcKz+LAZMYPRgW9ZCBQCASQYiUT93QVrsZlgs7vA3gx433ODDKpLHP5bapoEQhX+WimkGSN4V2uCs5cBLIvgKdCY/yV8K9iXQSyKQK4U7nlFJ2YkFbPfkQ91zYRk4Oeb2zw9MvumO+tfCfeMdq6Ufq9DljYmJ5rLKUR3weoe5Mfn/LkZuf1lRaQ/nPyu5OuTzlm5au3MSzMoNZO/npjE2bXIV2f2pC3G3V4KvL7k35M1zQh+AIuS9Wk250QdHxqp7HT9E48rwi0/ACzPQilWdQ1sp6I5ahwvcuDP1wmOV8aPPd0DwpDCwflPebrUeGahbXuMTs3MK9RfnL7VtNCC9C55YMoT7ojxs3DEdWm2VqWMq1edejTug5bcPetucdiwzOmWyzZxfSh42tNf33CFJPoCmf2ycstppebUG668wUtpgVMhi/a1pAjemN7ZTpv8OzRZky+eIPp8eY+TvcooHx7O88N+14T5aRmkS709lWmS3zhdTvfqFGmb1u/rhqdQes3dpxd76Rq285baddSPdw0fKfv8Eg6z/NzG8z3cpfg26e8xz9/6jBUvWY/HMIu+3lLTZxVwd9P+4FdS+tE/PTzZkKwZnfT8X3pVHO60gKsn/6Jv5mBUz58N379nyPs2nc0be7612zsloy16xv2nOUgbtq9BUfPfin6/z8LTfVywLbmxTm/3nCpv990uti3Hh3Cell19JbyBrfX2uf/k2/jo5gawArc+IrlI95zsvkVtqTsuucsM+/Fsop2VJbbQqk9J9gx0qy2B93pow+YdpFS1h0PsOtiueNtdAMr3FLQBPCaLTAD+MkhMQPIxZ6M+j1fuCNp61xZIRDAnu68pxAwV+9QMUwmT5KWXUgYafuixRu0gaHJI3V+TPjMkbxHDzYwfOqdnWEDQyV4UdrAOAG0RSZOAJ0AOgG0H2itTgA3+c1cyARw2i+/EVsD+PqZtMkb9BrAowF8NnzPd/262Dqcq6DOOpgIIM2hGQLmbKEtr+xe+Jv6NACWJgCQETQ9A2kE/WCwmLFlZ1Y4XUATyEwjaA6Uvi4PL5oQsM8AKtuJzRL5DKDNIvkMoM8A+gygPRsKLQTsBDD9PU8fYS4AfRsnrRlMyOIOFjYowlKoBHCwdMWgIoD3LdopwjUVcWPT+kWvQxhmSrmFkXrSADoBdALIseEhYA8BcxwUewj4uAfPjZ6VG5IVBasBjAjgsP6nue1obsEQmQFkCjiKLbh+gF7A2YtAaEFXlMUJYLxuH1IEUFow5juVpm77kCc2e8p1UcjXS/8+6flmV5i2bl3KPl8bO00LdOeG3bBzpbnxKJesYJcvoLSB0spxf2/aP+ndGjqGQd51Olb6Pr2Wpo6awIYOexjqmJUp826UF19ViemUeJyuR1588imsKrVjVGdjR0X6/nXO8SFf7LOtpoGivk/1n2k2bdqIcIyBZ0zCAAAgAElEQVTOI50ftXq6v1TQScqvb3mSE9nA7CrTUTWkqtJ5Y6X9y841LD/A8WWNYE5eFmnJdE5pLq9fQU904IRJT6c9A6VZlG7uiufN/+7kXUw2e2zts+kfFOXB9/DVNtOSyaPwB7ccj2F87LIcbv6Po6ttJlV6xKVr7d5SyQT2nrEk+v/sbZgMCDj7MU78AxPGmCZw5WrTGibKUzhzN9N1S+v4uwcPjV5vy4RP1CUebo+4i999Y1qrp4VNwuq5ZpMg/2e15ed95Y0JuHBPU5lc/n8nRNvGA8zHriP413Um7QfBafs/lO7LRPheaQnj/oaFtCwFNjZVovIJ09LVLLVP0oEXMOEQ8MQaEwFuM9w0hidPfBSfv//k6P9ZP7ZFFQtONo1ecrp58X12b8OF5/nrT94R/f/h8++KthoHS1psUuPpb5nDxS5fexH1SdMDvniLzd4G+0Yk7dIw5fem5dzj7vVYuNHO+ep19qMxpPlGq3UTZr3XfBzXNg/HadPMjlXj6ntP0E8fqH3SPm+TP7AIYyqtv98z1mRYd6y1HMUPPGnav4P3pYIHoBZw2t7L7CRMB3XEpswe0ofl/R8RwLUtw7HmQfuct8wM3o6tJiVhWfyJL3a7tnwuApl69TdjE8AlZ0ULZAcyb3E++o6Dj6aachnJxzkHxTmcAMbrJieATgCdAIYZZSeA9sPHCaATwIIggL/YAgTw7H4RwE8DIPOdFEgXPfge38RXLTN+nRNm6bjIk3Yt1PMrZWtmVb5PORhXB/WWKSz7VMow1pViJt73/pCp7QQwXlc6AXQC6ATQCWD0FPEZQKZTB3wGsEBmALcOAWSShT+EBRePBZLGzFqcdjYrie6FCy5/EzJzcSqZU87U8dOp4/NZxzJEQS0fQwGcBu8rAaS7B5M7MNkE0xNlawCHQpq7fjEZJ4D9gi1dadAQwBvm74dJZRZS6k0D6CFgDwFzfHgI2EPAHgLu+YvhvQ9wMqvLxmZzIeBvvu1WfPvJYzDzSgv9t7e34N4nvsN/BzKsGn0vTf3FRfFDwGcz+VVO10rSR32DiSSp8gCo6biCvw16QJWLMKkDODJjH+P+BwA4JOM95u/VTN7XNpEqtqeOk4IpW/tH/sP3uhh7PD4w6Go7AYzXZYOKAErz1RY0StJ3ZWui6HU3NmGap0XBE0+at3/WmXfaM+u2jbZ/2PmPaA2eacNLu3++LljCxdXAio21eMck0/jsNmxptB1TagutpbHTQhTqCbO1f9IYSsNXEbR61PfJK0/dqHuUx59mZbhfuXS1j/o9FukOpQFUCI/79F5beEZUpHMWm76R+9cGvzvpDXW90jdSAyhd32tBHyc8s+8/0x9wbJlhtDJo//Ra7bLNxW1c4NalgZO2UHjoPNT/6Vzy+KtvN03Yv5aZDqtxo2m2frrPDWmN401rgy5wrGnWHtxg+rFDRsyLttTwKQ+vFhV97+YP2D7j06ioB5IHmsYt2WK4ff+Am6KtfBV/t8qe9Uu+sxNGftFSyp05ham7gV8t5+I94AMT+fwHblttuXufXzYZV+57XfS/cL3tg5YnNzmO3xfAayfZrNT3j7wBv1tu+1R2HWnegQeMsNRl0gR++5sfx8UX/yp677rVlo/3jdMYzQLmXmC60U/tZz54yySKY7wriOruWml4vv6M5UuuXlaC0XNt0mHx++2R+/79TEN54AimLAcWhH6kBvT3rx4YvTflG/Z5mnu2nXPHWaaNW7zG9H3H7/g86pLmtVgWRHryf5Q29pVGGx8za9ZgWZMJ+MZX2bi6e571ZecGw2hK0E0e8c2HcPMfDPNtHjHtXss4+6y8sa/136nvvzvaXvP0Ibj8YE7WAE9utDzHN/7N6k690zTBjd/ciLdOpBMXsKbV+uWR+810vnInm3ypHWbRvjFVTVi4rmtR5pzjIwJSMEUEcFmj6VCT/xmLT5z5j+h/6VBbOwzPaRWWu3mrEcCrtgABPCcnAshBwgFDn95bMjqNiyw4+I7roSM5A8j0rpylY5iYSRwI6LUh1KsqbIOrFM/fTKrYnsbKYZsZQPagKcLiBDBepzsBdALoBDBrVbkTQCeAfKw6ARwyBJC/9u0XnBWudrEVL90LHdX5K4W/tB7J2HUZAJIwzur1VGi9xqQN5CP8hcE0rNQEqtDEmeFb/hrlL4XeUsXG+zY3IvqNkGwibluDor4TwHjd5ATQCaATQCeA0VPEZwB9BjBiR4UyA/jzLTAD+KkeZ2C5MqSnHb0RQKa54fJ8m9ruXmgrwClkhnUZPmbCBy7wuCYkb2BePSZn4AyhVvEOFAHkdDTDC5b+qAiKE8B4newE0AmgE0AngE4ABygE3LyxEh3NFnZmybZViff47r320fd/Ltq5tM7C5juM5eJUoD2kyBsUIeD/+1Z8DeCnuXYCfZ0B7E8I+AG6/4RVw+qQUwD8km5ZAN4XsntZDkIr1OxRH0FtH3MsZu6LMyQ4y0mNkxPAOCgWUd1BQwCvffVASOu3uM08vlSkfRpTZpqdsYlG3N9onmiLms0set9ay6CjvK9VJfaZW5kakdZO3VxvvmfLmu2hKV3SriOWY/sKWwAmH8DhJRZBUA5cWYhYzl7TG0mzJ42Zrlf+eJn3oPyz2fo+6d6k5cuso3a0j45+LG3Bfy/z2NWpkBM1eL1pIQ39/dZ1mK6pywfQNFZ1KdPY0YNQ97I2tKPcuvLmeyP4KvL61U9jEtaOrlM+a7ouYilvQPWhju3SgtlvvJpEC+6tM82X9GGnjDf/trsbd4221z+1f7SdNm01ztuO3qld/SV9o7z9ZgVfR15/S8jNqxzNZ/ydyXeAMc+bV96aA5IorbLxUjnMtHCX7HlrtH252XRyNy0ybenEi8vxOhM4ApgxzkJox0+0fMGTym0Rk3C4be1e6Aj60/nrg1feP03zNvZF05S9sZ/pGlv23YhL97EskfNbzacwGa5buYXXt5ue7ok3puG9Uy13tcbPH28wjXqwb8QnTzfdF4uw1li+9AWzQRn3R2uvsi6Jhe+zXMDDZpjm7fxdTEOne5HX41V3H4VJIXhW+6r1/5q9TAO4frZpAsNHD0ce/hz+85DhNvIV6+eS9xhmTS32GVIqt9oZdahfaro1VJs4c9KddsyI103v++6r74+2d74xG8v/Zf6E295pvo1tE+xeFp5k57nhiKui7UkPnImLD7C+/Ol8wyh5l/XFxONMy/nqq5OBNhsLnWEcjJpoEcXmVruG9uCdt8u2K9GYNKyWrDQtYCERwIY2u7ZJwbeR429kueG3otnwnTLcMFsb9I5PPLoTdvqOaaDbO9pw9/rId3jgF4FsOQKYy7VyFo9aPlstY4tAOBC42KOnRSAUw/Jhw9StKjTC5MpgPlg58Myos6v8ltJYSntpa5m1L85LJ4Bx0CvCuk4AnQA6AXQCGD36nAA6ASwkArjtlfFnAJeeG80A5kIAZQPDX4EkgrRqOZHe4SELBy1iqBOknx8LQ8m0e+HxCgHz1wWJIdvqqQxUCNgJYBGSuDi37ATQCaATQCeATgDzNAM4fcYqNLTYrO6IylaMrrTZt9KSLgeCWw/hZFP/y4cePgcb2m1mMjsEPJhmALe9YgsQwM/kTAAJGy1gZATN6Xsu8iC5YyF5WwTg4+E14/tc4MF0PwwHrAZwe3jPplLfXJwA9n94d6vpGsB4QA4aAsjbvHqerYZnOJBFtgUKNc4LqcsYGlNY7J21NsOuEKPCXQplMjz782VHRMdMG74+2lYmLMS0XSU/ywBDy5odybRG4b5RpfYAzwzr6ro2dtpDWGnUZNeiNjK7TvvaOs3SKduKhbY25eFLQtYwVSU2/FMhrJsM3yEtnaVpU9/scLBCwTo329J1Kkwq6xWFoxkC1nWtTtVEVVcrlV6I5ynMPbGs/k2p4NRetuUNw8jjQqo+pXNTSFRfiC9utBDrvPoJWLzaQmpX7fenaLs8WPz8cZktznv1JbP2+dTb/4MxwYKmK6Ruoa+aYA80IWEhvLmt22BOE7XfQEO7fTHf87SFlBMbLOzXPjKFytE25jo7DPMf70M/V+D5Zgs13n6pjaGOBNBwnIU+K8ptHJ2xo4WqFbr+69K32OvfTMBnLrF2frHYrEdeX2C5ene+OliQbG94l575Br64vaVL++NKs3aZu8aOPXVHSxCgsU0Rv0LdC5osnLmu1UKgB4zhdxcws9Jy2/1l5X545R46VwAnf8CsYWT/ceNxlpZu445j8JZvmYXNHXdYmP28E26LtneuNqzW/chSBFbUJ9E4zbA+/HyLBf/tTrOvecshr0Rblbq2KlQE+5c5r1s6su0mm1atqix4WrZan4wbthHPL7axMPUvpqmrftgsaPb9r93L6jaTMtz1wFsw8XH7MIx80T7TbeNt38LjzeLkR8f8MdpeNOe9OGWmWQTNbzI8n73CwtLv/5LJCBY3j8WD11tKuredbPY3j660+123yvpnzGP2Wa8/vAljaq3vVi8dHW3furulnVu20UKs+SCAc2+yFHiT3mth7FEVNn5fvsXen/k+w2596zCUhz4oKzUJybyF1hcljYbziIX2OXjhx+cjn6ngtiIBjO53EBafARyEnbY1L9kJoBNAJ4BOAKNnkBNAJ4AFRQB/dnHsRSBLP0tXlJxCwFvz+zjuuRl6/rrbwMSFsXjqOwF0AugE0AmgE0CfAUShzQBOvTw+AVzyuSFHALmaj9pCrtBjSMCml4u0eAg4Xsc7AXQC6ATQCaATwAIigMPL2qL++POBdBLJvVADOBRCwFN/ugUI4HmDmgBSX8KMItQfUOdxBgDmu9kxjArqj44GYMvfi7D8P3vnHV9VlbXhlZub3hsJCRAgBAgdQlFAFEVpooBlnNGxjh3rWD97wV7HrqNjQ0WxYgFBqohIB6khpJJKers1+X7rvHvdhAiScGNI2fuPnHtvTt17n3Pes9dz3qUFoHuN3q4E4JI0ZCjIsINr+jIPLNWDPcEjRZhgzeLv4aEMUYgqVBbFKpU+TixahLHLc4TQaxlgC/uHgiUaF4yHqnqLl3qbJknjJuyfsGpiJcIsn1isSHo4sVARzq9xqjjeluyPLCvrr+f9nC4GMMQETtBeJyki0Qm8PJRtibOWJK1dhfh+KWZPWD1hFoscgRTqiXRZxU5wUrtqwMSNCwSzxSnShvnCRkc4QeHv5BjFboR5TPks7GNFLTiuPAfsdYQJ5H0occCeRlLspSiLkygzGL11FeDTlmck0l2DFhufxXpEjkGYy1e3oB0vHLSeiuxY75igQy2xhAV9PwccXVZZKHmZ0b5FheC5zHlg2OwRsHzxDraRh0oT+O5IdnAgkuO+8r3rje/x34D3TvlnCAX1AXdWmo/1jR2IVG0xvrBQWfQFmEVLVC3dNxnWLr+Us38s0Yr9mEZ+A2bPrwD7UNrHmxQOSdau+M03FFxX44ugw2GiUT3AfmVWgEOTckX8GuPjw8tmGtOo+GK6NgH3j82VcKv47mfwbn3f4MxVSB/35LgFxmdJRydp6DKqlNWJ2ovSe7tTr6eQZm9zIZg9sU7y9cJ+F1WibQJ8bFSnuFUfxUtWLwB/5piOOvRVvxf9HknBaTjSmAXol3tfZI9dool98H15Ku6Lfhv8KfZncJjWSPS90gTwbGUjIK4eOhHWLzuq46jGCX4vxwJGb2gw0j1K+sP06giK80P7Cn/8azFMo0O9wQBnK74vbV8MBaRhW1XdcH6G9sSyXYPQ/iU1aFtfs50cdThn80tglTOyO9qNy7EKQF523BK4koSql0xy52F/S5UVT+KHOL8OPmyjbkGwJ9q9PMG1bf4QlIHG8a7A9Jf5/25VBlALQGJAmDv5K0R0Hp+SnB1SCUHuXJz5g33OACB3wqIFoHuNrgWgFoBaAGoBaFxFtADUArDh7eS4C8DnH3GbAcy6hZG4dssAcrJvNpJmOxp+0uI3pMY1SFPHbyuxIeehxrjuaYJ2tbQWgO41lxaAWgBqAagFoBaAegSw7Y0APtcCAvDWdi0AOTTB4RiEpoh4WHtIg0wf7AifozKLuKcE2unSWgC613BaAGoBqAWgFoBaALZBAbhpSRL1Phm2PVx+mMApZv9Y/rb2GuPH9WmwJKq1e1JcLML37ToErAUgh3ljiAhpqIga27xoAeie/un0S7crASg+gJJ2aow/2KrG/nJRnlWUakcKOPGrE55HeLxKJ9ggLpLOzF8xhMKwdfdCWipjHg+wQy4vvwbGrfy7cGiWOjNJajbx1xPm7XDrqKhVvJny/xPfuiCT2p6iGZkN9PUAU9TY/8/fQ7wDwerY6+qo0InfeH+4lCsOr7oO2ytXx891KQygHKvwcRfHwseN66y3F65B1crbsEj5Ae618PWJaLg/GEFnnQdZ1DxByiNR2kvqXnwQy5z+Lv9EYfOKVaq5aOUP+GU+OM9wn2qaFr7N+Cwc2jXdVhrfCx1g7Z57b7YxfePKV1z7aVPHL236ShZwmR07cbMM2utJNTgEcoTCe458UM/JiTim87usp0df+4fxuXIEuLu5Y74yps88fYExjf6JH8SJdt0fQc+Nm298Fo/ANOXFF+UDLi3RlyM7SA3XeN8lTVpVEto//lM845qrHJR2NvqsMwjM4t9Hw5tWPC+3FIO5q3knjsyXYhsH8sEA1tnBmt00BincFhUMMKazYzaTtM+eaqSYS70RKfdCn+aEB0TrtyUQKQZy4vCdxm9p5Ti/IvzAj3qbUHcWpxeJ2bCdTRG5P6q+mJsHBpRUblxTjYmcgTiWIf3Avm1PU9xgAbi8PqPwu9VhpuxNYFNVpkW68FROw0r0XRa8CMt3gEcMOOBBNnQJUrtF118KTljO0+9yBxnfx0amuUyYK504Nxxqv03qfCu2BbjSQgrPKMcb6Anu+IAFx8bHv+tbsIhVvVV/UnXnFYg2DfgFDKRlQgXV7VY+j1gNqcuAC+w8VgGYGI/2L6rGtnqFQhAGq7Rve57B8VdcWE4nxkJkLt6Kekx6Cf0092T0ncmXw8fyqaGftS4D+GwLjAD+u12PAPKFiN8EwklGxMAxG1gC2kSauSv1CKCqDT1pdg1oAagFoBaAWgAaFw4tALUAbFMC8JkWEIC3tWsByM7s9Slijnx7n9jsO38HWUCHgN1rSC0AtQDUAlALQC0AeXRXjwC2rRFALQDdu7t3gqXdEYDvcERExdUbVhWPmb9ERJd3gvprNwLwo31jXOmtdAj40BRwOgSsQ8B8rdIh4I4dAt65F6kOOZ8wlwAvhJSDvRA/bsgAdogQ8NOPuv8W8O33ctXw693w4NGlQ9WAOwKQ4RM2nRLAUiqGX6lmgALwVMcu7UoACr8zwAdsknjkSRMdUD5zbx84iQLVxbFvIF6g8jXBg0yK5E1l7k/+J78Jo9ZFcWj8u3ja2QlckzBrRco7T74zaya8oWxL/OpkHmHheJ2yvgDFGAoD2ND/j9fDOX3tyjNM1huujqlCgUOyjMUFEtV7mck2xWdQ9rvCyYbyKKGeyGE6Lx8eeTfHIvcse/719kZe5EJ1vMJWLshLNn6f0w1sWYEj2MVJit+h1C97DnIRAS9T/k14q4N2iDl/xVYJT3nAFkZf7kKO1geTwXMJ+/nCHnB9FWU4lhfGzidpQ2mLfVYM8+1VnNumt7GuOg9y+etRf7BPY7qB/ftXNPzx0u2RNO9i9lslyrgdEZkHhnxnTH8oGmxMD14MJ4ZdN0aRKVwBXflg9qZPQP5YyTHt44G++PGKcXTlJNTb4lwwecLN9QiCD17eA/Bm892aQbufhe/dXSMX4TfV/vssYPd+enS8MeXS69bdxrS7H9YjfXtLKUSEtyfYu8KaAMrZALbOH9gYxX2WZkxvWL3MmK6p6Evfv468wAMuAQNY7QCjtzMf2w77Am1bdX4ZxYYAUdqjcjMPHASOz6bYupQMtIVPpjdZ41FXXWPglSe+mkUVYNcm99plTIPNFvomA9xaaR4887xDsKzTAb6x1opzM7FnHl0UBz7ykS/YPo1o3ETkBC+1oY/szMU+OO0mMpnBfEaHwRtPth0TAs0Q5G2l7amot5OS4BEq6ymzoY0zU1APkRtMdDAZ6wuPxzFNioMvovTFJfPgA1k50EYTB6Kd9pYiDZ1T+XYaX7heFWPJIu/XHegLjQXgrjTVfnvRJt3PyHB5DRbkw9pGeNYDFfheWILzrLbQh8K3of4s0M9k6YI+7vTDcVw4Hgzg3CFftCoD2OMp9wVg5h3tWgAyWPp3Tn+uugMnQa+/YHMaeDCA6GidsByLAOSrBy/HV0amdXFnQ+EryAwiekK9ft3Rq1QLQC0AXX1cC0AtALkzaAEI8akFoBaAx1kA3E5Ew1iDq/3gJxR2w8eTChE/qX/Cz8THeT+P2+aPRQByr/4zsJL/9wA/8By3o2q9DWsBqAWgFoB48ZT0CCCqQQtALQC5Hxz3EcAnW2AE8M52PQLIw9gP84vuDQQghy4kvdEsIuJcd7BK6ITlWAQg54vi5Ti+cQ5nwGpQbwxVcPwHng4dv7R5AfjMrslGK8R6lbosHHQIGB1Th4BBaegQsA4Bcz/QIeAOFgLWApAzf4xlQkBJkQ1ExDkckauQiPNksjcW+ItOWI5FAEo1ceLLLCMNa+ct7UYAcm7YWDN4JvF0k2bbUI18sYvzkSv45Kh9Lj5KmLfGTSzef5zvV5gsyccr80YpBjDUJDZM8MTjUupEPk/xBWP/Oy68PuHWGuf1La0FviF5ZHle4diED5RlvZQHmb+aWus8qFz56wUohizIhK5rV+PZpbXYt1hPB2Wp/KbCDUo+XmHBGjKAckyS+1jy/QpzmeiTT1IHUm/7bVHYlmoTWcd+G1gm4zgJbJHkTzapgXdh1/LtIRTtBV5M8vvmq5zFRYoF3HAQ3Fv6gSgKCEHe1Wv7gc3LtcF7LTkAzJrs/6sHTqUSK+o6SAHy13cHz7bTguG+d/aAc4x6058GPgp/wRIb2nRLLuZ5Yijy9N730iVkhv0fdf07tjU2Ag/hH35ymjHtshFcn8PfRJWxYNGquqNhHNH43z+Hcz53oh+fBk933X0LXC82vfohkydECpMjLxXkUU1Msd/lUnV/1Hn6TKzXwxvtP2vwFmPqo0zvor3KXQ9LH87H/g2ZCg5tXyn8+3qH4Ll3/Y7edOmJPxufP/gRuZQnnLTdmDJ3x+Wbn0dSz4F4Jpa+2zcE5MzyVZyYgCg4Ff2/ZIiTzh4N5nHFAeQ1HtYFy67ciPNz8CAwljmVwQZfxyVeMY8J/lhvtgUedH6eeNEhszqcNqeiL3gHoD6FF0yKBudbaYcAYv7v4Y1nGp9jPoe1y7QHlxvTSJVj+rV9E4zvpdkhNGPUZuOzMJrldnB9v8zn6BtR9fAaqnWgTXvE8T25vl+xPyWXjd/AQ8/TQhQzA8zjvmycC57e4C1jI9DX+4cAO8+aEUj7r0MdjTgdrGO5YgotTjzcOJ4Gqxh5b7rLwy/1EfCi0XehD0b5orNsfxhca/htGeTrCQ/CdSk9jampBHVTG6byW2fhO/cz9fIzhY1DPU6Oxb6kVqG/cflgzH+xf+XlFBJicIR/5YsVxn0pngWgb71fq2tnmvih1mKhjPY9AsidazQRAWD9Y2EAmUcJceHqhMUdASjVxZXHrrA4I+oL7godu2gBqAWgS9RqAagFIF/utADUApD7gRaAx/3mz8LvKSJ6/wh7chkR3UZEeProhMUdAciPN/8jIrze98eCR76OXbQA1AJQC0A9Amhc5fQIIEbN9AhgGxkBfGKu+yOAd93DTfpXjlb+lQrhESK6RI0Cqnf0XZtjBxMe/WNxaICOnbG4IwD5lWoOA9/M0QqOpPCouqrMfzcALztyvWoBqAWgFoBaAGoBqEPAbS8E/HgLCMC727UAZK8eFnnsQfSBYgGZ/+jPpANnIFTiUN4K7sha5bDH5o4AzCWis4noN2USOVJV8FlEdAcR1ZtqddxqbfMCUKp+WXo/ilAsXqYDfNCT+6cY04Rg5Ozt4QeuKcjTQsLfiTee8GyBDOkwJ6M879irTtg/4dB6ex9qDcl8WxdPnGOy3jwH/LRKVe5aCZ/GmMtcHoEyrzB7eSp/rnB4QSYrSZ5gYf7q/f8OzftbVUdUq1g/k+r1FuULKMuUqrzCvc0Wylf5V8vVb+xlx4X3j4swgcww2lw+gthmnvJTzLSCF+vmXexiKkM8wTzJ8UaY4B2YagfvxF5nklNZ8ibX+/+BspBt22vNVK58CJfngIUaHwOuaWQgWDvhBXldeXbU+bcF4M4mRWGkQuozwhM2LuwpWNvIM1HWs7AYXNfGd8BLeVXX0aw7lhqfv8uBz9z/JXxvTNMV5/j+w2dS3hkYHbo8eY0xlf4Ubsbxby4Hn7Z2dwI9NO5r4/P3B7Gf+95Bbt0ypIglRwy4tguGrSeHqvtCKzjubr7gXIVvi1Qw4PwHplLICuS+Tr+6rzG9+IIlxlR4yWonGNDFy0aQTzE6SXV38GcTR+4wpoGKqVv1Ll/uiCoS6ujUcaBdypRHnrfix4qtCInv2tmdouJxbpVuBRfWZyzyx5ZbES4tLMf+W0t9aPJQbCvaBz56tYqPXVmANs4tQjv2iSmk4mpsw+yJvndOd/B4n2bAX7JrINaRGFRIa55kHIqoaCDY0qSTU43pKRHCyBtfqdgRQN+8DcYvajO40Yw5WP9jI8B1io/l8qL+NCYMfW5HJfz0Vq2Et6PYaTpDHHTKIDCUBy14Q7hUMabVX4LRKxmG/jFt5FYqs4M/5bzAXHYuQsMHngi+sVswzsHNO3rS4CTwgsIvij+jWbG/4jOYUxBKk/piH5YvRR+eOAnsp3CSX23Cy6Ce/g5ySr7lKgSyzFXKK9GsPP6C0S/mnLSUXl5xuvHZuwTz3jh7oTE1/t8f7KyUVmUAtQDkaueb3eOckpytWlU7sO/fp3ypavQS6yFt1Rm+uCMA+crCV0vK+T4AACAASURBVGi+kjGVzNne+erei68FnQSs1AJQC0AtALUANO4VWgBqAdimBOBjLTAC+H/tegSwoYZjrSNv5fCTxOGs7MYREb8prJzoO74EdEcArlfhXjZW5NQCrKrv5gcgIjqXB5Y6fvWRFoBaAGoBqAWgFoB6BLDNjQD2nOu+AEy/p8MIwKbIER7U4uFh8QlsyjLteh53BCC7a/MY/bvsJEFEnF+JY14cn2Hwcn67rpmm7bwWgFoAagGoBaAWgFoAHlYAri3DOMi8MW8Z09YMAWsB2LSbeIO5mFNqaBTd7BW0twXcEYCNj5VhFIYrGcqA2VPHL+1GAP6S0dvlQXbJ2suNlukVg2Y6tQsYIOH7mKsTDzppQpfPnsqfKtwY+/c15Mx4fvG8EzauljyoXHn4CQtYXgv2SfLZig/eAJ8cFx8m2xQWUPz1xMcw1FRDwv75KuZH9le4Prvipzi/r68HuJ2qOniEybIW9V1YqyhPC6XYwUkKoxjkCRYqyAMMpHCPmfZwivMCd7bbihDYlgp2RSIaHYwHSWYj5TglZ6/sp1V5Dw73BxPGPnFSF3L8v1XhJpJeDabwoBUc1RlRu4hz/HJJ9MVLbjHKF1DqXrhMbqNt1div8YFgoaRNgk04tkVlYO5ODd5Jv1aCNyu0Iefp9PCtxnRpKRwTfvoCDFzwSQX0UtLHxueX8+CZ1zcAfmiSu7VLA189Od6P9mH5UH9s+5Ro5Ijt4VPUINcxmMckX+Sulj4px5RqjXb16U92g3mb1gf83Ld7wKF57QFPFr6zlgKysK2syag/WyiiQHUmTGtV7lafUAsF+iMK5HCC/RrdFazZ6h/APkZuAxOXO9tKibFgXquUj17O7+DaTLHgPeOjiimrCO1kO4j98QiEn5x/ELZTVY7zISDYQsF+6GPVNuWZqXL3+uai31qj0I+Th+ynqZHwHPwwC/lxR0ayNStRRhUS05beBk/Gv727mD7PG2F8piuxD3uuAXdap7g2j1rcDqaftJFW5cAbtMvj8AEsSEad1U5C2tSp8chpvKcimnYvR/80DQJvaFqHXMN9Z4C57OpXRinliL7tO4BteqfieH1UFlaPU8FIzuq5jd5fCj/FgN5g/YZFo/1/W4y+N+x09N+GJbca/TTaDxxrdiU4ybJVMPm2hdSR0xft7NsN89isqN/QEHCoJaU4Rj9/GzlVu/v7gjet3IxzrzYR8ybGgEccGJJHP753gvE5+e9gQX/7AufRsJmoIy7HRQA+2gIjgPd2qhFALQBdPfbwH547yv8b/vvWZswrs3II+TEielG9XSy/s+ssp5bjqxxf/Zje5RQXuKL/sfDdlN9QblxeJaLr1Y/85jKuNPXlDSK6phn7rQWgFoBaAGoBaFwytADUApD7QZsRgI+0gAC8TwvAZuiBdjdrc0cAYQd/9MKPWqcefbZD5hil3szhqwhvh+1luLD44/Ayv8nDr1fx62L8GM6vCh4J1uTHzYY+hPx6Ir/yN1FZ1vB6WQDy0BfnApTCj+y4ijWtaAGoBaAWgFoAagGoRwDb3gigFoBNu4vXz6VHAJtbYy00P3sgbOLsTurFEh7hEwHI+Z9YvN3nxrZeICLObcR+AvL2DwvAhts5ltW3WQG4cD/CEPXh0modAtYhYKNP6BCwDgFzP9Ah4I4dAu71sPsjgGn3d6oRQP0SyLGooBZY5j3lx3OLGpkTYcbACANF/Gbx39WbxbuJiHslEnAevTBIxMk0OXzN4WUpLAAZKuFRUAaoeHSRncPrE9f+cd0MxACKQWHwJLusrIyCg8G9tJXSWACyJ9vqEvhphXghch6jfMYkxyz7/3Fp6APno5i/xgyg5O7l+cXTLsiE5cW/rkrl3mVmS/4n9VNdh2qU9cSZFQzEbI4yEGu8HskfLMwh71OABxidUBMGg20qf65wfZLLN8hkc/mpCVMoDGBFLVizQifacG1lH0qtBLM0qws/lxAleoNrE05Q9pF/s6vB5uUVyDH6wS/sJkA0ajB81gYHg2HiIvsu+yBMXEoV2KjpEdtceX1DldeiMIClyh8tyhsME3vcCWc3QHFyMu+4AHCdxU74y3Gbcu5gLgcd4KXeWw6vt9oAMGUeTgQEvMMs5OMNXzZfL7Bq4jMX7IM2lnAn56J11IKTq1CedtEB8HxMCgaXyMf86T54rIUFoO/dlfCDMd2lcgt/kQVvtrz0CPJQuWpvHvmT8duqYvj2yXZ2LwNz1nfifhoSgrrdUsper0RZZbD6SooEl5f2OpYN21ZG+Y+C27u//7fGdO6TFxvT4sH4vS4Ux3pd8kraWw12LEjl882pwXqF4SxVnn/l/+lOfe8Ed7hiD7blmQO+zYSuSbZwJ3mEYN11lRAdJovylfND3XfpAY50SGQOdfUB+yZ9ZVkB1lvxLo6x8HT0dR9/O42KA5s4IhjTL3PAKOaVoi8nXIc8wmN+yqH1xaBinHPUtcoD7Z16AXhBpx+ejSeN30pmE/Yr9QrU9YFJOP7q7qirgcPBrHI+Yl8z+oqUERHZxkdrLZhF9mjctAfbjliH47cqR7bZFyEv9YJP0Renn/sr2VV/2vwgmMXMWdjm4ASsV85B7oOZ+dj3hFgwefK/rDXwlUyYh35Q8JyJkqOwfC+VL5l9NLmIJ6WcX6uyEig+HO3xz1jkn75n3Uxj6rcTbdtvGvhGb5OTbCoBtfTPPavAT3LZew/f0upLa74E0uuhx9zOBJL2AFvltdtMIIfUfRO+6BHAJlRSS89ygRJ0HALmu0vDkTmma9cqccg5+1gY8lWbRwo5rAty/M8LG0B+pPIV42qIcpXyL+TfeLjsSWVqPftPVvcgET3Q+P9aAOKGpAWgFoDcD7QA1AKQ+4EWgMdZAD7YAgLwwU4lAI+mJTrc/5vLALZ0BfBjGhsvnkFEeM3wUAE4VplLM/9n9ERV+HWr75Tv4NH2iX0K+Vl8xlFmZGaRhxz49UcM3fyx6BFANSKoRwDrEVM9AqhHAI0Llx4BNK6YegSwjYwAagEod3Aezn+GiNimgEMtjXVPw/cFjqYnOtT/j7cA5HF1zi2EeAMKNwbHInjcn3NA8Vj7P9npoME87DHIcQf2IvyzwnEH9uLgUT3klzpyYTCIY2ucH41FY1NKm2EAp6/iKDlRfCDsFKaGwpJAGEAdAtYhYB0C1iFgviboEDDCxR09BNz7AfdHAPc/1CFGAJk3YQ+sl9m56TBZQI6mDZqiBdrlPMdbADKM1Niu5X9sqaZCsgzXMLjxTqOXQDjhJTdqw1HBwzUAh2yvJiIeaTwUVPnj3AxuMVfIEA3U09FLmxWAA/wR7Za8sf4GA4eRGuHj+DcuwqE15P2EB5SRPrvLOw/VKN95/bHKB09YQMnPK2whzyPzCzsnfFNPL+QhDlYsH/v1Sf5dqX5fE7ZZ7ETeU381b2mtv4s/ZE/AhscmPoDiqdfdzLwcunuBWk+WA95ePb1wQyhSvNyPpYPpqqiVxm8pKp9thfIxDFf5cnuYwQjZyJO2WsAb5ShPvpWF8NArqsbLBgMj88hbHUOENxDTAX5g14THq3AibLi/OpICVL7ZYC/wdgtT4X923QDwUsJjMv9Xpo4l0gzu7vkd8OLrFYl6/UdXzoWO9nrsy3OMz1Kc/op9C0H9Du0ND7ko30pKqwBbFeuPl+JTSsFE+prBsqVlgFkM2+xF5YlqPRHoTyckIA/xtAicRvf/djb5BwIT8PXG8sVFYBN7xsKLsk61TanFj3qF4iFmUDD68KYS1O8FXTntOLmO+Zll08hkVSydN/i1aSciF+6vb8IXMOJ31HfKv8zUozu2dX08UyZETz7N2SuJikZjn24ei5zGZU4/2lXR1fgsLJzNqXi2GrSpXXFfRWtiyNIFz68+RRhICN6PfQn8J9qYj82qlp8ci/zLsnykF3jOfDu4PGbYsmrgGbgxG8ddl4K66rIR2zkwHfVNNhN52NCnz5mAuvlqMTzpEp+GV96Bd9BO4+P2049LUSc9FiuGMAesYf7JmKe0H/b77Im/0UGVW7lgDvYh+1TsnyUa8wwaDY/LgupA8vLEfkX4oq6Zi+Oybi9nBSU6sV8qdfFB/9z8ALi+jJlYz3nJG41pSgX6V8ErvWjgv+FtWKT8LivvhL9m+NPonw51HduwsxedMoRvFfX5fEtsaJ+dnyN/dNzb4DMPvBdHp3QDt1dsx3XEzxPtvvp7cJNXnsdmE0SvfzOFZk/5xfgs7fPhWxykIopeBx/A4GdU39yUQNGJ6FdO5aPIn9dPbYibG/82SmsygL3vbwEB+HCHEIDc+U5SCFl9Y+hPfxgKbQtV0vjtXH4b+CEiukI1IGcZYR6QGUAJ1XLolkcSWeFL4TsD34nYpfauRgfGZDNf/TlrPd8pmQF8XonNxt6Af1YnWgBqAagFoBaAxjVCC0AtAOVmoQVgW5ASrn1gR26OFuLpUBdXDRzvEcDDNcXh7FlYwLGBMw9LMCt4R6O3gPmVNE5JxyN+UviRjUO5/CiIVyLrCz/WckiZRSQ/MvKjJQvIR9ubD+Azu9gPm2h5IZ54JQSsRwAxwqBHAPUIIPcDPQKoRwA73QjgfY+Rpy8iC8dSnBYL7X+kQ4wAshb4t4oG4vV1XYwaaIsCsD01zXEfARQBWGJH6ENCFtEqJZikzSp2BpCEfMUqQcK8YiUiFe/jYXdZuUjIVlKZSQo4CfeywJKwqKwnQtmXiA2MpHDj9eeoFGsn+GHwtnHaM5NHHYl1i+yPr8eh0XsJ7xY4A4nDwFwk7Cz7t7Ea4adKFVqdE7HKgEq5pDsQzvqyGOnI7oheZkxTVBju4dQZ9GIiUlm/UgA/cz8Vll2dC1uM5wd8qvbf7EpRVuRAqE7C25L2bkHxKPolv6fxv4oqXJDjI1WYM5SRFKIRAbgu7ajpRmJHUeWAVc7uYoTougUjZHd519XGdJ81hiRsv6YUYed+jdKwfbQTx+i0mMk7ECFaew7qzCMCocBu0Qhn16jUYz1DSmhYCMJtYWaEvKR+o8wICXdXYf8Lf7uCbIVILeZTiBCot3L0qRiG9YdGVFGgDz77qRByxs9ISxc4AqHq6d0Rqhvin0lvZsESRGxlZnfnl//r7Tq6eaPuviscQpv3YT1BYRh9inpJ7Usa1rvvcVjfOGye5OmF0OTFAxAuzbOhHwQrq5euase5H0u/eXcl9kUyDQalIeRcgyYhe7SdImLQLoI8VK1BODN5Bo7p5LA9tLoUVi6Dg2BF8vI69KurRwM1kD7Ndjjlq7ByX5VQ0/8g9jt3LLbtVCnrmJaelPy78VuVA1ZG6S/iQTBkJxphyPsIkXJYedOSJOOzD6qPYn9CHeWPBwpRMgTbSRqQZaQ44/LtFwgp2yJw9iQORb/oFYhls6rCqKAK/b6wEHU9sCdC3ztSYVvjm+5FlmiFeUvaORW67zkQodQYf4SID94WTxlT0D/HTQZCYHHCOmbtNthY9euH9e/Li6Le0agkCTvvWYfzrNco7Kfnjbgu5p0cQWMvQ7i5TNkpGV8MeyH0Wwn3R3pX0uKvRxu/JU9FOrcteUip1yfi0O2t39yHEgZgfySsz59Xnfa0rP6QaauGgO9tAQH4aLsVgHxRE89fbgPuCMxx8IUCsf/6At6lExYtAN1rdC0AtQDUAlALQOMqogWgFoByO9EC0L0bawsszahYUwv7EHfKogWge82uBaAWgFoAagGoBaAeAWx7I4D3tMAI4NxjGgFkXOt2HthVyNYNymP3SHdb5vyvVW/q8hDrAmXxhrfgiO5WTh79efCdiPgNnTs5mY17t2+9tBaA7vWBVhWAb+zB+ykSYuRwZ44dlvo6BIwQlQ4BI2OJhON1CFiHgLk/6BBw5wsBJ/yf+wIw9bFmC8C/EdH7RHQNvwiuUrqep1h8pGU5tPDLmOzycbkSdsxKMM//CRHdqmbl17P5+3oVxuVXrJnfZ+d9MCpNK2yn0NDzj7kEcBqdtGgB6F7DH3cB6KX4OGHyvJSloqRRW1cNZq0h5xeoUr7JMjZl8SL2IkGesFThIuyfcHzCgoV6grni7Ysglf8Jsyd2M5Y6bxI+LsIM24tyxebFmMFPBas0cswAeqtjkFRtkjYtQOXyle/FTh/aYQWbs6ES7E+N4oXyasB3Te8CS4mzg3bRbhvEcg+1zSAgVbRd/X7jZk5KQ/R+8v8o3BPM2syNnDCG6KWh/DI50U6VukysbzjVmjCQP5byNYloYgi4K0nlxnUndb2pBvuZY4HVR3dfAFliAxNurqJcG0RLpRMMYL5FpfUKgF1NsuIFM2yRJPYvS4qRhaTSjmUySrH+IF8cR6XVm6qq8b/pibjmFSvLjKkRqKNlpWDElm4fQLOGIwWeCOrUKnBtiYG4hq87CPemnNIQo7643HzXHGNa2gcVq5xvKHZyJg1QTNmYINiHFKp0dPOemIbvycB17pn8FcV7gbPKc6AechU3+tY3sOKoVZYvN0773tWvv8tF3dOT4OeqYsCNzX3wLexTrT99X4z82EkB4NvKHOAFP94BTtLPH4yk02mi5DgwZFmV6DNlX8OKxIJqIJ9h4CYfHLCQ8hyY56ll041pwqdAjFIvQT08OX4BbauGnYq06Y/pPJhBdG4f8I07le3M7m8SKSAPdeH0xuXZhsx99Nj1qGfhWz/6+hSaPh0v+UiqutI5SGEX+jIeBCJ8cL6t+CKZ1GlPqnu5WMCKkRhouX4E7HFe386OGSieZjxYeZoUAxiJtkkMQl/8bGMymXzA99VacW8NSEXdq1OcrD1sdFJ/JG2K9gXr98WKMVjPMKSw4/OeS3f/UlcGEbF7kX05aAHPV+PA+m0OM5mVBU2AF9puTwYPOjH9hboL2QQ20j6xjJwqxZy1DByuOQDLxEfhHOzih7riVJliJyP7EGjGebRX2SGJnc8nn51CCafB9ojLdxP+4/p8uA+tyQAeJwHIHZKFGi4GTEbgJcuXiOiJw9QJO3fwhQf+VSjPcvZCdi86QmXyWcgXIh4RgTfW4Qt3ZE4By1nGuHDnY8BUdA93On6LEv5PnbBoAeheo2sBqAWgFoBaABpXES0AtQDsoAKQ3+aBckdhNQxFfGhhtc0jA+eyJWWDfzFjx09JZx9mGR4BfFVlA+O3sziNCmf5+oCfeY5we+Y33viJYjAR4S2owxd+audkzi+qf/Mx8FNahhKBnD2Bn2QPNUh1TxO0q6W1AHSvubQA1AJQC0AtALUA1COARh9oUwLw7hYIAT9+2FwL7Mvb0HJN7qI8TM6vRHMK17UNbq1PqdE6DPv+sbAQ41RtrEf4Td3XFRN4uHl5RPEbJSiPNEIoy7Hz90VKBPJvLADZ9RthCKLhSmxieL8TFi0A3Wv0VhGAYvUSpMKkDRlAHQLWIWDuwjoErEPA3A90CFiHgJmoYWqhz13uC8B9TxgCsKkjgEcSgOyJw+FYeAodWk5RfN+9ihnk0T0esWN245HDzP8aZzpV4WF4Kh25MMvErAWP+HHhlLDME4JfwugfewSDjemERQtA9xq9VQVgmQP+WJFeGI0f4ZvuyvUr3F1VLZiXFBsuhJsrwWrF+pa4Ur419nYTP0BhAFlUOg10g1NTYepiAJUhmr9JebyZql3p5kSg1irEQtgoE9W6GDhJqSbM2wBfeGgFqPRu/Fm4syAFkYlHWlUdQkwV6hgLncF0QPFhC/PBd0l5the/SEYUpNil0lpP13JSV7LeK3dyqmmiolL4mS0c9wptVOndvi/CemdGwkTepI5/SxXq9afcfuSn+KNzYjFPbx9wchVOMGbspbihEr6EwmKKF5mkfZsSBs+zefknUqwvuEjhA4VVE34sz6IYQbs3eSsGKtoX/nybCsGaFRYBHBvfB36LCQGF1MsH3FZvL8Xx1YAPlXqQYyuwBdO+KpUezAZeqqQGfa90IyC4uRcgNff6yvrE99unYJlLV8Nn79FXkWrNr6iOSpJwqXEEgCV7Yioz3fXp91747Czje+KENIrzx/FveGsY9m8m9vuSXhzNIXp6DafrJvJwepB3OPi1gGVou5A08HcZ09BXbpyEtN7MjVYrpjLEjOv/p1lIjZadBR88nxD06RO7p1OoF+bp5w9e8JNscIJVn2CwoGg4uLerT1nmOq+2l+Nh5LQIpHtbeAq4zN3/15vOHAemcl9lpDHNKQfXWbULrGYosFE6OLqWpo0CF7g+Hx6HoXPBvqX8E/ep+yciusZpADOtsDBb8wJ86+KvAmu3owAs4LRe2Jev9gwmb2/4aZ7WA/Ms/gHHFJIK/u6yOxca0wJ7MK3Ih+de9kYck0k5p9njUd/du4Kby8yMIqrBNcIcif957sH+WnuiPsf1TSX21uMi3Kkwf9LnfNS5vrs0mroGoC8LfxnlB85/8w7ws4OTwA2aTbWUW4l6zMsFhxn3Ldq9YCSmPcaA5by426+0rgJ9Vc693z9G+3SbDYZP9im1KIKqy3DuCrso+xIfBPazUnl0Hpzbk3rcX59n4IMx/zX+f6TSmgxgCwpAvuCgUf68HEsImE1N+cTmt4al8Kjdm2wTyregBr8zL8hhZDbnrAcvj7xPfKE7nxHYI8zC4vMztg892oF11P9rAehey2oBqAWgFoDqHNICUAtA7gpaALYhAejjRiYQq4XUCGBTBSAfOL8Ewk+AbP3ChZ8OWLGzeDvcSyDszs0vYbCti5S/qzeDWQDykxbrFH6JZBYRsWjDE8zRCz/R8NMjv2F8uMJvG/MT2ZlHX1XHnEMLQPfaVQtALQC1ANQC0KgBPQKoRwDb1AjgnY+Rp7sC8MljtoFh+wQWguzxx6NwHIrlV9PZIobDPuztx4VZQrZ74flZPHIImMO8LAzZUoYLvyTC4QQe/Wvo/cehgnrLij/eyycqcclvAnMYWmxoOFTBgvMm9fIJUkF1wqIFoHuN/pcIwA9SgEqkWRFS81eWJDoErEPA3B90CFiHgLkf6BBw2wsBZ1cBz1h+KjuZ/LG0agj4+AhAPmi2gBEjaOYZ+CUP+BUhHMt5Ly9V3/mlj3uIiDkcHkLnETseuePfVFLJQ1K6NazUy5Rn4J/dxa/jzJ3q5RIOYzPvwI3EPATnB+aRyU5btAB0r+lbRQAK+1XjBN8nfNKsEOS25JLlAAuUZcM0W00dipsL9qwhYfOciuuL94Gnl7Bq4v/HLJ8wgE7lp+WpfLrkpZNQExgpT49aFx8oXneFKteu8D08H9+suDR+kSXIhAe4CLW+qjovF5Pmr8CjKMXxlSkapLwO9VDoDKQ9FjBZiwrA81zXHbhHrBnXjqWVisOq7Eo51eCFAhWzd3PcEuN7qg1C+92sE43pC4nzabetq9pf7N/qCuRY/SED66vIB3M2uH8mzYoG+7ezBiHAk4PAXQlTuaR8EImPnlkxhN6KeeI8sVyilTfh3JTpZPkW+1MzEaznoBhwaFE++F5oBd+XXx1Ik2KwfLXqG3E+YJTefgOedOFngrEcEJZHK7OQL7hOZcgUXzS5CMRHgOuyOsyUsQvHXxcEbiw0EgzXcwORA1nYS/YOXLMYnGT892C1+r+M458UCr/BO9+/hLr+As+1st5ou7KJ6D89lAdbqA9GjzLKwqh7MNou7XMwiuInKPl3LXEA0oYkZtHJkeCvhvqCC3votiuMacCN4MPFF27OqOUuVu/db2A55vRFRXw4G/cA4Vxvee0qmjeHBw2IVlSj3d9PwwuMgS/jBh96N7a3PSuWZvSHj+LOMnC3J0Qgr/O2MvSHlO8TKPls1MX+cvCGheswb9xq1EsacEnq2a2QTo3GMb33Ox4E+1yG+tz9MrteEN08FrZl+2uiaOdtePklfyRCfZdexow70d4qrD/YC/X628EelJkN/rDODmav/2to06LhOKawi8DLnRmznb7K4ZclidJ24fwKScEyvsWos4m3cTIGou2lsQaLx+WEcLxcmVGDY1yWjvzHkZ/6U9ebwKKW28AxyjK7doJZNVdg/XU9amhQHPJjy74HKA++1QtGGL9XxYO/9Iqw0Dn9wEsuf4ZfPOW8xvjfHSd/b0zTLTjmnr4H6d3HZhifr7rnS2P62jP8TgCRdwX2f/jtOI85V7r4AP6WCta3fw+cg+E+6Lc5VbiWcJHrKV8LpbQFAZh4h/sjgClPNXsE0FUHbegDdzK2pgHYihAyQ+Lo8J24aAHoXuNrAagFoBaAWgAaVxEtALUAbFMC8PYWEIBPdwgB2NS7PPsP/ou955u6QHufTwtA91pQC0AtALUA1AJQC0A9AuhyZODO0CZGALUAbO7dvbFPYHOXb3fzawHoXpO1uACcv2+UYRvCRRhAHQJGaEWHgHUImPuBDgHrEDD3g7YcAk47EEX9nwESUefNmBvR4vUPUGsygH1vc38EcO8znWoEUAtA9/RQp1v6LxGAOcrbLkVBT3vKwIQlhwNZuCbiZ2NaWutFpbXwq3o+C3lS/c3go2KUl1yY8jMzUR2J35+0kjB/jfP8cj5aYf+sdci7KYyat8o9LF56zARa1Dzi8ce+f8aNWv1urzNTvh2c0SBfHIPwMqGKAfRVeX7BAGJ5yQUsOYAt4kmo1svzMAfI5ZEUvMn/bhK/ZEaUqfLI3r6T0Q+i4V2y6bro5di2Wr8sK/Uh/oPsi1fkhJfZrYsvxDLK6yxmCHKsnh0H375orzI6wRfMl2zTrvKNJ3qBqZuy5noymcBO+fmC+Xpi0BfGNMsGXurxdextyjCfmfwz8QBQ3QM804zRYJOya+B1dqACdXlDwjJXHuZ7tsw0fps36m2sPxs5dnNeBkeXP9qDPpwF1u3dQuR6XZsDXzXzYqy3GtZxZEuwkLcf+pGtBu1fV47pjDHYF/Eo/N+eE+hvifC4+yoDLKDHd+BQb71lvjFNtUbTurOA31h7gcnafwmePf1SwATGTASzl7kpjoKSwDFWW1Qe1xrcQK9PBt8pbCX3qXwrWKzlS5VnoEpQlTxlp/H7rz8jvzE/Mws/eAAAIABJREFUU9059Wvjs/CtTy1Fn3ljGupM8jS/sWYi3TQefOjL3+OFkx4/oj6E1Zs2GNxfbk2Ii2ursILDSwyDb6F4PC7d249OTIBtWXo56mZgGJiytJvByaVeh/q4acQyF7e24k1wh9HzsK1dzyN/8PUnoB+vL+1JlRfi/N91O1i9M0Zj3m1F+F5cAfbWVulNUavQhpG/gv2lEvgt7r0NfWTQGIRwR4elu/Ikby0Fx1j0PjwJo9Zi2b3/QjteMOlnivQCS+ijmN3FhQON72d3AZ/3zPvnULefME/GNJyvJ0xGBq8Ef9TVO6vY2g3ejiOSwQsyi8fF34xzJs4XbGhGNc6ZDZndyW8Djq+qB64ZfQajH02ORvszJ8klqyqMMsvQz8/vhT785jps01jn5XfQuCX1TiR9Q/HCaKUdbSr7sGUemEv1jE6M3vachraN9IXgW7kHff24C8B/t4AAfFYLQFcn6YAf9Aige42qBaAWgFoAagFoXEW0AIRI1gJQC0D3bqvHbWk9Anjcqr59blgLQC0AtQDUAlALQD0C2PZGAG9tgRHA5/QIYPuUJk3baz0C2LR6OtJcbgvA79MQUrDUIjzD4VIdAtYhYO4LOgSsQ8DcD3QIuGOEgGNeySRbpY0+Oc1Io9ic7BrNvUsZ96V+t7gvAPc8rwVgcyu/Pc2vBaB7rdXiAnCPtSv9XgHuRvJnnhu23vgeo3KY5jjA0ZTX+tK9O9kcnagsG1xY0gAwdkND4f8W7YUUjtW13i6/Ki9lrCaefFIF/iZcaNnPT/z+hJcSjz/J2cu5VbkEmaxUqnLeFiser/Gy7DMYZcZ+CPvnZWT4wba4lNaC5YkwVVGBEz53w1Tu2nQ7eDxhCr0VL/hj+SAqUv/LqAJbNT4C/NCsYHA+vsqbi/nBRw7AG++GroDol1ZAfA/3B8OXYQPXFOtVQoneYJOeyZ1sTDd/gnmjZqB+n05ArmHO7cvtYCxvx/LvZsJPcGbcVmO6vrQXpZTgfw/1/8aYZtnBMT27EN5k5hrUp6eFqLq/AtmUX9+4fjimcMVzJgXkGN9f3nUy3TrgJ+OzjwfCb9IG/907zvge9jb6itPbRKfeD3ZUWNA3toEFDFgHjqyyBzZYZ66jukC0j18ofBCnqJyymdWo56mRYM0KHUH0xgawVAEh8Jy7fyC7KRD9UgHfwUVpSWTPwH4kfgjubN+F6K8jTkBWJ4sTnN/uvGg6PQGJcYV9Tc0GAxsViT5UvgFcFyOh1q44bo8acJNRvYuM6Y19YO6/tRo+bp+vGk0jk/cZn7v5gzEUz0TjC7NbB8HjRftVULgXeK519yLH7oyn0Gc+fh5MYP8rwJiVWv1c+ZirboP3Xo+X0F7C0f6a3ZNGx8E30MuEev31U/jsVcbjYeeOSd8aU87D+8lnnO2KqPsS7MPB4ej/MX9DP5VzO98aRDlXgc1LuRj1OeoEeAgeUD51fooJ7hZQSjuK4O0YMg117hmE82zvg+jbF07itKzIxf3xfE6iwF6JxoS8lSVvxE7FhoagvnOnOOiMAagL8f0M9UKfSfJDP11WkkSpT4LFzJ6CPjZuCPZT+nSpHX2QvUotTjwM7ypAuwcqbrZE8YweKaiPWq86umEG/P5WFMGv0VELP8Ht28C3kmJv+WP6tbfht2aUAV9xogqiwdFgNkPUsYV7o20+WXsC+eaiLvjc5RLzGz4UjEDlVfSqpZNHoY5aVQDe3AIC8IV2LwC5M73BmHgT8gdzdhLOQiIG1M3oKe1zVi0A3Ws3LQC1ANQCUAtA4yqiBSDErBaAWgC6d1tt8aVZ0A1vggBs8Q239RVqAeheC2kBqAWgFoBaAGoBqEcA294I4E0tMAL4YrsfAeRz8z0i4lfSOSWcLg1qQAtA97pDswXg4jSkEhPLEQlrCgOoQ8A6BKxDwDoEzNcIHQI+fiHg/g9AK5iGA1lojyHg/je6LwB3/6dDCMB7Vd5f5mU4fyri9/XlP+7JgPa7tBaA7rWd2wKwSHFzu1Qe2W+zB9G/+8CDLEblh43xhIdWnppXWD1m7+Zs/rvxP0+Vj3NyT9w8xZtL5q1w+pK1FpyVvydYv0gzjIWlCBvG+Xkbs3oyT7AJ4Q0RsPzZUof1VigWzqa+i7gtdgRSuBnHIPyecIKlym/v53JYJ6RVRtAXieDkrHXIQ1tYC26K/fm4/GoB9/TUnjPIasO2Y0LAh73f7yNjWqyYslTF5e2yxNHFocidLP8T78EgYENUpZg73k6hgp/uTYO/XspucJlxCWADn+uLnLg7rHEujnG/DWzax6kjjekHQ/9nTPOcQXT1Us5bTnTl2JXG9K2V4LwCssAPKXtFqu5WR49Oh3/e21ng+HKXqHypJ4JdszuwTE2pH50/AnyotMeawt7G9wg/5CxNex/1GppqpZrbgbb0DIY/4bZ8eMX1j4Ln2eBgcKPvbhhLvVCNdOBkvIhhC0cbTBoF/7YRQRnG9LU3z6LzLoMv3fhA5CVOV/WQqTwOOU9xiBlcWJkDrNeSN5G79eI5PxhTZim5MKsaZsb1+fkV4O0S+oElS8sDR+m3BeuYeeFql6ff15+hrkaeiZy7CQFopwIbOLdFq0bQGSeBC43xQV+RHK7lDrBaeRZwdLsOdqGw18EsZpyFznHThB+N6QtrJxnTCQPBsJlUjmxjWxdh/wZ/Bj+9+evBD3pYTSRpYs8av8H4TXwU5fxcXw5mbccrgyngYrTDNT1WGdOX0sHj5W4BYzh+Ao7RUWciRy3qLd4f7GOuOoZtheD9KveGGdOYwfmUFAYPy6xrexnToqGoG6/z0f6TY5FzmOul1A4md1AAfPWe/QzngUo1TeG7FHPa34sqEupz4PI85irU2blT1hhTvu58uwiehv6D0PcqKtCGsweAkxVeMqcmlPxV7l85tlU7wGaGbgUbWDYC16+kXjk0vQtY1JXFYAClPaQ+Dljg/cdl3pi3XJ+P9kEE4ClnweOyyoEcxikv4QG+1xxcZwPMNlq6Djma/XNx3NPO+9WYLvwauZzPnbma5Lx0VFlpzdmGH+df/hKIFoCuVoZR4+ELX/Vx0eyERQtA9xpdC0AtALUA1ALQuIpoAYiLqRaAbUQA3tACI4AvdYgRQPfu8h14aS0A3WtcLQC1ANQCUAtALQAbXEe1AGwbAjBpjvsCcNfL7V4A8rAxD9dy2h8MceviqgEtAN3rDFoAagGoBaAWgFoAagHY5kLAWgC6OiUzFcxuaAHYSO9oAdjKAlA2t3A/8qaK6XOZE8xNL58CijOD9RKGrr8XWL0KhdpstIIJe2jbdAoLAFs1KCIXy/shV6fk5ax2gl3hIgygeJGFeGJZzmfLRbg89uaTfKvC3Qnn1zAHMC/D/xdPQOEZZb9rVe5e9hIUxkum4lsnx73gwAhjH+y1nvTVQMMolZx1gPIqFJtXrXinjRZ4u/Gy77yNHLqeE8EWzR+CvK7vl4C/uTJ8rTHNd/q6ODk5JpvK2Ss5R1PtgM4H+xygLAfYIWEUX3j0b8Z374vgB9YrCNs7MTTVJQBffQR5h0vOBsM2qhu83yaE7qVvC9DeJ4QDR/lw/mnGVHzWrNFgqvzCLHRWArimgf5gwd6+bZYxzb0I/oAOG7ivOruJJg4EixThDeav0gFmb8lKdj0gCk7FKc5NUZoMdioqGu0tvmoPDIcHXXUt+sqPRQNp70HwjI8OQv7ch5+4xJhOmQOvOGb1uKzM6UPF6airqF6oE28zeMFxXcDCce5WX08cX98AcGghntjfPdVg1RbuAEc1rm+qUV8N9yfTCu9B5sO4rNsNZGfG0K20uRhsZnYu5rllFPz6DljBvgkb+cvBXi4+bFI06kxKlgXLlikvumKrP6UvAZNnH4y2vHjAb8Y0z4bcw2tywNHR0nBSi1Pvj8HSpVyOulPe7uRp8aAzp6wzfhOubWsJ+MusneD6av1wcj8w4WsqVlzs7irUzZItyK173ijwg/kWsHulNj+K9AVbW60YtexKcIwHK+CVZy3EdSWx3wHam4r1RceBBfV7BXWUeQbYtUsmgjkssgWSn+KEP18CT8taH5yED0/5zJje9+N5WO/HFkq5BH0hqhuuW0OjwGwu3QHvvxmDt7muESMCwY4uKcIxjQ2DN+NvZajPgppA6h+Mely8H7mPa/crL8tY8MeTk+Cp188/n8qUB2mJYhblmlNgRR1xmX/i667P7n4Yv/QOHKtf/XsEoerci/JGW0if+/JT+GyOnQnO8b8j36Xy8nIKCTHa6C9nAJOub4ERwFfa/Qgg1zUfBIOk/+JT0N0+0JGW1wLQvdZs9gigbE4LQAgCLQDRI7QA1AKQ+4EWgFoAundLMpY27ktJ17WAAHy1QwjAL4mIn7hZofPTdeO3gGe3QJ23y1VoAehes2kBqEcA9QigHgE0riJ6BFCPALapEUAtAOXuDjuGIxdYNHTCogWge42uBaAWgFoAagGoBaAOAVNbCwEPaAEBuLNjjAC6d5fvwEtrAehe4x6zAJTNSihYmDv+3a7YtDhPsFrlis2SfLmvZ8NDLtK3iqKVp1kXb3ibCQMj62f/Py6+JoRcuYj3mHCCcV54cjcROCT2AJT9kand5e0H3z2T8uTjeUsVvyjzNM4f3LCKD9jBHYUqBmyfJdr4Pj8VDOBzQz4lyVH8YRG84q6OhHfeASf4JtmnazZcRB67wAcFj4Tv28RY5Dm9NPwXY1pVB++wddW9aZQf+LuqOjBL4gMXp/IU2xWzWFrrR0WKw1pdAX+xL7eCqevaFXVVV4dT544+i1wC0PiB93fR5cbUJxqc27l9trj4s88WIG+uP1A4mnPr58ZUPPNW3D6Ocq4A6/fE8C+M6aIS8INrPh9mTKv6geXzDrLRZUlgHOe9e7ox9TsIVstLmRqK/9yB0+oooCsYpUcU1yd5g7EnRGJG/l7aCdQjBJzYhHDweFsqwJ3alJfkL78pPsvfSVeMQY7hPVVoyyAzWC3pi2aTkyJUbl3hLT/+HV6JkT+ifx6cjGUmJe6h0cFgB9k/kkugSrLqqfrn+grwYpwru0bljd1c3M34ze4EH3lFT3jQbaoEL7oktT89Ohw8I+cv5iL4gTBbB+3YHrOyXspXc8GaUcZvF45Hf6pUTK3wiDsKosmhfBnj3kS/Sp+Bc8TDiT4yMLnehuzgq2ALz7wX3okHrOAatzyJ/jX7wR8powZ+gsLWLt4L77nuUWAs88rAITpSAyl5PNpnay6YQksZ6tOjCvXw4GT0Lz63M1Wua+n3v4zDdhKXo7+FqlzThbZAw4+TS0IQmOJg1aYO5de4YC3qJemJA7TrAbCFss3zTsJbsMUqTzenhvvmOzC5tiggWHPGg9WUPlLsALM4f+WJFLUR9VYVjWnoJHC3s7pxMgeiHZU41uW7+tHwBOTm9jfjnJBcvfz51RFgiY9XOW35rcamY/1xbf5gzH9blQEccK37IeCdr3WIELB0AYZz+YLOF0k+cXDT6MRFC0D3Gl8LQC0AtQDUAtC4imgBCNGpBSBuKsdbAA68xn0BuOP1DiEA+eniJX6Xy3gWQuE31d4nohv4HSr3ZED7XVoLQPfa7pgF4Nf7MZojo256BFCPAHJ/0COAegRQjwDqEUD3bkt4CUQLQFctvqFsYOZwIEX9Op6IOAUcp9261s36breLawHoXtO1mABk6xUuoaYaClB5wdKVFUmVCgFLyE7CphzWlVCspFqTsFnDkK8hNBuGdRuEg/l/sSoELCFctoPxMh6Q6sOt1RI2VQ9QEo7jtG8SAg5WoTqbChNJ1Qabaqi8FqmfKpRtg/zvoArHfZk91Pjp5t4/0VcHVTgsCqnbQk14QJMQ+J3rzjG++23zo4BTMIof4Y8Xu2ZEbzOmA31goSLL8D518YSdjohtSfP2Rp9PjN/THQitsVWNpDObFogQ25KqBGP6VjpfN4jmDeD84kSLqvrTlgqkpkurhJ3ImTGwccmyIozGIv/rbxDO9lVBB8dpCO9zyNvYTxVyfuXW8ylzhvETJfSBtc+oCNjJhKsUaRKyfH3BVLpwFkKJ2RaE1otsCKXt/lqlgNuPsH7ueA+q9cVn/2iEgi9MhK3I11kIMYstjJ+vjQZ3wbZHBsO24+sczHN2LOpXLIN+r4pzhQv3FyCkWOtUKbH6ImVZsLmGvkrD8lVl6AchGxEuNU9BiFH6cWmlP9lrEEK9ZTTChGIdJOjCU+smG7/7BtrIkgebkzofHBvb6HAJC0SfqbJiO8OjD1C5HeHRbdkIIXp6YpmgACwzuwfsOkocAa6ws2ASxj+I6PVfgV/4ZQItsIXVUdgOXEaj1uBY9lyLflBnRjjep9CT+k9MNT7//hssbP5xOux05i1Hf7pwIsLojFR8+hG2oagO8qrGemyzgR+c3wsp7T596zS68CqkqNteATucdT/BXiUQXYZOuhr2NX6edurqjbD+2ynoi7H3Yr3770Md/aM/+gOjAMXSj8pgjXRBN/xvTWkfY7rpW4Sl478ppr2Xoe9J/wrtjr4tdjB5NUE0NBTno6ShxN4R7ShHW+RU4dwr/SmGorYgnJtxJvpBnSf2c9oYhIClfL9tMJlK0A7jxsIapmFqvvdGwxKqLZXWtIEZeHULjAC+0SFGAPnEZI+uFY36AudY5AswfJs6YWlrAvBuInqMiF5kLdCgPdiMai4RcUJJViZ8JeC7AIzs/lgeZEeFRj9zolKASyh8N3iWr218jWbbKSK6jm22mtEPtADUAlALQMWLaQGoBSBfO7UAPPIdpNUFoDceeo6lOG0W2tExBCA/DSYfxgian5b4CQlPzZ2wtCUByFQxq3EmZnlYQwQgi79FRPQ4+8UqI0ceLmKiG8NmhxeArPiRuR2F6WNcoVFeI6Lp/L4AD5cTEWfo5iEBZJVvWtECUAtALQC1ADSuFnoEECOqWgAe+eahBWDTbqwtPNdPHBxRDCCG+4k4FMFhHB6ub6gTWnjTbXt1bUUA8ut3m9QI3L1qhE8EIL9SxnH6+5pRlTwCOJOIANr9sfDrpByM+wcRLVD/5tFBThXDghOvsR29NFsALk5D6MSiQqrCAOoQsA4Bc7/QIWAdAtYhYB0CPvqt50/nMO5Lg656jDzdHAH8/c0OEQJml/kfVNSPOQ9mClgb8ADSGUwiuFnf7XbxtiIAWYmzx8EtKk7PIV4WgAygcEj2RiL6O2NRKrHzPUQEaObwhQXg7WpkjxU/e2VweFmRMXQqEfFTAcMrAGNQGHh6gYieb2KLHrMAlJRqwqiFm8BleXs4KU/ZnYhlirdiAoW1k9RVMT5lLksX4aSEDxNGT46Df2e+iIvwVsJx1SquL8iEiDrbuAgnJ9tm1q9hacj9OZUlimxb1peubCf6++S4GEBJNyaWI/9LB490UTxSZaVaulCiH6Lwg31g8SCp5V4+gPRpmR+Bx6ubXkxJEZj3wTikMytXwnq/DVjH8jJE/Xv7HaShfuDZYhQLmOeEHYjUx/0pZxvf3076gKLwQiNV1aLOFldxJiGix1ZzTnEir2AMPocFVVOlBSnU/pbIzzBEkpZKrDPYVmT+BthmeFiw4qDusIa4ewAPbtenVnvvjrModxzm8U7EPGIzYqsB7/TkibD24HrJsIK7k9RsaVX4nvIe7GuKRsH+h7d74Ungn4uUPcf6ArCLZRXg8jzU1YAZPP9gPChf2Q/2J7L+eSmwb4l4F1GT2DtTyeJE39iVBxsYD5Ni1nIwD/N5nhWqQuPQxwIVd2d8YRp70HxjesARRhk2sJON0w4+sxpp/wL3YnvVI2ooPpof7IluiF9mTOVBKlzZDJUq5jTPEUJWZQn03E70I7sd6wlYqSJAU2GzUlbqT3XKwiU0Amyp9LN+gehvnLKQy+7KGNr4O2xp+r8M9i1jJvbfGo568C30oF6TYQUT7gM2MaUU/TMxFFDoxlzY2FhTg2nQGJVCrwxsXfk+TM+fiLaQlI6L0pJoUDQsUmqp/lL++dhXjd8alkt+u4KqVZrAPUXY9r/7g7HkckniL9Tz3SeNz9eNXkFiyyK2PTVOcILLP0b73/Cvr4zpi+/NJF80ARWfgL42qh+OtZs/mEUfU332rXIHQpJiJ7S5EOxiUQEYQGb6nMGY36MGdWwKw7k2pT84P1m2yuFNXXzA9X67HOfXBZPqbwtzh8BGqS2V1hwBHHRlCwjAtzqEAOQuwBe5ixQGxicLd6Z5f4KRtaVu85ftS1sQgMzgsaDjM5jvOgxqigBk8ygWb3xlvk39zq9yM6s3iK+jR6gZvlPwqCJzf2xSxTwgX2l4Gb5i8Mgfu4PXJ8rFipgH4PDznUdYL8/fcBlWENllZWUUHIwL2NGKjABqAQgxowWgFoDcD7QA1ALQEHtaAB7tFtKU/2MEUAtAqSse/OEnt3caVR4btvLTEJ58OmE53gKQ3WX59TIehsUreIcKQB4e4mEL5v+MRxFV+FXE79SoXlOajd1WefiHnTn51bAjCcD1amTwriOs9HAvl5AWgOhGegRQjwByP9AjgHoEkPuBHgH8412kNUcAB//L/RHA7f/tECOA6eqejyH0+sIvlbIFBIbwO2E53gKQOT1O1AzPERQe9+fYCcfeOI61j4j+SUQNbd05XsRxggub0WYs7jjmwU8DxxoCPuYRQPH98/UA36JHAPUIIPcDHQLGGaxHAPUIIPcDPQLYjDvakWc1RgAHX9ECAvDtDiEAObKYxE5djaqMPZk4FHzsr0q3SHMdv5UcbwHIIVTkaqovHJrdrYZlGc7MVkO3DV8CYSMshjobjgr+WS1yOJj5Px7BY/NHeQmEuUIAVUQMeXHI+C95CaSxABRvP0mbFuEJBpDZuhyVLk3YN0mXJCmxyhTXFO5Z5Rp1EwZQ/P8k3ZNLVXvUulJ9yW8yr+yL7AOzgY19BIXZE8ZK1sH7L/+rUF5/wg9Kyi32r5N5fimHj9i2g/D/qrSALbp7IEKhadYudFEoa3WiFFUPu62Y9/mVU4ypKRgi+vyBm+iqcHBtdknNljHb+G5TjNZ5MfAS5FRrU4PgYSc+hcxbcrlzH3wFi6vhKXd7/x9JOEZJWdfdDD5MPBon/3ST8d1c4E3+SWCdru+LlHX5dqSs+2gveCmTqZaqK3CN8fTGNp8YCT5J2vbOxUxCkJEGa+h12E9hE2dG4BikDsWv8ZuiYVRuw3p7BmD/hioDuO5eALN2WiAsOK1YtUpjJm27uRzp3YRrk/61IHMEVS6F/1vlEPBXJ/Xl5zCi1XvRfr0+xKUj6fHt9P1a+Db2Ssoxpmn7Yoyp90EwdmxfGHgSWLcXkuC5KMfwcg54vM3ZYMFuHrLMlQJOWLd5a8GJJvTF+q/vAe/Dnl5FlOcEeiHnjPT7VaVgNncVgUuMDymhceE4BhmpttbiIeTVn/l5kMg/U1K4EUX+Dg7NEgoOrWgQjnfq6fDDy7GgjbNf60PhK8CWkg/6cvZZqm+PAEcZHlHpYtwSeoAh7BF4KB+3R/nt9QwqppQyMHq2D1GPPmXgUHPGKX/FU9Ef1rw1kgZeWs+vc6qxPys933ra+Pd148BLcrljAF9GUQbeBfTZPrKSvLzQT2f3RmCmphbHtuEg+oz4Qb6ycCrFrQD7d9pT9V6G/J39FLkwIyx1HqZSAr6x/mTjf2HrsN4qND/dfO43rv15ZhEMMc1x4DBtxejr143H/nPqPjlPS+3gWAM8640hXhz+sWtdbeVDq44AagEozc6o2EONBpH4fzywxL/DnLMTluMtAA9X5Q0ZQOOaoBrpCsUAXqJ4QOb54K6KFzp4JJGtXLg8oyxj+MrMV2NuZH7rh1/Blfx/bAMzTdnAMG3PqWK44G7TtNLkl0C0AMTNVgtALQC5H2gBqAUg9wMtAJt2o2nmXMZ9acjl7o8AbnunQ4wA3sHPOurFUHn64SfPp5QXMCNmnbK0BwHIDcNM3vXKs4cfSbkxG74FzDH+d9UIH8/PwwwT+GVFJfh4Xn7RRAQjzyNG0DwK2NAIGq/VNa1oAahHAPUIoB4BxNVCjwAa1SBuA3oE8I83kdYcARxyWQsIwP91CAHIOucJ5SaCIWe8cMovfzzctFt9x5yrLQrA9lTTTRKAn6UmG/YuhupUDKAOAesQMPcHHQLWIWDuBzoEjMu+DgG3yO0PI4CXtoAAfLdDCECpVEbBmAVkLyoOCx8pkUSLNEJ7WIkWgO61UrMFoGyucX5b+Z29/4RNEu814Zq8lJ+WiAbOUxpkAmckjI14+0mITfzBeB3CGzY+ZPH6ivaCjxkzfPIUL/PKPghzI0KW1yn/E2ZRlhHPvwqnL+2shuBLU1kjHLXgmUprwO641l/nQf3CEaWf0xU+ZU9kcqSe6O0EeHbbjXeEOM+vJ/kqb8N7s84yfvM345yO9wMTV2Tjc57oxi7LyVKHbQYoMV7oBFO0xQI/vPcz2XWIKNjHQiNC4UE4JRg8XkUd5g1QAv7ShVcb37svraXMc8BoXT0SDGCIJ7zu1pbCr5DbYOMiGID/81ymFYiSfJEb9Y4vGEMhuutsJhiIntgy2cVfOVVO3c9Gv2n8b9YXbJNJVBsO5mrGoG1kNuHBoqs32m5dKV5oOyMCbNjzn/B7VkSWHnaiGhz/nInsq050wAp/OeGnNnyMfL3/uuo7en7d6cZnDzOO7ZxByMO67FXUkX067DOHRR+gNalAaPy2oy2lVPUDqzk4IZvi/DH/rHDwa1l2vCm7oxrwl6cH2rS3b4Exva4fOD8uK9PB8wlbWqpY0xRrDMV7I7mPrO+5rRCUDwyHL2SZE1znqx/OIIfaPWs0+L6pwxVrqbb9/Xr4xvvke5KzP7iz4J/AsYWk41gOXIXp9ATU71erR1Gf+TgHzflog513w4vRR3lFjovfT8u2832HKOk/YH33n4e6H3cG9kGOf/nKITR2PPzuVu/EcSe+hW3mnIR9OfE8cHm51cFUYa93pVp1Ghi/Yy3JV4IBPPeWJZROU/LfAAAgAElEQVRaDQZ06Tr20CW69ORVxlTyMZepyvx42XgyWRvdRnqh7mLC4WP5j+7r6aAdnpuf7BthTIM+B7tZEY9lx87EMXXzLaElD3Hwhujvj7DRA1Gmyqm99QrkOY59DcxltcOHKu0Y0OkThGuG5DDmz0djIo0FWrm06gigFoCt3Lrtb3NaALrXZloAagGoBaAWgMZVRAtAdTHVAvCId5XWFIBDL3F/BHDre8c0Asi4Fidi4LeYWNnfoDx2j1QvzPlfy+9GqXSt/KTPbh2Sto2Xa+463buzd5KltQB0r6G1ANQCUAtALQC1AGx4HdUCsG0IwItbQAC+32wB+DcOphDRNRyQUC9xnqcs3TC8f2hhT142aGZTZvbp42Fv5vmZ42ffXi7NXad7d/VOtLQWgO41thaAWgBqAagFoBaAWgA26U7SqiOAx0cAsuhjH685qkKYO2Gehl02+EWMxoWdO5iPALuB8iwRsUnzePW9uetsUlvomahBAkldG8dSA00WgMIv9TDD/mGvHYzN4hIwNtHe4GWMPLwmMF6Sb1R2LMQMtsykuDdep8wrnnGND0K4PPEJvKlB/s9jOeDmLPPGHnh9/V7VjYrtYLH2l4H9qrHDFsZmh89aWACOzeIwk68ZjNZL/eDjJfv+XM5k4/vzPeAVZquro2hPsHn5TkQLLtvLD5RED/f+2pgO9QY/VVxrpwwHOKTyWiwTrPjJG5/j6APR0rvYPYhoxvaLaVBErvF5doTyfVOehMaPDbz+/vfdaeQIAocX2gMMWFwQpn2D8cA7I3QzXbHwKuPzE1Phgyc85twdyG/r44Vjvrffdy6LlMd2w/fQ/ku4MY2fwi+7E02IRAbEjJoIWvTbUOyQGQyduRT1WRuH+ggNRe7ZmT22kb/ySBPG9NO3cM2NPBu8Yxc/8Gm//dzfdWXoOhQvxQ8PB7O4+Ad4G3bZiGMecu8WWvQzfACdgfht/EDs31XRYCIv+/IaCkpEv7+pL9g+zvkspak5W5/fxQmDiF5YO8mY9v6kjq577VPjc4Xyxnz2HXg6VncDu3jVqWAuP/jodIraijrOmIrn3pOS2W6UKM4PfOInm5BPdmifLOoZAB/FhatxvH0+AtdWHYd+PODu7cb0hOBUenjJLONz/Hc4fv90rC/lMrCAZ5y2iZakIid1t7fQ73PHgV0bNQUs4S/pYDd7vuZB4Y+hPUyKTcx+LtH4HrgP14iMszixEcruh8CFtkSZOBkZsc77zyKqVHzsK2snGr9dMQamC6sPgmsdEFJvlpBdA55xXzHO7fN7sU0r5wDGdSzKXEEP/AwWNWg7jr9SeST6+GOesxJQn1tL46jsLTC51z4A5vfFFPg0drkb7Tbo/b3GdMGmZPIOAvPLnCWXYqvK60xEX48XVzDjX22itKYAHPbPueTpfewex06bhbZ8wOYZhm8uOt+fF+7UfME5l/HYBrO+x5cito88zOJ8webk1XxycypWBooZ/vyAU6+znegxrPNo+6n/r2pAjwC61xW0APyT+tMCUAtA7h5aAGoBqAUgLpStKgAvagEB+KEhANlNvqLBpZ5V9+HeoOU3/fhJkb101zaYn/32eDSAR/UOV25U3r2sR9iN/XXFBPK8x7pO9+7snWRpLQDda2gtALUA1COAegTQOAv0CKAeATza7aSdCsDGh8WJFTirVuNyJLHGr6efxAPmh1nmFMX73auYQU419CIRvUVEj/yJAPyzdR6tGfT/9Qhgi/QBLQC1ANQCUAvA4yYAk85AejsuX457xa2LWkcLAW/ZHU+zR8J2qMAaRN7KRmtzAdIjctkynTVG65XWFIDDL3R/BHDzvGaNAB5LuHY1Ef2q3hqWhriIiNj7ij28eESwuWHl1mvQdr4lPQLoXgM2WQBKHlfZ3Nzt8LbrGgperF8IwoVR3pUuDz5haGQZXw8wTOL1x95/4tcnDKD4/jU8rH/15XPs+Jbpq3iUnyi7HDlUPVTPE6872btAX6uLDxQG6sUB4ObiOKksEW23Ib/rGJ8CqgD6RqUqV2mcJ5i/69PBZT3YHbzgHWnn0FO9JO0zlvFSLOWVt+Jls1effcGYflw6hn4ugLedsIrXJsAHzVaHfLHLi8B0bV7dl5IncApponWbkSfXOxo8o3B9t/T/iYTF9CSwafL9v3vHGd/vHwjfOmbZ8h3wSBNWL9KM6EuuDezXvF85XTWzZmayhaECAlQ+4p6hYO2EYTs5GJzbflsXF9c1PwVebLGq750QAbZwXxW4vHUpvcjzIFitMyciL7OXCfv9zbd4iA9JxXaLBxFFDIAX36mxYLPEV7KnL37nfMrCcfL3SxL5Zb/mF0EKHl+GHLFJTx6gjIvAiz102YfG9KM87N+BN8Gqic9cyP46yjsdvNltYxYb02eXTjem/5iAfNLiu7lw7yAy7QVLproT2RVaFv8D2pZM6MCxj6fSuVHgRMsVN/ffm5CP2j8NbbHztjDy8FQdVeWs9qgEqzlsBNg1s+qL5aeWk+lHsIOBZvRlKSV3Iw+vx8/wzMt88AQacGrLCcAbNl1orHfbfUNp1GMQTl/tAaM8PRGsIguphvtrqzW72ntwMDjREgc4yS/XJxtTn1wzWePVsdSi3swB+H7VINR9scobvPW0SNr3KgTaTYORteu5H9FOvb7CMmNfZFSMaFtZHO1ZgnamoThHvMzgMMsL4P/ZqQXgP1pAAH7ULAaQq5xf2OAGYusXLvwSSKZK03q4l0C4o7Hh651qfp5wdi5+M5gbkRu0uetssCr98c9qQAtA9/qHFoBNrD8tACGotADUApD7gRaAWgA28cWKJl5hD5nNuC8NPz4CUCxb+K03FoLs8Xc+EfETc76yiOEnBfb548KhZH4C5/lZ6PFT9GtExMKQ18XlaOs8ljrSy/B1SNeCWzWgBWATq08LQC0ApavoEUAtAPUIYJPfrG3iFfaPAnDE390fAdz0cbNHAHlH2AJGjKA5hRCHf1jccVlBRBxyuFR955AKb4RTInFaIE7pslD9htfpUf5sncdSR3oZLQDd7gNaADahCuPffooGJWYbc+oQsA4BZ1gR4rx/EMLzTS06BKxDwNxXdAj4qGeMcV8acUELCMBPjkkAHnUH9Qxtowb0CKB77dAkAdhwE4mfPWp8DQ4ES9Q3DDks4/zAAgZ71pDk/PVUOW8lz29D/z9Z59X94LXWlgsLwLAYMDri+2ezgaXz8gKz43CAiTKbnRQVDD+6FxLnG9MDDrBv/VXeV8npG+tJlOFAfttlVci1mujDUQaiwd6YOtUgd0WtmaoUJxhsgoNBVR1G5W58BJ6lV94O78CKWl965Sd4zsX0Rfs8mAixsqQcTFRaFd549Pe0UZAXPPeGBsC/7T+74Z02LAZMVBefCqpwwI+rwo5pcgjymb79ObZj7Yl9MnnW0rkD4aMmDOTn34MTjNoE8WhygCfLnlFLXoEIpZ3TD7l6u3rjoXlLBcRCXg14wlDvGmM/uKwr6Il5A2HttWsJ2EV7INZ7+VSwV1yk74kfXNdlaKfCEbh09E7OojviFxmfS2vBfkkO6ELlu5hlCafuvsjNXO2sz13bXAE4d8eZxjreWs8vFBL5pXpTzw8YLyIqeA3bvrgXBhpWFPUzpgmB4BC3Xj+ECkZgnoreOM5af/S9s0dtMqbCKfIxH1Q5pM8IB/v2Siq/rEh0sARsWdyn8PGrjPWkUZeh7q+IAid6w/3AnyLWY9s7bw2jmcnYRrnqByt+HWR8HzQM/GVmGfp41wsPUMp9+F/f0egj7I1plCfBaHotBwNonziUAu/FgxWXhSex1+6xl2s2Ii91xnnR1Pdz9N1S5d/pp2DIMjsSKhtekTyC0KOawoPhNWn9IcqYliUr3q8C+/3QGZ/ToiIc0y+/w9NQIY909Xj4Qq4oRN7jile6U/45OJ/uHo5+JQ8M8/eAXQ30x7kS5ldNWWvRzx1+irFUdzSvCnyoVVUXmZxPhVtRfx6O+tteyt0t56NorPwopTVfAkn+m/sCcON8LQCP1qbt+f9aALrXeloANqH+tADUAlALQC0AtQBsXR9ALQCbcHPq5LNoAeheB9ACsAn1pwWgFoBaAGoBqAVgKwvA81tgBPBTPQLYhFtcu51FC0D3mk4LwCbUnxaAWgBqAagFoBaArS8AzV7HngrOYbfQRi0Am3CHa7+zaAHoXts1WwA23tw92+AZJnl++bNwa8IANlzmjgE/uLfHx2Hp+7bPov3VAP/X7AQD5J8K/s4aDnbHEQqPw9huxXRSdKrxeXMJ/MBeTEC+1zwn+KtnM5ETeFbMZlpZAtbrhe7ghfYrXqrYCd4ryhM8IdelRXn4pduxLw9sg59ckB+YI2/lIfb5gA9oypYrjN/Gdk0zpmeGgfNKt4FzSvRGLlTOx5zjQC7UPZauxlRYMqsCkGrJRH4mcFHhysvwpSU4ht6fg2dKvx583/8NW0RpVmxDll+egzqLvBgc3e4X4o3p3DFf0fpK5JD98jd4ro0bCi++9VnwxxvTHRxZpE+li+dbX4T/ldSgjqq2I9fwlDPgZ5dZHUa7VsJfTVgtYal8B4IxrLGg/b488XWXD9w1u+EjV1KJ9VqqwMnNGLid8ixgEbl8NpZdHppfuB9xCfJEe721YxwlPILPhWPAZI6/Dh5xYV7g0qqd2Idf7xtNtmDwi+IHSCb0vfOHwPNO6vuH1AHU4z9gS/ddAYjsptHIKfziBuSlJeXn57/bm+K/Aie6/wK0m5dKmhW6H4yh7cpiKq0EO1ebij7s9Ed7jx6JvMn7S9EG4bMyKOVJtGXCUDCl+RXw3ut6Py7Xphx4hpad3IcibgBDyMVdBnDCT/ziJlGIt4UifdV5o/IR1zjR3n6e8FKscqBe1+5JIN80fPZX6YGvu/UL4/tTC3Bt+9fMH6nMgePPt6Ef7H4cTODUh/mlUCLxuvwqfxhNjEQf5sLXu4d/P8v1nbnRicv+bXw/sLabKyd1nSfqpiYS7dblZ/CX1lhsL2OqFyWPRl1zOdY+6FrBMX5oVQbw/LmkBeAxNlQnWUwLQPcaWgvAJtSfFoBaAGoBqAUgXyq0ACynkBDDDJ//4C2sli/GfSn5vEfdF4CfcYa2v3RfW/7o9RqbXANaADa5qg47oxaATag/LQC1ANQCUAtALQBbNwQ88lz3BeCGBVoANuEW125n0QLQvabTAvBP6u+9lLHGf/dZonUIWIeAXT3lWMNvOgT85yFgScH33YT/HNNVTYeAj6namrVQa4aAtQBsVtN0ypm1AHSv2d0WgM/sAgsmpbbO5GK1+Ldbkn50bw+P49IiADkXbFUt/N/uWTfTmIatBpys0CCqSAATRWE2+sdg5J/dXRljTG+ORR1sssC/7sXFKo/ygHzqGQwurtKO9Vc7wCrVqKl46Y2NSqNIBWftrgKrd6AaeYkXJH5lTCduBcM2b9C79H3lAONzT+U9uLoCrGGp8kFbvmKI8b378By6qDu85xYWDMUyAUXG1Ecln1/4+ViaMhvzSH7ceU/gGMzV4NB8rskxppd2X0tvpsPn7txu8AP0V76FX44Gl7f7JXimzRm1nA7awYetfhg5cE+6n/OqE205FUxZxHdYf6R3JS1ZMNr4PGE2XkjY+PJwY1o4EXxily6ISFm/j6KykfhtaG9waFKv4vknuYzveusyqh4EDu+q4cg5/eZm7P95g7AdLk8N/cz1+Vg/3LYVmaG+/QLHytaCl1wHJvaDNBxbxFz0q5wJSN4bkIPjL+nvQfauOKZ/JqOO/D3BX24qBVO5fTHqtc6TqPtP8OnM/zfmOac3GFBnHRizzBrUb2p5BFk+QT9Vtp0UtQownLUn5sm80kGXDcQ2JSc457HlUmkHP5e2Bbxr37m7Ke895LoO9kW9Zu3E+vs/B28+ZxQ8AzOnBpFKP0x9x4FV5XKsAtC1AmYTF/2f8dXmBDdZnoptmstx/F5Iy03mmvpzuKo7mMdbTgWP+/KXyOF75tR1LsZZfCXFy3HiQOSq5vK/Uf9ruAtN+jxjNTwX079C7u6IHWhjryXoe85T0ce7zwX/997ot5u03r9qplYVgOe0wAjg53oE8K/qC21hvVoAutcKWgD+Sf1pAYgXW7QA1AJQC0AIai0AW48BHDXLfQG4/kstAN2TCG17aS0A3WufYxaAz+9CBgh541d2Q48A6hFAPQJ4+JNSjwDqEcDGPUOPAB72XDHuS1oAundz7wxLawHoXiu7LQB9TLBVqLcOQQhTSnu0fWlcpa/umUj/TUU6s6Jc2DJ4F+A4g5DJi5RbB5UNt5FPMMJu1yT9bExH+MHq4tZd5xnTLgGwqHiq1+cuCxLZ5uJK2EsctMNuY2MxUkVxKPiZ3guwLZUeTmxhAjzQBg9mwW7irm7fU4ETodUsO+xF0i2wjpHyVQpCwB67AskWivD1w1MQ5sy2I/T3wV6EJatzAykoDt4gw6JxE8+uQvg5az1Cf8NPgvXF1Mjt9MNBpJs78//buw4oqYq0eycyMIGcGckZAQHFAAoGDBgWc0IXA4Y1u+Yc1shvXN2Vdc1xVRQzgoAomEERAckMySEPA0ye+c9Xt97QjIPT3dXdM9391TmcevS8qvfera+7b1fdul9zpvyauoVp7ma/yWc7/TxakrRL3YwJuUyP5S11n9iClib3v8jl0iF/4dLlwMwVePBHyg0u3YfpA59/6ShTW0cT2Ex5SOyfhxRriXNrz4/NORtKOW7TNjEFWLcMptp77/Wh2NGdy273DaH9h5Qzu3i53ytfcj7wLJO8ZxVLHe/9U1TOeBq/kHHW4RLe34LbuTSY2rIAV/Vhirt1xVzOfPlLnnvUYOLcKIWzVKKlm3MJl/PXDuVScpfjaE3UtgFtcL58hVYtWTllOOcfkrseeCmHS9OZV9E6JqGIuBSOL0eHTEoVpv3Q29SHDFxg6iV5jKvEp2khk/HrBvR7m9daup2vzZ3CpelOL1EmsL0PU5rtGJuHvAWMNUnJ55XJwx6tPA72oPvd7KOkIZfQUzfzq8KzBSpqZlOvtShEuU3H2DWbmB/b6hdTv7eWGEqZduj/BXsrf9qu34e3mb8nfUgcGi/kGCbNmmfq3Iv3NfUB53JJ+N8DXw7LffjbaSSXgPf9SwhmAN/TGUB/xzYaz1MC6DZqSgD9wE8JoBJAP8KkxlOUACoBrBokSgCrfduY76X9TnAngN9NVAJY4wdTFJ+gBNBt8JQA+oGfEkAlgH6ESY2nKAFUAqgEsMa3iZxAAnj8Pc4+gN+9b2ZYw+lZ6NcD6UnhQUAJoBuuARPAh+Yfba7oiaF1CZgDoEvAugS8p7eiR/y8v+sSsC4Be7GgM4B/MgOoBNDt2z0OWisBdBvkoAmgd1nP4sP7UvO0TNFs/1IdpBOWWssRqyX7v3mHm9OS51Crl2I98UvTgZ29aX+RXI+2Ens1o36qWRrTez2YPdHUy0qzKrWT28pptPv5Vtq3ZCRTf9WhHtN09UvLQX4FLULmF9KCo1ca9Xhby6jzen0dNXubCxsgI5U6xDPbMLXYogJacbRI5Y0+8fNwUyctboC/HDfLHHdOY4quzzZR5/XLWtrNFBekIDGJOsGMTD5bowbUKiVa75CWDahrNOeX03rDiwnPKsTzeWuXTh2abBjaWsznblKPvhyL86gb215EW5z9W1E/eUDWUjy+mGnMtixj6rq0XFp6JBNWbOtNLWRKRrFJSedbcu24rbepvLwfMJKmbkynr3lNz5PEpvDarQOH/3gE8NVvDzC9DOxNy5PGqTvxVQ41fm0bE5PVX1Hz2em/FJeu/Sfjq0XGdhzT8ldzvKyAGHmWPp4OL3cBidXlIz7FhJu5SSt3EMfi6GMYB9tKGUO9M+xsXFkalu5gf2t3UidZOo6x0mAB42HbwNZYcwTHP2kb++uz/zJTr9vONg3vZvq8iuQEDHiSuk1PFzz/QmpAE4q5q3zh1XymivIEJKbyPSJl+dk3VR6H6qDnu3eZrtp5+H7ONIJWconhI2fjy7eoQx0wivi2TKPeNbeQOlopLw9+NlS3VG0//a6kZrHFD3wfJM7+jRj1YxrFjbfz82D2MfeG9T5q6jySGsDBx7nPAH77gc4A1jSm0fx3JYBuo6cE0E/8lAAqAfQzVP5wmhJAJYA1xY4SwN0QMt9Lg48NAQHkJhtdAq4pAKP070oA3QZOCaCf+CkBVALoZ6goAdQZwIBDRQmgEsCAg0YbWD8MBSJYBMJGAGX5x9MHys1d2WNKsPdYJ9opAVQCGGggXv8zbX+85WZdAtYl4KoxdNDkG8xL2z+0Eo0aloALilLQt9W6ym7ePODfgYal0/mRXALef6T7DOA3H+kMoNOA1/HGOgPoNkBBE0DPi877ckuyWjB5XbRdUmKJAFaFefJyLmtd+OW5pm4+nSmxijMSYOV2KM6mDq91S+q72mXkmTq3gBqo7IytlT6AO0vZPjmRRGtTATVVj3d9s/LSz2w8xBx/9xQ1SwXHU8/3wN70r/M0Vze9ek6lJ96bZ1JbdM3iU009rCVTSn29qaOp123Lwgv9XjDHO6yR3tg5o83/C3fwnjq02Ygsqyk8sjl1Ut613l3T3/y/YT1qAxf83hItGlFDtX4rNVSX9GaKtdOy6G32Sh791V5Zui9aZvLc9hlbTJ1fQo3at3O6mLpeKwr82jTKQyN7jYXrqXXzvgi/XcIUe+nzqBssGrgDZcXUqlVYj7fenaiXlBKKVGOVnf3JgUcANxRzvL9ZzftskEY9185ZzWBtNHHLBW+Y195dz7HNSCGeCx+hd+K2Doko2Nv6/BXRp69tO6bs26/5SlOXWu3lx4t7ocvD1ENuvofXOi6b2L/0yTBTn3wkdZ87SushOZE6vI8/GMz7YyY4NFnAe0jZsB0LL6HuskEbaj2bZVKrtmoBiUvGMr7nRVt3yujp5viLa5lLO3UT73vxNYynawbwx+CKouZYvoM+lVLeOfDpyuNIHZw069LK95pcc/ph4yJ1aXMdjwAWTCCONlMjGq7gmGT8xvfFquOaoXww3+9xQwCPudt5F/A3H98ukOkScESjOnIXUwLohrUSwCDxUwJIgqUEcM8BpARQCWBNHy9KAKtFyHwv7a8EsKbwifu/KwF0CwElgEHipwRQCWBNoaMEUAlgTTGiBHDPBPCAo91nAL/+RGcAa4rBaP67EkC30VMCGCR+SgCVANYUOkoAlQDWFCNKAP+EAB4VAgL4qRLAmmIwmv+uBNBt9AImgHfPY77ZJJtUs0GizRdaQV1SSXkyMpOoHZISa36A3nN9uYIaNQ+H85673Pw/c1UFihoyLLd1o44n3eqmMtOoCWxan/qp0opEJHvJSW3H/RutNkc/bKFf2a3tPzT15vIMXPM9NxWUFRBrLwnui8PpUXb5L6ebOn9lQ1x3BNt1TaWg68Ot1OoNyqAH3WvrqPe6PPtzvLmBx54O0fPvS7T3VlyejM2F9Os7vCX9yRomUZuXW8KcwCMymT/158K98MXm7uZ48WvMAfv0dU+a2stZPGae1U2mb0dpObVjZ7T93tSer+CLVzHO1gxjjtwxIz/HwekLzfE5H1xi6v+dwH6f3zjE1JMW0UOxogLo2ZbP3bMh87tKGddvl56y8sUIHFz9E8dlwmzq+6458DNTiydhTgFzwHZoQD1fgdVhrikgrn0y6df3zeaO2PRPagiLsojZyVdPNvUz31LXJ88t5Yi+8zFjZWdz3OY/1N2tOJN/TP+VOskdvfi+HT3gm0od6nuvDjWv/eUsaja/vI2+hekLN6GgIzWAaw5hf72GMt/vyjy+nvo/1h0vXoQfcqyX4eO85uKzGDuJTRj/I7tTR7qx2PpX7j/e/F/LLgQ866Ap4xjb6euKUZbCcc85hj++pCy/7NqIwhbJTSAHKAGM6NhG48XqGgEUJ9P7RLsP4CofQOWT9B8A5JtWWIE4pUpme6qj/1iknxMBSOZ6OUcU27JdjN++LKK05q6AXeUZABcHMJBKAAMAy/dUJYBKAP0NHSWASgD9jRXvPCWAyDvwSPcZwFmTdAYw0NiLpvPrEgHcVyYlZOIHwDQfAijkT9IS3A/gA5n4kcQOACQdBH8S/7HI+bItUKZFZLpHSKVsB5QpDk4fkQBK/i0T4bbIt7LNSeHXMCoB9AumP56kBFAJoL+howRQCaC/saIE0CBgvpcOPOIu513AsybfIf3pLuBAAzBKzq8rBFB8HmYDuFRW7ewMnzcD+A0AWasxhkRBFsnVJHmZZMZvhg8BlJlE35nGQLsPOwHMK+OH/519JkIsF7xSG5YPgYJT3fme9i8tgTYbugQcPUvAv22jfcwHQ7l0HKmiBFAJYKCx5u8M4JB9F5iuw52qzrv/SC4BH3h4CAjgFCWAgcZeNJ1fVwjgiyLTEsmbnZnziJl844gI6QqROUm6VbH2AnALgK8CAFoEZ2LgtjcAGnpxBlCStgoGIniS2cV7RMr1J/2KAIgiIBYxaludl5eHrCzm9KypPPMbV53zyuhTV2iTau602qXSiiQkgl52DZKoM5ISawTw91ISnxbJnHC9aPpfTd1sZjIS6XeLTbRwQ3katVBpbemh1rABNZJDWi3Dr3n0/8ov5rCUWU3c+J6vmP/P2kktV6uUPHRNYV7gY96/xtSNFlATdMoln5t64qq+pl6f2xAn95ffI6jsv1V93qfn0VhYRm1dWlIJspJ5P/XteG0v4700TeFks+jUZk3lw7Tdl356rdPZ319bMow97V5+eRrmFlC/OOu8gaYe8OxcUw/PnG/qy9680NSvnPEENpVTB3brgr/Y++NbuvXfCeKi85mntmGvzXiw5zvmeOyHF5j6tiPfNfX8AuZGfnumTMIDiY2LUb6dz9evJz3yvDzEchxpAnjbL6PMPXi6zuwG9IXs0mA98m3+Ye+HxMvzqMc8b2/69Hk6zE/W9UbOkpbmtWyb5rgshVitOYpa0xat2W+nRohMAaUAACAASURBVMw9LaW31RBOvpX6vq1dqB9tcxxx+T0/E72aUSe5PI+efBt/JlE+bsS3pp7x1GC0mEYt4qJLmR969IgvTF1mPT9fnUfsG8ypDxs2KOHQYntP/lgaPYg5l99542BTNzyE1/16xAM8UUslAt4Gos0WxK8+6IfsKXw/buvMz95uf+P7SYoSwD8GT2lJIWYpAYzpd1VdIICi8BZCJ5+A8k0qxMwjgPvL55slh3+3r59jZwrlG5WuvH9e5Fv+ffm+F87gc+pY0WBLDnfxBgXwoHgEW+3gnnq8E4D5SeRblADWNAS7/u7NACoBVALob9QoAVQC6G+seOcpAUTeQTIDmExj+GBKaWkhZioBDAa6qGlT2wRQtrv9IJsgZQOkRc2XAIoPwkyr/7vZB1WZEvkIgGz2qKn8C8DRlvxxi2j15VAAMhUks4XcovfHojOANaFdw9+VAOoMYKAhpARQCWCgMaMEEHkHHXanOwH8XOY8VAMYaPxFy/m1TQBl3UrWobgGwyJ79GXNT9ZBxQ9jiax+yIqXzzniRyFrXGfVAPQ/AZwAQNZM6N+x5yILLrLGeJQ4Yvg5gLtpAB9feLh9ALuEay1eZHnKW9bdWc7lwUCXgOdsza5Mcybto00D+OJiepq1SWZqJp0BjN4ZwKxULnm/Ovg/fr5N3E5TAqgEMNAIUgKoBDDQmInH82ubAIqGrn0V4J+3Oj9ZkhXDK5m1e67KJpA5AD4B4Dsr6NuNPJco1UU8JCZf/iwVH2R1hbLDmKKrmkvABLDE+v15mqVFNvGtp/cTTZPoAH3L4nzquIptrlI5/uRgccqJnuIRwCZJ1PEtK6ZOatYW+gH+MJOed8cc+iPSk7m5++1JJI1ZSxmmmwdS19ZqL2q0rug8FQPrrTLHR35EXd+kkY+Y+qci6toeWiyTy8DglisxqvGP5nhtKT3X7pgqTkFAZmvm092ez+WShIQK9M5mwvgm9SgJ9e5pQyHz8263uYfPbP0tNpZS/zm2EV2GDpt7pqkf6UHfvMKKFNx74RhznDuQPwDSh1GPWFBMrd3xHegD2CJ1G7z80G/+/Rjz2sYLqF26oodsjt/lHSjHG4p5P599NMjUbacTu4Ry6iaXnkfsmjbLx9hO1BuOm0C94KhjqJN7a4YoLYDULdREZu27ARvWU6O5dydOmqcm8TeaaB69EikCWHlBe3DKLPoYdkzfiIIy+uqVWC1divVeXGg3rBSUEt+Hur6NTWXMKXzFLJETAy0+41ikbuOzbepNfV9xFnDAMHrteWX+f0UuDKTnMgZzRvFH3qn7/IDcIo7/rnzU7G/RFr5vNy1rgoaLiG3KDo7LxgFsn1hqP4LLWKfkJ6CotRXB2osnFrDtIQd48mXg+X3lY1LLnhAY/S11rjN+5eeKlPSljAUp8/8hcvPIl0huAhlyqPsM4FdTdQYw8lESuSvWNgGs7kl9l4Dl77JL9y4A51sNoLjgih5QNIDeUq0s3cpMosz4SZGs6PItLLN/vt5/edYXUHYGyN8/ls9nqwF81JLNqt6AfzYaSgD9jFUlgEoA/QyVGk9TAqgEsKYgUQKIvCHDQ0AApykBrCnWovnv0UAABd8bAfxNJmSsVvD6KruAVwB4QTbL2sGwnv5/GBr5FpbzRHsoS8pCImXpV6aRhEDe6+ID6M8SsM4A6gygmY3QGcCgPzeVACoBrCl4lAAqAawpRvTvtEDREjwCZgbw1m9GIC0jBRk2hVuSZ+NSjQYwHgng078NR3oilyZ1CTh2loD7ZK7BTb1kEj2yRQmgEsCaIk4JIPKGDrvDeRPIl9Nl8U03gdQUb9H6dyWAbiO3GwH0PMcaJjFDXWYi69SEXXtc0hKpodpqfQA9H7BcqyOTXMCeJ2CB9ZqT8x/f53W3O63F1kIAG9nct55v2/hldOQpmtbM1PWGbzT1wz3fxs8FlIW+f+Vhu931+n2o98o8VDy9gaPazscZDZkDd6vdXPNLUTvz/398Rp1bQglDvLxxCdIbczySEqm/qqjg30pKqbn0vO5aNdqG9hncrNIvixrDIuvX+OYy5qPdtpF6sn5dc9DW+tJd1Izebml2vL172lpeH5e/Tu++dp+TCK+7jLXnW3hmd9kMD6P/++9nsiEdaGJlaDtGUqN4eS9RRwCz84nPzPf7ofnP1IvV28D+fr+R8dWpCXPjbny8o6nXHFuKWw9gfmMv5ryNSOtLqGGb8KvIX4EWH6ch7yRe88TO3Jz/6hz663XMJvYjWtFAtzYIoLmwLTfOPdkctUzhppoVhfTi87wZ521rY/4/utUs3PEr8yMfmi0JgIABGfTyu2OWKEWAhrOtnnB4Hk7vQh/ILaX0jHtvHrFp/BV1og1Opka0R6P1qG/f054P5DsLmTf64E6yfw2YtaojGr3LeCnJYMwVjOD9dmhKPevprW0clzXAv14fyXOzuJCR0Z2xKOXnY8WqVEu0IhBJDeDQg0NAAGcoAYzWWPPnvpUA+oPSns9RAugHfkoAlQD6ESZBnaIEMCjYtFEtIRAnBFDkWtfJfj0r2brceuxWh7r8qq1Ody9LC/wlBMivJ3E7l1/18gtPHD2eAPDvWhrGmLmsEkC3oVQC6Ad+SgCVAPoRJkGdogQwKNi0US0hEEkCePDQ252XgGd8ebcgFUgu4NMAvATgYgCSCkc2cZ5iLd24fLB7EV0/p95ZhODJsoNs4xa9vpTxMnFvXxO9v1g7yEZPsXGQJA9agkRACWCQwNlmSgD9wE8JoBJAP8IkqFOUAAYFmzaqJQQiSgCHhIAAfhUwARTSJ3qGyyzE4mEkOhqxZfMnZ6EQRrmo5Eyk/xXTt4qnlq/+QTy9xAru1loaypi4rBJAt2GsdhOIt9EhKYEaHtkUkpJArZanhfN0WJ4hsmcQLedc1J1aslgpQgDzypjQfu42avR++qinqQvaUR/Zu2eOqW9p/yH+b414cQMLN9ArsMVTbJu8nfq2padSl5XeKQ+v9BeLSGBRCc9dW0KPv9dXMrfqhq3UXlWsrY+yTF4raSd91coy+P+De1MTdkhjOgZNzO2PvGJqvR7u8rape6bw3L7vSVpqoP0nHNudl25BUSn9407rRN3Y4RkU7+WUyo9bYGVxMzw1hX6Ee31G/eHKkdafrz31XWM6Ms+rxMEbj/Pc8uOpD7urp6SpBuYVELtFO5jTNue2bkhdT5/CbT2p47vuHvqlryymB92Hfxtu6tyBaUg6hP01SCWOh7bic68ooG7uu6m9TF3SrgjI5zPVb83P4G7N6VvYPZP5Zz3vwOWXX2v+X9vlzSUc70yrv/XeX0uKbK7osjRk2k1ar6zguTd2Y1LghxYdaerNv1KPetIRX6N1qjhGAe+upfav2I6xpx8d2pIOVO1SN2NpIWOvbT2O5U6bC/qFX+ivmLg6zfj7SSlox8+BW4dx4mKH1a5O2cD3w9CmSyrzGxeVcwwWbOMzSIl0HubKC+tBSBCIKAE86Db3GcCZhnPJBw9FwSwiOKboePciM3nygSTC3Pd8/vSiTcVKse2fFzFElQ9DSdXqFZkB3McuAUvqVvH2lTeQLBHPqKlD/fueEVAC6BYdSgD9wE8JoBJAP8LE6RQlgE7waeMIIRClBLAqOrIzxLNc8/2b7LiSXJfi4M9ftCwPWZ0fd5Ltuexnl43lvO98ThPrBCGB59gMYPIrWnbVyVKzFgcElAA6gCebDauzgdEZwN1BVQKoBNDtbVZzayWANWOkZ9Q+ApEkgIcc6D4D+MWsgGYA90QAH5bJbQCcEt9zecaSx72rnCKJH4TwSS1b9yW16/0209eU2h/V6L0DJYBuY6cE0A/8lAAqAfQjTJxOUQLoBJ82jhACESWAB9zqvAT8xdeSG8HvTSAuS8Ci6xFvpdvF9cxnOET/I3oMSev6kc/rz9qlaeqFtASFgBLAoGCrbGQI4APfH4K0jORKs+NEUB/m6f5EC+iZQxfbPL+5pcy1uqyAWq28EurcpNRWjlU3KPbc+rSvL8bq7XzeNWupi7MQoUkLSksu60KPu6bJ2zFnJ33uXpsoUg8xW2P2kDb/4Waxsvr07ds5dgse6fmWOV5cTF3cvbOONXVCss21mkvj5fLUikrtX0UH6uYG7UWPv3NbMUfuUfWLTd3xwwvRvwc94t7pQp3YohJ6CF54NXOI5mfzHooaAwl9+AyZ9QtNfX23Sab2fPaWFLbElCeY17j1ueJgACyZ3MnU9QZTlzeoJe+lXdoWfPQIXRESTqU34lVdJNMhMHenJLABvrj/AFM3mrIYBYPYz7oLee3xA6gBnLWTOZbHT6WXYvfxW7BkNLEvbcD49PLQ7j1wmfn/z0vZf72Vqai31Ryi+XG8ry0F1F3u+JF6wetOm2DqC7p9yRPrSDl6xpXmTvZuKFIh4O35Ih0ChnZeiiap1DPm7CQOI5pSq1li35OPzaX/4oguC3FAJj38lhYxrhpYU/cy652/vZQaUWm7oZg60/UFzMv8+w7W+Tt4TnF+PSCJ8ZjagPrL4p02L20Z9agoZJ3eZjv6t5JVNJZY+yyofLA4PYhxAiijKptAZPlWrF+kSGCLwFvStP7ZJpC/WlsXSeJOE1MW8x0raeLtpg/vdZktFJNTCqa1BIWAEsCgYNs9OJUA/jmISgCVALq9zfxvrQTQf6z0zMgjEEkCOGyw+wzg9G8DmgEUQD0bGNnEIURQdvWeKj/jZS+a1e3JL5ybqqAvvyTl9dOrGRWZHZAdWrKzWH6Zyy/kfwG4xtaRH8gYuaISQLeB1BlAP/BTAqgE0I8wCckpSgBDAqN2EiYEIkoA97vFeQl4+nf/ECQC8QGU84WoeUbQPwEQ6wSZGZQiZE68/GTGzyvdAIgFg8zmTa4GetkGL5o/+btM3wsJlE0hj+5aSwrTgMV4t0oA3QZ4NwLoLfP6dnlZj6nmv7f9IhIGILeIdh1Sxg+S3fGxX+6cdwK+WM8lSW8psaiYFhdDs7n8ODBTPhOAB74/Cvt15vLrgY25DOdtqrn/eflxCbT8kUu1e92zCGc252YzL9Waze6G0kyb7s2L8KwS9G7PZcGRLcVpABj34xGm7teeS25vdBJbKaDXjPNxYAcu1T6ZzeXco+edZep6j9Bm5oVnHzP1bWtGYtFTtE/ZcgKXGBuk8f6u7sal27SEEtwwVX4EAy+O+I+pH8qhdKXkBkoAVlg3lQf2mYDXc6mVnpdL+48x3b/hOTbN2dx7mWosY9k2/HYBl9bb9qA9i5fe7m8duaT+4EKukLS+oRwlLbhUuXTM7suOg/empcnPn3Y3dXkqUNyYtjedexKz0kfElgsY+SDjeV0xr/to/zdMXddK3w9ESgRkptnUe/Nb4uIj+N3ipSP0Uv+99YHo0wHruoLSFiXIbMqxbJZB+UFJOZf86yXRxuWkNnNMPWlDb2wrpsygUT0uw6/MY4wUlTDGpaQkE88xnRmvKwppOfPuXC5Rn9qPqQDXFDYytS79VkIXUwdxQgBjasxi+WGUALqNrhJAP/BTAqgE0I8wCekpSgBDCqd2FiIEIkoA9w3BDOD3Qc0Ahggt7SbcCCgBdENYCaAf+CkBVALoR5iE9BQlgCGFUzsLEQKRJIDDB93svAQ87Yf75MkDXQIOEVraTbgRUALohrASQD/wUwKoBNCPMAnpKUoAQwqndhYiBJQAhghI7SYkCCgBdIPREMC8vDxkZe3S9rl1GZ+tX1tCk/hJm/vgyCaS+hHomkpd2ynTJa+4iLdonZGeQw1b/Y0V2HwYdVfJK2ijU2K1f216MO/4GdmSlhL4afteuLf1Z+Z4bRm1WZO29zH1JY2oCVxVRnuUMx+7FqeMmWaOD8lYaOpzJzEzUfv3qS387Nl/m3phSQlOfIvWME3ZDVLO4n2f0HauqSVd2C2fUgP40NGvmXpDKePl3fMPN3XONdSIJSZUoF1jerAc1Iz6yPXFtBXJSuazzrydWGUs2IQFN1NvltWEmrXDspnerVkKtWuvv0AbmEbLylBvE7WJufsRq+LBPOeUrtSzlRnHBuCD5X2wI8+m38sl5m0GiEUXy4zDxNc1OsrLi6mn/GF7R9RPogXLRy8dxOeldA/p6zju29vx47AkAyhtQazataMjRWYqtYSpiRynoU0Xm/rbLZ3w64eiYQdOP5Mx0yaVKeGeXEQbo9KyJOxYbcdwL6aY26cldadbi3fZP00cIk4ZWmIZgYgSwIE3uc8A/ih7L3QGMFZjUgmg28gqAXTDr7K1EkAlgCEKpd26UQIYDlS1z2ARiCgBHHATkpPoRRlMKS0rxLTZSgCDwS5a2igBdBspJYBu+CkB1BnAEEVQ9d0oAQwrvNp5gAhEkgAeus+NzgRw6hzj3awawADHOVpOVwLoNlJKAN3ww+MLuQTaPJnZNHQJWJeAHUNKZwBDCaD2FVIElACGFE7tzBEBJYBuACoBdMOvkgCWV1B/1qneehSWU3d230J65eWtoudcu67U9R3fhmK7l14YUanjKuhKjVbnbJ7z7670p/u+kOnNVhY3xe9F7Gd+Hv31RG8n5Y4O75s6LYEeb40SSzB6wTnm+PrO9AHcUU7B2H8vOIH38G/64fVPy6lMu/b8AqZoa/0cz904linnHun7P6y3mr+HFhxpXrurN6/5xN9ofL/2YKa5S+yRj+KV9OsbOJg6s84ZG0w98S1q17InkywXN07D8pPNIVI2W1/FYcSm1PrW/Tixt/l/0wVlKEknxonFfO61x1ITV1HK18fsN9PUHv4P9H2bncdY6Xu1+McCmaup55Qy8y1rxAigw/iHceVQega2S6UGcKcd/9wSxtCinUwR17H+RrzxPHWWTeYTz4RrGYOrNlKfObr3d5U+gilWQ/h+zq5897OPMdkWtMQBAhElgP1lBtAKXYPAtrSsCFN/0hnAIKCLmiZKAN2GSgmgG35KAJUAOkZQ4M2VAAaOmbYIDQIRJYD9bnAngD8/KA+uS8ChGf4614sSQLchUQLohp8SQCWAjhEUeHMlgIFjpi1Cg4ASwNDgqL2EBgElgG44KgF0w08JoBJAxwgKvHldI4BDWzMV3+P7vB74w2iLqEIgogRw7xDMAP6iM4BRFWAB3qwSwAABq3K6EkA3/PDPhYeaHp586zhTP3H2f3Dpd8y7W76BFgZjD2VO3RMzJa84MOpfkmccyFhbgfwTqIc7uxv9/j5eS81bcSlzt27Oo56uR5vfMX8V89k2bERtXnYW/fZy8ph/9YYe9AkcWj8HG8qoQ/S88dITqO864+G/m7rPWfNNfW3rSVhawny+N35/kqmbfcz73tyTb6+03lvRqQm1ZPs0WmXqozPpEXjjhfQ4XDOWvnMN0wuwfi3vZ8xgavLeeIt+ctbaD83nUu+Y3y4V+cfRy69wO7U+Xk7ZCZ8eaP6fus1U2Nm2DGcNmWWOZ9xOreKa0/hMNwygznHC78xLK2XSIcx1rGV3BK7+iZrNHaXEe+rU/siiXSOan8Ec1sOa04uxyGpZGyfvgKdxfTNnoPnb+t+YC/i4ocwBLEUJYOxHWyQJ4GF9rndeAv583kMyKLoEHKOhqQTQbWCVALrhpwRQCaBjBEW2uRLAyOIda1dTAhhrIxrdz6ME0G38lAC64acEUAmgYwRFtrkSwMjiHWtXiygB7H2d+wzgrybrj84Axlog2udRAug2sEoA3fBTAqgE0DGCIttcCWBk8Y61q0WUAPb6uzsBnD9OCWCsBaHP8ygBdBtcJYBu+GHisv6mhw2lzJUq5ZEXTjT1qNO/NPVhWb+aek0JfdU6pGw09ehpY4FCetgtGcXcvN8V0dvt5osuMnX5dTz3ya5v4Mynr+EFaIOHkv2on2vViEK5oc0pxs8tzsIpTb8zx40SC0y9rZy6vhvvZ07gh2/m9TITi/DSRvrzffrZIFMnMy0vRp78jak/eH9/JPbhNfq3Zg7Yv7b8ytT3XHueqVcfwTZIL8XhvZh/uEc68+8+NZsawFbNmUc2bRxxWHZaAhq3ogaysIiaxev7UMfYPJnXu/xT+hmed/AMfPZ7D3Nc715qDPd/gvqzd94Zauq2w6hPlPL58Ecqj/Xgjwic8Q3joGfG73jz5eHmeHt3aiqP6Et96OS5vUydlFaKAzovN8fDG3Nsiyvo2yjl0u7MIawl9hGIKAHsea07AVzwfzIoOgMYo6GpBNBtYJUAuuGnBFAJoGME1U5zJYC1g3u0X1UJYLSPYGzdvxJAt/FUAuiGnxJAJYCOEVQ7zZUA1g7u0X7ViBLA7iGYAfxNZwCjPeb+7P6VALqNrhJAN/yUACoBdIygyDR/btEQc6GFBbQSWlXAZXhdAo4M/rFylUgSwMO7XeO8BDxlkZGC6BJwrARgledQAug2sEoA3fCrtvWNc5ngtm8DatJum3iaqWeebgTJyLcavtN/GYNW6dTA3bjXx6ZOTSgz9fWXXGLqtBvWmvqT7h/juW3M3zruNWoM2x7C/lduaGLqfu147qaCBthRzNy8T/d+zdRdk9nvMVdfZeq+19OT8Iym3+LVjfTV++WRvqZef0KhqecePN7U+zx/FVJ4myjdlwd39/3A1P3rURN48eIzTH15+6lIsc9w2dTR5rXz96cW8uUPqDXLsFK9/mPmYt5GEpJtBdQoFhdQC3jLvsQjM5H3csPMk4Ht1J11eo9atU73U4/W0poFbijepcMcP+hF8zctRKAqAZy0inrKI7MXYsJnHP+W/XNNPahZjqnf+3EA/9+L+r93Dnxa4YxzBJQAxnkA1LHHVwLoNiBKAN3wq7a1EkCSTSWAYQiuILtUAhgkcNpsNwQiSgC7Xu0+A7j4Ubl/nQGM0ThWAug2sEoA3fBTAqgzgGGIoNB3qQQw9JjGY48RJYCdr3IngEtNRiAlgDEarEoA3QZWCaAbfkoAlQCGIYJC1+Wzi2iRk2i9gzwNoC4Bhw7jeOpJCWA8jXbdf1YlgG5jpATQDT+/Wo9bcKQ57+k5h5j68f3fMPV3Ozrjm00dzPGarfIjFWj0GnVspfVtaJ9BH8DWGdtwduuveU4icwF3TmEu4An51O69smw/vt54E35c0t4cZzWmqd+nA5419SqbA3ZreX3z/+ZJ23HFIuaHLX2eGsPMC1eb+sFO75j6qsWnobiU+rvfc3mflw76wtTDMxaYOi2h1NRjF5yNp3pQd/jlzm6mfnTqUaZu+hPzG289lN6E5/b5BkkJ9D2cktvd1MtXMy/xCXv/bOoBGcxP+3zOgcid3tYcl1PeiOJO1AcO6UL/QykvD+ZzaiECHgH0fCqX7mxhXu9Yn3El5T8zGZdn7c/4+m4zYydnMzeKXNObuawv6s4x1xK/CESUAHa60n0GcNnjMlg6AxijIVvXCOBNAO6T73j53vTBXFTW/wAwGIAIpESBL6yA34TVl78BuE78cwHIt+Hl8tnsc6qo5mWPu3x7S2b3SfK9LD7AAYy1EsAAwAr2VCWASgCDjR3XdkoAXRHU9r4IRJYAXoHkRPlqC66UlhdhyrInlAAGB19UtKpLBHBfAP+TDY0AxBrfI4BC/j4FcL8kVZCJFtmwCWAigKI9oCzbRl+SzZUAvrV9nQJApknW2zb/kmQNkpQBgKRY+KdMjgBgWgf/ihJA/3ByOksJoBJApwByaKwE0AE8bfoHBJQAalDUJQTqCgHMADDbzsDdamf4PAIo+bQmixtIAMAJ6fteNlLaNpIvTMwzngTwgJ3S3gDgTABv23PE10HW44RwModXzUUJYM0YOZ+hBFAJoHMQBdjB07/RcifVLs2HYgn4ge85jsvPloUOLfGIQEQJYMfL3WcAl8tXpi4Bx2qs1hUCKKZjmwFcDWC6DwEUwY0syV4hlmsizxIvVrE5A8Bkqn8sonASkZeYyb3n82e5hiRBPQHAoZLuFICIdCgEYxHBlGx7Mnvfqykyn+47py6Cs9V5eXnIyhIuqCWcCPy2qo3pPqeUOrrNZRm44UuZ2AUS61FD1/YtCtxaXUdd2y+TqKOr6JePJhnU/o1uL78PgA6p8hsA6JoioQfcveYYUz/ffjr6/lMUBEDDg6kI2KcZdX2jGv+42yOKBm9hEe/ryfnM2VvxE2Nh2PHymwYY0+wrFFdQv7eqtKmp5+7MNvWEDznhXNSK3nztsjehQUqxOd60M539fcg2bU6nn1zvhswR/EteG5RX8C28eWcDttkqv6WAFk3oN3hUW+alXV3YGIvyqA/csI3nSFkw6o7KYz3YhYBHALeUcgzqJXJ8WiYzH3M5mIN6ZVEzpCQy9jwd59zt1FrWs6/P29Iaa36nHlCKEsD4jbSIEsD2l7kTwJWyMKYEMFYjti4QQNHgCaGTJWBRpfsSwP0BiLJavqH/bomhZLcXrV4fAIurGRj5NhZ33QNtW++UhwCIWlt0hDLz93wVMifniUZQlp9v2MOA3wngD9+YSgAj8/ZQAqgEMDKRBigBjBTS8XWdiBLAvS51J4A5xrxcN4HEaJjWNgGUaZAfAIywGzUEZl8CKCRuptX/3ewzBnMBfASgurWUPRHAhwGIp4OQyj0RQFk2lpnBG/cw3joDWItvBCWASgAjFX5KACOFdHxdRwlgfI13XX/a2iaAfwHwrt3Z62Ela2WS7Es2ZMimjSWyaicuHT5gvmk3g5xVDcDhXAKuejnVAEYwwpUAKgGMVLgpAYwU0vF1nYgSwOxL3GcAV8leSZ0BjNUorW0CKBo6mmbtKrI0Kzq/BwH8KvIlScVZZRPIHEnvCsB3VtC3DxF5yXKuWL9IEcGOJOgUQYPvJhDRFdKsDRCx2G+6CaTuh/ptv4wyN/napKGAjeDms5kgOGUn62P+MdXULy+it1/x4iygPTWAqanUbFVY/dyrA/9r/v/O1kGmvrn5j8gvp+br9POvNPWGS+g4dN/elJWuKaGmKy2hBGlWH1ZYzjy8+9cnUTttzgWmfqW/hC/Qb69V+Hi5KBeArWXUlr24hnlkL8vm/cqmq75lgQAAEzFJREFUg3Er6Xs4qjXzDc/O51ukpJw6wub1tps6EeVYsE1cjoD8YkpTyy0gHTOpazTX2I/Pp8UdAW9DUonVdHJc6PHoeTI+N49jWlbA16Ws+OueVCXu96Q9RA8CESWAbS92J4Br/i3g6hJw9IRYQHda2wSwupv1XQKWv8tu4LsAnG81gOdaPaB8k3oOtrJsKzOJRrEq373WBmasJYLSx6kAZKev5/MnP21E9S82MGI9Y7Y7We2gvyDqDKC/SIXwPCWASgBDGE4BdaUEMCC49OQqCCgB1JCoSwhEAwEUvESTJ9sym1it4PVVdgGvAPACANmk4RWxgPGMoGUqRXYSc/sni2cELbOAvkbQvwcwQEoAAwArVKcqAVQCGKpYCrSfYAlgh+wNmH7YuEAvp+fHGAIRJYBtLnKfAVz7jIyAzgDGWBx6j1MXCWA0Qa0EsBZG67lFQ8xV88vTKtOkdXqXy7o5I2gD89TJTGn2zibZXA4szW+Kra/TegWjmMZr03ratdx30ARTLy+iTcrJWbMx8mvZaA60fl1+JwCrT+KS8Jl9ZZ8QcHCmqBSAwopUPLFSXIWAWzrKviSWQzuImoDl+xymq9t3L/mdUnPxSIZnOTJlS2/T6NvVXAouzOdyb0VZApK27VpmlNcSWtn0bp12pXfTJeCaMXc5w/tBsr2M4/L+55QdlDVhTHbYi97zSgBdUI6NthElgK2FANq8j0HAV1pejCnrlAAGAV3UNFEC6DZUSgDd8AuqtRJAJYBBBU6YGikBDBOwMdhtnBDAmtKw+o6sSL6YTHv38rHN1OW92tPuC5Bz5VevGJyeZLX9MRgpkXkkJYBuOCsBdMMvqNZKAJUABhU4YWqkBDBMwMZgtxElgK3Gus8A/j5eRiGQJWB/0rD6jqzIunynKcX1/mcAsoNOZF1SJAGEbOqU3WyvW82+LItIxi4vtWsMRkv4H0kJoBvGSgDd8AuqtRJAJYBBBU4YGnX41ziMHiJWpUBNS8DnZDPD5Hnd9pTEKAw3qF3WKQQiSgBbXOBOANcbKU0gBLCmNKw1jYds2Lxb1DcAdtiT35DN9tYOrqb2+vcAEFACGABY1ZyqBNANv6BaP77wcNNu/AsjYZ1X0GSh2EYC9zxsfrFiRzlJ0nc75McjsK6oIeY8088cFx7LdF478urb69M65qJBM0zdJ201Lpt+tjlO3kKNXaPf+Fa54rq3TD0hd4CpT275A7KSqLuTckInWreEstw4V7IasjzQ10tdDYyccQUW/kB9YZsv+fwb+tEqRsrCOyWzopZwIiAE8PpDqf387zLxrQc2rpGMkyLIZFwllCbi9uG7slIqAQzniNTtvmOcAPrjwVvTAP1iM3iJg4cUsXCTD2zJ5CXi731Erm2TQ/imeq2pX/17NQgoAXQLCyWAbvgF1VoJIGFTAhhU+IS0kRLAkMIZ851FlAA2P999BnCD8RBtJ3vufAanSNKXVzNY/qRh/bMxlt1TMoMo6VplyVeKGJ1K8nMxcb3Vpmo9SvbuARgO4IuYD5owPqASQDdwlQC64RdUayWASgCDCpwwNFICGAZQY7jLiBLAZue5E8CNNLGvUsSX19dyzfuzP2lY/2x0ZcuxTKPv7XOS16do/ySFq1fet0vEYuOmJUgElAAGCZxtpgTQDb+gWisBVAIYVOCEoVEwBPCFVcwUMuMwSU+uJZ4QiCgBbDLGnQBulsRcfs8AuiwBN7AzfbcDeNwnJqRP0QIK6bzX53XJFCZLwgfFU/yE+lmVALohqgTQDT+n1kOmXI/N00UrDDReWGbqWx7mxrEVxfT0W1HYzNTvfXQgmgzkhrHszK2m/mlNW1NXUKplypJTZZUBuP7nU0zdIlWSxADlFSJFAZ6ZO9TUj+0numTguE5zdzXWo7hGwNsN3CCJq2Pv33OYqTtc+RsGNlzJ19b2rcRICWD8hUuUEsBAN4H8WRrWPQ26ZOSSvHPyobypykmzbNav0T6vS+Yvyc/pOysYfwHl+MRKAN0AVALohp9TayWATvBp4xAjoAQwxIDGYHeRJICHNT7XeQbw8y0vyigEQgBrSsP6kqRSB3BTleH90r5+ejXDLsnf37TZwKYBEA3gYwCGVckIFoMRE95HUgLohq8SQDf8nForAXSCTxuHGAElgCEGNAa7iygBbHQOkhMcMoFUFOPzrcLXAiKAcv6fpWEV42dJiSQzfl7pBkBSJ40AMHkPw36eJY2yIUXOvQPAxBgMkYg+khJAN7iVALrh59RaCaATfNo4xAgESgCHNl+KeolMFyfl9j6ia9cSywjECQGM5SGMqWdTAug2nEoA3fBzbn357LNMHxuL00399eJOpr5+v0mmzikSY3ngs6cPQsGR1PO1zKKjwRntmNdXykXd1U3AeTDivIP75x9jEEgCRaXPvicTGsDZx09DUTn9JF+by9zUXk5pJYDxFTQRJYANR7vPAOa9LAMUyBJwfA1olD+tEkC3AVQC6Iafc2slgM4QagchQkAJYIiAjOFuIkoAM89yJ4D5ryoBjOF4VALoNrhKAN3wc26tBNAZQu0gRAgoAQwRkDHcjRLAGB7cKHw0JYBug6YE0A0/59ZKAJ0h1A5ChIASwBABGcPdRJQAZpzpPgO4/TWdAYzheFQC6Da4SgDd8AtZ6zeXUFv17GrxBmWZPOxReK//uIM5cx/qx1y+WhSBUCMwbsGRu3X53G80fB7d7btKH8mP1/auPGfmEeJlqyWeEIgkATy0wenOBHDqTuN3qhrAGA1SJYBuA6sE0A2/kLVWAhgyKLWjIBFQAhgkcHHUTAlgHA12FDyqEkC3QVIC6IZfyForAQwZlNpRkAgoAQwSuDhqFlECWP809xnAAvFf1hnAWA1RJYBuI6sE0A2/kLVWAhgyKLWjIBFQAhgkcHHULKIEsN6p7gSw6H9KAGM4PpUAug2uEkA3/LS1IhAzCHgE0MsbXQZ+vBaVp2Dyuh7meGjLpZXP+0Dft2Pm2fVB/EMgogQw9RQkJ6T4d2PVnFVaUYKpxUYzrRrAoFGs2w2VALqNjxJAN/y0tSIQMwgoAYyZoQzbgygBDBu02nEQCCgBDAI0nyZKAN3w09aKQMwgoAQwZoYybA8SSQI4PPlk5xnAaaVmllpnAMMWEbXbsRJAN/yVALrhp60VgahH4KH5R5tnSEwoN7UuAUf9kIbtASJKAJNOdCeAZROUAIYtGmq/YyWAbmOgBNANP22tCEQ9Ah4BlAe5vtcnf3ieO+edsNtrd/aZGPXPrA8QHAJKAIPDTVuFBwElgG64KgF0w09bKwJRj4ASwKgfwog9QCQJ4LCEUc4zgNMr3tUZwIhFR+QvpATQDXNDAFetWoWsLDnUoggoAvGGwGMLT6p85Kt6vPOHx79//um7vXZTL5NdQUscIiAEMDs7O9ykynwvDcExSIbDLmCU4Ct8HO57jcMoqDuPrATQbSzaAljt1oW2VgQUAUVAEYgzBNoBWBOmZ04DsBxAqxD0/zuAjgAKQ9CXdlHHEFAC6DYggl8bAPkAMi0ZlDe2/F9LaBFQfEOLp29vim34sJWeFV/Ft+r7bS2AijDCIiQwNQT9Fyv5CwGKdbQLJYChGxgz7a5b5kMHaJWeFN+wQQvFNnzYSs+Kr+IbXgS0d0UgCASUAAYB2h6a6Id86LCsrifFN3z4Krbhw1YJYHixVXzDj69eIUYRUAIYuoHVL9HQYakEMLxYVu1dYze8eCu+im94EdDeFYEgEFACGARoe2hSD8BNAO6X9J+h61Z7sggovuELBcU2fNhKz4qv4hteBLR3RSAIBJQABgGaNlEEFAFFQBFQBBQBRSCaEVACGM2jp/euCCgCioAioAgoAopAEAgoAQwCNG2iCCgCioAioAgoAopANCOgBDCaR0/vXRFQBBQBRUARUAQUgSAQUAIYBGjaRBFQBBQBRUARUAQUgWhGQAmg++hJOrgHARwNoAGAJQDGAPjBveu472EFgPbVoPA0gL/FPTruACQBuBPA2TZtlGQneAHAvWHOUuB+59HRg2QAuQfAKAAtAMwBcCWA76Pj9uvcXR4M4DoAAwG0tri+53OX8n12F4ALATQCMBPAJQAW17kn0RtSBOoAAkoA3Qahsf1QnwbgXwA2AOgKYKn959a7tm4OQEiKV/oAmAxgOIDpCo8zAjcDuAbAuQB+BTAIwPMAbgHwhHPv2sGbACRmhYQIuRaifTWAXmHMAxvLqMuP7IMAzAbwTjUE8AZrxfVXAMss+d7b4q25bGM5MvTZgkJACWBQsFU2esB+IA1160Zb+4nAYwCOtSQ7nHk0/bydqD/tQwC5AM73eRL5Yi2wZCXqH7AWH6C+zQl+AoCPfO7jRwCfALi1Fu8tFi4t73+ZWfVmAOW7TEj2/wEYZx+woY1vIYRvxMJD6zMoAqFEQAmgG5rzAUwC0A7AIfZXvSxP/setW21dDQKS2Fw+4B8BcJ8iFBIEZAZwLIARABYB6AfgMzsr+GpIrhC/ncjy7zYAhwP43AeGrwCUAhgWv9CE5MmrEsBOdtVlHwA/+VzhC/t/WXrXoggoAj4IKAF0CwdvWUFIyVsA9gMgs1QXAXjJrWttXQWBUwG8BmAvSwQVIHcEEi2Zvh5AmV1ul+VfyWajxR2BWQCKAZxpZ6LOAPCi1Ql3d+8+rnuoSgAPtJq/NgDW+SDzP6tnPS2u0dKHVwSqQUAJoFtYyIe7bPaQDx+viHZqXwAHuHWtrasgIDOtgvdxikzIEDgdwMNWWC8awP72B4zoAoWoaHFDoDOA5wDI5gUh2KJdk5nWAVaX5tZ7fLf2lwDKD3PBXmJdiyKgCPggoATQLRxW2k0JF/h0I4Jv0ffI7mAtoUFAdgKLqPtEABND06X2AmAVANGxPuWDhsSubFbooQiFDIF0AFl2Zko2hmQAGBmy3uOzI10Cjs9x16cOIQJKAN3AlCXJbAC+m0AeBTC4yqyg21W0tViVyLK6YC36KS2hQWCT/bEiO9i9cpO1MeoWmktoLz4IiGvAcgCy5D5ekXFCYE+bQGQDiGwEkSKkez0A3QTiBLU2jlUElAC6jaws9YrO5w4AojURDaBsABFhvYro3bD1WotOTb40XwdwY2i61F4sAuL5J5sUhFzLErAI6IWYyLKlWGpocUPgSADyGfsbgC52ub0IwBAAJW5dx2VrmTkVHKWIp6JIFcSCazOAHBuz8hkhtkbymSEejH3VBiYuY0Uf2g8ElAD6AVINp4gtiYjmxf9PPnRkQ4juAnbH1etBdqiK/k9E86Kf0hI6BKoaFcsuayHad1u9ZeiuFJ89ycYl+WwQlwAhKWKxI5ts8uITDuenlp3TQviqFtGryiyfZwQtP8DFCFp2XF+qnxvOuGsHMYqAEsAYHVh9LEVAEVAEFAFFQBFQBPaEgBJAjQ1FQBFQBBQBRUARUATiDAElgHE24Pq4ioAioAgoAoqAIqAIKAHUGFAEFAFFQBFQBBQBRSDOEFACGGcDro+rCCgCioAioAgoAoqAEkCNAUVAEVAEFAFFQBFQBOIMASWAcTbg+riKgCKgCCgCioAioAgoAdQYUAQUAUVAEVAEFAFFIM4QUAIYZwOuj6sIhACB6QB+AnBVCPrSLhQBRUARUARqAQElgLUAul5SEYhyBJQARvkA6u0rAoqAIqAEUGNAEVAEAkVACWCgiOn5ioAioAjUMQSUANaxAdHbUQSiAAFfAtgYwOMAjgNQD8AXAK4AsNg+h+RofQzAabbOtjlaxwBYFwXPqreoCCgCikBMIqAEMCaHVR9KEQgrAr4EcCKArgAuArANwIMAOgPoBaAEgBDA8ZYY3gSgHMArAOYAOCusd6mdKwKKgCKgCOwRASWAGhyKgCIQKAIeAXwKwCIABwGYZTtpCmAVgHMBvGUJ4PMAugBYas+5FMDtAFoFemE9XxFQBBQBRSA0CCgBDA2O2osiEE8IeARwKoB3AKQBKPMBQGb33gVwtyWAQhTTff4+yrZLjCfQ9FkVAUVAEahLCCgBrEujofeiCEQHAh4BnAbg7WoIoFjECDG8xxJA0QA28nm0v1iCqJ8/0THeepeKgCIQgwjoB3AMDqo+kiIQZgT8WQI+x5JDbxOIEsAwD4p2rwgoAopAIAgoAQwELT1XEVAEBAHfTSDv+WwCyQfwgNX7+W4C0RlAjRtFQBFQBOoYAkoA69iA6O0oAlGAQHU2MMcDSAUwA8Dl1djA6AxgFAys3qIioAjEDwJKAONnrPVJFQFFQBFQBBQBRUARMAgoAdRAUAQUAUVAEVAEFAFFIM4QUAIYZwOuj6sIKAKKgCKgCCgCioASQI0BRUARUAQUAUVAEVAE4gwBJYBxNuD6uIqAIqAIKAKKgCKgCCgB1BhQBBQBRUARUAQUAUUgzhBQAhhnA66PqwgoAoqAIqAIKAKKgBJAjQFFQBFQBBQBRUARUATiDAElgHE24Pq4ioAioAgoAoqAIqAIKAHUGFAEFAFFQBFQBBQBRSDOEFACGGcDro+rCCgCioAioAgoAoqAEkCNAUVAEVAEFAFFQBFQBOIMASWAcTbg+riKgCKgCCgCioAioAj8P3G/XVhglwSHAAAAAElFTkSuQmCC\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"covrr.corr_Gh_GB_mean.plot(x = 'lon', y = 'lat')\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"meteo.corr_Gh_GB_t.plot(x = 'lon', y = 'lat')\n",
"plt.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compare mean to total in terms of time"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"covrr.corr_Gh_GB_mean_s.plot(x = 'hour', y = 'month', vmin = 0.6, vmax = 1)\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"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": [
"[]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure()\n",
"meteo.corr_Gh_GB_s.groupby('date.month').mean('date').plot(x = 'hour', y = 'month', vmin = 0.6, vmax = 1)\n",
"plt.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map sample correlation per timestamp per pixel"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"meteo['lon_idx'] = xr.DataArray(data = range(len(meteo.lon)), dims = 'lon', coords = {'lon' : meteo.lon})\n",
"meteo['lat_idx'] = xr.DataArray(data = range(len(meteo.lat)), dims = 'lat', coords = {'lat' : meteo.lat})"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"fp = '/work/hyenergy/output/solar_potential/physical_potential/results/physical_potential_dhm25_200_CH_ELM50_1000.nc'\n",
"phys = xr.open_mfdataset(fp)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 159, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" SIS (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" SISDIR (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" apparent_zenith (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" azimuth (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" DHI (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" DNI (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" rel_airmass (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" DNI_extra (timestamp) float64 dask.array<shape=(159,), chunksize=(159,)>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"phys"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"pixel_locs = phys.isel(timestamp = 5).SIS.to_dataframe().dropna().reset_index().drop(columns = ['SIS', 'timestamp'])"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"lonlat = meteo[['lon_idx', 'lat_idx']].to_dataframe().dropna().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"wgs84 = pyproj.Proj(proj='latlong')\n",
"swiss = pyproj.Proj(init='EPSG:21781')\n",
"swiss(600000, 200000, inverse=True)\n",
"transformer = lambda x, y: pyproj.transform(swiss, wgs84, x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Match pixels in lonlat with pixels in xy (match to closest pixel)"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [],
"source": [
"pixel_locs['lon'], pixel_locs['lat'] = transformer(pixel_locs['x'].values, pixel_locs['y'].values)"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [],
"source": [
"interval = 0.25/12\n",
"\n",
"# go from bottom left corner to center coordinate\n",
"lonlat['lon_rnd'] = np.round(lonlat.lon, 4)\n",
"lonlat['lat_rnd'] = np.round(lonlat.lat, 4)\n",
"\n",
"# round to intervals as center coordinates\n",
"pixel_locs['lon_rnd'] = np.round(util.round_to_interval(pixel_locs.lon, interval), 4)\n",
"pixel_locs['lat_rnd'] = np.round(util.round_to_interval(pixel_locs.lat, interval), 4)"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [],
"source": [
"pixel_merged = pixel_locs.merge(lonlat, on = ['lon_rnd', 'lat_rnd'], how = 'left')"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \"\"\"\n"
]
}
],
"source": [
"pixel_inside = pixel_merged[np.logical_not(pixel_merged.lon_idx.isna())]\n",
"\n",
"pixel_outside = pixel_locs[pixel_merged.lon_idx.isna()]\n",
"pixel_outside['lon0'] = pixel_outside.lon_rnd\n",
"pixel_outside['lat0'] = pixel_outside.lat_rnd"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.0417 -0.0208\n",
"63\n",
"-0.0417 0\n",
"44\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" import sys\n",
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.0417 0.0208\n",
"42\n",
"-0.0208 -0.0208\n",
"41\n",
"-0.0208 0\n",
"35\n",
"-0.0208 0.0208\n",
"35\n",
"0 -0.0208\n",
"26\n",
"0 0\n",
"26\n",
"0 0.0208\n",
"26\n",
"0.0208 -0.0208\n",
"26\n",
"0.0208 0\n",
"26\n",
"0.0208 0.0208\n",
"26\n",
"0.0417 -0.0208\n",
"26\n",
"filled all gaps\n"
]
}
],
"source": [
"# spiral around origin until all gaps are filled\n",
"for x_tol in [np.round(-2*interval, 4), np.round(-interval, 4), 0, np.round(interval, 4), np.round(2*interval, 4)]:\n",
" for y_tol in [np.round(-interval, 4), 0, np.round(interval, 4)]:\n",
" print(x_tol, y_tol)\n",
" print(len(pixel_outside))\n",
"\n",
" pixel_outside['lon_rnd'] = pixel_outside['lon0'] + x_tol\n",
" pixel_outside['lat_rnd'] = pixel_outside['lat0'] + y_tol\n",
"\n",
" # try if the new combination is inside Switzerland\n",
" test = pixel_outside.merge(lonlat, on = ['lon_rnd', 'lat_rnd'], how = 'left')\n",
"\n",
" # find the pixels which are now inside Switzerland\n",
" hit = test[np.logical_not(test.lon_idx.isna())].drop(columns = ['lon0', 'lat0'])\n",
" \n",
" # update the information on what is now inside and what is outside Switzerland\n",
" pixel_inside = pd.concat([pixel_inside, hit])\n",
" pixel_outside = pixel_outside[test.lon_idx.isna().values]\n",
"\n",
" if len(pixel_outside) == 0:\n",
" print('filled all gaps')\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [],
"source": [
"pixel_mapping = pixel_inside[['x', 'y', 'lon_rnd', 'lat_rnd']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Apply pixel mapping to create 3D covariance and correlation matrixes"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>lon_rnd</th>\n",
" <th>lat_rnd</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>486487.5</td>\n",
" <td>110087.5</td>\n",
" <td>5.9792</td>\n",
" <td>46.1250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>486487.5</td>\n",
" <td>110287.5</td>\n",
" <td>5.9792</td>\n",
" <td>46.1458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>486487.5</td>\n",
" <td>110487.5</td>\n",
" <td>5.9792</td>\n",
" <td>46.1458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>486487.5</td>\n",
" <td>110687.5</td>\n",
" <td>5.9792</td>\n",
" <td>46.1458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>486487.5</td>\n",
" <td>110887.5</td>\n",
" <td>5.9792</td>\n",
" <td>46.1458</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x y lon_rnd lat_rnd\n",
"26 486487.5 110087.5 5.9792 46.1250\n",
"27 486487.5 110287.5 5.9792 46.1458\n",
"28 486487.5 110487.5 5.9792 46.1458\n",
"29 486487.5 110687.5 5.9792 46.1458\n",
"30 486487.5 110887.5 5.9792 46.1458"
]
},
"execution_count": 257,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pixel_mapping.head()"
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (hour: 17, lat: 95, lon: 217, month: 12)\n",
"Coordinates:\n",
" * lat (lat) float64 45.83 45.85 45.88 ... 47.75 47.77 47.79\n",
" * hour (hour) int64 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n",
" * lon (lon) float64 5.979 6.0 6.021 6.042 ... 10.44 10.46 10.48\n",
" * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
"Data variables:\n",
" SIS_mean (month, lon, lat, hour) float64 nan nan nan ... nan nan\n",
" SISDIR_mean (month, lon, lat, hour) float64 nan nan nan ... nan nan\n",
" SIS_std (month, lon, lat, hour) float64 nan nan nan ... nan nan\n",
" SISDIR_std (month, lon, lat, hour) float64 nan nan nan ... nan nan\n",
" n (month) int64 31 28 31 30 31 30 31 31 30 31 30 31\n",
" cov_Gh_GB (month, lon, lat, hour) float64 0.0 0.0 0.0 ... 0.0 0.0\n",
" corr_Gh_GB (month, lon, lat, hour) float64 nan nan nan ... nan nan\n",
" cov_Gh_GB_mean_t (lon, lat) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
" corr_Gh_GB_mean_t (lon, lat) float64 nan nan nan nan ... nan nan nan nan\n",
" cov_Gh_GB_mean_s (month, hour) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
" corr_Gh_GB_mean_s (month, hour) float64 nan nan nan 0.1611 ... nan nan nan"
]
},
"execution_count": 255,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"covrr"
]
},
{
"cell_type": "code",
"execution_count": 256,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 159, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" SIS (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" SISDIR (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" apparent_zenith (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" azimuth (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" DHI (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" DNI (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" rel_airmass (x, y, timestamp) float64 dask.array<shape=(1742, 1103, 159), chunksize=(1742, 1103, 159)>\n",
" DNI_extra (timestamp) float64 dask.array<shape=(159,), chunksize=(159,)>"
]
},
"execution_count": 256,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"phys"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2001-01-15T07:00:00.000000000\n",
"1560437838.7253907\n",
"1560437856.012751\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T08:00:00.000000000\n",
"1560437856.9160771\n",
"1560437875.781386\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T09:00:00.000000000\n",
"1560437876.6024055\n",
"1560437895.3533504\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T09:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T10:00:00.000000000\n",
"1560437896.1905448\n",
"1560437914.3534937\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T10:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T11:00:00.000000000\n",
"1560437915.1970277\n",
"1560437933.3389106\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T11:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T12:00:00.000000000\n",
"1560437934.2833335\n",
"1560437952.7554343\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T12:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T13:00:00.000000000\n",
"1560437953.6333265\n",
"1560437971.702714\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T13:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T14:00:00.000000000\n",
"1560437972.5840352\n",
"1560437990.8243527\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T14:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T15:00:00.000000000\n",
"1560437991.7419271\n",
"1560438010.3971493\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T15:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-01-15T16:00:00.000000000\n",
"1560438011.3386192\n",
"1560438029.814575\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T16:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T06:00:00.000000000\n",
"1560438030.8569589\n",
"1560438049.951456\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-02-15T06:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T07:00:00.000000000\n",
"1560438050.8506029\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T08:00:00.000000000\n",
"1560438070.6735878\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T09:00:00.000000000\n",
"1560438090.696676\n",
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"<xarray.Dataset>\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T10:00:00.000000000\n",
"1560438109.619331\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T11:00:00.000000000\n",
"1560438129.4743092\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T12:00:00.000000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1560438149.095921\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T13:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T14:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T15:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-02-15T16:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T05:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T06:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T07:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T08:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T09:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T10:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T11:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T12:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T13:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T14:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T15:00:00.000000000\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T16:00:00.000000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1560438458.7959857\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-03-15T17:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T04:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T05:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T06:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T07:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T08:00:00.000000000\n",
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"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T09:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T10:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T11:00:00.000000000\n",
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"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T12:00:00.000000000\n",
"1560438648.6498685\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T13:00:00.000000000\n",
"1560438666.4583607\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T14:00:00.000000000\n",
"1560438685.3982308\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T15:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T16:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T17:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-04-15T18:00:00.000000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1560438762.406377\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T03:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T04:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T05:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T06:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T07:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T08:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T09:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T10:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T11:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T12:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T13:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T14:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T15:00:00.000000000\n",
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"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T16:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T17:00:00.000000000\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-05-15T18:00:00.000000000\n"
]
},
{
"name": "stdout",
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"text": [
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T03:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T04:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T05:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T06:00:00.000000000\n",
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"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T07:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T08:00:00.000000000\n",
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"1560439195.8960927\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T09:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T10:00:00.000000000\n",
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"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-06-15T10:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T11:00:00.000000000\n",
"1560439232.5131636\n",
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"<xarray.Dataset>\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T12:00:00.000000000\n",
"1560439252.0039928\n",
"1560439269.0996666\n",
"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T13:00:00.000000000\n",
"1560439269.9003859\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T14:00:00.000000000\n",
"1560439287.784381\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T15:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T16:00:00.000000000\n",
"1560439323.879556\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T17:00:00.000000000\n",
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"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-06-15T17:00:00\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T18:00:00.000000000\n"
]
},
{
"name": "stdout",
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"text": [
"1560439360.15893\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-06-15T19:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T03:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T04:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T05:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T06:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T07:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T08:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T09:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T10:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T11:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T12:00:00.000000000\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T13:00:00.000000000\n",
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"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T14:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-07-15T14:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T15:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T16:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T17:00:00.000000000\n"
]
},
{
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"text": [
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T18:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-07-15T19:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T04:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T05:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T06:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T07:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-08-15T07:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T08:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-08-15T08:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T09:00:00.000000000\n",
"1560439811.295351\n",
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"<xarray.Dataset>\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T10:00:00.000000000\n",
"1560439830.0090992\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T11:00:00.000000000\n",
"1560439848.3750188\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T12:00:00.000000000\n",
"1560439867.0519638\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T13:00:00.000000000\n",
"1560439885.0831625\n",
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"<xarray.Dataset>\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T14:00:00.000000000\n",
"1560439903.061544\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T15:00:00.000000000\n",
"1560439921.4118578\n",
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"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-08-15T15:00:00\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T16:00:00.000000000\n",
"1560439940.184597\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T17:00:00.000000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1560439958.723766\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-08-15T18:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T04:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T05:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T06:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T07:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T08:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T09:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T10:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T11:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T12:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T13:00:00.000000000\n",
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"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T14:00:00.000000000\n",
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"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T15:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T16:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-09-15T17:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T05:00:00.000000000\n"
]
},
{
"name": "stdout",
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"text": [
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T06:00:00.000000000\n",
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"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T07:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T08:00:00.000000000\n",
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"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T09:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T10:00:00.000000000\n",
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"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T11:00:00.000000000\n",
"1560440370.2839546\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T12:00:00.000000000\n",
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"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T13:00:00.000000000\n",
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" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T14:00:00.000000000\n",
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" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-10-15T15:00:00.000000000\n",
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" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-11-15T09:00:00.000000000\n",
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"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-11-15T10:00:00.000000000\n"
]
},
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"name": "stdout",
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" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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"Data variables:\n",
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" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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"Data variables:\n",
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" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-12-15T12:00:00.000000000\n",
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" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-12-15T13:00:00.000000000\n",
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"Coordinates:\n",
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" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-12-15T14:00:00.000000000\n",
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"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-12-15T14:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
"2001-12-15T15:00:00.000000000\n",
"1560440820.6362317\n",
"1560440838.4671183\n",
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 1, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-12-15T15:00:00\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n",
" corr_Gh_GB (timestamp, x, y) float64 nan nan nan nan ... nan nan nan nan\n"
]
}
],
"source": [
"covrr_xr = []\n",
"\n",
"for i, timestamp in enumerate(phys.timestamp):\n",
" print(timestamp.values)\n",
" \n",
" covrr_new = covrr.sel(month = int(timestamp.dt.month), hour = int(timestamp.dt.hour))[['cov_Gh_GB', 'corr_Gh_GB']]\n",
" covrr_df = covrr_new.to_dataframe().reset_index().dropna()\n",
" \n",
" covrr_df['lon_rnd'] = np.round(covrr_df['lon'], 4)\n",
" covrr_df['lat_rnd'] = np.round(covrr_df['lat'], 4)\n",
" \n",
" new_df = pixel_mapping.merge(covrr_df, on = ['lon_rnd', 'lat_rnd'], how = 'left')\n",
" new_df['timestamp'] = timestamp.values\n",
" print(time.time())\n",
" new_xr = new_df.set_index(['timestamp', 'x', 'y'])[['cov_Gh_GB', 'corr_Gh_GB']].to_xarray()\n",
" print(time.time())\n",
" \n",
" print(new_xr)\n",
" new_xr.to_netcdf('/scratch/walch/scratch/covrr_%d.nc' %i)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"covrr_new = []\n",
"for i in range(10):\n",
" covrr_new.append(xr.open_mfdataset('/scratch/walch/scratch/covrr_%d*' %i, chunks = {'x': 200, 'y': 200}))\n",
" \n",
"covrr_new = xr.concat(covrr_new, dim = 'timestamp').sortby('timestamp')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"covrr_new.to_netcdf('/work/hyenergy/output/solar_potential/physical_potential/results/covrr_Gh_GB_nearest.nc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Verify"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"covrr_new = xr.open_dataset('/work/hyenergy/output/solar_potential/physical_potential/results/covrr_Gh_GB_nearest.nc')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (timestamp: 159, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * x (x) float64 4.855e+05 4.857e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 ... 2.955e+05 2.957e+05\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" cov_Gh_GB (timestamp, x, y) float64 ...\n",
" corr_Gh_GB (timestamp, x, y) float64 ..."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"covrr_new"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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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": [
"<matplotlib.collections.QuadMesh at 0x7fe79d7764a8>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"covrr_new.isel(timestamp = 110).cov_Gh_GB.plot(x = 'x', y = 'y')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 143)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
"Data variables:\n",
" poa_direct (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(9639231, 143)>\n",
" poa_sky_diffuse (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(9639231, 143)>\n",
" poa_ground_diffuse (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(9639231, 143)>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xr.open_mfdataset('/scratch/walch/workspace_tilted_irrad/tilted_irradiance_interp_clean.nc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Obtain mean covariance for all rooftops"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"cov_all = xr.open_mfdataset('/scratch/walch/workspace_uncertainty/final/cov_G_0-9639231.nc', \n",
" chunks={'DF_UID' : 100000})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 158)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
"Data variables:\n",
" corr_Gh_GB (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(100000, 158)>\n",
" cov_Gh_GB (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(100000, 158)>\n",
" cov_GB_GD (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(100000, 158)>\n",
" cov_Gh_GD (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(100000, 158)>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cov_all"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Data variables:\n",
" corr_Gh_GB float64 0.8731\n",
" cov_Gh_GB float64 2.285e+03\n",
" cov_GB_GD float64 -356.4\n",
" cov_Gh_GD float64 89.53"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cov_all.mean().compute()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Data variables:\n",
" corr_Gh_GB float64 0.9972\n",
" cov_Gh_GB float64 1.39e+04\n",
" cov_GB_GD float64 666.6\n",
" cov_Gh_GD float64 2.325e+03"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cov_all.max().compute()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Data variables:\n",
" corr_Gh_GB float64 -0.4939\n",
" cov_Gh_GB float64 -42.84\n",
" cov_GB_GD float64 -6.851e+03\n",
" cov_Gh_GD float64 -3.369e+03"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cov_all.min().compute()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"data_path = '/scratch/walch/workspace_uncertainty/'"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 147)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" SIS (DF_UID, timestamp) float64 dask.array<shape=(9639231, 147), chunksize=(9639231, 1)>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fp = os.path.join( data_path, 'SIS_dhm25_200_CH_ELM50_1000_CPU_total_unc_interp_roofs.nc' )\n",
"sigma_Gh = xr.open_mfdataset(fp, chunks = {'timestamp' : 1})\n",
"sigma_Gh"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 147)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" SISDIR (DF_UID, timestamp) float64 dask.array<shape=(9639231, 147), chunksize=(9639231, 1)>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fp = os.path.join( data_path, 'SISDIR_dhm25_200_CH_ELM50_1000_CPU_total_unc_interp_roofs.nc' )\n",
"sigma_GB = xr.open_mfdataset(fp, chunks = {'timestamp' : 1})\n",
"sigma_GB"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"var_G = xr.merge([sigma_Gh.rename({'SIS' : 'var_Gh'})**2, sigma_GB.rename({'SISDIR' : 'var_GB'})**2])\n",
"sigma_GD = xr.ufuncs.sqrt(xr.ufuncs.maximum(var_G.var_Gh + var_G.var_GB - 2 * cov_all.cov_Gh_GB, 0))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray (DF_UID: 9639231, timestamp: 147)>\n",
"dask.array<shape=(9639231, 147), dtype=float64, chunksize=(9639231, 1)>\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sigma_GD"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"cov_all['corr_GB_GD'] = cov_all.cov_GB_GD / (sigma_GB.SISDIR * sigma_GD)\n",
"cov_all['corr_Gh_GD'] = cov_all.cov_Gh_GD / (sigma_Gh.SIS * sigma_GD)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 158)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
"Data variables:\n",
" corr_Gh_GB (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(9639231, 1)>\n",
" cov_Gh_GB (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(9639231, 1)>\n",
" cov_GB_GD (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(9639231, 1)>\n",
" cov_Gh_GD (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(9639231, 1)>\n",
" corr_GB_GD (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(9639231, 1)>\n",
" corr_Gh_GD (DF_UID, timestamp) float64 dask.array<shape=(9639231, 158), chunksize=(9639231, 1)>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cov_all"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"corr_mean = cov_all.mean(dim = 'DF_UID')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/.local/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide\n",
" x = np.divide(x1, x2, out)\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~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mto_netcdf\u001b[0;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile)\u001b[0m\n\u001b[1;32m 753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m \u001b[0mwrites\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/common.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, compute)\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m flush=True)\n\u001b[0m\u001b[1;32m 180\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msources\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~/.local/lib/python3.6/site-packages/dask/array/core.py\u001b[0m in \u001b[0;36mstore\u001b[0;34m(sources, targets, lock, regions, compute, return_stored, **kwargs)\u001b[0m\n\u001b[1;32m 865\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 866\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 867\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 154\u001b[0m \"\"\"\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 156\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[0mpostcomputes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 392\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 393\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\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~/.local/lib/python3.6/site-packages/dask/threaded.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(dsk, result, cache, num_workers, **kwargs)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_thread_get_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m pack_exception=pack_exception, **kwargs)\n\u001b[0m\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/dask/local.py\u001b[0m in \u001b[0;36mget_async\u001b[0;34m(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'waiting'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ready'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'running'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfailed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 493\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfailed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/dask/local.py\u001b[0m in \u001b[0;36mqueue_get\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mq\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[0m\n\u001b[0m\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_qsize\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--> 164\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\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 165\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/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 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[0;31mKeyboardInterrupt\u001b[0m: ",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-a5bd18275dca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorr_mean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdata_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'corr_G.nc'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mto_netcdf\u001b[0;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute)\u001b[0m\n\u001b[1;32m 1230\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1231\u001b[0m \u001b[0munlimited_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munlimited_dims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1232\u001b[0;31m compute=compute)\n\u001b[0m\u001b[1;32m 1233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1234\u001b[0m def to_zarr(self, store=None, mode='w-', synchronizer=None, group=None,\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mto_netcdf\u001b[0;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile)\u001b[0m\n\u001b[1;32m 759\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 760\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmultifile\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 761\u001b[0;31m \u001b[0mstore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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 762\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 763\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcompute\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mclose\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 483\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36mclose\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_key\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__del__\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mclose\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\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[1;32m 240\u001b[0m \u001b[0;31m# netCDF4 only allows closing the root group\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m \u001b[0mfind_root\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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 242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"corr_mean.to_netcdf(os.path.join( data_path, 'corr_G.nc' ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"corr_mean_vals = corr_mean.compute()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"corr_mean_vals.mean()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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 overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(corr_mean_vals.corr_Gh_GB.values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.4"
+ "version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
diff --git a/Solar/Spielwiese/Compile xarray datasets.ipynb b/Solar/Spielwiese/Compile xarray datasets.ipynb
index 43516eb..67169c1 100644
--- a/Solar/Spielwiese/Compile xarray datasets.ipynb
+++ b/Solar/Spielwiese/Compile xarray datasets.ipynb
@@ -1,4099 +1,4119 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+ " from ._conv import register_converters as _register_converters\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"import xarray as xr\n",
"import pickle\n",
"import util\n",
"\n",
"import matplotlib\n",
"%matplotlib notebook\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import LinearSegmentedColormap\n",
"from visualise_spacetime import plot_map_year"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"colorsRGB = [(47,111,191),\n",
" (51,148,190),\n",
" (65,195,153),\n",
" (105,219,77),\n",
" (183,233,81),\n",
" (249,239,88),\n",
" (250,201,53),\n",
" (246,135,26),\n",
" (220,82,7),\n",
" (193,31,4)]\n",
"colors = []\n",
"for row in colorsRGB:\n",
" colors.append(tuple([i/255.0 for i in row]))\n",
" \n",
"cm = LinearSegmentedColormap.from_list('radiation', colors, N=500)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def check_df(arr, cols):\n",
" if arr.shape[1] in (cols, cols-1):\n",
" return arr\n",
" else: return arr.T"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"path = '/work/hyenergy/output/solar_potential/physical_potential/results' # '/Users/alinawalch/Documents/EPFL/data/meteo/results/2001_XX_all_SIS_4D'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge information from the model outputs"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def merge_models(dataset, savefile = None):\n",
" # inputs:\n",
" # - dataset: python lambda of structure dataset = lambda month : (\"PATH_%02d_file\" %month)\n",
" # - savefile: name of target file\n",
" \n",
" merged_data = []\n",
" \n",
" for month in range(1,13):\n",
" new_data = xr.open_dataset(dataset(month)).drop('z')\n",
" new_data.coords['date'] = pd.to_datetime('2001-%02d-15' %month)\n",
" merged_data.append(new_data)\n",
" \n",
" merged_data = xr.concat(merged_data, dim = 'date')\n",
" \n",
" if savefile is not None:\n",
" merged_data.to_netcdf(savefile)\n",
" \n",
" return merged_data"
]
},
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {},
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [
{
- "data": {
- "text/plain": [
- "<xarray.Dataset>\n",
- "Dimensions: (date: 12, hour: 17, x: 1393, y: 882)\n",
- "Coordinates:\n",
- " * hour (hour) float64 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 ...\n",
- " * x (x) float64 4.855e+05 4.858e+05 4.86e+05 4.862e+05 4.865e+05 ...\n",
- " * y (y) float64 7.55e+04 7.575e+04 7.6e+04 7.625e+04 7.65e+04 ...\n",
- " * date (date) datetime64[ns] 2001-01-15 2001-02-15 2001-03-15 ...\n",
- "Data variables:\n",
- " SIS (date, x, hour, y) float64 nan nan nan nan nan nan nan nan nan ..."
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: b'/work/hyenergy/output/solar_potential/physical_potential/results/datasets/01/prediction.nc'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36macquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 167\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\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/xarray/backends/lru_cache.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0mmove_to_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: [<function _open_netcdf4_group at 0x7fe6d092b158>, ('/work/hyenergy/output/solar_potential/physical_potential/results/datasets/01/prediction.nc', CombinedLock([<SerializableLock: 263421f2-2840-42b5-b76a-5e21b369deb2>, <SerializableLock: b870e6d4-2b4a-4d5a-a3ff-72bda16009fb>])), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('group', None), ('persist', False))]",
+ "\nDuring handling of the above exception, another exception occurred:\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-6-dbfd9d7aba64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mmonth\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'datasets'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'%02d'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'prediction.nc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmerge_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m<ipython-input-5-7fc6dd306969>\u001b[0m in \u001b[0;36mmerge_models\u001b[0;34m(dataset, savefile)\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmonth\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2001-%02d-15'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mmerged_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\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/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'netcdf4'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m store = backends.NetCDF4DataStore.open(\n\u001b[0;32m--> 321\u001b[0;31m filename_or_obj, group=group, lock=lock, **backend_kwargs)\n\u001b[0m\u001b[1;32m 322\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'scipy'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0mstore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScipyDataStore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mbackend_kwargs\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 353\u001b[0m kwargs=dict(group=group, clobber=clobber, diskless=diskless,\n\u001b[1;32m 354\u001b[0m persist=persist, format=format))\n\u001b[0;32m--> 355\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mautoclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautoclose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, manager, lock, autoclose)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_remote\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_remote_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mds\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--> 359\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_manager\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[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_store_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\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/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36macquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mode'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;31m# ensure file doesn't get overriden when opened again\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m_open_netcdf4_group\u001b[0;34m(filename, lock, mode, group, **kwargs)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnetCDF4\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnc4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnc4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mclose_on_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: b'/work/hyenergy/output/solar_potential/physical_potential/results/datasets/01/prediction.nc'"
+ ]
}
],
"source": [
"pred = lambda month : os.path.join(path, 'datasets', ('%02d' %month), 'prediction.nc')\n",
"merge_models(pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prediction = []\n",
"model_uncertainty = []\n",
"data_uncertainty = []\n",
"\n",
"for month in range(1,13):\n",
" new_pred = xr.open_dataset( os.path.join(path, 'datasets', ('%02d' %month), 'prediction.nc') ).drop('z')\n",
" new_model_unc = xr.open_dataset( os.path.join(path, 'datasets', ('%02d' %month), 'model_unc.nc' ) ).drop('z')\n",
" new_data_unc = xr.open_dataset( os.path.join(path, 'datasets', ('%02d' %month), 'data_unc.nc' ) ).drop('z')\n",
" \n",
" new_pred.coords['date'] = pd.to_datetime('2001-%02d-15' %month)\n",
" prediction.append(new_pred)\n",
" \n",
" new_model_unc.coords['date'] = pd.to_datetime('2001-%02d-15' %month)\n",
" model_uncertainty.append(new_model_unc)\n",
" \n",
" new_data_unc.coords['date'] = pd.to_datetime('2001-%02d-15' %month)\n",
" data_uncertainty.append(new_data_unc)"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU time: 42.90 seconds\n",
- "Wall clock time: 80.82 seconds\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"tt = util.Timer()\n",
"data_uncertainty = xr.concat(data_uncertainty, dim = 'date')\n",
"model_uncertainty = xr.concat(model_uncertainty, dim = 'date')\n",
"prediction = xr.concat(prediction, dim = 'date')\n",
"tt.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"prediction.to_netcdf( os.path.join(path, 'datasets', '2001_prediction.nc' ))\n",
"data_uncertainty.to_netcdf( os.path.join(path, 'datasets', '2001_data_uncertainty.nc' ))\n",
"model_uncertainty.to_netcdf( os.path.join(path, 'datasets', '2001_model_uncertainty.nc'))\n",
"total_uncertainty.to_netcdf( os.path.join(path, 'datasets', '2001_total_uncertainty.nc'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and plot information from model outputs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"prediction = xr.open_dataset( os.path.join(path, 'datasets', '2001_prediction.nc' ))\n",
"data_uncertainty = xr.open_dataset( os.path.join(path, 'datasets', '2001_data_uncertainty.nc' ))\n",
"model_uncertainty = xr.open_dataset( os.path.join(path, 'datasets', '2001_model_uncertainty.nc'))"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"prediction = xr.open_mfdataset( os.path.join(path, 'SISDIR_dhm25_200_CH_ELM50_1000_CPU_prediction.nc'), chunks = {'x':200, 'y':200})\n",
"data_uncertainty = xr.open_mfdataset( os.path.join(path, 'SISDIR_dhm25_200_CH_ELM50_1000_CPU_data_unc.nc' ), chunks = {'x':200, 'y':200})\n",
"model_uncertainty= xr.open_mfdataset( os.path.join(path, 'SISDIR_dhm25_200_CH_ELM50_1000_CPU_model_unc.nc'), chunks = {'x':200, 'y':200})"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"total_uncertainty = xr.ufuncs.sqrt( model_uncertainty**2 + data_uncertainty**2 )"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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 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"
],
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\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(485375.0, 833625.0, 75375.0, 295875.0)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure(figsize = (10,5))\n",
"model_uncertainty.SIS.isel(hour = 13, date = 2).plot(x = 'x', y = 'y', vmin = 0, vmax = 30, cmap = cm)\n",
"plt.axis('off')\n",
"plt.axis('equal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute yearly sum"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"yearly_sum_m_unc = model_uncertainty.sum(dim = ['date', 'hour'])/1000*365/12\n",
"yearly_sum_d_unc = data_uncertainty.sum(dim = ['date', 'hour'])/1000*365/12\n",
"yearly_sum_pred = prediction.sum(dim = ['date', 'hour'])/1000*365/12"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 12, hour: 17, x: 1742, y: 1103)\n",
"Coordinates:\n",
" * hour (hour) float64 3.0 4.0 5.0 6.0 7.0 8.0 ... 15.0 16.0 17.0 18.0 19.0\n",
" * x (x) float64 4.855e+05 4.857e+05 4.859e+05 ... 8.335e+05 8.337e+05\n",
" * y (y) float64 7.529e+04 7.549e+04 7.569e+04 ... 2.955e+05 2.957e+05\n",
" * date (date) datetime64[ns] 2001-01-15 2001-02-15 ... 2001-12-15\n",
"Data variables:\n",
" SIS (date, x, hour, y) float64 dask.array<shape=(12, 1742, 17, 1103), chunksize=(12, 1742, 17, 1103)>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_uncertainty"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot yearly mean"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"scale = 0.7\n",
"vmin = 150\n",
"vmax = 250\n",
"ctext = 'Yearly data uncertainty (in $kWh/m^2$)'\n",
"title = 'data unc'\n",
"figname = 'yearly_d_unc2.png'\n",
"data = yearly_sum_d_unc"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"BASE_FONT = 16*scale\n",
"TIMELABEL_FONT = 20*scale\n",
"FIG_WIDTH = 15*scale\n",
"FIG_HEIGHT = 15*scale\n",
"data = data.SIS.where(yearly_sum_pred.SIS>0, drop = True)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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 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": [
"<img 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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"matplotlib.rcParams.update({'font.size': BASE_FONT})\n",
"fig, ax = plt.subplots() # figsize = (FIG_WIDTH,FIG_HEIGHT)\n",
"\n",
"ax.axis('equal')\n",
"ax.axis('off')\n",
"\n",
"nx = len(data.x.values)\n",
"X, Y = np.meshgrid(data.x.values, data.y.values,indexing='xy')\n",
"Z = data.values[:-1, :-1]\n",
"Zm = np.ma.masked_invalid(Z)\n",
"\n",
"p = ax.pcolormesh(X,Y,check_df(Zm,nx), vmin = vmin, vmax = vmax)\n",
"p.set_cmap(cm)\n",
"cbar = plt.colorbar(p, orientation = 'horizontal' , pad = 0.05, shrink = 0.7)\n",
"cbar.set_label(ctext, labelpad=5)\n",
"\n",
"labels = [item.get_text() for item in cbar.ax.get_xticklabels()]\n",
"#labels[-1] = '>' + labels[-1]\n",
"cbar.ax.set_xticklabels(labels)\n",
"\n",
"plt.show()\n",
" \n",
"# fig.subplots_adjust(top=0.83)\n",
"# plt.title(title, position = (0.5,1.1), fontsize = int(TIMELABEL_FONT * 1.2))\n",
" \n",
"plt.savefig(figname, dpi = 1000)"
]
},
{
"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\", \"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 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": [
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BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIsAABeoQbl6ohgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCAQXIEAPbsWRCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIRFiBAj3DjUjUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgeACBOjBrTgSAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDCAgToEW5cqoYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFyAAD24FUcigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCERYgAA9wo1L1RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEggsQoAe34kgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQgLEKBHuHGpGgIIIIAAAgiUrcDNN99st9xyi7voTTfdZPq5UMt//vMf22mnndzt7bDDDqafKQgggAACCCCAAAIIpBPYZ5997IsvvnCHfPbZZ6afKQjkKtC+fXsbP368e/vYsWNNPxdqeeGFF6xLly7u9jp37mz6mYIAAggggAAC0RUgQI9u21IzBBBAIE7gmGOOsXfeeSepyiabbGJbbrmlbbHFFrb11lvb/vvvbwcccIC1bNnSBWsUBBAIJkCAHsyJoxBAAAEEEEAAgUIQ8A8ozNf9PP3009ajR498na7gzkOAXnBNUqFviAC9QjcfN48AAggggECkBQjQI928VA4BBBD4f4F0AXoqp8qVK1vr1q3tkksuseOPP75cOP33/dZbb5l+piCQTGDHHXe0+fPnu5e+/fZb089lXcorQM9lNnku7ylrT66HAAIIIIAAAgiEKVCRAvQZM2bYQQcd5DiaNWtm+rk8CgF6eajn/5o//vij1a9f3524bt26pp/Lo5RXgJ7LbPJc3lMeplwTAQQQQAABBPIjQICeH0fOggACCBS8gD+I1oOXgw8+OHbPGzZssF9++cWWL1/uluPzQkh/pc4880x76KGH3Ez1siwE6GWpXbGvRYCe3XLsBOgV+/PO3SOAAAIIIIBA6QX++9//Wv/+/dOeaNq0aTZ9+nR3TOPGja1Tp05pj1e/qXnz5qW/uYQzEKDnnbSoT0iAnv1y7AToRf0nQ+URQAABBIpQgAC9CBudKiOAQHEK+IPoTHszL1261J577jl78MEHbeHChTEwLes+depUq1mzZpkhEqCXGXWFv1AhBOjlhUgYXl7yXBcBBBBAAAEEoi7gX2Ho6KOPtrfffrtcqkyAXi7skb1ooQTo5QVMGF5e8lwXAQQQQACBiiNAgF5x2oo7RQABBEolkE2A7l3ot99+s3PPPddGjx4du3bnzp1Nnc2yKgToZSVd8a9DgJ7dDPSK3+LUAAEEEEAAAQQQCF+AAD3emCXcw//MlcUVCNCzn4FeFu3CNRBAAAEEEECgcAQI0AunLbgTBBBAIFSBXAJ03VBJSYmddNJJNm7cuNj9leVe5ATooX4sInVyAnQC9Eh9oKkMAggggAACCBSEAAE6AXpBfBDzfBME6AToef5IcToEEEAAAQQiJ0CAHrkmpUIIIIBAcoFcA3SdTXsDKpz89ddf3cmPO+44e+2111JSz5w501599VV777337Msvv7QffvjB1qxZY3Xq1LEmTZpYixYtrFevXrb99tunPEelSpUCN+XTTz9tPXr0iDte1xw/frxbYvFf//qX29dd97/ZZptZw4YN7dBDD7Vu3bpZmzZtAl8nyIH++9bgg0wlyACBZMeoTZ544gm3GsC3335rK1eutEaNGjnbyy67zDQzJJsyceJEGzNmjGuzJUuW2IoVK5zVzjvvbAcddJCdcMIJ1q5dO6tatWra086ePdst///66687859//tm23HJLdx5Z9+7d2+0dma4kq6/uSe2se/zuu+9s2bJltvnmm9snn3xiO+30Z3AcpCQb/KH7nDBhgk2ZMsU+++wzW7Bgga1atcq22GIL22abbezwww+3s88+2w455JCMl/A/YE21VcLQoUPtnHPOcefSefWzyj//+U8bMmSIffrpp/b99987t6ZNm9pZZ51lp59+uiX7m/CfK+PN/W9AjHdcLsu+//TTT+5+J02aZHPmzHHtsOmmm7o21WdP9TrwwAMz3or+Nl5++WUbNWqU6b8Xat/ff//d/TeiXr167nxaIlWfmWbNmlnlypUznpMDEEAAAQQQQACBMATyEaB/9dVX7nue+lD6LvvLL79Y3bp1bZdddrG2bdvaeeedZ1tvvXXS27/77rvtqquuClQ1nVPBqL/oe9eHH37o+mcffPCB+w6nvtL69ettq622st12282OPfZY1z9LdQ/+8+VzBnouy9LXqlXL9X1U1L/Tz4kl2TFz5861Rx991H3v1/d9ueywww52/PHH2xVXXOH6iEGL+rYjR4409aGmT5/uvhOr/1C7dm3nedhhh1nHjh1dPyJTUR9E34nfeecdW7x4sauT2nHvvfe29u3bu89Gsjr6z5usvl988YX7zKnddV71H9XO6o916dIl023FXk9mrP7K5MmT7d1333X9fW0BJxPVX/0+fY/X50n/P1NRHdVvVxk7dqyrc2Lp06ePDRo0yP36oYceMv2svsOzzz5rw4YNM/19qX7169d3/fwLL7zQWrZsmfTS/nNlujf1Q/QZ9Uouy75/88037j71t69+u+5T/Uw9X2ndurVrj3TPRbxrr1692tVVfSg939Dfuf6G9VlR/0nnU/uq/7T77rtnqhqvI4AAAggggEAAAQL0AEgcggACCERBoDQBuup/ySWXuM6qioI8ddj0wCWxHHzwwe4hQqZSrVo1+/vf/279+vVLemhpAnTt3X755Ze7DmWmoo61Hlio45mPUhYBuuxOPfVUW7RoUdJbrlKlij3yyCN2/vnnZ6ySHqxo8IH/wUCqN+maelCUrPzxxx926aWXulA/nXuNGjVs4MCB7qFHqpIYoOsBo4JZhfH+ooC5tAG6Hkbec8897gFaptK9e3d78sknrWbNmikPzSVAf+CBB1xI/sorr6Q8rx6svvTSSyY/fynLAF0Pra6//nr3wDdV0edfbaXPX/Xq1ZMepsEBnTp1cg9wgxQ9bNKgHQoCCCCAAAIIIFAeAqUJ0Dds2GDXXnut3XvvvbZu3bqUt6+BofqOrDAtsZQ2QFeIqeAuU9GgSPWjMvUhKmKAru/RClUVcicr6ldoxbUjjjgiE5ML4C+66CI3WDhTueuuu+zKK69MephCZ/XDFESnKw0aNLB//OMf1qpVq5SHJQbogwcPthtuuMHWrl0b9558BOjqP2tQcqaigde6h/79+ycdCOy9P5cAXX2jU045xQ08TlU0KEJ/O4mlrAJ02auvqT5Uur/9TTbZxP72t7+lfC6i+581a5adfPLJpgHQQYr6zRrMQEEAAQQQQACB0gkQoJfOj3cjgAACFUagtAH61KlT7aijjorVVzOBO3TosFH9NXJfAZk6gho1r1kVeiChgFKzTD/66KO4WRF33nln0s6iF7BqVq5GzKtoFL9mBCeWM88805o3bx779TXXXGM6r4oeGO25555uNLoeCi1fvtzNMlZw7BXN8tWsDN1zaUvYAboGMeghnPan18OUI4880oX/CtPffPNN08h0Fc3Y1YwAjcBPVTQ7X8vzeysL6DiNftcgCA2O0MyOf//73+7BhB4AqL3V7olFx2mku2ave0WzwjUTWTOK1YF///334wL/2267za677rqkt+b/rOo4PbTU9VVPfQY1wl6zZvQgQf/0UEZFI/u9uiiQ1oPIxNK3b1/bddddY7/WKgTPP/+8e6ijkfr6p+tokIJmW+v8mq3iFc3C18O1VAM8sg3QzzjjDPd38cYbb7jAWbNVtEqDZlTob04zlLyiB6oKpv1Ff0+a8a96q/4qqrfqn6w8/PDDsV9nMwNdqxrcf//9sffKSDPyNVNc9yqnzz//PPa6HmppJknizHENrtDfqmade0UPYfVPD3l0Lj1M1GdOLioE6Cn/hHkBAQQQQAABBMpAoDQBugZg+gegql+kVXvUN1m4cKELIvX9xysaYKwBi/6i74nqE2mWswb+quj9Xbt23aj2ClLvuOOOuN8ryNR3MA0CVf9M3zU1A1a/Ux9C/SD1kbzy1FNP2bnnnptStqIF6FrFSvVRf1R9U62upZW2vv76a/d9W4McVNT/0ex82aYqmsF+8cUXx96j4/bdd1/3XVbfwdXv0XdinUfXu/XWW12InFjUv1CY7Q/h//KXv7hz6d70PVj35rWL2lAzj9UXSVb8AboGYniD1DUrWd/Z9br6FRpoPWDAADcoWJ87tbWK+sk9e/ZMem4N/vAPjJWh7l+/22uvvVzfSp9r9Y/0PX7atGnueYBXbrzxRhcQpyrZBuha5euZZ55xYbLM1T9Un0Qzu9Uf9g+6Vj0T6zV69Gg321910KpaKvqbUP8lsWiFAv/qD0FnoGtwuVZw09+uV9TP1mdP/SgNSNZgYn9fT318tU1ikaWcVT8VtaHOI3d9VtQX13nUf/I+LwToKT9uvIAAAggggEBWAgToWXFxMAIIIFBxBUoboGu0vjrG3ujpVB08jcZXJ1gPhhJny0pPD2oU+CkgV2dPQaVC2lTLcAdZ4jyxVbRUne5Xs1yTBe46XsueqTPtzbxO9XAj2xYPO0BXyK820IMRrQrgX1JdSxHqoYoXZKoN9BAhWdGxBxxwQGwwg/wVziZb0l4dcD2s0/JzmkWRWLQMuRfe6uGDHiwlzhhWuz/++ONueXk9UFDHXw+FkgX8/jb3HvjpocvVV1/tPi9e0Xn8gx5y2QNd9dFDMn1mFcwnK7pPPXRT/VX0+VXwnaxkG6Dr/lUPLR2p2fv+z6vaWX9n3swJfbbmzZvnludLLNmE4d57g75Hf0/egyc9fJOZPBJnmOsBsAazeCsjJBscowEY+rtU0ZYDehjsH/zir5cGuchasy00qIOCAAIIIIAAAgiUh0CuAbpmASts9cpf//pXN8jX30fSql76nuWtRKTBh9oKSd/jE0suy53rHFodSA2Aw7UAACAASURBVN9dNfA22QpBWnr7scceczOl9f8VyinYTbVCV0UL0PV9W4MHFKQnDgDXYFT1n7xwUmGp+lnJigYfq4/jrbal5dnVxgq+E4v6WlopSv2MxFUF9N1ffSANQFVRu6gfpsEN/qIB0wqLFWCrqD20XVaygN8foKv/pDorHNcKYv7i7z/luge6BiQr/Nc/fVYSiwYkaICy6q1twfSZ1gB2hcDJSrYButd/0t+TAmf/8vYKpk877TS3SoCK+htqC/U9E0vQMNz/vqDv0TMRb+CzgnN9ThSo+4ucNBBA/43QIHj19TRwWK7+ogEYGlSuouBcAwAU7CcWfS41GEaDBrSSRLLB5EkbgF8igAACCCCAQEoBAnQ+HAgggECRCJQ2QBeTQlZv2TAFaN6I9VwI1anW7F8VjZD3ZownniuXAD3o/aiDvccee7iR8uk610HPp+PCDtB1DT3guuCCC5LelsJzPcTRjAfdi8JM1S2x6CGa9lBTUQdcD4+C7HmYeB7/ygTbbrutW74/3f6B3gwQnUej/LVvYLo212vJZuIkq3wuAXrQttXnXisZaKaGwlx5JSvZBug6hx6aaaBDsv3l1Y4KmL1tETSjSAMJEkvQMNz/viDv0cx2PfTRbAY9eNJ9+leiSLwPPdTTwAw56SGfZkP4l7zXg1ktma+iAQPa05GCAAIIIIAAAggUskAuAbq+C+m7sVY0UtGy6BpMmqxo0KSCWc2KVVFIplm8iSXXAD2orZaa9lYBU4icat/1ihag6zu2VuZKNWhTIaaWUldRmylwTSz6Tq6AW991VRRyarWlXFYwu++++9x2YyraK1x7lKfa+kjHKGBVAKuiMFUDvxOLP0DXa+pjJZtR7X9frgF60M+TfLz9zBUoe3uYJ74/2wBd71d4rpA4WVG/RavQeTPRNSAlMZTW+4KG4f5rBHmPBkaoP6SiFePUj0u3x7kCcW81CW0hoP61v/ifh+jcWj2PggACCCCAAAJlI0CAXjbOXAUBBBAod4F8BOjqrHn7jGkWqfaSy7VohLSWbNbIenUw/Us6p+owaoar6pHP4h8drlnpWjavNCXsAF33p/tMV/RwyHvoNnbs2NiDC+89CtUVNnurCQR5wJLqevoceMu6p5uZ7b1fD580+0DLGspKS7Enzvz2f1Y1I1tBb7JwOfGewgzQdS3NTpGV7lsPZrT0ZWLJJUDXw9BmzZqlbFLNXtDnVKVz587uYU9iCRKG5/Ie7c+uve1VNDtKMxoyFc020SAPlRdffNHNIPeKBn4oOFdJtQ1EpvPzOgIIIIAAAgggUJYCuQTo2rNaK/OoaBUvDSpM9t3Rq4dW3lEw7ZVk3w/DDtC1Opg3m7d169Yp9+auaAG6ZuBrRaVURbOytXy7tz+6lk9PHBDsD4MVmmv571QrnaX7bKovpD6Lt3T3l19+6QbppiuaHa/70ZZWGtCuFakSiz9AV59F95uphB2g6/oaoK3+3m677eZWnUtWsg3Q9Xek7Q/SzbBWv8Vr89tvv920xVtiCRKG5/Ie/d3r719F/bjEFQiSGWjrM+95iFZ/8AfuelbirVageufyucv0WeB1BBBAAAEEEEguQIDOJwMBBBAoEoF8BOgaEe3tc61ZElpiLF3RUm0ff/yxC0C1fJseTviLt2e1luXWLI3E/ZJ1bGlnoKvDrqXMNFtAo9D1YEgPLryioNmb3av78R505fqxCDtA11Lm2kcuXfEHmNoz3ZtJ4r3HP8tCe6d99dVXOVVXAbwGQchUAbfaONmy/Ykn9w9a0F5+2ofdX/xtrmXqFeIGKaUN0PUgS58HeSgg11J6/s+K9rBT8K8yZcoUN3M8sWQboGt2hH+P9WT11OfXW+peQbu37YD/2LACdC3pr1kxKkH3Itdy/95ykVqy31t2UufQagLe51fL1mu50iCDI4K0P8cggAACCCCAAAJhCOQSoGu1Lq28pBJ0EOJhhx3m9kVW0ZY5WrnHX0oboOt7rYI4/dMsa31315Lt/qJBkPqOr5mz/n2s/cdUtABdy3nre2e64g8w1QbaN9xfNOP54Ycfdr/SKmojRozI6aOm/rG35Pt+++1nn3zySaDzaAUsr8+qwdDa89tf/AF60D5tPgJ0DQBQ8Pvtt9+6fb0T+/ty0gAA9ZE1cN6/MpV3/9kG6JqtrdXs0hW1ldpMpVevXm6LscQSRoCuvzEtse+tPKG616lTJ2Mba0U+b6s0rRKnZei90rFjR1OfWSXVNnoZL8ABCCCAAAIIIJCTAAF6Tmy8CQEEEKh4AvkI0NXJ92Y/a1apZpcmKwpotR9ZNsFsqs5lrgG6OvNa6lozhr196jK1moI+BX6lKWEH6NozWp3odOX66693/irJlj+/8MILYw8R0i0nmclBgyO8mdN6aKO90IMULX/uhcB6uOHfG1Lv97e5PktnnXVWkNO62Rwasa+ihzjJ9gpPdiI9JNOsBC2X5w/M011Uqy94e3n7j8s2QNc+jN4M/lTX+/rrr92sDZVddtnF9HNiCStA1wwPLeOuosEl6WZOefekmRHeQ55TTjnF7dPnFQ1A0PKX2vNPRQM4NCtIDzX1MDHZIJpAjc9BCCCAAAIIIIBASAK5BOj7779/LBzVTFh938lUtGT63Xff7Q7r3r27DR8+PO4tuQbo+t6lAFEBnbcdV6Z70dY93mpVicdWtABdgwW0NHu64h80OmnSJNPP/qJl9b3+S7rttDK5ahl/BboqGkibKdj3zqdVxbxZ6xpcm7gcvT9A12oGqfYb999faQJ0Bdga2K0+d9Ci+99uu+02OjzbAF1L2Gsp+3RFwb0XQqca8BBGgK7nH7vvvru7Na1UEHS7KvWrvcEziVt2+eui82qgjbZj0yoRTZo0CcrPcQgggAACCCCQgwABeg5ovAUBBBCoiAL5CND9AaU6g95SzJ6HwkfNsPBmW2TjlLhUmffeXAL0yZMnm4LJxBHwme7nlltusf79+2c6LO3rYQfo2oO6RYsWae8hU4irwQ8K4lVSLWkXBGHcuHF24oknBjk05THJHoD42zzIjBHv5LkE6Hqgqc9y0ODcu1aqYD+Tvd4/dOjQ2ENUDQ7QudKVIOF4kGMSr5HpPZolkm5pxCANr/0Gte+gv9x///1u38dEc4Xzmmmv9tfgBO/hU5DrcAwCCCCAAAIIIBCWQC4BugJbzRRW0SpGLVu2zHh7/j3IW7VqFVsFyHtjLgG6QvAuXbpkHLCZ7Oa0ylSyGcMVLUDXTPtM32kzhbhaUtvbGz3oqkzJTP2fpYwfiBQHJNt+yx+gawU2zYLOVHIN0LW6War9zNNdM1Wwn8le5/RfU3ufe7PLU10vSDge5JjE82d6z9tvv52xr56pXTQJQCG6V9Rn6tGjh2llgcSipf21SqCeD6iPn7j1QKZr8ToCCCCAAAIIpBcgQOcTggACCBSJQGkDdD1A0f593mxujfpW+Okv/hH1+r06w1rKWTMwtFeXHsBUr1499pYggWe2AfqyZcvcrFYtIaeifeK0pLk6lvr/WnJ80003dcvIqQQJPLP5iIQdoAfZBz5TnfRAzgs1ky3xHrS+Wl5Oo99LU2666SbXBv6SbZt77w3yefJfR8v6a9azN7tG/19hukLcHXbYwT1o02fFK3pw4YXdGiSinxNLJnsd7w/QNWtfP6crmYJuvTfIMYnXyPQePfTNNFsnU9sfffTRpgdJieXdd991qyPoAaQ3Gz3xGD1oVti+7777ZroMryOAAAIIIIAAAqEJ5BKgq8/h9UeSzRhOdrP+74haQtybkeodm0uArlWptDqVivopWh2oc+fO7juwlgFX/0zbaXnFH8RqFSJvT3T//Va0AD1VPfx1yhTiahlubfGkkmyJ96AfPq12pu+3pSmaja779Zcg7ZZ4zVwCdK2KcPrpp8dOpX6l+oPap1v9/c022yyuvx/ks5LJXhfzB+hB+q+Zgm6dM8gxiWaZ3qNVuDKtFpep7a+44orYShT+Y0eOHOl+7+2VnngerRqhlSt0jPaepyCAAAIIIIBA6QUI0EtvyBkQQACBCiFQ2gD9nXfecTNDvaLZxyeccEJc3bXMtLe89G233WbXXXddWpu6deu6PdFUUi25nW2Y6n9IpOBee1Une/Dj3Zg6qN4ezcnC3GwbN9sA/aijjnJLh6ukCsezNcgU4uZrBrqWHveWMW/atKnbTzEfJdv6etfMNkC/4IILYqsotGvXzs3M8T9ATKyLHjZq6XaVYgjQ9ZDQv2efftYgmnwWPbjTf1v0N6B/2gfSH6jXqFHDheyHH354Pi/LuRBAAAEEEEAAgcACuQTohTADXd+pFKTp+5ZKpqXHdXzVqlVjqwQVaoCu0H/16tWuTqnuMdtAOVOIm68Z6BqErn6ySrIV3QJ/KBMOzLa+ensuAbqCcq/Ppy2wtJpZuqJByd7S89r/XYF6Yslkr+MrSoCuQeoaVKCSauutXNvYe58GQav/pOcc6j8lbu+l//Zo0I4GNFAQQAABBBBAoHQCBOil8+PdCCCAQIURKG2Arn2qBw8e7OqrfYp/+uknN5vbK1rSTg8WVBS6aek4PYBJVbSUnt7vLeOcrwBdS0ZrmXMV7dGusDhd0SwMby/3fAToCmC9Gc1r165Na6D78u8rX1YBun8PdIXIepiWS9HsC+3BpqLlt3/++ee87GFdVgG69oybN2+eu3+N5NcDoXTlwAMPjI34L4YAXRaage9thTB9+nSTQZhFD/K0z5/2VPQe9mrP9M8//zzMy3JuBBBAAAEEEEAgpUAuAbp/D/RU3xsTL9ivXz+3T7lKPvZAV9Dpfb/VSlze995UFfWvTqRjyiJA//TTT00Dcb1+kQZTpivqX/lXNCurAN2/B7pWXTv//PNz+ovR7OlLLrnEvTfVSk25nLgsAnT19bbaait3exrEoO/qGuyaqqxZs8at6KX/VSmGAF111OoOKvqc6pmH9kIPs+jv+qmnnnIzzz3rIKuchXlPnBsBBBBAAIGoCBCgR6UlqQcCCCCQQaA0Abo6x3rooj2RVY4//njT3tT+8tFHH5mWGlRJtuRg4u3p/f4Z7KkCdO3n5S0BHWT58j333NPmzJnjLqf/TbePspaj1wjtpUuXuuPzEaDroYIeLqjovOmWT1MHVzN6f//9d3d8WQXoWobcW35cy91/9dVXOf39aOaHBkt4AauW5c7HTOFcA3R9RvXgTyXV58lfUT3w8ez1v+kebmgJTu0lqId2KoUWoM+fP980A19FMz08h3QNm2kJd73Xv0KCllz3lgDN6QOTxZv8gzP0trlz59rOO++cxRk4FAEEEEAAAQQQyI9ALgH6ueee674vqihsVeiaqeh79Pvvv+8OU5B+5ZVXxr1FAz69wYzNmjUzLemerowfPz621Ldm+Wrp73RFeywrePNKWQToCv80qFVF/TJvn/FU9/nxxx+b6p7pHrMNlDPNgtae2w8//LC7bLdu3dyAz1zKtGnTrHnz5u6tCli1/ZgGIpe2ZFtfXU8D4uvVq+curZXhvMGrqe5Fe5h7M8j1vwqL0xXNkNYgAa8UWoCuQfQaTK+ilca0PHumkmkJdw2kl6WCc5VJkyZZmzZtMp02L6/7B2eoj+6t9JeXk3MSBBBAAAEEilSAAL1IG55qI4BA8QnkGqBrhrgeKPgD82RBqWanHnzwwQ5Wo641myBdOfHEE03LwHslVeCpsF4dT5VXX301tiRaqnNrtuqXX37pXtYDFs3+SFX8nWYdk48AXQ90dN0gHWZ/B1zHl1WAvnjxYheyejPlS9Ox19LnEydOdPUN+uAh019frgG6f/CEBgVocEC6oj36Vq1a5Q7RAwb/cuWJ77vnnnviHmIWWoD+/fffW8OGDd1taz9L7V+eqQQJ0AcOHGhXX321O5UeamqJQP++8JmuUZrX/Vs86GGy9qanIIAAAggggAACZS2QS4D+j3/8w84880x3q/qOqWWs020r9e9//9v0XdZbnSvZ6kj+ADNIf0vf0fVdXaVly5b2xhtvpKXTylL+fdfLIkDXQFzNUvYGqSpQ9kLdZDerZcPvvPPO2EtlNQPdP/hbg241uDOXJbK1TL7e5w3g1hLoqlNpSy4B+sqVK2OfSYX4GjCcrsyePdv22msvd4gGtsogXdEqCtq32yuFFqD7B5icdNJJpv3LM5VMAbre37VrVxs9erQ7lcJz71lGpnOX9nU9T/EPONZg/XTbk5X2erwfAQQQQACBYhAgQC+GVqaOCCCAgJnbv1x7ZakEDYo147xnz542atSomOHpp59ueiCUWDRivUGDBu6hj5Z4V4DpzSZIPPb55593I/f9JVWAfsYZZ9iwYcPcoUGWP/QH8+nqqYczCrv9sxyCuqT7QPmXR083O0Gj0rWkov/BQ1kF6Lp/v6vCdK0gkG62fKo66zOlz5ZXgrSRd6weHHmhr//8uQboRxxxhL333nvuVEFWK9h3331jS4Onu2+Fxvqs6AFdpnpm2n9e7x86dKidc8457lRBltcLEnTr4aOWUvT2rtTAgEwPTIKcVw/SNLNd+5+r6L61RGClSpUy/ndV/03QA+MqVarEjtXv0j0U9Q7UKg76bHjLEKoNtI8gBQEEEEAAAQQQKGuBXAJ0rW6kwYea5auiPZw1QzRZ0apYCtq8gFvLhWumcmLRd+dGjRq5XweZMaxQfo899nDHa+UlffdTfy1ZGTRokLtHfymLAF3X8y+PrmWor7jiiqT3qNXF9J3cGwCrg8oqQFcfVwO1FSKraNswBbC5LM/tH5irdlF/SgZBigbNJuuz5RKge58Lb0Uu9U81mCFV0bMBfbf3BmGnCsT1fg2UV7/cXwotQNc+4d4A3SArOqguQQJ0rQzhb0/93Sf+baUyTtY/Dtp/8vfLtcqd138L8rniGAQQQAABBBBILkCAzicDAQQQKBKBbAJ0ddwUkj/44INxAbOWm9Ny6qlmoKoDqo6oimZ+a2k7/xLqCvceeeQRu+yyy1zQp4DP67CnCtA1w8Abla8l37X0YLrwTvt59+7d292DHmg8+eSTLiz2F80Q1+/0AESzkDX6XiUfAfrUqVPdstdeSdZh1sMfXV8zS3SP3hLoZRmga+CA2sh7qKflz9U2yZaYU+dbgyi++eYb04zkxKLl4LUsvIraRg+91GZ6sJdYVFeNwleIrFnSyR4O5hqg+wcv6P8PHjw47V/3tddea3fccYc7Rg+D9HlNrL8eZJ511lmmWfv+z0qhzUBXHfwz8DVIRbMf0pUgAbre7w/89bP+DvVw03sg67+GHi7qvwEa9CIjPeTzz7bS7Cc9KNRnRjOiZJpYNENLS51qxQmV0mwzUCT/eaeaCCCAAAIIIBCiQC4Bum5H30Uvvvji2J3pO7K2xPH3pfRd/IILLrCXXnrJHaeByK+//rppG6tkRVsKeUtt63uqvlulK7vttptbQUjlyCOPtOeee86tROUVzfzW97obbrjB9c0UjirQVymrAP2+++6zyy+/3F1T3w3HjBljxx13XFy13nzzTdd/0iBo9SP1L909ZhsoZ1rCXddSX0335V1bS+6r/6RBuYlFfS19h9aAhV69esW9rP6vBv6qL+jVWX2S8847L2k/W30xmaifu99++9mjjz660fWyra93Av/gBQ2S1dYD6UqrVq3c51NFs9HV5/CWddfv1BcYMmSIC4zV79P3fm/AQ6EF6Pp8165d27WnBvzq2UCmFcyCBOhyUP01KMUr+vnGG29MOoBFA4Zfe+0115/WKhP65y8afKzBCJrIoH5y1apVN2oiPd847bTTTINmVDT7f/jw4WnbkhcRQAABBBBAILMAAXpmI45AAAEEIiHgDyXVUfaWW1fl1GnUiHN1zrX8ucLsxKLO9AMPPJB26UE9xGndunXsoYIewujBgpYS04h1hctLlixxp77tttvcXoDau1klVYCuBz4K4b3lDNVB1/KC/tHxmunt7QeojrqWNPTv661gUQ8b9LDq888/j+0XqN8pMPVC4XwE6KqL/wGMflbQqH3hFS6rU6uAUeYKEVVvb2WAsgzQdV9qr44dO8b2ttfv9EBNnw3t5a42k+Mnn3zillXs0KGDe3iTWGSuZe+8wFOva08/fc60CoEenGgms2bb68GJN2gi1Uj/XAN0PczRQx2vaMCHZvlrZrZXFKx7KyP88MMP7oGPHsR5RcfrYZDaSg8ivAcY+pzoAZgeOqoUYoCuB5/6u1LR354sNGvbPxNdD0i9EjRA1/H9+/e3W2+9NfZe+chO/7TkowahaEDErFmz4mY7JD549bet7kvv14NdzZLQsfrvgff3oYvpYZZmsLRt2zYS/x2mEggggAACCCBQ8QRyDdBV08RlrDVoU+G4BppqgKaC4dWrV8dQ9H1L3+lSFQ0UVpCqor6NvqNqtSAvVFMAfcstt8TerqWk/YMq9b1cfSlvhSH1Q7xAXudVkO0NLi6rAF0Bq74T+vugGpitGd/qZ/i/k2sfcm0vlOkesw2UgwToQtXg6L59+8b6pvqdAnT9U/9Uqyipv6kwVv3XVO25cOFCN4vd32fV+1VvrVyggRQ6lwZe6583qEFhfD4DdAX3GlTsfe/WAAH1vf39hwEDBri+nYqW+NdgcW8Wuj53+jypf6V21FZv3lZSOu8rr7wS608VWoCu+qiPMXnyZFc39Wn0s7bD8lbQ2m677Vx7eyVogC4f7a/uXxZepnpmof6Z+qfqH+sz/69//Sv23wDZq739xf9Z1t9306ZN3d+vfq8BOHq+4N+PXoMC9DejAfIUBBBAAAEEECidAAF66fx4NwIIIFBhBPzBVdCbVsddnchLL700497j3jnVof/rX/8a61QnXkvn1EMhPYhSpy5TgK73a7S2ZmukKolhph5EaO/0efPmpXyPgn2NmH/iiSdiD5nyFaBrP209zNLybamKBiRoVoqOK68AXfemveq1jHimPet1bKrl+/WaHuroYZ2WJPQvq5iq/nqAoNk2egiWWHIN0HUe7TWZbIsB7xqJgxT0EEjhv/fgMNn9apCBZpDo4Yk3074QA3QNgtFDNw2CSVW8gSh6PZsAXcdrFQKtHqGHvUGKBmJMmTIlbmlL/xYLmc6hAQsaZKOBGxQEEEAAAQQQQKC8BEoToGvQrALf+++/P2X/SPVSGKZBvRrsma5odR8NEvX6UInHJlvaXftsX3/99XGhr/99+l6uIFXheZDgWWG3N8g03TLe2bSXzqfBn95g68T3KqhVPa688spA9xikHv5rBA3Q9R6FolpZwAuK09VTs+vVl05W9N1d59FMYW9We7pzKWDXZ8Rbbc1/bLb19d6rwRuaDa/ANVVJHEjx7LPPutWivK2WEt+ngbZqJ7WXBqxn+qwEsffP6A6yJHrQoFv9Jq3MoP57spI44DvoeXUu9btkoH8amJ6pKLRXX1b9TH/Rtg1aITBI0UBwraqmCQUUBBBAAAEEECi9AAF66Q05AwIIIFAhBNIF6BpRrhHXmgWqPdW0tLdGR2tUvEZdZ1v0IEUPCxRWKmzTTIdtttnGLTGo4FjnV9HI6SABuo7Vst9aDk6htB4c+UPaZGGmHkho2TQth6hR2erga09lzQ7Q8mZdunRxMzWC7Fmdbf11vGZsa/l4dWD10ECdZnV+NStbwbE3UzpIWBzkGP89Zlsnde41s1z/FCjLV7M69JnQ6gEKQhV8KuzXAIh0RbO59VBFs8H1QELBtCx0Ls1ul79m3Wj5bi1BmaxkW1//OVQXDYzQEuKaOa/rezPedVyyWf6aia6HmtoewBt0obbSAxMtFent3+dfqr4QA3TVT+2mpSRVF8180aoS8vdKaQJ0nUOzgEaOHOlmakyfPt3N3tdnW7Mh9Deu1R70EErtq5nliUXX1yx1rX7w0UcfuXvUDBzdt7Yz0GdCD3z0fv2d6nNDQQABBBBAAAEEylOgNAG6d98a4KslsrVUs7arUV9Fs9E1G1UDfxVIJtvbOlm9NTNZ/ZyJEye6fo5msnozglPtja5tk7Q9lwbuelvsaKazVg/r2bOn+w6nEiSIDSNA17X1vVXfyRVQa+Uq1Un3qD7kRRddFAsFg9xjkGP8tkFCXP/x6l9oZaoJEya477b6Tqzv3GpTLQOuwdqdO3eOW/Ut1WdYbai+i7ZK05ZZXpirmcSa2a0Zx+o7qq38K2v5z5dtff3vVYiuQatyV79V7eAPx5OtRKBZ0vfee6/7Tq+BBPoer76AZqerv++tdhfksxLEPqwAXQ66fw3qVv9V/qqvN+O/NAG6Z6z29PrHWp1A/VP1qdTP2X777d3qC+ofa5usZP8N0OdKM/v1t6v+lz4vCtT1GdTnQTPmtYpap06d3L9kS7yX538/uTYCCCCAAAIVWYAAvSK3HveOAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJA3AQL0vFFyIgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBiixAgF6RW497RwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDImwABet4oORECCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQEUWIECvyK3HvSOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII5E2AAD1vlJwIAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAiCxCgV+TW494RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPImQICeN0pOhAACCCCAAAIIIIAAAggUrsCKFStswoQJNn36dJsxY4YtWrTIli1bZqtXr7batWvbXnvtZe3atbOePXta3bp1M1bkgw8+sMGDB9vUqVNt6dKlVqdOHdtvv/2sR48e1q1bt4zv9w4YOXKkPf300/avf/3Lfv75Z2vYsKEdeeSRdvHFF9shhxwS+DwcLaw3cQAAIABJREFUiAACCCCAAAIIIIAAAggggAACCORDgAA9H4qcAwEEEEAAAQQQQAABBBAocIHXX3/dWrVqlfEu69WrZ//4xz+sTZs2KY/929/+Zrfccott2LAh6TEnnniijRo1yjbddNOU5/j999+tS5cuNm7cuKTHVK5c2W6++Wa78cYbM94zByCAAAIIIIAAAggggAACCCCAAAL5EiBAz5ck50EAAQQQQAABBBBAAAEEClhAAfq5555rLVq0sGbNmtl2221njRo1ciH4woUL7YUXXrCXXnrJ1q9fb9WrV3cz1f/yl79sVKMnn3zSzj//fPf7Jk2a2HXXXWf77ruvLV682B544AF766233Gunn366C+JTFb0+fPhw97LuqW/fvta4cWP77LPPbMCAATZ37lz32hNPPGHnnXdeActyawgggAACCCCAAAIIIIAAAgggECUBAvQotSZ1QQABBBBAAAEEEEAAAQRSCCgYr1KlSlqfMWPGWKdOndwxJ598sr344otxxy9fvtx22mkn0/9uv/32NnPmTNOMda/oGnr/2LFj3a/eeecdO+qooza6pn5/zDHHuN9rtvo///nPuHv78ccfXcj/3XffuaXh582b55aZpyCAAAIIIIAAAggggAACCCCAAAJhCxCghy3M+RFAAAEEEEAAAQQQQACBCiSw55572pw5c1wwrj3S/eWuu+6yfv36uV+NGDEi6V7nms2+4447upns7du3j4Xp/vOccMIJbj92Bfr/+c9/bNttt91ISHujd+/e3f3+7rvvtiuuuKICKXKrCCCAAAIIIIAAAggggAACCCBQUQUI0Ctqy3HfCCCAAAIIIIAAAggggEAIAgceeKCbWV6rVi379ddf465w+OGH2/vvv29bbLGFC9e11Huy0rZtW5s8ebJtsskmptnkOpdXfvvtNxfO//HHH6bjJk6cmPQca9assfr169uKFSvssMMOs/feey+E2nJKBBBAAAEEEEAAAQQQQAABBBBAIF6AAJ1PBAIIIIAAAggggAACCCCAgBOYPXu2289cs8cVpGsfdK8o0N5ss81s3bp11qZNG5s0aVJKtdtvv93tja7y5ptvuj3OvaKfjz32WPejjrvmmmtSnkfXefXVV61q1aq2atUqq1atGi2FAAIIIIAAAggggAACCCCAAAIIhCpAgB4qLydHAAEEEEAAAQQQQAABBApbQMH0okWL3FLrAwcOtO+//97d8HPPPWdnnHFG7Oa/+OIL22effdzPffv2tfvvvz9lxbSnufZQVxk0aJBddNFFsWP1c58+fdzPOq5jx44pz6PrPPjgg+51XX+vvfYqbEzuDgEEEEAAAQQQQAABBBBAAAEEKrwAAXqFb0IqgAACCCCAAAIIIIAAAmUhoJnXS5cuDf1SS5YscUuXa9Z1upJs3/CgNzd06FA755xzUh5+5ZVXujC9UqVKsWM04/z44493P2svdB2TqsyYMcMOOugg97JmmGumuVf085133ul+1Ax3zXRPVbT3+VVXXeVe1vU1I52CAAIIIIAAAggggAACCCCAAAIIhClAgB6mLudGAAEEEEAAAQQQQACByAgsXLjQtttuu4KpT0lJSc73kipAb9q0qT366KPWvHnzjc49evRo69q1q/v9I488Yr179055fS0F780W12zzhx56KHbsxRdfbIMHD3Y/67g99tgj5Xl0HW/2+gsvvGCdO3fOuc68EQEEEEAAAQQQQAABBApL4Oeff46tOHXZZZfZFltsUVg3yN0ggEDRChCgF23TU3EEEEAAAQQQQAABBBDIRiBKAfry5ctN9VFZvXq1zZ0710aNGuWWVG/SpIlbnr19+/ZxPFrS/ayzznK/e+qpp+zcc89NyTdv3jx3HpWePXvak08+GTtWPw8ZMsT9rOvuvPPOKc+j43S8SuKS8tm0HccigAACCCCAAAIIIIBA4Qn079/fbr31Vndjt912m1133XWFd5PcEQIIFKUAAXpRNjuVRgABBBBAAAEEEEAAgWwF/AH64IZVrH76FdazPb0tW2d20dL17n3Tpk2zRo0apT1HaZZwT3VihdRnn322W7pdIXmPHj1ihzIDPesm5Q0IIIAAAggggAACCCCQQuC///2vbV+vrq3838JaW221lX377bfMQucTgwACBSFAgF4QzcBNIIAAAggggAACCCCAQKEL+AP0F7arYg2q/v/+4Pm49x/WldgpC/4M0BcsWGBhBORB7vPUU091s9E322wzdx916tRxb2MP9CB6HIMAAggggAACCCCAAAJBBLzZ51tUNltfYi5IZxZ6EDmOQQCBshAgQC8LZa6BAAIIIIAAAggggAACFV4gLkDfPqQA/bvyD9CHDx9up59+umuvYcOG2Wmnneb+/+eff2777ruv+/99+/Z1y7ynKloK/uSTT3YvDxo0KLaPuX5++OGH7a9//at7Tcd17Ngx5Xl0nQcffNC9/sUXX8T2Va/wHyYqgAACCCCAAAIIIIBAEQv4Z5+fV7uyrSkpsWd/KTFmoRfxh4KqI1BgAgToBdYg3A4CCCCAAAIIIIAAAggUpoA/QH9xh3AC9M7zyz9Af+2116x169auEQYMGGDXXnut+/9r1qyxmjVr2vr1661NmzZuRnqqcvvtt8f2L3zzzTetRYsWsUP187HHHut+1nHXXHNNyvPoOq+++qpVrVrVVq5cadWrVy/MDwd3hQACCCCAAAIIIIAAAoEF/LPPR25Txc1A77ZoPbPQAwtyIAIIhC1AgB62MOdHAAEEEEAAAQQQQACBSAjEBeg7hhSg/6f8A/ShQ4faOeec49pMs7+92eL6+bDDDrMPPvjA7Uu4bNmylIF227ZtbfLkybbJJpu44zbffPPYZ+DXX3+1evXquUBex02cODHp50Ov169f31asWGGHHnqovf/++5H4HFEJBBBAAAEEEEAAAQSKWSBx9vmZW1Z2HE8tX88s9GL+YFB3BApMgAC9wBqE20EAAQQQQAABBBBAAIHCFPAH6C8pQK+W5z3Q15bYyQUQoJ9wwgk2YcIE1whvvfWWHXPMMbEGGThwoF199dXu5xEjRli3bt02aiw57bjjjm6mert27Wz8+PEbHaPfKzjXzPJvv/026X7vI0eOtO7du7v36rpXXXVVYX4wuCsEEEAAAQQQQAABBBAILJA4+3yzyn/2q1asL2EWemBFDkQAgbAFCNDDFub8CCCAAAIIIIAAAgggEAmBih6ga2a5Au9NN900ZXvcd999dvnll7vXFYJ//fXXLuT2imaL7LzzzvbLL7/YDjvsYDNnzrS6devGXldo3qlTJxs7dqz7XeLy7d6B/mXcTzrpJHvppZesSpUqsfP8+OOP1qxZM/vuu++sdu3aNm/ePKtTp04kPkdUAgEEEEAAAQQQQACBYhVINfvc82AWerF+Mqg3AoUnQIBeeG3CHSGAAAIIIIAAAggggEABCvgD9H/uFM4M9E7fhreEuwJxLZ/euXNnO+KII6xJkyZWq1Yt97vPPvvMhg0bZu+9956T117jmjl+3HHHbdQSjz32mPXu3dv9Xue4/vrrbd9997XFixfb/fff72atq2j2+PDhw1O2pF7XLHMV7ZF+6aWXWuPGjd293HbbbTZ37lz32qOPPmq9evUqwE8Et4QAAggggAACCCCAAALZCKSafe6dg1no2WhyLAIIhClAgB6mLudGAAEEEEAAAQQQQACByAjEBeg7hxSgzws3QJ8/f37G9th2221tyJAh1qpVq5TH3nTTTXbrrbdaSUlJ0mO0RPuLL76Ydrb76tWr7ZRTToktF594osqVK9uNN95oN998c8Z75gAEEEAAAQQQQAABBBAobIFMs8+9u2cWemG3I3eHQLEIEKAXS0tTTwQQQAABBBBAAAEEECiVgD9AH9Okaih7oHecu87d44IFC5LuC16aCmhG9+uvv+5miM+ePdu+//57++mnn1zIvfXWW1vTpk2tffv21rVrV6tZs2bGS73//vs2aNAgmzp1qjuXllrfb7/97JxzzontXZ7xJGZulrqWl//0009t+fLl7l6OPPJI69Onjx166KFBTsExCCCAAAIIIIAAAgggUOACmWafe7fPLPQCb0huD4EiESBAL5KGppoIIIAAAggggAACCCBQOoG4AH2XkAL0b8IL0EtXe96NAAIIIIAAAggggAACCOQmEHT2uXd2ZqHn5sy7EEAgfwIE6Pmz5EwIIIAAAggggAACCCAQYQF/gP7yruEE6B2+JkCP8EeIqiGAAAIIIIAAAgggUJQCQWefezjMQi/KjwmVRqCgBAjQC6o5uBkEEEAAAQT+j703AZeyuPL/Cy4XERV1DK64RIkRiKKjmYgCiiIYxfziFnWSOBo3EjNxJtExrtG4xsSIiRiZMdH5OZr8XXD+BqOIAZW4o4krioJG4k7cEBeay/0953Sd6lPV9Xa/3fd9+/bt/r7Pk3Bvv/XW8qnq63vOt84pEAABEAABEGhWAp6Avm1OAvoiCOjNOv/oFwiAAAiAAAiAAAiAAAiAQO0Eao0+lxYaHYX+wQcfmD/84Q/m0UcfNQsWLDCvvvqqefvtt83HH3/Mx1WNHDnS7LfffuaYY44xG2ywQVUQDz74oLnyyiv5yKs33njDrL/++nzk1VFHHWUOP/zwqs9Lgd/97nfmmmuuMU8++aR59913zcYbb8xHXp144olm1113TV0PCoIACNRGAAJ6bbxQGgRAAARAAARAAARAAARAoE0JaAH9ts/nI6B/5XkI6G26vDBsEAABEAABEAABEAABEGhJArVGnwuERkeh33333WafffapOgef+cxnzP/8z/+YyZMnJ5b98Y9/bM4991yzevXqaJkDDjjA3HjjjWbQoEGJdXzyySfm0EMPNbNmzYqW6d+/vznnnHPMWWedVbXPKAACIFA7AQjotTPDEyAAAiAAAiAAAiAAAiAAAm1IwBPQt8tJQH8OAnobLi0MGQRAAARAAARAAARAAARakkC90ecCo5FR6CSgf+tb3zITJkwwO++8s9l8883NJptswiI42YI333yzmTlzpunq6jIDBw7kSPUddtihbN6uvvpqc9xxx/Hn22yzjTn99NPN9ttvb1577TVz+eWXm3nz5vG9r3/96yzEJ110/4YbbuDb1KeTTjrJbLrppuapp54yF154oVm8eDHf+6//+i9z7LHHtuT6waBAoDcJQEDvTfpoGwRAAARAAARAAARAAARAoM8QCAX0jQb2y7Tvb67sNl+BgJ4pU1QGAiAAAiAAAiAAAiAAAiDQewTqjT6XHjcyCp2E8Y6Ojoqw/vd//9cceOCBXOaggw4yt9xyi1f+vffeM5/97GcN/bvFFluYxx57zFDEulzUBj3/+9//nj+69957zfjx48vapM/33HNP/pyi1W+99Vavb8uWLWOR/5VXXuHU8EuWLOE087hAAASyIwABPTuWqAkEQAAEQAAEQAAEQAAEQKCFCXgC+ogBJhcBfSEi0Ft4CWFoIAACIAACIAACIAACINA2BHoafS6gGhmFnmZyRowYYZ577jkWxumMdH399Kc/Nf/xH//BH/32t7+NnnVOduVWW23FkexTpkxxYrquZ//99+fz2EnQf/nll82wYcPKukZnox9xxBH8+c9+9jPzgx/8IE33UQYEQCAlAQjoKUGhGAiAAAiAAAiAAAiAAAiAQHsT0AL670fmI6Af8CwE9PZeZRg9CIAACIAACIAACIAACLQGgZ5GnwuFRkahpyG/yy67cGT52muvbZYvX+49svvuu5sHHnjADBkyhMV1SvUeu/bdd18ze/Zss8YaaxiKJqe65Prwww9ZnP/0008NlbvjjjuidaxcudIMHTrUfPDBB2a33XYz999/f5ruowwIgEBKAhDQU4JCMRAAARAAARAAARAAARAAgfYmAAG9vecfowcBEAABEAABEAABEAABEEhHIKvoc2mtWaLQFy5cyOeZU/Q4Cel0DrpcJGivtdZaZtWqVWby5MnmzjvvTIR10UUX8dnodM2dO5fPOJeLft977735Vyr3wx/+MLEeaueuu+4yAwYMMB999JHp7OxMN0EoBQIgUJUABPSqiFAABEAABEAABEAABEAABEAABIzRAvqsUflEoE95BhHoWGsgAAIgAAIgAAIgAAIgAAJ9m0BW0edCoTej0EmYfvXVVznV+iWXXGLefPNN7tZ1111nvvGNb7iJeuaZZ8wXvvAF/v2kk04y06ZNS5xEOtOczlCna/r06eY73/mOK0u/f/e73+XfqdxXv/rVxHqonV/84hd8n9ofOXJk31446D0INBEBCOhNNBnoCgiAAAiAAAiAAAiAAAiAQPMS8AT0L+QkoD8NAb15VwB6BgIgAAIgAAIgAAIgAAIgUI1A1tHn0p6OQv/Tn/5k1llnnWpdiZ4dXvUhY8y1115rjj766MSiJ598Movp/fr1c2Uo4vzLX/4y/05noVOZpGvBggXmi1/8It+mCHOKNJeLfv/JT37Cv1KEO0W6J1109vkpp5zCt6l9ikjHBQIgkA0BCOjZcEQtIAACIAACIAACIAACIAACLU5AC+i3b5+PgL7/UxDQW3wZYXggAAIgAAIgAAIgAAIg0NIEso4+F1g6Cj0twO7u7rRFvXJJAvqOO+5orrrqKvOlL32prN6bbrrJfO1rX+PPf/WrX5mpU6cmtk2p4CVanKLNf/nLX7qyJ554ornyyiv5dyq33XbbJdZD7Uj0+s0332wOPvjgusaLh0AABMoJQEDHqgABEAABEAABEAABEAABEACBFAQ8AX2HnAT0JyGgp5gKFAEBEAABEAABEAABEAABEGhSAhRZTRHW3xjSzxy3fkemvZz+Tpe5cXl6UbxeAf29997jI7zo+vjjj83ixYvNjTfeyCnVt9lmG07PPmXKFG9slNL9yCOP5M9+/etfm29961uJY1+yZAnXQ9cxxxxjrr76aleWfv/Nb37Dv1O7W2+9dWI9VI7K0xWmlM8UPCoDgTYkAAG9DScdQwYBEAABEAABEAABEAABEKidgBbQ/zA6HwF9vycgoNc+M3gCBEAABEAABEAABEAABECgWQiIgP6v6/c3hwzpn2m3bnh/tZnx3mpOfz5z5syqdQ8bNqxqmVoKkEj9L//yL5y6nUTyo446yj2OCPRaSKIsCDQ/AQjozT9H6CEIgAAIgAAIgAAIgAAIgEATEPAE9B1zEtD/AgG9CaYaXQABEAABEAABEAABEAABEKiTgBPQ/yEnAf3d1Wbs2LFm/vz5dfawZ48ddthhHI2+1lprmaVLl5r111+fK8QZ6D3jiqdBoNkIQEBvthlBf0AABEAABEAABEAABEAABJqSgBbQ7yABfY1+mfbzzU+7zZchoGfKFJWBAAiAAAiAAAiAAAiAAAg0loAI6N8jAX3djCPQ31ttruplAf2GG24wX//61xnq9ddfb/75n/+Zf3766afN9ttvzz+fdNJJnOY96aJU8AcddBDfnj59ujvHnH6/4oorzL/+67/yPSr31a9+NbEeaucXv/gF33/mmWfcueqNnXG0BgKtSQACemvOK0YFAiAAAiAAAiAAAiAAAiCQMQFPQN8pJwH9z4hAz3jaUB0IgAAIgAAIgAAIgAAIgEADCTgBfYOcBPR3ejcCfc6cOWbSpElM9MILLzSnnXYa/7xy5UozePBg09XVZSZPnswR6UnXRRddZE4//XS+PXfuXDNhwgRXlH7fe++9+Xcq98Mf/jCxHmrnrrvuMgMGDDArVqwwAwcObOBMoykQaG0CENBbe34xOhAAARAAARAAARAAARAAgYwIaAH9zn/szCUCfd/HC9xbSgWY9Xl9GWFANSAAAiAAAiAAAiAAAiAAAiCQSEAE9JM+098csl62EejXv7vaXPX33hXQr732WnP00Ufz+Cn6W6LF6ffddtvNPPjgg2bIkCHm7bffThS09913XzN79myzxhprcLl11lnH8Vy+fLn5zGc+w4I8lbvjjjuirOn+0KFDzQcffGDGjBljHnjgAaxKEACBDAlAQM8QJqoCARAAARAAARAAARAAARBoXQKegL5zTgL6YxDQW3cFYWQgAAIgAAIgAAIgAAIg0PoEnIA+tL85NGsB/Z3V5le9LKDvv//+5g9/+ANP5Lx588yee+7pJvWSSy4xp556Kv/+29/+1hx++OFlE0525VZbbcWR6vvtt5+5/fbby8rQ5yScU2T5Sy+9FN1c/bvf/c4cccQR/Cy1e8opp7T+4sIIQaCBBCCgNxA2mgIBEAABEAABEAABEAABEOi7BLSAPnuXfAT0yQsgoPfdFYKegwAIgAAIgAAIgAAIgAAI9FUBnSLLSfAeNGhQ4iRedtll5vvf/z7fJxH8hRdeYJFbrnfeecdsvfXW5v333zdbbrmleeyxx8wGG2zg7pNofuCBB5rf//73/FmYvl0K6jTuX/nKV8zMmTNNR0eHq2fZsmVm5513Nq+88opZb731zJIlS8z666+PxQcCIJAhAQjoGcJEVSAAAiAAAiAAAiAAAiAAAq1LwBPQv5iTgP4oBPTWXUEYGQiAAAiAAAiAAAiAAAi0PgER0P+NItDXzziF+zurzZXL8knhToI4pU8/+OCDzdixY80222xj1l57bf7sqaeeMtdff725//77eQLprHGKHJ84cWLZhM6YMcNMnTqVP6c6zjjjDLP99tub1157zUybNo2j1umi6PEbbrghcUHQfYoyp4vOSP+3f/s3s+mmm3JfLrjgArN48WK+d9VVV5kTTjih9RcWRggCDSYAAb3BwNEcCIAACIAACIAACIAACIBA3ySgBfS7chLQJ0FA75uLA70GARAAARAAARAAARAAARBgAk5A3zAHAf3v+Qrof/3rX6vO4rBhw8xvfvMbs88++ySW/dGPfmTOO+88093dHS1DKdpvueWWitHuH3/8sTnkkENcuviwov79+5uzzjrLnHPOOVX7jAIgAAK1E4CAXjszPAECIAACIAACIAACIAACINCGBDwB/Z/yiUCf9Agi0NtwaWHIIAACIAACIAACIAACINAyBLSA/rV/KKUdz2KA//P3LnPl2/lEoFNE9913380R4gsXLjRvvvmm+fvf/84i90YbbWR23HFHM2XKFPO1r33NDB48uOpwHnjgATN9+nQzf/58rotSrY8ePdocffTR7uzyqpUYw1HqlF7+iSeeMO+99x73Zdy4cea73/2uGTNmTJoqUAYEQKAOAhDQ64CGR0AABEAABEAABEAABEAABNqPgCegf6nTbLxGv0whvPFpt5n0MAT0TKGiMhAAARAAARAAARAAARAAgYYScAL6Rv1NLgL6W/kI6A2FhMZAAASangAE9KafInQQBEAABEAABEAABEAABECgGQh4AvquOQnoD0FAb4a5Rh9AAARAAARAAARAAARAAATqIyAC+r+TgL5BxhHoy7rMdAjo9U0MngIBEKiJAAT0mnChMAiAAAiAAAiAAAiAAAiAQLsS0AL6HBLQB2Ucgf5Jt9kHAnq7Li+MGwRAAARAAARAAARAAARagoAT0DfOSUB/ExHoLbFQMAgQaHICENCbfILQPRAAARAAARAAARAAARAAgeYg4AnoY3IS0B9EBHpzzDZ6AQIgAAIgAAIgAAIgAAIgUA8BJ6Bv0t8clnEE+nVvd5npENDrmRY8AwIgUCMBCOg1AkNxEAABEAABEAABEAABEACB9iSgBfS7d8tHQJ/4AAT09lxdGDUIgAAIgAAIgAAIgAAItAYBT0D/TLYp3FlAfwMR6K2xUjAKEGhuAhDQm3t+0DsQAAEQAAEQAAEQAAEQAIEmIeAJ6LvnJKDfDwG9SaYb3QABEAABEAABEAABEAABEKiDgAjo39+0vzksawH9rS5zBQT0OmYFj4AACNRKAAJ6rcRQHgRAAARAAARAAARAAARAoC0JeAL62JwE9D9BQG/LxYVBgwAIgAAIgAAIgAAIgECLEPAE9KEZR6CTgP46ItBbZKlgGCDQ1AQgoDf19KBzIAACIAACIAACIAACIAACzUJAC+h/HJePgL73fAjozTLf6AcIgAAIgAAIgAAIgAAIgEDtBERA/8Fm/c1hGQvo//dNCOi1zwieAAEQqIcABPR6qOEZEAABEAABEAABEAABEACBtiPgCejjcxLQ74OA3nYLCwMGARAAARAAARAAARAAgRYioAX0wzfMNgKdBPRfvoYI9BZaLhgKCDQtAQjoTTs16BgIgAAIgAAIgAAIgAAIgEAzEdAC+tw98hHQ97oXAnozzTn6AgIgAAIgAAIgAAIgAAIgUBsBJ6AP628yF9DfgIBe22ygNAiAQL0EIKDXSw7PgQAIgAAIgAAIgAAIgAAItBUBCOhtNd0YLAiAAAiAAAiAAAiAAAiAQB0EREA/efPsBfT/JgH9VUSg1zEteAQEQKBGAhDQawSG4iAAAiAAAiAAAiAAAiAAAu1JQAvo8/bMJwJ9wj2IQG/P1YVRgwAIgAAIgAAIgAAIgEBrEPAE9I2yTeH+369DQG+NVYJRgEDzE4CA3vxzhB6CAAiAAAiAAAiAAAiAAAg0AYEyAX3Nfpn26o2Puw0E9EyRojIQAAEQAAEQAAEQAAEQAIEGE3AC+hb9zRE5COi/+Bsi0Bs8pWgOBNqSAAT0tpx2DBoEQAAEQAAEQAAEQAAEQKBWAlpAv2fCQLNxDgL6nvNWcreWLl1qhg0bVmsXUR4EQAAEQAAEQAAEQAAEQAAEepWAJ6BvnH0E+i+WQkDv1QlG4yDQJgQgoLfJRGOYIAACIAACIAACIAACIAACPSPgCeh75SSgz4WA3rNZwtMgAAIgAAIgAAIgAAIgAAK9SUAE9FO27G+OyFhAv/a1LgMBvTdnF22DQPsQgIDePnONkYIACIAACIAACIAACIAACPSAgBbQ752Yj4C+x90Q0HswRXgUBEAABEAABEAABEAABECglwl4Avom2Uags4D+CiLQe3mKpyjZAAAgAElEQVSK0TwItAUBCOhtMc0YJAiAAAiAAAiAAAiAAAiAQE8JaAH9vn3yEdDHz4GA3tN5wvMgAAIgAAIgAAIgAAIgAAK9R8AJ6Ft1mH/OWkB/tctc/kqXGTt2rJk/f37vDRItgwAItDwBCOgtP8UYIAiAAAiAAAiAAAiAAAiAQBYEPAF9Uk4C+l0Q0LOYK9QBAiAAAiAAAiAAAiAAAiDQOwScgP7ZnAT0v0JA752ZRasg0F4EIKC313xjtCAAAiAAAiAAAiAAAiAAAnUS8AT0yQPNJmv2q7Om+GOvf9xtxs+GgJ4pVFQGAiAAAiAAAiAAAiAAAiDQUAIioP8HCeibZpvC/Zq/dZnLIaA3dD7RGAi0KwEI6O068xg3CIAACIAACIAACIAACIBATQQ8AX3fnAT0OyGg1zQpKAwCIAACIAACIAACIAACINBUBJyAvnVOAvrLiEBvqglHZ0CgRQlAQG/RicWwQAAEQAAEQAAEQAAEQAAEsiWgBfT5JKAPzjgC/aNuMw4CeraThtpAAARAAARAAARAAARAAAQaSkAL6F/PIQJ9GgT0hs4nGgOBdiUAAb1dZx7jBgEQAAEQAAEQAAEQAAEQqImAJ6B/OScB/Q5EoNc0KSgMAiAAAiAAAiAAAiAAAiDQVAQ8AX2zjFO4L+0yENCbarrRGRBoWQIQ0Ft2ajEwEAABEAABEAABEAABEACBLAloAf1PX14jlwj0sXd8yl1eunSpGTZsWJbdR10gAAIgAAIgAAIgAAIgAAIgkDsBEdBP3abDfH1YtgL6b17pMtNeQgr33CcRDYAACBgI6FgEIAACIAACIAACIAACIAACIJCCgCeg75eTgP4HCOgppgJFQAAEQAAEQAAEQAAEQAAEmpSACOg/HJ6PgH7ZEgjoTTr16BYItBQBCOgtNZ0YDAiAAAiAAAiAAAiAAAiAQF4EtIB+//75COi73w4BPa/5Q70gAAIgAAIgAAIgAAIgAAL5E3AC+udyEND/2mUgoOc/h2gBBEDAIAIdiwAEQAAEQAAEQAAEQAAEQAAE0hDwBPQpg3JJ4b77rE+4K0jhnmZGUAYEQAAEQAAEQAAEQAAEQKDZCGgB/RubZ5zC/a9d5ueLEYHebHOO/oBAKxJABHorzirGBAIgAAIgAAIgAAIgAAIgkDkBLaA/cEA+Avpuv4eAnvnEoUIQAAEQAAEQAAEQAAEQAIGGEXAC+rYdJhcB/UUI6A2bTDQEAm1MAAJ6G08+hg4CIAACIAACIAACIAACIJCegCegfyUnAf02COjpZwQlQQAEQAAEQAAEQAAEQAAEmo2AJ6BvkXEE+std5ucQ0JttytEfEGhJAhDQW3JaMSgQAAEQAAEQAAEQAAEQAIGsCWgB/cH/QwJ6/0ybeP2j1WbM/w8BPVOoqAwEQAAEQAAEQAAEQAAEQKChBERAP+3zHeYbGQvovyYB/QVEoDd0QtEYCLQpAQjobTrxGDYIgAAIgAAIgAAIgAAIgEBtBDwB/as5Cej/CwG9tllBaRAAARAAARAAARAAARAAgWYi4AnoW2Ybgf7rlyCgN9Ncoy8g0MoEIKC38uxibCAAAiAAAiAAAiAAAiAAApkR8AT0A3MS0G+FgJ7ZhKEiEAABEAABEAABEAABEACBhhNwAvp2HeabOQjoly5CBHrDJxUNgkAbEoCA3oaTjiGDAAiAAAiAAAiAAAiAAAjUTkAL6A8duKbZZK2MU7ivWG12vfVj7tjSpUvNsGHDau8kngABEAABEAABEAABEAABEACBXiQAAb0X4aNpEACBzAhAQM8MJSoCARAAARAAARBoVgIrX97PDNzqD83aPfQLBECgjxDwBPSDchLQZ0JA7yPLAd0EARAAARAAgZYiUHhyHI+nc4f5LTUuDAYEQKDxBERAP50i0LfKNoX71Uu6DCLQGz+naBEE2pEABPR2nHWMGQRAAARAAATaiACJ55UuLaxLWYjtbbRAMFQQqIGAFtAfPjgfAf1Lt0BAr2FKUBQEQAAEQAAEQCAjAoWnxhdrGjDAmO5uY/rbTDvd3aZzxFyvlcLzE03n5+/OqGVUAwIg0GoEnIA+IicB/XmkcG+1NYPxgEAzEoCA3oyzgj6BAAiAAAiAAAhkRgACemYoUREItD0BT0A/ZHAuKdy/dPNHzBkp3Nt+uQEACIAACIAACDSUQGHhXqX2AgGdxHQRzEk856ufMZ3bQkRv6CShMRDoIwScgD6ywxy51YBMe3314lXmZxDQM2WKykAABOIEIKBjZYAACIAACIAACLQ0gZUvfbk0vn79itEU7PDpx/9ItHkotCMKvaWXBQYHAnUR0AL6I4fmI6D/000Q0OuaHDwEAiAAAiAAAiDQIwIsoFPUedFMKirkfBXtJxHLC4usgG7vQkTvEXY8DAItScAT0D+bg4D+HCLQW3LhYFAg0GQEIKA32YSgOyAAAiAAAiAAAtkRIPF84GfvMJ6IHlRP9+lKilSHkJ7dfKAmEOjrBDwB/Ws5Ceg3QkDv6+sE/QcBEAABEACBvkRAIs8pTTuncacU7h1yZjGJ50UhvXPbOfxvYdE+dnh2Y7IS1/vSuNFXEACB/AiIgH7GqA5z5NbZCuj/9eIq87OFENDzmz3UDAIgIAQgoGMtgAAIgAAIgAAItBSBlUv2LUVLkK9H/DoukqJ8uE5El2h1G51OJSGgt9TywGBAoEcEPAH9sLXMpmvZs0F7VGvp4ddWrDb/9P+t4A+Qwj0jqKgGBEAABEAABEAgSoBTsZON1LW6eJ+iz1evLv1LmbtITLe2Uefn7uJiRQHdGln0D6d774d07lhnIAACjoAI6Gd+IQcB/YVV5qcQ0LHaQAAEGkAAAnoDIKMJEAABEAABEACBfAkURfPwiijmCSJ6mYAeVCX38x0FagcBEGh2Ap6AfnhOAvrv8hXQH3/8cXPnnXea+fPnm6efftq89dZbprOz02y66aZmt912M8ccc4wZN25cxam49tprzdFHH51quq655hpz1FFHVSz70UcfmenTp5ubbrrJvPjii2blypVm8803N/vvv7/53ve+Z7bYYotUbaEQCIAACIAACIBAZQKF5/YuFuivDaN+ReH844+N6d9hzBoDnWheLNvfmO7VpRTuL0wq1sFHY5VHqGMOQAAEQMAT0LfJOAKdBPRnEYGOVQYCIJA/AQjo+TNGCyAAAiAAAiAAAjkQKCyeXPTbqGjx8mYqhJ3bwgO3tincl9iz0iOPaIEdYnoOk4kqQaCPENAC+qNH5COgf/G3+Qnoe+yxh7nvvvuq0v7mN79prr76ajNw4MBo2SwF9MWLF7NQ/vzzz0fbWnfddc0NN9xg9ttvv6r9RgEQAAEQAAEQAIFyAk4017dYQA8Mn+XLjRk8uCiYd5Rn2ekcLhHodAZ6v6LITiI6/a+fPiO9mOJdUr5jTkAABNqPgBPQt+8w/5KxgP6fJKA/AwG9/VYVRgwCjScAAb3xzNEiCIAACIAACIBADwmIeC7VJIvoksO9upAe7ZI8FqSBh4jewwnE4yDQRwn4AvraZtO1M07h/uFq88Xffsh08kjhPnz4cEOCNUWbH3rooRxpTtHdXV1d5sEHHzSXXnqpefXVV7n9I444goXr2KUF9NmzZ3N9SdewYcPMeuutF7394YcfGnKuPffcc3z/uOOOM4cffrhZc801zbx588xFF11kqMzgwYO5fzvssEMfXTnoNgiAAAiAAAj0DgE+31w2HLt/qS/W0BERnCLJKZW7nH2uI9QlyJyi1LVwLkPiOooR6qXz0Ys3IaL3zryjVRDobQJOQN8hBwF9EQT03p5ftA8C7UIAAnq7zDTGCQIgAAIgAAItRCAU0GloMRG9X3d38HmNQnoooIuvyQrqEr3eQmgxFBAAgQoEtIC+4J/zEdB3uSE/AX3KlCnmyCOPNAcffLDpEAe5Gu+yZcvM7rvvbhYtWsSfUrR6LJ27FtBfeukls9VWW9W1bs455xxz7rnn8rOXXHKJOeWUU7x6SDQfP368WbVqlZkwYYKZO3duXe3gIRAAARAAARBoRwIsntNFEeVysX2jxHM2pLqNWbWqKLTHBHQqw+K5elYDFRHeifE+bYjo7bj6MOZ2JyAC+lkkoA/PNoX7fy5aZS55GhHo7b7GMH4QaAQBCOiNoIw2QAAEQAAEQAAEMiMQE8/DyiunddeOHwktT+qeOJfs8X5BMQjomU0rKgKBPkHAE9C/npOAfn1+AnoayLNmzTIHHHAAF6Xzxy+//PKyx7IQ0AuFgtlwww3Ne++9Z0aMGMHnsffXDn7b6tSpU82MGTP4twULFpidd945zTBQBgRAAARAAATamoAnnrukXGF2rtL55Qyrq8uYAQPKMrvzPW02ccr2YGMyn4dO2rz9XH5HFHpbr0MMvn0JOAF9dA4C+vMQ0Nt3ZWHkINBYAhDQG8sbrYEACIAACIAACPSQQBoBnR08yqdTXVBP6lTliHUI6D2cTDwOAn2MgBbQH/vGOrmkcN/5f5YzlTxSuKfBTSnT11lnHS5KZ5OToB5eWQjoc+bMMZMmTeKqL774YnPqqadGu/fQQw+ZMWPG8L3TTz/dXHDBBWmGgTIgAAIgAAIg0NYEWEAPN6Z5ojdFnXfZtO39/ehzTY6e0br76oh4LuVDEZ0+7+5GGve2XokYfLsSKAnoA8xRGUegz2ABfZUZO3asmT9/frsixrhBAAQaQAACegMgowkQAAEQAAEQAIFsCBRenOxXVOmI80BE1w+SoF6e3j3sY7J4nkY4X7nkyyZNuWzIoBYQAIFGEPAE9G/mJKBf17sC+jvvvGM22GADxkmR6LfddlsZ2iwE9LPPPtucd955XDelat91112jU0jp2+kM9RUrVnA693vvvbcRU402QAAEQAAEQKDPEig8t3ex7xXPPKcCNmrc2U1iXPHD/vNSRv9ri3FbEqFeY+S5nJmONO99drmh4yAQJeAJ6J/LNoU7C+hPQUDH0gMBEMifAAT0/BmjBRAAARAAARAAgYwIlAnoUm/ZWeWBep6ghdcjpKcRxUk8pytN2YzQoBoQAIEGENAC+uM5Cej/2MsC+q233moOOuggpklnktPZ5OGlBfQ99tjDLFy40Lz77rtmyJAhZvjw4WbixInm29/+ttlss80SZ+XQQw81N998M9+nZ0kkT7pGjx5tnnzySTN06FDz1ltvNWCm0QQIgAAIgAAI9F0CLKBL5DgPQ+065nPKVxtDkeQrPiqmbB+0RnlKdn4sMKJCm0v/HqR17/zcXVUBinhOBSGgV8WFAiDQpwiIgH72jgPMUVkL6M+tMj+BgN6n1gM6CwJ9lQAE9L46c+g3CIAACIAACLQhgUQB3bEIQyMCSEHEuhbQKSKdrmrp3gdufWci+ZVL9rX3St6kSuXbcAoxZBDo0wQ8Af3IIbmkcP/H//sBM3rkkUfMJptsUpHXsGHDMuW5evVqTpdObdP16KOPml122aWsDS2gJ3Vg0KBBZtq0aeaEE06IFqGI84cfftistdZahtLGV7qmTJlibr/9di7yySefmDXWWCPTcaMyEAABEAABEGglAoXnJwbnmAdC+OrVnFrdrFpVTPMu/yMILLyrc8x1FLtEmevqErJ+dQ6vLKBr8ZyahYDeSisQYwEBY0oCeqc5atuMI9CfK5ifPIkIdKwzEACB/AlAQM+fMVoAARAAARAAARDIgEDhRTorV4c5VDqfPCG3YJCVUHermnCuy8ZE8ZJ47g8WAnoGk48qQKBJCHgC+r/kJKD/d1FAT3N1qzSpacpXK3PppZeak08+mYsdeOCBZubMmdFHSECn9OsUqU6C++abb87llixZYm655RaOLJe+zZgxwxx//PFl9YwaNco8++yzZqONNjJvvPFGxa4ddthh5sYbb+Qyy5Ytcynmq40H90EABEAABECg3QiUiecUaR6ehd7VVfyMhHS5SCjvb+2rfv2Ln0pUebVjs6hsaJp1G5MUhe7Ec46GF1UeInq7rVWMt7UJOAF9p05zdMYC+lULIaC39urB6ECgeQhAQG+euUBPQAAEQAAEQAAEEggUxXPxzJScLF46Qu/ZWBldgCIr4o2JkC4R6ew7CtIXyr3ObWa7SpIEdN0KxHQscRDo2wS0gP7no/IR0He6tncEdDpbnFKv05njG264IadMJ3E7dr3//vucrr1fmNrVFp41axaL64VCwQwePNgsXrzYbLzxxl5V22yzDQvuJL6/8sorFRfGkUceaa677jous3TpUpN15H3fXpXoPQiAAAiAAAiUCJQJ6LyvOGL40GeFgjEdHUUhnf5lw6db2UlKVOd7wX5mMc/CCdCmGEWXq3TuhRcmeaI59y3YEIhodKxoEOj7BJyA/o85CehPIAK9768SjAAEmp8ABPTmnyP0EARAAARAAATamkBJPA8xJEWjW4+Nc/CE5ZTnJxJNUU1A18J6rQI6PaufaeuJxeBBoA8S8AT0o9fNJYX7Tte8z2QamcL9mWeeMePGjeOzyCk9+uzZsw2dbd6T64ILLjBnnnkmV3H++eebM844w6sOEeg9oYtnQQAEQAAEQMAnwMI5XWXp1cNzzNXvdBb6qq6iwN7R3xgdeV5mekl0ur2RlM49MjFlArqUqZBJByI6VjgI9G0CIqD/iAT0z3dmOpirni2Yi58omLFjx5r58+dnWjcqAwEQAAFNAAI61gMIgAAIgAAIgEBTEiiddx6EMHieodihe4GA7qIutJcnENFrJUApCYeXos/p8cLiydVroWZ1mkIVjVH9YZQAARDobQJaQP8LCejr2BSnGXXsteWrzY5WQG9UpPVLL73EzqfXXnvNdHR0mJtuuonTt/f0euuttzjqnFK577PPPuauu/yzUHEGek8J43kQAAEQAAEQKBJw4nkIJIw8D38nAV3MIkr17qLQKbW72mnM56LbCHRpI5KyPWk+EgV0eiBJRKfoeGX3dY6ah+kGARDoQwScgL5zpzl6u4wF9GcK5uK/QEDvQ8sBXQWBPksAAnqfnTp0HARAAARAAARamwAL6DqFoBflkOYsdC2khx4fYhdEUdSIs1IkeZmYLo4hckwFjqukswFr7A6KgwAINICAJ6B/KycB/TfFCPRGCOgkmlPkOaVSp3TsdLY5pUvP6qJU8G+//bYZOXKkoSh3fR1yyCF8XjpdFPm+3nrrJTY7evRoTik/dOhQQ8I8LhAAARAAARAAgSKBaOQ5n3tOtk6F6PMQIInpEoFOKd35URLOE8690uZYhcmoZOsUFk0stqlFdDqfnaLiqf8s3Bcb6hwxF1MOAiDQhwiIgH7OLgNzENBXmov+DAG9Dy0HdBUE+iwBCOh9durQcRAAARAAARBobQIudbt3nHmCA6dmFEHOwWoRFZH6KwroJP5bnxP/y81123/9yiCg1zx5eAAEeo2AFtCfOGa9XCLQR//6PR5f3gL6smXLOE37s88+y+1dccUV5sQTT8yULQne1E5MQD/77LPNeeedx+09+OCDhiLSYxedyU7i+ooVK8z48eMNndWOCwRAAARAAARAoEjAnXmexmZKEsOTYIqwXetzqr5qtk7Zmej0LIno/fqZzu3+aArP7c210c+4QAAE+g4BT0AfkXUE+kpz0eMQ0PvOakBPQaDvEoCA3nfnDj0HARAAARAAgZYmwAK65wii4WYloFNd5dHgDDR2nl8K0lpQd+nnvciMkoBezZGUojlXpJTqvvhRmFq+lrpQFgRAoDIBT0A/NicB/er8BfT333/f7LXXXubxxx/nAV988cXm1FNPzXT6dQr3iRMnmjlz5nj1U0r3yZOLm40qtf/QQw+ZMWPGcLnTTjvNXHjhhZn2E5WBAAiAAAiAQF8mwFHcYsOQ4N2/wvEy9QrhnBVMn52eYEelABlN5x6mcaffu7szFc15o4E+SguCfIrZQhEQqJ+AE9C/ONB8K2MB/VdPQ0Cvf2bwJAiAQC0EIKDXQgtlQQAEQAAEQAAEGkrAE9EljWBZD8Jz0GPnoocPVSmTRqePVCEieihqu9YzOv/cRedX3FBQfnZ853D/DOKGTiYaA4EWIKAF9CePy0dA3+G/8hXQP/roIzNp0iRz//3384ycccYZ5vzzz898dqjOs846i+ulSPMzzzzTa2PlypWGUryTmD9ixAhO8U5p5MNr6tSpZsaMGfzxI488YsgZhwsEQAAEQAAEQKBEwInoSanbpWitAroI5xlEonvzReL4tnMMR5/TFT0HvWjLdG57d4+m2jsfPum89UgLiHjvEXY8DAL8zr5gwQJzzj+RgD4wUyIsoD+20owdO9bMnz8/07pRGQiAAAhoAhDQsR5AAARAAARAAASalkBh0T6laIeKDh+lZtuIheK5f+oqE7zVGej0TDWHU08phee5W8dRrdWWxPNanyyWh4heHzc8BQJEwBPQjycBvSNTMK8t7zI7/Gd+AjqJ1gcccICh6G+6TjrpJDNt2rSaxvDyyy/zmeU77bRT4nOzZs0yBx98sKH2Bg0aZF588UWz2WablZXXadwvueQSc8opp3hlKLU7pW2nNO6Ubv6ee+6pqa8oDAIgAAIgAALtQEDSnOszw6PjDu2p8KzzmMAcPsNlKAK9QqQ7NR5GrXt2md3oS3WHbVKf3PntxQxenZ+vXUT3hPMQRkohHSJ6O3x7MMa8CIiAfi4J6CMzFtCfWmkuhICe19ShXhAAAUUAAjqWAwiAAAiAAAiAQFMScBEJ1Lto9Ll1vMSCyUkMp4ucL2Vp4G2F+txzctSEl3PclHlc6kslv7qr7DmKvEh79VQ4r9YOhPVqhHAfBAIB/YT1zGYZC+ivkoA+Iz8BnUTtmTNn8lRSCncSz2NR3zLXAwcONNtuu6039SRiT5gwgdOqkxi/4447ciR5d3e3WbJkibn55pv5f/Q7XZXOVl++fLnZZZddzKJFi7js8ccfbw4//HCz5pprmnnz5nG69g8//JB/f+CBB7gtXCAAAiAAAiAAAiUCvnjuDKd0iMQG0iK5trvkcy2Gh3YTlak1sp16V0nEtmegu0HQxuMRc1ONqaJwnlRDSkGdHoeonmoaUAgEXAT6uV/KSUBfgAh0LDMQAIH8CUBAz58xWgABEAABEAABEKiDAAvoLog8TOtbOk+cq+4OBHCJiNDOEHbuqI7oiActspc94x1kritQP9sIdlbrbfkwAp4/l3L20f79UkWEe+I5B31Ih8vTtLv2yxxhVSaBojs+Vz3FO/UFYnsdCxqPtAQBLwJ96vr5COhXvcusli5daoYNG5Ypt0pieayhLbfc0lDEub5EQK/WscGDB5vLLruMRfFKF0Wn77fffuaFF16IFhsyZIi5/vrrzZQpU6o1ifsgAAIgAAIg0HYEnGCceNwVG0tFLvSPFsDpvHR6TjYfO3upgigudohk/ZJnwoj0pLTvEsEuZkylY7q6KBq9GOmeJgo9Kp7r/sZWRx3ifxoRvbBwr2K/Uwr/bbdwMeCWJ+Ai0L+0hjlmVLYR6Fc++am5MEcB/fHHHzd33nknp4d/+umnzVtvvWU6OzvNpptuanbbbTdzzDHHmHHjxlWcw2uvvdYcffTRqeb5mmuuMUcddVTFsnQM1/Tp081NN93E2b0o09fmm29u9t9/f/O9733PbLHFFqnaQiEQAIHaCEBAr40XSoMACIAACIAACDSIQLmAHojFTkMW8VyHlJc6SefmuWh2q22TAFw8pzxSpxanyRGkRfdoNHsAxHMGVYBly1USrX0Gti5xcFHfRUyXzQDiAIo5oupwDnm9j+xhkPtphPcGLRs0AwK5EtAC+lPfzkdA3/5XzS2gU9T4bbfdZii9Op1r+Prrr5tly5ZxmvX111/fjBo1yuy9997m2GOP5cj0NNeKFSuiDiES1inNPAn5uEAABEAABEAABMoJsGhcUTynZ6zdIJHdYer0fv04stqdpU4VWtuB3vO9zGBcnU3j7tlG9pkaormrz2epgUpnoVeMOpcNA3JcV0zY76mdFA4kgQHE9OozjhKtQ8AJ6LvmJKA/mk8EOh0bdd9991WdiG9+85vm6quvNpSxK3ZlKaAvXryYhfLnn38+2ta6665rbrjhBt6UjAsEQCBbAhDQs+WJ2kAABEAABEAABHIg4KcvD4RycoZ4UePFDlRyskgXiyK6cip5zg4bTS5RFbFg76SxJkVcUPlAiA6jucscVNKGOKpsd73oEd2PWBpFr005+z1UxHs+cRDSe84QNTQ3gTIBfUi2Z6C/+kGXyVNAb2666B0IgAAIgAAIgEBPCBQW7VPh8W5jKKI7uNKKus5G0ZuN3YbmbhcpXlv/w93Jks0rbniF9l3Vc86pr2QrkojOx2nZi+yljo7iJoEk8TwLUR1Cem3LAaVbioAI6D8mAf0LGUegP/GpuSAnAX348OGGBGuKNj/00EM50pyiu7u6ungD8aWXXmpeffVVnqsjjjiChevYpQX02bNnc31JF2UdW2+99aK36TgrYvncc8/x/eOOO8478uqiiy7iI68o+xf1b4cddmipdYTBgEBvE4CA3tszgPZBAARAAARAAARSEYieAR6mIVSacDUBvSSeU/MqrSH/qpxLYTrCar1lRw2lG/TyxfvOmaSo9rDuUDSX+7Ez28NnXTR6RChPilLJwlFEmxdSpIKvhhH3QaAZCWgB/env/IPZLAcB/QtXvsNDzyOFezMyRZ9AAARAAARAAASyI5AooouNIk3Jez8dKbXt3YkdkPo6t51TikQPRXT6nW2fGjfokk0j56uHx21VQhLT16UeSS1Pz4t4TrYdB81bG8+mha8qolMdWdhHFaLy025gyG6FoCYQaAwBJ6CPIQF9jUwbvZIE9Ec+NWPHjuU061ledGzUkUceaQ4++GDTQRttgosyb+2+++5m0aJFfIei1WPp3LWA/tJLL5mtttqqrm6ec8455txzz+VnL7nkEnPKKad49ZBoPn78eM4GNmHCBDN37ty62sFDIAACcQ5dj2cAACAASURBVAIQ0LEyQAAEQAAEQAAE+hwBl15QnB8yAicskyNoTuK4fPGciikvjDtjPEW+9jB1OlUljiDduhPTbfR8NVGenDsuVbuqSNoLnTDiMIqNOOb0STwfPSa21+gIU32AkN7nvlrocBUCnoB+Yk4C+nQI6FiIIAACIAACIAACPSfANtOqLmMGkAik3uldtqxiG0kCeuWIdumfjfL2jpJK2XeKiGfB23aP6tD2XcwMCcVz2Rig09JLX+iei7qnn7uM6RhQROHZSBSJbvucRjBPUyaGABHpKRcGirUCASeg77aGOTZjAX36X/IT0NOwnzVrljnggAO4KJ0/fvnll5c9loWAXigU+Eis9957z4wYMYLPY+8f+sCMMVOnTjUzZszgPtARWzvvvHOaYaAMCIBACgIQ0FNAQhEQAAEQAAEQAIHmIsDOIHfWuC+WS6R6mBpdj6BYJiEMXKciDM9IrxRV4Zw21okUIisTrSt4hCLpFZ2Tp95zBasJ6dRf16UKonktDiPb10qbGZprZaE3IFCZgCegfzcnAf0KCOhYhyAAAiAAAiAAAj0nwDaT2BUd/blCEct1ZHlSS6kE9CTbJM0eXIkQj21KZtukQop1uu8E8u6SIK7Fcy5j/89tOJbjwORYK1WGz35XNKrZPdXux8BWseUQkd7zdY8amoOAJ6Bvn20EOgvoD+cTgZ6GHqVMX2eddbgonU1Ognp4ZSGgz5kzx0yaRL4rYy6++GJz6qmnRrv30EMPmTFjxvC9008/3VxwwQVphoEyIAACKQhAQE8BCUVAAARAAARAAASajwA5hKqlaU/qtXf2ORUi502SA0SnKIxWGDhhyEvjifDWuaOfFWeQTjFI92k3cfiZPKejKrKajmqiepnjK40nLLlzENKzmjjU01sEtID+zL9ukEsK91G//DsPDynce2uW0S4IgAAIgAAItA6BwnN782A6t/tjzYOKC+gSLi7VRc4tl4xeoa0RmhJSTuwtiUaXqvWRWKHwrIVy3aVQQKe6dBerRbVXM3cqieZpBfUUG6IhpNe8XPFAkxEQAf283QeZY7MW0P/8qTn/4U9ySeGeBuM777xjNthgAy5Kkei33XZb2WNZCOhnn322Oe+887huStW+6667RrtH6dvpDPUVK1ZwOvd77703zTBQBgRAIAUBCOgpIKEICIAACIAACIBA3yBQeH5iWUc7P19+pp93nrpL2a5S+bm0hv2KgnaY4p1aSRSXVV5BScMec5LExPJKInnMGZTltCSJ6e5cQt1YNc9S9Y5BTK/OCCWaj4AW0J89KR8BfeTlENCbb+bRIxAAARAAARBoHQKFhXsVB6Psi5hgW11AD/Kpx9KrR20M37ziI7DocgK6rSiMQA+PrdKCe5iCPWZ/pd2QHJ7kFe4Z8MyiBLuompieQkSnZiCkt873rt1GIgL6+SSg75BtBPoVJKA/1HsC+q233moOOuggnlI6k5zOJg8vLaDvscceZuHChebdd981Q4YMMcOHDzcTJ0403/72t81mm22WuDQOPfRQc/PNN/N9epZE8qRr9OjR5sknnzRDhw41b731VrstN4wXBHIjAAE9N7SoGARAAARAAARAoNEEYgK6CN06Wt0J6JGAiaJgXumy92PFErLCJ0aVpwVUTViv2ue0DQXlxPETplX0/EQ9E9MhpNc5N3isVwh4Avq/fSaXCPSR05bx2BCB3itTjEZBAARAAARAoOUJSFQ6D1RvHA4i1SsL6FVspjAjF7UVS9Wu7RgvEt2d12WMRKLHBHM9W5UyioWbkdOK6bHVUElgT+pD7PMabDgI6S3/tWy5AToBfeyaOQjon5jzH/zEUBszZ86sym7YsGFVy6QtsHr1ak6X/sgjj/Ajjz76qNlll13KHtcCelLdgwYNMtOmTTMnnHBCtAhFnD/88MNmrbXWMpQ2vtI1ZcoUc/vtt3ORTz75xKyxRrabFtLyQTkQaDUCENBbbUYxHhAAARAAARDImUBh8WTXQuc2s3NuLV31fL4fXaEgHmi77sw/PgNdXTEHT2LTCQJ6TDyX7O7aGST1JkVFxNpNcvDoz7VTpgZnTDrCKkIlmoqxZyI6TVy96fhT9x8FQSADAlpAX/jv+QjoIy6DgJ7BVKEKEAABEAABEOh1Ary5174mN8O7rttsHBOtVbS3zuBVLqJX22xssVeyryrZLfo4Kzm/nKukzGAqUr1/R23nlVdaDbXYTtWGH0bC63YzsNcgpPf61xodSEnAE9BHZyvmXvF4UUBPe3XX8h2vUumll15qTj75ZC514IEHJgr4JKBT+nWKVCfBffPNN+dnlixZYm655RaOLJd+zZgxwxx//PFlLY8aNco8++yzZqONNjJvvPFGxZ4ddthh5sYbb+Qyy5Ytcynm0zJCORAAgTgBCOhYGSAAAiAAAiAAAjUR0AI6PahFdLnXCGHdiea69y69nk29zk6KkpcjGoVOz1cU0BO8JDr1e5JjJIlseM65pHrX5/zJs2nP2Ys5wmqaWVU4TI8YG5/uV1Q/Tyuq+3ybwblYLzY81/oEIKC3/hxjhCAAAiAAAiCQBQG2VYLU324z76J9XBONyMbEEecx8ZZsD91Hskn69TO+gF5+RFaUT5KwHNtIXC29uTQQno1OAjq1Q/2m47Cyuiqlew83Mnuivo3g98ZjbaBKQnpP+93dbTpHzutpLXgeBHIl4AT0cWua43IQ0M97oPECOp0tTqnX6czxDTfckFOmk7gdu95//31O194v4e/drFmzWFwvFApm8ODBZvHixWbjjTf2qtpmm21YcCfx/ZVXXqk4X0ceeaS57rrruAwymeW6tFF5mxGAgN5mE47hggAIgAAIgEBPCITiedq6shbU+dy+Dus00c4aOVdcOzqUQ4ScQd7559ZJxOMoc5xob1LEI5Q2ar1SKsFQ9C6L7u5XSnWoYev0hyJ2h/2vZ5d1rVHx1CeX5j3tapBy/uaG8GkI6bXyRPlGENAC+nP/PtRstm5Hps2++n6X2e6yt+H4yJQqKgMBEAABEACBxhKIbvTlLlTZYEri9efuyrSzbDdJdLm868c27SrbpnO7P3Ifksehuhg7EkuPICagJ42wWppz3nRsG4yNIVNytjLPXpMGJCOZshHL+q6E9LQbBtL2X9u3ENLTUkO5BhNwAvp4EtAHZdr6FY99Ys574OOGpnB/5plnzLhx4/gsckqPPnv2bENnm/fkuuCCC8yZZ57JVZx//vnmjDPO8KpDBHpP6OJZEMiGAAT0bDiiFhAAARAAARBoeQJOPCeDXYRndx6dcjCQryDiJMhKROcIChGN2RlEbVuh2fk0tODdzX4WiaRgAT3Uw7VjJJpaLxDTQ/FcR2xXE6ErRXdT//XYallVsXbTiOhpyjDiCg4/lfKxml8w7ZAgoKclhXKNJOAJ6N/PSUD/OQT0Rs4p2gIBEAABEACBrAk44VlsprL3aDEm1Pu1LZOlgF54doL/Dk+bjcV2Cg0ilV2LBPTU4nkleFlmyIq1U82uqnViyzYkRxuNbLy25fQmZ/eo2KtV7Kke9BXR6LXCQ/lGEBAB/QIS0HfMVkD/JQno939sxo4da+bPn5/7cF566SVu67XXXjMdHR3mpptu4vTtPb3eeustjjqnVO777LOPuesufwMVzkDvKWE8DwI9JwABvecMUQMIgAAIgAAItDwBL/Kc04/bIYcCurtRchR0W2fQwK3vzIQTC+hycftVxHPPeaEiATwRXDmxxMHlHChhJHoQOa3rSStGS5+8NOg27TzdqydKISbiZxGVHs5aUt9cJHqQtrAHsw4RvQfw8GguBLSA/vwP8hHQP38pBPRcJg+VggAIgAAIgEADCHjCsz5eKto2pUz3U5FnJaBz5Lm2OSpFbOvNxdVOYUpK1Z6GLduRQQVZi+Bp+pFURtu5XKbCYJPsvqRIdLbxbMP12HqxPts+QEDvyaTj2bwIOAF9jxwE9AWNE9BJNKfIc0qlTunY6WxzSpee1UWp4N9++20zcuRIQ1Hu+jrkkEP4vHS6KPJ9vfXWS2x29OjRnFJ+6NChhoR5XCAAAtkQgICeDUfUAgIgAAIg0OIECs9McCPsHDXPFJ7ew9nTndvf25Kj90VzNUR2cgQir5wFpyIX+Gw65RzILAKdzhPkDliHhnY6lTkyIsJ4tdlKSjNYzZkk9WoGaQR1nW4+FNSr9VXfT4pAT3JUsRPHeXH8liqNNY3DJ8OIdIjotSwClM2bgCegn7xhLincP/+zosMDZ9flPZuoHwRAAARAIGsChT/vXqxS3iU/XlnMXEVnNo99LOvmmqK+8khtGXwkytz1WG1I1u/NxmSWwr3w/ETPZCpFnlMnQhFboaxkByTpyaEdUrYhOWGqYnZXGlsjj5nnLGv2jHWuX4n93CcVSR5rPyqcxzINaNZpDcwKA4aInsdqQJ0ZECgJ6IPN8TtlHIG+4GPz4z/lH4G+bNkyTtP+7LPPMpErrrjCnHjiiRnQKVVBgje1ExPQzz77bHPeeedx4QcffNBQRHrsojPZSVxfsWKFGT9+vKGz2nGBAAhkQwACejYcUQsIgAAIgEALE9DieXGYNoU5OThaVTx/cbJ/RJ9zllhnTyxNOzvHrNOB7/c3LKLbKzMB/YVJyautkoBOfUnj9CHHiS4bcwil9XVYh2HVr0fgOKtaXheoJtKLgB4T0qmeWkX0tE6tcExpmUUGDxG9phWBwjkS0AL6olPyEdC3/SkE9BynEFWDAAiAAAjkSIAFdP3ORwI6iee7Lcix1d6tuiSgp3jZdRtnu43pWl3sOH3W0VGymTI8A72waJ8InAriOfen+iNFkzhiWNUTTR6meQ+P00pre2SxDOSMdRbS1fgkkwDZtwnHlcWbD2AmLZGejhEiehazjzoyJuAE9D1zENAfzV9Af//9981ee+1lHn/8cSZz8cUXm1NPPTVTSjqF+8SJE82cOXO8+iml++TJk6u2/9BDD5kxY8ZwudNOO81ceOGFmfYTlYFAOxOAgN7Os4+xgwAIgAAIeATCKHO6WS6es7eAn+scdU+PCHJUQOyKGNaNEBALJJrT5WUoDxwH0l85O0/3VQR0djzYa8CAUp32oyyE9IIV0SnFofzM1XuOHNsPdngEad6lf1pU1s+Gjhs9T+IY4jrVDZ3pvbRMSgUq1V/JaVJJIJd71ZwuUo6cQWWXGkRPI8/DuiGi9+hvBB5uPgKegP4fOQnol0BAb76ZR49AAARAAASEgIsypw/69zOdo//EtwpPjPUhcURvcYNt584P1Q2wzB6r8r7aOWJu3W2ledCPONfR5p4RVTQZwyOOnA2yuiSg02ckoAfv85mlcvdE9JjoHYw65BseV5WUPavMFqtyLFW1TcBpJiPrMtpmClG5xF3VNkok3M/azkoYe97rP2vkqK91CYiAfiEJ6P+YbQT6L0hAn59fBPpHH31kJk2aZO6//36eoDPOOMOcf/75mU8W1XnWWWdxvRRpfuaZZ3ptrFy50lCKdxLzR4wYwSneKY18eE2dOtXMmDGDP37kkUcMsccFAiCQDQEI6NlwRC0gAAIgAAItQKAsTfuzE6zjI8xyV0rn1vmF+lMj8VneMeFT+15EqCbBflt/N2qWyIviuRacTdGpQ4Ir9VFHZMvvSWnsVnUVhWxO4d6/+HNH6Wy/agK6E/JpzMNnR4dZeNFGoYtoLf868d5Gyuv+R1O9qzGL0ydJlE5KTVhNvK42Ubq9JEeSTm+ox6zrDqMhklInhkJ+PZEi1cYk9zUb50BM+7BfrhGbSOrrGZ5qJwKegH7qRmbYuqWIsSw4/O39LrPtT97kqpDCPQuiqAMEQAAEQCBrAoW/2DTtVHG/ooBeeGJcsRmtJ5NtsWoVf5yJgB4TIEXMVe+ceQuInoAu2aZ4U25xQ4G72GayNlDMXqBnurpKG30lg5easEoiut5EXLFcpSh0OXpK2wdit1XL3JVkU/S2MC5zUdzBED/HPK39xgzCXdIJ36ikjQfOLkp6rpogX/s3OO/vQO09whPtSMAJ6BNIQF8zUwS/eIQE9I/M2LFjzfz58zOtm0TrAw44wFD0N10nnXSSmTZtWk1tvPzyy3xm+U477ZT43KxZs8zBBx9sqL1BgwaZF1980Wy22WZl5XUa90suucSccsopXhlK7U5p2ymNO6Wbv+eengX61DRQFAaBNiAAAb0NJhlDBAEQAAEQiBMoPLNnQo66aqnr1Hlo3fWncU+MQA+7qx1COYnoLFqv7iqK5hI1HEYrkwOI7HtxBImHLLT5yZEkziBxoFBUhXYoiS8iGCsJ5twXJ+oWHRb8OUWdl0W827ngutVN+p36L+Ph362oL23GnEI6Ij10rITOoJjwHBOMw/lM61RyERAu7KNYk9euOIWCsTfDlz7k10MBXYYEIb0ZJrd9++AJ6D/MSUC/GAJ6+64wjBwEQAAEmpMAR5cnbbqMisP2Zd8K6PRbvSJ6gTY1x+yjCpmY8hIQOSW6CN/UJxm7bOLVG1rdBmL7nh7jRDaTtg3IznK2VvJacFm4arErxM5xm6Ft/WRqaLuP2tdzXWmDb6WNuHKvwjxlttq1aO5+TrDpvXmocq55Zh20ayXkmlbIr6MfeX0H6ugKHmlTAk5A32uwOSEHAf3c+/IR0EnUnjlzJs8apXAn8TwW9S3TOnDgQLPtttt6s0wi9oQJEzitOonxO+64I0eSd3d3myVLlpibb76Z/0e/01XpbPXly5ebXXbZxSxatIjLHn/88ebwww83a665ppk3bx6na//www/59wceeIDbwgUCIJAdAQjo2bFETSAAAiAAAn2MQE0Cutt9Tg4GX3mt5xz01OI5O2bc/+UShe6iz92ZbyJK24gJcaiIsE5OFR3ZHUaii9NCysiZ8RKRrsYTXTIk5NMVOo+SoiDI8UR1yzmC2lkljiIS8MVBFaYVF6eOdg5VcgbVss5jInylz6S/3lj1hg19wzp8XH3inIs4aGrpc61lI5E/XhVlQnqtDfjlIaD3jB+e7hkBT0A/beN8ItAveoM7iQj0ns0VngYBEAABEMiOQCk9u46wVvXr9z2xBbppMysJxEV7pscCeg1iY17iYfFMcXV+OSGgMdKxVVqEDjfV6mh0PS0uAt1+KK/6oYgdTmWlTb9eWamwX0mop2epr+rcdX5E6hRbj01QNd8xIb2SOB4T0GPCfA3z6g/NRv27LGqSWcweHcD9twHkshFcV8DtJqznWr46lQLU6x1bLe1XKJvX9yCj7qGaFidQEtDXMifsnHEE+sMfmbwE9EpieWzKttxyS0MR5/oSAb3aFA8ePNhcdtllLIpXuig6fb/99jMvvPBCtNiQIUPM9ddfb6ZMmVKtSdwHARCokQAE9BqBoTgIgAAIgEBrEIifbW7HFsuixunt/POj603fXrt4XrT+s0jhXkx9HhkgOW9EeKYU7DoKXQRScaJ4Qc82NWGYrlG8FcKNxG12XJDQbet3UePW2SEp1ll4p26W0r6XOUvkA3FKyNxIlLmOPKef0wjoupE0AnraaArNL83Xx9ugYcVzp5vb37Vo7qVuVyK6OHMa5bhJaif2eQ8zFUJET7OQUCYPAp6AfvrGZth6Gadwf6/LbHshBPQ85g51ggAIgAAI1EfApWfXj8u7XBjNS2VoM6ykNifhfKcH6mrYizyv8X22p8Khf865MzyKP5CtIgI3RdiLEK035YotJTaF/B4K0s6Wse/4IsKL7SJNs51mRXDZjCxlEyPWrcCs37tdYqvuol1GddBcDejwx6VTy4sNGG42ps+T7A1tJ1H90fViRXupv9oqCQV8vcFdb9qQetTegeJGgCDa3NuErFTwWu0U3U61MaQda5p6Upbp6XchZTMoBgJlBERAv2jvfAT0c+7NJwI9CwGdosZvu+02Q+nVFyxYYF5//XWzbNkyTrO+/vrrm1GjRpm9997bHHvssRyZnuZasWKFmT59urnppps43Tulft98881ZWKc08yTk4wIBEMieAAT07JmiRhAAARAAgSYnULN4zs6BomXcOapn5wlVFc9DUbZK9HlZWnPFvnN48cwmukrnistOfZ1y0Dq5ZJydFEHBynfR0UAOFLnE0SBRFCLees4YG6XPjiJ1ljrVoZ1E9LuI6hIJIe2EjiD6XaWALD6nF5pS8LnrNrKCokHkZzemWHS7dSJpx0wlR12sXBrRvdp3Q5w/ErXP5e08yMaFsA7PIRXx+IQfpXFApt0cUNaXCh6njIV0EtFD5yaE9WoLDPd7SsAT0M8gAX1AT6v0nv/be6vMthdAQM8UKioDARAAARCoi4AnnFd8nwxurl5tOkf37ExaJ56neW8N7RSy2bb7ozfmajZY5+fvduW990svuljZRfSuzOnWSdS2+5Np066LDO9X3Jws9kHsLHR+35aH7b9k79CPVC89L3VS70jkpkuyboltpSPGpT9hSnY2KdRxVmIvyeda8Kf6+Xkq32E3Nqs5DrKxRcVxT2y3aJ3daO2ucFXGItyTVq7eXCw2XpL94mw0tdFY2JXVX6t6riqo9dFa1nZd3+DSQySi83fKcuscNa+HNeJxEKhOQAT0iyeuZU7YZXD1B2oocflDH5lz7lmRyxnoNXQDRUEABNqAAAT0NphkDBEEQAAEQKBIoJiyna3lZCRJt6yBW88O7sJze9tmEyqPpbITo14J6PRRGIXOAnqVIZXGay3mwqriMyQu07jYUaMim1l0ttEI5JDRqc+lX+KckWgIrlDS5qloAiEdE+H5EetA0WK7G7uNipAIDznH3GNjxXp9BrqO/qCfxfkk7Um0ghcBopaEFqSTRPHQKVRPVEXo5HHiuc12kBTNoB1GOpKizAmj1ptmp9dLkuNGxh1zfqV5Pqy3moOoVodTwjcYAjr+2udNwBPQz8xJQD8fAnre84j6QQAEQAAEKhMoPDmupO1K0ej7XPASZ3+tJ1NX2Vnn1d4fwyEE5bWIXk1Aj5qH9M5NmblIuJZNwdSmHFNFUeLyObXNm3btizrbSlZkF3skzK7lBHQHuGh3SUYtsXnoX87mJQKwrVve17XQTn3SthA9K+K92Ht6o7IuKzaXlJNMYk5ct/0kO1FHb1ezg5ztYp9Peu/3NiuoydXPexuKdaaupA3H4cMVXAFp1neqPxzhgK3xVMXPkKrqegu5eS5WAAG9XpB4rhYCnoD+xRwE9HkQ0GuZD5QFARCojwAE9Pq44SkQAAEQAIE+RqAknlsDNqn/McM2dMaMmFt19E40T2xHNRQTabWgLeJwmShZ0qy5mTQipEQxSOpBEcilnxJZzun81Nl4OoqByopjhxw2FJkg4jnfo4gF5eARAb5aVAE5fCSNIJ+XbgV0cdq4c9dtandxoOh6RdjXziD9PNWhhWp2IMWcHDZavpLATGPVYnYaJx+PyYXr+1EpobMr5KW7KY4raVOcdnoe9doLIy/4nnU0iTMuLC+/y4aD6Fq2WQZiYn8lET3jaHTpGgT0qn+aUKCHBDwB/aycBPTzIKD3cJrwOAiAAAiAQJ0EXNS5tiu897aIYB4TPmXzcYrsXWXCOds1aQwbO0i9KTXpucTPK4DiSGwVtS22iYxXUrfTZmS6J7YMH41l7Ro51opsDhbc1Vnk9C4espM2Y90S20fsJN6g3K8o8JPQTxdvmrW2mJQTnmJLuXf8SAYuiWanspLGXVKwhxnCpO+0MUCiy6vZE2yTqGO6PEE8aS6sUO7WhawN9XmS3eJtWA5SuMfWrRtTzFavpvzLZvIKtqU3RFVfWHUt6z/Ndz3YFA0BPQ00lOkpASeg77OWOSFrAf3Bj8w5ENB7OkV4HgRAIAUBCOgpIKEICIAACIBA3ybgi+d6LPGIiehoq4joTjBPiliOORNkl792YmgHBzs5rENCUgRK2aTzwSv5mjiKwkabi6jMjpb+RUeLGNZy5p6I7DJ2LaKL0C4p2MsEdNtR6qcI6EkOFflcnDLi3BEHk7djXtIlKseLlJcoD6ovFKM5haIV9zk6xJ5n7zlJxINjIcaE5XAMCf4R8ZWVikdCJ0KHjitsOyXnuouA7ZXXAn/gRAodLqEQz/eDsSY5vULxvGx9V/kOVXNiZiykQ0Dv23+r+0LvPQH97E3ySeH+49cZxdKlS82wYcP6Ahb0EQRAAARAoAUIlJ11XhZZnMJ2Cm2mQEAvO0qrWlRu0kZjZ590G9NlBWyxS3TEtd5sGpujpPblDHcR0Nn2senaxY4Qe0rEa3mflo3Q3J59ztlMnjFR2owr/aC6tH2ij9USe8vZTBwiXjqP3b23Uz9tlLhuV9sEeoOxtkXDaHVOF28j63l8oVhuRenYO70n2qYMQxf7R9cnm5+lfWd32nF6VassAOGGA7GzK23qDrspmQTkWZnTsrWkxPyEAPSyR9ieV7uQY2sxSyFdjbtzJFK4t8Cf7KYfghPQJ61tpmYuoK8wP5qLCPSmXwToIAi0AAEI6C0wiRgCCIAACIBAMoHC03uUblYS85IiaCsI4pLOvbBwryCaQPUnZqCHwrlEJLOjw56l51KnU10qzTkJwQUVYSDOHE7lrdL5he1yOj+JDLeOBZ1+UFKsaycTOXC000AYSTQD/ctOliB0QBxO1mfE93V/wihwKUf161TjOvWhFvh5M0EgoItTQ4v9XoSHSl/oxHNJ+2cHJo4k6QfVqaNOtHMpXHLOUZKgqMfWgY560I6ysN8uYt7OG60BJ4IHTjjvV33efdBhaUM2YoRRRrLuNXddhfsuRTw9YV1JX0/9fQy/mzUEHenqIaDjvwZ5E9AC+gs/ykdA/9y5ENDznkfUDwIgAAIgUE6g8MTY4odl72VOSVd2VeTdsuz9uHTWdeeoe4wnnofvelr81UJxTIR1adKt3US/e2d+i41Cqc+7SvaV3kAsfQ2zbNHnsvGWzTAS6O3G286O4s9hVDqXIeGbjo4im8vab/K71Env3aFNIGK7bNxlTdxyczaTJ14A/gAAIABJREFUsi9kbqSMq8+emc7P2OOguF0b9S5jked0PdrGETtI2yNii5Atqe1BWSuxuiyC8k3FrrHSOL05rvbNtEZTKKZLHfSvRMzLfIfruWwOpLOeseObuG5+9PntwSKOrSVmlDCmUEAPmveeykpIh4hebYHhfoYEPAH9nzJO4f4ABPQMpwpVgQAIVCAAAR3LAwRAAARAoOUJOBE90fBMiMatIJ7XBE3vmiebn50yanc8VcZitjisrAPEOW3I8WOjhkW0JnFbRznI2Oj8crpEEHciuRW6Q/FYn5Onz9YjgZqi1cXrIcY2C/aUgpBEXBuBoGFohw2PS8R/W0jEdefksJ9rDd6J6NZDI32UZ0Q812I5OyfUJgLpUyxqXhxVroyN3qDfw9SE2gEj86E3WyTo5X5K+4RCwb4Db/7LFpiuI+Jx1GvVpYjnDpd7bfS6EW5l3i2V4jDmDIoK+KrTaUT0ROdsyuMIIl9CCOg1/WVC4ToIeAL6uZvmEoH+uR+9xj1DBHodE4RHQAAEQAAE6ibgCehus6X3UufXneZ9zz1RIXo93Ijq3mtt9LZnv5B9QZt87WZaOkpKxE05CoqE3o4Bxc/dJuEgcjq2GVfaJTtH2yzyOq2Pm+L061Ywl3b51duKzHIEF6VXp6ssfbsFo4Vf+pkjxq0NJfckCl1zoXb0meXShrNXbF3uXT9or5KATu3rDd9arBcb1m2ktvWK/SHT7G2IiC1JO+lR8Txm98gkaAE9okyH4ri2NyoJ59rW9dZsYEt547M2i2YZM/sSBfSU361wDuv+hvtrAFHoPQWJ56sREAH9J5PXNlMzFtCnkYD+R0SgV5sD3AcBEOg5AQjoPWeIGkAABEAABPoQARbTPeFOznBWqqgWGHU0cj3jFPGcn7UGuD6PTpwc9C87X8gZImeKWyGdHRU22kEcGjQGLZRqRweJ6CIS63ToVEbSAorTRdL6handddRBzClAY6B22O63SjANT84A1M4gKpIkeusIjliKQu0w0I68mFiuy4btu0iViAcjKSpBb3xw8xR6TWT8enEoz4mbf+Ukcn1Xa0KqiQrTWrGXdlR9ZYJ+4JDSfp+yTSSKhx6a/Czp/aWr0ry30SBpF4GtxG0MUYx0P2IbW5IcTSm+gxDRU0BCkboJlAno69tNS3XX6D/4t3dXGQjoGcFENSAAAiAAAnUT4HTu4XFGziaw1UY3ZUqTVV7mZCOp92+QBjs8wkrex+nVk9O2W7GZhWcrpOuzu6m8CNfhplqxT8S+EJFYH2FFdo1Etw/sLLHkTcZ2864TmtV7r5TUdphscpZ7Iuy7srTpWEX/66OtqAz3X+xWK9RLBLMn/kqqeft+LnaHsNMrQtt7us+yMaCW1aMFdBl3bCN2rE5tL3tHWNnsZPIMz5FsnLAZzmICubYZowI9wyjWGrPVwyUcthGzp2Tu9DqTDRHhmGOmnWszOB/efd4D4yhhHiWbXi3TjLIgUAuBkoC+jpn6pWwj0KfdTwL6h2bs2LFm/vz5tXQLZUEABECgJgIQ0GvChcIgAAIgAAJ9jYAXfR5GE/NgahDQdZRvJRC6nHZoiKhdJtiq1OUUUSF53iQCQcp7adaVc0QEUikn0eHctko7qPvM6dkpEl6ljaf7zuFhHQosoNp6wuj2TuVIEueXOJSkLe1M0f3Tad69sgnb9kOfQegMiomw4hThcdkxeNEmtq1qZ50750ro7bBOvrLz6LVDRkVLeOtHNh1IGsDY+YHq/MIyx4sW6eVmikgOdijZ9ZYYaaEaC7MlsHNIHDuRuSr7jgQCfUw4h4De1/6stnV/PQH9x5uZYXkI6Ge/yowRgd7WSw2DBwEQAIGGE+AIdHnHLjvGSGUnCjdnpnmnrDQafi0O3rPlDG8Rc91GWysyS1p1FrzJLtKR6LT52B55xWeQK6Fd23801jASXd5lRYTl883V+zy14/obGCginorwrzc7dwYb7vSmY7cx2rYTCulhNii2o+wZ51oA1u/pemNCkp0UmxOpW5+d7tqzKfNjz2mhWvobCvyxjenhpmdnd9lGxF7V9m4t44mZliLSc1t6MCpDnNiHsU0j7EIoM07tUWQ23X+0TNiealuqc/VGNmTUMu6Ufz0goqcEhWJ1EXAC+r7rmG/nIKCffTcE9LomBg+BAAjURAACek24UBgEQAAEQKAvEig8s2ex22VibiSaWAxT58iwlrUIjyI408cx4VVHUbud6Fa8dfCs+C0OAjmrjlL8eU4aRVui1sPoCWfUq3PS6TESPmXXPzmY+Fw+Ywy1wWcByq59lc5QztkTh4mOShDHE3dJDPrAcSDiuXOESP+Vl01H34fOtvDMPnmcxxg47cJ6YtEHes4lzaJ2Bklkv3PmqPFw6kebclHmSS9+draIgG77pz0wIQO91OhnGatzkKnx6XbCCIqkYG9+Rs50tz+7uVLRPC5SJzi63jlttDgu65SyHVjnpI58qfjHQAv8wVmB2imoN0J44+7ZXxpEofeMH55OJuAJ6OflJKCfBQEdaxAEQAAEQKDxBApPjis2KnaQvLOFIqKYA9oO8d7bVcQ0fR4ekSTvnbJxVyJ12WRSKdBZxA82bpIdo6OzQ2GV3+vtJmH93iwp1eV9XfruCcU2GxjZY2QzecciqXFQHyVSXT8vDPR4ywKH7QfOblLzLMIu9z/B1hS7hO47u1Bt6vU2u9q602wWZruJ2FNddqz63G+xm3hDgjo2S6LC9ThDe0XmVNsAslHBbQBQGyhE/Oc+2Y0QzkZXEdpJtoNrP8Fw0naa7puwFRtKr5lwI7SMRduz+nsS3pfvldeGHUAo4jtbW21c1nwzFNIhoDf+72w7tQgBvZ1mG2MFgdYlAAG9decWIwMBEACBigQKz+1tOrf7Y1tQKk/bbr0+XrSDRaEFPXFKuAgMa807A1oJi86RY9Ot6+hx7RgQRxAZ5PK5OAYookEb8ToSg5qWNiSS3RniNlKZf7eRASyg24sEdB2d4IRea4nLmePiCBLnDbEQEVn64oT0QBFmR5t1JrGzQ3sC1M9hKveoE0Eiqa0TTcYVcxxogTlWl3ymo//1XJU5dGwjslHCDVNFhHCdAleH4Ei/I84aEdzLHI3BxgCp1gnaatChE89NsGpX2tHfbB0RoteQOGd0m55DRtaHXUt6ykWs1+2Ekfg6usO14QZW/CSM7ggdQmVOx/R/siCip2eFkukJeAL6+cPyiUA/82/cIUSgp58XlAQBEACBvAgU/rI7V9254/15NdFU9bKILptXw/fSMkHaRo7L+3i4Oda9l9pI7/D9urCqaN+ERwPJOy/ZFpQBi/4l20ZEZxGt5f2+LNOUPgZL2yHK/hIRmPoe2li8obm0Z7j4sxWN6WfZCKvFeL3xVmxHZ/dYkGw22D44O9OmZpe6XMY0Fe2tNzVrIVfbmNrWCwX8tIKr8NR2H3XdbWwQu8emzNdR8GUZqgJF3ROg1ZLX/XbCubKvpI2yCVGCu56rNOK5tpfCTc9uYwQr4EXbXF96s334zRU7XH8e2uD0O60V3a7nk9C7B8IMZcpuTDunKf66QERPAQlF6iIgAvolFIG+61p11ZH00GV/+tAgAj1TpKgMBEAggQAEdCwNEAABEGgzAiScu6tfP9P5+btTESg8O8FLVdaXDK1iBHqgxGkx0BnmgZEqZ+i5CAArEsvOfqcFBunsONrbWu9iIIsjhpw19JmkHSRnEP9sy7uU6dZbocV6L3LCGvTad6CNdi2ghzPsnEKKCbVD/ZCIDXZEBGew8RmCykMhgjlFaIT91mkVQ6eDdjw4R02VKAtuNpzDoP+xlSxOCx3NIg4wSUfofEERoV/4O8dVkqJrn/VS4EuHAmG9LOW752Up/lLNKRITyr0z5NX6kfp4DLYtzxllP4tGgwepLb0o9wp/OmRThvYBSXHZPCLOWYnQ0NWFDsdUf6XKC0FErxMcHksk4AnoF+YkoJ8OAR1LEARAAAR6m0Dh8d2KQim959LVv7/p3KH6OaucBl2yQJHwPvpPvT2UmtovPLWHXz7QQYvvqepdVd6VxW6i+2Qz8HFRauOk21ysNmbSsxL1LHYM2VH8Pi3CusqyJe/u4butvOfTe6rerEt1uDO11fs498Xab0nZkKgNLyLbjsXZD0rklY3OwoLtIpvNSt6FJRuYS+Vu++M2OutMUioCPCaYxzba8qt/ZBNvGruCxyp87MZrHaGvWcjqEBuLN0CoTQEdlKo+oR96Zel515+XnYEeRpyrut2GjYSodFevysbFnIJMB3ojgkTYy7O80aFCJjTdd8n05RjpzeCWEc8H9Vcb8cGYwg3i8qXT37tKQn5N3/hi4b7k26ljeHiklwg4Af3LOQnoc5DCvZemFs2CQFsRgIDeVtONwYIACLQzgcLzE8uN6nA3e2A8FRbuVUJWFlHQbTpHzut1pOLk6dz+3sS+uBTuMRGdx2U9G6EDhRwJFB3u0guKxc2Wb/ExiTbQz0qKdE7/Z8+761pV7J92Dkn0hD7Tz+tjkCJcC/fscLLn31G9oSOIPyPBXxnksqM+jBJxO+Ct0yk8a10b63wmtnXQSH064sI5DCgaXRxBNhUgOURiaQ7DyOpQUE2KWNAic5LDiKfM9kNHdIjjSadSD+vQ0Sgyv55wrXdh6Kj70Gkkmx2sA5A3ZGjHiV1PzC7mTKkUhh04XHTURaJTSfcvYe3LPMa+E3rDiTsTMvj6yRwmRc17jj+bClLPs263bPjBB26c5X8CIKD3+p/oluuAJ6BftHk+EeinLWVuiEBvueWDAYEACPQRAiye00XviSR48gZT/0gabXsUnhqv3rnV+556T+ocXV18zxNPUdgvXkmiPo9D2yKVXkHDzZ5iNwkn/V4tUd7u/c5WzMK3Eo7JbqLPxJYiG0OEVtmULKK7jrKWOsQ+k3ZEqKS+kS1G/1I9ZM9oG87ZL3buZHOzzsTlRFZbmDOI2XHo91rZVMznstt1I/ZGJ23GiOxIoFdzGaeU1c87203bCmoTg8xFaK/XsqC0PaiPPXP2kEqnLpHZYkaHc0FZ1cSuidlxWrCOrReZT1k34fzo3+lnPf6qm5Blo4BiqefW2aF24zzXrzIH0O9exgQFmedb/c4+BPW78Crro/o+hHPmyqq15gT47CLSIaDX8mVB2bQEnIC+3zrmO1lHoM//0JwFAT3tVKAcCIBADwhAQO8BPDwKAiAAAn2JQFoBveqYtDMkYXN556jGCesuSkKJrp2j7nHDKE/frqxYiQgQA1+LdmJI865zK1ZLOW04swFt65QoC4lApzrY8dKvmHLQnRdnd6NrAT3cba9FZHYs6PSGgXjOzgUbHa4dDRwhbkV2dkCRQK7OrNPOBu3ckJSCelx0X4vfUl5HCuv1wOtEOSjkbHftVCKOevNA4JgM9zs450jodKgmnnM79jxFvQlB+U2888PD6GyJ3HYOJJvKL9YPHYEeOnfEGaQjY2SdhZsGtOMqdJyUfUlVRI12fkmEt5QP17t2MqpIKb/6QNwWh6abnAqCv3Ymhk5HT0C3nqnwMx1ZEXPgOkbWeRT+bvqZzm3nmMKifXhI9DMuEOgpAU9AvzgnAf2HENB7Ok94HgRAAATqJVD48+5WLLfvqZJpiSuMiJ/yDhW+u7l3MtuTYN+l9K9RwroW0LUA2Ln9fSW7Ka2AHm5ipd/lXd+Jq/YdkSOUrb0km3v1O7UT0K3dJHaIvMfKe7nLdmWj1qkdPg9dhGo1PTorlwjunGnL2kwSNc1TKtHTtl7ZLKyj2cN3enlONh3L7/JuTUOnevSm5UrZwbQN5SLw2Tgo2lNii3g2aZi9y74P80bdSjsfgm9GaNs7O1CEYx3dbTvqRaurjcEyXr0JWURjzZptMBUVLs8520zd88R59V3ynrGNlNkXCX8FxC6Jnc0u33NnN9kxa7sktD31RoJwM3O4uSHpD5O225LSxuu+6TUnf5oqbR7Qmyu0XabWCononHWQ7KYmCJSo9284nmseAiUBfYj5zpiMU7iTgH7XcjN27Fgzf37vblBrHuLoCQiAQB4EIKDnQRV1ggAIgEATEvBSt8d2eod9riRKOuO2fKANFc8pNXtkp70I6HyGnxYrxegUI5OfVTu3nSFqxyWpCMkYFaFXC8cuotk6OMjBJsY8CeYc5WAjD6jKMBrXOQSUCCnRDJKuXYx3T/y0E0D1SVpJGQfVSQ4iKq8dUBTRIakDtVNFOx9kPO455TCRZ1hED9K7yzLgiBEdZa48Ji6qgpxAoq0HTphouj+9296K/9UiC8JlqedO5lgcBxIJ7jk5JfLfpn10Tjjbce3wCdePrKnQ+cRTpj1IwdrTzq6wLzq6JSqmBwK624gQemqtGO74xJzAGp6kbw/THibsnNH99jYEqHZjEemurNqI4m2mCCJLnNMniKSX7wJXE3ccQkRvwv849bEueQL6T7bIJwL91FeYCiLQ+9jiQHdBAARagkDhiXHFF1V635AIYtqoypmXlKhZtpmwwvAT7Ko0KeGzgsrjCjdsklBmBXS+r1Ou69cp/Qob2kvSQb0ptuw4K1uIedrU5vIOTTaTvNJJ++G7u2zi1e/vVA+J9rRJWOyT2Ls221Yk8lI5K5brDcd0T595LvdcW+qZcFNEmQCr3mXlnT1JzC5Y8V8Ee23rySZobbOK/eFSyOsNA3Zd6rbcxoUqKygU0PWmbt7woDYOi00sfdXjl2b0hmuZR9nEre0m3mtt63ZR92J7qnd8zyZQhrFnG4e2SUJkdjh/EiEeitz0e8ze1Hazs39jGw2sre7sv2AOvO4qP4DbAGAzlYV/N2Kbo6Wvek+zaz7YdC/doDT7kb8FfNu2CQE9q7+87V2PCOg/3T97Af3nJKDPhoDe3isMoweBxhCAgN4YzmgFBEAABHqdQDQCvapIniSUxYfTSPGcesDR5exYsNZmbDe5CMoSnRBGjrgd/ZHz/Nh5oVILyrDDtGnaCaN3yesIcG1kh/i0IC2OBDlDTTuCnAMjyPQtjiOpl5xWfM56l2UTCJMi1uqNFIlrQT2rHSJ6DM6Z0G0MOYPokqgL+pn6IeImtS0bEzwhOphHmVfpozgHkhwalb5h0o52hIlDx60dVYGbA3VWHY/dOjO0g7C4AO06VBsDdBSSc9bo1J9uMSVpvaXIDC2aJzk7qDpPWFZiv2ZXMXLc9kmY6+gRmeNwnYRON64iFgUSMtITpsqLY0gcgLH2wnG677YcZWA3qzgnd/nigJDe6/9J6rMdKBPQ/8Ee05HRiP72zirzOQjoGdFENSAAAiBQO4FidiulGNMrTKFgzIBOfbR56f2NzSUpH26W1O+Xvl3VK+K5vF9Lt+idSjbe0nuubBKQFNzx/Yi+uCjvjSKah6KpbELg92hrI7Atos9Ct2Kqs100UnWkFfWVNyxbIV6/s/JYrA0k4xLBXNsVAweW+i+bh7VNojdf6/d5qUPOdhdRWN5VdYSyvHtrQVsLnPSzFu11enovetsKoJx+frUxKwvGfGJtrXUG2fWozn2nvojNot+Pw6+B7nPFd21Z27YCzgJgzwSXTSZ66VMx3a7YriEjsZ/02fVio2gBX+we93xw1rnelK3HIWstaf3KXOisY7JudVY3r05lF7rvT5J4rjYHhCaPs5WsvRJuOtYielkkumzulo089nsUlpNpE776vttworLSybrRfxfszxDSa/9vCJ4oERAB/WdThpjv7LZ2pmh+ft9yc+adENAzhYrKQAAEogQgoGNhgAAIgECbEGABXV86QjaJQUxUTdDUqYpGC+i62yymizirb2ijVMbjCcHW8tZODzY6JT2gFYBDA5odBCo1n+yoFweI3rUujgZyfkg0s/B3xrpNb8ipCG291Ib8zv2x8PXZ5iRUizODbpNTKXRsxERJEdE9Mdie1U3tOseP7UtSVK84rmSjgbDTjhIt4FKUiQtBV6kGnTNBvAbq/HS+Z8uKQySM5og6ZtRCCCNZpJ/aWROLKpAq9Bi0k01Yue+FitYWh6owEuFXBU940Q1yX6JouI+RL2dMRHeisirvHDDKyeJuW29XbLNAeKa9J1iXIhO8qsSJ5njYjofivXZ0ybzqc9/pvjha3RypjQkyz+Ks0xtWyiJFVOR70uYPpHZvk/8CZjdMT0D/6ZZmWB4C+il/5Q4jAj27eUNNIAACIJCWgLdB173n2iORvHf/oMZEGym4Yd9lGimg657yOedi52i9371/qdBefo+z2adCgPLeLO9fEhlNIrc+0kg2GodiNgmVLGBqId12iKLGWaC2bUuEsgieYm+FG6N5vqzIyJsBVCYwKqvTqut7OkOSbGyW/mrhO6zT2WZkj8mm4wgvvQS0HaHtTb1JWDaAO/vI2kLyu4voVu/lYitKP2Tu6BkdeR9uCgjn1b1r2067DSLWPnSiuIqA1+MTfrKMtK3h3v2tkaNtI5lj38Ao9U7b6o6hvOvrjcN2PcnaionoZd9VtZlX235ii1DbbrOFAla2WVeYaXYacMS+De0gbetqu0eWgP7OSll3bBrVL+2J70D8B+rYuegfQ5Vxo7OzlOVP1aePyUv79xTl2puAJ6DvnrGAfi8E9PZeXRg9CDSOAAT0xrFGSyAAAiDQqwQ8AT0UAGM9i4nn1tZNGkivCuiUrj3cAe8M4EiPxRkk4rI2rsVIdiK3PQc9FCl1ajxxFtAzLGLb88Y9m9katWzjW6NWIj30zm8xhsl5JM4CckbJJQK6CKmcVlIiv8kZFRjvYdS2GMKSlk8LvM4ZZh1BTswNdqlzf60zQRw10sdQlJdIhDCKWfdTO2f0edxavHXjUMKo9J3YxJg7H4I4FBSb0CHGjolIdIFeI9JPmRevfZXiUDs8wqiA0MkU+FX4V53CX/lBvHR72ukU+1JKvyv+5VHp6mMpJsXJJg7L8O+C3iSg+6m/e/JdCyNqmLdKUUj91RE4ek2E3z23iUB9ceV7FUt5r523QXp3RKP36n+a+lzjnoD+s5wE9JMhoPe5hYEOgwAItBSBghwTpTfnydnU9D6jN8yGIlcZiXJlXZ873hvg+Dx0/f7v3ttiaqMSKUN7Sdeh7SYasn5n5Hc8tUGYbBe3sVhFFat9tE4JlHokUp7qlg3DYhtoQZXeWcvEc37p9I/WojkU0Va3S+/DUq/elCy2ht4IK1MrmbZ0VL0wleOtpI9u47O1TTyB2fYxtB9DeyYUpbW9JHOiBfRwWrUIG1uA8g6uI/S5nN1sHT6vbSq9IcKbB/usyx6gsr+5DQxqw7j0y6XgDwRgHqfa9J1wfJM2K73MEuG4w7WtmetNIt5GZv1QjeK5fEdkfbOtIsdn2Z89n41epHZjuaxh74gsxZAzMlhuYXYvvSlA27/0M2XbsNOtBXkI6L3x17pvt+kE9APWNd/JQ0C/4wOcgd63lwh6DwJ9ggAE9D4xTegkCIAACGRDwInoZTvLI6mlo0JZcj96UzynXvF553rHdczxEBr7bKja8/C8M9nUeWFlu+FtxWLgSvSCOBpEVJe0fCJAS9s6WiOMdqCqqZwI52LY6ogNmRdxaIjhLE4DJ1JaMZfLWyNcG8fsdFApyakNMrLZWJYoCjKg7bnuDJki6K2jQtohPmyYWwFbNgaI0S0RFDqdnxuD2hTA5a1jQAvDek49cVs5FsocIKqe8F4lUVbvwAjXv3Dhcdoxs+ODUvyrNJSyCPUmgljEQtgvF0mtIthjAnjMsagFY6lXO3yLkxqEs6uzAWNfa89hYwuEwr8Wz2ObV6SvbrNKJAU+OxGtA4y/S/ZLoB2Brv+2H6ET0B2pEBtn4FijZ/XGF0SgZ/MflzaqxRPQL90qnwj0H7zMRBGB3kYLC0MFARBoOgKFZyaoPilxLHzHCgX08JXL1VJ6kepNAZ3Fc37dkjFp9DqCVUUYUxG2mZKiq23mLhG5+X3Z1s82gH2W21UbTuVs+bLjf4JU3cUOl78IssAdfO42VEaONwqzPJEN42w9lSqe38tF8JUz0CVtvLXVZOOuvFcG75ecol0LvMJb21DaXtAbTXWGJW376alyNoE2cIPNGm6jb2xjRDC/GrFes946CV74Y2tIv//LRnEC4X7WXyvbX50ZT2xXbYvpzQeywUGqkU0OLho8NFiUbeF9d4NNuEl/gcpsqkjB0KaXMYS2pbNzbB38XbD9kP7rDALOrlZp+vlRK7TL+o/ZbZLBIbpmrD3kMj1oFrTe1eYGEdN7Odtg0/0HAh1KRcAJ6F9Z13xnbMYR6PcsN2f+AQJ6qolAIRAAgR4RgIDeI3x4GARAAAT6FoFWFNALT4xT2qByfHhGdyTFnHZoSJSBGMgs+naVBGVvV7fy3UgEik4PLueKcapyuYJQBTlbTW6LEKvTsCUJiOJg0M4JFiGt+C11a2cQ+w26jRkwoHSmO30m4qWOLpZNBXxfoiBs/3UUvI7M4JTf1tIWA17z1w4mN2bliNQ776t9pRwX6wwRYTiNc0Me4TbEYWP/dc5O5XQJRXSXGUAcfzq6RiIG7AD0mX7ivIqtI+EWprXkaoJIA2856ZT+ch6ecmpqHpU2A4RLlNHYTQH6XmxenNNMM/Mz9Bc3RYhjSDkehT9vilAR9/SLl8FAcwi+UmGfEteA2iwgUe7WMYno82pfONwPCWgB/cWf5yOgD/8+BHSsPBAAARDobQJFAT0QJ+U9V96D9XutLuq9c+qRFN+ZekNAL/xl9/LNqvp92L2Py9nMNhJbZ0XSmyK1eUN2h2zElbHL+6/efCvR1/x6p46hkkhl+VeyZFE5OY9d3t+dKGy5Sup4PsNdHYXl+qMESnnvpHddFtPtuyn3WWVl0naBG4fdbKzrINuK61FR61SXS3NuN6nrTcBaKJd2ZYnIplK9vsqEZHlvjnxDwnd+vXHYKx4I6ik2fTg+XI9sVlabocP1L/PhyivbS0f5Cy9tDzrbWjZYq40dLpJaibwyR+HaEDtR6pON77L+hEni+CNMsKTwAAAgAElEQVSbDEIby5lBIQD3x6H4g7bttcYv49bZC2T+nYAum7XFbrX85Qg1t4cksmG6bOOJ7acUZWbBd166TqytgN7bwRK9/d8DtF8fASeg/x8S0Nepr5KEp34+jwT09xGBnilVVAYCIBAjAAEd6wIEQAAE2ohA4bm9/dHGBDAdBRzYfXFU1hE06p6Gkyw6glQKaN2DYAO+l/pajFJyrEg5nWZcDH6JInf1yll81mhlsdlGLlAZiVYQnwQZtTqCXVKek7OFnQU2RTv1QRxGbNBbpxKlNmSD23ZAHFBu3kwpFaFLXx6cK64Fd3HwaEeTPmRb6pVNAGGUhnYa0s+x6Igwyt05DAJ28jlzoPMPreMp7SrSQrpsAHDPyvwElfH4tMNIifiac/TgcTsxIi6z00E9r/uvv0PCw82xFZR1pAW1J5sZtKOW+cpZeTrKXY1Lnx2phxv4xcqG5DZgBAW9DQoBP63n64Wp16P228h65bJhKsLIHMn0hN/dpDWhx1BpE4Y4y9jJqQR9RJ+n/bahnCLgCeiXkYBu01xmROlv7xTM8H+HgJ4RTlQDAiAAAnURiEafixhHm2TlXV5qj21WTHif6dz+3rr61NOHCn8mu0lljKIKV64yprOjaE/w+7hsxrT2hNsIqTY48juvLSfZgziS1W7A1O+CkqVKnpH3WmqbN+CqS+wy2VQqtoZsbBa7S0d26/d63kysGnfR7bYNeS8NI5blnZ4+l7kVm4kedbaUjUTXorREnctY3Ht/ZPKTNnqGa0dH7oeTroXgagvCE96toaPfl53YHBoNqmxsQ643NJU5y7NDlMDL1QWZDURA14K3NtMk7X24sZY+l3T5XCZh44K2y8RGLhPXvQ6rX2Jf3CBzV8ggOheBmB0K6J6ArexgZz/ZDAhsRkXmiD4LjyCwU1e0H01pUwutT2YifoVgYxBv3AiOD6Bz0JVd3zlyXrUVh/sgUEbAE9DH5SCg3w4BHcsOBEAgfwIQ0PNnjBZAAARAoNcIFBZNLLYd2+Usxqz+N9bTRDGrdKM3zsNi8Vxfup+hRiqGaFK0iAjZ5AQhh5js9vaiyK2hyef3WWHTtW/FQXFqhOc464he6kuHFdBXFUopDSWFGoupdI45pQnvMJz+r5J9LyKrOFtEfBfnkxc1nrAUtXNMzgd00RT2GRGAk5w/UrUW0PXacunh7TnX1dZdpW9NWXRFgqOD6ijTy8MU6TI+EcOTFpWtjOcyEN7FiSgONb2xQMapnTYuKkW+nFpcDtoXYV07U0I2lTYQlKFRWRpE1PaaDBxcYVueL0hxCPww/FjYZ7fZxBYO/Er8jGMnGwfCTQ+RSBDv+xE6mOwmF86mEDhqIaBX+pbhXgIBT0C/PCcB/SQI6FiAIAACINBoAoWn97BCVbi50G70lDTUOkpaOtnEAnphwa7FcclGXv3OzkMNXsjkXV/EcfecTdNOr2ZkD4mtIDaCFrCljLO9whTe6jxsfv/rV27ziE2k7/HZ59QPK5aL3cMbA+w7uhbOnRiv3ktlg7Nk7RK7jWwmeqdeWTBmjYHF90ZJSS/2lGy4lvd6Fh3VcVcxAT0mQjN2u87CDbmuf5ZxLTZRxbJi88SO0KogosuruLdJWtkwelOEHhf9LDaPzsKlxfNwQ7ZUq1Ofy0ZrnoPu0vu8+87ZjQ3eGd92vTs7zK4xEbF12Uq2rf5eu0xx+itTadev+l7pzcYibjsB3aZx9yLQ7REG4Vn3+u+N7g+PUzKIWVuOfv90lTFrdPqb3oWJ992380//cIY92XSss60ZAxG90f9V6vvtOQH9q+ua72QtoM9dbs6EgN73FwlGAAJ9gAAE9D4wSegiCIAACNRLoExA93aiBwafpIzTjcXEt7AzgcOo8wulqIrCU+O5dJo0he4Mc2u/de4wPzrsisK5cwjZH1zEtU3TJzvQXRSENezJCcPODznLm9IQrrJCaYJTScQ4vaudmpXPSTAm45OdOUp0FUGUHEAi0Ms57CyWB4IhO6/kPHM7LomUECcFR8yrMwAl4paqEqeXOJg8A9s6AMRg19ETrm7bJjuV7O700DmiZyop8kJHlVB553RTi0wi40WI1k63mF8nulaTdlLwwItPhE5O7TgpSx+uFpWbh4iAXqy4VLeI6hLNU5Y+T+oNROhY9LtwkLPsuE4ZRwBGL1eJ+qnk2+G5VCCFRTTdfKTPZX8PtHAu69VGKrk1WrarQS1s1YYT3CNjjP118Bxg1nEmkUHOSeQ/iBTu0T+z+LACAV9A/6wZtkHGEeh/L5jhJ73EPcAZ6FiKIAACINA4AkUBPXb2dtAHshEkGtq9tgQvW9VsqO5uo20dsW86d7w/1YD5HHP7Xt45+k+JzxT+vJsVgeUFUYTFcBej3XAYCnbaZqJW5B3URarbKFd99rm8csu7l7yPyju42Ar62Cm6J/aLy5RlhVG2pyhrmBVP5Xkvm5O1wWQTtLyXe5sD1CYC2dgrGwEkKp3ewwurjBlo/9suNomIuNQX2eSsN2aHIqxER1eazXDTsZSVDQIiLnOq++BdWDYLCFMaZ7hxVR6J2gGhLSOFI6KvN59qQMIm/A6EtkR4NJRkg9JZvXQqfe/nYJOBZ4pENv3yhgllVsjP/F2hz+U8e/uz+lr8P/beNdq2qjoTHYdzNgch1o2KGgTJRURAb6owcC3UIAkQJT5CREnAKrlXKUU9xHBRoSk0/2izGYQiliIWUYtKAsRHMKHAV6KJcnNVJFIaFUTFYNQQQC0FUdjncVvvq399f6PPMeZa+5y9zmv31Rqcvdcac8wxvjHm2uPrX38MHOQHEes9MkoAt6LcK/GcNwQN1OHXRZw0is9ijOKPw4EJgXm0/Fw5AdBF1dzhcNxx2rA+UkCf6es5GxECSwL6L5ZXPWOFI9A/9ZNywXUZgZ4bLhFIBOaPQAro88c475AIJAKJwHZFYPG23wwh5/TrWOQuC+hjRLvifY2GzQiMYCT68jFLvUSSW2lrxgQhFrYIKJM/5cKBTSIKQSPHiZQiJRx7eysxtf88vTsPkARnF4qDSA1hGIYZFvHlPTW4rTVh21K8w+gBwZBh5drYFVb2S4xe6KXKBuIQ1GG84LqGwBcGIcZGDEGtunxxd/cEdDUGmTOBGEawHnH8XDPQDTNkFGpFCYwaKVvWBRt0NCgM9lfs2KJlWgYjNvYAk1Z5gVY7GKZaedYrgwqN24fWcBjwa9gIHI070UDMz1rHuNMS9wffKVSCwGxTEyMQ/tew5rWMTXiWW99FPfuV742QZlEcQ9R4O7wwBfTt+udpt7hZJaD/lzkJ6K9OAX232Cw5iUQgEdjpEVj86q/T+aB1Fgo8QI8zFulZRbzSVGfhURx1HJ2RSURXobzFvXDebkU3w7HWRUOub4zDmTnFQnTje7hgS+cmz3ZEZyxci8jtAQe0tuAT+rkRN3akhXgNoR1pqXFPXM/COqLG3fHVxlpF2Yezr07dHJPBaSDiwwFA8IRYLpfj3v5v4JK6DsYFmdNw2vexCOfWE6JOAOLMbQ6zGhE8zKJU1VkHfwXXjWJ680k0fJj/xXa9cz7aNTmRidR8/m85L3P2tAH1oNrxWCvmPrzuyluJdFS2hNCxP86h5BnjBeEa4wcXj5w1Ou7ydS3niB5fZUcMPB+8JlE4H6tp3sJRnxuUUjOcGCOeV4w+93WeDD4F9J3+T9pON0AX0J//i2XDCgvoF38yBfSdbsFzQInAbopACui76cLmtBKBRGD1IlAJ6CBqHVFbUeqKhg2hq2XA6RFo7lsIl0WUL37pmEGmwKVuoxjYENBhpKjIPnnLKy8k73P53dOuw+jF1iIzUogBRaPOyY4GA1Q0kLlBJdT1hhDIaf40zaOlZOcxC0H19Op0Uxlr9T6n+AOTD/Odtt0R0VGtFUUKe6QvGlDaaxX810zGBAeAVupv4M4knPcdjEFu4CEPd46q8KgSwSTU1YPhLdSxnjb9+vPQ58ABIYq9DeGZU+TFm8PIAUMMP1/6HmU5aBYm7yjG6K9ydGkYdWkJl5xJqIZeHO/AqMd7a8Z9NjCKkTjfMmTiOekJ5z7GhlLeE8+x/6r5WWMYZBGNHjBIEX15T9Bqb10J6O943Hwi0H//doU5I9BX+27L+ScCicC8EahrnbdK/4QRQNSVrFE4s1cR1oOD4XAKA0GMIruNEkgkuovnLR4HSqDnH3AfOxdCJPbryNs4nqOa50ASjmMULERsvz9lp4o0BbdlAVKFaKRvjzXFSXBXR2brm3mKvxe4FaOMbF98zkaWLxm/RJcPHMct8nz9+iUnZ/Sja25nWx1/KAelAqhFibNwKpnGlEfBiTtshSYHQVS18VPgLSXG0FeLe7jo3xHFIazrtU0Pj/Y+jc2b5oGGowL3xiJ7S5DHHuY9JT+744RlYkOd7qpvCO1I4tYhCs7B+eLQtiu82zVjHETnQI4DY19c0dzBDsBYn4F9Zux7JTgO97BHXXnsZ3YWgRMClyVDFgbqb+HwT837Kzn7380QcAH95IeVDceubAS6CujX/q/ya7/2a+WGG9rZK3czOHM6iUAisIMQSAF9BwGft00EEoFEYF4IuIDO5GxAVhtEt+LSI8R6mhEnEj9MtOl1HQU6YuV8H/amFwOBeOHD89y9tu1aTy2ICAEW4SlaHOMUI44I50raKRUcPNkh7lYprUm4RCR1C2MQ6WgAgAEENe4qowYMBmI4gSGMRN3KcYAcIHrRDTDqeNrvxs5DmnhMC4ZARDtg/FITEEYXGINa98WaxNSKHIWuhgYzNqH/1kPBdQohXDvx54wD0arREZc5OgD3iwas+Cyw0NtyOOE1aUVTuOGI0mbGdPDdL4RgzamMM73nxzrD3pVrolOG34+MLtVa2n31FnDwYEzHjG92bRTPWTiP862MR1McA/ha4NEyAKtgLrUp29Hn6CYF9Hn9Ndo9+60E9HfOSUA/KwX03XP35KwSgURgZ0NgIKCPDpDOQzj7oVRP0+dwRj6FZjGCFdxE/0XwdhCs8dngXGVvOMfoRJzjjMfHTT4Xx5/Bv1DaSc/zODMiqpfOcSxcwpFxcG4O50tkD/MIdOF9UubKzqTKo+w9OAmzSIso3ngm5zM8+KSOn7ACR8O5mR0I9BwrYrilrZcfpCSXOhabwA4OJeK5tIdQL5xqjO8wJuCjVRkwu4dnVAsLjutVtLfzL6d5Zy4dz8xjDq2xpNNAQA97vGUn4PcGziPY2FRXXMcTssphfSMVASeUa5C+Xa6VcQtWkUvzXMmvRG+o96SyaPHxddzIthA5CT+zs3zZtXh0i2tWWbRsYA5dENBbQj7jV1FmclrhDHGtsdu4Mgp9loXNNkDABfQXzEFA/5sU0HOnJQKJwPZBIAX07YNz3iURSAQSge2GgNY9d0NMQyjHSLqezTMQYZ5NFBtH++8YkppkuwEZGx5AeiHSehRzqAHH0eMwEiESgo1fTuiJlGIITERZFJT3hZzLJSzc83xUaLZ0gXgf0RcuMJM1oBIszSqGyIsWTiwgqvHADDggyjAEca1C78cYtKdltAlHA48bp4ANrWOL+PP1LKKrIclqqbOhCCntpz0lvEaVAYYMGZUQa/g1jQ6NOQz67BingLl3YT9EQ13VztrAuUC6bjpl9ECIRslpYEGsp4wMvBa8z6LRNBrxqnqgs+yBIPrzvm0ZdZrOH50wj7GxKiT2TOLZbKRtbyGXIvq0/ZSfA4FKQL/04PlEoG/4lt4uI9Bz3yUCiUAiMF8EZhfQG+cfnP+5prMeRaaEqlaCIs6qoVZxizJBGBzcwxyAB7elLFIuJgbxD07JUbDUYZlgrD9bFLBcjp/5HNs667ETsmAiYrNiZmK4cxaaLGqM65mc7u/8UqLBzUlaOZmNq9om9j7EUoztvgdK2XOPUtbvuSSWMp9UzmaOzFyGybmWiKtWjkuGjPlxpq+WOM10hD9nMTfyZ+UJxJkAEWq2t/ZZ5Im8VuCFjlPPgbbDfVyspc+5z6lcPtgkKkE3lAXQCH97jirBngYBbOAEDp7LDi2od976CmGHF70H2w/IqT7uIV0/Gkd85rriNQ2i9fXA13XMJcPMAdTRlK+ciSNHWHPMTXi4PJtxb0bcyHa08KS/ne8Xc/a+2yBQCei//m9WdF4X/82PywV/lRHoKwpqdpYIJAJNBFJAz42RCCQCicBuhsDi10+YzKgyzgQm1hTPA7GdhsuA3HUMS1U/jXFEotkkjYG1O5lG40AIXcA1w5GQRhe+TWRGBAVEXRg3YLBgYboi2cRQq+j0YJCCYQNRFDDygJy6qG431vux9z0MRETqYUxqGgIs2kAMADDuwJjAhibsDRh+WsabVoRE696tazFv/DuIQrda6h7RYakQZxHRB/sWNesbRonKgMXGjq5VYum5GRiZwvPE+HNbFtB7hhBsn+rzKWPy+5EVKV7SM5rG+wCXCkuKZmLxnJ8B7Bt2CmCjVsvw6uJ147uB98lgPzesQGOGIcyJsyJMiTpvPUIpok/70s/PBYFKQL9sTgL6K1NAz92WCCQCicD2QGC6gB7OMC78BdG6cuDsnOtaAueEtC1lwXInX8ye+7LzrAqj9r462YqoS+VzcGkl9oV+4EipDocibLci20nAhqMwUoVrfW47nEEQ1/sKJ7NU1lyiSW6vYr2J59LUU61TGvp4Th3lRzbRKN5XEeic0YtEUo3SJkEc48H5lMfh3I1SdMcz8thmbYnLY+dgPqMrjzIHZB2jRLIbxqP3DGSl6SRs64czPO+B3ph7HAg8IY6JuS32OtrqPUItcr8+iNSxH+alcS2YM/CzwmNjnggBPT57zP9b43Kh3/a9P3ecMcKe74oTEe5xTJGfYS9X3YTrg5lk9HvTsSJnEWSGqNa28R1GeygF9O3x12n3uIcL6C98WNmw0gL6X6eAvnvskpxFIrDzI5AC+s6/RjnCRCARSARmQkCFc/aSbnqCs0iObjviVu+uPeGcifNYmzGCHe8ZxcaWsAlCCmOC1MxDKj01CBhB5OmiX03PR3Xs0D+8tFlMBDcmzX6p1noQ8uU6GDgGadhtIBD0PT2hpYyLqfKkOSLQK5Jtv0QHhCpFoNUYhFiunucjRpdKdCbjXPS+jwQbxjSsLadojOOTNlyHEH0JHr208D2h3rEiwyPwxDx713b3N6Xx5xqBPUMS78nKaaWydNjdyMLRjZqJG60zUDYkNcVlSkUoXVSODLMI9h3FusrAEMooDqIwLPNCNIZW+7gZhrGUTrPVdvBcWu1JpLWEIWzsm5P3uvWXAvpMf2pWfaNKQH/34+cTgf6KbyrOGYG+6rdbApAIJAJzQGDxK8dStGXPQ6/Bj1SMk6jgcJb3KNhOX01ORsSERTMX7cJZbUwk889YjOTzLDgDgVlllzKH42kpxuP8VdjFOINAr8dZE5z1TE58BuN1jkAp0PX4bOI8f17VHTdnARbjIZQiipzP54sbJ+K91BHnklbgK7FcFwTlMc603H3pa2yYgQeMcR/wKpSz8vPvHpO5CCaRu/Xoh46X9nQlQBtXnkWIjUI294vx0q2GMPEYqCxBbMhjgRMFxoz9xNdg/bHvfB8hM1sDK/av5gh0d7K3G0THgoh57ysE4+aIdf/yiBfRhAfCfViY1qVi/0BWPDgCyL5fWDfk/tj3vL+b+yh+By19p6SAvtwvgNXbfklAf3jZ8BsrHIEuAvpf/ihroK/e7ZUzTwS2GwIpoG83qPNGiUAikAjMD4HFW49fMlIweR2QUZBWfNAwDrUEzy4RRno9M5AMdLmGUNeNvghjwq8wLKgwrqzYapxR36j1Jn2jZp0QRnm1ahOCmIqBpkox3og0HkQ1UP09IaoagQFPc2LiSk6l9rSl+otpxOVzCMlcOw+RBty+qiW9BIP+xAYwF8j3MBJNzgHAqCWSt0jz2D6K+wq1zW15fFytiA1dEzEebapTmMOwhnFOe1wG+8jmWqXus05gROPx9J6N5jZsGHv4evTfFdDDc1AZVMJA0JSNJzCUsP2EDViVIYV+YQOgjA0GoWnYNj+naIeGjZR9DfCYeumCloDe23PY95VRC3ueIn90r8EhhGud8yYcmWjj/guHfGKrkMmLVhcClYD+X+ckoJ+ZAvrq2lU520QgEdheCKh4jjPEtJsOhG1EApOTIpdBik6bXc7TF6Zi8p7JEE24UgddOudxN/hMeAG4kJ+VgsMjnI65RraLyxRBHp0N/Sxpwifqe7vjsTkXwJFRz7vCk0igbTm26ngsIxWLxJytyvmRnfPAwdBean8r9zN8WLBGin11EAjR/jJG5ZrmyLyS4nnkBTjbQviPAmbDuVOXX9bT66Eb92PH49Y+blDwpWbMmbCfeF+RA8OAL/UemmBTcAeGlrpMa+Cc3ARuXjcI5bBNRJGcHbzZWaOJR0dA5whvXg/YB0ZxpBu1uBr37U25IQ+UnF8GAnrnPuFy/RVZ92Tc/kxQWQmdozhgrK1RSgF92l+D/HwrEXAB/XcfXjYc979tZS/tyy7++P8qF3w4BfQVBTU7SwQSgSYCKaDnxkgEEoFEYDdAQAV0eXWjZFvCOYwawdAwE1EmNsnGpQGWjXbcpklKTRmE9zTILIxBKkpvtvR15lWuHteBGKuYbuK1GkxaipyJiogmcaONGZr8dxIPQUxlHi4MKvh1aTKJdFAiG+r8wdjkBi5STDnN/LTazW7vMPEQ8wcxZicBNw4FI0Zvv0x7JiIuLqBTenw1QCGtPEWhYJ/K2nj0g40LBj1EwERDUs+5o9pTYc+psYUMepyRYFp/3lUQ0HXt6aZoByNqMxqH2leCMllJesJ0NMrE52ZWAV33I8oZcM3IMUsNxh3rE45sEt6bVap+XNOzRoX9CSNma32jeK57vGEEbA2zI96ngD7twc/PBYFKQL/8kPlEoL/8Gwp2RqDnnksEEoFEYGURqAT0lpiLcyqf7fTcZwIrnzX8jDPD+TqeKXEfFwfZSzKQpdFznx1KcZRDWnLtgs53zA/03pQ2XX4XsY0zePnxz+aGs62eqW1AnmXLOKUPO4iEfK6HsMpiqIrBkkbeyjxBOOVxuiBrN+GztEZob5lEmQNPFg/ZOVMdjzF3E/aR/h7R6XIL8Ljlbr8Wt8L6gedBDMdYsFRwGvfjMvEP8CYVdk30h4g+yhlH9hX2IHDW+zacRGbCgDYpuG5XfG7wKn0WjafwOR1cIDojMBfV55AyrFVrwPO3n2P/in98hgkU7FW1TRgYfA9c2+RjmCu4Y+B9ek24d+ut3jLGtXlwcWnvIhuDjo943Np1tSNOhxcN7ErK2ye4LDzp72baFdkoEXAB/ffmJKBfkwJ67rJEIBGYPwIpoM8f47xDIpAIJAJzR6ApoA+E7S6LbQvvg+ZgbkR6neC3ptho12vf4I3aVEUxM6iwQMz10uRaMSpwdK3W8qO01TAwuKHMyCuu8WtDhGucFjDl6A8ZC4RgiHggqnK9pxfkunnoOAiXMDAN0riD8IPg23VyXzXwkNc6ROgeGd7W3dgyGBCh1u45bX00Bnm0ihngeN0giqIWo/QLTNR4QaJvNAY0t7c5SHDUihs5Qmr/Hi7R4OlztQsG2nMvnaVZQ9DerwsDH3lMm0Pk+0fDjQvQcZBrKPPCrDeMKv6UjdR6pqfuyREBnaNM/PExAyKMnD0Bfep9S0nxfFu/GFbP9QMBfd+FFZ38d+9ZLI9PAX1FMc3OEoFEIBEAAi6gN6kLnYlcoDNnXT5KwWGyJcBHATWeI50LhXvJWVU4j75IdIvjZMEtHu+cR1h2rFZKdsl+xWdRONkiylnL4VA9dT7+scNxhUeIHvYxG3+B8Mjlq5BRjP+Vecv40J6xlIhZCOXSPzJ9VSWK4ABtgipK+6jTstWJB08C/DKPsdT12/ro8H187WmNmYNGDsecA5m7MF+MGXMEv8LacakB5wMjk8G9IO4rbSFRmrlts5vIZ2JJrEgMKAqdndBZGB+Iy9Qn8wKI7zou4zjgBIJHJbYroVza425faIjovF64xyy0iafaWv/RyHTil7RNpvnV61UebEDfWcyZpBN5jnhdsc7Tvg91LJPJLDzxb7f1qcjrVxECSwL6I8qG41c4Av1j/6tccM0PM4X7KtpPOdVEYEchkAL6jkI+75sIJAKJwDYisHjbb5qXOKXpdqLXqHunn0Uv7FbK8jiwBiHmJk2hPfQRvcb94wYLxRARKeFGDZsnp3L3OSGy1lKXg+TB0CJpCeU6TnfHUdMQZ1WsNw94fa/DkjkywiNHaM7siY4uuC833oQUdtqXL2JjhxgjF0zEYNJaKk9T2PNK2IqNx4YHGKDEMANxHJ+jFjqMMDBKtQRM1POr0oqHGowQ0LE+jiFSZ5pH/diUPOVhw4KBdRi7vrUecJhgx4XqmaD6mFVuc7JCtvYN9zEQ5vvbYfAJ9lElKLOhSoyFNpbK8MhW0rHBzLC3li2gNy5AFgoMhaON5D2uG+mpCKMIH34Pxu4Uzrfi+2CVX1IJ6O95QjlgHgL6f7pNUc4I9FW+2XL6iUAisGIILP4jUreHdObNGjSdM1ePF4B3tM7lkfNIHypiI3U4Zb2SMzbOM0iLPuizkXHLhdfADXDWlnuJsyF+B3/SiG2qJy4CHJxYwXUQJQ1nVBZ8+Xyv2JDwX4n0LMibwIlzMMRyZBOTftiRmEVlGR+ijT2q3fga+KP8i9T0MhfnfkFU1KGao+4MjpZbtRExF5xn2SkYa+CcAo7jHX7nddA3T4RS3SeyruLwIJgaf0UqfGDgnJTE8N5kpA9kGKjOy+CfPd7V4vPRCaRhg8Bzg7Xgblw8p+ucs1BD7FndT5zBAKUIQnR6nLvTBIrQrp5nep8dxNFPnHoUz1mg726iDq+KXHAa/cIz5RyTvisgmsv+wJ6R8Yzt/cb33cLhn9qqRyEvWr0IuIB+6pwE9L9IAX317q6ceSKw/RBIAX37YZ13SgQSgURgRRFwAZ2F3iDzpi0AACAASURBVEHUAwnpFQkDqe0I2NHYwyPHPdhQASNAq12TYE65rxqVqAYfG1MQvVBFRMBoY57VXv/OopwZI4jPalgw48kgBeEYY2UDgAmRMWIcxhrcI4h2tRd5rOvc2ybkNW7DnoigNtYYyY2oXHUcoHSFLbLc2jec6k2NE2bQgZiNfiLxhiHIDQaoUx1YP5wbOBUk7skGJp2i1SzkyBO87xh0cPNoGVpvbupe+MFwMrZv+RmonFLooqpN9WAs/QIDB7IYKKb2ccd3Qz/lNmPGFI4wYIMnDIZsCBozCg8GNcWC0/u4a6RpXBDfiuI5DM8agcTZBMYF9BTMV/TP0KrsjAX0b713PgL6wWekgL4qN1dOOhFIBOaCwOI/PmPp8MTHBD+HhbNX6xxTncvCIa3rKIwDu/UPR1P5lWsVIwI3cpt49tHrrIwVkwlEY6NfTcNtJXvieRJpyuV9cCKc2XEuhPgqnyPy2zkTskER/wA3Y47B3MeFPTvTc0YpjkCP0eQ4Yw/4juHZOlfivsJblE/YOFlI9vrWFonc4zic+r3JC7BvrM77YH0M/Bgd7rsczq3WzkXOxgbEmoALKQ+yTG2CT1W2zPZJK7o9rlF84sDzmO/xNcybqmunPRMjxAaO19ofGRhwCcPBnBtOHv6elG8jzspp3XmsoPlR7EZ//hxx2vXO2HrfWLNytbHQ8mmCOe7d4uI93oQyB71rw3wWDvvkXL6Ts9PVg4AL6Kc9omw44RdXdOIXf/RH5YIPpYC+oqBmZ4lAItBEIAX03BiJQCKQCOyCCGjKdk5vVxluyNuYDSKRJDpJHYtCn8WjHIQSRgS6holvz/BUkeMgYgrJkygNGIIGRD0K3aUUiTaPRBwCugvzIig3UoiP7gUDMBrbcI1H9NqExOCkkRK2Hnp5Ryzveq8Tw5cfpWZZdT/7nOfLRjR1PLBIca0bb0Ij7sdRyDovMxRw5EbwF1hKjS+GOUs1GfeWp0xHXWquT037BOODgUcNF6hHaJ2yAQ7j4vGzSNxbP+DDaQl1OTBwRLQ30ro37T0h7eDA+GEXDQyq3YWuw6HIdjRLlj9/lFuis87RojJ4POpUYQ42uo2mKfZxI4w8LC2Dz1iEAxsoxyCqjD2h/qPfsyXGL72XAvou+AdvJxtyJaC/79C5RKAf/NKv66wzAn0nW/wcTiKQCOySCHQFdBxtWkI1Zto7BzIS0Wky1kbvnbEgSrcOe3zfeL0LfeAmOEbauU7b4yxqAwWlwLla/oUTIh8kOcqXPwengaMsY9Y740UHYqNCOjTmR1UJKxP/lSNsKWVh3dJ53XHA+RVOuoFjge95lHrgcIw7O2EqV+KSW3aGBkEanJcNVBGvmaNVewM145kL0VlVUrM7FogSbzj1ypg5Ch3n+1ZGMqXmQZxXR4UZylcBG3A03ofstAwBv+f52+NArT0Rnx+MX+6BTAzg0fgMewn7Vfij/Hz/Yin77LmUUa4nojun4AR9jWhz3GfaMxpNEiygex+tr88RlXxWAb3iR8Sz+X3w5RTQd8m/YbvyoCsB/TfnIKB/MAX0XXl/5NgTgV0FgRTQd5WVynEmAolAIkAIqIA+Jni1RF42mCj5rNj95JcB2aWGkdy6ESEwRm4HI4SMp0rVzazVRNtYb4+NMSwQc9SA1iA0MXyWHSJzFkODkEcxdoihZCC8GTDT+oVRCVHmcGjA+Dgag8cGo1fLIAfjgKYxpEhjeNNzWkUWbV0HZoMMxHMzBAHfFhnHWBDVgqiVKAxHg2CMqGBjAePA0exxXbVemxl5KrEX+4JSDrJRp2f8UiNFY5KelnCw+ZdWpxf5MVgrEsgr42fchCEN5eQhq9e2uW/tmepp2vzIMeaDaVOqexjS+H6yh91QGjuyDej3mlFAb+0vfB91RXS6qGeAivuGDUGV8bBvbVp4wl/P8i2RbRKBUQQqAf2/zUlAf0kK6LkNE4FEIBFYCQQm4jnzDjrPtI4MGqFtmY82bpxcKA6jiPKVy/Vn4xBw/vR7dA5v4Ec4E3kz8A5yZIXAXPEH+wUOr16mRw++ASrrE9xL+7NMTBAlWciUn+UsFTNqyb1EwJaXYIHPIaTGs+XYee/BjaUsTBxvt9z7YFnzkHWlrLP04zjT8RgxoxhRDAKrDrfkAKsd09qpky+cmCM+jYUHx8H2ACdpbUKuLy5YiLO3OHDHsk0u7lonVd+0biKi6yuUqIpnYnUohyBP0fy6t0wg533InNw5HCLzl4Lzq+3D+xLcqYUBeGW1D+M2pGfB+RI5T3Bz5vrs/AFnEDhY6zoDT8uyIL9yFDompHs8PB89xw+0w9bQudOzic/9PVrTOA/YBGYSwKc0Gvs4Ov60eBbPQTNKkDM+xt3hZxl93tr4+d5yEXAB/UX7lg0rLaB/5Eflgg/+IGugL3dRsn0ikAgsG4EU0JcNWV6QCCQCicCOR2Dx6ycseZbH4UShKXzOtasWv/obdXR3JZYROUUfAy90IqxVOj7y5p4ynonYGTztB0aLhmAphBBR1W54gPHB2Cbm4wYEMRCJEcxEdxb25Rac1rAi3Jg4pZaXaxEdX2HMYmCMOG+Mq0VakQLe67+bkaQnLuq0A8MW4o/5tDCO+0YcCgQnT59OEeawVCA9oHvh24A8RSKnFw+Gh8ooRDfHfdXIgVTttNFadbxhPOkZg3oGBKwxG7jYsIAxMu6+91lApv2oY+mJy1FAx3UNW+fga4UWuyeku+GDjEnVNpgioMMIO6j/yYPBOGayAtU23K5gzgOne/H+5tv590HYU1gnMQZ5+/Y4Uzzf8X+3dpcRVAL6nxw2nwj0029VuDICfXfZNTmPRCAR2FEILAnoOB+Qg6AMCo6cKojbuV2ySNn5cOHf/b8+9FqMJ8fj6pw25dBWOSRTqvXIhWI3OCPFc/FYCmg4qGKecO5lh2mc1TB3nAnhTKvOA3tMHI/5xUIu3odwyGfnjZvL5jvvL3vsv88Sx+DP+f76Pp2VhWd5DXO7CONSR2drK+dAjo5mUXcQ8dw5zyJ7FsT8MVzRlp3TUTqLMWLexE7IzjNoT7KAPOYcgYh1uY/yWKTU1zdqjsv8l/v3GtjMWWngELvZ+Z2vV+oOTjultjrvNRbRW18IlQhuW4EFdMY7cgzn/KHEQHUfKg8H/CIlQb8DZxLDF47Q3G/k52g605feDPyq1cSf147jOGOFucIRqLIvdHhTpm6fafWy0XQEIKBf/B/mI6Cf/4EU0KevQrZIBBKBbUUgBfRtRTCvTwQSgURgOyOg4rmQJtS2jvXijPgtx2t4YBBS+wV5XY+SXPP4R5uBd719UJFeq6cNglmR4BaRYw92pCc0Q5f0EQk3SGXUNREtThx4Utub7qkGAiHfINlsmLALo5GJ8UG9cTYYVJ9bhIlHr5ixDsYSHzNFEsBbXMbVEnejEUCdC8TQFSIzeBww/rD4zZENmuYv4hLXAYJ7jHqgG0URP665Cv2UvlDxZ6uhzYHronN6Sd+nElFB9dajwcwNPxC1gwiOIcucgQlvxZZxNBpBtY+wf6sa73gWQh1zvzd93ujKde4xTZt8PSbDoXSQnlrf7sPGF1+yRufxrZ5xKY45Om74uvaUcto3bJzjtIlYV6wT71OFPgX07fwnadXdbiCgP3LPFcXgu3c/WA5OAX1FMc3OEoFEYPUisPilY0xcjYct+/3BB/SHhSM/NzNIi18+ZqntQC/nrEoUbcs8yB06LZqdS9nw2SzyGHconHmoS1me/P44a1p09kBos8/hiMu3UjHbIrsxNkTq6tiC42hL5OMjIM78nmqdbuaOzcaTPNuQ/Y4zJTKC6bnR+KU4QMhrVvHcz+ocAR4w9sxaxGHZiQDnUebPiOBmoRvtWk6m4LODMzCNJZajYt6E/cFckTmTpoC3bAOCVUzJjtsI9hrNTxnkKs4DHkEO3nE+kQfP4mSCuaBttEXg84HTrG1GLm/nc7EodM50x/0MMrTZHlAMaNDgGLqnQhR8ay15+8g1zUj9jnjuXGfG57x3f8ZP2iB9O7dvXLscG9KMI8xmqxiBJQH9kWXDM1c4hfv1PywpoK/izZVTTwS2IwIpoG9HsPNWiUAikAisBAIqoJtRYCUJTlNEh/FB/yWyDA9yIWZCxkDIpUlF+o18QlSOqbpZmHMCR974vbpq7hGP2s4YoEWIV17jJrhLk6azQVQcTRTWiJSRV5O8A4CKNRNw+JEi0zlag8m5GIrcyGLGGhgzYpRKa5gw6qiASsYehcrSOMrPGo0iwrwZmzSa3xaGowsUP7UmLH3OxqRKiKcB4V6e7tHSK3KNQYyVRW7vgp0AGkZJN4JAPDeD0MBoZuP3uQcBnY2UPeNWNP7MIqC7MRH3C89RK2oBc+9FNAT/girdP+PGz+MAWzIQxZSTuG5gFI7fAY2Nh+fChXtrQ9tmpu/BXkAGG/zYYcIfveD0AYeiTN8+E+zZaDoClYD+p4eVA+YhoL84I9Cnr0S2SAQSgURgOgIqduMs8CufmX7BDC0Wv/RrQ4c9dxilLFfoq0p9TudpPRtRFi6cldgRmM9y1dh6B6U4AdTCxvuxlFBwanRBlsRw5XvmcMyCu/O4IGrHIVSCXch0XomUDadixZXrh/uBzzgM1XIH1/AyWjGFewszrhUe72MTAc9FDXHMh8XrFjfD2NFOo+ellFhwUvalobUZOB8QP45lqXDGh0gL7tcqvcWR9ho1b/0y/dbx8L4hzuTcwDYrHBd6qdxbwntlDwibhVOn67WWoYx5HZ61yk4QosurklmY40hmOB0GP4tkP6g4Rni/xTdn8BOezDruxy2lLEqpg4WeP3D724n34+DZMxuAZ3JoZAzg69esKStpX5rh6zSb7OYIuID+Hx9ZNjxrhQX0635Yzn9/RqDv5lsop5cI7BQIpIC+UyxDDiIRSAQSgR2PQDcKvSLK5I0NkbEi8SGSglNs+xRJsK4E2CB8Ka9sGDqYiDtBJXWODTHswc73ikI6xhYFy4FIHtYJxo2Bx3tUOM0Q0UsHyJEXLBC6MwJZNSrDVRDG3SAhdQxNCK9SPcIQtMlEc74+OB9ArIcQWuHKxibrU41b0SBk447p/REhUXnwS1QECftQhaM/BRuUGAsYpWDcau0fae/3iJHucW3hwEDv87PAeGB5m0p2wwDFt4oiNTuVoN1gO0UDEIww9GxFQ5j227rZ2N4kr4IxMZ+dIfxZ0s3bAo/eGzH+dgV0V+gn+41Te2IPML4wmh/yiR3/JZsj2C0QqAT0Pzt8PgL6f7xFscoU7rvFlslJJAKJwG6IwOLNT7cIZ+E+lk48Clk4dsWU4n4ei6V26OjEInrkOxw5XPGrMVE9nAG7wqaktbfMUNK3p0yfUbBnh4Dmuoez4djRlM/5Lg4L1yCO6NwjRI9HvtjjlE1BOHBSOFVDmGeuoRqv8Rc4djovsB/ggCz/suAcHZW1eUu4bXBiXSPr3yONIy+kOudOFSgTl0YkW/msFkfFvJjHRWdiLn81Fgk9uK7zpQBng5YTLwT1loM9UvozT3Q8qUSZ7mlzkneH6cgB4RDSed/5mTloANuZvudaBG02ajRT971GOlepgW4O82iXEejbBGtePB0BF9BfLAL6w6ZfsIwWF4uA/uf3ZA30ZWCWTROBRGDrEEgBfetwy6sSgUQgEdjtEPAIjUjcfabkfS7vCelGPTzS7QbRzpFURhFSyBwbQpjsai23jogeiTgMPLhe/3WGG8RZJqqU9pujtqPYqMMI3u2VOM2pwVmMJhFSbwuPfZsXDE0QoD3tepx3iLZvRYfDsONiaQc/GF2Yw0OIRJRMVV8cQjhHIJgYDSiRQrFHyLGvYASEkYnXEancW0YT7COG0/0mDHvpe63Vw8ZnbBjgCJAo6vN+YeNWtyYlCfC98fp6c/Q+TyRE4fC3ShSsK0cCM+zguYEBrfmtZBdC5B7sayspMKiDPsVQ2npmB2vfE9Bn6LvCotMezh1ISSj7GuUOFObJdQspnu92f6925IQqAf3KOQno/yEF9B25xnnvRCARSATGENC08HzG48YtR17wBRXu+IAXzumtaNYodLfE86bDcjWoujRXlVEqtjMRN9Y852aLm0vZtKWUh6xbejdGTIM39c5zLpRK2SkToNeZI65cG6P2wcGcN1EK8t6x0s/Hhrk7OMTVtYN8T2RnvLB8SCnvArqJtLEP+RxR31h/jgaONBFD03WniUUnDHYIHjv/V8691qfsF8Fca8dDQAc/oc5YQG9yHcKtxed5Lny9Z1RrDBx80Z0CQrr0wXxs3Lr/LMsZ9pa898BiKesX7HHdUsrGTaUs2L7lLHDOYYw/uHMCjdHXNjg5NKAbzmyZ3GdbvoIj99XxKXEzAV1KAW6c2HI0C0H9ygj0bQE/r40IVAL6iSssoP+PFNBzxyUCicD2QSAF9O2Dc94lEUgEEoGdHoFBPb9KwGPWSwSV66qp2B0836dwRQVl4Ckub5oxBWnM5S0YHTSy2gwr8rmQZEk/vnHjEsYg0ZMbLKUpVyNHwwLAUQ0wGLlAPUXk1FvYvIEHzxse8VUOfBsqBED1Bqe0cRzBAsOMCMOLD04w1kgI8lqAMc1FzY7BxXEkPHUoZPRRQw/VT8f84lpy3T81+IVaeppyL6SJG4i3jdTyVRQ6lpSMSDCOuKBMxiD5UbDU9bdrowMGp4vXqcMpIDyi0v9YnULGMjweAy1a74Po+sb+o6XkDPk+IsxV5hUdRXqGt+piW18vbdAYAzCLwn3rm6sSz+2C1nOF53swEez/zrMVn5+BDt8YP4yAbDRKAX2n/7uzKw6wEtCvnpOAfloK6Lvi3sgxJwKJwOpAYElAtzM0nyfpLXUCrs6ZnD69ce7fGvjc8dn4EM71GBOip3EmhzjtnCUe/HCuQzQ0iaQs8LccMqeN38dg5aO4j8ox2HCCEOqp0yn9OQRB5wMGvFyj5cXEudY4o2NAkdzuuKyHVXJwDpOQ/gbOxVZP2jkT6q2zU3g4vHLGNuYYWCcI0Hz7eNzl87eOyyKpK6cNREUbXwePw77AGoqQrGfn6tAdJ28R8ZRNC1CxwO0O7x3H7QHfmkKceE9Xwjv9gvT42FM6HpAiSYUuQvnasuXeB8uavdfVQnEUmGGL4BT4Y/xQUZqFMAHOWQwiBH2reUsU59WKjhvgi/E5Va5szuZwQKZ+Ujyf9iWWny8XARfQT39U2bDiAvoPyvlXZwT6ctck2ycCicDyEUgBffmY5RWJQCKQCOzyCCx+5djJHJyHNtJZ82eRoAlJE+JddTIioLd4chTFnIcS+WVRGfXnpJ2n0rM6gyKgx0gJiPkwMIAUt0T0mILdBWmuEx1qh1eYmIjOAjpEdSeuAQTB0B0EyIiGfiGcy1z33HMiwIqTAFLtwaHAxVUYbbheur0Xd6wbXGxO8D6HAaESWqkGeVyzyoECdRzVurO0uXppLd2mYOPVS8xIxTXafY+RUMtGJkRoyFgQ0QHDhvQT08rHuoEyVkTuxH3q0RQdHLV9IxK9aVMJae95m0s3lQGw8xWjRiLDmY2Bg+ZkCMRDLs+I4spqfe8+U5xGKmNVxyklGnJakfz+TE5JV1kZjfgB8U00cZyAIQjGwS1bykLWPt/l/17tbBOoBPQ/f+J8Urif+jWd9rxSuH/xi18sH/vYx8oNN9xQvvKVr5S77rqrLCwslMc85jHlaU97WjnjjDPKMccgwnL6Ckhfl19+ebnxxhvL3XffXR75yEeWpzzlKeXlL395OfHEE6d3UOTP28by3ve+t1x55ZXllltuKffdd1/Zf//9ywknnFBe/epXlyc+8Ykz9ZONEoFEIBFYaQRqwRwEKhz2WoLbQNylkeFM50cZa+zCZEd0mypimzgt5z+c073EDZ23uB89h9J85IyJ87tO10TZlrMkO/1G4AdnQWsgAp5yGtQ8J4daxxGOuHYNznjunBvwkTMunHAlwlj6EW4o7aszM0ReFnvJOTzCjmsVB6vH7k7jlAHLOWBHRGdHWuAZz9PTRPQooMuYYiQ67x9OGQ8+BXEajgYxsh381Y/bxh0qkT6exQnL6DzNe2LMkZfb8R5o8XZ3rADnrC6ecGVpI9HnMj84V/OzFvcy79VoF2h9ofg+mSak8+ct44d1Hpv1nh2HvuMMLXtT9sQDD5ay155Lzxg3R9Y4ROLT/FJAX+m/HtmfC+j/1xwE9GtTQM8dlggkAtsHgRTQtw/OeZdEIBFIBHYqBLoC+phoDgOKk199o46cZmIaCXOL9MNwIm0hDEeCLtEDamixenzKPckiBWLtqZttEhDcI/JIc85jdSMR5oOIERhGyKjlERJ1ELhGwVdjt4hjJtga7WBRC1FQZEKM2oPqJW7CYEwvL31xNHI1TxJQoyGI1xhiMqI4ENEPATymeBykjeS09ZTyDmvthpsYiU7GAtzbxx9FdFKa3djHjg32OURwdQZgsT04dsDogr5aKSqBURXZ3xDRowPKlICKQRR61Z6ilKL+zan5dftbhL2+b9Enjl/LkENZGLRdNODE9zpfV7qunDXAJsD7vhkRZLdsRXxgLOzEgOF5+4aDCb5+5F/UQgc25jyT4vlO9WdntxlMJaC/f04C+u/NT0A/9thjy2c+85mp6/HiF7+4vOc97yl7igNX57Vly5byile8QsXz3ktE9He/+91lzYgx+Ac/+EF5znOeUz7/+c83u1m/fn1517veVV760pdOHXc2SAQSgURgJRHQDF0Drapx/sG5pDpi0YWV/yIr6+GMA+dM8ITq3vaLi9bhpjjjytseoUtoeFp0yRplqbvx3ljKdupiy08Xy5p9LCU28zg/n1JdaJwP2alZ2glnw1iVz7CDJznkcqrzKOxiTMITwfmk/YKNbXA+5kmEozCfN6sU4bjGnBHYSULnYGdwzIE3ngrJ4cwMXsV8Bkdw3h+DuYILhXHr8gcHXcfFOmTuxrXaxTFAnRJCNrjWvZ0PUmp9nyuyKgSH69bf/MrJpCEAM34+L3tTr+15H9M6aZPAc362WIqUB2DBuLJBUHvGY2yIHf+WpSmMNRhZTzxHvS+xVsk6tI08i26jTeR3cCY8g+bwkML5Sv7VyL4YARfQ/+9HlQ3PfviKgnPxX/2gnH/l3VkDfUVRzc4SgUSghUAK6LkvEoFEIBFYhQgs/uOxddRtpbkF4wwIVzMq2IgYoqHd6z1YAyByK0mrrAsTNseGIDUqUM1oIfIi7IqAzunaKmsWeb7jXi3Didbdjmnm2Yhl5JINOSp6W00+JfMQLO06iJuaht0MVpXwbOxVPlcDUkcMhBFEjUBI900COj5n0bYphEZ8G3opR8pzxISMjz+r7mVrzfeMkdstJ3uQd07vHw0SUZhHykVESYD1c8QG+kBKd4y7qsVOe9n3mL03SOXOuIXrcG0sUcD4VM4TZAWLzxYbofBstQISYABqGWJh9IHjB0ebYJ/4dHifstg+1fKzBAiMSb3vSr5/ZbyhC7yP1n1DashqC+uF4XvDGrCRmY2ma9Zk3fNV+Hdte025EtA/8KT5RKD/7ld1OvOIQH/84x9fvvWtb2m0+SmnnKKR5gceeGDZtGlT+exnP1suvvji8r3vfU/vf9ppp5WrrrqqC+35559f3vKWt+jnT37yk8u5555bDj74YO3/wgsvLDfffLN+Ju3e/OY3N/uR+x533HEu6p988snlZS97WXn4wx+ugrpcJxHya9euLddff3151rOetb2WOu+TCCQCiUBZEtBZ4Wxk7uIznZ9P+LzSyDaE85PwHDidKo8hrsHnV48WhqMo+qfxgLPAGRfci4ePeuI6ZqvL3hDQt9y/saxZb/wG3GzNmrLl/sWyZq91pSw0OJXzJsoYJvdx50jjdTJ3vSc5g3JJLRF3q/nGzWgOtyqgm7gqHG9P1LtGe8Om5cTpQjZF2ddEtc7eBF4ic5HSWlV5Jjrwo1/nDTb5QftIBIw0tRyW+VwfuUGLL4F/saOy8zbmOVaCC30M7o2yX7EcFe1BuVY5NhymO6ncKwGd1ofnxvyeORP4vbRF1jGsFXiRva9p2x+6ftIr7Ary8883lrIX1UPXfUlO3uzA2xxrw4Gh+R05jWPZ4nAzzCE6Hgz2qDm+MO/EGFrXajYJmyML6uvXu/0lxfP8QzdPBFxAf8mj5yOg/9ldKaDPcwGz70QgEVAEUkDPjZAIJAKJwCpEYPEfnxGipQkE8H/9t2cgYgZG9fxAbjWSGYQ7qqXBmDQNfyWXJmKLIUJS/vXGxd7kLnqLIE1pABFpwSIkRz+AfKpuR4YhJ580HzZKqIBrtdudyIroLBHkIsAHI5NNa0LugQnXsrMU7y0yzDYHMRyp4cIwd6Mbanhbmnu9XyDdwNHTnFsKQ3nfcaIF4nb+dtgjldGQLAMczY3tw8Y8xgdYuvHC7lGJ8HaxGhYoikZuyWnb8TmLwDo/c1LoOYZMjkkWmUFp5pvpFWlCPYML4xVTGIbHqUrnjnFrv4i8D4Yvdx6wrAStQuoutmPDTTPu2IBjM38+UGee1h9rqKkPaYzAsnrWaf1a3wGVISnUCa0w3lLKeomQXRpoRp5P+1LNz7cFgYGA/qh+hPbW3Oe7dz1YDp6jgP7c5z63nH766eUFL3iBitLxdc8995SnP/3p5bbbbtOPJFq9lc79m9/8Zjn88MM19fpRRx2l7R7ykId4d/fff3+RaPebbrqprFu3rtx6660qrsfXFVdcUV7ykpfo26961avKpZdeWjWR+xx55JHlJz/5STnkkEPK1772Ne0vX4lAIpAIbA8EJunb7cwS6wrz+Q3HKx0UzjhLSZE8Xbl+HLiR8AQI2C3a1BPLIK5V53njHnIWE/EsOoz2zlyISOeU5Y22E3HS/u7xmRB8Ta5hwc7xIFFVM2CZUzLXaEeWrlh+aXCOpPXgDFQDzhQmoFggDTvxXIio7JgKzgHHBncwJqEYuFfOsXYejXwE6z5YXxb4jYdVfCtewKSExgAAIABJREFU0MlYwNzCRVecjUO5KTh/y5zg4M3OAWNR6C1HYMAMZw3w454D7oAr9WwFKDkQSg9wloDoGPHzRRPJjZPzOUeeMS/3tHnyfLTOE15iKjyrM1EncqRwgZ/2IU+VBfvel5nzXPteifwqfjcwHprKfVFrweu85d6SVQjPp90zBfTt8Zdk9d6jEtCfs8IR6H/5g3J+Cuird3PlzBOB7YhACujbEey8VSKQCCQCOwsCGk3RJWoc+RDSlDvpI8OQk2Zry17oboQZEdzjOJgYMikUY4Kmcl+sBXFcD2MME0dO1w5Bjj25OUpD+nHij3TdxmzZGz+SZzgKDNIP2sDWLVjkeZgo2TT8E47OGOaLnDRjURn1u9mhQMbDERtqDKKafVoT0YxWaMsRA6jpzSK6YoOUeLHWfUdAZ5wh8Pdq+3HfOkcSt81eUGUqiNkF1AECgi0i5SnTXxVdYCADA/QV9zYvSkzlDjzQhq9loxDbUKrlD6nv9bNu46X6dWyIgaMCIn2wD72rYKFxQ2BM+d74ImgNBWvExlJORan3pbqPbPDh53Cyic1wGiKS3OBMWSmATTTCwdCn3wvI7jCZy8Ihn9hZvmpzHLshAiyg3/7B/6McMAcB/XGnfEWRm0cE+ixLct1115XnPe952lTqj7/97W8fXLZhwwZNqy4viVw/+uijB20+97nPlac+9an6/llnnVXe8Y53DNo86UlPUlH8YQ97WBFs995770Gbt771reX1r3+9vv+hD31Ixf98JQKJQCKwPRBwAb11vhuc5ensRb6VfsRjXqPnJD08TXiCOsTSjJi39CbKAnpsI6nMPauVdbxpSylrOyogzspjqc/5HmjP50KdDpxPndTUI+MxRVEaZ7oohGsP8XBK82hNKWqy4EJyduU59s634FLgF3DuFcdoeVWfg1vRmmJM0RkA51dHheqou/OoRYXHaHjfIFFEp3O19ku8OzoKA0ZwR9QH170J3hudvom3AI+Kj9pkYhT6mEBc4YDF6nAh5xvgwdze1oJLEbhoDhHdMsDJMEVgl8wJyFjGfBVrgj0c12omAd07qfd9/K5oYYN5yr8IRnAxnzY0OzhwGQG3oVhGOamDLhkZ1LHZ+LH0S04DKZ5vj78iq/seLqC/9NFlw3MesaJgXPyX95Tz/zQj0FcU1OwsEUgEmgikgJ4bIxFIBBKBVYRAUzgP2naj0N+S2MWWnYFhwtK34X2NpqCIahd1ORVcAF8NJhTR7t70ooqtW4qkkMtQt43tBDwmiVRXcdEiMGDU0fZbJv1JexWKbZzSlg0dHGFeDdUEPrleUyRSynW0k34EAxha4j7rkfCW0SiK0ehLRVOLQGfSr8Y4ukHLe13T4XN0OgnjLipDHLe+KmMQGVcwfzfKkEjr84nCewCkii5nPBtia3QYYHuL/Iw11Dk2jEEwUHmqfIq4HzguAEs2CHaMdmxM4ucKe9SnzIau+CC1NgqcA1CCgAxZOhdycBgYPjF/tRC292rL1sO2qcGeDDXosT+jI4b+bnus5WTgzyQZArmkgI6L5064axSY7UExDhn2GX2+iv6g7aCpVgL6X8xJQH/BjhXQ77vvvvLQhz5UEZba5CKo80tqnz/2sY/VVO+HHXZYueWWW7qrIZ9//etfLwcccED5zne+U9VC/8Y3vlGe8IQn6LVSS/2yyy5r9nPnnXeW/fbbTz970YteVK688sodtPp520QgEVgtCCx+8WmTqUJ8iiWnmlpfEND1nMOZpsKZD+fRFqi4rzuVct/28+LmYRp16Us4kgijixud42z+3k9LeXBzWbPvek3LvmXjROxdI2Ki1IfW64i7tcbE9ZfBsdR5knhCjAiGcCf9MWdSId24o/Il4U1Sk7tFkEaUy47m2tyngiXO/riOnWRxLtVU4MRzPIrfegXX0GOqrQW4SXX+Nw4RM2i5I0U4l7Ooys6/9R/gRlYzu2kvRTzzabkHR9WLmAouAULRSuOuDthUqx7iss4FZ3UT/sGfxwR0mROXLahPGfXyMbb4mR032CmAr5T99vNNpeyzJzkjowEymNEFvs2Y5+giL+NrL+zVuHXRlTtgGFeV+cjzyjyWM8cNbBK07/CZi+nmkCPOAusXJs81nCOEM9EzlgL6MpY2m24VAi6gnzEHAf3DKaBv1aLkRYlAIrBsBFJAXzZkeUEikAgkArseAgPhvOKBHVLIUQ3apGEUcg4axHM3NgWs2MjgRqOQlg2kHQQZYqi0FyKsRJTqlfkYTHQFKeSoBgipmBMiYjWdmRlwpB/UMY8CNO7BnuEYn6RCkz5Qx0/awqudDSa9bROJNRvTgJdGpiCqgTridcF4WpHYMn9448s4WWBG7UHgzum3K6Ga1l/nR6l/KwcCFqNDNLxquI1U9opZAMK9/oOBwI0otG8q4xnS9ZsRB3hUURGGIYx3iIiutjnNF0YsN9ZYynjtpmMZwfh7hhOO9B/sjY41UI2VlOaTr2PxmqNsKgEa2DdS9GNueEYYN56jrqFNKq4R9gTmDCNi/P5g3NhI56k1EXlDhkk2qOI5FKMf9rWMZY89Mvp81/vztMuNuBLQr/mV+USgn/yPisuOikD/4Q9/WB7xiEmUiESiX3vttdU63X777Z6O/cwzzyzvfve7u+son19++eX6uVx30EEHedv3ve995YwzztDfr7766nLqqad2+zn00EM1rbzUa7/jjjt2uX2TA04EEoFdA4HF//n0JQfbeDbFFNjJsMWpIrVywdQ64LM+OFM8L7LoqZfhPBxEW82oJOI3Sj+ZAy+J51t+atm71u1R1ohYLm1bcxsR9LUe+t5UPoPFbj2D2vnQuR47TJv4Ks7Leo41IVuP0Wsm/KsnGOvUkZloREjHuboS4Lk9LYpHlVsNdhznkcIdPBBO1vq+rQGf78HzVEOG07i1c+ds40wcScznYo6u5jM2Z3Vq8oT4JvEl/ogzbbFDtjhgY546Dzq8Vw6x1Bmi8AccjSLDmbej9BSPJzpIsG2gx6k46t0FezhtBLGbn7WfPrgknke4JLW5tN2rUYan4jr07HkfrX1IXwpx27W4YOXgbx2rswnVkR+sOzn9t3AUnDS6fMtEjJeXBgxIRLs41ZizhPWb4vmu8TdpVx+lC+j/6ZfKhueucAT6NfeU8//kX7MG+q6+SXL8icAugEAK6LvAIuUQE4FEIBFYDgIqlo8YQDxadcz4w+TT7Q1TBHQfpHl0i7ezRIHjJYaAmDIPXYLQM8GMhiMVkeGlLZ1K2m6KcI9zZpFPmxshhQDp97R+EK2B8bIBCEYrF1zJSCTXyX8qTFNdvJ5QzI4Ikfyy+I/7c9pzNnyAz0N0xBwdIwjIiK438RSpy9GvrIFeQxgxBhUO9oFG6o8I6G5Ysfbcd8uDvmU0Y+NMnDfWvYpcILVW+0MafrM5wnAVnRo8qoQMTwPBl2qhs1GFcUKUAD+s0WBafWYLOIgYt70dox0wJzV4xZcZQtmoF423bihtRLvEfciR6j7fUIecQ6pg/PM9gbUY+ebCs65OLJZiUYV/TlsZ6kJy6nfpWqIqNMJ9MtmMPl/OX4psu7UIVAL6h+ckoD9/xwroH/7wh8vJJ5+sEL3uda8rF154YQXX9ddfX6SWurwuueSScvbZZ3fhlM/POecc/Vyue/azn+1tpe+LLrpIf7/55pvLEUcc0e3npJNOUiF/zZo15d577y377LPP1i5hXpcIJAKJgCOggrk66Vp0LVInMyfp8aqBDzKJ3NWZb6Co1ysQs/XAj7J3jlRh1/rsCeEyZpwxx1K3j+0FuU4jYoMCqKK3iHF0sYrIzBXlbEdnVulLazFbtDmfWblOdRwP86HmWEMpKecZ8Swayifh7BqdppHhCHOTfcHOnnAa5sh157PES3mscKyW9wbic8y0FTJeRcfVAY/CjRoZu3A/FqDhRA7ux6nM2YFA7zuSyj3yZHfgBhem0k6tdeNnavAcDTaB4RactKWZc0hzJGFBHvxmwHNKKQ8uTtKbN8dmbwKPwXM4RUDXcYVnAxks9LlE4AGVrOLsDgNHAusLHBDjwl6AvcXX0vYhY7x+/aQTwiIF9PxDuD0QYAH9rOetrIB+kQjo/z0F9O2xjnmPRGC1I5AC+mrfATn/RCAR2O0QmAjoRnjhDa8EmusNi4hGEbyOQqyhxhcGoxBHmQ6IpYnofH90FUllZJjRAAPyB+Fbfh+klgvMuzIUmMAGETaOFSkDBSBOOc87g8V2JtPyvtZflv8imQ6/c1RwTBeIe6kBT6LsG8QchrAqDSDVC0dUALz9OVoC7wFLpC9EtDnvF5Dxan2DQaiKLgCp14UL+4o2HQuxLazGItBba8cRFTA08iaH4cc/Y6/+GEXdGPfAgcL2HYv93Mb3ZCcChNeUhX81xJH4D0OeG/amfEVxv5XwT3uowoC+G+J3Akef6PManmMWt7lPbUtp91ms5+HzNcj2wGkxeY9wVoCInRhfvYTDxFCXdc93uz9lO+2EKgH9L//tfCLQf+fLOv8bb7zRU5f3AJHU6Cv52rx5s9Ytl3vL6wtf+EI56qijqltIxPkrX/lKfe+DH/xgeeELX9gdgtQsP+WUU/RzuU4i0vGSiPP3v//9+uvdd99d9t13324/UkP90ksv1c9vvfXWIhHp+UoEEoFEYFsRmNQ4l1c4v7XO4pVAzHcOPIR/9WsaKiG/hfTpLdEc722kEknqzNoQtmVYIm4vLk4GiHMrn+v5PKZ8cKqCuTRZiWiVFxwOxq6FAI/zpgjo8nMULiPWOPdz9q2WYzKXkALHiWdG5U00P+nTI6mJG4BnMe+UeeKcymKsi/oANail6qxMTqCytq3zOovpGKJ21RHRR50JrINB5gLb2+wwoGdsRM2jfJllUPNxj9Rhj3uG74kxtjJ/VXMMTy5vQexP3rOcuauiTdaotY99zbiMgjxDjUx2Bntdc4Gch6t93hLQw/cB7i1jFeHbSx1QCSrs84HaHvaTtIPAXjlQsCMNZRkTQd0zRJizC74rbFwpnm/rX468flYEXEB/2X5lxQX0v7g7BfRZFyLbJQKJwDYhkAL6NsGXFycCiUAisPMhoIYg5l3sfcw/k61oMosW8R4xCMWpK7GMBhgT6qPRxqNXIeSjpjhdH0Vy97YOzgA8D6RjV2EbBh6rEd4SPkEutSYfGZCi8cSNT2tCVP2aSWo07ttxAaGHFzjXG6fo6MC3fS2iccbXLqSqgxEIhhEfC1nHIIQ2RXEzmGhtO8Mf4jPGVtXUM2McRHS08RSE7GhBlhIXRxuR7pWhiABBnyw4s1heGYN8sPYDCd66Ryi1e+v5cEMSb+MgrGMOwHhgQIvGINrPPUNsNPi4wWXEoDn2TOsQRgw7rSgauaQywrJlFfsNhkBKrx7vhQhy/x6wcbh9MVzLGR3gGKKRMeZU4EYi+m5Sg7GlJyVjagroO9/fot11RJWA/ldzEtBPmgjos7ykHvlKvi6++OLy2te+Vrt8/vOfX6655ppB929729vKueeeq+9/9KMfLSeeeGJ3CPI5os4l2vw1r3mNt5X66h/5yEf095/97Gdlr7326vZz3nnneST8TTfdVI488siVnHb2lQgkAqsUAY1Ax6uVTQifbZN47p1MfmABMOIev9Nn+Y6PIrk49sYMR3y+xCBU3DOeIrWi96LsUrE9l8xpZU9iEXXzlrJlcbPWWq/Ol1p6J4jJMpZK9Kaa0FOjcsnpITqFQpzm8y3zCna6dYdiiw6WMSnPAScwJ2vF2RZMb203dd7EXNqEejif9hygtRuah9+DRPTIISoRNeytaQI64wTuA3GX5yFjUgxapbdiJHhwUBjgEjZ50IdH9eNqeuSYgIt8PUJggPO+GUuYR+qkOLXWWzds/9sy2jzYsQNjcuG81Rd9OTi3Ju6NZ5fX2UsJSEm5zVaSzviUPHMokWCjTvF8lf6x20HTrgT0317hCHQR0K/ICPQdtLR520RgVSGQAvqqWu6cbCKQCKwWBKqa5+zt3RXQSayrbPF8cYOYwugxEM/ZdbwR6R6NQRgXjDPOSzk1NUT2YGiAFz1uqfW+rWY4E9QBCbbdIO2R8kw8tlkM91R2FIEgfathxYxCMSW4bzJKnchtepxb7ssitEA/iOQlEdMNClRzD2I65sBRzBC8eX09HTgJnfi8FeUPg5n2H8RQCMFO6BvOFLATsEMC5tFyQoDxq5fJwKO32QDB9yURHePjmo3Yv44lGbD4y4Lnxs4FvM2jqN38smFh2hqw0awnyFfR6NEyw1bYKQYdfpyRoYAjx9nJQYfHETA0oUHqQRoTnDV4/rKXkKodOLFIjudDI3Yo7SDKNkhfeE418mkYOZIC+mr567bj51kJ6Nf+u3LAoxv1M7dhmN/91wfL4377SzP3sJIC+qc//elywgknlI0bN5ZHPepR5ctf/nJ59KMfPRjLm970pvLGN75R3//kJz9ZjjvuuO54P/WpT5Xjjz9eP5frLrjgAm8r78vn8tq0aVPZo/v3tOj95Hp53XDDDVrvMF+JQCKQCGwrAos3Swp36qXn8FidC/3gWN8+Hn0rvtNwdopHODSZJqJjjPjOxO8seldOs6qULtV2RzsuiRQiVF3URmkojKkp0Mo5zURV6VtqrMsLTo/yHpwf44Ix3loOy+qND3hIvDCI1TEVuzSvhEq7nnmKiI0yVD5vYg08rTzdp9Uf3ovZABQPqzOOtPXVHqKU8nHdWyI6zuTSR7VHK0W/kWGOnQyIk8tljAWL6Bhn5HoD4bjBmzAX54wYb+AvzKF0rex/refPP7eBMXdRPAhYcEefgznr974omsJ5aNzi1dqELo5OAXDKiOs14HqtAcDuAOFcCdpkUMyJ4UjBfFjLadkXi4wBAQWEawro2/pXI69fDgIuoL98v3LWb/czTS2nT7S96EMioN+ZNdC3Bry8JhFIBJaFQAroy4IrGycCiUAisGsgoFHoTOSnRU00Pw+Gnobdx9FwMtcQCCPp1d+ps8rYQ3XKgz1gYgSxO0bjh7zNqdVhCInCNa7D/TUKwiLDmfDzmJj4wpiDf1tRFLxFEDndEofjVpI2qBUYnR5k4ojuRSp4Ju1cBx1zY2MQ8PF5ccptu1nlXEGGL75PlSpxuQK6GRpcpCVxFqLrtDTuwMwNTUgbzoCFDcfYczTFrMZSGHRYRG+lKB+kpW8Z7oKhBX3iHh7RE8oURJHbHx9YXqd8L7EwjmfAI2s6ke4wyvQMmPqoNxw6eCiYLsRzj5CAMdKcV6pnxgR0PM8Q2uH4gGc2TDkF9F3jb9PuMMrtKaBvzxTuX/3qV8sxxxxTfvSjH5X169eXj3/84+XYY49tLllGoO8OOznnkAgkAkBg8UvikBOJB+EDUW5Ac0a4Uks8b/k88hG2J05H3sRnfQjAPH7lH3bORtkm8K8eT8B0cW7GGU/ex7gkJbum5h7ZOyzEQziX5j3OVIn5BrCeF8duQkIiztBor5mMjDMC23hG3ygptWle6nhNDskYL2MqeKBfnT4Js3zOZgE4Oh5HoZcjjJ0fW/kku8WSMysi4rG2HXzcMTeSJso2wPRBfSvo7K2OCIYH9lZTsI97IIjpEa/YR3zcogDNmPretXtCMPZnhzhly+m4en5CRHrrvoPtTQ79vk6B8FTzMYD5vRZP7O3xzZvLxr/757LuNx7bLuvmSwvM7Q2UK5D5inAOB4jglJACev7t254IuIB+5n7lrJNWWED/4N3l/P+WAvr2XM+8VyKwWhFIAX21rnzOOxFIBHZrBLyeH4hbFITd2AAi2jIAdRRzflsJ6Ug7vT/SuIN8soBO0RDa1AbMhF3rkpPHPDz92bBTib82JyaLMZKDI8vZkKRGIqoviD7UWBRSDk4Tz0GwkfquJdhiLm7oCWJmZYQhnGWM8CjX6ZpRhwVue9vXh1Ouw1ii/8IxoeH84A4I0bpAGMcxOn49j4tGXT+sfRS74xwqoxAZapq3ojdhwMPYkJIwPh+9bwW/L0WLaFtKp8+GHqyr9xeEczd82AR5L+NadnbQZhSZ7XsLJRBGvs6i2B/3SIz0wFyxb5sGH9xvxLgJPLCHxFiJupm4h+x7SSuIEgrVnCl9vjxrvN+jIeiQT+zW3+c5uZ0LgUpA/x9HzCcC/Xn/Uyf9z//8z2Wla5y30Pz2t7+t0Rvf//73y9q1a7WuuaRv772yBvrOtSdzNIlAIrD1CCz+w9FLWaXQDZcI6hyBB/ynyY8aZ+ve8ZjPmnLOeWBj2XL/prLmF6xUFKK6/QhmTsdwDtWIV3KQ5bPlcuHpCfnSDwvkkV/hPI82iIZlDsJjiRyiEk3HBPQpZY7Y0Rr48H3dgdX4Je6L65SHUjp7nJ1bQmx0ZojnapzZFTtKhy5rxbXY0Y55APMUOLYa/aij0GlyPQGd9xfzkLhFwcEVI/NjqNZvbF2M1/Cc2Ymg2ochPXqLJ2t7UqExJnAidj4ZcOYoYpP3CjsQ0JBrPbuh8PNbjlu4D2wSrX2gN4g357WbOIZv/NR3yrc/flc55G1P6Ty5oeycZgazgADhb8KZYkYJ6ikF9OV+IWb7bUHABfRXPGYOAvpd5fz3pYC+LeuT1yYCicBsCKSAPhtO2SoRSAQSgV0KgYGAzkRZfybrTTR++EcNCw/e8mtiGyaGIJQkivM4UK88IgtDh1ymqfXseiGC+J0FSkS3Inq7J6YjIkEIJtoqLzdPe4xDU0WbEQoOABgDi+ZNYkyTgSgeU9Ly+JzL2w8skrqQafPn9O4QmqOTAKJ1B8XkYLQhwZfrmrsBomEskCm1IsMVO4pCZ6OG9sd7g60ztB9YMIdhKNplYl/RKIY1HIjrdE+9jxmtNBUlahtaNMe0tfR9awaLKk19mE8zmoCMJWwMaxmCYuYIPA+8H2KafPTDz1LTCBlKNUSs437i9amWlIxQg5SZhEfl5BEedDzDcJCRtloT3eqfY75aL3NtHYERHBQy+nyX+vO0yw+2EtCvEwF9/YrO6bv/+kB53HO3n4AuorlEnt9+++1lzZo15Yorriinn3766Jyuu+668rznPU/bXHLJJeXss8/utpfPzznnHP38+uuv93ro8rvUWpea6/K6+eabyxFHHNHt56STTirXXnutjvHee+8t++yzz4rinp0lAonA6kRgIqCbk2zPqZD5i8PEXCqeccTpsSGex+NxFM3lTPSglKJaUzbf8/OyZq+1Zc0vLExSovP5sMV1Zl0+iSKX/lCOB9dF0ZzFSeYCzEG4HI/MBWWu1OnYROhpeivmgvtVnKNBCpi3shZZ/cxiqUV0D7qyuubMI5jGchQ8p8iGkwKXH1JMAGQof8Rn9MgJYyQ1hH3mn/5zSIVenfNp8SM+4GPRKQT8iukaztdwHEcKev0d53u98Qy7zTpm/gqscX1rDpH/Rk4LjBxu4+z6e9wQIeudP38NG8dgRq3ntzdvEtHdGdjuEelws6a83fyBB0rZc0E56z2Xfbns+6p/O8RZ8Yl83eYpfAmcKX6X0e8Lh/7NDOuXTRKBlUGgEtB/Z6Uj0O8q5783BfSVWansJRFIBMYQSAE990cikAgkArshAgMBveKJyxTPI8esBPcRkR0kuSKSiOCNhibrx8VyE7nVEEOEXe7tNcvN+MNiOLp1T3kjmeyF7WkIrc5eJZZbOj9OW42fQfKZ2AchT2/P6f1gQGKSL5+7UG6ibityXN7DPHAfGA38vmRQ43SFiGhgWwKLyPq+CZUw8DEf1/EG0ZuNHDAG6XtmJMMcYwrEXoaCSpyllOUtAd3T1ttN3BgV0yl29hXmI/tJBVt7Cb4cod0zRvkFhomXAiAHD9xjIJCTES/OGdhUxq5oseRF5PlRhLa+HX7H2vQiHWJdee6an9nWzy3DGT8zuDdSYsqUYiQEG39xD3mW2QlE+lkQQ9Kw5jmGsPD4jD7fDf+E7dRTqgT06+ckoD9n+wjo99xzj6Zp/9rXvqaYv/Od7ywbNmyYir+I7QcffLC2O/PMM4tEpPde8vnll1+uH8t1Bx10kDd93/veV8444wz9/eqrry6nnnpqt59DDz203HbbbeXAAw8sd9xxx9QxZoNEIBFIBGZFwOugR0fCcOxa+jXwnyhCesORdmjDZ0A5o24UAX2PpTris06C23m5HjuUimi+uLmUvdZS3Wu6gFKzb/n5pkl2qgc2l7KwpqxZv3ZyhluQfykbl2DlmZ388DuhD81zW6cWtUNkUbTxjDm4ByK3iTCgzJUe0y3FuR6NQ6R6jADXNuK8iZJENkYIxrwfmN9EkTfywUpQh4gLYZ2i0JFBLIrc7txg59+WiD4moEdOB7GcRfRqX0HsNiwkO1R00gCPHDg4DAhkY8caf2fhXFrBedqfBeJo8p5GVfeqK9CY2Zm3slOwPzcEbRa2W0L6mEjeehhZzO5cG9+O89ZuSYSn2/zkT24pv/Crjyh7/MqjJ/sZdhBpDn6M/ccCOg81xfOt+RbNa1YIARfQX/mYctbvPHKFep10c9EHRED/l6yBvqKoZmeJQCLQ/Gu/ZUs8YSRQiUAikAgkArs6Ai6gKzmNWdYDgYye/1Hw7AnoLqwR2e2R3N6fGr93uAmEzjFDViSGMUJdSCQIKeqcwyAhBioYgeS6KlU5ifmV93aDIEfiGo02EJfRD8RzFRSjAQXRKkaitS+qdye/xxrSMfqXDUVs6KmMWcTiIbR7ykL6rGk4MWNHFU0RxE0l98rqxx+jGMnggm8jsAHzivsBBqGxo4x/Rs4G0QEgRti3DH86m+DxH6N12ECiEQaNFJMcecCOAZw2MkYN+L0borluF3qmKzGehjwwGob0f+5IEZxZKqMgRdhUxp9gPBM8kRnCBXGpf2nPJEoosPFV5qGp3G3/y3g0dbsY8ehZCUaoFNB39b9Wu974awH9yfOJQH/OzQrMPFO4//jHPy7HHXdc+eIXv6j3eutb31rOO++8mRZE6KOklpfo9cMOO6zccsst3euICBFGAAAgAElEQVQOP/zwcuutt5b9999f5yMR5HiJIC7CuLxe8YpXlMsuu6zZz5133ln2228//ey0004rV1111UzjzEaJQCKQCMyCwOLNTxuKvl0hDucU67krno+0w6Aib+DBxvvHc/ksE0ObeF5FVi7uQ0R0+XoGV/JzujlBOm+yMxkfiXHGrM7B9gvmyA7GPn87pzb5FsRt4mw+H3MCZg4LHiH/asYyc/JlPlLxCFofzYBkwjvGK23dOdSES4iW7utqc9RxgA+T6O1cGfcKWc9YQNe+jT95/5gDlTkbOHmHjeCcoLFBPPsYjYebgRd6BgGphU6cDvyIcWyKwZ3Nyc673kdI5e6XsiME2TN0j5oDM7gyrmGbB37mezrHgHNFeEb93lNEdF1vvnZEOOd5av89hwPsP8JO1vqBB0tZv2cNqPN92neyZlIqK3LYFM+X802ZbeeAgAvoGx5Tznr+Cgvo77+rnP/HKaDPYdmyy0QgEQgIZAR6bolEIBFIBHYzBAbiufM7svDMYhRig1DkhU7yp5HHMRHVmK3zyMh6re9omMLYNXpYPNONcCpBtM7kvY0bl1YWBhYYQ1DPXKPPIRiaQYgNE7w3ILLCAMS10uMeiqnNdSomLMf0fSDTENejIYINBVpPnqKemSRDFOZ6epXm34h8iOmy3QDRMwREI4fhrUY1uxgGhSjgD56zhhjLxi9uz0I5GxCjIaT1LEcDh4xVUxLKi41juJgiYDg6vfc9gagXrGNT/CZsKocWMqLgXu6cQKI471/2iMH7HBHPThQ6RYjPJphHIT1mQ4Do3xPxZfzSh4jhUtc8PiPYk763zOiFlIKV2F8mArm0rcRzuwfuw0bcsA4poO9mf8B2gelUAvpHfnU+AvqzJ6L2vAT0+++/vzzzmc8sf//3f6/3Of/888ub3/zmZaH/qle9ygXvz372s+Xoo48eXP+5z32uPPWpT9X3pf2ll146aPPEJz5RBfiHP/zhOt+999570EbE/de//vX6/gc+8IFyyimnLGus2TgRSAQSgR4CmsId53YXPAN/qX5lPhV6dX5l9aMh9Ol5LJytg0/m1BWCuI2+OrSpcqpEp8oxStnywKayZo81k3/Xry1bRGhe3FLW/JuFpUxB1lbTvDNvQsYuvT9FD0cnVL0nSkYZVpwliyfq/aBT+hAll1rnUXCMKg0980ATs3k9cT7lteQsZs4HEYXOWaqIx3E9dO0TY7brZGxa8oui9RV8wi2ezXH2ZwHdp7MNUeiDTRVKObU+l/fAK2Qe7HQsc0B0Pjt/KA7sGG4d93ToeN8x7iRt2W5R8eRg24hmB9QF9/cbjsgDp+8RMZz3iI5rylNbOXGMiPIuyBNu8p5Gm68l5wz7XMcBO4rZQWSuUvbKOdPwfpm6feq3bDaYAwJLAvr+5ayTV1hA//N/nauALk7GH/vYx8oNN9xQvvKVr5S77rqrLCwslMc85jHlaU97mmbRkjJYs76kL8nKdeONN5a77767PPKRjyxPecpTystf/vJy4oknztTNxo0by3vf+95y5ZVXKn+677771En5hBNOKK9+9auL8Kp8JQKJwMojkAL6ymOaPSYCiUAisMMQqFK3g+S6IEdML0ap+og7RqGmgE6e9q3r9b1ZmTMIo7WHqIfhsCgIowSES408FyGPDBcYjxJ/6huGDhVR1QugFlFZwHXhNdSy49V1kRhe8jRfFucHBDu+QSSYSb5jGFILKrQyNzHSCLEmsRVY8S3kvSodIeEMYZ/n1TNkwNBVjdFEaDdS2T6Lqdx5K7huzCI6ifCDGoE8uBDVzeJ668mLeOrah73LESaKrRmC2EA49lRP2+Ys+qOfHsYciT5tD3JdQX9WQg1x0ugnhlAzhuHrYGsFdPo60Skhk4PvPzMW4msAkUs8Ztm7XNKAv5fkMxfQ2+CneL7D/tSs6htXAvpH5ySg/9b8BPQHH3xQ65d/4hOT8gd/8Ad/UP7oj/5o2Wsq0eNPetKTihhyjjrqqPKZz3ymPOQhD/F+fvazn5VnPOMZ5aabbirr1q3TNPGHHHLI4D6cxl3Sx0saeX5961vfKr/6q79afvKTn2jaeIlml/7ylQgkAonAtiKweNPRk3M0OzHGTsfO8PGsjWuZt4B7OB+xRi3xbURjG6VULb6EsUhK+HUT3rP5jvvKHvvvXbZs2jJJzc4v8AQ/B9tgcEZm8RB8icXzSlwMDrdKc5irEEWMjsMtVTIKqJWzaRASBSiI7zirts7zcEhG9Lkr3DZOnFtZhAdnwvr2BHTOcsalsoCt8xy7lzqdslAMrIBTcPjl+WDPRF7hePMihzVoPkB2fpfrxcmVS1/pvdhpIDpS0Aaexo1w72niubYj54jB80mO5dU+aTl3+4Oy5Bgg/XHps6p/mwTmEnlZ6xnm611AbwDNDtiYXnwedWzYj2bncPHcMjRIGwQV9OqfW78poG/rX4y8fmsQcAH9rDkJ6JfPJwJdSlwJt5n2evGLX1ze8573lD33DJki6ELJ3CWZtlDSqtWniOhSEoszdcV2P/jBD8pznvOc8vnPf745rPXr15d3vetd5aUvfem0YefniUAisEwEUkBfJmDZPBFIBBKBnRmBqdHnTCxlIhXxo18iIYzXKQhjrLHFBFvksZFino1OcgkiBqq6e2US/Sq3qYi9CYTRoKH92P0hPKNfJu54b2yRK4cEqu0cjTscqe7GIcYYAiOlZldYOWqFIlbwPvBRR4JGbWjFhYwbnLZRMIjitHwOQTlGakQcYDgZrH9MSUjGCN4mA2PKcqPQG/uuMqI1ah9Wl1B0uUahB4eHar7YSxzF3dgY0wxEsxiGBoZWGGymdW7jwd7DnpPsCzCwuECuVpw6HaVcjkhxLxeAObKllxaRo0zcoSLWm+Q+gqENzzT2enU7u4/cQ+v4WQQPP7+0BCmg78x/jXbfsdUC+pHziUD/rX9QAOcRgf6CF7ygXHPNNdq/pHAX8XzMWCMGoSc84QnNBZWocIkOl9eTn/xkTQEvIreI3n/4h39Ybr55kope2r3lLW9p9rFp0yatw45oeBnfy172svKwhz1MIzTe9KY3acTHHnvsUa677rryW7/1W7vv5sqZJQKJwHZDQOueiwMu6j37eWSEDzH36YnngzPyDJSpcbytanm3xjb23uYtRWuZC0f6+eay5hHrh7hCMIdA7pGraEriocyVs03hLDftjOuCI90+Rts6XnY/zrTFZ0WclcEH/exrTrFwGIXTqvMmSwGv/Cecq104tbOqOnWqkr1URosjesGZNDIY4yWRm+cS8eT9IudbfsG51DkbidjgdDinQ8AGt2xRhei42+L7kc/6eChaXt4T3KaJ6LofTLBGJPosFGZ0/4x0gCwA7PQx6zcH9iQ7/MKBgTHgeu3YW/yc8vB6JpExAX1gKuGb2EDYb4CFc3/2zXlAotT5e6yBa4rns26QbLfSCFQC+gsetaLdX3T1v5bzL//+XGqgP/7xj1c+I9HmkvlKIs0PPPDAIrxFMm9dfPHF5Xvf+57OZ1p5Kcn0BR4kfOncc891vnThhRc6XxrLCCb3Fd4GUf/kk09WviQZvERQl0xiwpfWrl1brr/++vKsZz1rRbHOzhKB1Y5ACuirfQfk/BOBRGC3QKASzpXEsrGmweoGpDm0WY5RqEKwctGefBL7YpFWP2+Mz2vpWX9IGyi/wrjBoqMbdajONcRlacdGBrWLoJ5eqK08zRDE9ZxR47oyZBj40r+nbWw4E8Q6gK3oBqwjjCDyL6IkOJpc5teqb436fx7RTKIsp6nXscb1J0EU61vZMWxOrrE2BHROcdjqgw1dMLxgzvJvK9V9ZYijum8+/FDXm/cW2iAzgPdFe8wNErRnHOutSE04bT/1vn2mXRc/9ygTKxOg+9T2oGOPeZoB0esgGjDAOzokxDG60wateVVT0UG0HxoCemvevLZyD4kwVexDrU27NsXz3eJP1y45iUpA/9icBPQT5yegj4nlrQX55V/+5fJP//RPzbXavHmzGm8kirz3kvSGEnEhAnjvdc8995RnP/vZ5Qtf+EKziYj4Epku98pXIpAIJALbgoBGnatDqUVgwxmwxUlawqO2CyPw82aLcy1jtE2HZbt+7DO+hUSco543R4jjHM8cAgKffkaCuTv+ot40BHQiA9POqtIlhNcqzXsQ5qUdR4xH8Rd4M8X0n+3cq7SESnqxM7Jcz39/BqKmORKrmE78BuddjJ2F+UHabz9sL63EmIAehV8Xc6kOOu8zrI87O3BJgBnP2b6/iZf2sl4tpW8zSEJWKd0uqANuU+a95FxhZO9vrXjegHrqE8b3itxQOIw40sge4fJvXoaA/C6c9zYc3FuDGBPQB+0bAjq3aQnu2PPrrATDCO4poE/dJdlgTgi4gP77+5ez5iGg/9f5COjPfe5zy+mnn17EsVdE6fgS7vL0pz+9SEYueYmw3Urn/s1vfrMcfvjh3YxdUlZLHImRsUsybYkzcnxdccUV5SUveYm+3SqLJfc58sgjNWOXZPySzF+ZsWtOmzq7XZUIpIC+Kpc9J50IJAK7EwL9qHNm3jTjlRbPmUzGNHVNcdysHsoTWZVlcl6ptTz42gue0+uxocL7NUHdRXQYWCitmxoBxLDfcx+P2MV2weCElNTSrFkXkKLMu8Q4pClHOzgPwGGAjUwcQSzR1VXkORu8KKof6+U1tBv3jQYON7I1UnRXkR2oM2d4VftkKXu+pwKMQqlHWtjkWWCN+6oS1iOo+N0ME56ysbHecdvBgAjRXckTrffYF8kshsXW9fE6rlPPte169xZscM3i4tIzBsx079h+x3eBGpJR59BwiW3YmAYDnvwbnS90XFRyQH+PVs+4GXiNbCwLUsevLZ5L6xTQd6e/YrvWXCoB/eNHzScC/Vk3KSjziEBfSQEdK/eRj3xERXIRwMWgtO+++xYxmJ155pkzR4xLKvg//uM/LldddZXW9PvpT3+qUR/HH3+8ppmXdPH5SgQSgURgWxGYpG2X+tbBqad5tgznkxa1Wq54zpwEohg7Qm7LBDdtKVq7XET0yEFQagciWxTN9bimil+dMVv5TMCqR9N47HBYro7lfCELuSZej9C/STc4X9J4YtmvSly3flHSi4+jnLbfa6EHXorMTSxMeimiIHZ7PfYwCb3WeA+fxeP6tER0piq+brG0WIeXsB4bHUEwDncKMM6KvTl4FojLYS68fzz9PbivjXHEca62AcRNP7LBxvaeDBNZBZDOfOx5wjzF0UOd1JGdDFxobBywZwSn82hnmUlEZ94U9yAm0Hhf12uPUoQzcaY+pl7CmZ7wN9vyrZLXJgLbhAAE9P/8+weUs164whHoV91Z3jAnAX2WSUtmLCmLJS+pP/72t799cJmUp5K06vKSyPWjjz560OZzn/tceepTn6rvn3XWWeUd73jHoI3wIBHFJUOXcNG999570EaygknWL3l96EMfUvE/X4lAIrAyCKSAvjI4Zi+JQCKQCOwQBBa/9GvDWmQyEo7I1ZEZ23UDDYYbRET+dTTSoSc24z7Wv0cDdNJgV/eglHEsysHjU4mtpekTwu/iuc0PvNJFzxBdDkMC/q2ESjLixJWMRDimxOb7cn3naQIqDCUwzvB9ENGrpNsiz2Vc0TjDddY9gpjTctNkote8R5dA6IawH9YW84gGv2qfmcEEgqdvL6vppwYvGks0vnBEPLYrG/H8Xg1rULWneZHDvoDI7hiOODLwHmCjmczPsyOMPPLT1r53KWMtqfiBxZSadoPuBBMxBi0+aH2QkQfjZ8Hc140NkzCWhbWDkSxG80QHB14qGOhmwkXSt+8xiUCH0TFMMMXzHfLnJm9qCNQC+v85JwF9Eok9DwE9FzIRSAQSgdWIwOI/mNEavCKeSQYcablcqcONplCmqja4nvus1vFyFol5E59zpQ9O1R7TgOMe4FV+8GzcfBbRvDqvWx/M9djBWT/uOAzH2+OMCcHQl8ZSWHv0OK0Z1452XO3zWLtdnU8loxdFVbvYTU4FvCWcx9kCVxyNSBef7f0MbSW4eA+2BHTgCbGanR8iltOEZae6dL5nAd2FXj7/02Fer6fN7B9Fh4GQzr01rlE+sBXiuYwLpd22hjPJXDZtnESiV8/gSHR/j9u0vkfGHAlaz0Dru4nv5w4QFhCA2st8HcGYAvpyvkyz7Uoj4AL6H8xBQL/yzvKGd88nAn0WHO67777y0Ic+VJtKbXIR1Pkltc8f+9jHaqr3ww47TB2Eey/5/Otf/3o54IADyne+852qvNY3vvENL6cltdQvu+yyZjd33nln2W+//fSzF73oReXKK6+cZRrZJhFIBGZAIAX0GUDKJolAIpAI7GwIuHCuJC8YW5qGGjJQRC/06pRHv/S80Hv1/7gfF5VNxFaiR0Jcz7O96sOMCxDN5TMhoBx9jfdYDGbBU/vrieMYZBRdw2pHAV0/DgYFvOWXRseEXo1oXGD9yZBQh42dDzzy3Iw4Om8T1gc1/XoGPDKIKCyhfvogooJwiESeoXMDU6h9B5wcvyjEUs3A6J2PPd0zbsYHko0Vce/JWolBxB0RgjFE349OA3aDaJzC3mNDCFL1DzAyA+hMorHdD8YzpBKEobORNsy3tvwQMxMsbpqI0BKBzk4nMEDCUOoGmJCGsUrJzp4XtE8rZwlywsDaVA4teA4HCxfeMPHcn3OsVW1MW3j8x3e2r+QczypCoBLQPzEnAf2ZKaCvoi2VU00EEoE5I7D4D08l/tDIOtUVz8MBPx6xB87G8fzf4BR+TmoI5dW5rKe8hz6bmYCMJ2itbcvGpef+kIFLbjGLY+is6+NDHht7g5dFYZaPjRXGfC2VcqpKY8m53oTQiiPxGZbOlYM1xJmcBGPwEsaBs3cp19g8OXtXB3TiE8yFW3wi8iW093tCdG9wOGQN6B2z+RjdsgMwV0G5MHZ21n1Czri4TyWiE766fsLZA9dUzoIyaj2hfEYBXfFZMxG+sY/l0h5nimsXMwCAM4F/49FnzgvH9Ra3a9oL7Ka6D8l2ID+3uGfVb8ShwePBl6qMfMOHNQX0Wb/Ast08EFgS0B9bzjplhSPQRUC/7HtzqYE+CxY//OEPyyMe8QhtKpHo1157bXXZ7bff7unYJSvXu9/97m638rlk85KXXHfQQQd5WymVJSWx5HX11VeXU089tdvPoYceqmnlpV77HXfcMcs0sk0ikAjMgEAK6DOAlE0SgUQgEdiZEJikbCdBliMVevaKmN5OJzTFMIOP3bDQE2YNHSbaQprjPaOBgj3yXSwOBg0WZ8UoUYm+qHdu17hxY4R4jy5kY36jBrVGTTQnx9YXDDpMxgfQk0AJDB27jtMBDBKOmxlsIJaykWMQ3UAgROMB15dnrHitYn9oh7WpvOxZWAXxp451qTgVYVhL3dvRB4LXaQbniCoqRzIY0PXN/mk/89xate5t+J6GUNcDxjO7z8ChY8q3CaJo+HmKxqC4xTX14JZSFqw+l0RhyDrI+xsXrS6kGU2BJ4wtiJhB6QHfn7Qvq7rxYfwxjfuYcM5GqAEMhJeMbWFhKfN7VRpA0rengL4z/U1abWOpBfSnlAN+af2KQvDdOx8oj3vmjdpnRqCvKLTZWSKQCKxCBFQ8lxd4RAsDOb+xM6K3GRHEK17T4hCNG/XEWm7aa8N8L0aZ81lXzmUxCheOqu6wGsTOldoXWyOgN7lWEBsxPoiUzPk47bbyLhLW9TrwxcA54Dg85uiq86HzaVwn+QhOpy4MgxzQOZrHj49Z9Izzg9N5xcFJQK/mRPNq7m3iq9X47ZfIKeAYzI7VuG4mik2OyigHBS404EnUFkvuTvFhMq1747llfrFuWKt4AAu4FvNl6UtEdAjzrftxFPhgzewN9NlybIl8iu/Rm3fz2aSMYdKnZO2CU0ajfQroK/UFl/1sDQIuoJ89JwH9XTtOQP/whz9cTj75ZIXlda97XbnwwgsriK6//voitdTldckll5Szzz67C6F8fs455+jnct2zn/1sbyt9X3TRRfr7zTffXI444ohuPyeddJIK+VKy69577y377LPP1ixbXpMIJALxGLJFckrkKxFIBBKBRGCXQUAFdBapWl77PpvgrTzTNz41UsOA/s+Ezg5zVoMGDAwWEStjYGMP0uLBUIE6zb0oY5BtF84bdcvdgMLLNwu7J4HbvcIbW2CagK5zjL4IIdq/Sfhxr1ArGoYFGIOAqxuDyJPfBVZLZy9dihEHhD2S8sphwe7fEtCbXvHWma9x+B1rLW+3IuLdK78VhU4COsbI/zb3bMMoVXn2B3wVG6qLVy2aGdZ6UeiYm3ZJoFZp59nQZzdDpA8uW04ketyKMV16/FwEc8FYDKb+nJVSFjdOUhpq3XdkCEBkPEWGcJ11YFUJ4WaM9Ge3sSjePtTJjGPtPg8s2O9Ryp4Q0IcXpIC+y/y52i0HWgvo/35OAvrnFbsU0HfLLZSTSgQSge2EwOIX/n0pKqix020UxRtn08ij6FjpvMjP2+T82Z1Xw+GzimAOF/I5WC6FuI9mPXEO58BqvpStDOfYWahSnEt0Bm7NtSmg89wDDgOeRcJ+tUyBz+LeghOEXnkPODlHonTifswkQR3lkvjsjPM+i7LOg2nS4MjgyRDv9bo4Z7uOubKey1ncp4h3tPN1Jh6O/isON5JqPJ7rx549dhjgPdeaDq+P7yfaWIwfZzmIFI4Fa2/X4RLT9m0lwo9MlNO9czMR0MX5GEvY66Jlu3BxPWS4cA5Mnc3KmbpTIPDlvuJ0nAL6dvqrkrdZLgKVgP67j17u5aPtL/qzfylveNf3itzjmmuumdq3pEdfqdfmzZu1bvmNN06cnr/whS+Uo446qupeIs5f+cpX6nsf/OAHywtf+MLu7aVm+SmnnKKfy3USkY6XRJy///3v11/vvvvusu+++3b7kRrql156qX5+6623FolIz1cikAhsOwIZgb7tGGYPiUAikAhsVwQqAT3eORobRsX1xsUumNtnKgLOMD0XJ2GkodTtHIGrnuJUyxxdg4i6McAMCtKvRls3Urf7sGYZoDS2ezcF+2DA4Cl3MWwZeWLENeZB9d11KNROjRLWl9alNuLu0evkxIDoZhgy5DoIuXAEaEWRD9aRjH0tPCqjSSPFJF/D+GCtYxS6zLkyApHTAY+N5xfrqXvkOFtkZlh7FYdNQNaIg5b4PrLH+RYeHQALzUjtxsr4CScHjrYfu2eYl/fVueaBxYmBuIpUt2iqjVLTz+bNzyIE/ipTRMehgw2HMK7FaKexL4qxZYrGVhmXzEMcZwbOGJP5p4A+w3dyNpkbApWA/tdzEtB/MwX0uS1gdpwIJAKrBoHFLz6tnUYaZ3GjBwrIQOMmUuX8yM7zfP6uMnp1zm9AHCK0lOrBz+A5OI/x+bfFmXj15Kzk0eiUqt3PgzOI+z1H5sgpIRACrK5DZEWiJr+A0yAiOeKv53Tp0M7JLuRSNDnGw868iOjlM6k7H2NRGw7dyrdwhqeU7xg6nE6jf+wAK+Z0Ev0eHi2/nrgL0xCccwc12W1jxjnL2+B9CpdhRlP1EcSxx99b3wKV47alpXdMqDRV5VxAHVXzp7UctGcQaGCYEziKp0wnQb3HKVzAnsIN1WliyyRqO77kuZT/sF957sKlWinpB53gi4THEfjigOMu9yuZHk5N4752wgNbjgzCm57w18u9QbZPBFYMARfQ/5/HlrPmIaBf+r2Zx7qS8aMXX3xxee1rX6v3fv7zn98U8N/2treVc889V9t89KMfLSeeeGJ3rPI5os4l2vw1r3mNt5X66h/5yEf095/97Gdlr7326vZz3nnneST8TTfdVI488siZ8cmGiUAi0EcgBfTcHYlAIpAI7CIITFK3d9KwYQ5RjIri78A4RBdWhhJ7X4l544Nu2wAmc0dNM23iMJq5eGoN2SgCsU8j0Cn6nI0FU9eOjV9kKInp+KJBhPutoiOCx7e0Y8zhZe5GDY4m4JTvqH0WxFykw4aQDgKPVHO6/mSQGAjoYqQSoxxNQKHtWDpY5PWt0KhnzfsIY+D2jBeMC9E4wGnpGbdZBXQY1zwCZgbxXO4DAd2NcXHTkHEx7usxI43OYSxqiWDHnlUjB1Iwjoyf9+M08VzG4XUXQ58y91gHnVO3Y5+6EdEfzOBowFY3zjYxJdq8VytwEHGEDUF7WwxBYtzqPJspok/98ssGc0KgEtD/5uj5RKCf8DkdfUagz2kRs9tEIBHY7RFYvPlpS2eI1lkinm0H3Mbe0HMk868gjLFqWonMLYitI0Q887ErNpcsQoMjKzkY4xwq/7qzsTlscurs1jCm4RG5jZ797czHZ8fYdxTdMcbKAdUu4jN0hFSbAHTwkrBAijWtC0f0K1fQQS/tAfCHiqMZT2DxtTUWFiYHn5NThY87+usSD6uon60nOx5H3hlF9IGAjnnSYgx44IyPuzq6G3Yx64HeZoy79O7RALRpRwgPAzhTTMFv023ebWx8fIHvhWB/EKdjrgGPOTv3HMOxJZzbPtbrp3Am7rr5PFQTsF/snhDQxWbScGhOAX3G/Z/N5oKAC+jnHFjO+r0VjkD/038pb3jnd2ce90oJ6J/+9KfLCSecUDZu3Fge9ahHlS9/+cvl0Y8ezu1Nb3pTeeMb36jj++QnP1mOO+647lg/9alPleOPP14/l+suuOACbyvvy+fy2rRpU9mjClapu5T7yfXyuuGGG7Q+fL4SgURg2xFIAX3bMcweEoFEIBGYOwJTxfMx4dy4m0dWVJq4XRh03GpC8hkL29qfvjnU1oW4RpFc2i+sW/LojqSV28v1YhhQ0kz/4Z4zieeYE9cPN+Ge5wkhcWz13LgThPhBlISBHKOx5e2Yfk/fs4jgyiBn6c8ZawibWINK8Kaodjgb8FxaZD8aC7l9NPrxureuw1ha4rrsMSHz+opODLgpLYYbs8xiwJkIvLkZMKvU6dMePZg3HXAAACAASURBVMs6ACxae1O74IiXXp9jD0nfR8F7a6VaBPnp7UWsK+oq2lDrETaMWb42IqBvmtRBx3tr11m9RpuPvC+RShJx4c92D5NgkesZeJANQf71qCNKsznN8UBuz7VKJTVheKWAPm3v5+fzQmAooPejALZmDN+98+flcSmgbw10eU0ikAgkAoqA1j2f5rAYnYwr7ODoGjhQTyCv3o/qJfXlZ1qr1S3nJJy/8Jk6D1oZHh6TnqmoVBPm57xJyviYw3E8ss7iKKxnQCsHhZ9x/5kib3HkD1m29EgZzqrRkbLy8yUeJ6Ijzu4obzVw/kXdcxs0n/VZZOAU64PxBN2xJfCOPlu8xi1+jAN8+Ez5DznXVufw4KjLDgDgLcyLW8J+C/uxecBZZFCDnS5ix94xnGYSnTuDaTkseHp3sROEOudoz44Q074LW8+EPIsqopsTgdo1kO3NOnxwcVJmapiyYmQTTXE8gD1A9oJnY+g5akfjgYzPbCYQ6sXuEnlTRqFP2xH5+ZwQ2B4C+vZM4f7Vr361HHPMMeVHP/pRWb9+ffn4xz9ejj322CZ6GYE+p02V3SYCOwCBFNB3AOh5y0QgEUgEloPA4pfEa9CIV8u5OYrnkXzzzSLRdbG2MaLK4z1cGLmbjA/e/iz2ynsgdUJI8ULqPBg5ICAi2py9znsptyMX1SFylIKBFcU6GCs4AiFOfxAhG41hdkHLGYH74rp8+j483UUMJiMKGyOwfoiscEOQYcmpGqVtTPvHwnbPYDXLBpwmoOt0OundVUBnL3gyhtkyTYZgG6laC06nT4vsqS218XAGLTFXa8Kjll3HuQM9MVZseMS+qqL4Rwwh3h/2iGEUtxAb9bg+ecsZAn1i7d2IRHuqNQ9EoItBSIVyMagEpwIRz1EDnVHF/pP2/DPWvbX+wBDCuaYWRVpOMwS5cS4sYWU8DdH7MsZGqsUU0Wd5kLPNSiNQCeiflAj0OQjox2cE+kqvW/aXCCQCuz8CmrKdzymV8yLNf5p4Lk1b9aejo+zAwbQhnvacV8GRonOniF/yntditnOxOETKC4K5nutQ4ooydbXOg62lx7hwTnNnUyUAS1fgPD929I1cNDpex/u3MPHzdnBaAF+qOCsRYvBPcFE/99OBHPug50jAvHbZ4rndB07PwK5DHZtZwtjZY7A3waGIV/DxnzMDYMqM5ZjzRNWeHLzlfeaBcf2cq5AzNzDk62a597SvJd13VFIODgeCE5yC2ZEAZbtgE6jKW02JohdbBewVKp5L2nZL96+YGBdvjrlpmKg5K3Od2LzCqmPD8PsOiCo2x+Q7QThTiFDNKPRpGy0/nxcCLqC/RiLQf2lFb3PRn0gE+j9rlLVEW8/79e1vf1vv9f3vf7+sXbtW65pL+vbeK2ugz3tFsv9EYPshkAL69sM675QIJAKJwFYh4NHnerWJWSC2AyJFRofW3djAwdyL27pwLsJjL8TUUuI5SQ1R6bBrwAggntowgGhtMRPVhOBJquZKbO3A1DPcKFG36GTFpRFtzkIviDeIcIvcDwR0GlMU+gbDNQBhwIl9IT077l+tAwgzeLD9zkI8xFaOpKjS23Xw6xnwxnZlNFrNuoNhWPFocbIiVXjQfpa+Y3pIdjjAvdlIwuOJW1VT2XPNucrbYdaZTJ4BGN5GDa6NLqtIeuwLqpHIBjuZuzwPEi2uL6r7yHNX5xKrcwfjKaeorJ7lzaVIfXTZi4jo1rqb9mizs0WFJYMZgGWBn5+d+BzFvgW7lngOZ4uYIhVbBoZijfawl32WAvoytnE2XTEEKgH9U0+dj4B+3Gd1vJnCfcWWLTtKBBKBVYDA4s1Pr1N/t5wS43l4IJhSmSWcS0fP0OwQ2658tXR+CTdr9avil0W+CqeBkC7nP+FMIhr2ONGsgiWXhmJH3djxsiLPldg0dtnI+bsl4nMXKAXEfAQiJoRVOe8rNyLOxGK7RvVap7ofSETdWrG89yzpOKlcWey/6yxq4+9psAotpaU3mjChCx3HYucOMzz4eka3c3rlUAxnis6GazlS6+0a3gOz7k04clS8JM6B1rFydAEwRnRk2Cqgo7QBHLw7mMiz9uCDQ+d06a7FBR3/1mBpzw2GH/CsotrBdci2wZjCSbmuK7FEkBCUsOeeg0mmiD7Ds5BNVhwBF9Bf+8vlrFNXWED/798vb3jH9hHQRTSXyPPbb7+9rFmzplxxxRXl9NNPH8XruuuuK8973vO0zSWXXFLOPvvsbnv5/JxzztHPr7/+eq+HLr9LrXWpuS6vm2++uRxxxBHdfk466aRy7bXX6hjvvffess8++6z4mmaHicBqRCAF9NW46jnnRCAR2OUQmESh4xXTTTfEyd4M4ZHeEs9dOKf+KmIMMkpRxyDDwgNRK42NQYgkh+AH8VyIrAh6mpqZSKQK6zxVql3XmpOL5zZmGFhiWxYyexHb8ZpmBAB5wOPeleHCDBBRxNe+4bVOwijGi3lrSjrrA2mwEVGh47E1gGGN8YeREFHK0QDhwnVYXxta1xgHo8pynho2UKEOo94nGg5DBD3S4bnBkyxN1XyC4aFlcBKDWhXVM01Ab4QF+BrY5MccK3r4uKGOogm4LYvZ/LzxfAc/W7pHwUuNqZbKMBqnZB/+7IHJ3SqjHRmWsP709TJpj6wSZpjD9QMnB7tQ2qs4T2lJ0Yd+N0RDkF2n5R3ks1hj1LY7IigWxGkA814CcOHgjy9nZ2bbRGCbEUgBfZshzA4SgUQgEZgrAov/cPRSBqyp53s0oFrW6nhoZ6tp4vmg/8bUBuffhnIrZyZk7cI5X51B7UwmjoSDcx4OkXbPXsS9TxFzpDOXjw3nZBaZmfcEp9d4lvXfzXnVx8q8J5z7kSGpmbHLeBOcYZUGmcgr95L1Uf4p3JFS1+M9nG8hrMvv6sSMMzHNd2w3shg/NRsVOV/E8zXO4g1t2Z11B+vbALnpsNoiQvZez9mCu0ZpAN6neoYnZ4AuUbSOIPDLmV5rcduEZxXOe2vQHb9xIM5wN+gj7GV5PrREQqfTRYtAB3dju0JvHIO+qGG8RrCRPb/exG30D0cR2BScM/HzTXYElILzNPJ2Izz/mGNGoc/170x2PhsCtYC+32wXzdjqIhXQvzP3CPR77rlH07R/7Wtf05G9853vLBs2bJg6ShHbDz74YG135plnFolI773k88svv1w/lusOOuggb/q+972vnHHGGfr71VdfXU499dRuP4ceemi57bbbyoEHHljuuOOOqWPMBolAIjAbAimgz4ZTtkoEEoFEYIchUEegyzCCsSTWmI7e4JWNpmGwgUECMwQRZNEOArm0aXmmow8YfyKZ1BRoZhiC6MfRxRx9qn1AEByBXe6p13HEOQmD7i1Oxh+MP9Yz692mR5y9Hp8YCazO2KaNS1EAVUS8GZI8Lb05G6jdhgxZiHSGJz2voxJriq5nwozPnOw3Ege4eBrqMLoAP8XI4uNs7J8Wdj0BXbcvZ1GIxjgzZrUE9Oo+0wT0LaVshJjbW9zGvnAjmRh+aK4e3bAVXwNYl544jrWUe6O+ZfUsBkOMO8EQdtK3OKOoMZHai1FJIimw73GtjmWG8n3+vMOoa33DmOl7tZX5wtqKEY2dBGCkg1EnOrTEKHXFb4+JMU7mF4zDKaBvxZ7MS7YJgUpA/9unzScC/Tf+Px1jRqBv01LlxYlAIrAKEdD658i6w7WMcQYFJpEfIbNTPKf6GYov6JyHW29XDrmhAQRzvYcNDNHhECG5jTTRmunhvOzn5jHexJmAmiqunbFa58529aTqbt4lfsCZ0Xgaso/JfOSMjhcyc8llENTlM42ktUbgmTh/+vmUeIULvpQlzYfCzgHgPC1FNAiWA+7FDgA9RZUcMVrp3HmtHQTLdjUqoI+sAZxbe8s/TUQXYddFWRtgZU8wIFvbBmvD5/Np41nO99KYgK68ifYS4znYnNSROB+3hPQHHliqf67fF9EB2Tql7aTvtJwacK2ngecO4xcFdYjnAQ7o4Fqe1Y0mFteMnymkcqfmGYG+nI2XbVcKgSUB/X8vZ5220gL698ob/st8BfQf//jH5bjjjitf/OIXFZK3vvWt5bzzzpsJni1btpQDDjhAU74fdthh5ZZbbuled/jhh5dbb7217L///sr/JIIcLxHERRiX1yte8Ypy2WWXNfu58847y377TTA+7bTTylVXXTXTOLNRIpAITEcgBfTpGGWLRCARSAR2GAKD+ufKvXpsrhPh66NvWHVY+Ea7ihQ2alz7GPSHpRppMN5APEV/MEgJ8VMBjGpjV8SP6mZPI/rStwptqHlOS4Txw/AEoR1CHATNWVa1F3WiRh6r6S4COgv50q9ggGgKrlkO44KnTqRICqThdhEX2ItQap78+hkpn3i/SisXhGFbJp1uLwKGSfmIPaha7zH8fA9wPfNZALc2UwV0BXmpQx4zjF2cqrx76wZWAweRKek4p00LY3NjUrQ+wShlgjgMVjHiHvt2IObbfpA1XL9+yYgj7SUloIjofEs8h7IHxWAW+43rD1HeDba8ocLkqzHzZySmswEVzwr2JUcW8dccHHMkWj0YqVJAn7YB8/OVRqAS0P/u6fMR0H/973XYKaCv9Oplf4lAIrC7IuDCuZyTwAEgoLcmzYIva774eXAeHzv/2A3iOWhMPNezjR7CaHR2c2TcYcdIFo9xRazvXM0z9AsHxgkhqL0o9dxtXAPcDGcvPo9N2zwVRyW8kB5chEtpg/Mn+sPZFvjp0JFxjRZH52BlwNCGRWrnsBDv6QAMOKqyYa3J2XsRb0RYI9pdxz6LiD6ybxzPKQL6GC/Wc/EU4jz2sfJkydoFjgns2QZAODpkBrYvj90kisnT9sy0z8fGXqUzb/COQd94vsTxeE+KlLeG4nTMjrxcmx7ZtJynBdjZKV1vY7wOP8ex8HdDDzPMryWe6y062b3kM3EQQAk9uneK6NM2XH6+0gi4gP66OQjoV8xXQL///vvLM5/5/7P37kG2bVd539p9Tvc592KMJctFIgm9H4AQWFgBPUAKhgACpDimqFThxE6MMVXUraLyTxJ04zwsrATn2iEPHBti5z/+IC7KxjxlEkCYQCShBxJvhN4SCKJgQPfcc/qcs1NjrPEb+1tjzbX37nO77+3uM7vq1j2991pzzTnmnKvn933j8dXDz//8iMsefvjh4bu+67tOZKJv//ZvT8H7F37hF4ZXvOIVs/t/8Rd/cXjlK1/pn9v13/u93zu75vM///NdgH/qU5/q+PDBBx+cXWPi/nd+53f65z/4gz84fNM3fdOJ+tov7hboFli2QBfQ++roFugW6BY4pxbIyPMZr9IQwhnDhKjJD+cjrE0kCdEwBoRBrTE2a6NFVkQUAHXOVUTU6GsiTPedC8C+g0epPQjAnKSciwjWFDJ3kAxLfZj0V2oTmoCOmG79QswmLXXW5ZPvLPJCI8cR1FviLcSQ1vCbEV8SpUHK95rGkfkiG8BkPguJt4+IvmuulPxiXBBj/n/S+zXW6eMV0I0IquTcrv7m9xBAcsPSloOo29Z2tWUSXSKc2/2MOYkWiCkizUv5AL9nzoEOJjJrvXCiSkwANzLFfmw/QrbRP9a3rQ9bnya8Z+1JIYl8HUpKxwnpQ6rESLWOE0Mq+FG6waMrwnmEPrmgH6lK1Z70z0hiHZdc00X0vRd3v/AULDAR0H/2jAT013YB/RSmqjfRLdAtcJ9Y4PhdrxoFQM32ZGNfEtCXRC3HFXr+m/yyOXi51hhOvLfuDMP1w02GpSVn1cnZJvCRnXG0xrn9jrOxpl/258XZi3NXSc88Nt86wEt5KXUasMs5jyF8uxYr5/N7gUxqMjJ2WTukxU8H6DA2TsfYjfT1RKBTIoyzcpb8aXQunR7kkCz6e8Iny3Sm+HZmS87oEuE+KTVUHYT3FdIV8yiIEBxWcf8u8bvOWWvPb23DsnaFU7hioUkE+glfJPcqoiuu2fnIsNnEsVgne2cD4/rX0gjWb7MFTho4qGg2BJplXeKYQiR83f9LdgQP2pipW+6ZGdTpQxxb7LmPPTYM16+PPfD12CR0xu+vX4ughakDfRfQ91gX/ZJTtcBGQH/u6Uegu4D+oTNJ4X7r1i2vX/6Wt7zF7fEd3/Edw/d8z/ec2DYWPf6Sl7xkuH379vDyl798eOtb3zo88MAD2c6NGzeG17zmNcM73vGO4erVq54m/oUvfOHsOZrG3dLHWxp5/Xn/+98/fPEXf/HwR3/0R5423qLZrb3+0y3QLXA6FugC+unYsbfSLdAt0C1wqhZw8byC3Up66BNnZA1fFmBVcZbdt+TVnADRCJvieW7fpfAWwCyjq8W72wBhphBrDEj73SSCGmblHjzlNULELte00tye0fElNffSrCH0AqDtukpekX7QDqYmoGvUhdnCvk9RNOyMwO7iutjJQLAK5DOwbddKPfgk0UKIZA63Ct91sCWdu329TyS62yL6s5SaMNeH1p4TAN+Mlggi0u51wmKBBMxhBMtSSRrr29Z6ePts1SKitwR0Xf/bmlwU0GNR5TqAsKy1I7XxIqITGcGcsCeOjgYX0vmxmn6W4r3+bCHU1mLXlfURgpd1Yt28cjCs7twd///YTa/pt14dDCtIHUglez5rhX3OPvX3w5Wx/W0Cuu0zT7c4H0YX0PdZ0/2a07LAVED/srOJQH/tv/Lu9gj005q13k63QLfAZbWAi+d+NimOmTNxKSywhKdaGGliNBFVb90e1jfvDKvrloVqPQyH8ezWmXTJ8PWs72nn40zUwkQ5nlZ2py0AACeCWiIHoVrPZ5rynj4s2ZFxLTmZcha0cfoZ0rBQ1C3X854L6IGLSF9NlLgf9Uu2romHg3VCD+QSNY2wrMK3w5dd4vfCgFoR2mn2XZ4G4uzdsqfjRRlLGZKbujpZq1a8T8r0pS6CSauDQOuZTUfvssAfj3h+kpeUOmfnfQ0FXj8CvurarRm84AC4ZtfUjpMz7TlzfHw84i/nW+IdYZjIs4FJRj5b07a+cL73rF/Rbpb3khro/l1rXwh+NSyIMF/WXBfRT7LQ+rWP1wITAf2bTzmFuwno/+PZCOjf+I3fOPzQD/2QD99SuJt4rmnVq12Ojo6GF73oRU1zWVS4RYfbz8te9jJPAW8it4ne3/3d3z28613v8u/suje/+c3NNu7cueN12ImGt/5967d+6/CUpzxleNvb3ja86U1vGj75yU8OBwcHw4/8yI8Mr3vd6x7v1PX7uwW6BcQCXUDvy6FboFugW+AcWSBTtjvoFxJgguWrKN4Qt3NMOwR0BevVDh5ZUVKD4SVNs/Y9KcrVA9zJiSCBJmKoiKQZZbzHBPjzyr2QLY4vg3zxy4huFiHSvqdW+Yx4Kc8HOPv4AxMn6aTIO4CsgVwbI6ItETB+fxgqU6xr7TxEeblu0RS0AznYQPPUWNOUcURXtNJDklofmzHYjFxvpDfX/jEnfKZETl5XBHT9fDZWWatGsPnPFtu4M4JG3sctRkzkuLetrW0CvfR7ljI95pD1sfSIJcJliVxtRabPSCjSWbYeKhNgpIzXRN+yXuifpXm3n4juVuF82Xp7sUk5hytbm/acWRr4aAdHACdJ1yGoRzQ6jyKN6eHVofbx6Hk/scdLpF/SLXA6FpgI6G/98rMR0F/zc97ZLqCfzpz1VroFugUupwWm4jlKmYy1RqDPtFERNidn3JaIuh4GizY/vDKsP/4nIYKthuHoYFh91vVpNKgKVncspfyOc5M7HFtdZhOYF65tnQmbZ+mwg55Tq4is52vO01yD83NToFxYR4kJ4tkTsdnukblJB2iOieFk7JmjiMw3XGfZulRkDFwwazvaMRGZiHceOUm3Ln23/qpQSvdc6KxiaC2BpZnPcJyg7S2ODNF9v7I6f+ftjfvhBOr6ZIkuRc/zHPiEli0o51TT4E/KZG1ZuxNhtjh7n+SVcxJYke2ql0HrYXs2avNtuJNsebUpbcac422dXsMpec9n+Hrc81rbh/YcE9/9p77X4nfrM+t94gwTWNF4D/ucCFSZqy6gn2Rx9msfrwVSQP9Pnzs89M1Pf7zNTe5/5H//6JkJ6NvE8tYgnv3sZw8f/OAHm+O7e/eui90WRb708y3f8i3D933f97kAvvTzB3/wB8PXfd3XDW9/+9ubl5iIb5Hp9qz+0y3QLXC6FugC+unas7fWLdAt0C1wYgts6pwHEnagWxzqvdUGmVO92POSBe95/TjFcwCvCLr+uEgVmLqcRkAXYVP7QY1ljxbdkqYbULhNaNQ0f/U6nqlCdVrfbBhEjAFPTVm9S0CnjWw/0g7W+zz9X9SRduAazgZKiqjHOf0k8kHngkj3SkRoumyPyojOmS1awi7gXEmvJIPUgNFQTcs/S9u4RUSvZNls5cfzeGwSOM4EjVdPuA8ZXEa9lPR0kF18PxOjh2G4XQmHBqE6eXjteOyHJCMaW3pbk01Hgi3Eic55LXFQxzeJ/Gi9aqLvJpx75EEaf36x1fkzQZs2I0JivZAmfTphJ33NrQePYr/xWDvdoHXTnuvp5s0BJvYc70ElVBeE/i6in3RO+vX3aoGZgP5vRjrNe22w3PfRTzw2PK8L6Kdkzd5Mt0C3wGWzwPHbv3QUm+0MY9l20gFRsJKe4RvH39EmC+K5f1VxlFx79+5w532fGlZHB8PBi58ydXiuGKIan7Mw528iUV08X0i1qmchxTmtibWMWC0HZTAIw6qleHBg9P6E8LZrLJh7ciYmu1U8KB2JAw9qZK33KbAmtZ6tzUwrH9m58uxLm4JRJzYIx3P6Qzs6DiLcFQe4vcBVggG0bQT3JaE58Zfd1AK20velDZm4XC5QDFXvS7wpWFuj/ukzachb3TKhtpY9U9vwzG3i7wTH7CkSz/bFvbyl1Km4gi8hUpYwW45tGIZr19r7hum05izTlq/PWGdmV8/2tceY9xXP1QzGDVjK9tlPZFBQvqg6+luXiD43PFjeaV1Av5f11u+5VwtsBPTnnZGA/sEzSeF+mgI6tvuxH/sxF8lNADcx/GlPe9pg9vm2b/u2vSPGLRX893//9w8/8AM/4DXRP/3pTw9Pf/rTh6/8yq/0NPOWLr7/dAt0C5y+BbqAfvo27S12C3QLdAvsZYGscY5aXoVGWlkSxRWwTngeQGMhf5riuXY1yCFvd0GAdwBWhqeCsYE5A5MZ7S3XJoAtoLZaC3LJSBaiqolI1oj4icAvRI0KtQYeW9ELGnWwbbZUnCeynOvd65v0aTF5kEGkY58I2kIqIZQisGfERNhGgXaL+JvNEcaVGnoahQHgb6WYq8RHti31GT19fMNQfu0OgXpC/sS1O9P1x00WfcI47FEeJWBR5wjwpVMuwJbI5ex27WdjQK01OlufIoarE0OrXjj3TogTntvYY74uJZV77buRNWrz1tzRrInoVv9OJ86iGdhXLaEf0hUbuxPM2N/1TIwf534VNf/WB5a6fe3R4Suzy927w7qQwSvL0mDCvT+7vJCsfRPxvX+SltAeDzke87/W9PRhoy6g7/Vnp190ChaYCuivGZ55JgL6W72nPQL9FCasN9Et0C1wKSxw/EuvjHO3pUwPsbl5ZmoJ4Hrs2CFmbhPPb90ehtvrYbh+ZSP0VuvO7i9ZxfRsSNT5omOonQvzIL08j+7QG06Rek72MzRnainJ5XglmvOzb6RX9zTycj5ujaXVCz3SKVbLDFxxrlPHBhft706dPR3DSZ/TcdYclcGnC+f52m/wYHWUmBz/BeukrWIws5IAYSfvkxlBsJ/+vlgabce6y36BcZgf7XAFhTpX4XScpcwQWmuUvEygC+jbcP8C3jqtN0oLW564bcVVpcGl9lkbrFuP1D4Mh/vogNnGS1Fho9IxxSaGd+xacM8Ex5dOGBaCn2B922eUcNDH3LwZ2bsaz7Z7HRdWR//A7DiQ4Bzg0GsccBfQT7zI+g2PwwITAf2vnEEE+vecjYD+OIbcb+0W6Ba4hBboAvolnNQ+pG6BboHzbYFNxLkQF47DJWU7QzixeD67cfyganUO7ETITqF4i+hO01X/0+gCTx0dns46DfsAZAWb1BfX2t5OtARpkQSMRo+IiE7aeBffdkXBl/Wi4vrkeWEzosUntQTDlgjo3mQQFzgBOD8hpAwCPASNEkPYQp0TcmqV6Ajihz4rgWPXT1IZlvp6LIwJ6TX5ZUMOLdVYnyyX2SIbe5y8n0/G5rOmN34hhhBTaYfaicxpa10hEM9eA5Vwqwu6lfWhNOJDiHG0yhu0ROl8jK1D2XPZdHxWa1FOHl326wJ3OGnSfvmMB4Q4tXSARvxSV68xNj4yBxjP4GAOKPFesm4eRq1yTZ2fxE3YxuzDHrF/X73iddF5EbmIbv1QUpeyA3advUNsPU/qY0okhTV0dNjTuM/Wd//gibLARED/uTMS0L+8C+hP1Hz253QLdAucbwu4cA5GcuEpD5bScTmDtkTfbedVvmuKxfGlndHBIThzNp9TzsI4JibOki4bVrEzT0t0a55ht8xTODNO6sCrk6c6HZOhS89qeb4uB+ttAjpH2nr8Jz0850EwSsVwNhw7D5oN7Dsv1SPYxeZa07oj9Ga0OqW2WqJyOKRyz+z4rU7CYddWjXPwx1asVI/3xQF5Mm1bsh6ogD7RzCvYWQDVLvaKkwRzus1pWeufN5fXwrPuJaK6tr8PN5D3VPKh9fsCCGvBv/psu9Xs5I7H8WNOx4ZZZuu7BJ2DC61NW8PsO8t+lynWhYswfENWOe2b/dtLOEh5MpsfnJ/Vftg/M7apiE4KdyuHFlxESePeBfTz/TfvsvUuBfT/7HnDQ3/lGac6vEf+yUeGN3YB/VRt2hvrFugWaFugC+h9ZXQLdAt0CzyBFjj+5S8PL/9GbZtKUtyTeN5AeYvAD0FYBdltxigEReLUiAo2AkQjzxPbCmsxwbsSVbEExNUm/jyJYKhkmHtiW9rB3xM33gAAIABJREFUVt2glhG2jFXJHr8s7ocAc/Im0k0T2ZD3mPAYwr0TQRo9QpRHiaTQensTIi9sp4K6Rmeo04ULnTEmnjuxhdqgkk1KHsl4lbhwEjCYpIXbJx1IflOIToiCWl+wNRVmNxdRw372fPXO13qOSjZAIGndy7Sfjk2i6tVpojX8GHYKyjn3Ym/uaxFCLiAH+aprHRJQxfPaF3+ENOoEzdVhsDSdi4RwGPqB62NdP/shZXsr9b/aH8HcHxvP1TnwPRmfu9jN2qnC/CbF4NoIKV+r47UrI4Psv7ot2TfWLs4T/plkHvB1cGUWEX/0vB9/At/k/VH3swVUQH//v3rtmUSgP//LftZN3CPQ7+eV1sfeLdAtcPyOV4xngIyM5lwSZZpm57dysLhn4VwOg5yb7FkpWJWzvV8uD6u4BvzAeYbaxAjsOtWTMYnC5p8v10adrpY4c/JchDz6hXPvUtp4Gqs15OuSbMErTQnOc0wItJ8sWSVlwtyudiYPB8ppaPw805WWtEq7I6ZzZlZAtIB/FE9x5l3EN429qOK8fg1WmrTJPEqaeZZYPjP+McNP9dktoDFsMkzxfKKc/dFhA9ay7Sds51mfFpxrt6Unf7wi+sIwtr/19pmggpkMTxCpveuV+qA5Hoct09k3bpKtmM3UMSgut6xaJsD7j6ZdL2UOtDGbMw9GONxAv1sq5BdPEN83ga9I38+zMgufCfPT8hCHL3zLLkv077sFTs0CGwH9+WckoH/gTFK4n5oBekPdAt0Cl8ICXUC/FNPYB9Et0C1wUSzgArrjKFeFpsIsg9hK9siXi5rwlmsgKXj2xNs+AF4aU8mHamERdiGDMoJCrkX0dgKntGFAzwDtkmc8YJ827HaP/qDWuJJnFsERntatxUC0RVOcbNyQRENE4WrnVSgnijY9/mVOEXUR0CFa3MxGEkndP/tMhfYk4dT5QMTMSSRzXIOATuRwzjXjg7SpaB/ngLhu4shRSCfIEsT0bX4JM7FciKGtpEukb8yoCIg1RNSwHc+GBNJpJJV7Dl1tpxeSJQBlFxvE/5UsQci1vaPR54i+zRqCtNPILqGkadpxh0EzgiDWDu+SSVS5rBkX0IMkMwFdIymWOKg65rShOAHkO0zfGQ0HgXg3uICuZQDW62Fl9dB93ctYMmuDjYF3jES2R3TS2tIslp8uoLdefP2zs7DAVED/t89IQP8Z73oX0M9iBnub3QLdAhfFAh597hHf4QzoHV+P2XQQ1hnMoiNy3KOD5rhV78lrCpZCCN7XcPWMpSKxfUeJnKbQrv2NQxllpFqYaSbsca4vadMztXc4Yi45s4LZ9hE49djKOVhFRI6kit+sXQR1+/ekRFbBHY654qw5c+YsE5qwtRxkZ22Awzhr6llWzu275nqC1STb2wR/NSLdt7Ybzr27cFJtw9Yn9eXBF4irzAdruLXmHHPJeTyhxJZFULHgvhi7Ye5dph6/b/WlrBfFx3a5ieCNsk/NqHLbW9eOxveK2cpwE3tGnYtzTRffjto9nO0ZXNo91mfL4cZF7yueaWsy3BtWfz0cyyft5MtPTBjYNh0oRsdj/ekC+n4rrl91OhZIAf0/PyMB/X/oAvrpzFRvpVugW2CbBbqA3tdHt0C3QLfAE2gBF9An0QMhOilXkv0pYpoCrabOtiC+tUgiJTeaaaV3GSVq5SEeqmczAFIBJl7RCUQRvBeesySeI6ZTI9kArYtqWyIyKqGzy2M+BXKtzQeBZbX64lku5EcECmIv0egpqlZyJtmd4AKCKPHLouZfRkQk4i5GEoHU+YRoQ1PAE9GctQcF5E8AfmHdMjp5SciFCNqDVUvBWcYx+2xh/p0IEvu6EKvPln635tO6bxHaGTmji7LYVVOUa3dan9N/jeJWAV0dY5ZIWfaeOiP4/t8mngdxZE4iNQW7m6Lc62siiCDSrds1GsVQTVKJHrWFXWv7jLTq3Ku2bxFBCOKHV4cUveNej0K3sTiJKv0nkqIK6ERlHBwM6wmRPgxdPN/1vu7fn6YFJgL6z5+RgP7qLqCf5pz1troFugUupgWO3/3qOW5K8TXOhq0z1ORY1DhfTYTfyS9zQ9VMUnEk22pRFXPBCpzJLbqU83urEc7z4AzHOXGua0GDlogNrsTh0/5v7ZqYuC2l972K5/Y8j2IWAVij1xPnUEc8HCj9eRHZr+fjxIHhRMt4MgMAYi9iZBimhXEmgqdeEM7dHtEuddfz/BnXLgnDOEbX5ZVCfyyUPO9vWzEcjhnHNpxVvwuHEk3Jn7hJ8XEjawJdcsfjch4fF+lyp3fh6X1eOXvAyWkzZf4mXyqmNRFcIs/5qsDevJ1SZQjo9oVhHsMq6nhi2MrtHHcqrNd1sIQh2fctzIfjgz3D9iklDXxvWSr3KH9l/VIRfYYDRUBHODf8KP3rAvo+i7Nfc1oWmAjo/8EzT6tZb+eRf/Lh4Y1/vwvop2rU3li3QLdA0wJdQO8Lo1ugW6Bb4Am2wCiiS0TqPmL4VvF8h3DO+AD024Q9tcW26wysUVuNtO0V0wJSIW8QQAGPLbtD3JDuOvtu/xAGxK7TdPHNOSR9YXy5T9pwtZVGRxAxThcmUbMComeR6IqwS9o+u400hIviqc5tSwSWGu8QOZkCMcR1SCHGX4lAJRNoY+ueCCG7NY9KUKhAjN2aYneMke+sf6S8wznAiSBJF46Q3WqPz0yc1ZSE9Vra9LGuhuH28XaiCHKFtcnU8DlZDtjb++6zGQvTMr6kr7exG4niP431oUTKZ3zGeBmf2X0movOjc+81KCO7Q93LunbsXk+xHuTlEvmFMwHi+2o1rI8sskPGZ5HoNy0y3kjXFgsFOT6Of20RGYXM6+L5E/wHrD9umAjo/7cJ6JHy85Rs89FP3Bie/6ouoJ+SOXsz3QLdAhfYAp7C3QTnTE8sBxjOgvW8tSSeL/kpLp3Xtjk27hL+9FmeKUnKXW0THhGUcUCsAmZDO83I4XxmORsSeZzn2Hp+5EhJtrA4tG8b40w0DqfXSbahOLJxtrRzXjprRpQvNl6AOOkgOjmzh9Oxd1MOsvVIrMK5nnuxaQrjCzcqVtc9lJ+Hvbauk4KZCiycHMj5bqswrUAr5tHO0D6WOPCnEKsZ77ZAHLtey1816ys1XiL3KqAv2mCfF1UFKJOJ2WAj65thntt3h+H60T4Nj47a16IGut1vdj2+1a6BDqcBFsIJI51AAlPZk1VMb3VfPzMMT9smfmv0vC1Tw3DuZB44EKyVZcKifrrda8ENmqXN/Gd66vb91kK/6lQtsBHQXzA8dCYC+u/0FO6nOmO9sW6BboGWBbqA3tdFt0C3QLfAGVvg+D2Rtp3naBqwJodRWImdxJCqYGUwk6iE8DzfBfRpYnKdiFte69xSr0s6sCU8q6muiYRdAtxJGhUiRwmFJDviwxw6LIkYtEYJO6/gbFsRHqvNQnj3lIJRq1z7QBSugVUHytJfu26SFr/FCGnEjEYPt5ge/AaUMJE6f4jlM5In2krhk1T02tcGM7aXgK4ETfwbE1YSQD9fEs/1cxwTJs4LjagbxttMZRmdsLZUmLV7bN0SEeP/ljr2pONvnpaowx331HXtyy6yE0BELTmszOzQWCOzaBeJ6Mlx2Piktl5LTP9TD25GYzb1OuiaMl0Ga+K0PdcIJ0hp+5p9oO8DJ4oaKdv1miTwhDwkmsKiyCMqYmX9IRp9sqdkDdvzrl4d1pDQ0e0unp/xH6/efNMCEwH9F77ibAT0V/60P7uncO+LsFugW+B+ssDxL71CIj7j/GDRk5zXJtmIFrL3zMRd+aCeU7Y5sLYE96pf1smp99j1hpcQwnaK5+CYENy1/W3i+QRPCmZL5+ElhVpsmGL2NuUcHFEGnjXWoz3GSZahdEA2B2hzwrQo2pLRi1JU1aacJ7V/NQqYeeEaXyel5JOK7YkF40bE+KZorhgT52T7bE8BHfyJTSbmlV92Cuhl8YFFcN7Gnvqclu0ma0qeb5ii5SA+mQ/t7451srTkmuNf8m7ZtQFa30ukPZjM8KBnyytzmbfHPdevbxpMbNJ4A9saBgdZk4pHyXynj9LSba0XurdBprXYF166Iv7N+8Ouszm6eTOyj5HJDkxmAnzUUbfxSvDA4Qt63fP76W/peRprCujf+cLhof/wlCPQ//GHhjf+vS6gn6f57n3pFrisFugC+mWd2T6uboFugXNhgYw21yjTjLJeQlDyOUTAgrY6XlkF90a7SjrMyCJBeErsTMTUAHXu0Vxqjbfws3fJUqAHweAAUITj2sUqntv3CHd+rQjmsyFLGnzahQARDmmS7qxV/ExFS+YIgibtHw1yrfURr/8U0onipc/FQJO5CGG76azQWh+NKHY3j9RUVwcBPte085rufibUQqTtIjK0pt8O8gfypg5H59f6kWJ2RDhr6kccH3QcGVET3vZuB+mL3W8e+m7b1SZtZRIesT4nWQMaNoevciLD1n7UoMNE3qf4hbHSd/bBNsJ2QqZC5EH+8PDoVwjJYxR5i6Ut/iEI6HbpjRsbUnoS7R1tQ84YgZbR/mEjF9SDxCFSXfdhRleoqK7MGQ4AMldHhymiu25uRJX9V99PZnMv1XCQaeCXhPPjX/2K4fDzR+Gx/3QLnJUFJgL6L/7FsxHQX/F/efe7gH5Ws9jb7RboFjhvFjh+16vGs6ALXXKGoKSP1/SVc149L+SxSAVzUobLOa119p0ZAwxTzpaJMxrWc8gT5zcXzkfnvzH9+pLYKOe8LA3FOAv20UdWJ8Z6zpyl3habaF1nTU/NeVlgV3ONLEGEdC4VAZMxWX8mWK/0ZyIuL9gKvEWnwGZk1qo4Tm2ux2mE9AkOkZFO5kpV0C3ia2LAYpyJmC3p1HOIBUPtcrAADzlmssjzwEyziQrx1cctGMswfPap2NmcGqijbu1pdHWrX9v6mmsIXFPtizN4netd+LO1ARXsKziJNee4JfiI7EblFdbjXqW8AgI1OM7vC5s6/owIdedE4jtrEgcEuw8hm9T6kz1K1i/J/OU4S8YC9uL/16L0A3PjAr85PQe2Yv+ClR+IaPphGFri+fF7X+s9P3zpz563PwW9P5fMAl1Av2QT2ofTLXCfWqAL6PfpxPdhdwt0CzwxFjh+72umipYK4rMuFNB4kmuVTFga2kyknbAJc3IHD2pIGItQLbWHm6XRHKMGICSiFHC3BLaJ/iUNtpIgSXA0QLbaiOEk5yb2nJAFKuyJsRDNFZsDnDUanTSSSdxE5LFGvC/Vz8s5EBImox34LPrd5GxEQOf7TNm+g3RAAF0SzevceF+X2lyohe4gnz76L9No5lynhcisESoqRNs9E698iKCIpHaCQgghXf9EU1h7ngFBo1HEwQPyo+4dSAv2AhHsrG8Vllnr1oYSLpXgxc4zsrP18NhLEGZGAtm/M/I8SBv7n0dDhK2ok46Abr87GZSd2xC6rFur++dpAamDKP1hbxlR5HZcBckW8wtBqs4hRH14F+taCGeaQxPRg1A0vtX6qBkebP3YMz363OZ4GI6e++OzN9zx+0YSKPtmhNDnjQIkP8fv/5phvToYVnfvDocv+MnN57/9Nf7vddaKj68ODoaj5/zoE/OHoj/lQllgKqB/5RkJ6P+n26QL6BdqafTOdgt0CzwOC7iAjsOt/2EWLFGdcPcRz62NmnXHzivUBd7aV3OWNCdXhLb4vwrker8fmaVet+ElF8/joib+oc1If55tS+allpasYwe7JKQLcNAqW5U4J87gpFP3c3vY289s4uzYslFCA8UICKWSJSvtL2KpllZSbDVJL++Tvzw7BS6l0wL2Vz21NlMhIHar2Mivaxmfvi0Ivzyvrk8i4itW1WeAoZo2F8da0q2Pp9eYLxGq6TvnaxyUyZRlt7mTSvnxElqUeVoau9yzU0CXPumjcGDwzxqYXPHvid4nldcIZ/zJUNjPJSud2cjStyM+W3mpdLCOa6l/7vYjmKA4VWuEew040CxbNhca7a/ZGKx99i94GBzrpazix+5/LLCdZ8yL95XNrVxXxXMXzm0fwukcrIbDl4xlg/g5/rW/OL7PzA7XryWmcrxl70/7zt6PtsaObw+P/tPfHD7rTb93otnqF98/FkgB/Y0Wgf45pzrwR/43i0B/f0/hfqpW7Y11C3QLtCzQBfS+LroFugW6Bc7IAp66XcHwSQTxCTlSO7gHqVBvmYi7+mWgVAdoC3yFgaOrAti4vUnqiPd1AsAFEXXWR+rnaT+I5KgPM7IHYk0a0jEgxms9PrsUMW8WuS92JbqZpu0r6o2p0G7PW/RqF7ItAfUOkVufp5xCi6fJz5YmrvlnfyNw+9dBvFAnPcX8ukZqWzsi0N3OC5HhkFS5H9TuUcPPxVcIvpLSzvpKNJISUU5WSlkBH16kJG/u8Ur8NS6y9qwNSAmIFQTqSviR8p9adEvvFiWdUnRGSBbClvsRrc2mGVUvUVHYmussVbulIcQej1nqdovGjzm3PYHgzj4x+ykpB/HmBHLsSXeiibSFeq3viSBItSb8bNnEmmAP2rUmjsu8rUxAtzTysU7WJuyn08EwrMIpgnrom8j1eJhHfF0ZDl/8U/7BrQ+8ztvyVPHsf9q+cjCsvHZkZIKwOu04KQip2YX0M/ojeUGbnQjo/88ZCehf2gX0C7o8ere7BboF7tECnr7dneUiElPF4ZbzIUeafJ6c6xC0Wxl3dp3NONNw9tNng5cWI7zN0+9wOcp3PIhsejCp7U6mLhH5lvqqgncV1FvljWaidwPEkXVLM1UtYb3aLxfkJMIagS2zkcUN9ezPmRLR189nBY/qWb+K5/RDHTi1b0ttgRHq3NZ28rxe8Jv3oyH+6prUtpwPCPssCbqcUbftn+oEkf3Q8l6S+tu+t7khY5ouP8P21dGiRqEvORG0cAxtpQOD4uAYVFN0jxvU8UHt2NTyJXX5DGsEbkwHDUpXKR8RjVoUN1Hn7FvHIeFI4HMY+xLHYMRse67iQdYyTuHVvkT3g8US54pzt47VrpuUrAinYns+uMn27LHNr5VEiFJlPh5Zr16aS+65cXO8nneRZSCz+ugv+enh+N2vHh2ZH3xwk02j2rfskfVvfXK48fbfH46eOka8P/CffPge/wL02y6rBTYC+ouGh/7qGQjoj/x2F9Av6+Lp4+oWOEcW6AL6OZqM3pVugW6By2OBrHueYLIBvH24DUFVhfYJ8N9HfG0prYi8C/ZdIoMA+0se1K3mIJ00YmOXhzokBoBSyRURzhajOJrOASWVtJplqT81WsCF07C5C+VVsI/vZgK6shMYaYnxOcGap4nZLScR0IXAyfGKEwVzsUg4CrvRWjdKckEU1T2A/bGb2rjlge/PgZQj7XqkLVTCzfqeJQaYg4gM0JSELftNODCpbU+6UDIkeLROEFE8IokUE/0hJKSm5NIU46yA+I1Hv6fjK1H8ECiZCjMadduMIrT/YEvrhwnPRFlZhMIsstsIE8T6iFzgdyWHnEQKAxnJQsQSjgW6/3hn4NzSEtNVPIeUunJlWJNCPtbgyggf+xwhnyHb557i9cqwPrgypn43mynRbqL80aFHm1vkuv3fnS7MBmQssCkyAd3S4WsUSHw/CunTdddrrp/gfXWJL50I6G/7qrOJQP+S0QGkR6Bf4oXUh9Yt0C2QFjh+R4jnrcjpPEq3MJOes+PfnNk0GrRl6xYe8LNxRFf6OU8cRvMcvpCSnfMYZ8om3qjiOYcbOZ+3YETtv2IWztM4qLaimhVbTs74iJfgHT/IbZ7WFC9b8BVHSsE74CYVHtUpVcfEeT5xyJbNobCnYjfmjM9xbE1cGn3HVno/Z29rg3rs2i93jkU4l3HqslzQ1EeTqvNxGZ/i5qWhK24CF+jZ36cuMFP2I9ZydTqnP3b21nXq5a/CsdT70VgA4D/fKrpu1DYMIvCM/Dr+s2GoJQF90g0yX0U/XfwtE4AdqrMBe5k1lqnPKbcQTtguSquAbt+PWbDGrks5AnsGjiMI6GQJ8xrkgSPcgSF4hEnkebTJctKSbPmZZH0Dn2kkujVhWMa+UyyFzQ032U8I5YM5WfscxwPAkDY++84i8bERIj5tYdPMUijZvz796LD+/UeHux/64+HKa587ywS2ZUf3ry65BVJAf/gMBPTv/9Dwxi6gX/IV1IfXLXA+LNAF9PMxD70X3QLdApfQAhmBrmObcD9VVIfEAKCVVNCLNlpiNxz9jHdVkXkicCtBhHCndQZL+4tkSgNEA7S2EWIKyhSUNsdbxOIEx9lIMV4Abb7eVzzX5ojIgAia1HuWeocTAC+itILMM1nnJxTQrQ+QBhPxM6L6F+1evqgC+sRJgXpswpFU4VzJvCoQQwDVKBpS3lFjnDYPj8a0gyaSIvJqd40oyLp0xROBZ2eaTyFDrI2MjgnxXNfcpH9BHrLtlvYJfVbyzoX6+M/IiyqgI+ZCyng9diNl5CEZER4OABaBztiMPLF2cVIhNboSO4j/pMO3PtjziKQgwt/usYgL3w/iVDJzlDgY52IWzSLrj3HyTE/VLhFMjbXowjoOEZmG1dJOWu1GS8FokV+x/jy6PN5LCP9kl0iiuWTN0LID1v7BwdgnM6sL8KPND5//k8OxpX+3dqI/h587Rg33n8ttgZmA/vQHTnXAH/34jeH5XUA/VZv2xroFugXOtwVcQG+JP4oRdAgJoUrKcMc8lh45znIqii5hgEm7kXWIM0Kedf3wHAKl4KY8M5S057NnNQ6F6sQYzWc68m3ThfCVx385f+p9et5s2bGKzxp5zvW12w0fhvFSwZt+zrJSP5HimXMzTqApSgvu1c/0GVXgrnaZjUEE23rexsa+RqROu3d/cWClhr1iiFh7S32cTbk4XuR38Q8VZ1tjRKxmzr2ut5zBXVgtjh1+TziPVnGaMP9W9i6cTnmWnpfVocDntdhxsu7DPrO1JOJ5y+wKa3mePyeWmfbBPysCOjjdHTfIbBZR3rrGNfuDicjpjHx3xD/qIE9mjLRJZM3LDGHxrMwSFmJ2Yt94Jy2J5+A6mw93Co6oeRXWWb82BspOuUhfF5r+HmnYEfoz/fqdTfQ8YryK4+ArcKe/R8OxJPexcEb2SGzReHdRVstTw4OZvqDXXj/ff5VPr3dTAf1Zp9fwMAyPfP8Hu4B+qhbtjXULdAssWaAL6H1tdAt0C3QLnJEFJgL6DCDKByqkbbtu1s9twnkyJVHXThpWL3wENPVYT/DbaH+fR+5DUOlYqgg+A8NbJmhyL0SM1IlTssT/XQYA6FaCrQXmSX/tYmoQFv5sA7hBWigo9i4HkXHmAvpJFzBkTfyfmngQLVubE/upgD4hfsRpgTYB7BAB+n97nhNtEr2NAFrJPCdF5TojGoxEOjIB/fZG8NWIilwjklEga9QLCepp4SkjEANSMgrxmfbsEreBkHW6Hlt7pZKZeq+vLa1B6Q/Y1NmzX5PICPLE1qUTRJKePETfFOSZp0cf29Tzc7E+Ut5zr4nNNl7mymshWtRCTJZ9Z3ZGqPf3VonIT9Iq+pepOwu563aQdeLEn6Ven6ZzH7dRiV6xj248lt3yVKmQfUmaR3u2NnCMgOhbIknre0/nVx0a8p0Ze1wE9PUD14ejZ/e66Sd9I12066cC+r8zPPNMBPR/6WbpEegXbXX0/nYLdAvcqwWO32k10Ms5k7/ZxfcxHQLr55OsUKWtfTpmz5tkpckD7ubMR3Yd/r8La4AJJs+Xjs8Ewx0dTWdIHlww5cLZaZP0bOH6Gm3cOsdOMNKC6Gxz4Gd1iRbOeQSjiXie9gtD0OySOF7PhdVJograVeS157XwH2ZXrChab2Zg0rO8tq1tZls6lzUCveHk3eqrNkFacl+nYSjO+TOn43AmmeCr8Uy9/v8eG1ZPNee/9XjuRzDVeUon61irE6wnjipqNxW3idhnTK3vWkud/ZDXy1rJ73RixNic3ZVbAXNMggqiTfDQJFW6lW0zgTmyW4HFkj8J3GX2IZW6YwEwavTNhelwTndn/Jh/IsIZC882MTyfGWtUHR+0NJqL1Qej0/Dsp2xcUtJbW5SpYo9mv8byV+k0oFkt6jbX91W+rxvp8elXFfjFCXr9wT8Yjl73y/u8mfs1F9gCKaD/Fy8eHvqrZyCg//e/1VO4X+D10bveLXBRLNAF9IsyU72f3QLdAhfWApnOPUfQIC4czNchLhATflmL1YiPK7mhUcYKdDxVsQGw2taWtrfNghIK+4roKm5CtCQxBJEiBIGSHmmzhge8m6iMY1uflKBrpXx0obLUZEQc9CiAOqeQDYh/S3O5jTXbNv/bGAdlbbY8l8hqbOVkA6kNqRe38BzIGsgAXZPJaSirWaK6tdkJQRNru5JAfn1E+hJNYGSP1XnLVJ0hPlM/24VkIT49hDhSGxKFrEvERdiIWJ6swdhXEHQTMkME9OoIsrRNNWUgzhWkFbeUgRA9RBcgLlOrm+e7OCw17GwsNmYbx0REl1qAFoVOivgrkfY9yRQjfkqEhLdp6R3LfoKYSpvFhLJ+jAQyJ4aoV54itto7xxkp6CFxLDU7EelL75uw9ermzZEEJeqBqAiIRZt/64tmINA2lVRrEZ+sa3WkIJKddrjv0NLGR9rDYT0cPetHLuzfrN7x7RaYCOhv/+qzEdD/rbd4J7qA3ldjt0C3wP1kgeP3fNk43FoeZvI3Nw4biIl852mSIyNNTU29dGxOLIFgJQ5/KpB7hOiYlWbyM4NQ+2Cqhni+AL0mz8ojPbgncIkWDW8Jw3qfj1dw1USwpa70MszkOJ794hxbMZadu0hHzfOI+k6BO+yQ/eHgLJHLKoSeZCOo4yNj1j5uE9Bn50S1R43yLg4XtY/gpcxeEBO9a77NNBMMIw3XdOxLmZt8L5gITP9D+OUcC4bjHO1n3bK+cYz1qXEwULINREktdVqwa/2+stZSEI9Ntw/MpR27d1LyK9oH22X/yAwnmJ1I5eZIAAAgAElEQVTvqkNA7u9wIlBcYvcgott97mQQttG5oQY62erMfpG1KjMCZAY0nA4KxrbrwVqI8DrWyXqU6HPw4QzvLmwUL3kVpax8GsMJg/VknxlmUiy1a44qjaFOBrleeMmKM4GV6Prwp8aOHt8djt7wvpPs7n7tBbPARED/a6csoH/fB4c3dgH9gq2I3t1ugYtpgS6gX8x5673uFugWuCAW2CqeA+jBFbMxbRNd9zGARtzG9RA/i+K5o8x547uA/r6CeW3ZwZvUYU6UL3Wo1bNAAToiZ5IgAvpnQt2CvSbCeQiwE9Il1eBNjeUEwoWAmpAHEZ3rc6tAWZHmQp8mAquugV0odoKwNwSH1lPTS4wI9LFIuwiRms68Anf73UkEMXJdH0mWRYS+fT9L7Sa2raRIKwqGKGNr27zkiZaw+dIUcy6WxvohJR7tLUULMUbaybSIMseWJt4EaH5SVG6skToVeU+QTxAXumbSoSEITOxl/7fadlqz0okkiTqHREkROmoaemTEalgHCbeyeX3ssU0tcLvPyBLWPNHl1PqDJGJ/WzsQ1IwJpwMluy3qwtaI1hdXB5u0R0Q8uCAec3Z4OKZMbzpRiGHDdl4DnR/6ppHoLuQHwWfXtbIdzL2XNpEi3peoizisx7rrVi4AEgqbewS91XKPaJDbdwatmX7rg1/vc+Zp4C0F/AtHgbT/XDwLTAT0d5yRgP7yLqBfvJXRe9wt0C3weCwwE89nZ8tyBtDvOYPgDNpKR147V4/kXiZIzn04MnqmKUpbLZx9mzioBZ5wsI3OqK/pLuMp7llyfiYtfMU3Hg0cY0sBEAOUfm7DfFXEp88TwVeiZ8EZnKvzrNvCNNEfdVTdBn2qaDubXxlIdZLcdW+rrbpe1Im4YkO7P52NxVl9ybaIzXzfclDOs67hZnFsba09zS7lbQpWqXvD10yUIMjnxvWKm6pNDDM5LpDMbFxTBXTO3xN8V1K8t/anZqtSnEEKey27ZPdzJtd16nhXs5zV9R7Ztzw1evnOMIZhJ+xNKnfvazjrgHGZc+1zrrNol/2A8O6AwHBY2N+cwN3Ju7F27VocjBkn2QMmThRbNrDZ4ebxRuT3YIB479ltYGtsnfMrc1X3AXOrTtzMJfjSfmfMZL5jLJZV7MEH/P2kpbCOf/0rx1ZirKSB3/Wa7N+fPwtMBfRnn2oHH/m+D3QB/VQt2hvrFugWWLJAF9D72ugW6BboFjhlCyQBNBOihQWYCcG1E3pt1bS3MRu0o3XZQszNKEoDijsiz7Uu3xL/g8eypi+rBMou2+LlrABLBd+J93KAVf+f1DyEUNom4s+8oBv17hJ0h+09kjeidEntDSHh/ycVHxEbpRYeYqOmwFbwPxHthNhwAC4gfknMbtq2lU6+hXTjGb4Oa/3IIAohCxgzBOLkuZUIJO13tK8ET00POCFwNItAISdt/ERAeHeF4IQw8LR31DkXgrA+H2JI10qNSLHbqWGnwq5dp0K/XUeUgdaqa80L/XBROMZnz/C5jqgm6rtjl12p/xHNaa/WI3cS2dLbj2L2OgTp1aM3pnXNqTnq46PmnjkpRBQC5AXjhUzDNvZ91leP4Vn9dY/cMMIqIjeIdIDEpf/2f+qsI+g7UR21x3c559xdDysjhEg3aP+nPY9OaxBiPqYowaB7LT/zCRuGQ4s2uzIMRqDxzs6IdnU84Z1qafg32RbcEcDs7xEl68HF/vj+8AU/uevt2L8/pxaYCuhfczYR6C8f10ePQD+ni6B3q1ugW+DULHD87lc3y7XkA1QMW3qq/f2+dXsYjkbHwTxfLZ3JJv6pcu7PaFzBSXEmmTRVsZFjBpzt/HAnl8cZF1yhR/59IB0tpcNxwUECj2YCYIrm4hhQRT0d2DaBt05IxU32Pc6PpIjm3I6jgDskyjkdHOLYT0R+P/sV9ZxfFUMrfqR/286NOGu6zaR+N20mdisLBzEcoVuf5efZMFwK4aRnbxhUP3IHTzJkFeyU2BXjNRbzEkb38UVZKnCkXytp5BPfSbuZJlyxYIlKZ3kzH/UMzjpQDOX7SrN2xTPrXOoQ1abMl7ctttYIdLiBpXeEZpOya2qUN2nRFTeYjcimZfPhTrIqbofzgW551jyYE26EsXqmNKlfb+WxcO51pwSLSL8SWdFic5NtQ7NqgZns/y5Gs/92vFQy21rsN82+Rhp3bYL9pM7JzIcuTcR8d3gvGdLq3lanenGYXn/iU8PqzzwwDIYjy9roAvrSwj7/n6eA/rdePDz0185AQP+7PYX7+V8FvYfdAhffAl1Av/hz2EfQLdAtcE4sMArnCviLKKrgD8BRuIFxKEIsJECf/WP8oGIkxOQK8F0wj/rd9n9E74ntojG8tIlC1WvSCztqH2uq5ZpKcde8qFgF6E2hVBBZ9Qav7aqYjvlmwK9FxISdDeQRYYpnuP1uAicCupNEmu5Ma/hF25N0k2Vi7NeaOq4SQ5NI8SAZ8OZWz/lFu4p43ow6L+vRo0BKhLwCYdLQzdZIAO5KaBL1nREXIULy2ATgkSp+KQJCl7qShZnifz0MWj/Orq/kphN3QRx5N4JcyEiF8PT3PVTmyoVe0plDcIQIbNcr4aR2bu3lwp+64E7qRAgt+91EbKLbKxkIASOvBjfRJEK71HeHdPC9fnUU+i3F+Go1rG7eGiPDiQAwMkjJP4tMIPWpPcNry4cQbtdmVL85GoSdPEI+3ke2Z5yACQIF26ddRbh2BwSdK3k38c5aWQR9KyKd96QJ6DHX9pE+l2gHzbhh/0bIxslAIyxYlzZ3EFU57+wxiOh4XbPG7DqyMxyshrWRYvb7zeNhZfa+c3dYm20PDu7LWum3PvwNm7+Rd9fD0XMuZr34iYD+S197NgL6X/gJ31JdQN91kOjfdwt0C1xECxy/61URaRnCT2sQk3OVYKOKfdSBjrPRkohaz2XqSMr5rqZrjmw+2cVZG5FJK89lCHycQynnFM52NARm2+UoyPkPLJLnwVa2rjinqPjUwk2cf+vZdZv2Vtv0SNkQVzl3gy91ThynxbWccfmew6OeffPf0jkVz7GXCuBpUxHF990YS0I6dk6BO+ax2gHcM8ucpEJrdQaIsy9CIm1UXJLn+cBUOaaFcU6cIwLUGYZMZ+w4hmlf07ZkTBOnUM72Wn9b7QqOzLO8OCYwj2DgfGZJ0U976qTAZ3AAKnY7jvIHx1XsL0lVX2G/ntN9XulDiYJn76szjOEz+A/HTIEh+QxMBoYmDfvVwEzOM4gTgmEQxmP7xUtDUFc9HPS9TJe9P+IdYteDixzzxFwZ5sIxwPFWPCczZzU2AZiJ950L6DhchIMC9+NQYmtoshbVASXWeTpYBzbMtRyp29lPzCn4Vt4h60/+4bB6+p8d/zYYhosa74cv+ql9d/Olue74V79iGG7cGAZ3sDgYDl/yMxd2bAjof+9vfe7w0H90ygL6P/rA8PDf/c1eA/3Cro7e8W6Bi2OBLqBfnLnqPe0W6BY4xxbYpGoXMOf9VfBfhfFAWjNtt5HSzLFJg9VoEUEV2CM+ce0kQiJR69hXIqubKduKcNqMYt8xSZAQGnlut0zILyJh8d4Xj3x1PEgwXIxQBfX04Jf5UBt5X0KQ9PrQ8W+epRHB9DuBfav+OaJsIX4g6dJEAjhTfI4lgyc6Ecsp2ioBo4L4bBEtT4SC+AkBUW4hMldtznpu3lci8hU4K3EByeKi7Y5U8HUU1Qve2gX423fXr43iqLcdIq6nZZcoewfsIqBTd87uQ0zGuaOSDJqWz8cXNfYmmQU2fI47YBBp7mssbOQibqQ+xAaZhp77pc7ihFyVeWce6p6/dm20gaVrNxHYnoGQboSEk0F3hsHS0jM3rAvEdc2+gCODC+jUFQzyhJT5RgJBlkH8ICaTkpCIC30nafp9SCjrFOI6BEsQQ2v6lWsD54H1sCLCgYiejICIPW0khH2m0ReQl2pLe5YJ3TZnOA+kc1TYv67pfF+Es0Y6/ggpZqL69QfGnt++PUnxfo7/vN1z12793l8enTXMnsyF7Zl4n10KAf2dZySgf3EX0O954fUbuwW6Bc61BTzi3M8t8veUHldBdyZWlqH5eU3FyMBLLVF6CTNxxuZcCr6xtreJ53mfZrqJ8/hEoNMay3tMDWepPH+XyGw9g9ezi2eLQmzV7E6kFC9YUvtZ02Fj++rIAJbz81tgNcOWlLbRrFGOqzjPTgDFRrTT0lW5DkIgVVjtHxXhuCWi72Hi2SW0Y//XKPQUtpnXRgYz8A7OqGovsB84QUVIxwSCR7xTEs3O/kjn7sBM25wclpwxbG4cM0XpLuujpv22zz0tugrT0h+wk/XFzsd+ppOzOusoHVrFCL4e1GG5UWJO1wiY2Nqkj9gPHKUOAdHN0Xy8UxpG0jZ8vBHlbfd4tqjIZOY1xcVx186x9h1O0NjCH2iZL47H9wTZFWjHMEd19rA+gAvBn9EM0++2Iqof3MSzwS8s4Ey9X99BgTkpKzVf8Ju5Po5occ++FheanTNdPI7OlA4r9eXtFqLPia7P/olzQ+0D/EyLTwKHW//tHeLjWA+XXUg/fu9rgr+w9RY8hvB2FzUKfyqgP+de3tCL9zzyj36nC+inatHeWLdAt8CSBbqA3tdGt0C3QLfA47TAVDyv7Ew0PiEhqsiuHXgc4rkL4IV5cgCowqakT6sgG4HDD+oyDucMENcB0wLU9iUvHBAHuEXosuYqEYRXtUaCqrd3gjKAcoNsAHDORHYh2lJYD7vh/Q15YfZI8Y4IC021F6AQAV77lfZTL/vwKtc679yDN739P8mVsE0SDyX6uaaHnGQ/2LKoiZqeefDH3IKeU0AXx4+ZE8AMDY8fQP6pd7kSerkWFurWt7q/SApFvTgngySFna9ZIYKYJ3VGSS06yE2IBt2vRijkHEd67uqkUQlfTGl9ZqzqkGFEC0I/BI5GV2BDt6P9UkhQm2uP/Ik16OtmGAkgnmciuUdeBxF09arXFl8ZuYGADig30gcCw+uWEy0RUQNOKFo68hDcIayZZ+uiOmZAwvHeqI4I9AmSyccbGTIYC1HslQyzSHqP1mDtiPGtxrj1jQwCEHxEbTFeFdZ1nWNr7G/P4D0Q0zCub5xsilORzYfZ+fDqsPKSAkEwCrm/NucGPr9yuSPRZwJ64c7NlBdRRJ9EoLuA/uDjPEVMb//oxx8dnt8F9FO1aW+sW6Bb4PxYwDN2VWGc7lXxUTFB1cRcaIpIRbsfR76l82KFaJwV8m8+tc7FuVPPNnq/Ot+qQx1ncW87cI+KcftOgx9fJcKb3x03UW4nDiY4Odbzkp5pVBxPsTI6kyK6wD8934HZqhhIuukcc5wV1b+XvmokcToFx/M1Jbfdm04BxVjVV3gJfy59vo+vcT5fsIz2z03eOPQjhk++L6W9Zs4gil8Za3xWHYz9zB/n3iUBfWnd58F1dN70H6Ki/R45nDG0SRkpEaW53L7HqVbXmfXTI6cbArpOpzoSMF84Luj84ZSQ6yhswDN0zDg7VDyltAuisNnBMA1r37FeCPa+XyUzBpm4JMNUCuxmx3TchisIx/xrDQFd8WNrvqwPONETge42uBIlzCLbRV3jWhoB7AjutvcifZ9sqcDJYCIchrGfOV77em5ElPM574l0RpasG/lOb4Hk2F/Wby8lFuW3En8HvsIBPLiJyyygH7/3taMdPADAnNzNaaC8b64cXEgnghTQ/0uLQD8DAf27ewT6vkeLfl23QLfAvVugC+j3brt+Z7dAt0C3gFtgFNABn37S3Rx4KwExsVkjCqDaVMU+/W6m0xfxPEVzhO6IEFBhUwE1AlOKyhOUO5I1ScxEm4C3bQK6AmgzS0YlSDSFPQpgjOBln6n4XUkPxk9/K5lQ04NrJoAareH9j5T0Cmy1v5OoV0k5DSZMYqOkU1SHBiWeavr2jMIh1TgiICnbSmo3RHXs62I49caYuyWjxVpNh4HW9eJoMetrYyGq88HMQSAIyYziEYeFGtHdeqfsQwgZ8UG67nTSiL3IHLmoS3rC8iAjVFpe+njAQwi1xsZaZb34el4gC0Jg9b3kaehjLrRfmcouBHIIHSMybL6JlHeSJaIArD2zr6cTXA3DjcfG/YNQbZ8hflsK93y2APNIETfcvFlKUQTJ4RHsIfzbc+x3qxHujgsh4LvJ7w6DRTPYd3adE6zhRW/jtGvTaSBqB7J/8z0TawahWd8FIfSvMxpe5jJSHaaIrpEakxqQEolVSZ8ZMSqZMJLkk1qdOu/WFZwWlAzSNjWCZRiGw+df3lronrId4l23HOQQBLEtz8/5FxfmRDER0N91RgL6y3oE+oVZEL2j3QLdAntbwKPP60/ryOR/O3A8lbrH3Isj32PHY91zBKLWmXFJeOfMkZHAIV6mwFxunGCvOL9NxPM4/1m/FU/5OSM6rmJeHr/jSxWPsmwRZ9kQY62vnHe9NIw5+9K+YD134BTsNokuRxBtlKHxx8mEII7qZ2BJnCMR7OyaVlQ1jg4OS0r6bnWGnEQ/i8EWjtSLi67auoWfl9acYrUq7C+d7xFuzcbqbFDPk4qXFUPrOqCt6ijKmf/xiOdqB08jHuXJNMOSCttL+E+dc7VNnDxwHvG2Ggu+it5kCatrD/E8tsDIC4Rjq/azFTnv4q/tgXAIDiz18Te/Y/jELx8Pf+EHvnT4w3/8K8Of+RsvHfcmpag0+pz1avtt4nhrzjqRnl2dZXWs7nRsTs2GiSITk+Esx4xlv7OHbRykd/dMXuG0D07BoZf06tYMDhHGUyQPUDL44SBt/anz5cEXgY0dswWXoA4UUFy8F9n/zHcdE+++dH6SRQvngrOE/Z/1hFN1vgfifWwCe2QlOHzx5U3l7inba4lFpRfzPXEwHL7wLXv/zT0PF24E9M8bHvqPT1lA/4e/Mzz83b/RU7ifh4nufegWuOQW6AL6JZ/gPrxugW6Bs7XAJvocdFeeNyFD8uTb6NRS5LmjkvYgIG0gkQDjfO7AU9K9LYnnrdZTey0RlvY5QE6B7pLIWQkXBEmIHW3DriWiAwIh223YACC9ZNaJiC5ODXY9YNH7wXekJhNgS/QIQFzJBECrgxsho5JYkqhh+zpF1RA+JyKritpBQEFo2FcesRxRA9Z/UmRDLiCcKqGg4FfT0AsAyxpqs+h1FTC37CEn7WKxpAjcYCqpp45Hf0buSJry5jpcZIrmV9+2FHvM4bR6gl8MKWLXucNBXIsw3FrDEH1ePzBqxOWTBdWyX3BcYXzay9w7JiCH4Owkk0UqS114iFZdXy68H26A9YTgivVsbSBoW800XxukIbSU5Fa3bxiGx25uSjWwF7Q2JYRr7t0Q54nasOh1RH2LpoYstP+bLe1+u4b3j40DOzpJIhEaOJ5Qc9DEfSdgZX4gYniv8f7xSHRbf5LFwO09Omh4zXFSubudxXmCe5gvIs2T2FVmNVJUZs362Bu6HnT+rQfXrg0rn9eIQKskKjXmL7GAfuujbxjXidt9gf3Ov1EXWEB/9+vOJgL9z/+4r+ZeA/1sz3C99W6BboEn1gJNAX0GoaSkVIqT0k8EWc4wKbQtnCnrURLcBMQCKxGxiXCazq3y7CW8wyUTEbhiprhI+6PttZwvOctXPGm/E6HqmbNKdLKKdPUcpYJpqy/5LAalWDAchWtNbeYQLKrpwZmnGhmcTgJxRsB2npGo4LZ9lilnsSXooG3sEuVpi36YfTnvtvqy63u3D46vNaMY6wIsGefOSRS6CNF1fFvX5IIx3Nn1ePF4lrh25mxtqYMkslrXGc4Wfvajrjb91v5Ln7LvJVp/shfLGLIkkOwva94ddiNzF7ji+HhYf/xP3MCrp17z/9/9wB8NH//nHxme8TdfNKz+9LVhePDBwZ2H7TGOdSJ9O2vg2DBN7C+Pxg7MhjPw2PrmrGvYy1O6B5Yxcd76hd0qhlOHamvGfqc5nG4Vx1j5LWsPJwh/fDhvVOcEsmwRyZ3vtlhz6SAT9oOncU4mxsl6p63Juy42/mRMzFd1nmhsOusv6d9xGEhnhWhHMotcagH9N74qKD9ZS/muic9iHV5YAf2/+vwzENDfPzz833UBfZ8/kf2aboFugcdngS6gPz779bu7BboF7mML7C+ewyoAsKrRijc+QCjx4gL4rfiEaFYXmkrq9uY8NdrVjzTqQIVsBV/qTd96BmQDXsuAIoBZ3lPEc/88SJpZbTwRm9W5gL7PSCOJplBwj70yZVykXKMdiB77vlU3PsXViBTW8SMcQQDsIoIAvj5mRMbwnLc+ew2w4/EJDthLdLKDbY0aZ8xbGCJSZi+uDQW+S+0I2VNFQgX09m/Ae9q3RB/XfiwSQgv7IUVu2U+KQcl+wHMgu5QIqn22MRlB4csx1mMlR5VgZa22xFrrC/XIjeRhTZmg7fMaYjCiru0VrycZpIgRJkQX0I6Oz/5t6wIPfqLEIXHs/x4pERFJrHvrixIzkLA+n0Hi4WRgJBDCvrVHNIV9Zs+9fn0joNf6hB5lYIRS1MO2+SWaguh465/Xy2ykZ2cNQbQIqbkmzSBzGyUcUsSGWNOMB+wX9ihzpuuYOa+Cfr4zSl1SrrcoD56p70DWnIn/V65eyPTl+/zJv/WJvzRda5PMD0qcxnvhYDUcPeOH92n6XFwziUB/99edkYD+Yz7WLqCfiynvnegW6BY4BQvsFs8RUuN86Gey8mC7RIVMHPHSkXXhjEhbqeeBMeILzl6IzfpYbXJ2Ni1gTM8Q2/rUOuOqI6qf7yS7mDtmCk4BZ4D56G8VrlPYiih1xXPcU8eHfT3qWcpVqW1wjjN74tTrAnGI+Y5rZP4m+KvMazpEUCapYJhdYvfSXOWZkOfVQ3NpWH/l0paAnpgtHsB5cnLWaWyYiplVdKzrxtr01NYxOWQ1aK2bk+KlEdSMZ3CvjS51qlW4tcvS4VzWj525m+s36nuzR/VMnubQM6A6vAR2o13O5l7/mlTipBMvmQ5Yl4aTeTbr0J2Lo79Rour2z3xsuPLyPzesnvZZIfSvN5HXdq0GCuj4sYfjTXHKJcMZfQcz2bOtT/6OMsxkjsyRGcIxmon1hoXM4deuC8Fa4bdFltcU6tl+OEFsezdToota4nbttkh064OPTZyYeHdiZ3WqSH4j1pSui9n+j2sm/Y368/aZrUeenU5BYYzAmxdNON73z+bxb37VuNeVw2rd7Hv17oVL454R6P+1CejP3dcse133yD98//Dwf/vrPQJ9L2v1i7oFugUejwW6gP54rNfv7RboFrivLdAU0CdAYobcW2h6A/YAsEm4cPkOMsiBTarH401EsZ4EVC89pjoQL7ZZhqeRFBAwE1DW8sCPTgCcnRiCPCjCqEaeNskfcQiHeFBBMj2pqfMc/VHSxP5NqjQAZE5Lo1+Aa8YLKVTJFjWVEi9cZ/Pn/4XoaCnKnRQIwE66M8gtB7xRvy0jz5UoovMxmS4IeoNl0goRqFEgzShSEe0RGZk7rldHBPoNhp4Ro9EfIofpnQF/5tDasO9VNDVygkhn7vGI80iVnnZdDYNFTW/7UULL05mH04IBW5wY8v5iL4gujZwgUnkyHZrqPxrzGnVS29v2CoSpRrTb+K2uHvvebGGR5xa9nRG/ke7UbISDAGSsOVvg5W92Ya25/WLP+POkLjwR82YPyizQj4w4j37Z7/YMF+o1+ipSCtZocOuf1Q0/WA0rHUOSwlLLHicM0jWGM4DVHM9sG/na3dRgXLnAr6lMSeEezkb2LN0PSv5NojWEZFRhnPmBgFeHmSQDDwZLO7+6e3eMnDcO63ljlPFl+3EB3dL4T6LP2QCkmA0yNux7YQX095yRgP5FXUC/bPuij6db4H62wPbU7XIWrZhDjVbFcwRJP4rFeWwbRpk4UnK9OB1Xtb6eUWdtt4BTUWF3thEDTKcASb+M0zHZbGZ/UyXylDTNiZnyMDT+A+dIPb9q37Tb4Ag979lnOH5yruecZ22C7/xMGTV8JxHUZSLVKVXPx2mHxpqoG6ja9sbxMFw3vFAyedm8unNpiKk4XWjq8AqZ7FmKB9KcW9R8xe9VRN62+Seio9R+VswkS3zSFM6uOMP64TKE2nRMECddbsbxld/BnFq/3Gxi+KvlRK72ADuYQO3YIbKTUVN70mE1dGBI9i84RZcugrNjZYGt6dweH4LzXLSWjG12nYuvq2H413883Pypjw3Xvv45w3D92pi2Pc//sZdcbC8lF8gohRMuzrDq8GLXWB/UXmZLy8jl+y8yidn/sb3hUWvLHLVZn4ofGbCmj6fkGGvGU8/rS7NkiwA3WR+8v+IYXfc/e8/+n3MnFzkuE+7D90h5z+yzT3CmcZG8cimxmOxzdZxYrYbDF/3LS/kn9Pi3v3p8N/n7shEEMOEHRweHixaJvxHQX3IGAvpvdwH9Uu6MPqhugfNngS6gn7856T3qFugWOIcWOH7Ply30StCcA4+TdL6kK2sK6C1yRjz3VThPYKO1r1r3Nz5beExzNPsI6NhCwRYEkAOuELfS21o8z/Eabz1cIylaZEIVqhGd04M8BlrT4iEYOukS4rL920TNFC/ju+pdTTRqglNBlNsIlBSa43rIrRAFUyAmAtrEUAXYGkFuzwF4aWpKt1cB1m430iOW72ZR6ZVRkt8hGBC0nZiMtQm5oGSni7VGIEQkQWt+iYao3ymhgJ2cTAmSw2yG4IyIidd+1rWzenQhPGv7rJlMyR3itj0n2wTUhr00KqgSPt6esgp1czWcFiA4WPvWPqk57XLIICVg0g4xmEdvTO1v3xuJhgMIdfXscne2IBJd0rxDnqjAbnaEyPj0DUlReDAMDzwwRm/ciNSHRhy5M0PU0COqvq5B6v3Ze+D6tWGMIF8NK4gm+sE60vaIqGdubDwekb/yqO7JTzhdrIhs92mRfXxwMAr3vK+c0C2OPZq2UdezRgstvfbVIWa1GtYPXM9MBkfP+pGT/LG4UK03JfgAACAASURBVNfe+vDrN+/4mirW35/xN6o6Jxjv+4x/fu7HOolA/+WvP5sI9C/8UbdDj0A/98uhd7BboFugWOD4na8SYWQhitzv4UxVGljSpTnTFo16vHtXSSABDbSvQhl/m+hK7cNe4nljKexsp0TUZ1pzO4tEDWQ/t22cAkdhN4xAZp8WNtMjvuLFyRm4+tJKNC4mw+HYzm44y1p77pRMWR77dziuqoPhLKOQ4IiaSn+GmRoiOuK4jm1m9qoMbgG5fk6LyH5tR894Kt4i/Ov/J/YUR8vWm4G2HP/Gtep4jOhtdrXvwUxLe0KFc56XTughlqZzr8wVa8rnOEo12b/tHO5YLp5dazLzDHCCb+M4V9t6SCcL6quXTAqJ02MCKQWWjhN7vE7pr2aoA4sh/jI/pHK3ax+7MZbDMmEWcZv5wN6KI9zRAsEbB3ap2Y3jLSnrreuOR2IfJPaIuTbB3J732GOjjRGIwfY2BnV0AdsbZmPfufOytRP71rN2IUKH7Ww9Z710y2p3ZfwCIR7HaZ6PydlTOEInqCdbR8l0x9zX+/U+rpnxY60NzJoIfO+O5ZsyXYcvuFh1v/dYyX6JC+gT7qBkhOBvAnwGDd++PRy+9K37PuZJvW4ioP/1U45A/1+7gP6kTm5/eLfAfWSBLqDfR5Pdh9ot0C1wcgt4xMRJBON8hICApqjeEM+1e46FAhAtki9E8oUgIbd4U81+n0A832fcLZMq6YBnvIvMQf4gajfBlHgjz9ou9kjAKLauQM0jtwWkKWlG3WcHlVKnl8uJcvV+SDp5jVywPrgQWbzV7RbSbWsk/mSOxZu+Cv8Gdg2EO5ER3vzZz/Bkx7YAVQRR7y59ijTw44IItK0AuCGgc//E/jEvCOR4jRsRQXo6ux6QbuOBEMD+RNPX9JLMIwIv3YQks++pv13rXS9xYrp2IVFaa1XXkIq3EKnpeBFOB2Zrj4aPxki5xxrBzioMMre5DnYI6Hm9kKUpaB+MTh1EqkM4WcS3RlyYjY0Eun60SSWOc4LdT5p3HCYgU5T4svmwviSpZCngQ3i3cTqJczRGcGiEPEsNsqcSuHzu9dIjSujw0EX0MVI8CGPWkO5Nu5foEGr/0b/4/9r6tETUhFC+unNnWNd1GFPqYrq+RyBWWQtL+1nfv0vvJq9leMXHevScUSC9bD+3PvaGcDzBjkZAmsOOEIO5xyOirf6tWa2Go6f/s3NrmqmA/g1nJKCPDhZdQD+3y6B3rFugW6BhgeN3vGKTQcn/ltY03tzUcDpeEgn5m9yMzuOP90ZomQaSS6MOl8SJa/K3p1ynY3u8eMrPB1tE3Ho+VEEMER3s5OdgiTLlLFNxRD6vYCfORxqN759Ftp8a7WrPx7HYU7VHpK2L6VcinX6kqM6pDWdEFyejv2AnzljVHojp1UlC9HaO2M2Nx3lSsc54mCsLUBrkqwKFJu2DRexD5glb17Ne7Zg6Ftt37tAaqbHpLwIy2QXIloRDrd3TWjqe/el4FBh9bYjzrzsSRyaz2qcty3By6S4OwOunN6JlwZ+seaLR7Xeyi6kDiF8Xe1f7thU7a414ycKg69gxeFxneGjSdmQtw9bWH8NG4czrdnCn/1j76ryO47/jppgbux/sgoM+8wIPYfY04dvFaUqDicX9tRQ105WrYA07Fo507pSysonXVPJk+NKMWtZHHAaoEU96dHsOmFInHzuyfeBvdL3PuB7Zatyf60NB2Z4LEHuYHa9eGS6reG4mPn7/1wzr1WpY3V2PTt1kCmBOqslIde+YPGxrNnrJz5zbM0kK6P/NS4aH/vrzTrWfj5iA/uZf6yncT9WqvbFugW6BlgW6gN7XRbdAt0C3QMMCY3r2ICggW/I6AQITAFGB+jbTioAOIJkckBfEYm+yRpjL7/rIGfg9gXgO8L3X1ZEilDRggF6jfEGzpOauz2qSLFvGijDn0xBzB9FD26TIQgyDQPKa4kRlq0At6IXIZpv+JIJCqFbCQMcBIbJEBFA3sJIxKaDf3IiMZi+ea4DXRFP70dqDE+WQ+S4MUUbUOuMSvY1riVpw+2l9t/Bet8sdzIb3PnPqNfRCQEfQhFzJFO5B5iiZovPigm10CSIogf0J1u4u0of1wdogugNBNlPyNY9NYx+tHrma19OfRwRLHV9mXpipuuMDnGCMSA+NPABAkzoQ4s3uMREfYo665qT5t89t/Xj0eZAxpFS3uVABnawGSrxhDxwKeOdQQ13XzIMPbER11gDjsft8XyUTtHHsMFvZGCL6YR1R8iNpsB6Gm5GSUEkgxHx3pggijn1r9wX55cK4ll3w/o+290h0m3eILch0Irv8GRLFZs+HFFOiMF7DE142XxUaOTD995p0jXfvXrr07Smc8z5y4jJKKNQ0jUyJz5WKFiKAhD3Po5A+E9Cf8eC9/qVs3vfRjz06PP8Lu4B+qkbtjXULdAucmQWO3/WqsW3O2C2NZHIEil+Iwl3SVLhHBTeOtJkxqUSeZ1tFEFdBmb/9aZFyrVrqNMRzznpLM9ASmqozpf2O6JpCLkf4OGuoVpxnkiJOqv38b3Gc9e1zHDHVPirmIKDjOOz3Cn6YRFWLY/HmGDb+zWccivOwAfitlT1r2wr2cQEiOKTxYDmwk4HLz701+h57ChbRuWvhUp6pOM/PwTkBm6hixHjNbuWYKfaORw6THUoinVvjdgwYG4R7EFYVS9R7t+mX2/ATZbLs7G4ddvHOHF61Tno1aDyMWt+sab8nUuozfvqp7VWMylxYs9T2ps9qf4RiwzP2b/u/2z5wPuXcPIV7A4MhnlubjhdCqAYf4DTseNDsETag5IH97s8is0FsOusHGEzLZtU1ZkK5i/fm8BwR5tZPSztvc50p9WX+mR/lpRQ32VjM6Ts5JxyyD0YH5hmfFXtHbaTX6LqaOVKUNPJND5DGolZcbV97KYKNg8Vlq39+63deJ0YY10jiYOX6hKpxoX29HgV3s7utZZsjAlUOTEj/6TP7W3+vDU8E9G85ZQH9H3QB/V7npd/XLdAtcDILdAH9ZPbqV3cLdAtcYgtMa5q3BrpLOAewtxiMCdKYRkgCSDLlmnjxVsJHQdZShPrkGgHwrSG1gPQ+AuS2dQCQqjgasVXvTeCLWKtETPlsG+g3MDkRuEL4ysgH8/gPoK/9MgFP6223yBE8+e0+BEGtp1z5GnW48HvKcyu41d+JBDbQjZe7RlVk9Dsp8wL4E7nheFfSAtZQ3KZ4HusWEsH64CSarHe7jygXbO3PlH4C6C2tN+Kujf1PPj3OOO0bILYfxkWdNYRmBGAnVeaC2tjWwgLctXbtGZAM1ja1xK05T6kXY9YokcW1HikPISi4LgnfIE4yMrwloJNxQaIpGIP1xfrLmkM0t8Gb2I9wrlsc5wVLVW/zkSnxpSYgorndhyOEz2PsN3MOsLlGdJ+tIVJORlpATT3o6684oHjEfjgMOekUzzGC6MrBsCbN/PruKHCbcK9ODNjX90ZE1fg8Ro1tyLdI07i2rAiVhPF+3R1Wjz42klcQW+wb+52oK9apR/kH0evzEGnpdRqdxBPHm8neC2JN01zG/l5fvTIcPfdy1T93Ad3qnpOtI99rWrdSWCAneONvJWuC/W5zBNEcZOrRM3/43Jw+JgL6e18/PPMsBPSX/gsfb49APzfT3jvSLdAtUCyQdc3521ctNDv2xAf17+iu+6qArmdKzuBVOJ+I5PEl10zOiudFPOcAFQNSfOT2MgGQslLi4JqYI0QrbLPtsKyOjTiL+7k8xFs7GyFw6rmGdN4apZ6QN/rNOVTx1wRP4FhY5iTP0BEJn2uidXYuC2YWfQ5QiHtbzha6pjB9a4frebJiRM5/KfpHAymgk8UJx+5wWMDRJObh44+8Z3jqCz9zuP6G54VYG1H+9r2eaekfuJLzJQ6zij+3jaX53QKw0vJPjkMCl5kYC37W7FXzFFCbp20rN+BzoLW3tZOsFRwTAjNV/GW3eJ/CSddKWz344Pg7mbdUaGcs9n+chzOKO7KAOfaNbG42F+wTHH7tXrKkVcdj9bK162yetKSU8gee9S6c+c3Odp3tt5tWIivWgT0bQR+ngMxApudrf/ltatHTZ3V0hm+4ejUzcs1EdLKCYafKs+R7nyx1gfUQc2vwQIrh0Vd4DrrO+0Uj3rHvwerSRaGbgL5a392I4bznK8kRy99Ec3+zFYeF9epgWD36aOCmMVvc4Rf93Lk6r2wE9C8YHjp1Af23egT6uZrt3plugctrgS6gX9657SPrFugWaFjg+Je/XGpFFVC+FaND+jTIH8DihERYMn9JWagAvqZpq0KsiukNXDl54kxIbADjexUh91lZgCIAMeCLiFLrHx7ctT0A1kREazxU7YHQlUSHgDMHjuHpriQH6fE0yrUVQQBAJ5U0HvSOYmq96+hnRsCGCOrjFXKL4WhbOFAgLGm9QxVVdW4B3Cna5j+mKdvpK/ORoF4Ad3qlB6InksTuQdTGG9/TzIVNrS0TbMMLf0w/FoKtkQkQLIjqCGNGREDikQbcxoM3/zaP9dba3SWeZ4R5jNkIDxPQWYtGUkDC2XeQKFvXe5BjCH4QhiPEHe/MtOf1BSPkofUBxwRInBRsLWp8FJt92lw8j+wDlYQm+sRsaG2a/TOVvtQ8d5Imot410t2js0ljGM9ijVaS1ubc2nDyJjIQID77nkOQD8LN+m+R5RZN4eYxB4bDYe116ccalKubtl6i7ALz6QRlmNOjWMLBA4KX/Wn/t1TwtaZfzJ+nhyfdJXPqUUBqRNkPHh0d/3n0idQiZR/hLICNfG0HCWbXXLni/fFn8xz/7PDSpXC/9eFv2JTpyPdbRFYpKcQ7y20ozjD5nr0zvgNse8jfiPMroL/hjAT00WGgC+j7HDj6Nd0C3QJnZYHjd706mz582c8PKZrveuA28TyxTziB6pmu3leFTs7SWdon/+DE2YI/+iEila/H80cq6dNR1LPlPlhKjntNk+w6mzZFKYneJtqbc1szS5IqcQsd0rHNnFNLxKj1yc52dp7Jcj6r8azP+d6Fd3HapX0cUN0xMc6ddiHna7MHjqGcAdRGeV4gZToX7RLSWzZonLsVd/mENUoJ1DUzw1g4BceFjJX7JlHVRVxEJL1zZ/jXP/Bbw6O/f3P47K99xnDweX9247zKUPwcJPWns2Y2Na6puY2z8RKwZ09s2bR1nYJdNNr6+vXNoe048J3jlzinU7ps27vBU8uz71EG5TBIFH7NqKb9s9vY/46Zwa2RESHaX//uHw6rz/6sDQ6r88gZHwdda8cwCnhfz/g2DzjgpoMJDsFRWkvP+mxD8L/da1mowKLuPB7zhxOL2dqci9PRPNK98w6wcdkcgNXMVoabwBf+ypP3npaqwiHbnon9AzOaU28ueZxEbOysAXUUpt+I5JM1H5wInIzOoQZXJMaOMl703+6z8z94y+2xKVt2+IKf3PVX50J9f+t3vjbE8ALm9VUW/3ZuRQIuMgI9to7//ukbnmVg/UePDUevfee5skUK6H/bBPTnn2rfHvkHvzU8/Hd+tadwP1Wr9sa6BboFWhboAnpfF90C3QL3lQVSQNeoX7VAC59rKuv8vlzoB9h64q2mjXsgHBBMAfATAbOIaxtoM210LyHxCRbP6aGCMBdPgwSw79WLf0Ie+ZclxFgjqoX4UmFPIyRUrMG2GS0tqQrNLBp5CgCvoiTAMQEqY4n7k8ySz11IDEKDyHcdd/MvsqahDBs0ibK42Z6R6biTFeDLEPd9sW36UonISf5CeT62CWEziQoT/4wAGNYjkDOCzbzXEc3pr9nMQL0RLJ5uLmpyZ9RLQvWSP7HBoub6IColREpsnIRBMapHX4cYW+cUcfuB6xsi1SIV7FkQDvqOEF11EqWeZJFG2QY5obagr5PIfnlf+FqO/htxYoK5fUaafq/7LUQTpAIOCvYV6cydpDkafzfBmNTu1jyR1/Zvsgog3LuYHIQOIjpOBDiesM8somNYD+srV0bRm6gLI34gVexaoss9YjzmgtT5R0cuJI9OFKNDjXnij9EZEV1e0zfa2MwWZpcsB0ENvzENYUaR6HvItoA939M6hsDN/GrkuS4h6szj0OJbKQR3ag7yXmPuIKUgSGM92Z5ZQZpZMyagX7IIdDPdrQ+/PjJ9yIZLoj72rV2oZJ8TlEGopoNTpH+Xa48+Z4zIPg8/kwj0952RgP4FXUA/D3Pd+9AtcL9bQAV0t0XVKbcaSHBPxQZVsGu1q2dWonYTK+mDa2krOlrgxDZB/CzEc+/GDlFThSU9a1a7pqOeg4nNt5xlVOTys1qkkOa8RzeaOrSWLBMHyOo46Cmx7XxmTpOBcfi7DQZGlCS7lF6n56kKmfOMS7S24pp9Ft3CNRNIKUaY4YIwaZ2DJi8QH05E3RCSwTzR3J23fWI4eM6f9nPP6jPHM+/d3310WH/i0eHKl3y2n3/u/MLHhyuvesZ4h51TwXc4n7fWwrZ9x5oiSpoyOojNS2vdzqkqHvMMxumOsxqBHg6xZLNivSf+3zaHtRNieER5xyB1AmRCHVuFYwfN2Zo1XPrxTw2rf+Mp04xldq3hIk+tHpiSUmEI3WCVmc0jbbzjs8gul/vDhN/AaZq9gTYs9bqK2Y/dHO1MW/7siD4nGp466aTqpzSXDd/a8jT0kT3LxfHIdqe4yaO3zRnHUu6Dq+J+IrsN1xds7vje+sdatHHAoyzxZ+pwrPuC9wOBFfoec/in76nAg4ch6IO1fD9cHS6bgG5mvfWB1w0rdzSCG9vsCy9JZsK4Zvywy3AsD75sjGIfna7W7/zQsHra9fMtoP+NUxbQv7cL6Pf7ObWPv1vgibJAF9CfKEv353QLdAs86RZw8TwJnPjHVkwO8aNEhRAXFdNNsGBlBoREMuBWQSwAQj2HaS+BSAGbe4nnjmbntl/ic3iWRmUvAfilGa3iOIA62wyiq0UqKcDa1j74eULctNgh+cxBbdRYhlzyqAip4VcjEUiVNomoZ+3EOIgOUFBpjyUKHVFSPayrGA94rYROvQ5bOmlQavSpg4Iv03AWWEzbvmRgiQxA3Hzgmot+LpYC9OgLgijCJGnxSf1m12m0OevR7GL3Wsrw1k/WjoPMC0CPyA1YZ8847g4nBlKX5zYUw7KerR1LN8+PpXBH9LZ2SKHHFtKo+1m75UVCP2g7wL9jXUt72PrR2oUZzRHOB5qT3NsIAtMJIGrshRBpRI2N0eyfZFi8BlSE9z5FHfIblr6eCHWJvHbhO9JyW59tTZP23ufbvluNKQatL2azrM039gdiZmUkkBFX9hNEkwvoOMyEGO1EAjannqAJ9DbuqHPu6wZSCdvas6gvmO95IqQifTs1t60tdQ5gvnQtsb60/mG1t86j7/kgqtgT2NsIKnPWYN5irs9TVPXS24DPb/3uX97Mc77LDjKS/tYn/tJIBt6wTA6Rqh9yk3ejR0+JYwgOHeaQgeOFR9MEscgcWSnEZ401wc/Djwrov31GAvoLuoB+Hqa696Fb4L61QNY1d1Y+6kVvyww0sVTBTfwNIIo6S1BtEZcRz1NX02j1irHKGUf7qY+Y4A75YklQzDFt6WfCjBDkVChr4Zy6oqogpQIukadZGqUB6fx+rbld/JC9vQaerSKXn7tiMFWABQPZ2YWsUimESXYtBDuEz3rm1Ht4HuOfRMvSX/CZGqXpAbCHZ0esH3XK0LmowrmYLS9bEg/BZC5Ujw6B69/7E8dLq6d/VuMdImOwM4+Lpla6JgR0j3S2jEuNW1143UQMz/oGLkYg9jUUDtd2MedUXTN2LbWuc4xiECKKPQJdfih9xVlPneXtdvtdS5/h0K2BCUtvWNK9Kwbxa2Pvg5ns2YnrorHqrO2CcGSRYm2CmRHQ7Vb7DsdlxXG+js3B2px4Lb16OOTSHzJWMX+MyW6zczGlrPjcnYCjNnuUn8psDeBuUs4jnpqDsp2hOSvbmMkSgVnMNjgAILCzVlRAJ5LecFNmfdusSc8i5zjLamsHJswI9sb+y/mPklb1PaJ4nlc3mB1HfdYm68Yd72NPuNP4wXD4/IsVgX7rg183it+Ba4n0P3reTwyetWu9duduc0b3f9+56+L4WAd9GFPr2++Rdc5FcluHkeXDcLVnObtzxwV0u2/90d8f1h//9HDtm3/jXJ1dNhHoLx0eOhMB/Vd6BPq5mvHemW6By2mBLqBfznnto+oW6BYoFkjxPIHhEhkiRMASRq8i6z5CtrWlAmcCB6mL5p+FZ7d+n4CxDGon4ZOIanrjlqH7hU2P9R1Lqoq3fvKXiHNvNwzq4Elr9YX3cXjPTggc2gEcNz3RiQRnwqoKLSQAXucq8qdXtUTGM1zqSPM7Y1Ibppgf909SsZOGOMZLhAjt6P+riasIm4B8YQIrMeXTr0J7rSkv7TiZIRHoLqYGeH3AIs4jwhlBi3nAOYCIaYg6UjyaLUjRTf/tHgODCdpl4JO0kpEGHscLnukpoJXMrRu1Tk4JcmdbeAr3GBuA3tO4R2RCi0xjL5KGWtd07lMWRC6a+EdEp2BLCE2IB7MhxFYl6TzySrI44JxQl7rZ1ARuu95EZyNXeJeocwprxQR0ixL3SHaLJAlDGDj3SO/j0R4e+WBR7VGX0R0fgkRCwLe+EzUA6WFddlB/ZyQFgtBzssDG79FMtwcX169ecQE8052TxtGu8brokT4x7hmF/SDogrT1dPDq8BN728kIJ6yCeLRhkr4dx4FchpFu0+cDMlkEdxX4dU9C/vI9NdSDAV2bYwNEppJLx8eDkSnn/efWx/7dzXogmsn+b+vH0zGOUT9uf8vmwFrhHW7/t7Xp0T8xWhfUw5Hl049uHFiIjPE1MF589KzzGYH+m2ckoL+oC+jnfUv0/nULXGoLuIBur18iI3eKwYieDZGXs1Keocs5tnWsxbk1xUkxN5iKv/cTzBSNbcVI8uW9YinvTjgxckbzM0kIn0v24nyHUKR/I6PJjc+kHPIQtf0ZUi85BfywTzppNzCNPqsloPv5OEpOcS4mvbQ9X2uw+/nKJ2JcJzoXOKOmp2+cq6z9iWOwYA+cFblGcQn/nqT1Pun2q4ZCUBcn1LoWZvAizuEV3zKnCOHUr2b8uv7BV2Qssmtt7Ai3nr67rGE/y0epJ9Yd2EHNgHMDgqXPWayV6tA4GSvPG8spuZkVJ7MuqoAOnlNHXm9KbS0Ct6Z4V76D/TPB+JFFwWzjJc0CwxKAQGYzHb89lnO2fu4O9CFkKr6ytjJdemDOnNtY19amr/socWU4w65xx2HZsJTf8kxXUSILm5P9yn7PCP/AQHredY6g8CesF9/X4ThBe5pGHdxNenRKa4G1Dc+xnr0c1uhU4Y7HMl8unrvztJQKYzrBx27bfEFs1qbyEKw7HDlm71lKPAk/Qlp+u6dExht+pAb4hcBMH/r60YEGxw1xKM9MaSaa//GfDOs//ZmjRY+PRxwc2H1tThP2eZQ+88hz+/1Tfzisn/bUsf3YF46xbW39waccNx2++h0nfUGe6fUpoL/pC89AQP/N4eHv6gL6mU5gb7xboFtgfP+u10uulN1C3QLdAt0Cl8MCG/EcoKPgTseoNeCWrl0SxwpgVBHHmyooHOJCxUFP1SR17ybkyx5k04ysaTBSC9qrW8FBW4lmnaXcbqyJpbFV8A2hoiTQ0hIjMpHvfWwlTXbeC0FQBXQB8EluIYiFRz4AM8G/EIDWvkagz8TzIFKYeonK9K6l6B9p1SAV0i5CJFU77Cue7xLitxFkuQYjKh8ywyMGIj2j/dvBfdTgxgMdJ4uc0xgAaeXcQ9oiKkxQk0gJA4EIn0ZaKBh3oiHWP33x+mvUWo/IZQgit7vMQbUhEROM0wVoiIlhGDwNeeF5fHwRGa9OHkvONMwpEeM2Zoswt+sRiRGuIbI8gsfSmcemS899IV0ne6ex9y1iJfdG/MPaMXLL2kf4hjBJITjmBUcOm5vrR8Ng6QTdruE44WJ4pEpHGE3R3QTt400tQLzrvXbd1ZGMsZRzTsAE4WT90OgimwYT7+PF45kNLOWiZTkw+3nqeciyIHPNYF5/U1MVxh6NCA5v0/ddTJgK6GZT1g5OGE7shMNErpOItmD+VisnbZyYwIFE11rOX0T9q0DghOfBsD4K8ZzIgWxn46VhTgaHL3jL0hvxSf/81sfeMNqPrABEpZgtbc4tEokoGyPniGbK6KEQ28lCYMvaI3NiPfG30OxHrXsihc65gP6MZ8S75JRm6WMfe3ToAvopGbM30y3QLXBiC2T0OVGMLbEu/n6PSWQiBTHnMtXPqtjY6s02bFKvJzoSx82WeK5nO/o0OzRxdioP2AdLMXb7P2cI/ibm2awxqIp/WoKtilUqSE36X70nVc+Smtkqcm1bBRXH6fk0jqrjkOO85ONWh/MibnK24oys5/EUw6Ph6jxtHyMypqBYMoV5Z0TA22uFy/Ws2W0O8Q0Tp9AI9sFhGrzgmMmix0Ocp7SSpgzPbAAxBHUo8HNRnI1ZB5zjPRrVhFwwm4jeiTftfBxnZLOJlEjaaSLWGuufPc9Y7XyHSJyNRaQy6cOVQ9AH6jpnvhHCs7SVCO04plgb9kxEXfadC61R+ofn6Nl78mxL2x5ZrHzJxcTa+LKMVOxjj/puOGjbdVGybDyfWialY8nQFYK5tY+4bWdba89xYThJGO49lgxiZM1i/iYZuHB6DzyJMzPPxjb2fztXe7m4cDowe/uzwxlax69r1HCXZg2z8mzYmj3MGPwdL5n89D1HSbA6F4jnE8eXWAy8EyaOEfG35PAwS8NlibhJ2zhKHAyHLzyfuOnWB7/e35crcfZ2Adz+84x2kbbdX2WBW4OTGiPR411ibVhkOfuG+bKSEAjrsRdceP/kp4bh8GA4/JK37dzyT+QFKaB/1xkI6P9LF9CfyLnsz+oWuJ8t0AX0+3n2+9i7Be4TC7iA3vQVEoYnSY2W2GBCLgAAIABJREFUUoboipdsiLktcFgFS3+uIMckRhDrA6gl+FhKb95oowLEyXxuEc8nQ9wIOJPb9xHO9YYEnA1R0z2uI3I1vfBJ0yy2dVAxQdmjEMZnSyKm9kO9sSFXAC8Abxe1QtSF9MhrwuPcrsUGkEQzUi0e3CIHbRiQA3aZR/BAMBbhPXV/GSDA0oGVpIqHlHGbCrhurW+Nnq3if4rnNl5E0xCwMwI7alhLSsIUWRXIphgo6TWxcTqSRFo5A/hOLglYzKjcIORciD0cASUCupnGyAiIUx1vnZf6HfYzJwAjPOx3e75FBOtyM3t6BPoeCy3btHUUkeX2WZIYQ7QfNvHU97YHggxiTdjYbawpFotHfs59EDPWZ+sfUSq6Rs2mpDe3Nu06CG3GgzOIevgbeUO0gtY+dAHd+hX1yHUvMHeRrt3roBs5Y9EC9m9dP6T/Y39hb4gvu+foaFjdinEZmWNRyiZY2xgQW11UjfrmJvYncRPEkpNAIeTqpIbzhffJxxI10CEKZw4csTZIpYnTg0fSm1OAEsbisEOkBd9PHCBGkd/EfSdBjECzqHuIqBTuN4vxPKYpvPWR10fUfpA+pM+E1LP58jUZqUhtfTxmmQMiUwDR6i6sB2FkGQvMjNR6xBaQtJG68jzVP7chagr333jf64ezENBf/AVjxP1HPvKR4ZnPfOZ9clrrw+wW6BZ4si2Q4nmKhHZeWxKD3SsxuiyCHlol5zvOKwuQI8ecTo4F89jn1paJT1fiXMVZJvHKAk6a4QqxcB1W65zfOuPPPisp1Dm/tSZzgjflvmobom4RrkZPhen5B5Fz9rx9xeW4Ts/NnOlxtLS2EzPZL+GMmI6KIW7jcOv9pqa6GkCNzVhkTI4BBVfFoyb4XUVe7/O28zo2WEjd3ujCZB26TeWw6ec86kzHdy5qGbYTR2l3JBR9X9OVs47ZW3rOT3FNbOZnIxFnPeI78LSaM89jIWjbWRcHE/qm51K/V0ozpAN4HtJHp1ccRzVjl9rF8Ajnt9Zan9gvztesWf5vzpecHc3Z1H7MPoYrcMbOM3ZkxMp9KpkNdHyeASowJE6+umetX9iM59lYyDrn7YfDsX1P5itwiLXl+DQcFvyaENbBGb48A1/b/ylxZW2ARf2sG6UfSNWOA3uIo26PSPE9ppC/Oq6vGzdG53bKamEznE9sfrnWa5nHduGMPknfzuSZgB7OwqxVsinYmLPtsi/gJtTGOh9NDo4gAs1IKH9D4ITc+Tr4E94r+k7ytbM6d+ndPUX7XRPQR4d5d1QAP8d7DHHdU7uTueT4tkeVu2CeQRzxjtV0/ObsIFH5ZiOLYDeH+MMv+rnWbnxSP5sK6C841b484gL6+3oK91O1am+sW6BboHms6RHofWF0C3QLXFYLHL9Hap7PQHYhDRKDK+pVywgQBzwq2bEk4kUE5BiRSuppidTwtgIAIrjOxOvC8OxD+LTIrgn5IqQDHssBQk60HiCC1DwQHImvJII0xUlJ2bVIbhWyqPlXLD7kma15zDlQokVET9Ke+RjiGgf2hSzZ1zCIqz63kZLf75UMBxOBLcAza8EvlX5UQTgjORYi2LFnElEB4jOtZIAyTwMekfg8O9NWm3e9eeyHA4MTMSF6s14ghYx08HVgTgIRIWHPToeI6IiTIUFE2Ed2n5JIal8XgyNCnYhuB+gyJ/RNIoVdJNVoDvYq+8EFaxONLfK11BA0YZYoCmw4sX00hvhHxgj2q43XCI0UrEPUZn0R6QLJiHhLdEXul0Jy8BzIGupoY1+cFTR9ILaGdLLv1F7u0BHEn0asE12ctefWhRyTl4+N49o1F8Cpn+5EgUZqM7+VPIE4MwE90uivPv3pMQr92rUxCt3a0WwFRPnbPLEHsKVFvtv6xlbY0m18d1h5RHtEZ6RTh0SO+XzLe9iJ48jGgcMFIjpj0TFBlMU2T/JYySQTzi2q4Mii7G3tixOVv3ujhjtEFIQKAvTtO8PRs39037fQqV+3iUAPAd2dIIxgjdSijNXnIfaq9YI6jnatrRVbm2lTqYdOKn3Ws5N1d85V7XOMqgL6r7/3bAT0z31pF9BPfRH3BrsFugW2WuD4na8av2+JbXlnOJrq3zfu0fOni+dRE3dfp1wVGMFGHN1vRmpk+/tif08QVqqArhhpJogviOx6/l6ykDoK6/Ut0b3V3uTs0MB1VUDHFpy17XcVmffpc3MsVciOi5hP+7+fnQI7xPnE1wRCHucp+44MX5zrEP4VwuEYYXOVn4tYlrqt9E3xHPczZrflNvFcB15Bs+Lftm/IrI+cPxE5EaD1zImjptnBha5G1jS7D8dUGwNipv87MAnd5SxNW9YpcFNdc+l4HM7Knjki9knOZYybzzmvzhxJEzht3gOGm2pkvF1mmMf5jUhxTpvZBI7VnK9jjOmUi1AeN9hz0nkaJ/GwE+sx64lDPMhc40xs9sF+dhlZu9g/6awQxvYof1kXfM/at9/tGuqZU0aL8y3nVn0u3dK1bfe74C1998xOIVr7fgqn0/p+te/MzkStW6kk6/KfejBKI4XADb7jHWm/u4AeDhO+5sYSVdP07ePkZf1z7sOJ3r+WDBTsxcT6knpeMTTjyEyA5aUEjwRHwr20y1zwztHsZGBVe7SWBIBCWd990oT1Wx9+vc/lKJKvvYxZllMIE5it/XOEcRzOnJsh+0KsDU/nHriR95C3M+4tfw4C+st+fumv2JP2+URA/9YzENDf1AX0J21y+4O7Be4jC/QI9PtosvtQuwXuJwuMUeeMuAGyq/CbxqmIvyAg11lK2ugWeUI62lovl+aTKAlQiWBL+9mfLWQP4GU2sZUs0AuiA+oRX9Og7bNQ3H6F7VHbqKBN5LXafMRpm/rM9ZlVNAYkAOAQvIhQnwnoFaBJRATdVnJB08M5sA8balRBnbvaZwgVJ0IQkNROsXa4DzLOyaoQqitpWZdujjOe0YqWVgHfnsU1iNImeBEhEtgrBS1PU4fYGFH4Bto8Ijc6k+nHA+hDahCJoQQF80Q96xQvo0a33eNkSEQVWNt4uxtZY5+r0G52tc8ZA6ATG/r6EFtCwB6GcGfrhbrfdZvcCAGduXN+IwhDT8Mo+95Fe0gyS9lnwDXqdDO/rC/rg64vbzOcaTzCQQilTEkq3vZEqUPgmU3seUYI2TOIDLF23X7U+h7ri/sP11FbkahhIsRdrLZrIdiI5o50hLrWbSwu7h4M6wceyHtcFCb1ur5f9F4tUQHJQ/1BCK4kXmIb2vcuaEekMikxkyQSgs4jvbVswGgTTxGPIw3ire8NCDohs5x0ihrt7EucJuo7t8nLIi4Y6SH7P4nveOdr5Er+mYmahDi7QJjbeyj6/WSI6Lc++Y0j6esZFsJmvBeIPJ+MwUjCaxunhXBcOT46HA5t39i68znhvazRXBLtdvfucPTMH97nL9ITeo0K6L92RgL653UB/Qmd0/6wboH72QLHv/TKTbYn+7tG+ZWqtaqow59NrmnpmUtwRKGWnoldXLw7DEeWccjOJSHkmmjOPXH+yOxMiQeioZYYW52Ka78qjtNzJHgF/KHOi9sWjYpIk/MGWEAM52f70n+E7E1Y9kaU1b+3KcxvGf9MjFuYtCry+9976Rjn0HBOHHFBiQLW7FgTvBztOGYIUZTvOaunrXUxSV9ZA2DstP/SItTPG4Lr0vzxeBN0bXwu3sXZh3OZNYfju+OiwB4pzorp8qwpIidjpg2wJ11GRGMdkZq9tg9Otb7ad7Z36U+KkGCKOGvhXMtcqB3AQLoW7BxuZz7dNzYm6oB7nW1xErfr7Hpzhs39IinXEUOzxE+A0RSuEQrJ3saEyLrJlNajGDxiXTIfaL34WG8W6c76MWdOX2sxH9YfLe0ETrB3jV0LlnI8F47nmukLkdmuzwhuyaBnzzJcYZnPDNOaPa0vnIO1Nnxrr4JHrZ3MLBDR73AvifXDHuwtj3YPp2DHg+E8zRhdSKekltV3NaeGOOuzbp1XCAxc3432uaaBr0L57D0oe7s6Vim3xDOzz4YBRzF68xPBEJ4B7WCMyKYWeKzVJyujF1m7PM26LUuEcjKQsRjNrO5gHHNgwwsb3v2n7x5WT7k2rL7ic0U813fylFfyCPT1MBy++KfO3XEmBfS/80XDQ9/6wlPt3yP/828MD7/pvT0C/VSt2hvrFugWaFmgC+h9XXQLdAtcSgts6p6X1FBVxGX0M+JHPkiiR1LttURzwJgffuOAOxNARTiGdAAk0Ga2XRieXYSPj6XFVsUAiPDTfgLY940OSdEXlqM8T0VG7058PyF4QjDWZ6pgriTSxM6I2hIByfwBIHVsubLLJChAcxIhQDdijkaNK+mhO6VyO7XPRGvbPROiR+ylXA590vFWIdhvFXs2nQw0bWAR5Q24O7A3+5NuUBhQTy0dJBHz7OniDOhHRApez2mzsB1rD0KI1JoQm5UcgLRIwqeI+njYW7tEXiNmRiRv1kYnYtfmUr34fV9ZKvXrG+B/47FNqnPEd/ru0eNB1tp3TggFwQLZ57W/wwsc0sD6R41Cex4khov9kWqP94GTW0QCxB4y4sKJG03JL5E/Rt44qWSRJRZhHxEbZnq7z773qKBwNiAiAoLIyZ7IhOA1yq84yeAEidY911TbE494SM/wgofgshp12MjX+Z1h5RERvDCFTMwIF2MKRIA1pM+43ZEg7nU7hlOFRtYTkZ4kVfTNfo/0eJtU7mMbHn2OA0dGD4U92XeQiTYOiCBfT9QU1CgaSk20Xu4aWCJRXJSw4J2Y5GW0oc8vpCr1/9ZO5AZ5xnyHQ8rRZ//Qmf4ddwGdKH7IQ/5uOdETkej6zp+UJgiCzefNnBoiwwB7hfe3Ens+Z+vh6FljJPZ5+lEB/VfOSEB/SRfQz9OU9750C1xqC0wEdH/HRwYlzjSJTxbMoH8OqXHNGSyOOpM7K1ThbGp/k4/vDuvju8PKhB2r8wpWeCD+BnIeSoElGtM2ZxjtBHiqhUdUtOHv1LYVocKx26blTCv9bmEKzkHYT4egmIP2wQcqqOHArCJ39rvVoGQf4DrtP86MZOjJtSJtcaarRyS3q2TcYl418pp7dA7oB4/wZvLCcCqQs+XiQpsB/ekMVpvaHPoZvjhDjsBgPCvaNRrp7GszaqBzvsQe4IuK93C2RUhnnJy1GKudwfycCxbmjBkljjwaPgRezcZFGm/rczr1i/M+ZaHYM6Satsc4RpASSrpk3Kkyaovb55Z+3SFl9MvswDohFbU6HGhqeTBW1nkPrINDQCrfjFWcuplFcKnjl3AmAGvamVPxHed88LDZxXGEOe6YSBvYmJTt9gyizh3DkaVKMIt9hrO52yAys4HV7EwMfvV3yDCWOUpxPmxH9jXdKxos4OuJcn+RGU65H7CC9dmwqWaIoF9u53AWtq2jAjrlrphH1p/vfd5jhcvQd2LinVKSsPIb+X4pjgbK0eQ4Zc3zLvZ3bDSSDuvxd8uEdDI3aFCEb927w9Fzf/zM/55nBHrgThfJbX2Rmn8d6fKH1Sig8zfX5pfx2NqwjAWOm0YhfuZple/F9bCyQACrePeSnz7z8Z30ARMB/W+egYD+t7uAftI56dd3C3QLnNwCXUA/uc36Hd0C3QIXxALH7371HBxDLOg3E3F38kXoOUt1yeNaRPkEOwhOC4ZCIElCoyXM7yB7rOmmiF9ZqegLgihd4t4QYPaeUh2rn+MN+BQA1yKxAHyVRGryGYVJYs702eoNTT9q+jsnElTglVHSFmnHaYPPU9TD41pAyzYOBsIwwfXCfdhIHQwchxcPb5/nwEt42EOQtNZAJZ1oLx0lQqjl+UYOkFYZcZE2iDD1+nGkRhdx1P6JeA0JQTYDgLn1USN/E5BH9Dqgm7pqGsUOGWQe+hCydf1yTab0i9Twdp2B55ryjbE5MRbRAEk6robBIhQgM5hnUuZpJgWE40naPyPSIjW3E1Zlb1Afz4R6+6miba6rsLGNzWvmRUQJdlDCwL63OWSO/JlB6JnNLELc5trsa4I3xJvV84YYe/TReJdIikUl6BCfrR3Sd1pbRgRpRPmdO5tIb0uPbxHqFglgdb8PrDZ6pCW8aSRgiNM1usENE+/DLCdgURpBfl29MqyvXxvrvFkkAuQthJlFoNu6zSgpS58XpQJwPqDGIWtPHRd0ffn6CeIU8k6dNVjLvIsgP2b7MuqFtghhngep5u9HyaLA92ZL1iG1FCFWjLc8awH9//33h+GxGy5oZ3rJB65vXqhkH9A/JPr3ifmxz4ysZU3ybk8bxosp9u2TEW2/z99CFdDfd0YC+hd0AX2fqejXdAt0C5ySBY7f8YpNZDB/x/SMvO05KnyqcC5HYP68ZzP+t45U4SHM4FRlkeemEx2a6CC1kzVK1//GxN+MFu7IBxWxp46jJezM/l5HG5wBTmLzxJjx910xjY4hzx8iECfu0Qeq0hsG1v6mY0FgB5ynUwBjUiRyPIFGPKeOv55P/TIRwzO1s6Ytl2w8tf0cK8JR2DfF+oaQXx0yOO96XyswC+BECYHZ92LPAjkZ2iRdPucysh8g8lGPPEtYhTDJ+TpLATWc57O+eNxD5iE3rThcIHpyVsWBMh0QY3+QaYpUznqeZY3P+AfJxGTPx9mVftMX+7+tH3fYldJk1jeiqOW86tgFMZ2N7+uPmuGBc6zPnk1LMj9lX0OQNpGeuuJgeq0PreM03IA4bjiBCHdshdO246RInY54Sdr7Bx/YOBqkM3jMn9nH09bHonF70nfBu/a1XYvDNUK8jUMdFOw87NHv4hhh82DXeXkvc4w+3hABjEfnk3f1xAE1nKTNvpYNyvrrqeIjKxklmMgm5sL/uKcdG7pDk9R3Z1M4bo39lv+OCcB5AEcF5Tl4J6nTLP2evNrE8b/5Dha7g0cngns0Zs87OtykdSe4IzmRgzMvieUR6GFDr2lumfDMJryHbwc+1fc+r1/mhz1jn2tte7WtOZnZcz79qN99HuufW79SQH/znx8eOm0B/X/69eHhLqCf5FTSr+0W6Ba4Rwt0Af0eDddv6xboFjj/FpgI6Co6bSMKJiSBHMRbYnUlRRTEL4msgNckOJ4A8Xw2VUIEnXQaAT9NMqJBUmE3FbK3CUkz+wc4rSmhU2iGgIsoSLzba3Q7AEvnUYVdb0/JPIkGHjFlRJCIwRC2+b+DQQNHpbb2pFYVa0rbEbtB0lRAab8Durwr4rE9IZFChKfJnC9SDYboDelotdOMvACgGwAm9Z+BbrufdOkQl+4dHU4TrGPqciOgZ810ycZg/U6PfCKPJS1cepVHdLAJofYcTxEYHvfYBRITIqJ6v9vvJoYrwYTd3JZBKmRtwbCbjY2UhCb44rnv3vkR+cAagtTC1p76PlL7+fjDE55xQTaQ6j3TuMegiM61vrkwHuvIok48vXkQTgBwaw8h31M2SqSBfWfrDsLLaoubmH372FOcu6hsP9Zfr5cOAVdSLNYSFA8+6Je6mKt7yf6d+0miu3Guyf1v190Zo8KZM7Mje4TIBbvevO7NFkYw2fiuHY710V08D1EcxyDrj6dvPxxr3PMesWfh/KFOBr5Xde0xiULOpIDeWHs2v9QxVxE4yX0l9XGKqWSUbHIlG3XN8m4x8sWcIZhT/m/jDkeGoz/3f5z0bX6i62994t+bllPwaJ1w7iBSquWMYAvGHCo8nWfMJeNKxx7+DsYXTmSOpOTR0//Zifr5RFysAvp7z0hAf2kX0J+IqezP6BboFhALeCQ6PxX3qEjdspo6f+m5ONuLf3DW5kyoZzP+/pmzlrVxNcRFFf74m+vNCYZq9rt0uo6hjhHBujVWdRjcd9UwVs5Izec3+piYI84kS5jS+8E1gQtSrOLsonghcA6lcIh0xJaMS9OV6/GIzznbYa8qFDOnE3G7nLPA4iocc59iF7V1yzkjn1EXndp1wYBxrJ5NJ58jwIN5MiNSOISCNb30lKQSz2xHUvrJ11rMBRGlCLB+3glhWs9R/z977wKs2VnWe767LztJN5cghkgSQlQOFlLDRTwIilhogBFwvBwQrVIJIxcZp7Q8R48lIhe5JMNRuYzMDDNVXuqUVGGQOXKAI6AgE4RwO2pEAQ8iBQeBSOSW7iS9u3tP/Z/1/J7vv95vfXvvdO+ddCdrVyW79/et9a73fd7Lev7P/7mwpzwKvseC6jwRxTjUSsfudXQwiT8HzKzvIJSdHPZ/Q/J65ik9O6Kck3BFzNLDcVYNvJCY0QlUnqd70YGjzymnIEjVr8zyVWs0HwIOUR+lX0b99CTGwUw8rzCjpaHn+XmMDOBG+a+zTnnIOSPP9SwI5dD9s6wSRD7rNpw/E2Pq2dLPhWXW1tqmHJe/8tW2efe7DXXGtRrcVpGLcKiLvTakI9c9rBXI95BX4mNlVSschPMw0cqbQ8p49Z0MUmULSeeEcG7dP0RA5xiCjGWs5RyfssFWomtx6mfz4IhA6QHfVDg1+L7oo8unztQlTJF7GpxcDh92xsVaHmQUmFBzFfg7bReSqTDkP32xHXz0h3d6kt/q64599n8anI4zhXvsUfYENixlScPhINeh5jzsSDgkjCLPDVuafS1wbv4c/Fdvv9V9vS1uGBPo99/VR/5GEOjXzSncd1Wqc2OzBGYJTElgJtDndTFLYJbAHVoCRaJPEeCM3AldgI++c+DVSwkQAlisNrYwdmxFnofiDHDsHradwScuX2XZcqMBxgWPOtjh9JezQCCsiZss6mCqvz3x7u31NhVvHa9hn6OR8SAjmSFwAuBbTWnacgLf2wd41rCshhvj5N4pUSJ6jC0YI9zIRJq6ybdw92G0YwJxg2T00Qg+v3Ukn6wT7tHRGA90e9RCT0NDeKVnSnHJjhR1MkYI+EcdtExnp7E5CI3oE5GC6cWv/hABEWA5x+Hp2wHjTqjSZo0nx6hIB8kCsjTWQl5E5Dl9QmYYLyD9o08Z4cIeq2tPtnb40EKKYeDM2m8io5G9RxxJLhHNk2A8jFVJ5obhhrTjSTBjdKEf1JeHaGdAGIo0LjkuSHQat8htNzhhwMVRQUaUcHLQPGR6N6KNGA+RD3qGnq95VUR3peDM+SXtHwarcsywhaZ7ZZRwA5quY0+OUpV7ZJEbNgcSfYiwyY134GBbU4Sz/g5yXQ4OmbYuo1M25ewhJwAZjDyzQBjZDwxGINLhs1fkmR9rQTXqszzB6F1g/apoilxkRH/0ZwQGZ9YaZwDtjgz9pH7PvUu0Up1LC4cLDGbIJGR8QsaVRbmFMHLJICbnEI31HNWH11obDEbrF/7RDg/0W3/Zsc//SDramBOOZHHzkDKwshHEWUxWknwO+zScJcw5qSL6/d2X6yINemc6gX7dHhHoD5oJ9Fu/SPfgjqNHj7ZXvvKV7eqrr26f+MQn2r59+9o3fuM3th/6oR9q/+7f/bt217vedQ+eOjc5S+D2kUClcncnTbpiEGLUO9eVVhG9qLUjZ9psxSMIwxEPPS/r2xZx3WcDMzKh+ugAZIKY7sU6RaBPXjNB1O9kikI2XXmeeN3RtwnsVmqJ1XMeEcrO+vYCB4tZBHORY6mWo7PrOfFutujXwpJdPWvGWlgjO1l6XOpyNZepT7tjbFxiDomQh07K+XMc17isfc7cgRhSaqto81VzRpkjshrpOs9WhIxCx039pvReZVFI4jUwTv4NTmENMA8e4YtuxLXori4HL03FeGustof0T2UGUh/LSdUi3yvDF2SoRZIHaZpOw+WkYPor5LJk4inC9cxwxJWjswkXB1l9hKNk/Nuj0HPNIGvkw1xAhKpfekatHWGO1HshG+mTHKBjLIaVWS+FF4kmz3brXDOSGOK78Oe+Qf9V33BApj2wsDv0gg8oRxbR0GuRpWtT2EZbMZxYFs8M4lSfa46/cEP8e/OrG23tmy4YHJ8dM+EooDZYT+qz+oQTuP7GqaNqmmdaeZzP5VyNs7Y4aDJJqN2RDUEOBKS2z3kmyxvT2BPofd31/iz0M3B0Jk5kn+AZzJXKe5yzPsiQc4X1o7WK3YHsXTm2yFC2ebKtfeb64b6Lvr4dvN/bVp0Kp/X5sc/+4CJqPPZtZlO55djgFIEzOhHkMfkt1snIqT3WG5urC7rJ81ZZAyplvYLvb4MU9bdWOCMC/dl7QKC/aCbQb+2c7Pb1M17abYnO7Z2JEpgJ9DNxVuY+zRKYJbBrEtj460dlW26k6AwOPZm7Fdmu1gBJus+J4VWGI90DmHSDQSjLp2nsGeDWCnl1jG8PUG6tlEeGkjSEeP+n5MZnJWNPS2cdWCLRs+9L5OrEPGJ8CU9lixKe6k8RfGb8CaKxPCEW6cncMAPRx5whixEvmFG8gMyyj5nn/SqDkE/jaNosesENmt43xBhgXshpfQDUo/Tiudb0vQC16oJHarhMpyyyrmp5y2s+2wCEkj5a11eqxtwLyH1l9DkdzEjjURqyBIOxp2xu9Qxqf4ug03ee9k4ycoMT+1L99cgLNam28ZT39aT205AxbKOUtchJrwmIkbFIbwIktNasfjkGDfVNhguMb2qbGum9M4Xa1lxERIhtAhHcIvHdQ98NqOxlke0xdhlRkshXmjhFuWO8IBU/MtIeCSJz4bE+qtlIZBJ1xUVYQ2ivKco7o0KiD+bVr9R/lRJxMADI6DNE4pthqjKA5HiPbwzRAJo39Q2jlAxipDeUEURyUv32iLhIA1lEFKwFqRyGEa8/aIaFIVpk3/AMHCDc2qdnRlr5jLyocyvXLkSvpyZ0G7afpSMiPWtSxlC7Qy4cH2wO9u8fDGteEkMykBMFeyb6ub+tRd1JRalk+xp7ruW9ItGjDjr7gudirFwaf39OZ8kEj87h3RX7iwwG+U5VxHoaUc8GAv2ii80R59a+Vyeu/6fPHm0zgb4LglzRxL/8y7+0Jz3pSfHts571rHbFFVdMXvnFL36xfe/3fm/727/92/h+M9/fa7nH73vf+7Z3vvMk8TrjAAAgAElEQVSd7bLLLtu7zs4tzxK4jSUwRKF3L7heny7SaYVPLX3udfs+NXSvE0OgK217pTVeQZz7axUdrmTV4aIpmNQTsb2cITL652w3H4jOccNwgCyJlffcoskuWjzIOQcFHa6reYJsT7zhcu0d2nj3BmGaUZkRWT2Br/o+1+PzH07q+TNxqnDZBVawzD9BflqZrJGeFAKbKMPVT+RI2Kn3Gp7bbq7ALRCjga3GfFXopJLPV460dvfDwzwKl8iZUY+SHKNWupHnYBYcCsqxIAlnnL5xOi7yryMP0RO5XuPxyN8guzPqWjqscE3pzbmHai96hHvWcFe/AtcZ4TxaO4ZBAweckxnIslHGHxHftoSCKDfhgwHUx3BOjQUy3CAnV8nfifnCNEn4+jxqjGQCCPyeDgtkAeMsmMLbkbEpnbEh5sGS4eBqTiWRFSyxBri3IqJTp43zKklcjVnp31WHWmMSFgNzi/CldFZmACjCPNKymz0or61pO358INBzDQztHB/WIMsf+YHrcfQFIyZmKMwRMtw/ZBPLrRbp22M86VhPB4LA9QM//6T0FP0HX4P5AgPj+NCthaV9mfu6t1NNzaFxyVrvgUmxy3FmK7I70/7r+8CmWudZSm2TzAFqa9++tn7pf97upLjV3x/7px/KkgXaa9qnQ8auNeHzejfYeeYmEPY17w0/m0fv1M22dvSmIcOB1l6+09Yve+ut7u9e31AE+pUPbf/rrhPoH22/+sKZQN+LOZzx0l5IdW7zbJbATKCfzbM3932WwCyBHUlg46+/265z0G+RDkV2duB+6gkAYTdqbEeMlrd0Alh/Xj1jG4PPJLE/ZRUqNJQtb1PDfSdSxEhS5JPflAiuNzo4IOwjwHvjjqfpoh2cDTxFVxgZ7EEYLDwyN56b0QP92FYZovgcMrgnqAH/S2OyB0TfMKB0hgRdRlqzKUcLlx39L5CYX2KQmjL6YYi6y12GdN8RudyloQdQ3+XwQLDrHgFKgWAIVdJSQybSFyItfC5YZm48iLRjMsTljxtL496MWNcEVVRMAnb3XscRICLCkwwmRSHkHXIgNTdrjN8B5nHEsOh1AOkUga7vVN+uiMJA88N6wrAQwDb/Jprfa9XJGaGiL7IOXZC3ZuzwdYlBCgeIIzctanuXHHOPeSSHZKNoE/UnUmOnEayMGB5NsR61wzXPAbaLuM2x1Pym8VBjCONIOiQoeoJIdrWxcTzJ2zbUOBeBq3TpYXjJtdU22+a6nmkRyZEdYux4FOncyYJA6m5SMQapng4JMirtP9DWJMeqL5le/eqf7hkZMdPJR/0V6e4Edr+fRT57VJCfMRhC1Q+PbNHfnFGsKebLSfS4rifS893j+ynXt+rFq10ZfELmMf71IaqEvhzbiMj0INCr7cEJYP3iP97Jib7lNcdu+NHhe49iwlAp2ZBJwR0K/Gxkz7iRSKS4fpQJgHWMsUy/o746xvxFGYwznUD/q795UtsLAv0h/8ObQ1yf+cxn2iWXXHLaczo3sJDAH//xH7cf/uEfbvv372+f/OQn233uc59J8Xz/939/e9vb3tZEmIs8P3DgQLvnPe/Zrr/++iLTH/7wh7drr712Fu8sgTuMBDY+/AgbS2KikS5i5Lq/A+M9OL61/gr9Neud874M/T0dHLkQnQ0nKvSter8Y0eISH+Gj3SDPLTo3nn0K0+uk6Sq9vZo1VgSCrzJZuZOv96MHE130OThLOoWnZI8SMEnoOGEVsmbcXdu0NUWg91jDCa6O3x56T5aZHDO6tesQIS/D6Csxdv+AvixUD658faZehs48lS0snRbLuZPbpRtJR46I8yTQ0Q9Lr0lylzXNHDiWCHLeSiKVzmSYg4xL4DyiwdWX6nvW21ZbkVI9HTEizXk6u4b+Rb8hhsk0MAVMjdSV/Ik+h3RFFuqfcKeu8XWl7/t03hCX6LaBPeUwrTJZ+UOEtzoufZ/NF/sis3xBTmv8UdbJxsz5EvLBsT31y8hslro8Dp1qV/2JCPPsg8aoklX6UX+izrtFYKPDcqaFY7qcC5SVSbI4Gc4Gm/v3Zbr2YS0Pf2fmKTA9+Jv5lTOtnRee0n3ojyLnyfyWZyiR7oGZc47BzEGqZwYu9mqUCVsRfa5+RWYp6pHnnkU4lTlKThKJ+Zi7sm3lumbtuoNGYfMVxpDePraKRE/nkyDIwf3S1QITZaYD2yOK/o81rGsie5vW0oCxdoNAP/bJ70+nGnunaS4g+AM7Z3a73kGte8fE/Afeyyx3fqYyHeG0PjiPhBOBke5nNoH+bXtEoP/1nML9FNSU7W6Z8dJ2Epq/v7NJYCbQ72wzPo93lsCdVAIbf/ldYzDuAKvIjtRg3aDQXwfwLYW+A/hu7InmAOgYfbrfMR+30thTc7iVRafTtvsxrVoHEMd8D3FO1GpPDHk7QbB0xrD+Oe697ZETyBnjAUCAiIXRtWYQCYNFEsX0LT2KA7S4V7/3xcflz6at6ic1BS0lMcatKRlSF63az+mt8eB1v03UzkiuXTCQY07mi8gAPSeiQqkxbg1JFgL4zBHgGsMmKdvCkJA1siHXI7V7evnH2u+spAH0LBo2ZIqxNPtSeyejUOL6jGCmm0S8K9pY+4K63jg2sF9EeLoRh3VAvz3VnwhqiFj6Dumo5xTZnZ0IY0nKsN//vWPFiAjMCArWIVHlGH0UlcA6xoCj72KsIiuPDYYRjBb17IzSiTrf8ug3T3oZg8I4kjUIw8CVhqiIOGgRmRyp7ohM6Oupc07pN8Y81ROPFOInzIs/I5SqhnnWaZNNi7rqRLJAhFMnO+r/jSNMhijqrLuovhHFgFMCEfWSiBwFtCIigjwNRkG2DhE2m0SkEJljjjBKbVcR+qwzjI5a65FmP41RbgjGqUC/ZWyJVImZJp+oKQhtHBKmDD2sBxxEwnkkHTpYj1VrPp0eeHYZP9cGBwh9LvLc5zvtxxFJcvBAW7/4Taf1lj/2xacsskE4ga65LWOkZQDA+WHkFOVGbHMggHDHAIdsKt2lGZLzjDnd8ZyWMFbc7DXQ/3KPCPSHzgT6XkxdtPkLv/AL7VWvelX77u/+7vbud7978jn6/DGPeUyQ5+eee257xSte0Z7+9Ke3gwcPhlODItch19/0pje1Jz7xiXvW37nhWQK3tQQWtdB5wVg68YIeRq47T9lDE8gN1EaI5VV4Ad0QPFVk0h6Q567jIeR4ng3i1pLnvAshTgt/TTWUQhk5ANhsI6vR90kkclm9e42E9+8KGyVejexG6WTs723q7o5CsLuV59jGI8TRy0rHcvl14yZaNGRfHR1H2pezaE+iTzJJXSe3kLNfSYSs1tsUZtK10v2O3NzaXZNMJQuXR/yCC9QGOAhiHZ3HnSlCmU09sAj67BjXaZjoseiH7AvJhlTykM0QqFXGKssk8SwySvkaR+8u54iULVHaRJX7PWpH5HUvYhGuQQznnPa2BJc7eBgZSH+f+qGEVWUpyP6BmcA+vf6stkJXzTrSQcQnwR8OzsKWSUKrDzjfSq+mzvh5Gc0bzgE5tinHEBymlSUrI+iD1FQ7Bw9WjfMgb/WD4wAYAOJfG0HPEmFKlL6IURHuzHGRwpQ2y5T7RLmr/XAuzprylGFjTYFTAtMMWdLUfiwR1ilOK8g21kZXvkJYBCdmnEaQeTh3pJODZFsZ5ywTWe88Pz374wxs/TVTNhbWXGLvIaPXYJeKUl7CM4ltAxcKl6bjxvqlg8Pqqf5EtHllpUg7GDaTKNG2wLiRcS1Ste8bUuZ3P0WekznEAz94BaptrSllEdBcdvvxjE7hftUeEOivUgT6TKCf6vrd6r4ZL+2FVOc2z2YJzAT62Tx7c99nCcwS2LEENv7rd46vdW/YQA9p5K+rEgW6kl4A2MjD3oG2NyyNDEBd7aIBtiyPof9o0rCynUWnMzL0USI7lRyGjiUDjTVQhrMEWlNRmf0YPN1WgXfaxGCXhG0Y2bpoECK9dcvIoGP9itpfmdZ8arxu0KCNqs3cEcRFfmVDbuRgvH0/ao3lPPt1bljrHhVPGH1GJLENou5PA6baVj1vAUTS2AVJaw0JVCr6XERuAPRMkwzZLHAdxGdGNMfaTbkT3QzhN9onXe1zHAy8xnYfHa51T4r2niTXM6l/LaOHhOEe6zGnVv8uQHqSpGo3IjAy1bz6qSjoaKcTrNoQeJ4y3kBmh5Eqb41xmTEZ+bK2MZJGSkiNQZH9aTTQNXj0U++cfVO1+0SgKwLCouWjzZwrGSqKqM1a9uq7+kTGARmWguw9ken9NoOgl2GnjDpBKGueM3qAMckApDXkax2HliJ9B2eIMAbpvgTuFSmdRtQgeCFfwwnj5BBNnXW9I8Ka1ORej9NJdgxLMhBElP5QP7AMRJqWLC0QBgTq+JUhdCDao4Y4ziG9s0RFwaRxg+2CQSoNV+GAwJjD6JeOHxg0nUDv3y392VMR47mw6iyx8hZxZGApSecUPsNQjbEojZGDfOXkkAZnjHBra23966/e0Yl/7J+fnAanjMyQ4TCi1XKthNEtU+FXOnzrZ60XjGXsuY6MqIwh6XQVkSC5b+pMHyZj/T67n1pxR8LY4iIn0P/rdXsTgf5tD5oj0E93nlbdL+L8ve99b3vJS17SfuVXfmXysp/+6Z9uv/u7vxsE+m/8xm8E6e4/t9xyS/vWb/3W9qlPfao97WlPa7/zO7+zV92d250lcJtLYONDHoWeejZ4JnrTRabTw14vwimtMESXotrfdfr3KII1SUbXN7fER92XUzBpK6J61JfunbWTGXAymPdYtNlHdfdEt2HDnh/muSsxWN7gJCgRrku4K8kad2hgvnBqi7I7W+DLEc6xlOV8jl5Ce575pyeCenmNvkcm2ZeK2oVw6mXoC3BqsibAuut/rlv77RCujEt6YDipWnQuhLYeEVg1o6rR7dwxkzWGHjlV+1z3QTwWkZ5tgxcCD5hTs3RkiYRa1zgF0O8YKw7hASyylneWPYNkVv/I5OQlhpCJ9GxhqjwCSlQ46OJQ2X/fi38kh5zLkGsCI8kRbJAkbzxL7SADj3yv6SdyNx26cUrm+bpXbeMUrvGEc+r+1ijfpfELv5HWP8j2dCqHxGbb3f2u3WIrg8goW15ESGvtJGE7ENdrCwI1xpIlpsgApucK/+R+jDaYE3cuoLwZ2djUI60HjU1t4IwcayHxpEefO4HOmg17QKZuL6cQK/fgEfC15ykRoDVl6e9Zd+Gs3BHGbodZlebDMXh/HiYkH00CDhpkhgtMnfsl3jFDPwfcSMa83BvHjw8lxbTM7v+OnZz6Lchz/Wj9hH0i157GppJbX7yhbV7w9VmbfjMytcWckhkkz+7I4KZ+1fnLWgJjLWxDa3qWMq/1mDZ7fKYT6D+7yyncf3Mm0He0Vk/lohkvnYrU5nvuyBKYCfQ78uzOY5slMEugJLBEoBfYcqKiqx1XaeXMouFGoNB0AaSBBo30sAeUIcGeVT3bxuBzSuS5TfwU8Fi1Lnxs6bVbEbNLqdunUEs2TBTo1HN8uCNZGkBArgDpMpxkgxVxnvKPvycMPnigA/6YK+9XGZIspXKR86S+NxI+1oSloRylOIuOL1pfstekMbDWVV46wtsrrvH1qnZ9LBqfiFOAdUSKZlo7rtNvRfFidImI9fU00phneBgVMnIB8pf5iPnqrHs4BUTksnlBQ/LhVc9cI2+iLgRgIbeplaZ2wniSUe8h4wTm5a1tRhIcJdRPiHSRr/Ez1MceyOucn/S8j8/0HMllCoRKVqTN7qORWIPufe/EKYZmxhn7Kb3yibryFIb6TqnjPUUeQFzXRa07y7SgZ+l7jU3zjaHj8HlR0y6869X3qKudaQU1/CCUzehW9w5176OGmv9AiNbYhjMy2kfWGAIjxXrWR9dIiOQnU4DAPhHzOc41OQzIaETkClH2GHyyLnhFkYdhMeuZa+1hQMADP4yNiz0aJL2uk+FK/cSgos/66KOUb0TTE9FCv2Jtmfwxktp4wykBA3C9F/IMiTSWXksya64j6/6sqLOxq3869T7QZ4qqcGIex6HaG2tt/YKtSfRjNzx1cKxQe1oXkc4yI0gkB7IJ6KyRPPmeZ1BGgH3XH8m8MmIMVlaEPUSKd+ZQl+3f3870FO4f3iMC/WEzgb5KUzntz+9///u3f/iHf2hvfetb2+Mf//jJ9lTX/NOf/nQ7dOhQpGzX7/7nqquuas997nPbgx70oPZXf/VXp92vuYFZAmeKBIJA77mgOttX4JaRo6TVau5xU+mz5nyV6lrhKMdNPLeuQb3zfpwGeb4k9B20lepkdc2dx1LXCvnxznb90PVyd5SLNieZxoXu7e1Uv5Mgoq3QkayEj+Mc3uk8y69Tf11nXbUYg/RKATgminHhNGpOA0WMUwPeFpYP1/Ue14X1b6Kxt9wgU+TlBNHudc8ru1qSqr38IWvRuZecEjKF+4gYpK77CqdIcFNgAHeeMH2PdPIQjMJ5kMfIE+dQ9Sl068xOhYxw9Ix9k5hJ4nDMFFgtMwqB/SjfhN4a+C4dk9UW2GNqLshwVQ668Mg50e684ffH2svSPZRwAvuz1/g89tfJgbAE/wcOSedW0l67AwD6ZzkdbwzXCx+RtQuHUem4TtqzxjUmnNT1mWRCGS1NtWdN0LAhSkWUBz7IVOrsET2jd6hWuSkiyDVOyHE/kpQi3gl90pHjvCBsj+NFkO6KeM75z4j5wGqdPSYIetoIXJ7R+ZwV8RzbsKz5GEPaB7BvUAaOsyIwV65h1obadedj+sjz6pzJhbLTQJCpM7Tft1Uyi/Jaaeexvm3nvHvsc0mch2PE+iLTnUStuvU4KwQG3hzS6Etm4cSRAQrHjg312fPHU/cPH9kk1VG22dZkE1B2uXQGiEs17sTb6/d9y5Yn5e3xJTXQf+Oqb2v/yy4T6L/1qo+2580R6HsyrTNe2hOxzo2exRKYCfSzePLmrs8SmCWwcwksEeilk1qEg5MfgFZXxCuCeOK5S8RGojUAnRMGdfsOjDRL7U4QxVNiGNmVdnhPDzowrCzZc1YYeJCPA6xBq7ceuoGDjy0dIUaZAFITBhiv4wfR7UYCBxwQkABhiMNRjW7cyNNwofYLtHe140fk+Yox9X4F/WUAxOhLJxasleW8sGKdsTZZWwL7o/rNabSB8KMZgWr9kIo5iLc0WJAa3Oto6/4yCnWdhSDWxwGMM+0c042xq2SGYcj2BYYM0t0N4doDEGUeKv0bjgVd+nfq5jH+qrGeg5ZBRiSz6n5DxOtv9f/o0QXBHoaZLstBkbVGfJIeEQK1ohuoI9jNWWw9zhhqpyeZKkcB6hWGrBNEZ4226KPGRwQBRi+izdUHyUpGpIzaj5R1+SPSOaIcoq95BoQjTBKYSsWutnW/RH/4UKUgrLVpziJKNSdwj5f8ENWdkf6Qt5ZKLtLTHTmyMCCLDD/vvEilJ9AfRH6lV8/MB4wdclbjK4cOjJGLlPZVl30/43MCfXOIGo81vzEYyqr+nMILssSD1micdbknmbPeGYZ954a6sBnlfZyfGGA0Xs2v6t65c4v6w9k0ZQzvPxudeVNnwhDhRkRLpeGPfqbhU5HcF7whbo4U7Zq7iFDK+vQYaxm7+hB7Jjc0bem3nCzC0JjGcoyG+m4UlT6VcSX3g7+SOGOsTuBwDgwlBHajpvvUK/J0PvMI9A/tEYH+7TOBfjpTtOW9559/fvva177WPvzhD7eHPOQhS9d+9rOfjbroij5/7GMf2/7kT/5ksr13vvOd7fLLL2/3uMc92g033LBn/Z0bniVwW0tg44PfsSg14nhpCZMY6ct7DgyQ779pUrhzKPb3Hu/HUFVSL+thzKgfExhnq+shsJeEugIrrYJQSzo8cKfDSCsJnWw4YFHqv+hoS3pA16bjBHQE9DAnuOMVbnWuHefyfi8ddYJAp56yy2oVgQ4xCN71545kZTq1z8WIQO9058B7I7Z9xZbYwWQJF6DDu0PzyDk6mw9H48xihFN26HD5nCCf07kSgpWe9XPoa3aKPF1V9grciD6vPvNciH1ITPTlEJ85UUDY4VCu7zw6mzThgSsOLjKZqR3JQPgjMkClgzbEcT8LELc4JqAjM3bkDeE6hYOLSCX7WMoX52o9U8639F/rLTJQkY5d0eTpNI1ctI41ZcIoerbGofbOy2h68Ld0Y7JZHduI8lf+E861cnbWbzksqw2wa+6nwkhOHuOoECRq1lLXnISDhgVfRD3344tSAFXb3AQlzMa+9ujzcHTNslRgVbLqee15OdwW6T00VSncccLAQUBfuuM7woC4Ze0zv/z2vc9nnE/YYvq1o+sUGS98GOsx66sz9J0S6Dt5WSamCYymn8CeSaKnmWrt89e3g99+bTv26SdV7fSSO2eAn0m33DLgY8xKtI3NifcjGbho45aNykSwLYEu2KVz4pZjQ1kvP1PA9sJ7l525BPp/2CMC/ddmAn0nK/9WXzPjpVstsvmGO7gEZgL9Dj7B8/BmCcwSGCQQBPoIV5vhQkonZCFg3g0eDggQ6EhpNY64vy8Ar6WbHqDK8rRMYf4lQ9UOifDdIM9XEsHufUy9864+FqODDJ+SZUkgkQrXOJgeEePmyY+hCS5+ieS1yHHqoIfnd0YlAzABoPwe9WHCADYipCecCKb8Cnym3QhJlH4ZWLrg7r4tn1MHk4DYAqtmLOtXGSn8IqogAWMAOJFs8izHeSAzMUDcetQ5xDlrs7zjrYNhKKAGe85befAr/d/6wvhEFLOerX9Tw5s6bk6gQ4CTmtqNfnpm1hVbmmdS7qktCHeNWwR6ANkkGpeir1PQsXaSKNZH0c806hIxEWvIIrtZo1MHsL4LT/w0DiFLjTnSyedzVT8PAK5TQ8YrT9un+4i0yFR0ELbx2CXivKs3n32LdOtRvy9rp1dEzhD1UwYBjD5uyIxUdyzIlGNEEqfhjtrzGJjCGNWdIRju1B+MOpwdYQBM2cbROdQjj7p1kn3MQ7dZcu2FAUb9m0pDOTqHcx62OqdindsaxwGGfRd9I8InI8LlQIARiC66sWmrTB2sG8hp3UdUOeT3KCor+6YsALouav9RpiGs2ou6hMwJxlVdT+rHyAihjAAngvSvNIPqT5R8yPeX+oNhGUcIjbHO7HyBILJ6l5kM9U8MaWHQznTucobQHBJJcdF/OuPUGCfQP7BHBPrD95hAV1T1Bz7wgfjvgx/8YPwHCayU5L/3e7+3rdx1jeqC7+RH6dCvuOKKLS89evRoe81rXtOuvvrq9olPfKIdO3YsiGzVF/+5n/u5dumll+7kUdteo5rmGxsbMfaHPexhS9e/8Y1vbE9+8pODQFeKd6V6n/q57rrrgoBXXXSldJ9/ZgncUSRQEej+rnRCTp87Wae/492ryMzUj+Rkla+M0fvThRS6IcqIdAB7b0EI7BZ5vi0WWTF7U9BrSd930JT/7slwJ1TRF0btoHPah9vywS48+mCEOXNWQ9P7VVmmIO3zHnQNMJOuj0xAifPc8Vjf9XjJZRvfm07M39xXmLqv/8tgbUwjYq4n0b3hfu4AiN5mOl+6TJYcCrp20EldB3Ndj3XP46L0EI6qrGsI0pQ1ZC+PKiy7iEStbGF6luMW7iFaWHKPMkk5J+xJ36vor4zFbQuRqjsjYyH1mWuIXPTxcJBM/KJ+BLmbuIExRP+SoCY6nIwI+sozS9DnKceJXq8Hq0Ubwm04gy+cREMHl6NsdCGdQ+UI6g6i6q8isqPv+V/0HUfaYe15ZK+n1g6iWbLf2GibciJI4n1Bmuf41Qz6vpPJgXW17m3fgb2QQ+GmbF5Eqe7BOVdY0B1gK116rjvJAcwd+FZ16w8MZbQcx/ielaMvWDccEyZ+fEv19imwGtfg4O/n18hZJfc4pLrqkR/YP2T/8qh09hdrx/fbiuN6RCprzETVY+dgPZY9Y9+AtZGPuoYMhGfJxEAKet3vthf9W6nf06FjE7sA+0htkBlQfdZzKSEg+SQGW2QsGGcvGA8zM8AJs7MP1N9KV9/amRh9rjEQgf7yqx666xHor3jVR9uvvfC69qhHPapdc801q1bG/PkpSGDGS6cgtPmWO7QEZgL9Dj298+BmCcwSkAQ2/rKrf14Etnk3U0tsK5E5IO2NBU6yF+BPpRawHW2fKnm+4t6+v6dDnpftZcIQVM+ZSElXhiAzhJVHtkXmbtU389otaxpgsbyVzRDihijIIUA+MsYYEKSOAH2C/zBImFe5k2DlTdyTcpCjCY75NWHvGRkDvRkfP+ALMrvsRUn+Q8AuGfzc2JhI1Y1BU4ahpTViddNJu67nS15Z+2uo4Zdpulm3AO+Y74xs9jR3PAfDASnmNAZIv1hamcqNKCM9hzTuqq2n5/RGHYweRAtzb/98tR/e+VmfW4aJiJiwdHUR8Z5GLiLv9b0IdOalxjIYSkYGpjKAJKkYBsY06HCfGxfK0Jh7AYOG+ugyUN8hSRUhUAbLTP8XxqxMBac0fyFH1ZTLVIIR8Xxg4clOdgGlD8yob91DNHREiKexLVKrY0QqhxQ9t3MKcIOQxlqGIFtk6jfrkLnHqOLRL8gIYxHRanzuz8LJA6NBOMF4xgDbaCdOtnAKKAeQPDu9D0Wqr7BOaw32xu7oX0caYHSr+c+HkJYRx5BVe7BkbRf4+wIni4wsiVr2mY5eTgRB0Kdxp5wt2JsaA4Y3NR/7e2MwtNwksi8NoVr7Wlt6RhhiN2M/au4jBWGlDpXDwoGM4s97iUIv55tOnm78jr2b46zpSqMlaxUjdc7t+r3PPPJcI+gJ9HtfvJze+3S0r8999mjbawJd5PCqn9uDQFdKdRHlH//4xye7dfe737297nWva094whNOR7Rx773vfe9Iy/76178+iPL+59//+38fdc8loze84Q3th3/4hyefee2117bv/M7vjPTuN95442n3a25glsCZIoGND1MDPc+JXqcJp8UTbfPYibZ2TjDR07EAACAASURBVDpAHctMMhpEECjdGQPBDnERul6+F4AdishET+CFsSWu6d85ExLc4qybxGRL7+vuA39/oR97iR3XH/XvDk4skYgVspjvw1GkdTamYbp+7+3Ws63UEbjG76Ef6DeOu0KMWeaGeSPyWt+h1/nYCnPxXidFexKSPTnd4+a4zYRTpZqCWV1E5qIHhXOnkfdLgnWGr5/EAlm9EjLgGceB/fyxntOBc+h2zpX+jZ4sWaNLxT2GC4g6dcdWnuMEOjIbkXy5l8Bpuq9qeOd+QddGtw0iOR2L6UuRmp3urHsVsS09bP1gO3HtZ9v+h997kaFM93tZH9ZU6Jhd2SfkAaZj79GvOhMsM1fZCybmLOY7y19BtkpnxSmeGvVGxFYq64zUDVJdUeOhR2d0vfol/Zh69uzjPE4i29bJzUjLXtHZNU8DUQ1Gi/Ts/uNR9jjb4pgsWfd6v/riDiruVKzxSy/XPYFXhV+Vkp31lR3W+CNzlzkV+54VgV6Y0Dqbe7LKZPXkfRwLCMVtPJ05q55rdonAbdnnwpL5bNaBp3XXc3CqL9xsfZ06A71//fmYODpwUpDM+4fodjKRCebhAMJeBeeVvS+fLzyJHMDt0Y5sDcJFWTu91kXnIE9mA3fonsLP/RbIUgCFRzU/Ub9eONuOsnxnrl/65okWzoyPikC/8qHtObucwv0Vr/5oe/5MoO/JRM94aU/EOjd6FktgJtDP4smbuz5LYJbAziSwTKAbapUCGtGuvYXD2u5Tk/fGmCDE0GQBM5aSqxT8CYP1lA17ydiz2tBdvewv2dJg1IGn8JbP1FVThI5r6W5wYcwQ1IDLJaMGSn7KWM/y5/h9I2I8BDcYAfBkH4HU9OLuSRpug3TT9xElqWhZwJu8jTN1V43JgC39K6J7mzVCuyPCvAa2vFAhyUiN5rad3n+hlmuX9tBbLWAH0M0vR/NpDbNm8aiOaG6ihqz2eD07I7UBbRiDloyqMiZRHztrUXsEh9pjrJK/iFsRgnh0U+NO0XwJeIk6jq6EF36g6MXoiTQggpVa1ETSBODMlI16TkUK2vxgGAsi2fYuEThBuLN+M9IK8j2MexkpTbf03ZQXP0ZKjZNnRdr5jMQh7Xe3YqI2nQQiI1fISkaJ460dPjwYhtSHqOV3YJE+jkiPNHKGMUge/tQvlzEh53EgSzM9OwCfeYvrLVJolOYSmXQHUBk8cyBlRJuYP4xnqme+f98Q8R7ZBHK9y/CDwYLfk0d/9kV13jXHOIOw1jl3MNJgAJ0yVo/2lkWXa/1jGORcx0hE1Ap7StH7EMsuv9r7lvYTI1Ku5ZADjhoSreaOKKQYT9ZOVN+1DipzQ8qNtI9VGiDnJx1TyqGClPuVVnKIfIhtynMqSifbiJT/KWtk6O/POhPyeoxeanR07tuZwz3MRdZDPxsI9Pdf96S2FwT6d+xxBLoT6IryfsADHtDe/va3x9yfCoH+tre9rV100UWTO1MfXnLJJU2pAKd+RD7LuPaxj30svn7mM5/ZfuzHfqydd9557V3vele78sorg6AWUf2+970vao6fzo/Ssiv9+k/+5E9ORto/8IEPbB/96EeDQP/85z/fLrjggsnHiVz/0R/90Xbf+963/eM//uPpdGm+d5bAGSWBjQ8/ctEfJybi36o/rPeOynbonZ4OWuhCpXss3smLlNepL42cDi0y2N8f2+Ka7oJtr+9FvANsFe/5vM+JcyKz42XZ4cclvdgU/HgfWnkq75KTRg4fAmOhg6HHm2zj2iR0w/EYUjfvIQtTkHcp/yLFuDcH6nWmiUymz+XYYGMuR8d8ps99kYf5XcmlyzpQjhKUOTJymnG5PFInWvZM6DFXN3Gd6GpekRlYoncURddT/yUfHO4hpNGzPMIWPU+/Q25diSjmnbkjC14QpWCxPkI/yVS1STQ4UbMQn+jIwjyav2gLAj7l+rWjrd3lvEU5p4gYz6jaffvayb//57bv3odau+vhLBGVGNrPAfVfbUdGscTSg+a4wNvIAwxeeDPJ395pYXI75ocaZ2SeStmAr/Q1mdBwwGQZBJaQM60IVI09CV3N4ZGbBtKaNvOewiAaSkZ9B+kqTJEZBQdS3ch11oOeUbgpszdUX7q51FosR5Y8Y1gjPg70ZselOBVECu+UBzYO9aHklBmpQmYrbFxOoIN30dmZb+YvbEVmh9jqjQV+4Po6vyjBldkZm+Q4OIYHzo01kZjYMbc/q9q2YbHPIltcjjVLHgzp6tMJKNYntomMyEfef/+Z1i77htbOIRtbrnthuSwrtiknYuGTPBsHh3QWTxtKacWeTKcc1tHo/LZXq+OtCXlGRD7rVxjZHXD6/bJ/fzsbCPT/bQ8I9FfOBPqe6Y8zXtoz0c4Nn6USmAn0s3Ti5m7PEpglsHMJLAh0A9eOJYKQypRXvZI+ZTxBaa06Rl2tVwc6UxHnDpyX7DlTCHIHRp5bazhyAiT60DfgLG6CYo/0BmABEkbYrItSr/HaMyAE3YgDsAHoAZIAmkFGYTTqjUn5EB8GdbkBf56O0EmoGB6pxjPiFrCjNorgnAKgE2vK++CGNIwwRGImsTnCtTS3lR1I34UHfqZhpP/UECY6NyIUlJrP+o3xITzxlb5uAQRHBs4Yvxn9MACRepD5Z25dfhHVnJG6LK0+wkP3i/CVfBRNrR/1LerTnRyMQxhGSG+ncXjUMUZCEZrav4pe14OROb9JB0+09jFLtYthMUjQg5k+1EA1xGJETuXaQN4yHOnZOAJQj4+0h6R9q/MiDQ/hhKA02wnSWddhjJOHfAh3yeARxOaRo5n6fYio3zx8aIhIFqmpMSiKXrI7Zz3Tu2F4zfR8yCQJ6ohi9mjsIG0zmkIp5KlFR1pIl4GfXUT4s1f1HQY5jK6cOUVsZwO6R5ECMgbRDoRzrPU0Pnrqx7jVjLDIkDWzsTE4CoTsZUTKKBIzdFSazVUp3ntjtprSWqQURJ4hRWxz3ui+OGsy/b2nJFz12koDICn5wmhSERu5DxV9T+YMlodkQuQK0WJ6xuHzhvvVjvorg5rWd6S132hrqu0ouVLbUg4iRqKH1DY3WxiLtE5Jn6nn3iyHl4zo6F8dTn703xXJkAZlfy+MDKnUs2xt/QxM365uewT6tdc9cU8I9Ec8aKhh+JnPfCbI593+ecELXhCktf678MIL26c+9an2jd/4jfGYUyHQRSBfdtllp9TNF77whe1FL3pR3Pvyl7+8/dIv/dKoHZHmj370o9vx48fbYx7zmCC/T+fnt37rt9ov/uIvtgMHDjQR/2qTH0W5/8RP/ESQ54985CPbe97znpWPUj9/8zd/M+qkq535Z5bAHUUCIwKd963enzrjb9kYyHORSEc2FhHopG4vvX5CZ4bQcz0tVJ4Vtc79PTESbodZJqHTVthpB7jK32E+lCBcJvQ0yN5+EYAnCht2+t0Ij5GFqoNlvCNDbtl3nBlJFa/nepru0JHT+Zf+ezslgvyHfnnUapBEqc8nGTVKXaznQcqD39DZ0MnqvW/6Go4Wpefy/BXlwJbkWYPZZrshpwlCPvqQJFfoUJYtzUsThN6p6xJzSgcLsjL1ZNrAWZIeVbR0Zuta2dPM0OWOtL1u5G2K/NWz0KOJwBYOiPlP50o9/8tHW7vH4UV5LMi6fznS1iJ6fH9rSkNeTgopr+PH2+aXjra1u53T2rnntqY06Mxj6L1pL9HveL45CGi94KzuDgWxl2w+pJPihOyloMCKyBWdXvcyVo/whjzXnOnfU9v6qzcunnWXw0N/VcJL+nGUO8qbAvMM/w68BXbWZ5HRSmW2ErdpvnBsQV/Xw/kee0Po8YYdfW7dqYK2AptrHHnWOibzNcQ5cOjccISOuthkaBM29DlxLF9tbGYGMl2L83T2szCrOe64jh7/js0/xqqsIz8H+3dB4Pyh7vsiTf7QTowBuVc2vOwwR6bjaSLMhVXkAI5zuD+f1PVkisNGwP4k4l1nwF9/qrWHftPCWUnYSXYKZTWTA4Wiy7X/9e7T3GmOlbrdM22ojFY4lZiTlL6P6yYcYrpzwWugxxok41iUP7Nz2u9jGvbtO2PTt6u7RKBfdeVD27N3OQL9Va/+aHvhHIG+J+rnjJf2RKxzo2exBGYC/SyevLnrswRmCWwvgaXa5yMyMWufSyftoxADvGT7YHUiJkYEnaG1sGuUdr9153qQ1xOtA4TbfoC32mg0MVYeVYBohNIWhoO4Lg0JgLsgpztjRoEra6cHP3wVHs3mQU1fiAjQ34AO6nw54VXPd+tWCiVIUcjQJLUwkhBxD/lEnyGOIa8smnTnk5FoJsZsKQ2D8bR0ldTBjjF2hkaXl/syED0ShoIcJ7W/MewIaOk6EdNet1ryEMm8cWwRhXJwfSDDINrVJoYi1mDIURHcZpzw9aoxeaQr3tK1hHPcUbPNnCuQLYaZiJzItOREV+gz3RfEeqYt1JxhMOz7oWdG6sM0WjEHZdySIWRj2FvVn1yD1AmUwUj3Sa5RD9qdCdJmoD6LrKa2mjtplN+BlQpgDYuMxMiRGRAWgHm8mSMtIHNPhIOIcuqeHz48RJRjqBTYV5/lpS6jWMxlrtqIhs5Icvqi3+EEoBroKY9KD5jzkIaOaghDURkY85xkP42iXXL9lyEh+0Lau+gfKbwHo18ZfejPqnMQY07u07WsUR/p+DD0QsKTKaPuYQ+m0WZUD9QiuTgDPAoCI5f6lY4dFS2uWzHmpRFoSJFvGQZGR2sayEbrX7XoDwyp2Zk7ZO2E/YkTFSnen0vRB4zgIstVy/zQoUEuaQRck+OHxE9ETu2FNMDecssiEj3GlKUDokb6MTOa22tqy3efOeO4YYnOl5EtPyCF+8V/vP2xeztc4QT6+/aIQH/kHhPovdhuLwJdtcjvda97tS9/+csRBf+Rj3yk7evPjNbaz/zMz7TXvva10e0PfehDk7XLd7oUvvSlL7Vv+ZZviZrv+/fvjxTt97vf/SLq/E1velM7qfNybS1Sxj/1qU9d2ez973//ptTzz33uc9uLX/zinT5+vm6WwBktgaH+uQEgO76j46GLnWgnv3BTW7vrwbZ2KPXsKbJE14Od+vd5YKbTJM5Lz+xEOomp6oWzc/mji68goQZ5pIB4pmMidzKeevc5XnIi3MkShweert2JudQPKvoc5VH9BzMVlkjdK9pKPYTplg6FzuTplAPzWb3pIv1zQbgjqNqMTFAixNARDNT43DjOLllPRPXHPBuBpHajDxNOGjW71iAO7z43OAaSAcjJSsYXOrCVKwKLIBvm39cA+Livdd6vupBZOjmXo0HqYKW/dhnb3ImTvaXnjCJvh5rXIf+bjg3R5jhuQ7zSl82TbfNzX21rX39ogfMy29WJj9zQ9j/8kgWBDumIbhyYLQl7MrpJPxxF4htmos60ZCdiEizgTsfqt/RNnEPAnz05S1/0PJHWzK9Hg+ue0FdzsGo76pYn5tYz5BwgmRw6NEQPa5kRXU4GBjCTlyIjrbxu4DqNL/TvTDEfDixJCke/INIz+xJOwezd6FcS87HUc71rHDglOF5IPb9Ssy/pTb1dZJFlLzCG+p3R0uEs7dnF+r1Y67NzXAbHsQ/AwsxX4dDsizCnz6UI58pgkI7elY3B7CWIw+0Galt/i0iXE0GUa9u3SANv+2NIyZ/nXX9mkHY99tGJaKeclbUmhLewBeSzAk/l9XFeHh/ui7WkMQmnO9mfOK7I9hWvIuwBnoFsKBWwwrnI5X+WEOhX7gGB/uqZQN+5TnMrr5zx0q0U2Hz5HV4CM4F+h5/ieYCzBO7cEggCvYCiE72p2U55hDpID1CQwL1Pw+Z815bkQTcHu0WeG76qJ2xpNHKidoJwjkY6Y4SDeoC3OwqMiN+8F6Duza00klga47CFZJ0v5gXSS79lOIAcxYjkqZFH/U8veYxBAFDaA7D2BHp5QCfQFQjCyLHTrdQbqabukzwC6Im8nbABAZ7LKGNGCKKwPcW2PpNBQkAOEjRJxXo8xJpHpZOOECNOHw4PGemEdKw7W8QYQTxKmzE5aQ64LzI5I7iRl36TPpIaZaReJxOAnktawsoAkSNk/QSJ7DW9M8215KNnhyEiHQW8Bpnak4xEjKv/irJStDrZEjA0Av4jfV4CeDkreAQAjhJyWCACO2RhHugijEf7tSPQ1U+MP5pXySRq2GVauUPntc2Kzj8xeMErQl0e9xpDZ9Qt73wMXBJbzH9G0khmEcWcKelwZqCGXRlE0rnA5RKp46mpnen+tWQxco2cgTZbEN5mXAg5YKDF2D5Boo3Ochk1cNaQcUPyxPiGM0asSeoT5qJ0mdv5sYgOUEpB1YfH8NWVb8BgQT3Bmte1gfw+cTLSrSvlX9XbWzqn0khKLUfWRYwhI3o2Ty7GVxH5pD1PQ5hlQQnjH44MaWQNo4/m4fChYV41R4qmwImESJWKqhmM7UXgQ7IzFxJhnc05G24kWnVGsnaiHXPSUT/Zj27wP7C/nanp2zVEJ9Dfu0cE+nfeSQj0d7zjHe1xj3tcrJyrrrqq/fIv//LkKlK9cUWE60eE9Utf+tKdvpEnr3vLW97SfuRHfqSJwPd09pt5bqrW+pvfvLqe5DXXXNO+53u+J+790z/901EU+2l1bL55lsDtKIEgz8EWkJS883R+Exl4/GTb3Dg5RJ8TeY5uiO6BQ9kkVuIh2/gKb6EjVT97eW2Hg3binOwyQC+G7C7if0W2LYdRkNFLHtnorV1d4eGNbSNygJDvTvUDpzYwWBA9GRlfZHbW4A5SK9ssPTUfUQ6CSdA4gZ6Rk9UZdMIRaZb9Rf8nMr+ckS36eyv9QN9NZYLze/y5S3Ky9dRHxsZXKQvJDTKSrF1gKiJY/ZmR9Yco8c2BNEs9aoiW7aJKIZXBeKvWmjsa920Il9JHd9pUv5hn+iiZcH3p4HJ6jEWa+NIcPHoCXfrx125qJz725bb/4RctIuv1fDIUgfFwjIAYRm8He6pPKjPl42HO9Fv6Oc6dgVOzNBb91n2UI4pxZept048jIlf3ydFZeEe6LbovOD+IzHT2D5tBrn+N59xzCneFzn/jkUGSKocl4tPOjsBUko/GpB9dDxbW55xvzCVz71mhaK+cUXLiyBTAmVk69tihpQjVUeYuO1BF6IJFPKNDHBud59PJEwOu0Q8p/5WOXNmtDG8tSN4J5xScI2JcnCl2lsVxQPR+fz/lDMwxgL4jJ2w0OBG77Yjzh2wXiSsiC1j2P3BufG+Oy5Q2Yy1gX4G8j+xv6eAv50kR6DjSB/ZOTBTODcO+Cryl5wbZr2j+LH2Xe2IoO4aj0nBGUh5r6Xj38yZEpsjzzMKnbHLsM86xUZBD3hy4eV9bv/Q/rzplb/fPiUB/2R4R6C+aI9D3bI5nvLRnop0bPgslMBPoZ+GkzV2eJTBLYGcSGJHnKPUOOlGmnSQGzBQ+wVM4leHJR/eM+Ir+TV02aejZQXs7bsv6gvHHjUEo5iOgBSgycFQkSRohVjr9Wxq8kCXAyjJ9Aex4JgSqy8LnJP7dN5QgI5pPo5JHiupjGUqKAE4gA0EI0IXg43lETJD2MKKduzGVl3bvbJBjBVcCMJ0g9qXh348+9znLoRO1TD/dc501KyOGfohExoCFjCB8PVo8SPys1ca4fG0RXQBxG9jRwK9APZEmoykhtTT1rNNhAPnqWqIR1F/mQ8aRIGwtPT0RCPos0nJb2kDIVgC7e+9L7nyv5wGoZXzp7ZO+HjTnEb2x2drXbhzIeAB7rKusJ0eqfK0/UqvH92mswbii8QTRTTaCwai1FHluSzwMO5JFGJSoaQm5nWk2zz23DZ7pg7EgSGmlmTtPxHpGzWQacVL8xe9YHwOxWoYuHAHU5zKWZTpQ9gCRFLoPx4MikclOgPHHxljkuC0s39IY9GJNs58njK61N4dIjiHN+Ykh9TjrUs4DmZ1hiP5mUabTQe6bILa97rs7AsUeM8NTGADT6ITh0M8n9gO/RYLvV2aALuLfjb+st4g4y3dL9iEMMzq7iCBS6j9Pz55rMIj22NOZmUH3k3EjUrXL6DOs9ZCJsh8QyXDk6NA/0ijqmWpLbRw71hHoaYhdZfXZ7nXl8w+RHtOS9SJlhAoDZc5/Eg7rF79pxYv09v/YCfT3iEC/6NCudupz/3S0PepOQqA///nPr+htpWp/xCOSwOskqvTtqqF+5MiRSOf+7ne/+7Rl/hd/8Rft3/7bf9s++MEPVlvnnntue8YznhGp5PXvVT+Pf/zjm8j/r/u6r2tf+MIXIpJ9/pklcLZLYESg9zpd6GAn2+bNJ9qaSPODkJLdSwC9e+QE112zk/fGSJgTN/Qf7RZxPnqukeSoBZCTPZFReqXrDxZlv5WDcukq6EBZb9ixD87JXMu7VZ9npGR55Or9CrFapKkpvpDAQQqa3obTKrJU28II6L7lFGkZttCPIZhC17HFg7Ol692lQ5kyGISTE3+dou7YIzq0EogO/XXs1WN6famxhDNCRq3iNMn86/M+Ar2cHicix7mPKOSV5PlG6tDdaSGdTv30euZORoPH+CzIveNDHyFYJWt9blmIau7IKMW+TOfwzeuPtLWLzx9I+yTiiovVNcIEU44C6iu6LO8/cEtEGqd+G+nVM9NEjDHx+E03mQASOIcDh5W4ijk0zBnbI/9m7YLzhA8ju5LZKcJZNPeUdFxhhIi2TpL3yJGBCD/v3MXnvU1IMo1a6plRiv7LsVv6a2RMI/154lOuoZRUkcqJff1sLJya44Kk5ZwZLea0CdRZt8VBqq+S9A+nWOYwyrul835GcIcMghw3rIMc4nxgqnKe2Hplx7G96Fgn9mHvaETmsjx3dMk56+MU7H0gxpTNg+cEEZ4O8Pm8irAvm8m+tomjR52bdvbpvogiz7UeOC5tAfpu4/iAnXP9xfPCBkDQQpLsnJPYgXyLT32WSzPmozKFbA5l2HKOo5yWBwwQTML6T+y0ft+h7NOZ+uME+rN2OYW7ItB/fSbQ93TqZ7y0p+KdGz+LJDAT6GfRZM1dnSUwS+DWSWBEoBepSUotU+p7AgSggKFgx4bRbYBM3/1JY892VqUV0RrbGY6mQA6gMmQzwfIukUQGeoqMZ1CdYWbpY2fM7Mu+HfVF8qa+XIARA8sevdg36WQZoCTTk40AN0QghoJqP4mzAntWixBw3Dsh9HM6ZbAxW9qYg+qMArQ1RaxDPLE8INTVd4Ep1mh59WcN5epf1maTPPCER85hRMk05b09CjIPj3qISl1PfcS4P28soJ3pyyU3GVTKWJBGC/Xb64SFwSi9xtUvT8NHm963SMMHkDdwTiq43gECwCw5Rs11EbQZKa2+RXR3piOUPInI1bXqm/4mRb5kSvQ/ADaMTJkKH1AfxiURl+uDU0CC3HHUOQt17CMyEOi3ZF34zIKgSyUDGZ00H6sI9MOHCmyHN35PWqoRIm3UV8agNvWfZEG6QRkQ3KFhZESDsLV0+cxJnkejyPL+aMPwFmu0W3gYzZAlzhMprhhX1Vk0I4hqGIbDweZAArNPdF8ZHdPQEnLJDRJGYiLVzYCr54fhT0aS/L43rvm+dSOOPsfgMvXq8vdOvmtIIRn7hTUjQtsJ9Eh1n5kP1C7RF2qP52V02tqRm2Lc4VChNRtZBgZj+NpNIq0HR4WKepc8FKFOBoiRo4cPwiZzq1cWhnuucaOfzgaNUSRlEOiLA339DE3djgScQL9mjwj07z7LCHRFYysFutL93e1ud4uU6Jdffnl7znOe0y6++OKVyttTnvKU9oY3vCG+170iyVf9PPjBD27XXXddu+CCC9r1119/6xTCLa7+3Oc+1z796U+39fX1SCO/FXFOM3/3d3/XFK1++PDhU679vmsDmBuaJbALEtj4cO+8YhZ/Ig83kmDRV17zPByi8l1JOuftIr1XvTuW8MzEhf1H22Gg7frSy4+hO6GEukba+SUCPd9h3jevc7wtgU4nUqfl/ek60giDdPXSwS71zNSrIKNcHwpHz0yx7mOHUHN5QqLjSAtmKlwJUdeRnUUapvPopO7UEejlzNiDkRTqiJzbhkCP+bLMT8yLSFp0QjIIEUHvfYRAd2cAxgSO7Mek/gWBnc9w2RL9j+7q35XOZwQ6Y4UU19+BTXIg4Sg5lG2qDEN+LX0FJ6JTeqY1jSMydqX+KN0RJ2EcDPQMdMsg5jMLlfrvDp/6O8jZzGJFffE4GzJyv6Ys1zjkM7LQ83FiBjfq+dQFV9vgUd0TDhDIQw61KQ+flyC/j2eUuaKtqfl9YkjhLswncpX1QQpxPR+nbGSta8AS4Zi6nngqHXrVCDIqbJ2D09g9i0MR5xwsuX8SZxd+Yv0FjqW29mhhmZOBfa6Ic/AdBO0Ir6czdmAxy17HXKkpyRtbGDgauU+dH/oOsrsyb03tU7Mngb11GbjNRFLQwM+kwGZDKbvAQ7HvUjaCNVmfvBx1I626rk8HeO6Pc2BYQ6pzXk7mwlmsudgH6ttGpmkf5BVp3lkfymSE3aocH3zcvFAm3mVaq5UVYyiHMtgJ7P0b8shxan1xXpmczhYC/aVXPqQ9c5cJ9P/91R9rL54J9F3QArdvYsZL28tovuKOLYGZQL9jz+88ulkCd2oJVP3zXnen7tuIIHZRJWkySZy78tsrxyvEPWUk2pI8d8Y129yxoWlFH1aRPm4YGOExG5t7vnI9wJq/V7UfQMvGMPU8j/7EmFHtdZ7KlcrYUwmbAWZkkBCgylR27u1NNEQBdgOkFSmRBCuGBtIfb7ejRlOXXvZb3RPz2l3nZLna09+kDcR4htFA45NXPYYz6prF311qQT1HcoAcJhofwyhrzMcAySljkJNgujcMKlnrUPfICCHiOn6nYU7RAPoJQG5EO1HZ7AMMdJ6i39dFgccEuxozZHlEOSTYDGJfBKEZMiE1iyhPAxFyDiCakQtcqxTo+pFxh8gDyQ1DUER8KAKFOo8TmRdkkCHa4a53iwAAIABJREFUwrBzeJLHT3cwAY4VNW71uxVlXmRw1GFLIleR5hmdHkaSjBxX6rmIroZIxtmBqHFIc31OPXV1BwMS9fzYg0TcjDZyDgGjn8bHdcxpGAEyEiTmjyweeb6WsRfDKBulM9pHBLbViuSyMA5izB/WNqn8qIkec+RrgXUU0RZ571R6dd8LpOB3gp12mMl06KgoD8aGDEdG34kDgX2mo0B7LUpZDNkFBjI7U4jmcTG0kGMmfaV+E0GEU41qmcuBJWr8pbFPa0hp2jnr9F2UqrDU/XJAIEKFMY7eWateSBPRMf250huXIpWlMn2cPKPTtvusOYH+/+0Rgf7oJNA/8IEPtHvf+95bvnkuueSS7d5M235/ujXQVz1AZPQrX/nK9uxnP3vyEkWcv//97w8i+sYbb9yyn0960pOaUgnq5+abb27nkHVl29HNF8wSmCWwnQSWCfS8A+IVMgbdqV7ZqacnoVHvp60euGNMswPy3J+zE/J9O0F0vMXy5dukbu8jR0Nend43iiaf6FDptZ1DHxhglCUn1RzXhUdQNfVhMIHr1uUokJHYkGmOmdQ9v6cn0PW9q7OFm4hm5cttMDPZm0Ic4Lqt7llBoBcktInkM+q4f/Voa3eVnp8YBj1w5CSaBHGUd8r5wynVdc+RLQECPesW+9RGxqSsk6znUbIqhpsOKOGIS6po239Bgotcy6h5MI+cfJmPfp70N9/peuGm+EnnCemLlHEKrJk1ogv3pZ6suWf8rOPIsCTclKWlRCQjYzATRLfujUxWImRzfGQtYOHoO50f6iOOOMhOMhGWvMthi4TOccTeyuUSmCadeOKzTIUNLoyyVweG6HPwg+SnNPB+lhGJHeQ+Oq3068S8MV+av/XheWof0j2dUittfL+1IXspv+U2gICxixJYYRqAvI3fGdGfQx81PbKzZJmsnsB30jXWhjIsJKkcGMOynMUz0jbhjrD69yQW8kMz90DZWuww4rKDIqgPDpmw3HmF823KHtHLkvdNpKBfkOiB/zQPzAnvBDA2Kesj40UKbv+BtvmhT7a1b/+mINJD1vv2R2R8ZHgjpTyZuzL7m3AUzw5HZxxY/Eycetdh22EdjtYDGeoWDlKqT194mjMs+75+nzM3bbtPGRHoL9kDAv23ZwJ9O61m/n6WwCyBXZLATKDvkiDnZmYJzBI48ySwlMI9wKVFKzq5Ecpupg326L6lYfVa/RSSsZt2RJ5vQ8rv2NC0zRw4MCm7Q2eAQAZu8BkZkwyleWR4D3q8KyHnHlDlBW54ACiWASMNOjFvFi0KMFlKpT4B4PBKdmcIwHN0wVKTOcnFMAGgGIROdZmvsPPQhXFkuj0Ew6X67MalAIIJgIkeIEp3sp652aNIbc9jPKW7zwcA2iOFId9lWCAVur6PqABSlWfDpPLTn3jy699cV57xmeqaedE4/fqyjlBPbHOIWg2Am3WfMSzod6QbzOhoCbaAqW0k1quMOUTbBnmexhfqoIfhJqMXIuVjGng0jiAsk8wOA0qOI8YrY9f6wuCFMSdlOhhFukVRTilDrbQAyxHJMkSFBzhXXbRMPel1rcPzXaR5RPWvDWC/fjKyBCcQ9lJEs6gOfaYfJOWjk7UYg3wP+x5gfykt4traQMqqH0S6lNGH6ACR55mmM/Z5ngtTZ9yJwbEhxh2lAiy1IBvGDTwHZJCRgXF/W7v55nTiyDO9t8G640Z/hPMda2SKQO8jMUjXzxpeRbb3htb+vZNzHtEHWrdhXBmcYYb0gR5lMQxqiJLItRIEeq5RrZ2bbm5RBx1DXNY3HQxEi7N8SGm5cLipufQts1NiIoyA3NgZzjhTyhsn+xAOCEMKxfV7/7+nesreZvf1BPo37HIK98//09EGgb6TQVG3eyfXrrrmVAn0F7/4xVFLXPXJ73Of+0Tzn/zkJ9sf/dEfRWQ5fXvta1/bnvWsZy09/oEPfGBTNPeFF17YPv/5z285hKc+9antD//wD+OaL37xi+2e97zn6Qx5vneWwCwBk8BKAl06IgTaiKjJm9FFi3TdwsGqf9/y/C2disfqzNKkFWHq755VD9rBlK/CPEX29bjJ5DAVOQiu4r1X2KUb16hZ+6PXFUf6WBLAPqyRg3Pqo55BpxyRFymU4/bQ56xsmbdJH4pQtPq+PYQNB9p0mI37tiHO/Tmuf9Xnq+73xZgXh6z5dzfXkJWktg9Sl1rJ1G02hwVfm+i0GksQwZndaWo5gSUgpYlGh1hO59a4lbTq6ltFCee4wHxEkKNbQdi6ru5t9n2SDimiNMpRYevYN2S5khMaaavRI6nz7cQ884+zLo7XnnVCuqf6CKaATIyI8kzdLqwC2UlkONOoa6iTTmQv6xIMPBobG9V+hxyzPrnuEWZCt11fD305CHRkrRriclL2yGzmOByWcy2DNbAlRP+yTBx6ujtf0CWX4bDJEh+mMwTkMfuuMpVlpPToTE3iv3ducZkogxTlo6gH3u/dWAMdgY6jNU44S7aAdIZ2xxHGw1Eb88jatcwPXCeZRIa5dLTX35DsWUu8htLv4am1CP4MO0A6CZcdZ62tyelC/Y0ABPb14MwU0ei5v4KcFl7/+Gdae8ClbU0ZsbLsQGBKrclblAks14LjsD5CBJvN6EzrNiTOSBEwIIyfGepSjovo80WyCUqOFbbCeWBtra1fcuaWu/KRF4H+soe0Z+xyBLoI9Je86Lr2qEc9ql1zzTVTp/L82SyBWQKzBHZFAjOBvitinBuZJTBL4EyTwCR5DiAYAZoEM0XeZTpiBwanMrgdEeegDh7Qs6zZyG4Q6E5wO8jxsaHUA9LLkJGgqDcC6WPJsgwzFn3o4It/qz0nwRz7EqlKlCwWECce6WsApPQAX0VMF4kzUcOeuXak1kdUYKAKQ8XxZbJzhPK2YsdHyHK4a9IA2c11GStWtZ3OBQJREMTUqcOwUX1Mg88I0FHzsIS6qKGo+0WUE80eXtppXCNlmdfJ5nuGum4GGRkYiIpl7qPPSgOY7YrsXOqb9Wv0ZRo+NGaBTgHkc88b5igjdiNFu8SpfmSEdoHocDLQfKaRZZXBEHJZY4NAJ/KEiFn2pcYjkB3R6sesnp4MNaTIzFrwGJQs8qTAcjgNZBSw2o76allXNyPPgwwVWQ256oZjItcj1WDWaosI8WzHa/jF3rWacUG4dxYL5sflT7/diCLiGsNZnbFpSCMVIA4fIsU1V+x3NwT5uRylCDLynPT+/TkMiQ0Rr35kvcqIutYPZxoGO4825/zROorIlTTsxB7NfadfEAeMmT6T3q8MJjnH/Vr29vw7Ul9SUiH2z5CWcKiDnrXQ0yAYURXaMmWIPB4OC5WePdsJQ4uMomFw3NfWlKIy/z2kHEynhJGBbMVLxj4elx7ww9sGVecuh0F+pygOslEUweDCSIcX3b9vX1u/8I39bJ9RfzuB/q7rntD2gkB/zIPeuuMx314E+le+8pVI1742SX619uY3vznI9Y2NjXbo0KH2D//wD+0bvuEbRuP65m/+5iDcRb4rjfpWPz/1Uz/V/uN//I9xyWc+85m2G5H3OxbyfOEsgTuwBKL2+dRroMhzO/PR+cAEvCO9ge1wy5QzWcl3+/dRXBqYwgjT0Tm0qgMrJnHqlVZYSS9ey+hUarmlWi+nOhoyx1939OPxFdHdOfnV7egg6O8dWdxfN5Jdd2/oOElkexavJVGQ8SudSV2ejg+DfFuRYUvfeVRpjD2dJZkzdKJJsjw7VUQYnZzAx94/J+qnYBNrVXqV9F3pJPrRuNy5gOcuEYg9Zspo4Wgj9Ufd06dnhyAL4jnJYyK6iTRHryaaW7qeO0zjSIseq7ZuHhwj62eJqJ1Y5xFtnoRi4KX8OSbC8OQgEyfBiRzHiRU5BWFJtHTnnE0/yGSFo7T01nIIyBJFkleQnHKKPmfAKpDfrJHAa+kIXedMLKRhnWW5ojoLKEGlazUeyVL3h3MtqbFbOinL6TcdKCgLpXFF7essJQZmZK3hUI5zfeA2W3A4f7M/Ay9m+voYU2IKd8jIfTHO2GX7a21fWztxfOFUjeM/+C/XbDjZqi/g8ymTEs41jkX1WTgpW+Y6nCnCOcAyfelvcKk7uo/sKgRM2H2cj6xBjVnroTKese9XnM+cOawByrxBossRwjFTlfHCsVzzKUw5YOzAQpqjwJkn2qbsBWlvCjlG7XqR8lnGTNfhkBDz1TnBg3GnCHTWcjikZyaL2ru2iatkVtqttAZxmm8qO6Ao9Nh4iwCKdB44G0h0CPQX7wGB/pqZQF+xceaPZwnMEthtCcwE+m5LdG5vlsAsgTNCAiMCvcgQi2h25XUECFI53ckozE4yunzKbrNkYJ66yNGOWXNWtVeE9g4664RQXN4hKwj2xKVL6QYrCtgi9T2aYqsuuJErvLm7+n6Mj0hmb3eJbAExUku7rwdmqRW5F8ALuVNArzMIFsgx8QRIzrRcNUYzZCXg2sEMLIl8y3v6Oe+BMAYpgdFImZ6Gm1H0eRp8MCxN+mfkgyrqJIlPRSaMjAKkw0+veBl6ylCQbYSRQh751LrL/UYNdEA4EQa6Xp7eIus9whig7QasMMoglCTRdR1tUecwjCdppDrvvEUEr0A3hho3BmCQ8rkPYK9ojYyG0N+qF02kCucFRq0kbSsV/8hTPveLjEDUNA/jTEYf6OuQ/ZDifkjdrpRwaXSJCJmsWS7iU5HppA0kRV1EwmTNN8maCAKMdBD81JaDwNZN8d3+wbAXnzuYz/Xjht5wXCDd90B2Rl8xYriNNwwM2j+ZKcDJaDzvOYr02Kqb2hbe/lUPzzwFMMKxn50kXz84RMJX5EjeJ0OEfiL6RRkTvI4rNR/TGajOqFxybiSKtTHMTRDZMbbBMSgMcXqcG1CXjMBdWkRqJWJEUvuZkr1I9DAUam2cGKImYr8MGQnCcUFyw4inGn1R81zGyiEzBNkJwhi0f1+mbve0sH4SdQdP9+eYROe+vIhrp0gM0lzG+6VbY75mMsPF2USgv3OPCPTvTQL9TE7hvpP33ktf+tL2vOc9Ly59yUte0n71V391dNvtEYG+HVG/k3H111x66aWnctt8zyyBM0ICQaCj/3Mml9NjZuYhn3FPnPd1eXeCgdDvdoKNVrXXO/1Wm1N1grcQ8xSWG302kbIdch1Z9HprT+5CAIUXXFd7vH++/032K4hIavg6JvOhQYyNhsszUx/qydYl/LeoL1zNuKx5j/ckM/ocEbBT1zG2Kfzqn1XbU2y46x49bqYTJgAuUZvoc0Rj48DYr8cRIWhtlaNE6jKh+6bu2EfR6nMiXaXj6XvpOIGf0vkjrklnSRxonUBX3w9k6S316SY5DStdekZXs2enlvfIKVX6fm4kiHL089CXNzOV+YAlN//719raZV/Xmsj1cIroydB8oOtvOOzGFCRucfwp3AMe0RjPUe32zAqI8zwO4ZoX4UM9XBH8YEuISvcekM6NnNH9zz0ns2KdDIyy9pWvDuO4+12ThGwD3tIzKIvFGhBGA49EZLZl9ArcmdmhcFT2vkwR6F6znf5nCbLAT5yrdf6aUHHYLfkPlw96fjqiJ2YJJ9vATHa++NlZZ1TOJ+sT5+GKsM5IcT7XfVPfqb/gJRx0eIkkLqqXitcTLyifWF5y7x17zNQzdhRR2vl0BCELhPpHKSvhnPw+MrFR2uvk5hCRrr9xatB9G3JEBkup3cHBO7J06SecBbrsFKP30dTLqcM5sT4zW97Sfl3gp6XIc86KqXOR9wvZzxQzcIZHokOg//oeEegvnSPQ90SXnPHSnoh1bvQslsBMoJ/Fkzd3fZbALIHVEigCvScPg3TBczQ9+/kM4LYTwU4ZXFYB2SUD0aoLHYlmJ1bo5gNG2erLvL+PznBDiZPobgghOrSIbgBeJ5ggh9JY4cYH/h2/03ADmR0gsDN4aBhFanUy8JpfLjYHFBiWpsJnINmohc4QisAzAhnQn0OKSz36PBwBEuy7vFbad+yLrWxA/lwX8Vbzy7rGGEEadkA2IJY2+n3AM/ne94DAXp+eUM/xVNZEKwRhm+nlI5oiDT0Y+YgEVvsRyUT0eV8PUkYKq7/Wjx3Dl49HYxXAxBue+/W5DETUkuYeTynfb51+68V4lQr7XEtJmNES7uUeqeTdISQnsKKV8qCQF7nkSn2/rHMX5LkZQiMFeqbDj7rUGApoL6IH7Bmk64ux5Rdek0+XynARpHemma/9Qgq/NEbgXU9ER0XJZJp6yTHT2VWK73AAyPThYZwhMiWNg0HyJ2kd+zfPkjCYDJEAYQiij5xrMmqEE4c53NTeMMI29qTGnVFcItBlLIs6koNjQfxIjj5PpN7X8aSa8e4c4fdxfvTGVRlhVEtRfdfzkH1vOPa9rXnxCHh959Ep4ayTHVakTBgN01lBBLqcJzJtn8YXctf650xX6n4cXzKdYZDsMgZhAOz31dIrxMnt5RfhMoHeXd+3z/7I6Pk6P+v8MaKD98DBA239Xn+0/PAz6BOPQP+zPSLQvy8J9Nsq0vpUUrjvZEquv/76iDpXlPxjH/vY9va3v3102+1RA32f9uxO9KedDDBUsbV2vI883OG982WzBM4ECRSBPtKDTafQ5xGhyDsqo0hvLXk+whr9C2gC00zBHPbuFIEO1tipUJd0wQkcFm3l54VvTHXr9YRVz6Zv6HP+KJeL6+dOTJUeku9NT7c80vW7MioQvI7DGPfSOWhR6E7ABrawklerxqgx4YzZY0F3wOD+KSxdspgATtthqVWwONTEvJlSVNRI7slh+obOuNV8kkoZMtUdGMAmkrGI3SAR2zgSXV0SkVxr+mRrx0S4pW6r79dFPJuuJP1VOq3aDGLT/Iur7ziAmkCUAUrtavw4HQfeyxINKqVz4mQ78eHPtf3/+uIhknkqor5fq3omjuojh2LDddRI129fcz5fPkfhdHxsGCdlynSfY6awQRj5KvmKEE/dGJwinAWZOo5Ut5rVuieyjCXGl/4Nbh05FycO0nwQ8U2fyl5hhCvEv2eBcPwXuGm8aIcoaat/npguPpeM0il5cJRN5w05DkteXIOTjjftZ2PgwI4o9wh0nCGku3s0fuAp4W4rYVaZAyc2Nc695aRPBDYlIKgf35VeAA/0e1BjkAOzUvGzdvVYPodQTqfjyDoQmQ3SCRnnYs50nIz1HEWxVyky1kYfaT51GGxhiyOjgttaRk2sDU70/ISzRdqawi6WGRR8jXAGsu7S2eNsIdBf9LKHtJ/e5RTu/8erP9ZetocEunCMnJn13wc/+MH474YbbohZe9rTntZ+7/d+b1uNQ9c8/elP3/Y6XfC7v/u77Yorrtjy2qNHj7bXvOY17eqrr26f+MQn2rFjxyKT1xOf+MT2cz/3c223nHpnvLSjKZsvuhNJYCbQ70STPQ91lsCdSQIbH35kDjdJcgjXMv4k2YNQzItzpZx6oOcXbmXk6ZTl5fY7QhnFvleYl4DQip66gcfxDCBj5Cnc1XsrgOXk74TFwkHfFIGoZ/GcnlyHdK80ZBPPcqKt5GDp9p046w1ao76lEQEjB/IY9SlTiek7bwuA6aT0KiMXJFZNyRbkeQDDRf2twbs5jSBThomlnO8GuCHGo+8TRhTJOMC3pWYrAjZJx1hXGc3NGom0dxlxEGT5Ir3Z4OGulHppwMGIoXsj5V+mf9fnHmnsEegx/iRTBfplVFGb8hDXmIjMVT/okxtu3dikuYVoRf4yEEEa40WvttU3ojxEZhOVTv1A97JX/0QAapzqm/pJajwZmoK8tDVDekP1wR0QSI+OUSdSR5LibnNIKSei85z1IYr52Eb8uwxSdW1GQkSE/2BEGaKec0I5HyJiYqiJPooaITKCs5D9hzENQxkZBHR/GIkyvTsGkIi+tvSPbpQZnZFWv1tEcxo4YlxKr0dqfCIGIqojCf8sHzAYSLLWZJ0buV5Hxt2UgQw96n+Nf0FIY2AJmUCgk0XACfRVeyn6v29Ij+4R53U+mCGvX7Nh+MtIfV8b+flQgzEdFSDZlZJe5yD1OnPYER1BWksZhzBcysgYcss0hZ5Bgj26hRFn8m0y8V5bkOjdl26Q1/7GwUXy0sRqr0U6/jR2UQ5CfaZfmWbzbIpA/9M9ItAvv4MQ6Jrue93rXu2f//mf27d+67e2v/3bvx0ttSc/+clRL10/X/rSl9r555+/QrFp7cEPfnC77rrr2gUXXNBk0DrVHxmEdvNHBPoJP/93s/G5rVkCt5EENj74HcOTpCN4ZpfjqZ+eo4jH7IzeTUUQruhgT872jmij21a8T/ya0vs6nWf0fusZ2S3IDdrmkl7H4J3bkzirdBHX33vYBMmOw6Q7N9MP2q3nLfTEIvbqWlK/uyP4qpT2HXjtMWBhJpMd+kovc+lMkzgFHwNbO3rvbyWT+L5bO6O27fsec5ZDw8Ta6/E+Ogdru+8/Y9TnjLv2gaXu7x/lUaXo24F7SJOe7xmiz4lqVsYnRC1CmzriQYwqKjZ1QWSjawITpQMDuEPXBokuzJSOrMfyXtZuEKwWfa7PPaqZfS6dXriDuuh6hmqjq++U9CJq3zEgjsvI3J1h9SwiwnWvk+escc9wVbp+OtjqjFG/hLfcQT5I8xOL2tHCZdhwkOvRo8NnoX/GZA7OrqT+ThK6FmCUjUrcxNoqZ+JcA5DB4UicmbtCnrm/fE+bHh/Zn5Ad88KadIcLZBj4Iu0CctbNdO9rzEc6PBRO1FypD9L9IwpdayAXj+OAPuMa692xiTuTgAU5XwPPp+OU+u9Ov0sbOQcq0UD0c77hiIWNAPuJn8P0VWPXeF2/AvOA26u82kCsUx4uHKPLEWFzkE06fAe2Yv8qtTvOE2FHyUU0YfqKBvtXysj2kucjdpX+HcjnnH1aG+BQMqSRMY39w7kjp3ocJuoMo4b62ROBLgL9f95lAv3/3GMCfSuH29uDQFc5LBHlH//4xyeVr7vf/e7tda97XXvCE56wQjnb+cczXtq5rOYr7xwSmAn0O8c8z6OcJXCnlMDGhx9h0ZqmCYdinsozmvBODao7tc046Tsl/WpnBXle5HJ3805JdAelfdRAkc+07WnPJ6LNJ40NAPnsv1/jhGoNz67jswJLE2nXMbwATEbEvj17ibgGzGbjAlABDrOmsJOMbkjojUT6zqMo3LA1YPGsAdcZk0aAizGnnMPwZIRrpUfL9lYZpephU2jOEGfJ1VGoE+gZKU5kMb8lHwHLAJ4i2zNtvboN0Ui9wCB7k7QVAR0p9nJM7CE3qjphpvGRqhBDkkhpALKMNB7RrnULaPYagSzbIB/TWMWzMQjIWMFniANPcEjwJEJHaDgIadUqTIOM2gliV58dT4K/DakOifTGACKjF17/YYTAkJgTc/jQ4Amv9oqsPtDWlCJRbROhrDEokjqcPtIAl57oQ4r31jYlq95pwJwKwhgQzgGWMcH3JXtCjUVaxXSywMib50ek/MYBIOUd/QrZpmA5y+J3d54ppXgYpobxRcQE9x/baGEQktwO7h+IabWL0ebYsbbG+krSfTDs555m/7rhkkwJ6oY+JxpDfSMSxDMdqD9utOuPY5dZ1JIf5iOiur1cAudtTxC400fIPDMxuFNOphgsZwiMSxh6MJxmBJhqIZYcKpLFakpKBpLdyLhlZ9DolTJiDqY8cMZ2y5ExyF5GvgbUJxk82WfUENS4Yi1lHcCQjRvb07C7ttbWL/pPZ7TO4hHo79gjAv2xdyACXYT3F7/4xUkC/fnPf3578YtfHPP9vve9rykifepHEd4i148cOdIe/ehHt3e/+92nvEZ2GgVyax6giJH5Z5bA2S6BjQ88fCDPg5A52TaPnWxr52apEPS2IAiNTF81aH9frCTPu/fIdm25zo++wmc4ZU6+41Y0PHoFduWlRreYcsBY4rc59vr1U9DOHYNH2Mgiux1rBFZzAtf1fSeX88FOqoR+5LIl4jPHOCJ9LB256yw4/jEu9MkpUdJvMg7hFDxoTHZHp3P0sGYk21GCpgXp3OPwkdzNsWCrzdhjVmQNrojsadk5PQ+nwMAxIvUys5Lp3XV96rSlV4fT5smBdJUOqfrl+iGlutoAN0XN4y67Qeiu5oBJtq9Y/ypPlSnh6Qt6aziTygnVhMwzIdXRXRUF71n4fFwQlJSeKtJX+CudnMk2hr6H7MkGdei8xZmR9aWH6HEcZN2RPLMd6HlyYiYdOIS22sYpIYjsJEbTsTeIcqWhl2MqmCl1d2VyihWpSGNldfKIY4haTw1eng7pBBv7Kon9ILzJapgZpNgzYNd0PI5yS6vsN0acD1m5TrSofZ24JXDjvv1RBz3wt2St7845J8s15Tkhx2Tp3ZHxCRtI7j/WLfNS/UvbCFjane099bxjL7Bq74gzWrjCXPmOSJwy4J+M7AbHrUrdzp6IZ5ndIfZGOjMfPNgR38NcLEj0xBo4PDPmnKNI5W4ZFytzmZ8bPQE++m7qgOnOXD+r6qvORqOjhqx3vFOEjcvuknsjHAZyPhMPRg/S1rF+6X/e6sS73b8jhfsL94hAv3IPI9CdQFeU9wMe8IDKpnUqBPrb3va2dtFFF62ck0suuWSlM/GNN97YJMuPfexjcf8zn/nM9mM/9mPtvPPOa+9617valVde2XTNoUOHAlM96EEPOq25n/HSaYlvvvkOKIGZQL8DTuo8pFkCswTGEqioClO8K9LR0w92Ou1KzO8gqBd2Eb0rZqGeYYTylIIeADgV5fiVQACD0XaT3BuZnLSe4mGrvf7L1P4BePx28szTEk/KsHv4iPTujFJT5Dtt9sYB96zuJwtQW/fiMJEDDTCYhC7rwmUKsHfv8AKL2Shth1Ep6z3XlK2IrChQu8Uk6JoR6Mo/IBYBtRmlGwsFY1j8NmJZ/w7wPVErnnXl44fkgpylLQwrHtksYwUR4mqjjC8yvu4fIhn4YdxlgDEStH9+RGAkSaiBpfGgIvXVtkjnAt5p6MJZhO4GAAAgAElEQVQrPZ6RdctYr752+r2jaxhHRPtm1AXPhtiVvEVKk74eUlcyIdrdSdWYxzRsRDp4EcSZei4NNIOBJ40DN988zGPUXheBLpJ2iPwKwlyp5oIUztp3ZFXItqifHv0r8jiNpfJcJ8IZA0+uozI2xDx0qfV9P4bccm31Z1bs6YUBLGrKHZBxI2WZETkxDo0xUuodHwjyNKRENIXGRE3vW0S+D+tgSJOeqf6IWIaIJxqGszHWfEbpE6mtrgdRnVH+tKFrRxHuZrj0SCtkxn7w7evR6/0ZxVrLtSkjSURFeM1zXXPgwGD02X9gSMOe5PJgODOngjDqLjJDDCQ+e2k4NwaDIBkt0hDphvSpI360J7oXnP05TuFOqlcM8zgVJZlfxD/7I9cHZ3Ws0yTRI1OG1tYQPbL+DW/c7g13u37vBPrb//oJ7cKLDu1qf77wT0fb4x781mjzjpTC/fLLL2/veMc7RrJSSvfHP/7x8dlVV13VfvmXf3lSltdee2175COHzEK/8iu/0l72spftqsznxmYJzBIYJLBx7b8e/oH+G46PSeCF3rEDSbmOULihv88a8neULpsi3EdZVawtvUYgz50YdZ2m421RZ6qVntAuTDF1YzcOMM2UXJyoLlKS9x7t9Dd2uKiwYL7/PetS6f4GAgo/2js55jMdO+PxHTFPxGfNG3WFIeJwdN4ijTs6oJ4V0bqQiiav0ImNmHY82Ke238Eym7wE/AThxNgiqjf17f5GSEF0PTDTTbe0dt45A2kJoQXgcuITYlBDky5Dlib9jY6qZ0bq65T9unAKDtmpRyl9e6zb1IlwOtZnaqfP/oT8cCRlXJGmHgeMJP2P3LwYtWSEXHRtjnfzX25sa+cfXnzne4g+pP4+OBFkNjV3fkZn1RjAqdJvRYZLv2WN1RohW1aXzlzYC8zI+nVH1EhZnmSxrgUbSybKzpQkefRTmbP0N7gxSHkwS/5GhujUrivrejBXYI8AHDlXC7GiIy+I+g57dzYc0rUHJlAfhXUCZw4lrnD8pdxXYCrI9SxhtoimXlsQ6JzXOGsTPS6HA3C+4xWud4eGyADgjvXpOAWu6s9aP1Qdj5Vjk51z7Jf+CETm6XgQjtfhEJEl3vy9cFC46cAQWW4p66P8FfPjtpRNpXFPx+pY/306eavp7ueDv8uWzviJQ7/kar5DZseK8mwnheeH90BlEcMxJ0qOZQfoP+MepctPPA6BfpbUQBeB/vRn/6tTPd0n7/u/Xv2xduWL/qY96lGPatdcc82utq3GXvCCFwRprf8uvPDCdiolrzyF+z/+4z+2yy677JT6+cIXvrC96EUvintf/vKXt1/6pV8atSPSXE7Gcjp+zGMe0975znee0nPmm2YJzBKYlsBMoM8rY5bALIE7tASGVO5JlkJoacRB0iR43coYtC3ZgJJb2vFqefpzJoFH11ZPoLuyv92s0f6WZLk3suLCInx1bRpo+nGMvJB78teDD/L+kZEkowUgfCoVWhI/eqwBD1JzDT3PPo+IdAMdkH9lEJkw/E0ZtxCLGyTKIJXPDICeBkUngeqejkB3EMeYRiRcJ39FPnvNOwxeESGepFmA2G4hlGEiDRFE3oe4SPdNHTm7l8gST2+nz4LQTC9yPSvI3owYwPihZiry1+SCQQWjQ3jAp8x6ArbWWRrp3LAShiSlDDfZE9UMia12ReBHO0YyhqwByf1ezb8jOiTT3kFYhqf/YNCIFIAAW5Hcmu9I657GQSdaYz1ZpAKgXtfIeCQDiYhx+i8DAEYdGZYiLeHBgUSvdpX+b6h1XTXf0jgQdeCIsnbDIAYO5FHG6dxMkWpf5x/11zpifDQ/tuGX7Lw5X1lfriJKmHOJmL5kem7JLSLRNW4ZioiWSINfGBOI9g7HhuGMqPreOHfgse/GSZY0xq5uDw9p8tOIGmuMWux99LztDdZV7hH1PbIGuIGDfePvmP6MTxKijEG9sRnjGfu0HAvkeEFaz3QoSQK+yHg3hMVex0C/XF9xdGKM5nPFi9A+XiLQkTNGOjUeezJ/y/BcRmYi3yxCzM+wNOauX/zH273dbvfvnUB/2x4R6I+/gxDoL3nJS9qv/dqvxZwp0vx5z3veaP5Ut08p3r/yla9EVIdSvE+lS/yZn/mZ9trXvjbuVR1CGbLmn1kCswR2XwJRD931Gz0i3skTOnSvy9X7x/o1dY2TT5PfT4xrla5fel6XPjwwk5FcUzBniR/3i3rFvevTFHHeOw5E37p3K1GehVE6x0U8aF2HoFtOfMf3XR97TDN6Bo6OnSCqe9bPILB26CzhYkE/QzevR1EyyiK7wbjoT0uAZou1XfLIf4BBiMzUY/77V9vaRXdNXZro1y4lO/IpvJX6O3rL125um0c22tqFdxk6Q51r6S5gI5w7WZ9EjOt6dExFfENai0CXfEOvTwdsfad2FL3uzgxxj9kqXO8cyT2zRqBXBpmc8wkhjFMzZYGSgK3rhHsUzU6daDAI67DaTL1Z0c5Bsiqr1saiFBmkvx4P8YlTaxGdtm7RF1l+0d/9g3z83FHfap5VFkjR1ir71QYHZRx9cI6vTBqJmcsZgfJSyopEunxPE27OoYUp0pE15iazSU0Q6LFEtjrvEosVIUz7gbfzvBKmC0Ld0qVnZHXgPTl16CecC7I/chhI3BVR6NgsyAQV+CUDCQgQ8PUTtgJbL7H+83p32NG+dpJ9aYum7LQmYi8aUU0EOO8Id6SJz7rMC4H9rdwbzuG1HofvFqn50+G8HF1wYE9Mkg5EQwms7ln9ecK4ejK8x3hT5305V/QlKPIIkU0h50XOzoF5cU7XnMpBgrOldyxDBr4u5Xd/6Zt3XxHY5RaJQH/BHhHoV+0hgd6L4vYi0Dc2NgIvffnLXw689JGPfKRNpVh3vPShD32oPexhD9vl2ZybmyVw55XATKDfeed+HvksgTuFBEbR5yjyAiUi5LYizl2Zn1KQ9f0qkqkU7/yHGzT4DsPB1Cz0ZCIkWHCD23R6S+LcO+KpCvtOmMHJ+7vK0IQRCUNANTdlrcovIZ2DVB2iURe2kwmjEKAFUBfYe4VgnbAJmZGiLEEvZDRz6IDIRbEU4Q64TMANOOwdCHpCtYhfa3wrBwoZGqgH7SgPoxvGmr6NGqulA9QjAYsYMMKhJCNUfa1KDnim6/MA72aE8chZjwKgjR60QsoD8EfLzMg09mV8P2G44D6iBdR/0h16ZD2GsBoT67j7verku0nkuIwMeQH196ihFw4BGQUda6zf6Glk5pokfgdCnRSog8EjUoAHGW/15dW+5krXIsv0vq9sFPLEV9Q2qe8FtjE+al2LRPc9QtaCfo3rGpH31KJnP1F7LyOZK7rEZeoGUV1Punj2I5EdOEv0jiiKto608UN6ybUwBqXjhYwhGjtRJ+W0cXIRleH7kusimoGI5jSMsDbCoJjGxazfLgeGcGQQQY/Bl/NA/ceoFvsga+qFoWZI+xgyJq0l+3Ar8hz5VeRKpnKnLfab+hfRPYqUUL3BtYxM3x/jHwxzGWEec22LeenVYOT5qI/mi5IGu4hk17OI2LF2a8xlGLQINMlY+0NrEsM+76iIQsp3rc6eqO+XnQwDXkYd6aNMMXqmpyBE2k6g/8keEej/4xlOoMuApJrlD33oQ1edqO3Nb35z+zf/5t80keTnnntu+8QnPtEuvvjipes9jft2ERXf8z3f0/78z/985TP38osbbrghjFX3uMc99vIxc9uzBG43CWy8718PEbQ6k3lvrmf5kl7fmuol+keqc6W7oN/1OufKkfYvtBX1tNF7o52udjvv9Pxq6VEjTNPjFfuyx1V915aw4Ap2ZgmfORvaja/vLLgFspGMQn23i2Cntna+74PQTkLLMeaUbBw3LeE+c2qenH//0OQAfgH7Tuku/lnIyhWRFQtF1+HI10Hc0tF5tmdLc52WaFc+A2v452Am8GWtrdRlIMjJoAQ/DCmu+85JfZ0IdP1dKaST4EePjN+jTTT0DrzpJG5vG2APar5vPtY2bznR1u5xeIGZQ0e3LGD6t/RwHHf1DD0/nFp9w/LvFLTWoHCTfnCYwAE27AGJKSH1azy26CBuwS+e7lv/FjEeRKoi908Muqb0funJUe87ifsotyW8mjo7j9AzNZ4jR4Z2pK/SH/8d68hSeo/0+Rw366yCGdIO4ZgkCd3JsyblERmiRKA6ocx6CucGr8WeY1dWMjKRSQak8dceWV/POulpI4rnZLS1nJRj3bD3kygnvb9jkFpTeX1g13QcqPWeNgXaRM93YhfiO+5H5+/KavXLaspcpFvVfvyXa7bsAJbKPR3f16Jk3GZkPyMDhpyMi2DfkvhfYV8rMjwXVNSkV3a1zJKX9p5yjGbMbuPL83YgyltkCFM2NK2BcOgGbyM34fxbbmmbWvu2VYYU+Hku5nyuXzZkqjobfiDQf+1lD2lXPGt3I9Bf++qPtZf/+t5FoPfyvb0IdGXwetzjHhfd2WnGruc+97ntpS996W2+RGa8dJuLfH7gbSSBmUC/jQQ9P2aWwCyB21YCEUEB0HWisVKVTaShmyLKd0Seh0o8HuA2PPeYLLZby7CRWnMYsKiDvF2jCeac4FryEsgv3fN3iZSHcMR+kWAaRX7JoGI19WooZgzqbUkQnQFYVUsO8nwV4d7NFXUEPdIYoD5hcwvgpedU/Sg6acar3su3LukMOBCUZQjDw7izBkEiRv7tbGwr0txXD2s0gC1e0mkYhIxcRZ7jga/2iMQQUNcPnuMJrpd2JJ7eQQ5mTTk84mPuqWGc6dpJV05f1G/WEmA9HtKleqYtDFJuVCHyguh+2naSU+15ekSMR04S8lwGiTEr5NAtEuQcUegpq+hzps+TwYVbMBIwPs4Y1rST/BDhMqgJ2GsuzzlY3uYBwKtuYBKMkmmkIkwiNVIwZqo3QLf+vvHIIsoX8tvXBKS9k9jIIg0SQWJn9FLVOR+FVecNAvcYluIsWjg/xH2MzY2z6gvZC1h/7AOlBFQqexHlGyLQMcAN8xrR6USrYzQ+odp+qulOxHvW0WPuyRDQn2s+L5r3cBpII4bmknkdpfBPw4gTwRhy4sxRH8wBxSOuVu2tTvbxXIyUXm8xMxRo/BGdL2lnFBiEuqfKrz0cfZ04P6O0RH4ugjwjCEkbWRErqhdp0WaVAj4MO/uGtPMQ6MqkENETVgsS55LY7jEpQ4YDUhHqjNd4mVei5uycWL/3mV333M9LJ9D/y19//56kcP/+B/+XeORepXB/z3veE4Q2P6pRTirA7/qu72rPeMYzRq+IK664YvS3SGylB1Ra9R/4gR9oD3nIQyIyYnNzs33yk59sb3jDG+I//a2f3/7t324/+7M/u/Ta0Qdf+9rX2rd/+7e3v//7v4/vn/WsZ41q+ildu2r6qcbfe9/73njWbfXzhS98ISLo3/jGN4bDgH7udre7tR/8wR9sv/7rv94uvfTS26or83NmCeypBIo8V+3zI8fb2qEDrSnNtM5xJ1dWYQZIsx7HRK87vcudqeq9wfB4n3VgwzEdr7tRtF/qzKXjddhhJWmbXzg5Sldc11+8cId/0b1eHtXOxHsZfTMacHBkWXBGr/KeFc4H40AX72aXV16Pk2u8p5PI7C+rPuTA0Jf058i5sgt03wEULVGhH9W4Owwajqbm3BxisSjxXq2ZwvWQeC5S/q32+6hNOgfO0O+IKM9Ib5wjidTmeq8lrs/kaBLOvIqGVsRwCsYdTTR+EeaSrfaSIsxDr5UTq3BpRvn3jthB4HZqXTnnGlEa6lZm9sJ5Gzy8sRHR85s33NL2/at7LtYrZLHGQKQzc0/GLuls4cid8zPamrnGdA01ztnDPhe9E6u3UVgRQjAzlKH/S27SgSNzV0bohiyzlJYc78PZNXVL1VgH7yBfnPp1nXAGtdIhY2ONLPTjmCONX+vASXFwBmWJGEdGNC8OZdsYSaSCN1DPq755YQhb4IE5M7sZBLmeEY6o+wMbhS4eqesTv2j9rKvkl9J+5zqLuRxsAJHyXT/9HsD5mv2J3s4QygHeCHTHGJRgArN7ffEYe+5p/XZ7ABgO+bJmp465OIdoK7EXGIs202EjHH1xopbjuXCLj60v3RBzDZ6zNbCYzHTcIGtcYs4oBXZwSJVPebGyUwztVKmu/Dxwln4iU8PxtvbVr7XN8+8+/DvlUGncD+5va0eOts3zzls4vHCu1Jk2NLd+hqdsd1Hq306gP22XCfT/+05CoLuzsVK1P+IRj+jFHH8rffv555/fjhw5Eunc3/3ud09et9sfznhptyU6t3cmSmAm0M/EWZn7NEtglsBpS6AIdFpCWfZ6WvrODR9TBGf//ZLhKBrZPprdFd8RKO7AE/2NjzulfvLZJio3UIwMONbWyHizygC0wjriQCeHPRgC0thRQ7Ex4fk/6rsbBjzlIv10o9kWjg5TxrSabyNy8GBWsxnVuZQ9AIC8auUB1iC7RsYOs4PF/dZ/RNGvrZClyY4a6pEyzep3Y/yKeUtDkI8REg4SWiQ2ILL3tnZjBmsfkEz/AL0BzhNg4ngQxouMvhf5iczUB4HJIExleMjIBtZazJPtkwD2JxapCiFyeXYt1xxvGBUSgNb+6AylrK8yAiWJ101HpPsjPSEyVd8B9ew5oihIEx4p9ahNaGnwkHWlx891F9EQ1DGXce3YEOF77jmD97lqXUfkxGZGcFhGCD1b9+raSG9u0crINuq46bsuqmhE+qYxJg1xUSevDL5JhBNB7o4e7hwg+WXKxCBYcdBQhLTkRnpA1n2dbSz8/IC1h1OHDBzyrlda+qNGoMtIJNmRApM9ot8yNlCDnnWQ3vsRKRNp8XN9+n5mHeqzqDV+YDBCdQalwSiSqfvVfhgPjShnH7AOlhwWOkPR1FkyiqbYHAwtWgdVlzENZSXnrHvv5w2ixagoI46cLqJfm4sIeTcA5n4PpwmNXxEsujoJbtWaDwOczqUTJ4YSAzqiMPxoneg5WkuHD+UezzqQ4QgiY1LKyskLzgPmiRqVEDL5Tln/+qtP+51/WzbgBPpb94hAf8IeE+gixH//939/x2KDCOcGCPTtGjh06FB7xSteEaT4Vj8i85/whCe0//bf/tvkZSKt/+AP/qA96UlP2u6R237/+c9/vn3bt31bXCdy/DnPec7kPXIEkPHpc5/7XDkC1Ot3bS0MVH/2Z392mxL62w5uvmCWwClKIAh0ZWf5l1va2vnntHZuEnykk53CIP17EN2yMMwEnpjUtzu9v8ZgJDM6v5MvPlbe9ZA6o+86odTjzAF4ql/oiXV73tgPazt85o8fvZvzhe5EveO0XmcLp0rKMeE0DVYM4S+croug7wjw4e0/FghihkR0Ah690+/YCYmO/lYkuhGW1QfkSWr3rvxVdNWduK3fkHOVXQtMlZGvpZ8blp7S23Aw0PXoYqVnZf/CmZ2I6pQnnwWRC2ay2uKB2RLjHuwieSF+Qwez0lWBYXAg8CxCmd49HBGzLTCabx2R5v98c1u75G5jLCo5on+rv1HTPcsD+doOlT1TtAfhJ51az8xodPUtyG2lCU9HUAh5J89Zp46pmEvOBnAXGI9yWWqXzFYiEt32EATyhjk7Z+fV18J2No+FcYWjJpz1e8zkeNMxk8uoxwBLtpfEiHKExklDs6G1VRgmN9zIrsJniSV0r+vV5yjS/EBbO3rTYs1IR5djMJmyPOhCeEDzRL1x8Jna5PrIYJZrF1sW+xYiHBxAhg+3F0gurBEcZsGQ5YAvG0Fis6kzxOFikdp5IQS62y90jZcpS8fckLUwZb/vvT/l3CKHYq7nHZBZzDhv9OzMCjbUWRdmk4OFsGI6OCduIvo9cP7B9Vif0Q/tHu0dZTwTMQ5+yjEs6qCnjYNSEvTZ0+5HBroBL2+ed25bv/hNp/imv31ug0B/3sse0vaCQP8PZ1EEujJpffSjHw2nXGGb+93vfu3yyy8PLDKVpYsZe8pTnhKOyfrRvcIgq34e/OAHt+uuu65dcMEF7frrrz/lSZ/x0imLbr7xDiqBmUC/g07sPKxZAndWCSwR5xJEEkhDGuVM3b7KEITy3gtwpXFkBXm+ChAsEUw8qLdGWAOroqO9j258caDXR5S6Z/+qMTLWaNOMRU7q8zwnLB1E0vaS8SkmJL/N9j3V21IUpcmlZDcRcDG14IlMCa/0jOJfInmyP5MGPWsUgKiP+Hdnf1oE2Nj4Aoj2F1pECAYuEYAYVJxcjygQS4MXyCkaXUQxaA6CkBL5lXXcBL4gq8oAN5Bjo5rg5RneGeVqWWaERQBSq/mN4UbXBSmYbXvta4xLRcbnXoy0ewmmE6BWmjaim8Ob3SPNzbAQMjDAz5JiLYZhJ9MP9sQ6qc/LeCAi3whMycrJcxnmIGZFdDKV4b2fHuk8X98RdS3DiQhZZKY+9Z9pDJKD5l0gvKLfsw+KvGC9Mk9uuNHzcJjwiBf1J8cXYN8zANi+DGMOUQPs0zLsmsGRzASy61EjXkYKzaOMQb7HcTjQZ4xdQgtDZ65bzYmMEufIQWBjaCOMvgPhX+ndcZrgvowKiuhpfcZ+CWI858kN66wH/0ztp4FiEWWNQ0+mZVe/ZYhjnYUB5MBg/OL8LCOrGXb7SAs9pxwzUvBlkFKEREZM9KkPMbpp76djxmCIyY2s8SsKwgw5EbWvZ0UK/jSuV2RaPlulA2TI0ZoJI4zSWR4conIwuum7W25ZEOhE3Mh5AQI99vv+1s5dH4yopM/3M3i07+wLIoTivM3UjHHtZlu/8I1njdriBPpb9ohAf+IZTqAravxNb3pTUxSEauyJZFYUuyIflOL8gQ98YPu+7/u+iGRXZPpOfhQt8ZrXvKZdffXVER2v1O/3uc99glj/+Z//+Xbf+953J81se83rX//69uM//uNtfX29ffazn233vOc9J+95+MMfHmPjR3256KKL2t/93d9F1Lx+vuVbvqX9zd/8TTvA+3bbp88XzBI4sySwca2Icy9Lo3dqns/pQDddssrI7ckhdbjGddFR1HQpJrwo87dhENfdccgq/GGOhLrFnTH7ptHX4v2e/+tJ1V5ndyzUj7OwQwIUJ3t1rfcVMk/95vO+D/3fPfbU2HAEdh0r+pUCnsSMACj6aQOhz4wF0gYZTenbO1nC6Jehkxh2mZp7x95T8h/hUkvTXU7RpDf3FN7+3A6LoTMTfQ5e6TEDf2sM0l/Q/egv6dhD/NJtbc2HTpa6dBCB5iwQmCmdjcFTGgtllzwyX9eJgKSPEfme+xNZ3qxI85vb2sV3W6S77p0pILqZVzDyMZXgSadRCDzGExhlY4gEZ43p78g8ZTjBnUPD+zLxIvPjONEhcTgMZ0p2Shzp+8gIdmKRwj104CRiyZAWWdBMN5bzbWS2yrTuRbRynCDvtAf48VSlmjIK3de3myzAwm5zGeEm0p5nVgDKcKHvC4O6AwyYwElrzgbmCX2ZLG9JokYXIdBxcA58sHA4jyxhOEW4g7A5x47Kfvm5CC6r89SiwOtc7TLWsc+d8OZ88dJYS+dw53jSYzkyNcSZaqXU9HeVTVgbHBacRNcaD0f0xJ1aa9X24Kgw4HRwkzuzCzPl2uQsi/blDHE8otzJEDlkcdscni+cVQS6+kQwQtofC/OJ7F/g4pGTO3uEe+McGp8vZzOB/lO7HIH+/7z6Y00Eukh6ZYva7ueSSy7Z7pItvz/dFO6rGlepq1e+8pXt2c9+9uQlijh///vf3w4fPhxZubb6kbPxW97ylrjk5ptvbv8/e+8C7Nl6lnWufbp7d58+CRcdCCEXFKIWWEVgCI6E4GVUgs4ADqCgYGRGBGa4DcwwclNLk1CWzgyDSRwjSgSGoNwEAgbBKpVwKyZcRCiKXChNxlASVHI5p8/p3d176nnW+3v/z/r2+u++pHdOnz5rV53Te///6/J97/etb33P87yXi5QjvM2eb3jpNg22HX7fW2AT0O/7Id46uFngyWGBo5+tNDYpWCKca9NJ6thRaFozT4LCVfMNou5pJh5Bv8FJqutFEKU4zPVGUXckZvK+ewX0RjnLEASu1X0dUuj5tBK+RmGcc8cIR8gBi0bUP8v0gkO6NIOB8rSGYFmzw51O4RRpetxXxOxTSae6OYIdAm+ZZzbTcM0E22vf6zMDvrkG9Cx0k6K90vu1x3Sl97MgOdaALPFO14IoEDCDWMl0c5A9CyIwalwiglJ7jT4RDT/WDoRsTXDXntMVqQExhbBgMun6nOpQP0pn2DXWI80iYNjCdUV9Q+IWOPb5pPJPwkv3ICpaREqB3SauiJrW+fpOgAIBkHuZ0JHHOGnVox2OdCFaRNH2NV4I585wUSDeabgVKaGo5hvT8YNzPbMW1S2cR2kBjTmkkeaDCCvNJTz3iZ5Q+0SkqC9dksJX7ielAbuIhIU4HnNVvzLPAPG9jsTlkhhzivGZBMAjv+0BCYHjg+dckWiQE5BFJRKbdHD69CIXq0a5HQo8r4vkhjxXdPRjVf8Pkol0i7mGEPUBwZyOEaSk72f4eCeOu073TIpYNO92l+NMOrOMAvkooNMGnAl6XZ8zKiiifyZdqiE8Y+Ww4HqHaVNFk/AM2U6HZd+KJiljec5BNsac8LzUvNZzw7izLjKvLabPJQM6pSARMrK9IzBq7YKoTEeG7OM823erd5PzlX3DY1LlNQ4OpsMPujnpcaevgrt9XgrorzkjAf2Tz1hAv9s2eSJdT1Eer3zlK6cXvvCF02tfO6fKH39Uv/1TPuVTpoODAzsEvPrVr+7ag1euXJm++Iu/eHrVq17l7/XdZ37mZz6RTLC1dbPAdPQzv2+2AkIfAmC/p0eH40EQ3+04BmuuHNfieWW/yXcD78DxnZFX1TsLjHEaFhoFZ66RsGvx2Z4I9BEjhZZvsTFF6n3tOSEEj40p0RXxLEU0bwsiGjuFev3u/cKAPUZMlvZrZ4MYmxPYpfZhIQztLpEGqOCx35UAACAASURBVE/3TQdOslBUzrD0x/+WsL04P8Bf4ngdD5ZXuxCZy/GucRH7XBymuQefpy3AKhZqtedD4CqHZcSqbGtHCg82J937kfZhIZITeb6IMK5G2HF4SE2tr8A3jWlqj/5olBW6VPWdeWbe9dg0PXS4K1FGRq1K+90Zzfz8lKi7mL8VzQueECZLoV1zCsG2REjjDrAKAjlYBizB3htRXHtL9up+XoSDVa6pcGCWC6MPzkRVWc00121rCf2FmfW77m9H3rKfcJYwk/CS17biInRPHGPT0dvzi8mREzKc1PM5YT7ls8lz2OtAja3HubJKKXoYTAuOQAjW7cG97M11rUVmKqUOP5wzVikCn6YW9pz35drb45yOiD44O+OsYUeccApOvJIclfmEuhkcBOI72Ib50tco3gf8pGto7HivMDewA2Og7zuFfY114gZwozCO8Tl1yMncda7LTZmqkc2d7U5Y+4Ed/sERup5z47CxJF05DVhAx6nF41v2SB4MEd4C+g7TS2Sf2zHUki/OYcbmscLi0JLTsd9nNQasVXXs4TO+f3j33rt/EoH+tV//UdNZCOj/+1//N7fc+TGj1i2fWAfeqYD+4he/ePq0T/s0l76SQ65+lOnqe77nexYlr4RP1rJ2yTFZDrxPe9rTJkWGn/YjTPKd3/mdPkTOzfschW/W9w0v3cxC2/dPNgtsAvqTbcS3/m4WuM8scPRzzz9ZLy0xEJ70bOxPkCu3q9jWxdeIgxTLR7IihSmA7wkCKYDc2M7TCCNdr6+P92x5WHOv0wgeorO7XTVJUqxMADl6RxNFvTBlibP9WaGEjsYID3Bs5X7U/04jgvbNYYBWnotw1inI1k4u0uM0G+u7FrsKlNNWABA2SrDJ7QwUC1TqdwF5gSjICoBoe0LXXGgvccTzuhn3ROAGiFdU6i46Yehvg7UYn9HuTv0u0mGI2m8xs4D9SEiqnh+ibEQtdwsceV6pCUUe4U0PKGX+4ayAPTU/FFmQRBRAU6CXSF1IL6dGvzGfw9xKb3uiEyBcHL2rCA+B8iKYJE6apBQ5c7SLSue+IpQ0dnil93lz/W6n1Dbhc20WmpW2vT6bU70V2ZNEnqMrqqa4vqfOe5IsJp1EHg1e9yPITrG1x3dI98m6AWGTx1lYDU/8Il1ct5zohiJqiEw+EEFCLXHX5Ds3R5gzp1M893clnkOiFNnkFO7OFlICutoFKada6I9e2WUwgAjrcgXjcxOpTp3ecY782kWhV6Q8Dii6V2askL2JmliQijWPdZ76lwQLY+u1p8g/5kmMu8kdyFsLGNVW2V5kTzqe1No2Ez1zrciFgO52VOpCkzKRArTXIJ1TZJDJrF16fvcxojBaKDfhWGlIFVHhKPSqEWkHGSKAllr5eomMIp7cF9aWctJ64IHp8Gnf84TZnaSA/gNnJKB/yiagn9l8eP7zn+8ojr/1t/7W9BVf8RWr91GEuiIvJJB/8zd/8/Tn//yfXxwnAk612H/pl37J4rlE9O1ns8ATwQLO1JXvfRqd4grv6xaKT1NLU1Qcjmvh3Av/rZnHe81BsV4Te/tqcd21W+xrXu/h64DEXYgb4/VoVousg2Mrjp3sr3Jf1e3NzXNdMMvF+JwojaW2jJHBuZenrWtjemsWn49CqLbTuQS4QVAcx+9mw5l7P8Q3j2MJR2AKrpPjlONt4bayKYE33WAcj2OfNwroo01SxEOgpLQb+/i02Zp4mlgfoTOFeOO5ciRm/2psFHtqfa607rqWI9ERXecIWn+utl+tPZ/+1vHsyXx9cH6Jy9Sl1rHKLlQpqo1TsqZ9thUvVUVGOxL8wixIu0xXRRzjlIktdZz26cJB9DOFRfppAVxOxQ+UCD84H4ATKdvk+xXuUhvtICyBv/aZHjuyopV91TYyYdmm5ViaeJ22eW9dwnI6wMyTfx51sDhzYFx3Ft9Hun3WzsSt8E9KsW4BvcTt5gwqklr9y7JVZLnDto5qLscGROPmG6rkl4Xuc46M9uOVc1nYQDXgM3W7+pfp25lLXktiPbRDcdlGmI007Z7X5SBNtjCeC7BMtpFngiwOI18DHsGO3c+ITLcDt3BTlS8D96ktjZliMVHUfzstFP5hTtNF2SSzgGWblY7dAvocdT6Hnh/M9792fS5D1lm/avqQpU1/Ogp9vtGcMUycwexUlOL5XL+dBk1zqSz+c7a73Xe9NNWYPJGi0BHQv+brnzv9ubscgf73//avTv/HPS6gv+Md73C6dmGKtR857UpcPzo6mlT66s1vfvP0QR/0QYtDP+zDPsyCu8T3t7zlLae+3V/0ohdN3/Zt3+Zj3vrWt053GnW/4aXb2URtxz4ZLLAJ6E+GUd76uFngPrbALKAXkBo5CQApRAcb9gRLbZsB2C1stocQMuYavjtNqK79987ldA/7kO1swTkJImrFVSPZ8LPB77DN+P6m7YTJyI7XPSGzIGlmOFAHxjGc2u2ICIokg3QcQmSCKKLW8z5pCwHfBExNYBWQY7wBddwHwiRJj7ZH2PUEoB4eHNql2ldNvEVg/0j45N+Z+q+JKc3bqneOgJZCm5oGqC2BzADYXuaqSVcR0pXSudPgNamy58Hv+ZIkT451ndfEE+m4CyzrawCyiZK4T0dVlDgHaNa/RFI7/WBFemiuOoX6yvOHyI7zy9ozKbIgRTwdg7DdwD9T6hUZQFQFzysiLlEAugY1+HRNRYOLzJH9TdhIVK80nOqLvqvIf0fuqm5fRfMaYBdBZmBMpLBEdYuoVZdPX1EjD6cAHjWeAwgnhGMtBQL0I8kHIcA4NbkzPLsjaVte902u57Mj739HmVQUPv1yuxXxUWnwOAeiRW0re1mAlwUyjT/jWsTYLPJGBDpNhti5enU6cBrEmDMnnCsimifJzCLg5jroVecxr89cY2whV5JQ8vGRul1/r6X2LELL3cs05ybDisTLtZto/Vq3HC1uJ6aZTHc0PEQrxI4jsq5VBH0K5xh1eMcosl7XtPOKR2JXioD1089TRY1R8qQjyVj2i+hceV2ceCdCTOp+ZN7AQcOpDOaxeiLVQR8F9A/84Mt7Fts7+/g33vbItAnod2a7WzlLJJQiSH7kR37EaebXfp7+9KdP/+E//AfXGNS/F4hei4O/8Ru/cfryL//y6cM//MOnX/7lX76VW2/HbBZ43C1gAT1Fy3xNdHRmrc25z65XhjuQAm73aA9WWjjp7un+CIdGcXshXK9dg1IsNC7/5r0V543XHy/J/fg8/04ssXgHDp3oPUpEua85JCT2QTgFbywEuXA+O5ENKyJmPU7RfzWrBZpq4+K6OPguxaq5a9mnwSh7IOyJ0UEMwxEybXrC7iHeeShrr9UOBIFVdWgKpTqe/SmHJd7hGgjNRDgvym+cwgV4L0mt85XOs9fkAUlhTp8J5yyOqXGSMO624ySgzFPlcMxcs3hezrN2PsXBYtiLXionyfG5zaHU/tniKQ6nFY2c0caIzZ2NQPv4qzsnWddHv7bD8mAHIvIR2WVjtfdRpYmPrHZkCCM7IBjXe/mIHK/+W2R0m7Fh/a7xVrsS6+Tc7n6EKMmcyyHU3ERQ5h4nggt4dnBQ5TljP1wGBbur7Vk2qvFw2d57+hLVs2wC8x5h3U7e5aAMJ6RbsWf3PeYIdLJaOeqZzF7KRKYxz2clsEWLtbmueZ6HeE5ABePTNip83VH49cx2how960diNg7R/TuSnnJ0la3AkfaFm3AIkU0yO96FCxV1fmMW2uGC9K/LXtX5mgo9l4LXwO69ftacqYxkvRSqnWRewMZylsc5W/hVInk6TIxYPZZSOyVrtI5vzOI56y7zCC4jl99yVj/8Hf90zwv13vsYAf2rz0BA/wcloN/LKdxvZURe+tKXTl/3dV/nQ1/ykpdMX/u1X7s47fGIQN/w0q2M3HbMk8kCm4D+ZBrtra+bBe5DCxz9wseXp7ZVhh3YB1gkeb8mmi5sMgLn2PgnBtjjPXiqeRPELw5cAReIZeGR2gCHc8frrZJZp7QIUsL/kr69LjKKQxZsSxzrS66QB81rpAdz1SYUWFnYMCLkEauIJkhw62jHStfmiMc5mrfrOuuzBlAlSJOKS9fRtU0kBRBMsTFJBux+s+eENizIsSGVu67r+5R9uSZp9ajFjVFs30pZNoI4wBzz22CqbIF4joh7M/F8nD8362t+rzZChC2IjBDRJXoL7PccUvq/iriGxFNkBKQKKfmybqDuiW01fq5BVmnqAZb+t4RZ7AKgH8cXIhHxF/s5vXrVNTc5ULWvIYycbl61y8+7Dp+EXYNufSYnCtooMVLfy/u/ala71rTavvCQ30Xo+ziD63ooUmA1aSIBn/TeEa3ekf1RtztJSSI//G9E5fhZqGcu15WeW0lKQmDWugChjoe/7GmyoGq4Q/jicKG/Lx46UvmAyJAifeSx7/Ooux23nSNLKvK60hLuUrbuoucPRMTpuqzDRGnkuOdzAGnF812ODrMwHYRei+axDq7NqXwmcEzIZzaIJs0DpxAcidSyqUke2s2Y2VZVry+cehytriaf1zwjlXqtzSLIIC5z7Vk8LzuyT9foNbQiKEzc2G4VVWPbnJsmZZbQ5653TtrYPUJJr91BAo3ruZ51ojQqm8Hhb//Ht7MSPe7HpoD+/b/wx6ezENA/9aPm1OLvSeTA426oe7QBT33qU6dHHnlk+rmf+7npuc997olWKsLjOc95jiNFVEfw+79/PU3mj/3Yj01/6A/9oel93/d9p//8n//zPdrbrVmbBXYWOPr5j9+JUGmY3CvzOSKM9wmDFdNRdi0yeR/eGiHP2rVreze/8EpsWrSp/kis4vceJw7i+Wrb69jcN4wTpd+lwxeJAcfz8zve6e7jAgAt8Wr2EWGJLDHd78BMtMv7E/oRYrNvxeYqHK7dhsLJtl28nNm/IYS242pu0vJlvjIn9tlF9/IeNyKw02EwhbscZ3Ct9kukpkawwun4Vsav9zYlTOse2geSkWdvZoSbiOk5LU4cWh9YmK0IahyFGTf963Ykvinx+LESp3W+yl3hdCw8ZTxdczyfFYb9fEWq5/PLdzqXbFsLvFCd0XA71XplVmK/jOCqeukpWhLxv9g/VwmqzmB0o6LVwe4H0/TgpaVYyvqT/WF+MsfJBoUzvXCAxErhuMTSugYckCPviwfgOWRfz/g1XhrWGwTMxDbsjzvTxPB8wDcQAS7HY2EhOJSspe1yY5E1CzxBP3WOxkJ21LGUmMoU5DgICDudO18C+nzRuQzT/PzPEejcjMlae314EPDGwj5E/ZM1ak8iEXAYc2NvNoxwzOI5yPFpkbw4JxwGFg7xNTHAvtwzHPPtcAEmqnM7shuIKzti68WaGk489Qy5pBZ8GQ7NLKVen1QuTQJ4OEhrroCVVyizhViOA8L4HsM2use169PBlSvT8a/95nT43/zi+La65/9OAf1z7nIEugT0//Ov/5vpBS94wfS6173uzG1xJyncb6VRv/Ebv+Goc2W4+mN/7I/ZyTd/Ho8a6BteupWR2455MllgE9CfTKO99XWzwH1mgaNf/IQgTALYI56kpz8e/nuBMqhtjzB8M9EcEL/vuCQ6ehwKeEECLciHAaSMgpCbm9G/QxpBgGI6EKxxAWvciNs3OCMAVPKeneZ75cILEZOau+GdzS0gi+w9HgC+RbsZmCxq9jptN7XcKoqaCFB9ToR4aziRGk+3GB0Tck6EYLX3cQFcNiBOb/Q6y+IcaZ4LxCKsAeosCleqbPqPVzNi89gIg+VK8Vd1veb05kR0r7Sa9gIUMXQTZHFOElmA7UWacdnvejkyEFVT/yo9u84X2UPqalJ6M976jhrozHuRPYicaz4sJay2AGwwrGwEldqQqHGdazuWcJz3zmdL54s4EylBRIUFS31eqdtFNjSgrVrpFhKr7hpkhtpdBMfxg5dmwVg/ZVtHOiOEEz0M2DepWIKk7k97NfaO7qi0fib/rP7u/vU8Rogu5xRE9yS2WTd4HuOxXmTCZB7mOoajgYgZ5grEFVHkskP+qK2Io0RXBDGrNPad1n3tWURAT+GW67MGqf5feetPckRRn1hH+jWAGBxpBnluet5TxqAWirZRTMLFOja8GywwlzMLjhLMsyIVd6nii6gZr1dCtgXt6p+j81m/W6Cf2+Rx8Pyq570iLHDcWKgctnsOOOtrEli7tb5rnvv6UXICIrAj0924nc1zzdA84l0LkZjiQDspVQ3Qgwemww/+vr1L7b36RQro33dGAvqf3AT0Mxv+Bx98cLp69er00z/905NIvfHnO77jO6bP/uzPtoD+V//qX53+yl/5K6tt+cVf/EWncT9//ryvt/1sFriXLWDxfH6TRDN3WUD6vTOGp6/AohOXqdfCfI0sIcV7Z7DM0IQTduP7LkcSIvQovKxhGwS9xAEjDmsn2KU5du1PUDI2eLgp+9nsyGJPUe9U3oG510IkYmg6OrPugXCT73Pe74tm7cGv2Q3al84B+p0axYjv7YTOlAlRKXFlXm/f5MfuiODsicftSY6HhaPCjmoTonFjv4jizfuOY0o/1EelQ9e1OmMUdaLXGl64E2dhz2tSyYdNRocJfTXiSDAs4qn/Vtr1sulhiW7ggKtVT12C+3n2p+GkjXhunDnMQ74Trur9YqQupw1dIioyMWlc9qWL1t5f5+A4SyQ+e2D2+9ovWvQ/nI93vfSKnpbQLXuSzlo4R9cVjgAPp4iLwA+GVd90vrBfCYpzRgA5RJSjve1f5Z9ybPjd++vgDcCNPY9Zv9LZo5xax/17PkORTalxwQOziD2n6657Uq89p1xj2DrOeKI4ICL0M419rx3FLZRjtXGExPLIZOeoa6cdLycO9Z9MdvAUCOjMXdqbvJO/g8upZ2EVM9ExbLbybLE2p81xBMgljLmGLYyFa14zz3UNMqCBlXH2oJ/K2EWgSK2xxkyjM3lyTfQNhyLeaXBmhf9mbBzvJjsSRKY7RaLjQA4HpBMyaxkvUzBTBpMkZsKhQ0vYs16zb7W9Zz9HQP+qr3/udLcF9G++TwR0Dd4HfuAHTm9/+9unj/iIjziR2eozPuMzXC9dP3LaVYasfT9yDhZG+YAP+IBJwvyd/mx46U4tt513v1pgE9Dv15Hd+rVZ4D61gFMP6kfADHEhyQc2+fo3QcGaPRbezXsMlhvj02yaZMjacUnc5LEJhFLMThDRIkpdmHMQlAwIUvQZUqSvaCi+EuCvTx3qs80H7cSUJibGOutJ8ozp5QsE6l77yCpAHEKYAGMeC3nisa0UX+5zRJgD+ADXI9fVaSmz39SQC2NDXCXw3jeemSZ7of6TSjEE9AK2JhLUVggEABuCeXqnJ4hifElBpuNIaUa08752mhQpb27shC0TaPLskLoNszhyW89b3ICUztRqs91rXoi4Uf+Ush2vdP2rzxHZmetEnutzE11xE85tAbnmo/6G/CFaO8+jj1V7vFP+NXiNeSRQLPJG3zmttQgCRZ6HGOLo/sP5e4zw2GPz7/pXY6uU7fohQl+p5CpyowVRHC6SaDWJdlTPYtmXucF4MBchNvW50/mRarvOM7nhh2kX7ZR95plKgoohDWG811UILZEPRJnUvU0IqB+u5561GCuFvokuOVpUesIiMR3FT/r1cb2DBCLKnmwNTImINnAUekb30w/WhLZdOcxA2kG+qM2ZRaHPX2Pi60vIm1yPHHkQqTQZW1KCynadpn9wcmIdID0gNcyHSCtlNpCNu5Ye5AwRZoogd21yv6zmY3Xtjhav5ybXhyRg6x3nWn/ZRz0L1Mr0OlXPM9fBmUdj6/IM9aLBsYrIidE5indPEY5PRCIoBfTv/YVPOpMI9E/7qB+2pbcI9LUX23v2meoHvu1tb5u+9Vu/1UL5+POlX/ql08tf/nIL6K95zWumP/En/sTqDX/iJ35i+oRP+ITpKU95yvTOd77zPWvUdvZmgTOywNFPfewc5cr+cXwlJMYYcdM+8VzXWBNmfe0S0G/l3JXXU5sB0enEjepdY4FnxCN7IqNT7Onfh074Xbgi/p8Yl8A843t13N/RPo5L0Q1sxp7bx4RQvRC4iDDH6XaoVbxoB5fBToXLGtfkwFCiaUgFnsKzm7UioK/No31zuEXEIXNRt6kERuzXwmykaaakDHtBxE+wgu69hjOJqDYOqYjeURzMduMknOIqovdh1aJOe2ivpecLQTtxal6XPafF5tqjafsokVz4SJ8pNTtTEKFRx+S0TAEyo3ezTbpe77UqtT19Vv+8R6154G2j7i2sWBhVbclpQp9w1DUGDZGe6Gjmr8Rx4SbWHONQ4ZajeYyUwt1RvVNl+lKKbtVLL64AYdyOx5VxjbmCCJ0YQvfKvefaPKSPuZ4gZML1cA+ePWzYfAnPyQBgWDtIg2/BVvt/1bcusVZ9kwOw/sYpOvGTeZIo1cQe2/j6+g7XLcRdnJYr4pwa3SxR5j4qZTm2Z01CEM81H0djHCTyncF9cQZPPqHtvWIXvuPZXuODwHsWpiONv/GDOJPAWsw747oo7VfvMWfpAqOCb4Z13WMyHXfmOJdCw8kg+TTejf0s0D/VR6ekGdAr1lAwsBwa6not4oPLkx9hLXX6dwIB4l3WPMDBdPghP3RGO4WzvWwK6J99lyPQJaB/w30Qga4RkOD9m7/5m6sCupx5X/ziF3ugfuqnfmpSRPraz7Vr1yyuP/zww9Mf+AN/YPpX/+pf3fHgbnjpjk23nXifWmAT0O/Tgd26tVngfrRAi+fqHCJLguAUhlybSwAG79BMfzdYZ00zYbN8MyE1iYUEq+MAAOozmqK9aocU6rFRXgDgBCEGLr75SfEcIJjod+BL1tMxjo0O8gUQU7dssQTxyAJw1cwCnDaYj/6NJAzAffQEpikAO+zW0QfVf53POMsrHUA3Ogd0NEXWdYyUh3iPp6fzrTxE7SGNWF4AOJ0bnNJcqQMjWtdibETHZu1zIi9sqyCVUuQU0aDrku5sra0Gy5GijBrkeWxi3ST+dhhxBrMiO3jmeh6WkKu+Wiytmn08TyZcigzx71HfL/slokaR6V0DLhqVRAEkhMYWIs3fl1c6TgoIdpAp1I5zuvs6FmKMeQKpJgAtQdsp8yoS2MB+SCPPPRA77WRQ0ekSMy9f7gyac6pz2abmJ2RQitsmDXQN0r+VDSDkEDzVPs2dKxLw8yeZbJGEVSuRNYn0mfobAZY1xIRVkRSQBTqm0uYb9HNOCaoHEs4hWCzWIpRXZgjmKtH2SShmzfdsNgK6nCMWUQaRqhKHk+s3dpH9TQqVPWQ6jwelJ4qoVBtcS7D6qnt05MJgzibRgrhh/tJvhPTFmkYbKmWkyadaq9LGEFeMj2uTF9GWTgA628RRrQUcn+2r94BF9Fofmwjaq3BkO33ROXM76y1ZJ/xVlL9YOIHUvNA84X3bZQciUgsikrWVd3aN/eGzn3iRFCmgf88ZCeifvgnot/IGvqNjlJb9ta997fSpn/qp0/d+7/cuV9Lj4+lDP/RDp3/37/7ddO7cORNZStG+9vPqV796+pzP+ZxJNQLf+MY33lFbtpM2C5ylBY5+8nmz8CJBjVI4KZiAF4h6ZO0/IaqPW4414FTvi9OEc10G7AIQSQdIfx/4Jt993YSou0vEX73GfEiK1KNgDZboNgzvbBzTTs1WlpvgsEvuobsP3eH5QAs7gZVGEYV+ZHaquVPLSMdMoz1oVt2iEc/k3twv/IriTsEI+yPMNob2RFn3msjxvhXM7NTVhW3YC4HdsCGiN/1kTwsushNvdWhNRBauwEGX30nZPuxB2l5EUbOPsYPrUO96tBXidu7J9LszcZExLJ+d6jdCqa4ft+g26xT3K+dXPRs4JFvAg9vgQAa9zrOdb5QDcD0bFnArSpv95mK+ljOwnF15NvM5okk41rInBtep3w5yqH8ZW8ZSWN3HVAS6Mn4dXpiOLx5GtHZl2EKYR1REZMa5H04guaAT8x5nhcj4NS5fYJQUysE17IHHvb75D55LosEL5/aevhwRcChmXQUHIaRj53Sqt5P8+dnBILkj7E/fL5y3ENz+EKzlCLf17HQaeTApfU3cx/Pf3E4FLeS8XzjnnvL2yuviADDOM+Y5zz2CPeNMO9r5oxwq9H1+tnhfzVhmJ1w3ocdDUfNs/rPrpa8FGiQf17/XeszYgZ3U9uYkKqtHOiQUV3Fw9bHp+NKlHW7OdVnjSBa9nG8eq7m9h7/jiS2g/28vfe50twX0V73sV6f/6z4Q0DOF+x/9o390+tEf/dHFA6aU7i984Qv92d/4G39j+kt/6S+tPoDKrvVxH/dx/u6rv/qrp6//+q8/5UE9/asNL92x6bYT71MLbAL6fTqwW7c2C9yPFrCA3l6+pKyujbE9ViOltAHiioB+GrEz4M5lzbpTLJrAYh95QNu4jEBSejwnoTSSSQnM8/xRPPeePoDCWl8TNCYXsujeaIg6qYkfRNFI59VCTZJfEX2eZE1hD99yTB29BmoBXNhr/BsB1eC9AK0joKOGNLYAcAK2mvAajHU7DhQIo4BRwCVtEXhN8XwU9yA4AJL0BzLHQLFS10HI6BquL7fyY0Gz6txVumuTMqTmy1OoR4wImmQRx+n+Oo6Id3u0R+QP7adGchIutjP1t0MU1rUR3Bf1+vqm8y85LCnGyqYjuJSwDCnFuOpfkUCLeuIBvIluIKMFTgcCt48qjbuIsAuzAwGPBedk/bpLF+f2KjJax12+tKsDT9SJRecSmnMMSMdtATk873Oesz7oM7WH6HpIJPoLoeho+hDGIWVMOJSXfHrBdxRXCb+yr9KpJwFip4M5HZ0JB/Xfz1jWG61r6/yFaF9ZAzQ++xw5SjS3V79/FzlYaTzTXpoTFfHsun76GQV8P+fhoAIJAwmbZM7ovJNrg+xqx4x5DkvM7vSI1LUciVPW4ZE80rXswFEkC88NEegdZb4Twuf78m4rI3Tkiv7mfvn0XQAAIABJREFUnTfNtdZ7LIqkzXV5TRBYEEL1wOl2KdonQYmTFFFCirrSdbvOeyzgOM143hTZzfu7HH+eiOnbZaUU0L/7jAT0z9gE9PX321349Ju+6ZumL/iCL3CE+ate9arpRS96UV/1b/7Nvzl91Vd9lb9TDcIf/uE5E8Daz5d8yZdMr3jFKxyh/oM/+IN3oWXbJTYL3D0LHP3M75tFRoS2FBnG9zZCFe9Ojl3FNOzlhg27jx1Ev7XujDhk7R68H9l3peDtfUztbxBPR9jCe9ivyVGQr3cnm0ywxV7RfI+zAH1rHFEf5H4sBXubLa6VYtKII9vJLpyF2e/xzk1Be7QpXUxdtV7xCzEeBwUEmnQQHPcyKbgt5s+wV9+Hg7EXUc7YKSOz9RnRpmkT444S/YkuxfkAjCQbOMuXnCgLp+p30pmzL845id00TtrTqsQU6eIvPLArOZV91+fY5krhTH00CuEcR3YfnCk9NhVprIjzHpfA8MZNNXjtgK+9bx1/WeJzOCWvYVawoNqO88w45+FKsImO1b5e2bUymr+fszIYTq70g2hyO1MeTJOciPXTpaUKl6qdsq8+v3hh/l24SRhMOAueQecKM434mva3M1CG5gdX0c9mcUFqL5xAciw8g/oex/aOwlfUd1yfckY4w3p+1nrH/Oz9cowde3+dJ0y+xmUgHGf/W0CvjGj5PHjOZAp3RaCHiJ7Zv65fn46VqU6nOHofOxVfgj3yuUvHJYThXtdyopehW+ANm4AxeXZ0aOKtXLNoA/bTs8Jz6LlX5aF0DmsEc17XxJGEKPIayxbSdY4czy9fmtO5g7eY/54HhVP8e2HZkX/j/ehxrwZoWap30lwHvb5LRx0w04jfei7VGmpuI8rRsc4wtg8cTIfP+IFcwZ4wvxOBLgH9z97lCHQJ6N94HwjoL3nJS6a//Jf/ssdUkeZf93VftxhflYpSivd3vOMd04d/+Ic7xbuwyvjzhV/4hdMrX/lKf/wzP/Mzq6WqbnXibHjpVi21HfdkscAmoD9ZRnrr52aBJ6gFjn7u+XPL2fAjIC7SMdUuvAmXEIgyAn1NUF4Tp7HVzUiABmh1/zw+AUOChtOis9kop4CT18QGAL6usT12LAisJJWSgBrnA0TE6jyBtCBaoWpdJ2BKIsj9qAuN98+m8t2+sfS4rzSoya66EQItABQxrYXsZkh2af/HGmkLAiwiPU61C32M+mZ4DluwVMRzpRfDWUHzNyOlEYUZ2xZRAzwi9rWHPN79axO6It0R0EnDZwG+nDZGBw1S2CGwK0Wh66QFCWWHD4nJleY56457nIoMMhgtEdjzOKKNSF+fQ6p7ikRJcgYSdpxHY23lqBttMkYRuIpgkGDOmuEIEpEwlW7Qc6OIV2p161jISqdTL/vaBsfTJHFc//kapJGL8eF89cHp+a5P04OXlsKijiHamUnNsw25Ru3AFp6ZtzXO1HNrcTrID9YM5orSGXrdqdT9kDJJhLT3fhEHrD9kGrCAHhE0ShVOpgHXKr8wHcjJYI0MZ05BlpCWntT4au9IzkGcUHNdUf8WnIPAonQBfbt6NLmWHcQYNcWdNr0yPqh9rAn1fTtULAjhyEiR9emIjCdCPB1z9hHnrEXMdUhpCLhhDXf6dM2YIq265nxOgfGdwnjVtTw2TbDtI/xX1owggebmknZyXi+OPQ5E89fzrXMQ2Xl2c76Z0K4IdRGgSZr53HKQEBn0tGUE8Oor6B77MAX07zojAf1PbQL6mY36I488Mv3e3/t7p7e85S2+x/Oe97zpOc95zvQrv/Ir07/+1/96OlbazYMDR6l/4id+4mo7dMyzn/1sp4L/a3/tr50gus6s8duFNwvcxAJHP/v7q4SH9nC7tfbEaS2kBIZpAX2o7donxztkrT75PtyU76+1PcPYuNFht7+PkjEpoK5hpRP3xLFsTfBd208PadJHAWvfOHDfxCA6lxrSjZcQ9ocLgXFyj8QhfJf9XRy38u5PHNYRsyEApTDG6R35WU4Ri71Y7Elxpm1x/xYfT6JkMxsV+9Xcf+ly7TBLJHE5IyKcIUqxdwbv2AGzxOMxg5auS18VEU2abAnUCJ6Jm9yOKkOlf40tSkhEvCO1de1vj6/dmA4kout49ky+b2DSzCSQezr9Lnw0zgGZXrXTn1pOuz22BbaSP2ixse7f39UYtRiYWFv9qoxinhfznnDhpFofNcBTG+V0TKYt2frixTmtPXZGOLdtJKDHd8IRwsrCTbqGjrEQWmWDcDyGc1hk0iAqv8atQWeDz/na6ivlstQmBO+ec8rqRvrscsZ31rKyndeayKaXAjNO0AvxHAG9HDL8/Mv5ubJ35TPJ3DCHFXjMzvbn5pJiCMi9DtTc5lzXWx8EdIm5Es4Dh3c9dua79+khUDMfaUc7SkSAQnuTx0PUa204zbNGjQ4HOm10DCb1Pf3DSYZxarE80uH7OsLkNU4QTzpWzhcaz8euTsfG4+s/LrGGfRDORxuPTiaL+V9OLzwnlRbe712EcJzWjQtx/Apc29kVwNuV1a05sljHFIH+BBfQv/IMBPR/eI8L6P/23/5b1yz/6I/+6L1zUU64n/7pnz5JJL906dL0pje9aXrGM55x4vhM4y6H36/8yq9cHKPU7krbrjTuf/AP/sHpX/7Lf3mLL+X1wza89B6Zbzv5PrTAJqDfh4O6dWmzwP1igaOff37U2Ku0zY3GqpeAGDb9bLIBNJAFKcJ48zuQJUmM7COA9hl2PJfz8/MmKAI4+3oB8jgvvXa5Z4rnYBYExQQN2cZ94vmiXXVCmiNFJf3egKrSn5koIfp8DyGVG3+3t/rtdOb1O8B1zV5r/ch2IQQSUZpCN/YTEJeQiGjHfRGaANWQLwxFEkftQTzUBzxtLgCCBdYRnnVN6nYjrkNutG0kVJVAS11u3YfIb5MfkU55bANp2ey1Hx7izD2RSe3dXSeP86pFVeqOKc21UpCX84RsJVJE9yKSXm3yOOBIgJgetQs5XzYmGuRqRW44Ar1Eun4mhnnZz2RNVP0jUCyxnD5AJAi4di2zelgg2nysyJmKntY5RB1Y6KvICKImdJ3LD+4EeKUZPJSYfm0Wk9WOFm8r1Z6urXmHLTWWTZLFQ5kkkMkdkUUrtc8YZ9dGq9qFPDusZRL4IUToEwQIIJ6+QWawjpyIPp8zDDgKmWdD4rmjx2tO6xyTTaRvD6cTHAF4DnQfasVRuzDnLoRwpYOkdnwTXNjGxBpRF0Hu6z7Xr3s85nTzB5NJEURr3QtilXWOYdC/YzQKziE1F+fIguP5mu5TlGvg2WKscWDQGEA8LYjoIiqbbJkN4fTtkOp+topc0SMVziWkJBwzszvKIteVE1pAETA8Kzi5jGtInzf/4r5X5ERHVmTETa7tOCfku5ZnaiSCdM1au5/oAvp3npGA/qc3AX3fW/aufK6oDInjql2eERwSxvXzF/7CX5gUebHv54d+6IemT/7kT/a5qoW+rxbhXWnsdpHNArdgAQvnC0yBOBI1sHnP8C5LzKTfe49YmY/GPVm3I6L0+Cyx0z4c1XvOOml06Mx3ih/Fwgu0O+3Au/GWxPNy9Ox3Phv+Ey/LukPcl3s2fAsn4vyOvYw+I+tKlp+ib96DcZsBW3AN+ut9f+wb+h2eGcDqWgiMiw1C3cjv5boQGEnXyvf16DDcTp9xfeoPZwR674kiKvYW5msfwp5U97OgFfO19l+zsAh2pE9Vlkf9df3u+lyYgv0rJYjWhlnX075b+1qnUmdQovEu4bRyMsdmZGmmjZcQf/14dsaygF57TZwoF89HlOqK2ug4cvn+EuJ1z6u1/3xQWaIqKl/NHTH8yAsgnsse43h1mukQb8FKzEeNDXgrhXcycj3lofl77fG1F6TGun7vkmUlyLM/B8cbI8nx+MHZ8NRLP4FPC5M0Z1DPNP0Z16EFZ3B+mYksMaf6gIALXipcsdvHG7Avs1nY7rHXH51M1C4/Q5UVwfgZB/VynmGPzdrg5z4yeenzzGDHPXFYAfe63vo8zw4qFfnxhfMzblPLbbMEQNMuXb6+s0NIYXL/nQETiL3xfCzWmsw0hzhOFPVQZpA2pNMJ87fnc95HQnT9zVrIOoWwnZHqXP/d756mpz5llzmu11NuNpTWyrnWz0rZy+sk9gunp8RLzEkyeOm5D8fhjkpvJ/xqR3Iu4NvkAZKfqmMPn/XEK3ul3hKBLgH9z3z+c27nDXHTYyWg/+2//kvTC17wgul1r3vdTY+/3QN+/Md/3II2PyrthHD98R//8dPnfd7nLS75uZ/7uYu/JWL/4T/8h51WXbjhoz7qoxxJrjX+137t16bv/u7v9n/gj5e//OXTF33RF602813vepedft/whjf4+8///M+fPuuzPmt68MEHp3/xL/6F07W/+93v9t8/+ZM/6Xu9pz8bXnpPLbidfz9ZYBPQ76fR3PqyWeA+sEBHnKfA0KCEzWxFVQLwSIsG0AMws09mMzyCq7QXgOVObDieC2gbr9+YYIV4Gr33s80AqgQAbPaTxBgxfopEAD/atMITdHPZxJtrWQGMPrAAFtEDKWxJ5Ov2FcDkb0fbCsAXqPSlKjWXfw/AkvYjSp0oXHm3Vz3mjk6AyDhh/+os9a0QUA0sAb8VUTmOP9caHQIWbRsMj82UAk/nk0oNj2TShOU1BIyo620iSOJsgW0iQNeIRl0DoZToYKUgHOeCjkG4HkSyHUEZ+BrhnzFxZHWBSKIwENNlG5EClSKuxWkJ5MyzTHmI0OYU4CIidX5FCHRN96He40gGCYSKlHFa9BI1ISIWnt/DgOoYk2WqSV5R6bK7yYOjZV0+6qFbkD70+HR6cc0dRbs//MguJbnsQdSE20KERTkU4LUPIIaEwhmF+dkkbA0U6x+iOVEGRDepi3oecBzQY3vx4nSgqBqT5CVyI6AzP3PuMM5qix09Dua+mqCY/3VEd7ex2kY6edYmtZXooVyvSIMu+0AG5/oMaa8oCqIuWBNpt6LNi+CxqDxJdFaEheZekVK1Dh0cXa1nqIgfbJcEKc8T48B3zE+ir522vqLcx3WN9SEJ6ZF8gfTRvzlmveYHqWWOrlJOMj6VSh+Bfa6VHqRUt2mIZlusASWG0Mdei4YXwXhO1fZr4T6JOGcVUP3zWu/dz4qgyKiynAf+njV3fj4On/Y9d/LWfVzPyQj0f/wLnzR9wAdXutK71Kq3v+2R6TM3Af0uWXP/Zd785jdPX/M1XzNJDFeUhX4+5EM+ZFJq9i//8i9fTY3I1SSYi1R6+tOfPv37f//vz7yt2w02C6xZ4Oj//a8qsxLfEplIZFyUt9IhfseUGMReXu8vBI3eh+8R0BcazPj+qC/3iee+P8J07Tf3HZsRnrxDRgOk+MR3iccW59VeCKyyOp3Y6JehTgiqw/d9jdir8d72v7W/tU1LuBz3/2lP9n65zwDjMXbsI8FnjGFHhY4iV7U5s98oZbYEt4ePpoPf9tB0/B/fPR0oolntlYj57kem6ZIyQ0WqckrWgJl0/xRwe/qF+G0cMTqsj+CkB263LzduujbPSeZrlnACi5DiWseSNUtYgmjzFHpzvNkXU3rmMd0rUr6ricYV2qCc4rSs6fxbV6eD96uMT9zD5x5Px5Vu/aAE+OPrx9OBaqPr+uyVOUf9u3p90jGeLi6bVcK7+gQGfkSp1Ss7mK41Ruj28xSZ6BBPsQf9143AF7oX5ZUWz3hkGBOm9TlEYtccFw6Qo7FwIo7j+syloKpO+3Xt1c9NB9ovai4Ju7nslvBJ4TE52DNfNBdT0M19NX0cHYl5VhCVLWCXwwF4EXtr76qU8WAaHF5ZG+04XpHNi2jpLI9UNnY2sxmTLIIzdC3j8sA9jXHLsRcBnnWjMwsKi52bx2Ssf562qHbb8XghkM9Ay1hBdgSDWQwuUVp4q+aGMQ6Zuli7knvKecW8YZ2iD+O8WWTFqi/tYF0O+OnMTZ+a54h1YoG7ai0FR3ouR130xE9Lz6NaS4b08yOWop85x7EZWL15L3iKiCZ3s3ON270zZofkenZ6Pav1hexqvPd67azj9RzVGnz4O1+7+va61z9EQP9fX/rc6bPusoD+LS/71ellZyigSxD/lm/5lls2MUI4JyCg3+wCly9fnr7hG77BovhpPxLzVTrqjW984+ph7/M+7zN9+7d/+6T65XfrZ8NLd8uS23We6BbYBPQn+ghu7d8scB9ZoMXzsU9Ey7F5pdZrCiAz5NyJM5BBAIX2Dt0H3t8DQyZhk2QQnydYzWjPvuVAvvA55EWCmBTh18Rzfx/euiMAMhAp5DGSQ010FHFmcaiuRzRnC8oFWgA2mZpqTOvYIKtqjAEq22u8UsQbhFU6rjxHTWihRpHch1UrrdIJR5cNpATMFynGigAAVFtElUhdAqOnTgnoCWb5nPFIkfA0gpDjW9CuVNI5xSD+7DktAbnIBhEUahvEA7W51+4HcLSIXKBQorXJliBNdQ+NI8KV7V8e949dm46v3pgORBKZ2Knz1FaeIRMcFYVO6kJILY7XuSIjnLL+Wo1BpV6sR3Pu/oCwnWpeQnalmB+j0U3slhCt04M4Oz68MKdrx85ucxEZ585ZWHVdaAR2iBJqwJnEIrVfRb1onjEWIh1wgnjo8kw4SpiuMfL9bZc6juwKWW8d+3SZApEXFUXBeoao3yREkTAI0bSfNHBJDkFEKDJe9bkXAnV42DM/kpBkXSKTBdEgupZSoFcEguudExnEddz2Ikd1Xtbba6BPyYAQWolqYCq4LzPxY8Gevl2bI8pFSh1H1ooDzXU/3/VjEnIeaxNYnZFidrKyPVgPTciVYwpkTc2tjjIX6Uk7ikzyNSDqur2LST03xiTOUKuvia5qBo4xvm8RcbzfklgfyMKFaA6Z43WvIob2OURB5kDuMX6ea7W+845gvTvxiowP6tddNAUpL7WOlQjDvbzuRKSdrl/jZXuV7Q8/6Imdwv0fnZGA/lmbgJ5vzDP9/caNG9Pb3/726fDwcHr/93//W7rXww8/7OPOnz8/XZQD0/azWeC9bAGL56zbeW/Wfd4jiJ+ILON7yXuhSPPcDqOjMBA3SawxN2JFLF0xSIraJ8TVOB4hoY+vRvE6yndkYrC8ZZ7bx4wvOAT9ivrkON7VXI9rIfoQ/d79DqE136mN0yItL/jG7/JsD6pQdCIxLNdNbJbOezkOft8qgxQioFJBPzZNhxen4//0rung/eT0leWqKqp+gTFrXBEQyeTj+1StckShxfwbxK8S6W76eLBfx1mTE3rfXxmx2D84tTuYr/YUcnY9DebLhjjQgo10fZx8S/z2tlFidgnox++6Oh08eH4Wxd91NN1428PTA7/7/aaDi8I8iLSBVSqS3KK5sAP75TCCrtdOCtpnKrrcn/Uk3x0NBmQfm/hdmA+syLgnP8Bc7WvUvrHnRgl7YABdI51OydhFa+AmdAx4iXrmODMoQxiCuG2pvXjNSexPmbMUb93GmoRkZPM6kSUl5OwQ13N7wuHHY1jYz/csbkHXxXFabePZ4ZgUL+n/iD+9b6/x5r6ZTcx4qgRqxHOurzaCt1IUxu79fNdaktgwORHmR2TtmjNQzZnjhJnmjFHCccoccLQT4cGZVbMebNFZsFJEZ9zgjXhPLObnilPReB5OGuMCQH+T68JW5ijKiVvHVV8X0eiUjuO64BrmiudxBKz0Whm1znN993IX9cu9ztaxjRkL+/Ie8OcEguCYVs8vz1u9jzubGOsyzwPziBKAXl6Dx/M9dvP/8JlP7Bro/8tLnzt95l0W0L/1Zb86vfweFtAVNf4DP/ADk9Krv/71r59+/dd/fVIUu9KsC3OopNQf+SN/xJHsiky/lR9hkFe84hXTd33Xdzk6Xqnfn/WsZ1lY/7Iv+zI7A5/Fz4aXzsKq2zWfSBbYBPQn0mhtbd0scJ9a4OhnP+6k1y4bWQiGFs+j7i+e8Gt1+JoMmtPv9qb4dm2YguKtnJtCJ0QMQlWm/xoB8oJEKZDJJlrXZLOeoIt7hSbeKcbYbLdITeTJ4H1L29p7OlLwNdhwQ0J4J9UdwKSipw006r8GNBFdbjG0CA/Ele4DdetLgE1ARCo4ahpTu3qRQjoEXxMRSu29MmA6J0kC6pF1Fwu8u4Z0RuDUtTKiIsd6bW7ItmpH1kxmXqsdFnQl6D1Q4vMD03RFteBKzKc29r55Z1GzUpEr/SCci8H8joMwEIMoktD98NF0LNFadfTUn0vnpoOH5PkfkUvcM8VzgKI+I60hNfoUrYJnvcZZIuejijBQKr8glRZ9KeAJwJWddF0TQQUWHV0sgfTCLOg6u8EMci2Qk/rPpFiJpYqER1i9dHE6ULQNRAbZEbAJbdP3OC3wr0ge9UMi7kOX7cmvlHhOFy7hPj3JLbTXXE8+gfHOfkNgEJGkNqgfkIwaO6KwIb7zOda1UijWYFcEv+vdETGt4zrrQjwfOD/kuqRrOBokahNW9IXTAGpsSMdN+0cx3E4aEfGNHYhAkS3zmYQ8V2R/ndf1+SBgHnhgkqMC5L7HckVAJ/qj0793pMBMRFiMz0iDenZ9fK/x4TjEekDqdvWd9Xx8J0AgQwYlyY2jDOPXqSLDTroXZGiuh006RU1yX5sU8kQuUPtxZaFoHjaO5TCcKRDymavp44IQs5i/kQ6S1J9+T8uWRKwVEQWpt9AIUmifpsOnf9+tvFnvqWMyAv07fv5sItD/zEf/sPv81re+dXrmM595T/V/a8xmgc0Cj68Fjn7m950sRZWEPXso9gCI5ylW9Loc7z6+9/vgVgX0utCIY/bti1ugP0XpbDErMEvuudmH7BPP+706CORslHuPHFm2MtIUoYt20BfvHUu8Q1zj3Zf9T/znvVa9pxHidX8Lw4VR21a5eQ8MwnkeFwQWHOGGNMm0S11HpOz7hKO5HaOJjo9azTluOk978P/vt6aDZ75fpaSmnFc3ZnYMJYvWiI16jE5Ttuumaq/2eYl1c89LVGs6Het79pkpeI7XkD2o061MXTl37GAc7SOVejkN33j7o9P0W49ZQD9438Pp4AMfnFOzI7ynzZS63dfXI7QTz4k+P37s+hxhflF4eOe4cfzItblm+qU5C9QCd/PMGJdHVLiw3+GMk3YYrvCT2sTzDL4DFyA+sudnH5sZvLoOeT2DYNl02pDjtMZdmJnU6zru8oPGS3IythOu9u0au8a+N+aU745Ijvmba5JtWh1nP4wzRe+Zy/AuJVY2yNJBud/VMWQ2IEtBRmazdzfWLOeW3O/397WmgB90DGIvTs26b2ImX6fGTf+y1lCWzTaIvT0ZwFLQZk02vq39dq4zHHtBTtCzIY2p8nli3YQHKQw8R6LX8cwRr6HVJvb4Oaf63hwTD0FHnw+ZEDgn3zOJbxHBwYrt/B08QmItN3pw/M9rY3NzN5Sx09hEpsS1dwgc4iLafHA8bkeKcJyerTgbYvG+Kt6C645OGOBhnQtPQb9oN/26cTwdPvuJl8adCPSvOAMB/dvOWEDP5X37fbPAZoEntwU2Af3JPf5b7zcL3BMWsIDOzwi8E297w4nIFylpAckpcHQ9Vu9K56vfCrmTFhnF8HGjPlpv7XiOSeCVosbYrgS5nMM1mshhc15f6JyM1nA7Q6DR4a5TRtr1aDikzCigQ6qk/RHciCoE7KXgCEnELdKzWp+J9ECMSuBngFDCL+I69tZ3TtEsMfPcNKnes366Tpr+qGiIBrElXmNfRYjZy71SfVm4LtBLxHB/Tzr3AFs5tiadAiCNczbHXG3GbtjYjgQF5A2kC/jvm3vuXiiPiFL29Bcpob5HjWNETITS8XzExaMbk4gc14y+LNE60s8RUaxrJ7jU704bWJEgFuAV8XEwkyH60fjIRg9Xuj9IokzP7rGtDpvki/RxODmcOz8dP3hpFsk9RkFwAbJ1CRPERT4whtTcdnrDIioF6B1VpLZXysBRQBcJpIhmpWdXf6g9x5xTnW1599tB4LGZDJJNRlHXXQvP9H7mYz2CyKE9jD9EkJ6VbnM948xb2QyCBRK2UmpbREf4BYDj2NIkbMwn5iPPH+K15sCFc3N/KSuQa6zamyI6US7Md32vNuJQwbOIE0oK7OM6mevSBaXOn+eno/9zzJtUq2fSbZjF4mOyEcgGmRVA548kK8+w216R60UYOTKD/uczmuvTSNQ2+VLPbhJE2Jt+5LOQ2UEgrBCxcdjodTjGcI0AyrVbjgjKKsBzXWORmQYWz3m3KS4ycN8Qbov3KyTs+B7AVt1OBJt5/XuiiegpoL/aAnrV7hznxx3+/fa3XZn+7Cag36H1ttM2C9zfFrB43vuF2Lf5ncHetBwZeUemMJHZTnjPaP9ARp4Ro6zpnqODagsAN7E978p9+2adnphuFFfANi1ArTSO98xClOpORQNJxww+jMhWBDqiAlsAq72mHRQSI2SE7JCqnqjVjoZEF0QkTZstQFeVAoq00bSD/RR/2041KOw/2Cto/yRnXomdfOZ9sRwmJdaGgzUiqZ2IhzT0vc+pdutvsvoQ5Qt2TLzNns/7gHDKWJ0qqmdeuInv1S3th9NBOyOux+uM+zHvaa/t6jxL3HZ7w9aK/r5UIh1jj12Er67dcLau6d1H041ff2Q69xHvX466dQ2ciRUBLOfk2NdZLJfYrqlz9fqcxr1Sxh8/en3GX/pRCvdrx3NUOxnDEpfFNbs0k+6rJjwoXFzCqjFq2XmB7QMnskfTvxpzHUdUtuYE4i9zQP8K87gNkaZf80T4iNTcds6+5s8soFskr4xL+p3nXrgNkdV97AVtZ7sTDpmF9cA+uZaQzl/X8nXremoLmBeMgn3A3gjhvV4UVzI6vfCg8NyDI+BYWKtcBku1y6umuJ/VGo/CGLMdI2tF7pmdWaEcdRKbYKJ02uUz2g6vISdz2R+hOJ8d5lQ6CfCeaHGfyb/04+hHzbYfXgLwK8ZvEam/eD73OAXdUb/bAAAgAElEQVQxNuapcGriOWKdrXXHmfsGDmYxl8AXUWqA9vBuyHcMcxq78B71vBmcO+C0so3j/D2Va9zN9c7klRnqVtfEevBZW1VZ4gksoH/5GQnorzjDCPS9w7J9sVlgs8CTzgKbgP6kG/Ktw5sF7i0LnIg+z+ZByOBFD+BoQYHI7CArAAGIQd5k32JqwcW9TyFlRnDO5jvPPwHgAxAAUGgX5+PVDTBI4kjH3IqADlHhDXmQLi1aDaJLAjLAQ9qM7xmDBB38TqR0ehwjrgMwdf8UK/EKbiBeKeTWUljZu/3CNF1RlG9F3yIuarypVSaCJEmW9FDPFKsmfepYg9xKgZ6CMwBZ7ZEQl1HUObYJbtPrmTHFkz3JxTwHMHcamWgcG4CUiCJHoCtyOogSREm8rcfHHfG/RGdHU4ioUQR5/ui6PkbAv8hFwLaiHUy6KN2gBOcS068o6v94mi6LTDk3pz+vGoAmgzSG+awMPKHnq8B+RZ0bIOP4gJCda4AIAkWYp7ir+5N9wDXucJwob/MmjQrcE5UlYdp1xItAg1SsCHDfR/3XtSE31D8/TiskLAD6hLBZncYJRX/a6aPICB1fRJCFa88fxiIisTwfVkR0Ox9E3UjukwJ6j0HW76M+Z0RXFbE+21hODEFK5XzuNYe+RS3uitjfRVhHZHJxAos5Qb+SV5YYLuJXNc+dSrKivzi/18uIGHFa9xLvm7iq6A+dRxRPr2+76DD3lWVSZKicKUjVn8/+vvcAQoXTNa4RQUUG4TjC+yzXgDWyK997rJ+5Zo3voXy+HjhXAnoQenp+lPZ/FFOamJoNnJEsInyoh74T0GsggtzppaTf4fHeyfcLadw/eI5Cv/qWT3YUDrXuNQ4XnvPPxlXscf87BfRvPyMB/bM3Af1xH+etAZsF7jULtHie74sm8GvRt7hcTqb5PmK/rS9JO43ggrNhpr8dxbs0xiig36qD8ok90YqFE6/sE9BzD3Jin1uiIu/f0Va1bVtgJGFG8AfnsfdOAdjtKYGGcjz7MGbu/1vAyeBiNjq54QnncMRPt6PKoeQpo6M0exIcIbzfrVTII8bQsdo7O5Vz7ftSmLJDRYjdiJLsL50RDCG9fvXx9ZnTd8cmBPzg8SfqfgVneyyjXn3uV0/stVfmzmKPFpHapPRm/whu4hKPymE0BLfF/unAWMbp2yWiK6vR+wj7xEERdT7vaevC6i6CuJpz5ZpTwc8bnhLMdV/9N0SuT5erJjiBA3ld/S6bEnVce+TGqgv+ANGy/m0H6Nr3gqGMPaqMVacfr7FIJ2fWEV3n0qW5L5k1S/NIzrfKHIbTK04cwlCcD07tPXDZrNcp/g4+A8wEViBTBM7PqrOOcO1668psJsxaGLAF0BJhjTdxIikchjjaQjyLXWEmr5UR9Yx4Df7W3NcPwQLMMXiFHpsMBCH6vPBBRp43TxKOC72OrqSFZ00Hf8keJ3gLzgvHHzv3Ul6qgg3yEWPt64yE9WU6GjR3AmeB0J5rXF40BPXMbMD8TmGd03Aaz8uwhvV6EU4Led18LphD7XRWttcx7Zy9JqDDyUR6d+7b79vRv4DJwZyOTF7jMnhi/iePOY/X4bN2EehXf+2TZqd+zbdHH5suPO+nVxbGx/8jItAloP/pv/icu9qg/+dlvzq94sW/NL3gBS+YXve6193Va28X2yywWWCzwOKtdXy8xgBuRtossFlgs8DZW2A18rw3kpFmzm7bu03n/FuKNXjdVxQzG+4k6vcJlONGNTffp5lgTUQdN/Pj34geaxv8kahKb2QE9zUBPcUjUsSnN2sCu25PRhWHcwFtWLOV2uCa4bpI2RsiRASMQJeBsYS4qhlsMbCAKRHiBuFF6nAfgCAEVkbk6hgBYwFBp/gWyVWpuKhRiLi1SEVczXSq8arRTG0yAzNSga94V7eoTXR4pQQ3qBq8yBExTyMab+dRYtxHMZ5IZNnAYDicRkQEdTrmuplqmysd4FMUtVHCYpE9JnBM6FRflAJQKcANQIukQzzPtutw3UvPl869cjRNTynyROBN5BLe40pBiDDsSHnqQ0/TpFp5EI4tHg9Ggjh0VIWIpnJyMMFXZA02J5UdZIXmIV73OsZkRjlN5DrSNiYzQ9Un13x2HcUgSKgVyBxXG8Y0gSkWci7AfdG9QMuA9CRLIDGUhk9kEMCYuca8TyKXun5Kh65zSGevsYcwkK3byaWEbEcyQLZXVM5Iklw8tIhqBwI7m5QR1Q36l2kY+Z4IMXgD2SQje/ge2yS/kOS8iLjD2RGiU/bnOePzxfysaBzXN09HHkXn5zhC4jkjgNaKEJlZqyvNfNuA9T/XMB3ryJghnaqXzEwfGFFA5aSwW5pbud8JHNm/XGfgo9beRYjZ9Z3nRIv5u/ZIQHcXWQ9T3ChSc64PP/6M6knMaX7l/btof8w7P8dE6lRd+3pHNgGqwLN7XED/tjMS0P/cJqDfzptzO3azwH1vgaOf+tidaNf7da/gc98RAnjPLtZzMFPUf9U1cDTz9YY6w0nsJy5AeMDip4nZdzIqvU8I58SFoFnR3ydeS4EVe48QwtT8tosSRyHU5rs8xRi/vysSm31p/nva3h8BHfF7AWTZR0XHGntUOSDGA/F90fYa78aAUcqF9NRpn0euOK32Yq8hLJGCPH0lInesh2zH6BUhjL0b2wKnVy9MAX7Vtdl/UpbrTubGGPWqa2BnMA3XpQSSyknJhkSGa0vjPtaBQNor1+aMXvo5Pwt7TtMuR8qrN3bCt+YnKd+FnzTt6l+fyzhKaLfIrH3ljIE64lxiuVK5Xzo3f6b7KTK9Itl1Pe7tVO/s1dPxfrG3KpGN7FvgbMoXdYr32vN3lLlwX5WrAivgxOyySyv72cRGYGw5IOPYbnwkZ+jC3tpTcz+3KziInE6jkMgAjceAGzPlOe1wVrDAZx19Xk7CsgdYCGxgp2hK05FZqya17m1cXlMfgZh5x1rInhuegOxkzPnEALkup/M01+a5g8vBSYCI6HmSzaMPDwP/4/eAHDKKt9A4GA9H1Hbil2yXzsU5oJbKnmI4a6SDNnZZeyZH3JBOAzlv0/FqfK+4L9Qk5/0VzinpwJPjceK5KNzBvKX/4/uRdV1t9RpaARr8W/ZpPNwO+Tq+nIdSQB/Xt1Ng0+yQXH0c1qXmPBhb2h/3N2bSGq+58+jRdOEFP3tHq+tZnoSA/j+fkYD+dzYB/SyHb7v2ZoHNAmWBLQJ9mwqbBTYLPC4WOPrZ3x+bxWHTOALEFG3d2thdplgD+Oi0WHsiz1cF4hUy4zTLjAIKx47ABIATeGcGPQVmus0DiZKgtcmJVP8CzDXoiD60WBIgq9sQ12niKzzURxCL/fHGHmvwEnUA0CFa2CRDEV0JErXpPyE8leBKzTVHb9ZxRAanbYnQ7BptIzKhsyMSqYFKsc5TivpkJTbjQa6+SWyToDqOOUImBNqq0HSbj5frTkdKTuZP1/qmhp9SL5ZtsWVFox8/crQjXiwgk84/bDTeg/tAAkEi6Vz1W/cqAshYWUTPY9fmCHSNxaOK6K/7KD2ivifFosb+0auzsGiHiIoM1tyAcMnhyzmp9N2HimKo80X0WMwuBwA9646UqfGzo0GJ9Uo3qLZRm89gudLmJchFbCaCgjSG+Qwy1oirEDhEtQD0G5DH/F2EolQ7k7SEzGGqMPfU9wfOzfW7IVQyvR3iOCSVvnM5hSKKUshd88DPtWchGpeDBH0+LAFddiU6G+E/I/ibGIu1Kddn5hjP3okoDoi1eGaYC7KFoqXVJmqpYwteCbk80rciHY8vHJokXKQz5LltgqtI1Y7MiwgCi/I154jmyMj7cW3g73z8eU7VJv9eZH8S8H1eObNA/o3LyNo7jGNyXOt61Ji3qcKpwlH6iuxfEOX1nHrpnN+hJ6LN55cYUOIkp86yu2hnvGOI3CITQEewzO9GO0qwVGtOK837s3/wNhfTszs8I9C/9YwE9BdtAvrZDeB25c0CTzALHP3Yx8x7LMr1+L2R78oQBPUx7//x/ZIReBaQqpYu+9cWNIbatWmvEAj749PeSbdj64WIsoJf2HusXbOdjNnTl0C+1qdy1vJliIhO3JQiS77uRgftFIBGG7XA4Zvt3pfsWdgTjbZLJ2h9x55k91asdPsluuQ+VPdBoFaUuMaVCFvPi0qdzf5lNsAybf7CtrE5V7sQ0RPDMcd0H4tPlZUKQTsxLIIfe/FbnhtrGK+E2jU8o3Zk6Z1M2057XOd8FsOP3ymcJ5xzPAvlErS1RVOWLv0u3wF9P/7UNY553iS6O+PWudlxWN9LQPe+q/4DT+maD56fjh8+mrdaOgdsoNrhJfgfPFQRzZSmY4/voevN1s65mGcZnExENo4VPPf6V98RnW2xtVLdy9E5U5DT73HciViX4zF7S8/ZKpNm4bWiwHUNY43B0UVdaHE4hOsRp3cbKiJdOJCyVpSSU3vAIjkXWRPVls6AEJHztDn38moTmJJ748iA0wbj5X19lRRDzNffwuUpgPOssNZksAR7d2wHbksn6HGNgZ9ZYJDCcFnmS3b32CU2DUd8nHXo32IOhSM245/O/vlOMZcChgguY1VAr+NOcHw90IXZKzKc9ZI1V+Oca0y2Y8Rg4xo7YtMFbiJYovqi+1H+SuWwzj0wOyR7Xd6XgW5JpFl0z3ebcZWus8ssdxJniY8qYR5xH0zcmOl4xm9q3zve6cAJZck4eNZvM79yLzkgI6B/2Us/cvpTdzkC/dtf9obp/94E9Ft+m24HbhbYLHDnFtgE9Du33XbmZoHNAndogVk8L9A+kkDJCLXX8Zrb5hCVjihxmnh+qwTPCBDXxJB9fU+P1pGA4Zz0ZE5yywAv0jRDBgFIEsBmXxaEUx3k6wye4wuQks4Fe8TzBEomByLCQcAV4ICQxb+A+6zBhkg52k1t72gJge5KNaZ7S0hEvG5CRqSEAL6IiTFStMiNnDcnHB3qS/qW/2Y9sgTbzK2+Vgm2CXw9naOe2Tiua/Ml54quRR1tIgh0jvoqEuyxjPwVOB4ApYmJSqtOHb/6zJET1DgfwTf95HOPs0gAATdFi1cttuwPc1QpD0XsCESKnJKYrh+iPUx0Vur2dngopwjXAKw0eBa4lZa7iL8GqiVaKmqDVIAc17aq9HsW0KvtkEHqB9kQ0pNfbfQzGOnzTHic39U0Z77TV4gHi9U1/wYw7L677rZIuJ0AOGdNKKcZR9xGSkRAsZ4tzXcJh+UUoNTprlkpAkaPK2QWUTW6v9qXERaOnGcNqO8halhH3CdSkKqtUV9ex5h0qsgR9+mB2YnBmQSU6r7GvL6bU/3XujVGn2NrC9BFquoYxH6TEJWSfVzHIMpNWFSktNpARA1EQq+zMbn5zs4Vdb7ICq0n2LGJiD3EbK+3kaWCdYm0+rR5dDra935YrDcIFBGR7vMGQobm5bsh3wV5L96nrEUI6LVet3jOXNDYKi37vqh55qfXt/lGIn3mOUcq/XiXrL2LOJdnKB1kRv8mRwHdaEHf406JEC0lz/iBfZZ9r34+Cuj/xV2ugf6bb7sybQL6e3VIt5ttFrhnLXD04x8zC3ja27FvcWtrAe33SnXB+w1E22GRxZERoYR3xWLPegtlr3L/ejct1++QVM8Ky7Tz2XDD3AN0pqlMvRvYgFMXuI5yYJkmvV687GnyvcV7cSGeN/CY74CYmLhpX0SthzKEp46sXMm+ht0Tl2hvkJjQY1vOr9nfMbtORrSfirfDb533uK67T7iW6bBNCua2yxDNSxv21kbfsz9j7ygs4NJF53cCpgUlCUm1R9GzUxHlPXNKzD5+RDXI67mSDW4cTzd+49HpgQ+8tBPnsA3/5j7H+/BpOi4nhwNn3xKWrSj1nKqqoy5B/sIDc0Q7uEyfP1Zt1aNn52jmkB7lqosukZp5uMgWkVmOKnKWbG3GSpWu387H2khdLGGOOV7R1+wx5bC85hiS3Ij21nZurLTv7WRcWI657jTihdeNySIjRO/TCxcxP7sGuBzFq6yW7s33OHKUY6zXOhxFDi+4XNG8WZW4WBnvwJUpJIM5yTDlLGYDD6PrJCZVv4y76nm1M3UERIBnwLfpdM1zXm1blHhi/c19/iJzVjxTrC9zJ5fO/VwHJwlzJeU04/tTPiHE825Xrfs+t+yu7+y0HON2M05s5ND672rv7bwvVrmayCLCM7mYVyvls/KeuaSkQxZOEGBU5jB8Gvbz2DNPcPKqG3hOL53ZLjznRyanWQ+8mGL5TlgfHUsoobXC0cHn3TieS5vdOJ6Of/0/TQfv++A0PXS5x09O0oe/45/ejsXP7NgU0D/jDAT0v7sJ6Gc2dtuFNwtsFthZYBPQt9mwWWCzwHvNAouU7WzYF8JDED254W7h0ictvZd9SokzAIexhl+ClrXeJnEBOTEKr+Pna9fJY7jmCDRauCjQxYZ9DbR1NEXUqsIEXGcVj5QAE56t4Cz/i5kXgkYQTBbRSuRCfOuoxYyiWPkdsiRTekHkjAJ+phSTeIjIyGUlIJJWWQCmwW3VMus5BImY0QF7XNebBCtDrAlf2U7636mvK8oX4qhF9MEhQZc/NVJmAKMS9kRaSDDFk17X0GcW/YpcsfAY4ib30ZilN/YCLJ4iEBJxLjuItKn0gQb2FpUPpklCuYhbxtFEhiKBVQMdckRRFhUlgU0UfaHr6HPPtfLY1ueKpDL5Uo4AHmuROyWyki9P5IvOvHB+jkI3WVXp2D0mJfZ6/pRgbMJAZQTwbo/+J7ngsRVBEyQQ4w1hAklEFDjETUYEqWt+jtWnijRBQIeIQMyXwE37HCkfZIbF8or+OH9uOn7qU1w/cBY4S6S2c0OVSMhnHzKnonVb4KSdrE2sKZCvkF4Qnkm6YgvdR+3WPMy1VLYkzZ0zQUQKRJxpWG88PkXkeM4GQZj3GYkN1zKXk8Scft4R5KTq5xHvtXZYlHlGLd5XDXA7pFCK4pRXX3LhKbTnNdO2aZdc89eIJETuxe3jhogfLWhUR5nf+95htKFsKxLRwrienyL8O1W71/gbs20dlV7PamotJiXd+b6johmu/tofDwE9iB1eMggOYzshlpgTScTzvN+4PovnFR13TCmFaMfhM77/lIF773yVAvo//PlPms5CQP/cLQL9vTOY2102C9zDFjj6iefNexvvRcJJc594rr509DmL7SiWkKqX60Ud2hY0VzbHIy7aZ7fEJ7dr235/DHv4NUFvxGXsJTpSmgjdiKZf3Q4HZnIfwZl1cL6rUiBp0XpUWEkrHumqMxPRInVAGWhtz8FYMozRtM4cgICGMJYOlen4nMJc7y1ijL1nKyEwbbTAizGf1sYVTMd92SN1RG4JW4ltE5N1u/ZMmhxv7RNwhmynY/WhshRJPMcR2GnY67mI/UeXs1rgwooiRiRj3jE+Wd7KwaGzWK4I9AMdK6wkzCTBfqyzTs3z8wezkC4HZOaQS5HNUee+Dll6lD7+ulLBV1R74iZwR5cIqDJCumbuwcEvOJ8jNjNX2Jsj/u6Lzs2sa7Z5OQzLuGAArm28h3MskefM9UjLDUbCOVbPhvZ9+jm6WiW4yviZilxzQQK6n5HC08JQTjGOEypRwnH/XsMqmwIOmhaM6/ienzVZuH5G/vMsIjSz5jH3+dw2C34F7NVOx+y904Em6nezBoxzlDYu8AYl1sqJgnPT6ZpnDL4lH7Xm0AqXMzeddSuCO2yWlXrs2ZbmfQqHd2qF014I+c4pA9Nv2tJrblyn1324oPqXz7Nd49qWnADPPGvElcem6cFL5Sxcc1AOIzRzkRkkxG8cOFSC6nf9iBvaAjpOyLmUytEj33uJARVAsODqCpPVM+8IdmfauDFN//Gd0/QB7zvjXUqzCL7fYwL6l770I6e7LaC/+mVvmDYB/XY3W9vxmwU2C9yJBTYB/U6stp2zWWCzwG1b4JbF83H/TKovgPOC0wnSh03t6JXNRv9WW7y2OT/t3MXGHKAQ3uAjaUE/uGYK6FwrowkAY0ngIJbpGifIoPpgIUqdIqByXdpBuyyeFBBNYdLfJ9OzxzhJtOEJn17ao7fwpUsByKpfgHrqEi8EvH0eudWenCeLyIbBFrTDEZrhUYz90nEAUKTPBKSd1n3wQk77mMSoSBDmIUJVAjuBH0WYiyCRkExEkPpNBLqiuvXTqf/K61x/IxQzFBn9AChTOyFkELbd3zpJ+N7ifF0PEkICez5DRL5bBCbNdBnbtc0jpaBrd5envonfIhGc0u54mi5XFARzEJsS3atp4PT9srVqKp6fDn7rnZVSr9IDZr1zRDbmK8+yRcKaz4i+ar/nE5HYRTjwvEHqOLKlHBYee2yXShNyCuIOMkV/I9J6vBTVrojtEgUvVbS97h9pqk1GWCg+P026j+5ZzgOeY6RVbDKvCCieKaIclG6daHEiw3MtYH5ClupvIkR45omoaOcQIfBK5ziKo6TPh7DPaC3PuSDoR5Kjn7daL1fEc0ePVykAp/UWgWbHiVh3WA/HZS4JJ7JcmKQbiCCWNIvKdZHxmvl3joHX4FxzI309TWyBP9IarokBC1Id4j7W4BPjGDZI4ksfD+s50Q4WysMJbY7sF3nD5+6QL6yoCf0cvfETmzDiM5FB9bDvotAoRZDvPJroS4YgQvvyfSsHiZhzy+jzek4PHpgOP+h7o+OPz68poL/q585GQP/v/8sfdufe+ta3Ts985jMfn45ud90ssFngcbPA0U9KPNdiXGU/+r0RQsGoc/Mu5t00vmuMkRBIQqhZczxe63liHvZ7rOMjvsnzx/P2XRsBO525RvFoFOj9bgxogqCY7+rF/mAFE6UgdQpk2jkgj84MI7ao1NX9Mb8MSnn2E5vkOOudmGIoTpfs06hTTX1zO5geldNj1bD2PcLJt8cr9xDDfuK0sWOMjBEropzMOey/mX/eV1WqZcojkfo4j+F+7NHz/o3DKpOTTPnIY/MRctCl3rrbU5mnhKmEP8A0itCUEE3fhWWYkxZGT9n76dxK5a5//SPHVoRu2bfGzJHkii6/KExRGIusYGrPtVlsd3T0U5Qxime5hPtHr88R535GQ6DXZ8rcpUxfYGo9s8Jduh77WmOSSvssLNER4CXk2sHg6u74FPBz/nW698IaEsuvXNk5WbRAh+NF1N4G8zgLQMxBtZPzyGIHLlSbPNfPzY7kGhvt+ZkzONEj4qutOJsbj8oxu5xtbQ8cjYdSFKwVnq91PyL7wUzpeJJZI8AIRMUjjuu+ie19XUqn4ZRS8ysju5MHyDWNZzuE2MVy2bg+sAwYoDP61RnYYeSiRiehfP7AxZ6DBzOmTU5nfgB2e/oMhuhnmoCGqHd/6pu0ngPWPtbjEdvwzqFEm9Y93jvGsBUdruPoc2M0GpBYDOeGgwpiUO34+uzhR6bp8uX5+umY5MuM75zj6cLv/lF/c/Qr/3Vnr7jwu/959/rozS+cz+w+kSlhl93R0ejYMB2NJKS7Xbv1wg7llBh49Mrc1oMDcwAz9zSL/YfPes2pln9vfUkE+peckYD+yi0C/b01lNt9Ngs8qS2wCehP6uHfOr9Z4L1jgVXxnP13N2Go5wfhrn+TlGmyiM12kRhsSNn4L8DASj9vVyg/zVRJDOVGnk1w6AaLtHqAk9jTd0ovb/7d+fo2QHaLEWOjgphZAIYhlXuelhv5kYhr4YdoCnmTnyKe+/jsDE2nDlaNsb3Sw5tXhz14cT5XgBvR2el8K/Uc6YIbxFXtRtupQKTNVcAVUXZGIrv03mvjmGKTQTCpvyOCuQUf1YwrMRtxdyGK1/0AOXletgVB1WTGnKJ7ulDpitVnnUdNOXnhX6noay7P3EpiLQH4QpiqQamafo6WiDkrosaEjQglpWxXbXM/nys1MDN1/NWKltZxEtMvB/DU+RYrw/b0Uff28XIWiPTVEksvX+50ZHoWjuX9rR+E6CtX5lTiImUYd/1OpAM20L+QHI6SqPvwvcmaikA3sTjUnZdzhM6xiH0wE05EPrPG6J6KgDCJVtELMjVtI+oGMkT90PWI1HC69vDiJ3IDMRwRX+c59WLUvNdpqgmua0FQ6jz1uYnwIrZkpxYmMxKr5jfnYEOi6CGlPG4XdukTmXtev6pNY+QKz5lA/8XDXTtTBE5yI7lnatopCp9a9xoFO2TUnPP8jPWlSbFcf4Iw1hjgFEKUvw5NZyQIEuZ+XnPgvBfzxYQxgnc4kPQzRHrYlbqA45p7IkqtSJrTxHPugwMJ/0IQer4+4Ch0Rzm4L/NLyVHqxzd2EQ7ylfjQ166tkic+m0V0bFzv4X3tTNJyXlwqFWmkOtQaVNkBXLPdY0a0x85Z7vDp33dL7TvLg1JA/+YzEtD/h01AP8sh3K69WeCetoDFc95RrlOeGCn2K/kOaRG4uoaQ0wJqRH76erX+sl8ehepbtdDt4qk1MX3ELKNwRVu419o1UgTP44hIH8/Nduc76sR+wpseNhy7zCz9ruX72ChYxAvhifceexddLt/V436jRTRl34lyVTgEk4kK8VjXe/cj0/Q+D5WgGuWwcLJlHwxW6m4Jc9QeFrsTcUq3E2voPOZk4h9qT6sviLjsF3UcDqnqA/tS2yU3gOHckdu5vs+NGaPgyIzjru7pvWztt7RfVCR4idhgHuEcCekLaK1tkaLC9TnNKSwvkdznjs6jzAYJ5eqB7hVCuocZbGUxXZm7bjh1+3zC8eR0707XXs+2nIvLaZk2WqSXX4DEf2Es4cJ8NsjSg30QqBcYsDK4cYz265TBymc8RXOEXv5l/02UNuOLYIkzMc6/Ol4Yp9tV2IdyWcxFHe9jr852yAxsOhc8ojHgevQNrkDn6BpgsXQCTnyfmSn0OTbofWY9nMxNnBJGJxdsDIbJQAii5MFGrMGJETI4gT3ziAWT98ox4vhc/xB8dQ0/l+V4q7+x2WL9jA6traOJ7WTTRQp4vyR2LdKv9LF5uyGYZH4YloKzyn4AACAASURBVG+T5nLqemCxXKPhzsZ1Grx+2ntnFM9zraM92U+uxXn+rtpMO/xv2C7uf+HD/tlN35ZHb3rhshQW79vF+2Zegyykp+OQPgzHY2emY1w8xsfz2nDhvEveddDAdDAdPvveE9A//S6ncP+Ol71h2gT0m07B7YDNApsF7oIFNgH9Lhhxu8Rmgc0C+y1wS5Hno4iwB6jOu8r6H174kEAL8WBls86GeR8oeU8GcQRAeS1vxhGLI8X4mmcxbeN66X0q0JobfMBW3msBJuqeTUoVMZC2NdgvsHVCyIkUXRaUJcJFzVvak/dcCP7VMERB6pmTPlkbfYHX2vDPHvRKe3d+BnwGtOURDCcFiUQNXoDbArRFRERzWUnO0K8SkQy45zTGnWLa9h+iVCvVsY8zqClvfvWL2s5cI4EPxIjFOcSrEpc5Ts0TiQIhJgJFkQYCQSKcdJzsphTmgGOAF2QR4FDHOrVh1KtnrvijimwgGt3nVWSG0rFLzJdNiHRPUK97JpGr/mSaRM5zSrzxgaqxpC6gzrWAHqntNOaKPHCfjieJaBL87HXNPHnkygwaEbRb9FUURgnFSSBBKHQ0f9XQJkX72MwkZFQf+tLFXe1x6sCNqfns7R0kQwvgJdoCwhFoRZJRp73tWw4hSQwoDZvsffHidPDIHPnhtGyO7ihiyeTT1cnR2RKG6x4WrXUMzh4lmLZ4CoHD/SSwni9Bnr7YnuXRT8SL7MYc6PW4Imd4NrCp50vUR8QZquZcm34kq00YzgKq+19tPXj4yi7iIcVzbMh1IGJYTz0HSGmYhFJdBGE919S914wJk2ID987PRtIn28N3IzGX83Fcu2/2juprDuQWEYedyl0p2+e1qGtFSjj/nbcmnNMMp3Ifx3r3kq7ItGHt7ffP7vNFxEWd76wD7TCS7/xpOnz6P7mZJc78+xTQ//4ZCeiftwnoZz6O2w02C9yLFnDadu9NQ/DOtXXt/ZFpnOlUvnPSiTAjDNkDeJldAwKDhW6KPW5i0TXBJvcE7It5lyZWGtuXmIlraN+y2LeWoLp4t9Yf+b5OmyWmSHzHGHifOAhJmWGFaGzt80Y822JXbWdasKm/x3GyAFmOwfQ3o1jpd4pNOIeyh2usF1HXo4O2r032L2/WllGmKaDj4Maem/vkeCD2UFoKHKn9q2tcl7Ny/1s4B+EqRUD1EedJMIT+JTMDTshyADZWqzHXOcJTOP7mfiv3V7JFpU/vxyz3q2vPRvXVwvu7y8HBz+zsGHGAI6ggjvAcP3Jgznrn6sMhmCmfPwnGVSrLMK3qoCuLVe71ZE+izHs/Fk6TPE/CAtQip3xU4m323YEJjD2FsSidBN5Ke3ivXg6PRI/re9LG615gAc0TXUt1ynGs1Wd2ai0nYc1tYSrO07WMmaqOeq8B5SStNoJPylFWpYlcCsgOxlOVKlqWcXA5I5yZuabsQURvZrFIjNLjWn1mHuEowL/pXExWOcYHLOixGbiG8dlerFvh4EFgAGPh52Zcl4TRy+E/105+x5Gms/RFtDjXg2PItWSo8T36v3QpCBaUfWt+z9fiAcDKfM7ayecaG+zTfQhubHyP+O9e2GPRPyVTGOtPO9Zr7RE3Vc+wrleBHhd+zy66/Fb2EcJMTrleP8eLeVGLb2CkuTY65UUiECbtSek72ejChblcV8+F2a73WgT6F7/0I6dPu8sC+j962Rumv7dFoN/KNNyO2SywWeA9tMAmoL+HBtxO3yywWWC/BW4eeT5EnbPZPW2zjYA+ir9j6vaRaLkVYuhOB5NN7ng+4Ih7JxGUAlZu+gEr/JtEjs8poXcNE+zrY4O/2ID7WNLirpBmeS28YC0aBwFTkY0d4Q2QS0Co4xWp7bTLBQTcjxI7Af+AHECrmgTxolR8juQowT+jKHyvBBajYRqhnUy5pa8s+pZIDbhP4sie7EUsue8RnY44qD4ALrvvRQYhQDK3bdciRuCndIyIH83hR6vONG1TBDaiudIyeg4N9daxWdoiI0Gclk7tkRhafc7xZVxE8iiKgkh0OyisZC+AGKNPEJb6u4XdiqpoAHs8OU09pJcEflK444yh6166OJMdqidokVmR1g/MovFTHpqmd7xzF4HufpXTBUSQ7ldRFAuCxuRLkZkikgxiV+Y9pKiuJ5LnwoWZiOkIazk31HirPwVaO7pZYNZ1GRUNU+nfsZfs3DW4Q1jEWaRSzavusyJxbQeicI+OZpFcUcOuD1iTR7Y5ujYd6PmgHqTuYxtWn1lPnWawCDGeYxxcSkw1YO/sCEVw8ozwfJgkrPRwXluitvlIFnvsKvJkXCfHdR6TVMr2WeCtCGSRh3IgybnWj3aNI+StPocchgykvTqUenFjRNmaMMDaPK6H2B8CPgk2r4NkcIi2ZXvXhAC+h6i+Gem09r4aSbIk62vdIFKCCPJbjTjP2+3EcwYt3iXUYh1tgr3jc5NJYz8VtaXnnbGzLXfryb0moH/TGQnof3ET0O90R7adt1ngCWkBC+dsSzoydYg8Z5/NewZMMPgqtQG0vqYzUv9O9HlkAbkZTkrRYnz/3o7FT7z7o/FZYiWFX/rNe37f+5E9uPfekUbZ79Vd9O8i+n7tmmCv3tPHvpt9+M7Iw36y9s04DKdtELkQufM7MIFFohBUeAeCwRCRvJ+7Nu+P1U76y16Td67fpTXOaQP259hydQ8UY8OvZC1KAR3MYrxUONHtCQFIxzjNdpV7wjkAW9vpszAI+zgcHIUddG1KVOGwixOv769sZpUVyiIlgmBP1vqlOpJOBLYlGcjmLFmkWu907ZztfbIiyM/NYvhjUcf6cBYxJax3ZLnF6ci2Rd/0GWWuOgMZKfFLqBMmQ0CX2K5sben0iUNC4jU/N8rgdDBHshKpihDOnt6OBhKu5322HYbZw1PuyCW1QmzO9Ycx18maE2QKk+1x3mVeas7qmtp/C0ORYS1xkfHIuVlc7+dYGQcUYYsDf+2t/Rzx347PcOmfEoSNmeQgTt34WDPsYJzzN58dykThGJCZr4x/ysHf4zA439PuFND5Pe8BHhoF9FFsz3WO52QUpnXOmmOzjmcc2Jd7NtVaFtv3RfZDBNhcc+E4eO/AIblOfdWibweVWshwyPHcXCmdNb4z8r7zpJ+P4L2TfEquVfk756Rw3thwhXMc32PNb+ye107N/qZP9JrC37fzytOxTuGe72k9c/vGu8Z6J6KX/Xg/JtYGb4qzwJnE/ZjX/HtNQP+iMxLQv2kT0G93Sm7HbxbYLHAHFtgE9Dsw2nbKZoHNAje3wEI8X5D6AWRH/WoUX05sruNcNrlNBg0pmO9EQB/bk+DCm/lwyM/N/T4yCGDVbaHectS1TUJqBFd0F6LFhEBEVyzA1IpH7Wi/FDRalFgZS8BGghaDp0i9DtCxoFwitsH0uWVNPO3eiSrGexjAnJ6yNEN9NMimbhoprsOzGoEJUT2mBdHLHizS0VnUlXiIuBl9BiAjxjb4rehhakwhAjLuCK0SM51iDjBe0ekAVAvXkXa6xbGKSlAqQouwirwvJwERp64TXmK3Be2LlbqvvKHVd9J6e54F0CTq4qFL8+fUG+T5yjk3zl3PtbKPCJ0UXVOgxAkiRWgLtkrTqGhfRVVcnNPy0T45B+h3kUAijUR0aVzw7i4xV+SJMTPRAdPxLKgV4XKgun7UZFNfcIDw/FLN7Asz+eJU6oFWTWaWqJztzucuwbTGVTXF1R4J+o7sr7TrJv7KVmpXCdk+3R7hu1SEjobwmNR/SSSo/RbE5+h+Ez1qm2xYkRr+m3sHKSBg7TRuHuOIdDJZV97ytLPXkpqn6TxDdLaug8Cu43EccYpQslCEMwlrr77cB+qdjSBSmC/IoJhrXjuGGuRORTc7t3Q0PetfzuHFfKajQeBAWOPw4/Wn1pdeCiJdXy6JI4HD8+8+8cwNGUg9eesitG0ke9busbIU3/SjUdBYe++lAK2pdQupBm9234ykoL76fA52zPSHZackjnxsRLv4mSi2jXXbpH+1BDLo+Piei0D/ez/7SdNv/+AHb2ay2/r+P77tyvT5H7PVQL8to20HbxZ4glvAArqjZaOsTOOSEeOEo9i4tuZeDQF9EWEa++nxXXWaDce9I3un27X73n3AkCqbd9coPub91kRfxJ4WcBM31b6DPXzivrVrIXT5fTSMQb+geJdF3V/EbcYG+3tvVU6t7BUSa9VecH6dlgDd25rCNQhJKfzrd5fx0Z5aTpzD+3O8b+5l1DZH/JZzpNXU+tspqWOC6RgyY7Hn53uLNzem43dfnQ6U0Wq0p/frlV3LwnrZAQyJ6G3BtoRkxCH6jCguh1/jq6oRr2vLMZch0Pd2xCvjsUdOm8U8On7kmgXxxnP6jhJUHovComxnVBNdqdmrPcdyUK7xdP3yUUDnGvmv7Vd7auMmolxrPj16NB1fu7FLBa8+qeY711D/EK+NySKiuYReCXRzNPaAQ3Uu4mpFbs84o6LpwemyX2Ox2I9pHMGWuXcTnlEkrDOIFQ6zLUsA1GcW2ut82UrfqX3UMFf2KeEm1bkGN+Wa5uxshUtKOF4Ih4mTrl3fORJ4ntaEsGNAiLrUMacvPA/gK05FPO+1D8cLypWVA0k7cNQ9Rq6qMeTgpL4Wgd7czYpDO1hE3WLvzLz2/CrHAxxo9u3Dk++C3xnXdRwLet0K56teo7ywLNefvPYJh4GReMsAgeI2RlyT6yG/D8/n/HHWX1+5bj6LY1+jBMWdCuZ5STks+3mIKbhbMEpIZ61jbY5/ZyG93h35nNMHOZ4w/nBHZbd7TkB/yUdO/91djkD/xy9/w7QJ6OMk3v7eLLBZ4CwssAnoZ2HV7ZqbBTYLTC2gt4CQRhm8QBGCT2zsVwzpvTYRkJF+fE04yI31zcZk3MMvwMAyk10LjBAr47URnsbv8TTdJ/6MQh6kRBI4/B4AcVEP7TRAsGjnKZ64bPCT1GkBvS4CyLM4mlGERZAAtPAytwhZKdoE0EnfruuSgkrHLsByRaoD/ACgsu9aakQDSKVguzYTT4u065UCrr3aK0pX3TF5VAIk6bXwbAe0QxQBaGQb9Ynjuk3VnxJz55TaRWoQ3aBrCPy770X+CKwpkkLisn5EBBFpoTYoWvvSxWlqQkHnVkQ5QhNCtfC6yBtFtS/GqaKFSdem66qOue6lH5FH+kFEl5APsYJgzfzK5w3g2unUizjzeFa6ckc6VCQB6eHVJ+yLQKZU4g9emiOwL5yvqOuZGDiWl7uuIWJGdtC4FaDk2Dl1X6UMpDY6JF0/j4FgIQPpl69XxIq+k83l1V0R3b6vrodDhfp88XAme5gjuq/GU+fZKeDCdOCa65VSPR0fKlIdpwHPWUhajcNTH5ompa1/9NF1Bx7mazoKqGajotgdaV3kJyR6Au+KbidNumuMQ+pwPaIt8nMTz/WTaxlzJ9cZR37X8YB/nqE1IsfnloAgEq7qey4EdIjhcc0/sR6HiGtCqCJHcGpZHD+sh/sIm+zbYl2+BQem066ZBHW/eyBk97yc6FNHWY3kfpBPQYhdeM7Na/Xd7HVpMqgcOJbHDlF6OT+aBGTuVPu8BgxEnElKBIQ6vt6Phx/0vTdr3pl/nyncX/mzLzwTAf0LPmYep7e+9a3TM5/5zDPv03aDzQKbBR4/C7jmeUaqsq/jncgeZfEOIqJ6JVov3yPeh1TUJDhjXHNv9s4b3/Vr7bmZ+fI9N4q/iLYpcul67QC35+LsP8d3s53taj+d6cIXe5d6t+7Df3nNVfuU01eLex2CWeJu1C5vx0YisiPSuPdIETWfzgmJ+9IpAIzTDoG5NyNzCw5+4ejIHkx9QigS9iLblWzn/fXQ1j6e9PPhuMl87SjRcJxkb2jMhINnCughZoKpMmW750HtLapG+KLMFA4Avs+c7tzT86mXdqXAFFEO1rFpZlt1fXM5oyp6XPgEMZr5muI915FTrY7VObod13bZBerbs3epceG6zCU7aJOBLFJwI7Tp/kfXOuX7gQR04SZlYxK+opyZrsteOx2lEZcloiuDmaFPZAdQs4ypymGX9OV2Tq/xyX077XaZLPZodT0ybyGga6y1tZPDs+dTCfw6TufX97YQx5RDuh2mS5w35kqhH/zkuaQo++q7BxOu5nh2wM5wX/AGODXXIjCXrmUuYHg24TNwjPb4BrbP58QTr/azPLfu+wpG6Od6cFAZHZN67zwI6N3fuvaagF5p7Oco7ljvuOVY+qNF3EFw9rMVjuN53NrSrO//0zun6be9z/wtbU0BHbuvnh9rGSXXCg8uItJ7PV9J184YZ6m48T028m75fijO6cLv+pGbvdlu+r0xUzlstHMJzhrKLNcOVtFv8H1xpO00j5MLa7fx/cF0LP4Lx7BwYrjXBPT/0QL6h93UZrdzwHe+/A3T33/xL08veMELpte97nW3c+p27GaBzQKbBW7LApuAflvm2g7eLLBZ4DQLHP3c81fTsS7OWZBCAexPE89POKhW3eqM0sibrIl7p5FDp4nnxY9kwGpH1OqaozdtRiWuedo2eUVkNeISaRaJ/MTbffBqxtnA90m33gGoN3CgczdhiPJrvFexqa5FhHYCzQR1tMffq26zvNslZM91mfwvwNTEiERI1UGvlOUIhzqGYzO1Ffe1GBrRGz3u5fWtaxJ90URTEQEW2M9VregiKyC+OoK3UB71rgwaIXqKVOKel4qcYR5APuD1rfMEcJLk0mcWzytyooCPL8l3IohEzJg8KcD60MU5Cl21sB9R6vCqR04tc5EwHqNqHAL6ifRgRbTKRhLsdbyEc/3uthLFULWvqWdOSvh8flOIJwo5U2U7/X1GmIgoqbTnaq8i0AHvnOe05ZWuOXGkU6ofdhSFIyr0o2Oo/e2IiCLiGDNd3xE0Y/q4Gn9dQ9fV90QWqK+elxqrinynHiOCtAmeiuog7byag4MDonuNvVMJivSyHeteHnPNKZFJF+w4MNccL9HZ80dp9R+d05drPlSEz2JNxXECkVTCpgX0EsRZh0YB84KiPFSP8tw0i+dFhqaXPNdkHFlzTU6HYJtEYxMQRdZ1msOV6Il2NjoZgX6s+V5R6Sb/lJUh1zwInFyHTwjRQQLpOFLbt0PMyhraxNApb7tRfOfQdC4YTx/fQWuiuc5JYWHfe81zv5wCUkBv8sQXCnPtnAkuPOc9J4OcilB36NqJQ3YWbDiSrDhcVfM6yq0Jw4p8g0Tvz3fCwuHT7i0B/e+ekYD+hZuAvm24Nwvc9xbotO0WJ0NoY3/DRiffH/3uiDW+RdwyWb5HENBZV3l/9Lv6ZvggN2NDzdm8xr53Gsfk9/zOOwFhb3ENBN56363NhlEQ4R3a16l3Sr57Fu/iZW3kE9ljbmsGhoBe4mc7EKYoBl5qR8VIp5/Rm4ib7GstxBUm0LsVkRvM1NgthDK1n/177lOIsEW40d9c33vT2q+1mFz7VnANe9kUbNWOh69O0+ULOy7AGKeypzndeERZI8g5Arn2n3Z0DBGbLF3jHlPHyYlYTsct2s99PVZ6dbXlKRfn1OTvnB1QLZojcKuP14Y07Qjoa8+acNmN4+n46g0L7RbjlSJdorbMre9zT4YzdD4f4Ce2ndrT6lzKFhlXhHhd2aeOr8yOGBbshZuEo4QjwZdgF55rbC7HY7VRNpHj8dWj6fjwwg5PlEC4w2nlcEvmtfF5xsFCbSZbFo7S2qPrPDsPz1HoXd5J44ODvLCGsktdujTXg9Z3EtD1o4xfpIK/dm1uJ07YcAmI6XKwfVCZf8LpxHN9dtS3cE+EudqUTsasEfrXTua1juo4UrtbFC8nHDCU2kbGrnyuiMRnzfVaVsEFjaGKs0ncxDqQdh4xe69nbJpzQSqCChyVQQO0j7YnXkq+bRTQee/4GQj8hD0SMo3r6LhWslYZt1b7kyPjHtwzHCBmbu1gmq4+Nk2XL59chfP91v2pXxb2HPqRzwjXWHNW4lmeDu44ZXs22pjJ47RLn98lFmj2QkSnFEaM+40bO8eQCo5whgnbqjL1jY5x92AN9C98yUdOf/IuC+jf9fI3TP9gE9Bva7eyHbxZYLPAnVlgE9DvzG7bWZsFNgsMFrB4nqBkFAuaf0mygl3jijlHPscb6/qQtNudLjbOP3HfmxFDdzCUCFbZ3wXI4ZqI31F3el/7ALwI9n29SMnWERp1/TyWW7ZefsLroI5YEYwAL32NQdzX5xaly+u6TRbEVANM0gcWqBWQFjiRsOiUfRUF7XtGPXXELX2OSJ7gxgQR6afD25rrAUrbg3wAUmkrwL/F+iJ2CoB0HyF8LM7FGKhNRJ53HcIitFwzTinwolabmiHSwzaMOnkicXzdIECJsADEc47+VtSBCIl3PzyL6hKgq9be4jr6XD+6vggcEyThEY99HxaJIc99iLaBIGWMReyonwsHg5owayQTAqs8obGp/nXt7xKxGXciPexFXUTGeRErF08QPMdPuTxde+ih6bxqoItAybnAHDABWP1AIIUcWKw/Ne+Ym0QVlFjrtojkESGkH6cbLDvoc0XJUxtd89q10hWBLiLoatcSnCSIi+iyza/PInXWR/f41lypun8W09MeIpF8XqWjJz1+2d4p3D2PIB9JAw+pdLCLfofkgSyQrR3lX6ntNXXSOaVtWSkm1V4IWsaQemsIyqNIz3yAhG0yJ5595lqL0rPYawFdrwu1G2eA8Nifn9Ua2L7vHk+sXB8g007M6WrIrb5DFiJ6L4rzL1xjvFb2dTilz9snQozX4tmGkEryjv76WmWTIlbuioD+phfO8zzTTY5jjB3clogEZB6Z0c5UxcOcWHy3i0a/1yLQ/84ZCej/0yagrz0h22ebBe4bCzjqnPfY6BCce5Z8p+Sa3u+T+oV3h96Z6QSqdZoI9IzGy+uOVuUdP753RqH+RNsGsbuF8qiNne84HPZyv8E7C4GPNtzKvb3fir123n9+ye7Sk/P3Pph42n1XZ+EgoLMnXeyVydqFA2U5TBIpiuMp5bCwz+J+9U63Q0QJ6donpt3wBMDZlHHCyU57Rl2G/WY6ydrhFBxYN24RrF7gJ0T5Id18psdm/o1OBGA9ZcJinMBcYCTwtvACeMliXrVLOCbENAnbnULdTrrTND3y2KQU6xbQ5VzMHkTR5L7WbM8DC9JD7XLsfvW6RfODSyq3dWPGX/nDPowMW8oAlqJt7wl5Vutf8BnPP/NXtrky44d2CNAfui8OBnaMUU15CZRZmqBwi/bRpHDPbFT0GYHTNg9nZ/bpPWfKxojjdvaes2UZQ2ju6Zo44qpdwlfMLaVRV2YxteFKZQ/T+eBsnSc8Yky22wfOAjrlymZHY98DZw9n/4rU93WuS2aRgUvzGNzE847DN3Ou18o5ErjLh4ERNEajU2diHtabvWtoPeO5R891KbGEhdthbsX8PrEMJMbN/bjHeKXueDq9wsmsrWUI7ojoPDN7BfcoX7GIOBkuvhbxno4/+9ZiBPgTbY01122s5yDbX8v+CafvXHNs97h5i913R0BXE47e+Ikztu3SHDsxfRbBpzkSvbnNIq5UxqznGNHq4UAlYf3GjTkbA1kpwk6Hz37N2gi/1z/72I/92On1r3/9JAH9U89AQP/mTUB/r4/pdsPNAk9GC2wC+pNx1Lc+bxa4yxZo8XzfdVfF89qNr+kdaxvoFG4gDCDY874pXKyRLYtjI63UrdrkdsTzcTMPGTS2NzfxBoB1ABvm9K7NPi3AefRljfQZRZkFiNpl9prx5YqAjjew+0TayIhEsV0i7Zm9wou06zTe1a8UztsWFR3qqAJFEZdgD5gE4AfZ0SmZ3bdwzCBaY8BVc3QAqdUqVTcAvoEmderLKz290fE8d1T2AIgtnFeNa4gj9UWCNoQK8wEbC9RLbGcciVjAPvqcaHSJ9iIXlMrbxEA5Zyj6QT/8rYgKgS1dQ9cz8C9DIK5reB+7Ptco1GeOKKljxghaUrjb0SDaSh/G50k3hDRJggGhWJ8RqaFbikRRmnTS+FtIvjinLK/n26SKohf0o7SJqqtOSnkiT5r8yShqhMNqpNcN1V+vueX07BV9rlSA9t5X6j/Zt2rHObq96pSrXyY5D+bIh2tH08GVR+doigcfnIVqET7qA+K8BHD12dHqmteVYYFUe2N0BXXM5gex2lPnEd0hokkRFgbMRfZ4DlT0hwR+9VMiv9oECZTiNQQcZCVkC89XOhOMayNkko4lojvHOn109tbzW1n4icaAPKAOop+tIuhGIhqSJAmotbU81xHeG3ayqHSNa/M516aRWOnv9gj2GbmR7RnX4cV3wzpcy9quGcOLMQX0vG4LzzvR2aRSETN3I4X70ZteWCkHa/2BBIaQHduDPdJcjF0SVxnNxiMQjnKHT/++W31Tn+lxmcL9Fa8/mxTuX/S8LYX7mQ7idvHNAo+TBRx1zvrmqLOhvvadiueKNL1ybTp46MJOhEmxDWemfJWMAvmp78LBYLcirqdQlKe349dQFzaFksRMa+30ew3nrHCazffP4veICs33e9oj37vj+2qvbUroaAc/6tPXTfx17bVb2Ika9hmVqT0Ve8bcS7FX41aJsdjLLAShssso7hHlSHQtgLNTXiPa1H6EMWBc2HOnEyt7kUevTZMEZjCQ2kx0NJmlah8/7+drDyYBnZ/sJyJWC491kJ1GhW0iE099dXz1+nQAnpHjsX4elRB7PB1fvzEd2LGYCGF9thPQHVHOPEMPHDVICewZSTrOCd0bsdXjkZmA4gHoeVbHS8RNnNRZBw7mZ5p2KWMY4jN4S3OmbV42oYSU/gSftu3ruaG2OY4M6WyhY8F/jLXuK2zjccVZVxHuUSJNmEP9lgNsOmcIJwm3SEDXj9p37tycWl44WFm4DoUFj5ZOwTgPyz5kmHN2u3ISqKjwhSgJjqN/Oq/KAtnp2FnJQmCuyG2J8Rbtjd3DYR87M59xDseJZE2oHt8tYKVxvRqje+n7yAAAIABJREFU0NcEdK414ofGXRFUwLE4/i+E5FxQdGCsSfu4uIxoPsHzjO+D5KEGJwA/B6zRo3Gq/Z7jQ0YQHzouxCuNtS1ivR2vxXOa/JgvHTxWYpG2+TRd+D3/fKXBt//RUkAfz5dIvnNW93yOtd48AmtcpuqvOaBn0A7NvBPpvx61e0xA/4IzEtBftQnotz8ptzM2C2wWuG0LbAL6bZtsO2GzwGaBtMCp4vmC7995Wi4AZV5sn+cpxxi8VI0fRIL8bhyaNcLFm+j3YAxvVUBHZEYMRbAeBUq+V5NS3AUYsaFuUqmiJ8AUvcFOAMIueyVlcoKTtk8Aln0kFfcHpCB0A24QotnAj4AyQaOBwEA4QezJbvo9gXyLjUr9HqIoYJa2IYyu4S2iOdI5AQLKILIiLvDkF8CG0EB8UrrrUXikn+ofwm5GQqstImhEqhD5WqkAJ8Rv/e20ghGByTE6X02T2C3B16J6EQ5XlQa/osghgZS+u4RyCJeuB2hwOEcz+DHQPdWGFvArzdqChKu5RKSDbDxi1yR7W9SrcdTpWb+M1PHcwykJH2wC+fjy5elAKduI4q8UhRaKHX1SQiDkEsK77aQ0kBWdkOuCyawi8tR2ETz6Wyn4XWdbhOXMks1EUN1D/zra/fqcWpC06BomkUPXr88p2yV6P/Wp03Tl0VmwFlGklHNKIShnBpE9HUEN6SOxvjI7MNcRm7FhOWk4gsRCeDF5TnFYZJtLElTN9XbcUIT8+a5Rbbsxx0nbGUL9ItoiH03WSuZlkhOQvIw992YuZTRNO3CE44366LYkN7K8uSNWyF5AH8Z1n3FuAT3fM0mWD/X8IKl0Xq4beT1+TxJ/QQCtPQj7WKi62PjuyfU2CaC19+K4NkP2pCOVxyzWkY7gm6a7EXl+2pvz6lv+2x1BZnJ+7UXLOAS51k4+NRkgp9V2IuHqvXL49H/yHry8796pKaC/7IwE9C/ZBPS7N2DblTYL3CMWaPHce5Iog5Lr5Sigd9vX3i+VolsRsv8/e28erNl2lvftr7vP6eFOkm2IwUIoCAQOEqkKZvS1MWYeTOJyRiepxEPyTyqOXXbixEOQAOMhdtlxzPBnnMGVwdg4IAYjproJCPiDojCE6JrBlm0ohLGkO/RwuvtLPc96f+t79jr7O0N3n1Zf3X2quvqc79t77bXeNez3eZ93eOXutHlGabIj+5BJsHIeTP2fd8WSXPZip+oY5/u+65beo3vJnyIw0kl3KTvJvvkbCZHuiFfkyLG+INzSN0byPImeUUazOSqyZzY3M4Vmp1uDB8ELejSRhqSgJgJ9RgItZG7p3ceBEVKqou7ps9+fQ91zyDR/V07EXe7D2hJm0k/XeSpC2bpdOaJ2DFb3si4YJynG0dvQcfQ9mFH6LwR66prsD+v9VWNc+8VOyUWAQ3S6/UjJXtOweboyoEnHu3vf6dctHpyVMwId9bOvl/pF2ENRoyZLp2lzQ3ih9pzHHwsIXRfdzCQYpd5GEnDXftPZYoFD7oqw1rpQJiulq9ePlsR1lZYqItsl0iQfShwVuS28Yt22nNnRdYlE1/zagUHlvCpTVq4dMJPnunBVOR2baBbWNAEuzKRnl9DJGOUo9HJ81mfCWsIlKkOmORVh3u+/76xeu3JSwnLlkK5hV9bB7dXDRnAb6wmjF45KvIGjux3NtWbrOlK1o1/a4bhkwHzJaZeIfQ3H8ql9xRnKGk2nlTzbfEbUmsjSYTiSQHD2AInCtWCS8Zyb4YJYJHyeRw5Yl/UzO2cWDtC0U/F1OjLvwysj5vDiDZtOx3yScc0R5+rsfCtnlOxaPjNtVGP3+7gj0yPX5D7M9wO/J0ZKAt37q4jro3vTwaf/4IVrDXd+4StKdI1A994K+5ccPtq+r1IRM/sM7+PKQscasw2jvTueFPJcfSEC/T/9hs+YvuYRR6D/7b/xvul/XAn0C1+v6wNWCawSmKT/jIhmFcsqgVUCqwTOJoFzkefZ5L5jZ8nePusKUcqkpV644ZiBIxo4tf1Txo3i3w0KA5jh9vF7ABXGi32PQXHX95CFx0iV8JanvVSos+2MYGfs3ZBRF2bkywgkcjy0lcapGftVaKan+VuwRnTj0WCo6c4FlU7dz61x0j/SiEO64XEP4Q3QdTcCzAGU9H+mFOQZNuIUwWjwKRK5yB+T1VXHXe06qjjSZuGhrmvUH/XV6eUUMRxjtDGzDCQG/hVxc3kzbW/dmzaKKCBtO8BPNckB9XqOiG6nji/CVM+RMYh6fnpG1UGHIGf6ZpEL+lCGVkj0qzUeLibiQGOX8YYa9Ta8blpUAfXB+1obHDDSGDSbj7rBpKgMQpVy/bpqXcsR4PK0ffqpRko71XkZTkRIHxxOm1tleGG9q48y/mAM1DyQlpJ7mVP1Q7/bKFOydZ0+PfPpRnY77bxSrav+eRG7RDA4TWA7d0y4l3Fxc/NmSz2oqHk5UMgLXE4A1N9j/Wk8NhpWtIlkq8a8HviujKUlC0e6C0yrD0nAq03qDmJAzH1P1Iei6rVmjiKio7aGa587PTqRI3sMrWloSOeVXseyHozBJg0UNi6yp2usvq4fpHVzWoDid2cHqD3Y3xlhpMnzNAl25i4N4T3jRj0bw6+NrpVGH+Ovz8FKR8rv+94d2a806OTe0O+nnf3uc0RQ9M2LiCJC0dcNkXc5TxFd1SIb27o6+JSHr32+79XF53f+0VfVOEqG4/7vchiMbZx1jgyqEhQY2B390yIrnkQC/a9fEIH+R1YC/bTltn6/SuA1JQGnbEc9zchzvyPqJdPfNfnS2UOc17tu+8qd6f5P//p0+TM/pumEOKxaD5LDXemnowPtks6fGCqlm5+n4+hJM7CElY6965IAiXGCVfif+8b+JUGia2b6RZSZyvDEpfe5H72U9nhwvptllxnnKPQXY7gi37pjYWKcElySfwmben9GfQmFoojRWWRnsFPSvfQn2I13rB9b/ZbuCeaw/lPYWpgDctFrpPThrneUvmhdM97lln+kzye9Nu999DRndmo6v/Vf68dDOSvho1fvThthFOv6Gm9zPOi4qetJG6dVd/Ytp36X6LeN7MZB9tbdaVuOyT1Vu4nwFpnun5Hc0+j0fH2VdcjH9O3KJgbRq/F3Xbn0NpwZZuRXbBwiq3M87E3awvlYchBmBA8KC2huKcUEllIGKrBFOm4YI4XTCpmx9GzmBCIWkl1OzfoRbhB2EeapjF16tnFERmPruRDOiUdUf13ZxYxpi1wvJ2iT6Sb3ax16vEe7SPPEps7oBTYPe5AdvVX3/VLDkZDcOS7dCxa0HQGsXfjL919uzrvgSf3O88E6mZ2uz1th/X7GRRCDM9cVDuJ/9jgR7vtsY5wlrFGOneStPX/hiJ/OzH3djanhcXStPdwxyGi/qQGmzan3vQ9+/iboDkI4MoczUCf265bEPjOsFLaF8T1jGXLGDoLh2WPAio++oXTg6Ozb3zvtgQdvezTR52P3+btl87o0bcqZwdnjxte9ZZIlMnAUj30sGeJQYgK9LZLDN3/nvkc/9s8h0P8TEeh/+K2P9PnfJgL9G35mev7556cXXnjhkba9NrZKYJXAKoGUwEqgr+thlcAqgQeSwIOR5wNBMODu0zsiAr2iKU4z8hwzspze+qlXmGctzRYAmoAnwUe/bkhNtaTQo9QDgHgOoJfvMUyAFRIsZechWGZkd11gI0pEf1IvaV+/dBsg3Kl0K5W3+1rGIEA2nu72Ro/nub9l7AG8S9Enepb+5j3IUmtExKaJroh+ToAvEpWobxPlBZDVL5GhkNOA5fQKpwY6oAmwj4e7P4c8r2gCxoOHPMYX1bxWvwHWihoQsa16eaqHN45T7cjoQzp1k9ZR548agCLKFXGh+m8ymsiooLbVbtYxz6h11gxRHPpbpHwR7TYkEYmhMVKnXdcrMlvkpcaHEYK50fcy9njMBbITVOs66p9nbbYEhFoDjlKQd7raKyOX5ktGFUhl5Oz0e5Jj1UlkH2C0YS3idCE5E/liQ3LVwCMyQvfLEYCIn6q7J4NQM+Ao8pxsBzVG9loZV1p90c0kAl1RFT1duqSiCIYkvNU/72Vq8V2etk5neamlL/Q6KYLea7cRhjZQKeUkGQdw7FB76rPTmpeTQfdYL8sKERsY11QjrdIW+tkaq/YGjicZUcBZgAGWdZtGDqJTWBfj2tb3Y5SCz506G/r1YXAeD2DJ1eKvPd3PwzBaj2sP4wjGnTRY5lnt90MQ/EtnUG8ranXPzlkOYu+kXWaTXOtLHAifzQiJJaeCelhGU+S7ImWPvDmveId8RAh05FKpiTHmdKIjssBgSEsDs98VRfxjTLXMNk8kgf7fXxCB/l+sBPp4Iqx/rxJ4zUrg6P/+zHKgi1qrnTQf3mlNO6uxnkyeS4/afujOtHnusOlxI/aRnu16xUVgjORxXn8arkL6vIuT3F6amcRL4/ep43fn1iDTcSjGGeC0vvGsdPQz2eWXYT0d2UcmrxwT5Fjva2ZMIVK0ovuTIOrX13Nw7hwJcd7THlPM6zECqbBVV49wmKvJy4jf7CsyIF06pCZ4FYdjO2+WsxoOdybid2LqTtyQfYkBjumLRUolZrWeWdHRKWPd61rZ22mS86yGpN/BXPodh+DcAozh/na6//6Xp81vvtFqmkuUowOy0rWLQBc+Ukp5PeTWkWuYe2cJTzFuotCBM3ZACSKydJQZgV5EfsdN0td/9ZVp83HP7CL7Z2S8SioVWdvJwxgcOCWxc+4X9HRKfqmP11RjXJjpsF1pnF5rXc6z16+2aG0waWahMmlYpJy/L8wsrMW+o0yZcENl1uoknp1yovY4bYCfsDOoX0WkG3eIJBemVPS6nqUu3D1q2b0gzJMUFrYXDlL0usd60JyTlZJf/Vcf0jlFaeVF6OszXQfBaoxL1q8qdbWEYStbh+uqZ0YynE+4p6eAL+cSy6zmMwla1jWy9nqNkhOJSU56s3E+5P5aur6fK5wTUX9ghjvGl4Aa4+w94X3DeynPpewHY0vyXhMl53LPdzg0zc7PJdC0B0jNzgTKBoYtIjHFkoxGAp1zxSKYY71Hlbr9pKnVd45C5/B1/5acrgeDqf+s/vKe6+XzdurD4Se++7THP7bvk0D/PRdAoP/NlUB/bHO5PmiVwOtZAiuB/nqe/XXsqwQeUAJnqnnewWNouwko9z77BKMRqQhPMtqcaJA6y4BPIHQgJWgmjUOp60N0jYSTCegl4BLRJ4D6kdBJD2SeP/NUjQ4A2mbWkPq+kxj1N8AvAeAoJoFn3af0bSL7MN7YGEQqPtqjPlymTwwjFBEZzKVTxy2kmu8Aojy6MeJEmupeZ04Eo4FEGbbcf0WuBpGPPJNogpzBu1y3Wf5BguN1jvc48nPaQaLNK7JYZCVy1Pev3naEcwfW9rSPlJ0itPW3jDoy6KhtSHN9ThS7vhfmVhT6jeuNOL15x8agjT4jhSCR7SwxSPGs8ZfXZg1B1wUsYK8xP/NUi6T+wD/fRZSwLtQ+UU0YSnCmkExkFHOavYjAAZx240iQ1HIMsJzlVFFpJ516cOe84Ehsy7tSDpqILWOE5nFWu6+MJZDSTt1XNcLthKEIjnJ2YP3K8KJU8orWxhDlCISKPNCYqS2O0er2bffJRLf7d2dyZLfJajl1VDr4niZPRjTqo8vwc69dJ7KeNIwAYUWfX1adcwyLFaXO+uuGioogGIB/N1phpHAkemtDnu4m+XGMYU9BRkNmYozxPM7rRFqeFe3RnSk0tz3SZThvOLN7dE58n2eSP65FQvQ693bjXNSGG8nofuYula8YDjZIdM6L0ZjVo9YrggKZ9/M/2ttjjzoeSpB9WGLXY5MN9pKZgW4kE/K1xe8Q0Y8hioJeOwJ9NsRKw16yznSCd/7J1+wMQN2oGPVwfaakQUsR6E9eDfS/9hNfNv2Gj79+FuXizNf8+j+7Of3Rz1proJ9ZYOuFqwSecAmYQDeJFERLP+7jXdB/PQGL1Gty+/KdaaNoWIjzpfdCJ8XqvTnK6dg9w8tsibgmam8fqd3fkfWeH9+tnYCtd30nhuK9zXs/if/UFUasN5L1qY+k7orTLLo+KodwDQQkeqq+cz+IHK/3Ealyuw4xkE7okKmnZR8geexcNpLlEdSNDsT1ZOLaR6S530MGAvRY4wvSsg/p3kPtMm6i3FQnl2KecISkLXTrvMcOG0M/dB3RzpRokmMxmEnPunln58yr663/7qIp7Wx85ZKdjq2TXrnkvztuK3LMRLkh4WaablSEdmEm1zknhbuGBc7Keup2gCgSXthM+EB4LYhBP5d2Og4q7NGdzmPS1RccCiCn9bVwDY7DjoaWo2ll4Or6cKnFmkvS3QtnCeOIQMf5ANuD5kY6v+RtmQceARNAerNvTFTXfDhjRTm8GqMcNadjPc8O4pJ9C2ZwxKxrjlc2K8h49q9kJ+do1wCvDF/CUsZKwkD3WqYt9UMYJR3lNYmdQNfZWY6/lK1ylgTOmHJSBr+QbQw7iD5nTMiVvQ32UWR94S6VDXN0fTlsO0qYMRqf1k/OpT5KZ1DwG3Yf+sC9iZdyrsezjXMpnYP6GRsHup9dpLn6hb1l0fY04I/ETqe+S/e8m/J9kCQwODvfCzP8FYNJ55xj76qwlY39Zb2xnvv7YwGczfoZNdADRz8u8txHwC98+dxhbpj/w7d8lyVx55fm+KqVaKiseJrrjqOebAL9D3/DZ0xf/YgJ9L/zN943/U8rgX7qzl0vWCWwSuDhJbAS6A8vw7WFVQKvSwmcTqIPRHgaJPZKLO8ZFHQplCaZgoA8plwvGabOOz0jgT/0AzsJhOwx0JEOA+XRz0dJeI9Eelf2I1XejKAfvA/wPvXwoo8jeV4GhR7NMhtOkBMj2ECBd5RveacbHFbKve4iG4YjR6K3etENBBepTdS5DS7lgaz20wjUQU+RuHjId0BKxHsZMUghTXsGxgORCiiG/NM1eOgKgFt0BTKL+LQhRu2QHo7oHeoBMm9OoR51qdWWIy5kKDhowFWe9jLOdFBW1xjxlNxkpLlVaQpt4KrFQt10osqFiWWsebbSjb9cKfXGVIKWV7ShsTx1tRmmPMfVBxtPqjY7Mmar6Lpnn2l/qS2NQ2Od7eGI5OG6BK6Q23per3VdDegzG4LkjFEEsNOpV8Rxpr2044LGftDmhKhzatFjdMDAgGHj8Gqvm9fT+Hkd1POqHp+jyO/LgFMZEZDF7Tst8lvT5wj5iny4f3/aytiFQVNtini3Q8WdZmTSM0TE6zPX4atIH7ejyPKDcjKQQ8BdG2i2ch6QgUae+uxhyHsMXe57RS6lIZl58/qOVPPXrk2bm7fanqRm5WhcYG7SoKB+YrS0IbGMlYw5z+A08PazhnTkQ2268czKM5A9kv+n0Z22u0F3SO3Xvy9hZJaJfAWkcauv9zA6+UzgwB4MTLPvTniv8G7o159ChBxrKlLG9vdLXcSZRtvd0DI0Eq+Li05BeJY37J33/57p8BO+4yyXTibW2Yce35NDnDOArIH+Vy+IQP9jK4F+pvWyXrRK4LUggaP3ftZOp8p3Sb57F2z9i2Pzexkiq4ih/m6Md0U6Oo7p23VZ3pP96O/GoUOJSTijTxP+eE8nqQsfQWSnLuf3HOmQeX+GA/KoxzAW+pL4bHyfj7oB+CDL//T3a2CEro8UMaxuLTke67qRYCM7kmU2ZOdh/KRP730vaEcqddKdux8hEzPFQ1piy6P6hzOx+1r6TZ+TurATedwTzpAQ42rHqd0La0Hmp4OqmtP8Ske2k0EsDvVHuAvynCGIiCY6WJHFzAdjwhGYMSl6+ZWWjczlrzoZ3j5X2nZn2CodqEWhH0zbl1rkc0/fTnvMSa0h10kXAX94aXL5K8pk4dTseR+c4XWdCGvN4Suv2gHZTsSWcwAn5sDrC0fQwEV+nkjqyNTEPiMK3e3KofpKk3PH6pUVDFmqHTCPCefCBrlXcK4QMSyMJd0f5xCIbGcMK1zv1O2V3lwjc0mznZOknZx1X+EcYauW0l84qmUSU3auVgarHEe0Xlzr/W7DTCb9y2EY/C2i/kAlzORwfjRthKtEzFdWObJrdXwnmfQMZWGjYC8lgc4ZKcdiZeby/g3ne/9ebeTcQErrehPjUcfemM11BHak5mhjYc/l3mVu2ANgJHDAae+HTsJH9ifWH2fqzHYz2I4yK0bfupEufN9Z7zEMUdP0eV/QiNsanx/vrn6W50PTCJHOVpxtpKgfHKhH2WeT43uv/n6cBPqSWI9+7ov88cGnff9pb1h/f+cXVUt9nqL/8C1PTvS5+kgE+h+6AAL9764E+pnWyXrRKoFVAg8vgZVAf3gZri2sEnhdSuD0FO6lGAd26fryosSWkAEIuEAlQH2PwrvT96Ot0wDHrC/7Lo5BGA+Hct4JjmGg3cAzj8BLTDJLtdgV/H0E+gAsOlCKAQCW07iUgMqXhkzTIARRSXPIGKKZdgSYMaAYABagASTZ07jSu3WwqpR9qitdKZMFpE0+V+0xG/EivWQB7h5VYJyFYaGigrtHd9Us1zU9uhXv2wB+o6ELY4SRvVBKpVjXc7Lutb6zp30RuYBdyYUoa6Km1YeeOlxGkEotbyBfZDXEpP6WYchAO4hs5H+rALwMRDIG3dX1m2l67plp+ucfblcRMcGyxdBU0RNOGy+jzkuv7qLbkeU4z/1vX9AitG0IuTJtb1ybNh/6cC2dcUPX9RiGZkbGcHaR3DCc6hbSsWNYxOCm5h1NIc9qGVbKQOToBRHg5cxhY1ANHGOy5N3rm1c2gDR4OCK8DBqOeG9REERkt/baudVr+umv69da2kAbbeo+iGzJ97ZqjN9vkRjVpV5bnLWkMUuWOApovGRzKENSe2bUEsS4nXvdcqRu3XCQQqA7er0ZtnZR5lH3cCRlbQzMqO6NnQaaUUpOKWWox/ha6ee7cbBHgERd7nSOyXnnJXCMm15YV3mGQr4jkyTxEUOexX0dhow4Y8YSDj4WI/J5THV71ncY80Qf+2IYzuictrGfPneHmquLY2GhDQ5lx15hm+ngbd/3mtNRTKKXwe/w45+MqPMUYhLof+WCCPQ/vhLor7l1u3Z4lcBJEjj68c/efd2dnx4Ar+hdQ6QwOl1/79QjTAxBmi04nI0dPQlXje8gSKOzTHfiJevpRSqO0ZSd1AWjDIRvkiA5VvqdfZzpHOCAes+nvNyV0nHAAqXSHi/LgiJQ84VD7ii3JM/ge4iUnekZ1Q56C8QnhDgEZk+7HvXFkXuPXs/MXzgBUiO90nujL+e6mzlcM64iw+zAWbiM7Fno52qDaEfprGBC5sgOrhXtzvLWOIWZhGd8Xa0F4Za8HudrcA8p1bs+3DCbSHKT4x2r3m8101XKICPIVQddBOyHb/XnUifd5DhO01oKd+63CHdqqVcUcncE2EcGQqBbFnIcqFJnYNklHY5o5HFNWDZBhPG9MWthZmetKmdt1gBy03pRG15PlaofkjrXKutUjsJgc4ht9qzmW7hKf/MdUdpVNq2lTG/4p0doe82C5UTO79K9O5W7IL/HIhJe9Z8LhzNGHFDU38PDcmIujH7v3g5vaZ7IatDtH5FJLuUOyTsjiSkL2Ep9OZU80fqUYoPknbUVzvisyx79W47aOEHgkJB4dWk9GIdETeulSOy+lzromZPQeR57XdRZ2zFFEMx9Uaej8wB22M/p9LT0vll6lzEersf2McNHC07Ws/M0Ur73sVUfU4bYd7h3xMgn4cbx/D4HaX2W19/jvObOL31lPW4zPWnkuToGgf4Hv/4zpq96xBHo3/5N75v+5zUC/XEut/VZqwRetxJYCfTX7dSvA18l8GgkcIxI78roQr3zvY88hgrmRqaKztyb/nxU6Lsyf94xPgyBbrQwr0kEaZgRBgkCAFxpDAmH3Oa5HVHskLcz8DV48HbiCsvNYPQ55mE81KlNIAKIhpjWdyKaZQRJj2z1Kz3mRRJiUNHYXKu5gKHSwAuYOqI5wBrGhA5aY+4g+PQR0bD+uvqewJVI20wZlkS/+t+BXfNm9w9GKhsMirxWu07LLkNQOh6UQUr32nO/atuJCNd0UNNb0b981ueuxmWDRIHwEUwDnAHcaUh67qlGLiuivOr5uUXXEid6v0h5/S2CV6nkuYa9kv+XKLvEJUPNk50eKl27UscrsgKDUEZVNIFWdgGcSxbmlnXktRAPlWxsqJPzQqQqp4YiqfaV6h3ZOLqiiF21RykA5Kwob6VLd63vinTHwMJaKzK83VvOD5tLrT65avUR8a6hIIurh9P26tWqcY5xIsbCXGIcLCNTr4GuOT08aJETJtGbY4mj3ZEPdfbGdYExIwn13AeSjZ09Lk1bReBv77exY4yCJNd17G2v/Uq3SZ/TQYbr0niNU0I3ssVcpzG79zPOqP49a6avuv2/zNqMfZgRddyd48oWifbp67e+LJK2Z8PASQC5nsUBK4nzvq8GwqL3b/hlaY4xbGX0Ceua/8f9y98Yph9j2vYzzOBH3SVJoP/lCyLQ/8RKoH/UrZt1QKsETKKPRvv8+yzvHL3DFwz/XbpEVXanwMgQm++S2TsyifwTOjHqBCdNKe+3/D/v7+/r0BE6wZLZYOJ3dBGem/qB1QqwJ5HZQZznPVxnnWDAWuP7dabLRJYnXYc+mTpIx25yjlVmrtJVfb1wC59VLWrrn+Wkq9+7g+9OL7XzaDomkuXJ11eWoz4+IsmLgOo15kuO6AnARN2H86nXR/SXSHLUPDAYOrvupVyRxpbZnGZro/T0Xoaryldp6nWfSFat69uZHpva6prWbYsIV9Yt9UX4JzGP5hPCGwL93v1p81SLfJ7u3Nulfq/5NQEPPr+/nbZ3t60kAvsLfZA+nrRf0tFB/VTEtORIOv9xn9DfTk7WBd3Jt8aZaynXvtadnknENmuxaov72eqD64Trd+HUWudejy1TVl+Lxia6Xs5j3nruAAAgAElEQVTK9T2R2NzfnTwKA3vd7lK5yxnZcwN2Ah/nfaPOmzq7bQm1J4zXak7JGCbMJOcBLWM/YxjPkv5/7JwMBxh1FkcYOVkIL6pdY8ZYT/SZucKBu6dJr33FXKVzEDaV7FueUeloy5j3nqlhz+lH5vjZPptPlNLr7de16XDE+ZtrjXNnnLt+Tg5Ov5KfHS7iPdKx8eA4NIuQr/0+OpAk7lnCW72v4Sw1G2Ma1ur5ifVqjZw12nvVYh5MAhDof+Dr33EBBPqL0/+yEugPNjHrXasEVgmcSwIrgX4uca0XrxJYJTBKYEagd1LsAsjzGbGxZx46cR4K8rmm7CTLFV6vCxHoekYq9ansAzA6eIg6hCOwGwn1mbGknjvzrA3HA8DZGHmxmJLLnQrJ1NhsCKq0e/SNqF/IOd3VU5o1MrCTnnhqK401ZCXpqw20K8U0NdnogZ7lVHAQqIMXNBGvuj6NSKPhijFV6u1uYKRmu2QBWcicmbisFHLqMwS+SV3Vl6sIaAwJRPQQga7rZZggIkB18mQIkhFDUcsiv/v6CJFjkIGsBGhSjy9TuZcxaHqq0pwLnCoN4j4CnfSF18uRoQwOM9I0wTFpuDuQl/GHiO8yZsiwIvlkRPWxvTXsEY+7yFqND6NURu7076veHsY1oqdMlJcBVH0gTV431MioWA4PjElrTgaXa1dbdILmCqMUhLZq8IkI172VDrA7ZDgsolKXExXudKLlUKEUkorQZq2IsK794np5Xqd1v+5hXbHtumGhQD2Edt6LM07uEf8+GAI4FzMCSIag69fa1a++2tY0e8V7rOSFUZe6hyxU5J1G1zQCQbQzd8N23S2L/GLhvBnHxvi78SQcklhLueYs8yCql4xndIFIqpkRqxrra3Mw2Gck+JLhaOnc57w/tjd2Pjjd0Wq8Jt9fS+QIhq3REJVyHIiYJyF9+5IoXuufJYH+3/34xdRA/y8/e62B/lpfJ2v/VwksSeDoJz5n9/F5yXO909BnUh1APShCq9fu5vPxfZtEdb63TiLmaQMcctq1vKvy/3yX9vfxHgIdnacTMZBfC2T/2G729ZjeELpplqiZySrwVb+/+kl0Z8/eE5lgGBP1rK1fJIFeGBBnPh4jAl0/lLxiTtCBycqUMiH6OHU17uuOxeFoio48qmMmUkvHtV6zS4FOJpju8Ay269glUnGD8ZwtriJxO9E/RKAL4xCNrjaVklz/i0BXO4lvPF3bXb3zJChFfCt1+9Nydi7dXRm7uEe4SbIWgT60ubla0dr37k/be0rvXvNoIn7YuUkGIsfcQzitOKK7iGVnWwsHXtZXqsZet/EBeCAf351+s8RZZQrACRyHYAh0R8OXoy7z2XG5otkro5aj1MuJRI7SepZrkUcac4hmYTCT3JUtC+cK68qF38n0pTb1fNdpP9il/3c2g8gcYBmWCIii1/fplFEZtXblD4pcTwxi/DCk7s5zbXaWDOUg7IxQ9c9djuuosraVY3cSyJy96RRgTFSZLFI/B/+BMSz/mu/U57EZ9WUwkuBezbUigqweI8zbol965Ry/1ziXfR73cD8ZH30mMUd75Dt7h22m6UMvT9tb96bNs1ebHHHOoGeet+hmnu+WL5uvFsbs+ohaNwQsJ5hFzJSiWJDpgl3x4Lf+wAnyW796GAkkgf6VjzgC/e9900qgP8zcrPeuElglcHYJrAT62WW1XrlKYJXACRI4+snPb9+OuvtJnPQSicszIHN7CrUTG2p3JSl4pueqswMhtXeMCwR6gucZcU0jBVD6nwPhQ5/Hvs90/ogW7cBoGBwpifF+zgjGTsIPFhOAHcR/E2Cl0hvALQAdcr1HaFc6d4HeHmVBer4CNZ0Uq5R/1AXjc5OpeLxXmulxHZGSzobDiMIwMFNfwyAF6DYgFaCOCPtOokWdP8aGdz4p8kjbrr5AskqEiuy11//dSkcYEeg2IEA4q062Ii4UUY2DQs0B5LgNEfen7U0ZK0TiX5ps0OF7PTtroP/GZ6fp1s1puql6dpHyPg07Nr5M03TtcJpkDBGJ30npqjXo9OmVDlBzqusUYZ2OEZKH5Kqa3F4aJWOiA/QZsofAZg2N4FdzLXnhJMHe7rXOi3Tm+SaYywDnunlVExBjpduLtdLXQcm3jIHbGzdapLiMIUTGQKZLRhgYWV8AcKLgIb65l30qGcggpHY1BtLPay5Vd/xOydxOGJcqQiTq6KUxjHNjyWi270whxaf6p7acVWEzbS9tem3ATiwjJ9cQPGjyqK0+K1VwSd+3SPSN1kJfs5WK0jafBePJYh/j/Otn2ZJRZ4hayMwPnAHOPrBQ35O5mjUL4R5pCnMcGFe9pwcD8RLXn6ncsw+MCUcDiHxegCeRCpy3owEpDTn5Htv3PsKgldfyPvJn7caVQN/7Qn+oL5JA/4sXRKD/yZVAf6g5Wm9eJfAkS6CT6N1x6oy93cePqB3SLPf3STqYFdxZep+i2+T/J3XnvAS63+dDul7K9/De7IoJWKsiyLt89F4vzLZAfMy6C1ZKJ+SuMySWKz14ia86Nv4k1ANPWZ+LlOZJUvI5jsS6DawEsdwzBEWZKp4N8WadBUKz9EqITHSJhLIZHd4zNVV5LfQ//a9+QcLjOMr3jsAtLIhu4XnXfeWUSR9wsIboJC2+HYtFopZzQNZATydhtaeMXHI+lnOw6pBbgSENdWXZKj3UdcpfOpqmqoHeI8k9pkaGW00Wnjq8PG1fvjNtDoUjgpzL9VAlsn709//U9Hn/6786ufwVcuip0OWIUJH+wiWSDYSvrqV8F/jBn0UdchokIhzdVJ9bzrHgMhMbawjcYKK2+mGMedQwiNr1M5WyvnRgOyaIvK7SULkOk8TGUV7O73rercpaBvENNut7sdYqWE+6MvfgABIp340vKWNAGS7JUP2j31orYDlH7lefIe9xvO6E/wIe6WfMcIbocz0Pp4ByjHc9d2UB4+zk3MChhFT0ZEHDOQBsx/yCBTApndTHxB59yk8ivbloiCBnH1o+Nfc4EHA+e98uBG0kee8xLzgiQ0znOYiNaTwvc13wuzLwsZ44E1NerB36orkhi8MMU+HQk6UTq1NgeOyE45ltnF/4kbUxjmtmx7s/HXz6D5305lu/ewgJQKD/x1//jukrLoBA/1trBPpDzM566yqBVQJnlcBKoJ9VUut1qwRWCZwogUUC/VQSew9o6NGS5f17mrGk44usubyvu0tGkCX2ZEkTr88gkzIaPD2Be3rrUPg7qbJQizDBx7HHnkSgF/DpRKQ7Nk/tmKR7d+oNQAF4T6Jd4JIfjYvIB2p5ZX0ygWT9M5AX8VakNaBdYEVGEeooU08N0KJU20Se65mzKNzqBPXZ0zCFsQjA7YiFiiafkV1lBNLzMe4wP3g6J4DDoOTU7fKCj1R3aleRvWpHYM91se+1yAkD14rO1/8yBmEouVUpCUk3SP+IvpCMicaQkUc/RKITfa57n3261T+X0WgGauvZCQRFoHteiijEyUJjUk051XpT3ToZEPC4x4hi44Dq6yldv6LoI6Vi32ukn4/0mBhkMIxgbDAxGh7/5e3fU/uzd4huxyCp5yuSXGnoFX2iSBP1e+xTPy9qXZvY3k7bG9dblLjWhSPqKxIH40j2KQ1dXEdEA/3VNT2avwx8EJgYVCQfb8tmUFU6eEfBk8YRQljfu757XZ8RIqOh2dckORznFXMlgvzwYNrIiHVJNcybccop3Ito91xjvOl7SUaLlmrPddMt57Zmet/TKNXPk9MiHU57YS7U+WbvE/lAH9kvGFVGh5FuOC65pGEUI6WzWAQRfxKB7jNxwdlp8XW18Ew/Z0jygTjS0JYiSsJDn+975zEG5oH153VXfQlj7Eqgn7YOH+z7kUB/48dff7CG9tz1L/7ZzWkl0B+pSNfGVgk8URKYEehn8A+edX58F0HM+t2JrrWn0cQBs3dQ4KezSOosJPqSvpDRjPze9abAAH4PUgc9akL39/MpnURvMREc7/7+LqZWcelyvHePNTvKMQgn5qGcD32rnoueyLxA3Pm9XpHe0mNFMBkTRar28fnWRyu61mOvLGE8K7En8h4J9B6hXn1Hv7GMCiMkAZ8ySqI1P3eEc+nEkLzWO0vP1vfCR8Ii6HSKzlaEua53LewSoDJ3OUoVoj0ybLnOeWAi6fZRq9zTJtyErqfvhZ/ub1v6dv3Q/qgbpm6pZ4g495qrqGzWWsn87nf/4nTlyz6xtUmK9nQSt06/maabN2tsg1DVjtOQ7/Ty1laRyfodHARpm33Q73YiL6zvcmjhIKxnU0INmXvMtSYTI6In6n8izlUmS/eJVM55V3979q4qn0UmLX0O1lO31D/wTTrWe6pDXwa7d2d+MsRFNDqY1JNcDkJOMR8OBDO8NGSt6kEF5aSAXm5Mp6x3lT2h9t/2UmEkMCL7CLJaz8JG0J1msKdwjlB2gLIQcVi7ryGHU8/ZXD+BocEY2H66o0vh8XQc6pHkeY5Vn+hP4g1kBia2c0vZHPYR6MwPeMQO4eX8DMnNWNP5lzOMfYnTzkwuYQNLpxPvCwI1Mt3+PuwVa2PETf35K4F+6pJ8iAsg0P+jr3v0BPr/9U0vTn/rz/3M9Pzzz08vvPDCQ/RyvXWVwCqBVQInS2Al0NcVskpglcAjkYBTuXdS82GazCjoPenOEwTyqJGEXrQdnWalWrJK5VgSCA0RmR0YZVD7AuOSins2neQOgAPjwggQR/FCNPn6AFmz6NAirOSdLkJboNvA6EpFB5fBAEKbZyTQF8jX3/QVklXXuh54Ea1EGpPaPMeRfYL8g7D0M4MoxFO9e1fLiCJjE+CvjE5EcgDYICu7sSSA7YyMh3CqtNYAY8al50tGjEt9v3GtGTvUZ3lYm0AvoyXGARlrdK3r65WRiEhyG4M20/alO46O2N6+N21uDLXCbCQpQKiUgxqvnvHUjWn64MuNbLfTQUTR6nvmEq95e2VfahHmt27tnBPKiGOSVQSvSNcy9G11/VPXp81LLzejign8kDnrCyMNfQVYA5y9JsvIQ2YDtY3RrEdRlBGTNcDelpGJVIRFTru/FRVMJLVJ3zKodLIY40HVGt9onpSer9e3U/14RWJX1JAdHdSPMvyQJYG92h0vKF9Qm0MyG42L3INxSJdiXLKsSOkf0T3stVzXSyncLZuIrk6jxUEbX1s3l5qjx82bLZq85G/5lTHWJHlEp7tuuuSjtSAyvkjqnqa+G04WWeThRKpzFqMMTjV9b5ehnv1pR5XKLMGZ0I3fZYTB6IIxqBu+BuMUkRRJVGNkOXYO1Xkz6332PbI8HHsN8Ny4fviI42zWPI4sS6+ajBTR9+koMHv+cPPSO6XuXQn0h9FF9t+bBPo3/viXTm/8uEdMoP/yzelPffbfdwfe//73T29605suZiBrq6sEVgl8RCRgAt1n9zkfv488x5GQ9+pSw7yTRww14qezdinJQ/SRJXziV+0CkZQZZkxaLugXS85sfYwndDT1zySUkDdkGM/cOw8FbDueCIxH3yCY0PGy5FPX++oBwhPSraXPd3J8INBx7u36dIwTfXXUJVK+1pMKDxIN3hWSwcECXYt+ZsYnMCJ67HY73f/5D0+XPvm5cjCuqHhHBxexD6HqKHDhIJHGh20AItCFj4w3ykEZPcefC/8HvqlU7HYaJgW7os+vXp42+gxC1PpeIyedpl2/KyPTdWGrSy0CXdHohUtU69yqIinbhcuI2tYXnk+tx4j6L+z4S3/mx6a3fOPntbnDqZx9jMM1Ed9Mm8lWlfYqh3PhDzt9V2Yq5nm2TovkVRvou4nHjKH0r/aMMWBGohc2Ep97+UrLPuV11UjH7aVLLWMVa9BzddAccU3i32uYQM905oC6H3I8dXSvj5K7ie5Kja9njQ7YnFM+E8JZ1JipMCt9Qi5ciyxmfglJBuf5URflvvAzygGg8C+l3iQjH2HYMlwTva0D13a37Aun1BhcyovIefetarhjkzGuDAedYyka/cS2SjqJHanVPYR0OihMgkOQHWPkgB9O9ZSkYO2xRskaMTsyR4eDwOwaczozJ6ZKD2HOTTAv75ZutxoyiYBt8rqTzvO0GeX7oTsj+ZfdqJbO8fHdk/h8eIWsKdzP+vI//3UQ6P/h171j+vJHHIH+Hd/04vS/rQT6+SdlvWOVwCqBc0tgJdDPLbL1hlUCqwRSAr0G+jGltVspzi4wlFqIBIjkUbkejT8GH+eJPt/XpXMS6AZ1Ad5mf+czQIknkCKnEehqbkaIg7gKAHVP68EgN4IvRfMK0AK8RkI7a4TrO8CvwL5IzdsVzavPbRRRzboC1rP0ipHWUCSexxfjz/Gml7hlGLLDS95rgFqC1Doj0re+A6wL9CaoJTW0LpOBYQa+64HqA7WwIUwxBLlmWxn35KGvqBH9vPxKM17IQKQ2JS+lY5dhB3I7DZaMi0h0GYz0Q9S5fl+KPJeRR32S/F96td2jCPeKsphuXG2GnqeeaqQ+Nd/ZE8883bIDOCqmwGzVxnOdcI0DY5CJ5svTpHteennavKIa2uXtr+dKxiJrZWTR2nJ0dRnEWFPdIFdrMQ27OAZA4EKkj4ZY0qKrLa1JUgna8FOlA+zwoCgCpR0XIV6kvedK6dSvN4PVzVvTRoY7tqFT2MfaZm2NxlzGS/9TDj2F/kLUVhkEt1pr03ZnpIIozjODOZodPcOZkucfZ6JLDdQes4xaNMVWJQZshJoaGV7XNUeDlgq/k+KUKygDi41Bt2+3KH+MMBjpjp097NGa4zxnOmFdDgNp9MgI/CUCHUNsd6gYUrjrsWksohu55mr+uzNBpgq0aNO5I87R2WthiPYYj/P8O19143uQ5/FuODbPo/Gs+oOBbMmwlIYn3n38H9cffOp7ZiNa/3h0EkgC/c9dEIH+p1cC/dFN2NrSKoEnSAJHP/7ZuxIyI1Q6iVBPJy3rM/WeyvI4HucJjl3o3vlueVACXY/Su/qk+0fyYvbOHl6sXTeqzyFJlmRiwp2SUQsXFKHaronn9Hdw6YAj59YvLaCAU+KxNkqhJBKXdzw6U6YV7rIGfIhoq8hz65ngm8oIlJjITpPJFEOwpUNCdbqI4+64y71uj2xRY4aCyGCl+yG21aUk0Pu8VZYxSELrZNJFi7S303FhBmEX4RRhFE2CcUjhKfQyEd7CRMJMIrY1XD2i/nftczsj6/770/aVo2nzjIhiOSeDixsWP0aMC1vJYRkCXZfdutvaMxFcTs6JP/ScxIl8588rQt5R28LCFeGu8d+83XAK2AKd1E4BRWajv1NGK0lJMgEQ2QxRDQZH3pQpM0kfawACnXVHNHzNqfDIppyhjU3u39/p+R27b4pALxsBTuU4BJCyvq/nWJd0hf7jsAv+TuJ81Gs7FihZgdF8viQJnWRybcjZmXFCDXT0ZvWLcVXmCOMj2w/uTRtS8ItAl73EmKmIczI5lF3CDgZV1swOCThf40ixZLOZnfeFx8E96ZSA0zAYotvHIptGPbs7ciMrzjxsLsaZQxYP7+eFiH2c5LVXcXzp/YJg5zAYMyqSMaT6qEwGzlIYBD/zQAaLfB+wDji76WPHT8M52G2EldkjuuU5BitaHuF87nMyShXG62El0C9OUUoC/cseMYH+nSuBfnETt7a8SmCVwEwCK4G+LohVAqsEHkoCxwn0RUvFKc/IdL4VFYpivC8KryvKg+HEQGF83EkWqcGAMz8iF/oNuTV8NRLpnTQqhR8idew3inwCeAxcaXjqAKJAj9sJAw+pvPZJ2iRZGWIwzrj9kA0pzXhuT8sWIA9QDlmta/SZwBaglBSF6ouIS/rePy9AjGGBlHUdLFWfMARgJHNKsYp2T6DUx1Yf2lu8PO0TiCMyIqE1dohCyESMFrpWn2G8EAjk2qefag969WYzBhHFYNJ829IAKk2hDDyWQUSKY+DSd3qWCHdFSfCj+/ST9+LA/tRhG5euyVTvej61xipyYOcgXin9HMFA/fhd5PXWzgDbyVHaBraXpunqwbR97rnp1SuXpxsf/FAzHuCQIMBuIxHRD0GuZ3o+jWEJoCJTPYc67OkZn0Ss5AN5jvx1v0h+zYHT7JFarsh0jUWg3e3U+sbglBE5+h5CfNybmUoDBw6T3+W4wbpl72qdytiyudTSn9sooaiPy80YU397XtnnGVkxkuhEFnm94hwynGHsNZPite68fxU9XtE09+6354tYpx7h9n6LsMDwUsYS36P650Sld/nMGN+2NtNYnpExRELkXrHRLw29pL4PBxvIcuaB828p+srrClnEOZHrBkMjcs3zdHYu7zss66DwOyjO7/HyJDN8xg2pA4+R5UtOUHGQJVmA4evYM8OAyLnKnDDeumcl0E+a34f7Lgn0r78gAv3PrgT6w03SevcqgSdQAibP0Y/oH/rqPqgyvmvai3hHwkNa+Z0V5PnS+PeRV+PnZ5FdkoT9VTYMInEM5Mn4WSd3w4E2I8+X+pZt5PeztuvFfAxLFZncnT4jqrx3P4gqHNoYI3pPZififUy9aOZYfbtzNG1/5ZVp8wnPzfUndNAcK2Q3CoWzNuGoQCrqwlCsC8YHZup/13pADx3WzfYDr0yb33Tj+ExDejJfXbcoJQcdizvVR0euU/KqSHLppHeEceTYKYKyUriD09Vf4SPhHf2vH/2Oc7FV2+20gezWlNy616LJmae792fEuUl05qKi0LevFmkurGSSsUWou+b5t/22ppenDinikDnB0UHPcwa3K9P25z8wbT7xjbs07eVQ+/6vfe/0CX/6X2vOzJDNJcPttWvThkxg7JskncmeBD5NnO62ao1mhH/fc6UXQ7Jeu7arJ41juEp3UVaM68oJmYxezeG21n3HTbXmWENkV8hV0zFB4L6etr+i7HE20DiodX2l4ZVOPLOvWO/9fCw8Nzr8d90e55BTDq3Ct32dgvflZFG4whiosAxZu4yjWNs4AshhQ47k6QyBQzikN/stMabuZ/8YQ5ZDOGtW/4N91L/MpEEpKl8zlBHjPuOpoSxF4kgcgkZRpaOI30cVqb90Ns+iHOJ90wntcG5IUtyvrfpOMsUhZexLt6fF+T2Tab3/wExLWI0SGemAUM3NcF28O1by/Cwv/Qe/BgL9P/i6d0xf+ofe+uANLdz57m9+cfrf1wj0RyrTtbFVAqsEliWwEujrylglsErggSXQ07bPWthHoM/cbuOO+BxiKomP04w6nXwqJLpIuJ9EoI8EUZHSAJ9F6Qz35J8mniriAYJ6H3lO20uGoNHgk0Cy10arB3eg3Rs83uuKhnVkgH+S1Gqp2zqwAXBAGCMLwIq8sonilPzVJmm+PYeqI1ePcOQvc1OEMuC4G3uy7mFZCQBieOWrD4Atp98uYwIGwwRQBn8B4HpNuUp1Rl02XUMKO7WHFzip5032liHINbSrjpzSuGuMd25Pjr53xGtFokMGYyDJeTMorqwFItn14+urr6QwNLiuG2XwQX7X5Tl/f5puiURW9MQuJaHbeMOzjdTvqfRoo0hmiE08zEX6ytDy3DPT5ld/raKV1e6VFr3d68oXmCal/lM3ps2vf3DnUJCGp4wgRi6dRK114PUi4j0cKZb2mcQiEI8He61zk+cieysNuw0fzzxj49zmpYqmd4RI1Vq0wayel8YAnjl65vcjIBjRPL7YB5pM1ilpHy1TZQRoRoxGoFeD2htEMLBnl0jiPK76+RdORl1uzdmhpXEswxf1zCWjW7dbZL5roh82x4c7d1pad0c3tLWlyHOtZ9eWx/gyrl823OjU4rTx9XxIX6LziYbg8240DO9/DGDezxhiItNEku9LayQNz/k9z8z95zMr0ynue/XFfGF4OukVksfueF0afU4k72N+MbAtvfvI4LD07hgcVlYCfd/8PvznSaB/3Y9dTAr3//Zz1hTuDz9TawurBJ4sCfTU7f3dVO+knkI6+osKsgRTuAwCppfIqS94F+U7Nt+zXf85Z+1z3dcJjkjPC9m6JO7x+hHfQBJZRxic5Eb8RGRmPudBCHRhliTgcHRMAn0W+W3hzUcn3Snf/9brIGPr2nJuuPfefzpd/tzf0q5++eY0PXVtpw+alINYKgKvz2/hppEw60R7zQe6UM6PidJIK63v0gHRc7ZnveE8DNnaZVV6lAlxOVBTgqpSnsupWHOIYzEYh+hs6eWKOrfj6YJiRVQ1a0HDu3u/RY2LXEePMo7ctpTtA2wX4b6t65TqfStHZWEGZQfTj56he1SfXf+DmVjDpKO3vIrwxNYgcv3DL7UsXZCt1DZXW7/2L6bpN75xV55BfVSpLPXHzr04VIN1wxGGddwzxFWGBZO35QCtdtzfYZ8zt+qvU8VXdLzm8frVSc7SJnsdmXupZfK6crk5HOsanT840BrX1/7AwbVnbsoF3w+RKEcR615fM184gswcY1qaehHR/sm08lnuC9K3k9ULkeZL5874mcaFM3GWWag13lOyS9bCcsJN2vXC+rbrgA0uTSLXndkso82PBTKQIaTWrb7HjpFEN2nWEyN5LUbQgxZqEuhL5y24OvZOF0G+D1jTHhx2GcYXYJf+JA4zridd/0KZKzKjqW2X/CuHbmFR247O+JOy6GfacFixJ7WWOQNGrJ77ZOaQ4MHPSmWtBPoZ5+YBL4NA//cviED/P1YC/QFnZr1tlcAqgfNIYCXQzyOt9dpVAqsEugSWU7ePSDwFlgh3/Lz+xrMXgLVIhse9KM97mtt9vMR+jBYprl7q5z72pNpIBb2Tl0WqErmQBp7RoDXrf6QlHNfbzCuXLzPCs0Blkjb2cI7UXYASiCHIeAxBjGU0BBFhoPsNAAv86zoRcjgL6DsiwAE9MweBAlPpUe/rajwJ8tLbXN/bAAMoK/Cj66n9DTnJuiBtmNrJFN4dbBUh3qe+kceWF+S5+m7QWjIUYX54ZZpEPAtkCxTqM/3IuIORAKOMPgesEqGu/nCtSOwJKaMAACAASURBVHMZc6rGn9vBcOAhlpFJpPlTIkCrLrs+VxR6ylZ1rwUINVaMa5ZnGUhJqec+VdR01WHshhXd57abYWl748a0efXV8ohvxlIbgzJSmbUDGSz5En2hZ5FKzWnW60eyw4DhPZJGwOoz0Qj6s1LtTdevm6B2xHy1awJYER+3brW05RV17bnJdZrOGKNRdrbXIotFHhPj0ZBrFSOj6q4rBfrRkesOtkwILbK7RX9sd/X0MKAkib7vqLFIKj0e+8prTs4qlUlBo62MAjbuvXrTBjNHl8twIWIfGYVh3vJTtDrZATLiYTRGZFSJ5o39biNSi+rxmnS2iMHIlQYRjcV1Ese9H1khcg0fO66HA6PPZ2YzqUnF4SDPRaJ9RsO+b0mjSgngpHmJo5jb/X/v83BGL19U+7Tmeendl+TCvndIvBNXAn22qR/pH0mgv+vHvnR6wyOugf7BX745fe1KoD/SOVsbWyXwkZTA3shz60jbeSmf2ftj6HW+i0xOVPphvwNLRwjfv04S0wzvCN59SzjqJEGlzgmJw/so/19qY+le3s9WOCMdfH9Hl77Ks0byPYldcNYxxwOwWhH0vKBnhNUo9FR+Si/Qf9ZLKz1wd/StCNhsD+fe0k0+/Dd/dnr6cz/W0dObN7+hnPlwZETvH52JS4h+ZuhTi7pofYguTV9NdFO+CcKrrk0SHrId2UEMqp3EQE5xHQqZ9Dhwj3VI1U4OUliEuh7nskzVl1tVRknPFDEuEhwyneeb+G+6kwl045Imh+0rd1sa94o+30DG59qQ4+hRI963qpuuSHZhKByZ1SffV1H6s6hdMEDV8851fvXqtP3lX582/9Ibmu5tklDjqPJgSUgyZzjHgFEt01ZSaee4HiWoKIuV9cPVB5fMGqKLcWrta7O+l7z1fEWeX7/W9PsiWJ2aHEJWKcqFm0wuVvp91jhOsH0v58ESTq/eOpXaPx008gxIvJR6tvCiHBkKPziTF1m4JK+UHWdFEsXnPdRxyAazyAnZw6r09a71LqeHy9PmpZem7RvfWE7HeTZFhjEciMFN4/naS8JVRjvhMDt/VKQ4NpoktdH1JTPIapy0x/GyNrvDcvSzY7Bgl/l15sjgQ2reMs4A2Zd+1uZZCeaK+uxnOfvTLrU0psSPCfcSV/GOsPNwORuM76Dcu3pOOmCprcBaK4F+3s10vush0H//171j+pJHHIH+Xd/84vR/rgT6+SZkvXqVwCqBB5LASqA/kNjWm1YJvL4lcL7I8xNkZaAhQFwe5CKcDAJLmU8SAaU4QYaudRsnzccSIT5eP7I4J5HreW9aaRaMF6OhJw1ASajTZBBhnfSeGZwKtNAO7FOmDNwriqxVzO9lDNI9Gf1CDbDsl+el6ijrWpHGMpwQfe7v8WQuQ4/mc0am1zUJzrO/x6YqjF02UBQhmV7y3A94h7UqQNzSiJdhESCq/tuLn5Tm1PCLNIQFoju4oh09TwYhRaF77d6bpldvt15kLXSId4xZaeSTUQgD0+270/b2vWnzdDMG+Xmse4xUREuQ2tDODEWK97VQ+0bEvvoq8jiNL6w3jGS6RoYEg/vKIpAe8b6+GZccHSBjgiKUNR555kMGQ5R3I0LVGiOK33tUaewVzVCOCd0gMBhg9EwTwhER0vtXkdxyEqC/ilwQYfzcs9OkVPN6htaGa73XXunRE8ftA91w1Q+Qcd8Hedr3ax42A0HcjWhhfOOMmhmrm1Ba6kKiSoqAXkqN58io6lvuMa85yavkqmuUPUAy0zrQ31oH6rI+k9FM0SY+Q+9XLUrde7elnu9OI5HdYSSYiXLu66kMErN0jxglI/rE0xEOPhWV5SF86numo/d98c6JAiMZhDxn/nY7HXz6D01HP/07a34x0tY5DHmfMndbFW3UCfTaYxiGUr7c6yeEYegY0b9w0HqvZxRHOjTVelw8n4fGOa/2kehLBMhsfbU1uhLoe1+GD/1FEujvvCAC/Z0rgf7Q87Q2sErgSZGACfQkq50ZSQRX4Z4RxyzBkCQ5deajL0Gco7ehdy3hjNk7btDB9glr1ANo1w6Cg95PGzlWPhsxEXpBPhe9Nzgf1Lmmy0dWJu7DadK6wvCuTSIGIhqddNafwHB9LuLd7OdW25SN4jrrTxCbwfYUbrj97f9wuvp7P2Wnx6UOTEYs96V0GpO5Qzt8lutiUEd3DrXVFk7EHTOVwHByZR5TDiZkI+oabJX4NPVR5h/ynLWXxK4cf0WUk8b7lkp+Ver2chjefuDWtPkYOQGX3pg4yISj9NuW6n378t1p85TwS6WABxMRvV6R6SLWZyngSeGuZ9C+MISnuQjqrkuVrHJvaa56KvLSLZmXrhOXI7WdS9VmRXIXburOyvrODvcVTW45ls6IEzcODj1j13AoaAx2+pZzbkX+o+8z5yJsU/dXCayK+O59EU5Ad0WXtxySuQzbDHO8tH/Zk30fxyL1r2Gz4YzIWvJgd8oIYJvwPNR+JKtcdwAJYj/35NJ5BoGuW3DudrR54WE7GrRzTZhSDtHtp+a7iGVnFJDMdS0OD2OwgtdZ2RV0P/tKv0Pgl5O1r0uncWTLvgN7VW9E9h79v7+7ulbrpts4yALY+nzwjh+ejn7q+Z3TRCeZKVM1m9By7uE8rDUw1hDnXF165yyd/Se9Wwa/jJnz9ew8D0yeeA7MxFo51l7sd3Blrt3auyuBvm+SHs3nEOj/3gUQ6N+9EuiPZpLWVlYJrBI4VQIrgX6qiNYLVgmsEhglcJxAH1H8WWQGURgRgwlAE+DS3BI4eSQEevb3GIu7MJjR0pWXFNgw4A9PcciajEgfjVsJtgFL3fizBGbqWUmUJuhKo4gNb0UcZ009AbkewYIHOgRkgSqi1AXC1X+D7YiqxFucutaIA4/gbnwrwq97jy+tkzAQAIL0f492LTI2jWLH0iwm7ieFmgwMGPvKqDGLBijwKhmR1l1Ai2jwJA5JRYYDgYxBIs+zNrmG5tTaFZWr369Vmjr67mcVANdzqHuu7xWRoQhzaqJj7JEsZEQagbb+9vwUmSrQLyeATiYHgQyglxFFz8fQlQR6n8MhvaDWkUj1SvnfavoVeCfCQe1QMgCZE9kwM2bWPmKeSYOIJ3+XU0W4aDxqV+vt4KD1oQyUvUYdzh/jGhuNsd0oFtkZ+nLMVN+D8SeNBb6e/V6RRBhBEpxrf8nAArhnLjNazMY0yiuEUZt9xh5jHXKtHSFIS9nSAzYC/eq0vXF92rz8ShhYdCaF04gew96UzCVf9to+o7jGwHc9CglDYKRfJU2h+l3XH3xyS0m99GMCnXUEge7jp1KbbjbTwad9v289+ukv2GUWYOxjo5y3GdHWCXPqNEZ0x3hW4lSBYWZcPzH1Mwcu1myeXUvrpPeX9RXnHsan0QiVxEEavsPBIAmalUA/ix7yYNckgf61F0Sgv2sl0B9scta7Vgk8YRLo5HnqVbwr9qVuH8fQ30Wlk8W7tUeeQ3KMEIV3RD7f79eTsExd3PX34R01jiV1nux74jY+nxHXkRKeNngUeg+6AN3NdyGR0ugm5v0Ke43ki3QvMv6QLayPg/FG5yHs+vu2XvzW86IOdzrjZt+ciafwC8+dyWl4//POBzty7ygXyyNwTpfnwBxlFq4k7U+aB/oAlrD+qhTfFTGN3ocTpNdWOWTaWTEIUta2CHRdr0hjCeRVOXJWdi2R3aprrixc+uzWvZZ+Xc0IAxGZjuOxCHRFlOf3RJXrWu4PGTn9u/5l5Lyff3na/tqr0+Y3P9MwLU7RHbtD2JVTKLhGbdtJWKR11E9HrpIJGEyfFS5winCtFaciL9zkqPJKoY6MuUcOr5KnyfDBCZjoefUJuwJrX/fo+coO58jlyhqnaHTS08spWt+rL52QLqeefcQ52KXrnJEdIvH07OzKrA+MITNFheM063xsnz0F/vE+LqeVtK3wXMjt8ZxBrtg8HLxR60CfUWbAnzfnYvPeM4eWOCd6sEHZGejn2Cdj7goW4WxjP/KszMjltbg50Qm2k+fqziz6vJ41kMJHP/U72t4ja9kxjFJn2+jAhFNH2pBw+kjbj99PsV/O8m7J+Urn8cRqx9ITxj5I/w73r7AwZyPvyPEcTfwU78GVQB+Vjkf7dyfQ3/WO6YsfcQS6CPS//Y0/Mz3//PPTCy+88Gg7vra2SmCVwCqBhAZbuWauP6sEVgmsEjijBObk+Wh8OeE4mYG/oZ4v9Z+yFnEq4/RtppzvOJT9XT8LGX7GgZ/pssGRAEIoAY6BdIAM2oVA85FcBrIExbquR1lElAJghxrPM1mFty7AEaMIJBWyN2AtQsm1AYncjpTyNgoojXgRbVlDMI1GABI+ywjQkfjtch3WDoCH6Fq1ZWMQ9ZET6Nc8d/IyrqF9xgnIz/p+rKtMFTfWR3M71UcZg2QI0vPUDl7sXIMci9ztt95SajjV3ZPhLdaKa58X+KReoAh5fZ6vaDsRFIGJ8UVGHKXrxiCjZyq9udpzZAtkcGxAGzRznTXy3RHRIlFtrCnDAs9nb5rovzJtn3tmml56paXaw0hJ1LujnCulob6TkQbDpddhEOesS4yfAsARnUyWAKeSVwQ8NenVXxHoSj2e0e+kl0znlR7JW0YwjCYyMGUKRc1JT1tZ8kEOaRjJsyC98v15Znqov/mPa9nXpEMnhaSXMY4pRDtE1IPbiRStGTlNW5obp9SstK5KbS+5ufY5KfRrHROdn2vM81BGiDTwMGbKALD+urG0osJ6Wv0D79fDN3/nmU7Oo5/7ot3Y01kA8r3IczW2I9AhwocaruNZlD1gzJIRRpp9Z+e4f2fzznFQBuP8DiPmPpKg7/0sFZCWIIzQQ0pExjUapnK8emb1++Bt7zmT7NeLzi+BJND/7HsvJoX713/uWgP9/DOz3rFK4MmTQCfQu0Nc1IgeM40swSirTGROIWNU4YAkldOba/Z5RG4vvT9OEhnv+tn77xTifXz3nZVM6Q5oJQTrZ5FFaiTQdZlJLr33Suexql56qd7t6JjWVSprlD4jC9U49pmzXNX1hggHA/AsdLZeC7sit4sAmxFKeg61rffKu3SCxExkzJo5+ZX+sUgQlpMA42Z9ZUp2dFLjrCgNhH7ke5u87v/Sh6ZLb3muYZ2M3kTvIIKeiFpjtdBd7JxZqcqFmzSnwkJXo3685yxSmCMfkeWviFzetCxd/NzbTtsP3Jw2b7ja0rvreaR3l/Ox2yt1XKS6cVPJRRj20ma698I/mS5/wZsrDXs5jYMBwQ84BCAPy63aMuF6WBiw0nEnCZhrXqQlpa7s/FqLzNHLd1tmKD0TJ2j+xjHgmENMkc7o69gV9EyIZbVlx+XaH+qDSkxdvtJwHjYHsshhE2CM7n852DM/1FVnHsAyRNl34rP00HQa4Z7u1LIQPIH9o9t/auA44fA5eKfbE4qkB7MwlqX90W0L2FlahLgj0CvrmhyP7YCMM71lgl2hHCY4p8sRo62vyiTWzzGV2SpHm7QHsTf1P7LTddon8s349B886UT2dybQcTbn/yHNexLCJtAhlL3nsyTYAimdPfA5Ghm/+jlRTiB5Rvu8CccgzsJxRJk1hPXV8e9ssezaG99p3qexOWbZCuKBJ71/on8rgX7qsnuoCyDQ/913vWP6okdMoH/PN784fdtKoD/U/Kw3rxJYJXA2CawR6GeT03rVKoFVAiWB/QT6Kb44SaDbkNE14R2hhtdxRlGk4jsS6E/6rNi4UwYcg68iMw2oinBK8oMoBgP0INoBoUTzQgabvC1jgYFkPauTnkP0sAA0EcmQ3xCWmQ5eIItnGJCWwY6UdFFLbe8U4CkO0FryZQiMFQWDW5N+poi8im7VZ5CC/l4RD1XXr4PoWFeQsPS/g7papzJQ5I89xItUS0MR46BTul0g15ERisi4Pzmdfa5T5O+U7mUkuHd/2t6Up//9afOsUgZCIm/nkeu5N6gd2OUR86lnyBBEejmB/250wZiTlsDYgGmcUHq/S5em7dM3punm7WZYSSNErqVyYtjiwb7ZtFrkWQfb0RQV+Y5BgmjwJFtN/tYEqD+UB3Ba+TJoqH0bd5T+sdL3V0089+GSIihu7/YVhgicHzzO2EcaCwSw6t1p7MyDnAdIUU9/7SAREfo4RuS6GVN2n3gmZa04iM5as5oDRYR4v5SBps9DRFSNRo8k0dVfRebjjHD58nTvmaeny5LZyy+3FPdqqsa8yfSR+kKfK+XllStN7tkXxiW5ZyRTd1qRkbQMdeWMoVsO/+XvPtMpbQLdnQsHgth7u+hzpW+vdW3Dxy5CfTGajv2EQQuDS55HHk+dc7k3OINnERlYZYNA9/lTaeI5z3MPpQRm77A4A3KfYWDMsyfvY67yntkzdn+sJPqZlt+5L0oC/c9cEIH+DSuBfu55WW9YJfCkSaDXPuddRB3j60EI0ul9MKrezX1sJpSGEiYj6aSLk5zfRyKcRi7sI0DOI+glImvpvTgj0Ov9CHkjvSbxku6HLDRmkU5IRiGie8sZODHXLFo8cRP6aBAynQiPKHney+BU60lJMFW/INdv3pmmp6/P9aZF2S2VlhkizZNIzWjqbE9yygxFiSlI6+1rCjv2NqN8lJ0Aqc9e/4+1z/VMyEGybXFPEuh2oi2CWuUK9LysgZ7PgehmPIomf/XutHnucBeJru9EoKv01dWIUPd6iMxorBX6QpsueyRn2XuVKau+AD8szU0Sd04BHoR4kqzgzQQ3dny+YoffmbMv/SMiXdgLZ1rdX2Tqse5kFLjkrxT0+jE+LjtDzb9qnW/Aus5O1RxkNzdvtnvIOmXMEzXMOV/UD2wEkq3KZl2/Pm3u3Z1Uq1w/DTdF1i6cD8DTWhsz/D+UNhodiFqru2GD4zzG2t8+D1smtA1ZtSCoeb7HFPtnZm+ozzUGO1Q0HCEncs3V/Xf/g+nSV769RegXVpXsjIvyjAIzHR42fNUjwYdyVcy17u2YqexD6m/YNg7e9n1nPllNoie20XPKuQAy2MR5H3s4zuf7YnauVPT9kTLr6R1VjgHd8T4i3jM6PQM/+oTLzsU5U8T6iJntlFNZLPr+kQjSQBDzxfIAY3VbSl3TcVOWKgx72z7pln1tJdHPvPzOfSEE+r9zQQT631kJ9HPPyXrDKoFVAueXwEqgn19m6x2rBF7XEjj6yc+fg5sujRMI9M7dLdSU7cAUYEQaqKFOlpXmBFWn1T5/EqZpYI2TgOukb/QT0kofmdAtgxEkdEbmkqIs0z4vDRmZCfADKDPtFiTRjGAq44mAGISZfk/yPZ9FqnZ9Nhp1AGndM71u3EeeE82gdkifbgAe5Hga0izTika3IabI2ExTn0Y1SL6q4d0jkEk5LYNC/mDQSMMEIJXIEBPXC+tfBHoZlhQlMV7jtIImi0sYOUbjx2gT8hnDluQkw0mmqAa824CQ/aH9GBjPUqpvRRVo/Ipk/9CHfa/TDKrvEOomz69M2+vXGmkuY8Ph1WnzyqvNMGi51x52SkAR8WP/IT6HvYxs9QyR2voREawICo3RkeeVQhJDjQxDr97aZQDopHM9U4+SQdX/k+Jcjg8yllQNdyLuh8gc1byzMYRnYfzQ/x5rRbJ7botozkj0PKeWjkWOBQP9koXrI15pxiDOgYxi8JlQBog0hoxrRGNVWxjTSIvfnQvuNqPI7duVlnAXqeN7tpr7IvBxJGDZYPzR/96DEdWTTgflDHD4SWcjz3O72SCktjAylXENItj1zyGsR+MNhrZuGOKsKYFBzvcMCNQN1P9kUgg5OwIr662WMagbbIbJZU/xnEw/299f9d5iTeraHpVWBuweTbJbGx5JPne2xvb0Y62DfmFKQBLof+qCCPRvXAn0C5u/teFVAo9LAkSfb1+606JoRzK8v1/39Ijrl4hudK7MGNVeFrvGlkj0k0jz7MZpxPeDCnFfuyNBTvtEonddsfQWE+jlSIzzZbaRhBU4irTLyGCAaX1IXUbD+zUJMJybGU8nHivzknRY9IElndwPq/aPqeyhLw9TegxvOBK4eq7fO3lYOgz4wTqsQURkW9qlGDdX9vMfnC69+elK+13OAOi9fSxSLiojmLECkail3/i7mCNFhYN39LlLV9U9uYbUd9273U7bV+SIW+WsqHWuNq5dnjYZVX7aGsz14NJT5VjL/xCw+3T13YJo91Ykt4nlX/m1afvxHzO5Hrb1dVLZFyYVzrTDsSKNVcap5OJsUOXQK/mRxl3YiTU+yoW/WZfMuRwC7FBcDrT6W1BYzsX1POv2RSw7E5X62qPQS98H10CY6+/SxR2NLSdwkchL+ABnd+a2Zzirc4j1A4Yiy1lypPn77HxKW1BkNhDOuHp155QAFsMBdXR87fs92eKaTzC9cC2/01ev4za/LZV7crvlcIwswYyZQULXp9MDZ3Y68uDAr231KftLXS0tdWMmarCzvtTOb/0BX95Stw9lJGbvi0g/nw8gG97soXUmcZYmBvN7KpycNA+sZdspyJ42EOMQ80tt+tmU4aPtsPmwh9WPnO8Mtth3PmCT6jbI3bpYSfTTDtUH+x4C/d9+19un3/2II9C/95tfnP7uN/7smsL9waZmvWuVwCqBc0hgJdDPIaz10lUCr3cJnIs8XzIGjAQHCi9exTZCZJRmSXz0UIZ4ek1MyADWAFgjgc7n3ZO7UpYnaYLhJg1BI0ljvFEpujHqqA0IdACgriPKXNernaylDtixB3J5Smt6ZKxa+snxJOGYqbzmbuitld6fAmaAsEoF3VNqp7HLYxSJWXXdTIoHAUZaO49J0csHu+iPjIY24V4e26SiczRB1S3HicHjr8hgGX1cyzzILhlgxnTrukeRD+rLrbvTVhEXrOMay+ZKEbEyBo0GzWN/l9DtWHC5RWTbaxvjW33egXxM0rAEZ9kf1D/JR5EFjsje1Vc3OS5jC9E1uiYjGvR8shFoHnuGgiKWexRMEb9EnXMO4CDhcRQoZj6uXeupyBUV7/TyRMxkZLQNqEVidwNiGVlElusHUF8kfI9a0PVEDbB+cFLBEFFe6W5HfYu0c65neOv2Tgaes85u784ypmKfga6iHlqNvKqF3sdYN0MK9/OgotJtGI31c6AU7gctk4BsqkTLuFuxEEz+R6kG+njvXkXBV0QJJH7ueQxtIuqVJYC9gaxMoF+aDj/pe859Qh/97BfO56zGm1EZRz/zu3bp17PUwEwWWWMxusGe73OSBHY5ACFrDF0YWpYMmzOjeK21fobvnBP6+knjuz5MAt39r7MYI2BGdeS7gHvHeRkkvhqCzr0Ez3RDEuj/zQUR6H9+JdDPNBfrRasEnmQJHP3E5zjzkN/tIgCX8MtJ5F2+M3gf9wxRdePoXOfXPdHYpOOu9/+oW+4T3kWR5/m8pWeM/Rt1kC6r0lFwJtbn0mXTQQ79j/eldBScaNHp+G6fDt5xVOlQOPCabS7dkzGhg8ipFh09HeJmsh7L/aDr4fA3E9ROf0t5JC7qvFT1y3Aa74Bqk76nzoYcvKamlrL9427s6kFbvy5MpGv5PR1ThYtKPN3xmSh3vnNGrnR8iOxixUvamVjZvXTZB+U4KwxXZPr1K9PmutKQX5o2StduXX0ENyEzcCgfmWRGV0bGSYgubcI+sbuGra9XBjJFK//kP542n/mWVs5KshFG6FnM6lrLrSK8hUuEG7R2ermkWkfKZpY/o/MLmBQHcyJ2dZ2ykeX6d7apS80pF6zD/Okz5tVOJoVdiMbWZ2oLB+rr11qvEp+QwQrMDmmZTv3O0hZZ+Oy4Xrg8cXuSmEmE9j0Z9gH2tPCH8ChR4ZDyGcXMerNHQZx/6PYd40dZPGUnS3zNOjJGxTl9F93c513zj3NBP9di/0nGrEH2X57n1afzEugShzFR4sAg0FlOR//gC3ZZxVKu+e4Aw+a2Gm159r+psbadustWkQR6PxACq3LW1m2+hD2JI3HarbAH2tYTENbyq7IQOIeA02hTeyvXVdpKco8tvA8P/pXTU+jPN+r611kkAIH+b4lA/4NvPcstZ77m73/LSqCfWVjrhasEVgk8lARWAv2hxLfevErg9SOBXer2EbAOoHPEoBA3MyU9PVELYOCxmyRUEhkGeeWBegJmfs3MiEFIkJ9d+Q+jEFHRgGiiqokuUIqt0UgAGIBcted3Ge0cdVBgFvKZiAm1pR+MIhjt1C9IwyTYy9fBwHw03iUgGQkrAFT3Ei8DRoKgcRKPRXfXw/G6dwRKGQm4V30g7bd+p/7zklFF92BkSONU9wov72YIVIhZRw2UcUnfiTDX/cwTgO/o3rS91VK/bYg4lyE1fzBKpVNJNwTWWpGBRGs/DRKK0O7e/AsRHbmfFA3jdH4DInbkQktJuH3jG6bpw0r3XbXQ7VBQQFXPUnuOmBD5WgYBrTHqnmtMGDQ8/iSUSyCuZR8OHJZZGVqIwHB0faWpRyYmG6svJin1D/I8UuMxZkUTyMBCBoO+9itVv6MrRIpH6QPIT6cD1f0ac4lslhrzcotSd4q/GgtRJccOoUjN6fUfF6ht9hxRHBhhMAThcMB66nunpVT03MnoU44zLU1kywqgaPrev2vXqr+RllHzljLVGu0p8CvinvqebqgMR66leLlFpdi5oQxyNoZcsvPDWdO3L53ZR+/74k4AjAalHqWOIUdzhWGL86a/K8JwTJRQPpD51prpERB1AU41eT1GqiQniLbxfGEJHjIQch/9os2M8k8HqdFIrvs7aTJks1gi/GVAi7rxSzJeP3twCSSB/l//6JdOz33c9QdvbOHOD/3yzekvfN5aA/2RCnVtbJXAY5ZAr32e0Xw4DI+6wPie0d+Q7ZBU4CR9Z31mcDjmnZURhuO78CwyeBzk+diPk57J+5l70IHATbx7IfGsB0SGH97zEOg9t3N0IvVtdC2UPz2vR3eSchg8NTjrSS/EmZT+DX6VTZUKIMvvM/Io+taxItGwGdEJlgSUVdvpQwvFowAAIABJREFULM39ZCbCYRWnVPoDsQjWSeJc10DcEv3MM6yLF0kpArxKLfkz6+3S5aV3T61kVe9P4SCycDFk6bMv3Zm2H7g1bd54dZoONi1dO8/13oG030yTItwh1UeM1x30ibQvR0WPB32tex/shI5eRQrr3reSb+FPOx7rI6LIjQkLU0ie0rtNqvNZkdUuwaao9Eqnbx36/rJTAGWz7DRQ+InlQ0R5lePqqc2F4bQH0DHBUSawaw44V3AKUB+vKkq/bC1chxM6WAFM3R0iyBqlzGgH09Z9bDJVdP4Wx+T795vTMXOXztDjnthHpOuZ6Mr8jxO112GUJuCcoG3bKwrvlsNId87GAaLadLp7iFs7O+xKNWU5LGcfIAse65O5zDUju0GW4KKv2LYegkDXY4yLau0vOc4e/cwXtn4SnJA2pzHKm/21QC57Wu1wXnvGZ2/sxVmgRC1SSHKwuZ2tR8w12C/IcJZEOPJkXXqudK5UAEU6OKh9MBNnV66NY1lbWuOr0/FZFIQHuyYJ9C+8AAL929cI9AebmPWuVQKrBM4lgZVAP5e41otXCbw+JWDy3AAk0QC/n0ago0CXMcPt1O8md0kpR/Re1bJz80O9qI8G4nxJbJAlM6K4xg7wA5jltZDeuSyRrcFeARtAZnrFd9lebum203hVqZybgaeibhc9eWtNzEihAkWjx7eNKIpSLvDfwfxQpyyB2zGD2rDWqJsNYch6svGsolFoQ0RhgsEO6Mv7Ho9w5GYkdXAc6KvfTst3uZHyMgxh/Mn6dYDIMiC5jp9IcyKQukEMEBpjG6MOOmkHEVuGH113/foukgCjTV8PpFOsFO02XATYzesE7kWgP/30tHn5laotWUQ5gNvpD4tAJ52/0wVWOkCI6g5SSYGNkaq8/L0GKsMCjh6sawPjSqsHAMb4mWvDcq3oc30OyMZwqf/VliOyr0wboo6I8qbeH20z7xibMMBqfJkGUUYqRWUUGd/TIep5ku8YkdyPzCEdeNrr2D84fNgBIY17QwQZBoxaoy1aQsbFS9NGZwJRMN3IVLX91HdFuBCtUuNohPm2RavLwKX0mYpcscOMHGii7h9rUXOoSHM9E0PQGIF+xvrn532r2lCE0Uo3c16xhtJ4jtGbfo9niue/1hLrdskJjLMDInup07RDm3mN5yJSHLKW0xCEwYjvMrU7ThScDUQz0Sbv5+j7wae+57yiXa8/owSSQP+TP/olF0Kg/8XPa7Uw3//+909vetObztiz9bJVAqsEPtISOPqR37aLkOXdw/vlTAR6Hehci45gvZl00eUIZ12+9Gq/D6NOMPhhiQjZJ6Rjevdjlua+50OiQ5Dyvvf7MvqYTge8S9GZTKBXlK+zvQTxMyO5YQYjiwwyhGQmbbIfHRmpTAzVPKX+1wmrImBTz9d1SRhyX3Di/qg7T1Y0cw48ZUD3c3wm6oqAtSNu6c9J3lOaR9+j4yeJnnoQmI7xGhsV8crYqoSVxyYCXfffota31mrpROOc37k3bfXvV25Ol37LU07b3n+6Qy3OBPWNSbpIlz3T2Yqk9veF39D9l/Q96Y0uCXB5mlSyKjOcoWuJXCUrl2VRBDjEuPeiCOXDpkNLFsLaJquFT5S57E77e4zyzy1nIleyK0d4152v0kI81+nh6zrhAf14fYLHq9MQ1mQVgzhnTRs/3W+1xRmP9wU1pCODgJ6t64WtTT6XPYf68EVWb4UtS9bGCumArWu6Y/44ERG1350Zau8gCzIq9HHhXFrOHEsEevXPuEmXJYnPHtGQKfXlvSnCtxw4lKWL+eprrCYMhxE7KZTDBM46hZm6gz2EvZ7prHnt3D745POlcD/r6ewodctf5dCqrEDHTLGH0uHqGF4qmw8YpxPW4dDCOweDIdhl9t6L+cH5Kc9zt1Ey4Tz3ehqcutkXnN3MC3uUNvR92sN4Jw/CW8nzs66mB7sOAv3ffNfbp9/1iAn07/uWF6e/txLoDzYx612rBFYJnEsCK4F+LnGtF68SeP1J4Dh5jgxGl3oZdQb54ClsgFBRnyNJkGQQ9bbxtkYJfl2IPQw2x8Zb31lWYTwj+hXAgqFjNJYJgEBMMieQXXhk40E9doO/04O4G4IKBI0Grb4OsP4EWwiY6p7rkRKNBcQ4x0gCo91KcWwZVZS5fsUYhCzsWV/GGRmCMmrbBFsZBACuaSxSG/YWr2dINqrZx3CuHe4MHnjw61IAsdK5Y1RSFPPtey16ghSGvjbmcml9A/BIo+c5rQ7MQHtEIKWTg8D/9astrZ/XSftyQz3xmbNGGV1JiXcziGCcLmRssCGkIp9IoYZhwQY3OWLwqCSMIxrI3v9FxLvu9+VmwMDAhCGGSKKcHwx2edBYTmqzol4UueKo65pjG6dk/Gjp9DexZ0hvaILdRq+IXBDRrL74vkrXKAFevjRtDw7djusBat1gpMrUhZ38zMmNSKVuLKg16lT6V9q68lqjJEEJ1AbJSPlJJFBFgvspmp/bd1rE/RjZnFE4kpH+lpyO7raxl/OFSfQyBnfngHQqwIhBlIbS2GcERi8LMU0Hb/3eCzm5G4FeBjvPSRgUbSRhg2FsKxnPpqL2P4buHokUhuyZgZs2cj6iQc5EZ7IY3oWcuTg0cBt7SH93Z4cy6mPcXTKqp2EYw6cNf+GkJkPc21YC/UIW4DRNSaD/VxdEoP+llUC/qOlb210lcKESMIEuMjH1UZ6Y0Cl7gV6K7ofODTmbBDJkALoJ72Wet0+nPGnUH2nifOzbSUT6efrKtZA+lDlK500clBdgbZ9D+mcnzIoAJ3PVoOZ1PTix2IzkhwAtXWXEzkl8p17vy6Peb39uNbDAQfqSlFePQg/nSHCM115Bje5wXQ4BgWk6fiI9O2v3sHRYoqoVGa4fOQ6rbUWfa1/YAbl+1/e37k3b6qPqm29dH31quIl9xBhMUsaYwL3oa6m3oR8hQ+vQ1NQeUsDrc6dAp872peYkrfaIKk85CndonEpvruu0rjoZXtihymN1nd4kOuW8yhk5dc9RR7XdRNH1V3fO58wf5cYynbZJ/yIee1R2OU3P1l/pithlDpRRqjnDCpP1dPM+Tyq6XiKXvo8zMmSmHeOvNJzp0lElF59Neo5KOgnn3WsOt50wV+aAKr8GHl/ETaNOXrq4sWQ5cIMhEze5zdpfbRO3ta2sYuXs7v6A2+2IX/r7TB+vNSxMXTiqEeiV6Us49vCgxlUBBzg2gPM40zmvOctxOvBZf+nc9c/P8xJrBHqNMd8bncAuWfX3z5ACnXMkz9OewYI07rzz6gwph4zdGRr17JkbY7khc4DPzcHJvq+Rkr3+xk6AIBKL9j2f2T6qbF7algZ72Uqin2dVne9aCPTf985HT6C/RwT6n19roJ9vRtarVwmsEngQCawE+oNIbb1nlcDrSAK71O37Bh0WhwRoCfo7qRJAPBVeSFUBGcjzVH5fR/JuQx0HP1hF8HKGsMRaM6sHTGo8oiwE1qPmMcCHmlykAffjC7wA7Dt5XrXVe/+qXxCHM+KcSQviNz+y8aoAWxJOx7yQQxad+I40yZCxHUAXkCPSQoDX5Gyk6c71BFAG1ON9rf9fujVNz1SKdNXiY2qU4k4EKx7lGDAkN6W0vrmrE++oc0Ve0K4bKQPQSYYTQJ3lVMa6cR/MjEJlGKj5U+ry6drVafPyqz1F9/bpG9NG0RSKGAC00iaRCDJIaGx2uCBaXfX1Kopd10NeSqY2MNVaYzwAcAPtIs/T+5xafeo/kf9OCV/tQFxfuTJtr1+bNuovkbqsgZRFPa8ZMe5N2xvXK8W8yPOaC62TSKvnFPWu1V3k+N2789rfeLLTF1IYkpXATkGs4XCGIMVhpkgErM+MohEZwhqUjDGwdSMpThORuQNyVYabGzd26SIrqmVWixEClmh+zZHuM2F/0CLOiZjRoxRRo7rmGDRGQxAOEsyx5tJ7FiOTotKb4eVhUrifdOTvUrjXgZNpW3sUeO0zNm03qpYBhXXM/56b2lA2apE+dzzzxmwA1YeMwmeP8+xsqzsyYLyuqB2iW9Kon3vUBv7hPZDpIHMfM9aKgFnr+T16BSIJ9D9xQQT6X14J9Ec/cWuLqwQegwSO3vtZu9qug4H+2ONTL+DLIJ+6/mdVOnRuHI6TRDjtWaPedB4i+jHIbfERD9vHTviUkyvOsjysR+MGPtW7086EJW8cMblHur++wpGhy710e0Mo6m0vjKpnEhi+y7Uw6rq81ztuqntHzESTCb161HY5s6O7OqVz4Kl08suSXZDUEOaKHIfI1u8m4EWIKmvV/Wm6emXndKz+kNZdfZeza8cRTV7bl46m7SvCHfdd53zzbJUeUs1zZAu+SNJe7SwR58j+mB2hPoBET+dYjV1ZpJ66sYuKhqRDR2a++z69Mk1yVNaaEoGMA6fmxOWVylkYjEu5Ma0vHBKW1jfPVf+EKYTLIMdRbdPBgfalv4vQl15Pavd0lh7lEeW0tlW2Ss8TQYwz7QYMhW49bdp3lzblfEtUuSLeo7QEtgUwk8bB+vLarv1DySqNWX3GHpSZNrxeqNUeDqeOti9HhFmq+oUIdAh0p7pvKeZFoOvHTtxam2BEO4aH423hd48bx+lORqtk1ZXmuAw2G3GTH1JOC5SeY98Wee54gAuKPtfjncJdc2kM2LIndGdfstelLSadTxJrGNaHAzPrMSPtNRg/J7J8qD3LFSfhKp3HnhodjHymlDMIZ0DH8DW/rNl+lle/cq24f9U4bXYvIU/MvGzCvXvTwdt/+CP1tvuofi4E+u9959unL3jEEejf/y0vTt+xEugf1etnHdwqgSdFAiuB/qTMxNqPVQJPsARmJHrwDLMuj57vADUr4Xh9FhkxegYbTJVXeumzT7A4HnPXFsJV8L4HrGGMMYAsgwKAISMUDSogZCN1fqQPO3FOMSZlpK09qQsM82wB5p7GDitOtcz6yZSTmcqMDmDgwGhCPXiIVsjZ7kldHswGrpGmHOcNIuzTWAGxSbSEZIPBwgRpAUwMboBBjDMyFsl41FNmAharDxqLU0jX36STBjgfI8QiOqKTyRgCoubhbJLKozuB5LWrjpIWyHXEeU9ffrkR4TJSZN10AGRG8soYZDK4Uk/3+udFLkIwO/1/dchLNS00NdmQ85pzasnpWRghbVhpqSUdBUG9+iSoiZqWLS5Sgx/9f1/ciWxHTij14K3brrnn1OTKAGAjlhwpjpyevBkM68zxemq13B0Nj1HKzhBlXMw+EzFQ0fPTFUWsyPBzZ2eE0XrW2oCI7QQ6E1f7kH3L+ejUjGSLiPVssbI2KqoCo68cJSDFKxqj10DHiNOdZSoixKnXSS15t0WUsD9kgLtUWQEgxnFyGLMWaNxOH9+ii1yDvab/oshzG4KIQMcgB9mg5UZaVZ9Lg5GHzqVhTA1mbUrOVH3ucZVTh/dy7e8kKdjLZPiYb4YyzIQhDqMVMiVTAiSIjYrUDa3znL5gcO2ZOupsSAMQRvcwGO2LqDj6+S+bRehcpPHuMb80L/xxSaD/8R+5mBTuf+Xz1xTuFz6R6wNWCVyABGYEeuq047OWyDPeT53MjCxD1odD4SKF8Uic7yOdz0OwX4BcHqrJfWM6S6NJDCFD3vVZKsXv2u79PSeBrM6WIxuk1yJgSuY6Mobltakz0y4RvbpupmsFiZ/OyyMJ77mN6GxIWKLk0WmN94rETiySDrBEIVtPGcD9aAMQpoBsV/at6y2zUS9vpefpczkT44isqHLrVyUUPcPOo0W+o7+h99JPR6YHGWbnxNCN0Z/Aj8g2Zc8eYJ+V/t9qXBfpB/msZ6mGuX6MiUhVXg0a21QUtPRIygPoc6LP+zwV0ay/uW7f2iWQQLLFOVOfkb5d90n/VUYsZ9CitnXZWyAzres3IR9+0vf4/6Of+6L21MKjTtfueumFeaxPtnaFC3YpzquzYCjNK7i8Z7CraH3WAyQ6hKZwFqS3thPZIJjfpewQrLfZ/o9yYBCyHadEVrtufohFq+dDpJYzcSe/IeGdcaDWQmRDa04FisgvIpqyZTjboLuDPUaHC91MloO+/tv8HHzyxWTsYok5Ap3xpeMIjhFJUo97HHmxH7EfgI84U/M6C6o2OEcIZysBD1qno4MHz8hgBrUFnuV6AhgYYLeHlTMEQRQ+T2tNgI98zldWDBySOqm+mQ4+/Qf3vlVsD5WTSv2sEet7RXXsiyTQf+cFEOjfuRLoZ5+M9cpVAqsEHlgCK4H+wKJbb1wl8PqRwNFPfv7x9OynDR8ACwEHkYuxH9IVJduK9zF38dOe8jr5fg+JjsED0g8wQvQDhLnBB6ACL2CiLYP8rnRrTaiZkqvQD0Dec7bLKN4MGjV/IhTtmV2Rvwl6u9GoSOf8jnptfEadbAO+8lo2wC/PcK+fSMcFSBoJdIAtdZyXVgykOYYQwJo+d53oqu2nPpGyW2uV+n4pC9JwY0hSW0pVqGtsOKDmchilxrVPbUaMdf6/ah4e2yMK9T3cefNr/IeHjTRWur2bN1tEOUQdABcniIw+sdEmIsztOFD1NfUMPTszFWCwMCgND/skhjV+auCJhCf1nQwXipDH879qcDcytyJiyjgEeXz45u88NntH//BLp63WnKJaRFpP22PGDdfgEylca1IR1v5bc4shy+nrr7bIjfv3G5luR4Pqj8bY66w346Nl7M8v7eoFMr9ZMgGHEsbFmdeBe4F5nAowwuS+xhBhg0WR62msMKHfokLaGHSNooHKEIThQGtD61jXygjwyquNQNe8FHnuQA2in9PITJQ5xpVKnd+e22Ry+JZ3X/iZbAMg66iMfS1VZET1s3eqvntzhom68hjRZiR77TM7KMWZ0yMxhihw9tCSkYxoF+7FGacb+ypVpKQl2XUiZIFAt5G4xudzzh3cyZl1gAHYc8ae3Dn4kNJde2Zu2OJ8v7+mfT/j6k0C/Y9dEIH+V1cC/YyzsV62SuDJksDRj3/28Q71d/hCNhGuxsmSjDG838guNXNWLedG2s0nLpHNS9ddlNgelOw+rY8P2i738W43Rik9k+jLBDWd+CHTUGfxGtZB5/X7O5zsliIfk7wf5Q1Odg3rctgFf/RrwxEVbOYIz3CwQ6fEyS4cPSnP0yN1Z2mPA48kgQ6+WXKaFB4iAh39Xrqq0rUr8lxjMuZQVqzK/KTvRKDrh7TujM9rfePMXYo+ty6k50K6o9fouTO9JspEzUoUhZD3EehkYEJP5290yI5DytmVOSazFI8w+V6YVzqZ7nOK9kut9nlFOncnTe1jdPKlvYfeCskKSUibdlglxXo4e4BvRXxTA32z2ZsFyrhJaeGL6G5Ot4EVyqFYa0ap1/F0sGOyMoKZvG+Z1ZTtzAQ+e8yYWY4GVYM9MxAa0x04bbozXWXpLDtRKPOZMmEVVobwRVbedxGIQTS35EQZMLdRe2bmv4/NQFil9OIsI2Z8UE7TXRYtK5cxtpyx5agujJll4WijZy3DAbacLcBN7FE7QhRWK33+4FMupu55LrEZgQ7m0DrlHNQ+TJKaOQADeq03B4JGxEewRpY/w+YHEZ5z10nuylqWvjnpuNSdnKPsWV8fe6LPE6uCmThTw87Qzu4qM8A5jxMBY4ozXQS5nbZrriYFJawE+gNpDhDo/8Y73z79jj/w1gdqY99NP/CtL07vXgn0RyrTtbFVAqsEliWwEujrylglsErgVAn0OugAAIwHg2P6jFS18kxUeSjBS0/L9M6n9ubRXXCRNWKP3vfFj6Cj+xwKSvD6z2m07+z4FCv+QXgDijp5HdxLj2jc01UDmpq7DkB07dLEF+jV8x2VHJECeQvABjCCp/yMaG8kcEsJKDBeAF6fqW36ksQR0RtZ/xliOokzDBR6PoYZhm+ASD1l0stV6neuxaBkA1OSWNWIvlcdQMuhojA0Ntc/D4KPvYQBbYw47+nuKuIYsi7JPCIvJBc9t3v9V8p4UnwmCaznygigOu5KV8g9dkYocGwjT9UEL4K41xrDIOF5gMgL55eUD+BcIJZIZa+FZpQyYS0g+uqrMyJdl5wnGvbOr/6+lpbe+6Glgjc5jhHERqjLzbAkMv3m7WYUIjI/yWWl9yOaXGufqBMcEHS/DCZFwkNmd8K5OybUuqE+O20lae61GJuDqAAi85mD9O434A+il345tWBF8KuOIekVk0DXo65dc72+zK7g6BPtm4xmyX2Zhgkvc5xryqCC48W0ufAoCj29EegRXY5DgLtWjiYYOjEke4+Q3q/Icchs9n83ogwGa6KxONNY9yOR7XYiWjAjo9jvrH8cUYhg4qxNB5s0UnWjN6nfY91wDnaSIGqwO9qs3sVEZ9EvDIZ5/N/fTgef9v2P4N310d3ESKA/+3HXH+mAP/zLN6eVQH+kIl0bWyXwWCXQSfSRFE5ddyRqMewTIcxZzXsjs6jsI5tHkvk0UvqcUrmo90MnKcb+nHWcZx0HmYWk9+CggL6DvtDlXThhnDOu43NwBfjLeGHAQNlmv75I4HSM6M9KfIEDHbWCiewsgt+RsKV/6J1P1qyMECZatusDOFFD9A1OeUmseSyVil2OqlwqIp1MCdK5cEaGZFeXiDoPlaVPFUS9CexyIBBxboygDEf1GeS5n1f9JoIX34aOPQoDg/sg8tLJ186vRZgmoQZWETaaOUZWtiVd27NUheMrDquUYIJsxCkd3bM7fi4sVo1HzzfxXPW90fu6Q0ThJkdvQxTvUlQfvK1lrTnLz51/9FWVcny7qwOO3mw82Bw7nc3MJa4KMyEXyULXyClXeIP1JexFBjq1QyR+1ULvDrpJpjprHNH9hSuE127e3jn/J+5ggBDB0tFZe87+Vc4APcVBSiQIWK87RdrvnEh7nXAyU6jd69daua/S+TvxrzFk9DmPASskTsEJSn12f3cZrh4Pgf6FO7moDzhagH87eV7BFsKJmr+cm17CLIIoWNsEbrB2JIvESpSEG51y6tjq2e76fg0HGXDyLLMB9daL+O/raSiLxRhwPM+1kya2ft4VZlraRFrbkkH+1HhOilo/y358PVwDgf6vXwCB/oMrgf56WELrGFcJPBESWAn0J2Ia1k6sEniyJTCvgx7Afey2voK0QwkmanM0giSofczDv0jifBzKwxHpZyDQRcR0Q1ABDjyh1RkIdMgiIiT0P6nLO4CJqPQOMhY+y+8yqpYoT6LdSSeYXuCd7KFeWNQ1c98qTXJ63kOgy6Nfv2McSoMRxLL6g2GBNab+jgQ99eRI1ZfpwtxfiCrSbVcqQ0dLVPpBtatnZYQEspGhh3SDSlfo6PaIesH4A8C3QS1qnQsock8agtL4pd9dJ/x6pWqvdOp4b4tQfeapafPhl3ep25j/jIT1/EeaaYhoogoMiCvtI44KRPb29NFZ07tFeHcnAkfft4j4Xdq07bR97rlpUk12QLHX66VzRzHf+adfM6uPpxTuBv1E0Ndcbx2p35wybOwZye6rh47K2BxVhIU93iuSovrWxqA05zWnmue791okhQ0Qta5Zb0RoEJmidoiqSOM57Ve7MtaoDmGPgPc+C3LW6wxjakQx28gTfYCExXhTdeVtbNIeu3mr9Z005pJVOmuwdzNCxY4gZXByf8t45TT7l6fDT/ruCz3Rnbaf/hAxQJSLo5dqn2HUxFiEXDDqcE8nxmuPY2hlH/Vo/JBrOj700db86G+cUWiDo5y9zHqh9AKRh31ewyGmp1uM9Kw97Wwd3rSPYT7Wn/ccUV12uLjUSiXMMkjEe32zmQ4+9T0XOoev9caTQP+jP/Il00UQ6H9tjUB/rS+Ttf+vUwkcI8+TpBhlQrpmznCINPRWdFPaQF/Lv2nzAsnziyLOl5bIucj0ccynrTkIH+n+lFSxXlYR6cZMQRDl3IEp/BlZuEr3TYwLNlmaD/SNrnfX8/rnCw7KkMZJyoM7EiaiR4wEehKj4EJjpiKfJBPqcqf8Ei+a0L6yi/TFuYP1iW6LHHWtsm+ZZCwdBDmTqv1mpQ7PtYzMVDed9O/Gq0Wedyfi7Cjpx6vUTs8uUPiy47vCORDolksRbhB/wpmKoLe+GDof+9Jpxmt95N5EJ3UmrIpc7unySVFfmdnoOvqaCffSo9FfdS7Y2TXSsttJVvW2K1JbS7DkcR69+84vfEXZaipKXMOREzKR9zXXzi6lfyLQcQ5Q33HY1neSl9YgjilECpv0L4K6ylOZhKYUkjJlQc4mboJ013Mo58WeHTPjgVuNZ640vVbjYD33iRoPhcooJtmZQE/dvlLtsx81RpVFY29tK8tZT/MOsR5rwjIqh4CeqQrMVPjYWLJt3sdhkzr6WdVAj8wAvHfSdsLc4QDC+gQDgSXYo4wRJxTsGmBEnL9nTscxF4lbcMTp52Jk9oAct62gziGe0R2CCw+DlXswBGU0ymEm914/s7FflHNFnmlcL7uCHOxZ/+OSurSZ1nTuJ798IdC/5p1vn55/xBHoP/StL07ftUagn6b9rN+vElgl8AgksBLoj0CIaxOrBD6aJeD07f4pTRdykUEn1gcEEj2RHqkoxSmsffzwBQn0cYCU07p+dkL9rMJRqrujXURp94Av446jJytdlsDsLF3hUO+vE98Lk0tEBYBU4A+CEkuSUsKp20pxxXxDtBmolNVBv84MQV4orW+QWqRYg0RUeyJAIbxn66iMD5DCpJDTNZ0kI11jEeJkPUiS2ACO9IyVrpw63Hou5Dm1nrtxqSJa9TeGK10rI5H+J4W5xtIdSwIcMtUm/OoeGWHSaMbvHfSWZz9GCo1V12BwsAHkYNo+92wjYY/utjp3+j694mn32B4vsJkOEh433t6klK9xYGwAQJOC0A4Pkl3V3JYB6De8sa0FRXcz5jKgHX7Cd5y2hY5970gKjDG93mM4NxxcaTW/n7reIhqUgg852ehT8yICvdIx9hp3rHEMBOUE4Gh2rS0BakVoKFW+jExVw6+lNa+IknLGaFFG9/FBAAAgAElEQVQLlVYdwxwOCBhwuCcjT7inh/3UejeQD8PPmF5vjNgoo4hS59sopmfcvj1tNA/UUFRfo0afiXwyGOTZkkdERYA7pb1rLV4wgf6+LwnjVNW2S7khFwx6GH3s4FMkeaSgbJkKyKYQziScJxiPvLaI7t6ThYP1n4R4lsbAyIpRDQPluKrTAEj/M0sFqUd9X/WlO2TUZ6xZnS8+B4MksOGxokvy3Vzn5UXXYzz3Jn/CbkgC/Y/8PxdDoP/1377WQH/Cpn3tziqBM0kgU7hvXz6aNjciZe+i7loOh2Cm7iy3gAOSqF0iHml/dFo+U8/joiJJngRS4Bihvm9sZyXTu3OdMkVVtGpio9S9U57pmMa716pDES/5fHRl7l9qk3fvrN/oFjH3dlwl80+98iHQwTnSGRLX6G874RYhOM5/1kLvDpJBOKt9nD15hnFN6UE4Yaeuw/VFuhoD9Xrnm2m6pQjOINO1zu1cWiQ2chRZjnODviPSHexoWZZ8RHYrUt24JvSy7lxZ0dTGb1X7XeORvopuR8kxOTQ69XrhGvBLOmziHNHXRemMmm/1QQQucz1zTI7SSzkXOFnqmc6yVo6zkoWydCkKXHXY8yjwmiuZuXTSd51rd9/5hS+3/FrmrEpb7v5D/rdU+tT81hrq5bXKCbmVfZK8rlbGq8JSPetV9Q8sJDyKLpv4QXXWK4OCSWrwtZ4J9jm2R2eHaJsvHBDI4pRp+FN4mCIkP8237iMzAI6x/VytdShsGA63lgV4zpi9HJKxgel5ZBCbZWurfru/VXqs0vyfJ+vauSZbUFgZEdkfROfjMMIZwRlEWTPmzUR17S3LrPYQdiWcScieYviBEwIYtewv4/no86/sLmOwjeYEUh2nEsoJOJgiyvlxVmDb6Jingkr0HPZwdwCvjmPzmTk2RYr3jg0LMzHujpt2WTGehHfledfG47w+CfTffgEE+nevBPrjnM71WasEXrcSWAn01+3UrwNfJXA2CTQC/QQyF4XYyngRbI4+hXAf/rfSebZnP8qrngTyfBzPyWT6OYQE6AV4p8HG86KoUqW2LoDsOSsUaUK3ojZ9X5DcGWU5I9Cj7p/AhAi5nr6v0n8Tfc58d5tQgY2seY4HcabQIgI9I84x6HTwHR7dPUpTKQMr/bjJ5Uj5xzgdfUCUagE7DB2AqVykmXavE5tldPDfMVeujVbREkmcWT6Q5tUndZ/adjgYIAPSw6vpHsWae6wi9dWGgK4MSPa8r8gYjVEGEEVUa+4L9MkgsnnlZqVyizrJEPuktLxTUdz0uQwcXitpROR3/Z8e5O5Ti1xoGQHuO32404wL/D51I+qLtwk9/Phvf6Atf+cXv6IiGcrACKkM6JcDg+p7X7/eos+dYrDmL9MGKhWhnR2mII2zhnhFK5QRyKkNSZEt5xET0RUZkvXu3Z9p2iBTvPa7c0Ktn57irzIcmJAmcoV+RLQMhoeMGugODtQKJK15GG5IQ5/1/EzYVr130iJS2496f73ftW7SCKb9KkcFjC7T5tyZBM46+Uci0DF2ZLaNTIvI/iaqin3KGQCBjnGICAf2QRq/MaAQzd8dGejxYOjGmIYRjjMIoyfOCtprpJ8fjaOdQK/9TxsYF3HUwTDkrtBInYv93VwGKpyB2B8jCVFOCDJyXrQTxFnn+km9Lgn0//yCCPT/YSXQn9TpX/u1SmCvBI7e+1lNZxPxp3eHCMR722l759600Rl8LUi8jALUu4BzvBPpC485C3n8MOR5kBxPIiFgMv208Z1GpKM/EElLpGRmhMp3chKzmhJ0/iTPc6qWHBu6jh0XmnwrXND7sDDn4C+3GwQ6DoGJEXQ7RJLJwSh3k033TFrVYBLjYDL0IMhDSKiu+gykdDoSWO+X8x7XbKfpVkVgX6s0yDivKm27fpJcNz6SU3XJB9Kcdnt6d2G5IV2+rtW+S1yH/HEshOTPqGaXeTpsAkYeGrsJ03I+TgIdB3XNTxKqqT/yXPBozgFO20S8kh4aJ3DKTinrVS+VtPOZPHzLux/oJFYEulOypyM7urKeLWwgfV7Pd61yyPHCCGArzc3169P2yuVdXXDwMuO1PFtmAI+hR3IvnW31GT6hknmWoeqZz8Z7I4KfLFrgmSRAj6nJkfUOPMbZkiS6MBP15VMeOGCrXc5yHJm743FgyNncF+kuPOx+tQPn4K3f+0BzetJNxkzo/eks0B0PouQE5aW0BrDpgY989sV5Vdi2ZZarMSBvk+jl4JzkdL7jGLf7USXkkL/sGSX3XXaDKlXBPuFc7naKOkfSAS33H+dtx3KYwuL8wD7G+chexjbD+WtQXxktar2uadxPXroQ6F/9tW+fHjWB/sPf+uL0PX/hZ6fnn39+euGFFx75HlobXCWwSmCVQFd9t9vTUMYqrFUCqwRerxI4Rp53xRGQE97eEBQAQZTgJaPBObjhh5X9k0ic7xvTjlA/r4AU+YzXenr6KvpZtX8ByhWt2jswEC75eYKEBDxJqs+yElTqPECayW7I6Uh1aABGXa1cP0XMdlKxkK4J7VhvnRSqKPZ03HDEQQG5jKhIwrcDL0Bttd3XbcwOoJF29ZWJ4s3kFH+OjKjn6X9qJRu8lWxxLOj7IdsvAl0f2ZAR30GcQ8J7HwV47fNQnwvsOr0ZEfaXp+2zz0ybW7d20bVEtSv6uhsL6pmOvDjYpfCz0SI8wGeGC9K9K6KiQCQe35ZjEHaOWlbESkXDHB5Od37TG6cDG7x2hr3Dj/22B97qd/7xV1ekjCdoR6ZLFo4GEbmrGuitppuNQZDBmk/SWxaA316/trvGGQ9wxKiIcqUKtNNIjUvjlew09h71UGsFw4HEJMNUktA9gqTkmUbcjJrWsOgj+ysND/qdVIh83rW8MHBi2Ki1bmeBKweVsr5S3svZAuNZOcZYXuwhDGecKd3hpCJ61KaGXkay80bGnGURHP38l+2yapD60dEKVdePyCyMW0UMz84GG4EqsgIHJAx+nCn63hEw5YCyRJyPxmzLP6IXMsqLlPs4b2BwwniUpAAODWqP6HHOW8Yn2VOuYgyIp69JorNH81yjD7yni8S5CCPeWeb2tXJNEuj/2QUR6N+0EuivleWw9nOVQJeACfSmikz3f/XmdOljr9ffUa5In4gAxCETnSl16iWSeOmzNOOcRiwvzdOCGehJJM73LbGjn/ui/avvJBMX+oH1rtIF+vsynFz92UJUeD51lHu2o3fsLBNXpIdHX0Mfd6R54aZsfyTQnf1qyP5EW6MzHjroSOr6/ozCLGfLMZLbtZDrUvRXPQvMRFS6o0Cr0+hUJuKrn7ePpu2v3zahvnm6MjIciqAt+UCQW/e5P01K4e6fYUDGFKWTJwHM+DsJXzhM4ybKGN9x9DTIc/Ss69d20bbo2shvVjqsyELpyu5/YWv0/yRhmcdcI+A5Y46or/7/s/euUbZV5bXoWPVY+wU5AfEoopwA4bkxAUEDQiABoiASEtET9J7EByIKNrX5bBxfP7TREPV4W1OUYPTqPQe4PvIioMQoJoCHp+IjAsKGgB4MDTSJsJ+1qmrd1vvofaxvzVqrXnut2lW75mwNdtVac445xjfGnDW+r/evf/jd5EnuR/O+uo1MdPZRpAAQjve/btFv3YmHX1LKXhV/yPtC73cF3pN0HVWonIGLsap/RW2LPhf8nABkUmUJxOk1HUl8rp+sioS2IUtf/Cz5KQ30x/Y0ENvr2fDiJBlBEvG4J/0tZf1Hxa+usEdl4+znFuRorvkArGqfTrIAwWGoq4FYoDIFiLWIgJ1LwAW5fscjukovCXTHmvM613M2DAUoxpcwXKtEmLxCxbFQRsKqayWrPrz+/Dg6tuPSX1FFAWOVEluXspdXK+zIOEoA4vGOcNKE3y0mz+BZcdmEKskczwP6grHA9i6fYAKQSRDxeSz+TyXmEn2meF2XslslSGMCDB7Tw29c9PO4Wi6MAPoLX3vgQId9EwH0e2sAfaBWrRurLVBboJcF6gz0el3UFqgt0NcCM+Tbeaaz28JllEaTQ+xNcXQa7Swusa1XEngeTUO28IKPUGPNYCccIDvhMcvcjuGs96iyJew5+aIegG9h/cJBylm3POzMOOvcAZMS3ELGtoIvBhFjvIoOl4I7MQBi8NaMdoPVUa646sgyQzZm3WscXqPoo+XA8Jn7THAUUoHKnMC9nDWBMa4RW7trIlUf3aB+MF2xD8HJykQYPDfwF0Eug2fVa2LgzdkiCFhYns9OI25lMDYCcgbqHSCjk60adjGLhYxry7ir3/4sBoMcmIvOKk5vjqf2057GC5t7XbPgVd7rgol//eNuSW9LAqKUAJxrZ/lqnbRHIF0oA2KcBSQXGYKkEwXESq1qSawqK9tS9pAeJFjsexR2v+uutXWvEAxy2yWgFgkJuq9BWAfYo/R4zJRwELNIbYbnFrbHe9kB0GomBjJCFLhiEM1S7pZkR2YEpPFL9lWo8e7glNe25Rz9LCJgNjK8euitTS/SHCl4yeyZrBDAAJdBf8yfM3cMmhvI9nvFtvOzz8CSyl3gnBgI4wJUBK5kofn9JfswmKRAnn/mn04F3dyes8nxHYNHklk3OYYBSkuZVv7elvaChH/MkkN7JniU93AoURHfrwbPRxpFsrN54A0DeTZ310YigH7hkAD0T9cA+u66fOpx7cYWaN36/AIKTj+xPTXWjqbGrynL0ONGVm4olZKz9wQoeL9XtdGgwfMVDpx3bbUXC6KbKEoVn1BiyQCK/86Wm9kBiY7IHIu5C0ztsdk3sG1giVsM77EF3kcA3X0y4GzJan9e/p4HCXZ/VoB6AbERuPd+qIvkib2JiKNdfQp7/zh8283yy/6O106n9uNbU2pNp3ZrOqWJ6TRyCMo5hYOERcm+R7l1j43+nOWkZctiP2Wdd6mChb11PzJFJF7jmSzgq0F6ERrgJ3guTUbE/tlkUhMyIjgYyQvup33N4t9JccogJMbqPSnmAoRdgqyDU3Ui6dj7xTb2+LIpfABLeMMPUu3yLp/PAH9rQkBorj9eeA4G2+3DKAO9Azxn6W3KuXvNwyYROKfdoD0OAnOoST7jUYtouHzTqEZX1rJA1rjn5pry//L6LPNrUDvuqf0MwW8aGck+BtaEieuQZLeMvNda9H+tUMDv5KehTRILBCgLrB0OgI64kkjvMS5SVAH0nf0T2jGUmgumKkB8JJk4NoExmUBTxqoh24YcLwgyJjjIh7J6Je4LMDyWZgBRwf4u+uL1QyJDKyvcObO8qLn4PRVIAH4m/a8JVHadTbbgu0zAfplHtVcUOjo+1fgR396NdzWDGZoB9DM/eGQaBoD+9zWAPpiJqlupLVBbYFYL1AB6vUBqC9QWmNUCMyXcK8ApwXPX7wuZt8XRlIMTSeRLYPOVCp7DNIsD0AUkIePYQYyugEHIYDYo1C+gMOf8dFG489n2CSmPLkcMn8MRskPGmlXOEI+Se3amIuPbwDYa0bqCY8MjBJewzgi+i/FtwMnevB0fZ5eaMUznrXjQHZv5PNvAgaDi4IfgFsaFYA4OZFIw4OOxyyCRPGIA01kTzjqPXAUEVglIW35Qv8M5BxDK7HFlBFua30GAEvRTIMHM6eLkioTA3zumzM+v6kPbXoWEoeCZM33tOBZ1Ad2Ljm/IPG9Arm80ZIPnoBCznn/9P6Xm3l+ac5XN94Rf/fu5aR36y8BNrjHOGufbd3RqytvZbjRSe88NdIwbrFsu5zuC0iXdRkEVrmNlUUgaH2Nh0AkgJwJcsCHGpkAUZfO5vBQg4IMt+cPqc8d3peT5Yya5A2ySPmTAxuUJTG5wRko1W8YyhmhX88Ks+a4MHAcXG6kBskEhHOSSAKwHP4pAUSdDhLUKEaxwYMsBDxJNlC3tjJrRkaHIEdKUyEL3e8bzgLUpGXn20zLuCqgy6NVupzZUCDAXfhdZot5BrKotGWD1O0/v0ZIxqPeuwflYSxOXlKwKvbNM1iilIPT+oOR/frewf9VafjHQh6ZwTgkABTJIlDS0Dfy8M6hUIUaFoCCeiaIcMOQ69vN9tpfreRFAf9Mtw6mB/pkTh1sD/fHHH0933HEH/7vzzjv53y9/+Uua/NWvfnX6whe+sCDz33DDDenKK69ke0888UR6+tOfnl7wghekN7zhDen001Fzde5jcnIyfe5zn0tXXXVVuvfee9PmzZvTfvvtl0477bT0lre8JR1xxBFzN1KfUVtgF1uAIDqBwqnUAAiArFj/3TcgWcic9pv8t6WPszQbgN7ru3426LPvX8kZdLNmoXsf1ssesAVLHyFjVna3ffy30deXLM0eGeKl7erchY22AdRyrgHgTlZx18acf991r0icLfv8juxzyZSNfSeptgLY+3sCYwHo4h7XGe3atzLLW5+bCIi9RATucZ3Ji/Z17Oe43BN8KIPhyELfMZXav9ieRvZdn9I6ZPpqbxSzi8s9gk/I/UzM5g+gGGXiXUtZYzMRxUTysvmNcs2ajHiu/SwDqs64rvo8yFLm3kpKWgTQQ4mluO4iiaL4oAKQ7aepxA5k07nXR3+lGtZen1Usmv9lcZLtvZb+xCNnVogaKTWe2tIBhA1Eo+8ob4U9KuqS43mhLyJFrfKcVMi79F9E4PWzwzHJNyIZuQO805/img8Z/ZGU0PN9Fp83OTewYwR/rfjEMnaKP7gtPx4xs9nqDmiahAqQgUeyv4dZURv0IVwLnXt9+YhQ9VIGfrkO64L+lTLzuebZmqTL5YtJwWv84G8M/C9Y64EX6W+QYhTONCeAHp59+3KxpIgDBraN4y3O8K+Cz1GZzokVJaZhYo/8o6hQF8+BvRk/UvkAWMTPit+B9uEK+Tm8L3G++4V/mdGuuI/fg/jXZBU/o/a9qMAXSARdfxfiuya/k2r59rmXrAH0l3zwyHT8gDPQb77igfSNGkCfexLqM2oL1BbYaQvUAPpOm7BuoLbA6rFAyUgvMQEwvp3RWwHPY0BnyOD5SgbLq6tn0eA5GoJDhDrLvZIj7JhUQZeFzE01+aKaTEGHBYzqZqgHF04iMKwbOihRmNjBsWXSRQB53W52X2fKIbq2b2Q927FykAjOv7PcSzsCQH09HCxIcft+duSLtGAIkKAtg+0xK6FMaCAZGDCPtrctHeTCv3iWDOJFwA7BEwBvsCuzZARUMvAX6hsS5A8y/QbpooPIDGnJrqOvlJ4XeBczTxzMirXoS6ArZH4wqBKkBR3oY7RnPMum43BQbcMG/tp8+lcG+uIkiE7nvpHSr57K9cajVL3tjXp9zqz29zFLwb2qBqQZHBnLBACA585wtpOP+ngAnGEPyX4z4EK5SUn8lcB1ILOUuZHsN+4f58tkCme5u8/Okq7WqGN/OtnyJWBj2zhoFQOizth3Bjrm0PXETVZxPxxsLusj10jMQbF2zgLA+kTNxiHW0p54+EwGvQk0y94MjjHbXgFZvi6yPQA487F2wMeBOb1SOrKp4f3ioFEkg+HnQqJQsMbZOzHr2+QH/8tnQO8bB8383lHg2X0sS5DPaQDLGbwT2SI8PQ2vFwa1FHh10MqKCH6nOdjpQGLpZ2NoZIeBPujLpLEIoL/xlj9Ie+4rmeYB9e+pf92WrhgygN6YBXRbCICOSmBvfOMbCZ73OwCiX3HFFWm2ewK8P/PMM9Ptt9/es5k1a9akT3/60+l1r3vdgKxcN1NbYPgWaH3n2M5N8Ppe2xTYJ8WRuTLP/e7u1dUIps42lApovpLB8l7DXHRddPyNZZkjA5/KbDQQGm1fCLyxB70cqMhO9bnReSp/4TsIsAl4kXDJe+t/3NPIJ+oCcnxSyCyN3bOf0ou0iT2g5dqjHxL3T9y/q+/2XwyAYq+7DXWKA8hk0BzX+OctE6pt3kjtbZOpsQ4xgyDfHDPNI9BsW5hoXMiBIgbQ7zBwLkDLJIhI5oTd6A/qHGdI89mRjH0k0DIrWvs1AnCBBEtzC6DlefK/Yl9NJIh+uJeE/VKrdRlM99i0f21zb9guhE/sYQcJnmMYUHGCSpQV4giMwwcwEcBrHwDyhvXc07MMlSXeSVBVOay4zMN19C3hB5q02sM/pU8lexN4NknXBFmuJT1nJgYXskUVQFcMIu6/7d8V5acQq3IcwqptEWA1wI22WP4rgOceB0u5yffxnBYwWlnVJk77PO/BPT6B9FltKvviw8hA55wbREf/6ZfiZnoeHT+IvoL/vpiMTKWOSA7PPkonaSIq+gVCmH0ztM3sciQfoFyYkh+8/h1vcLzKJBK+nwWgO8YQYg2T192fxs46NK/CCJp7XYKI8uTmlNYiEQHKCoq14Pvon6M/kVRfiPedkgOdcgW58d3tb+kwd0QFQP/Akem4IQDo//CRWsJ9mPNXt11boLaA/szUNdDrpVBboLbAQizQ+t4LO6cz40+ARRVkiI0uBKSdozO7E1jea6iLAtBtezgZyLo1mx4/WI7N9bnN8LajgX/p6Af2OD6D42DGfTy3nB+ui2zeUnvKAYjK5M8A0G0Fg+vqh7Mmw9cla7oaDHKbDnoVdrICRAS95CQ6MPLU9pTG5EjjunWqYU0nUkBsBNDxszPOWbfP2bthFmOmBTrroImBfWdQEGAM90YTBizLc6RgD2viIYCjDJn1a7PzCQm8cdSvRjawAVpNjoMCuL+DPA7msC+uDSiHFE6jgfWYfWu7OXBmh99BPQOtMSBgAoDBZisSUMYvBBYnW6m5798s5NUz67kTj56dv4fk+DYFRGkXBUYdLMM6MGEA47CtmBkcgpwlSKkgGceKzGwRBkpmBOT7mxlYJ8gtSUfVASRgizlyUNIOOeZ/O4JVkum23RxQiCC6g412+n1vtOnATwngATCW5KMePQKsfp4MbmO9EQgeTWnbtpyRzeCZM4z00DN4JJIUDax3ijPQDdY7CIFMFUgS6pkcRg109ILZM1EFwAE1zUGR6de7AGoImOuGA0GlVr3WpNcH+u0AK4cbanh6/cJulvnHOXwvTObPYkDRpAdLsxcyhYJIyjpnRktkPVVexTMWfvie4/Hz7yCU1zz67nnx+8oAvP9GoL7mEIkOA3vAl1lDEUC/YEgA+p8vIYD+nOc8Jx1++OHpG9/ImU8LAdDf+973pksuuYTXHX300end7353Ouigg9KDDz6YLrvssnT33XfzO5z34Q9/uOdMTk1NpVNOOSXddNNN/P5lL3tZOv/889Pee+9NQB3XIWN+dHQ0XX/99enFL37xMlsRdXdqC/S2QOuWYzpfAGz0XsH7JpOd+hFaZiG6zGlz7WN25yA/wXP/rZ7LIFXfAefDZ9LWhv9abSnua8ve1Sf2AMkL8NwPQFfnIsjs/ppEYZA87gH4tzqUxYpjKGuoB9CLtiOh1feyf+Pv/Hv8l8CaShOR0NxO0/f9MqU1o6nx682UWu3U2HM8l64qvqN8OJNl0T72wTgi2O2x4nODaAU4D9n9BvenTNgNGfe4B8sgiCwaJ9AgdSSsmmjqvXQszWTSoYnO5XmTD+fySfYzXRvafpEJlN6DVjP0y3IIEuMGCCNoCaIn9s6e7z7cjOZvDCYLnQA6SL8wq4HzouQVMnnhd1C9qs2M8VIP3ZLk8ZmLPqJVyXAtDoK28kVYE13qA+xAJtDntqUmZ/tGEBNropTcEpnUdjLg7XeBSROuk42MeX9XJWrg5uiPlaBwb2ci+znC/ARJ85yBbgDdzlYA8Av5RH6mCe/eexcAHSSDnOFPVSk8egf9/VxvskV9z9JXJZYhsq3tW3xJzX15J/EF0MkEjzGkEs/R3NKPEsHf6572yyoGOdNdyhdYcyZw2N81WYHxEb3zvGbs71afr6npdOsZN6fj//LYlKTUUFQYI7m4WmaylLhSR/lOkWKiSUK9VGJc4g/zVMu2L2gdGkA/fQgA+i1XPJC+WQPoC5qP+uTaArUFFmeBOgN9cXarr6otsOot0PrB72bn3I5pDPJ0/bxzptrdAfNondb9p3VkBOdrtmhrOn3KMIZzUkBAMYFngOeK0kSmPCWfnck8j04ww0H3jM5pYYi7Dd3EwQ2C88HDch8i+9/t+d8I5MaudY1ToCKzfyHZpcwDOocKlqH+HiQ1o8xbdKjdtjJAC/hZzlEwwKCXgzIFBFPgxYx3Z1BYnt2BNoOVBqKj/Qyq8Vxl0uJ7ZPhibu3Uw0m3k2gADd9hTuwElkCSA1HO9q/UB1SgLGfBW/q7Iu1u29jx5XxZElHXrFHmucdXAkvOjO603XzGX85jkc19ysT/+UMG45gZ7vXofmEebPPgRKOuIKXemcEMuTZL5wOhNaDOSSmZzIXe7mAjgi6YHmWoF7Ac9kGg3LLoaAUS9liPID5gDhG4RR/Jeher35kT+NxBPq8LOvOWtNOYPA84x+AtiQ8mNimIyl6GuoNoB2oLW7bloBWuJSielQNyFnsOUAGMzutttBNoM4BOCUa9L9AGAnIgFJCokCXDm/v97dwTuMAzJv7ljJyhYyDcmSnOpOoKduX3XK7zqCwuzAOzKSSpWEgkysyIGUlcMyAUKWNDRAqC8gw+tvIzGeUFrQTAOreQZYd8vBQJGPTWfEQFAT/TXFBBZrT6juf3qZsMUM2OI3kmEKhiMNHEmOnp1FZ9ymERHRY4rSvm9Aign3/LaUPJQP/sid+kPX72s5+lZz/72QO3zQc/+MGEgBb+e8YznpEefvjhdMABB/A+8wXQN23aROAd0uvHHnssAfB16zrZ+Fu3bk0nn3xyuuuuu9LY2Fi67777CK5XD8jFv/a1r+XHF154Ybr88su7TsF9jjnmmPTkk0+mgw8+ON1zzz1srz5qC6wUC7TueEEGaEw6i2Qm/42vDmaR4Pn4Yd9aKWbZ6X4WAL2fDXvdIYLQ3hd4H5j/NAdfrFJOiu1VQPIZ4Plsw+pC/HRi8MV8qfsYwaS4xy/faz9vwq/tYNKc/YPYJe9f8ZmBo6Kg43rMkjvG9xOt1P73HXuTyCEAACAASURBVKnxa82cxcnxgmypslWlz6GkDQmqILBaHlp7GpNF0UYE0L0/AmBeBelsMo+f5H2D5wG8LGugwkL0c2SbuL/FnspyNjhL369SKszz4P2bAVsD7/abnAXvNVL8a2UYRwDV/aWMtsfTuTfrhOPgv51xDiobfeKhM/K+2L6DyQ/R/6FKmZQIDHrTX0C2+GT2NXCY2Os2GEtQjMg+LGxEgm0miLtkUAE8/U6EuhTOVakl+lfYw8NnwmfoB3wQK3FFm3rvzLk2CcTS8SEOQZK71AXsb2s/XIBe2l5+k2Mj9jvsM8HPxbODccGWGls2CnyjkFXP/sYMeJ2P+zoTf5gZ6ADQTeT12rL/Fsnd1efZ48K5XitRDcDvTgLoQZkykvlBpPCerfibkrCPZAXMrX1Og+h+Hk0uMdECa3PHjnTr2ben57/5mWnszIPzHPxqc7r1VT9IJz3cSjv+9sg0cuDeeZ2iXSwB9lf+nF/1fJ/Kl922o1OWz1L2JrDLBuNH/tNO/+1abQ1EAP13BpyB/p0aQF9ty6keb22BXWaBGkDfZaavb1xbYGVboPXD3w0OX2CGFwc2xAV2Yqg1gD6L8arBNQPoZEVL6tvOWsxAjQ59NbMCAT5nrUcG8VxzaMfJcYsYoCrXhlrnBKh0gxiLcrDFkl2RqW+nqdcac7a8AMosCSdwDE5RkWmf7MgOltpfDl6FrHHcI47Jv9v5ikEgye2VAIfPjf2N4CyBO9c+BrCvbPPo8Jsd7bHannC0S/1pSVZbTtxBJw4HQS8B7XEeHUAy0BidRwPoOKcQY8QCL4SHyLQPsvu8BlKOo8zGzjX8EgMkDTD/0TZqeZPIoNp/IKU/5+/mWlnz+n7iiVek9NRTAkk9YNm5C4hWQIcZEGsyABvZ7w4MlqxeZbCXgIJJCDk4016H7AoEDLKkP8HoAlQ7AJcz/RutCWb7IkOcADrm0UEMz5eDDw4uet693pyhblJACbopm6cAvyGo6rlUhkEbKgasCznFDKEsy6iAAiYKawyZyfjXZBoCyKot7+CHQWKDzwbQIdlIYkFeK839BzPHcSFMPPwSEhLyey6QfmK9S8jq6/2RZc4VDCPpp93JpCmyklrbrUA+IrlFgRcTDDDXsA3fvwrAIdPKwUIGg6T0ADt43eOeeq8VuXa/nxWsjUA2xmjpSgRM+bsP9h8BxFDCoQSdFRgk2ULEDL9bTGDyWigA+mCymub1sO4GJ0UA/fU3DwdA/4vfHS6AXp2GxQDoF110EWXVcdx6663puOOOmzG7t912Wzr++OP5+Zvf/Ob0yU9+csY5GzduJCi+1157Jdh2/fr1M8659NJL08UXX8zPv/rVr6ZzzjlnN1hJ9RBWkwVad5/QKXsV5ab7AeW9Pve+tI/hVhV4fs/vd+SdbY/5kg68t7Kcsfd8hRDLHWywcq+UYH29IABdm27f36DQDNKx9vEx4zKSXON4DQwVAM9y5RWJ7bB/6Fo+JHaEOsjc2+l37J22T7J2eWPtaC5BUEwRyuXQXOxg2NeK/Bul4u0r+Pw4X1b5so/Kc3Uz1GTH5yY/FyAySHxHmxgYNCgbCc/2kaKfivNcIqn6Of23Sv1kEwQiqG91L9shEsNxnrNXPWbXWAZ4Gn10jIME5OyvZCC9UwJtEITHGWWQbDv7Is6s974WRFIRhgsojHOxh7QKk/fSEUSPjw18v7VrVNorxwO4jxVAmjO8G5kcqpJFUPEiIRf+h0nHVPqKZNdKebhIcCHJVyXuqqR3Kj5Esqr8Jvuo9ttdBx0k6CJl7zrwmlcCv/Jx7SZaUQL2oa8p0LZkbousDn/LmfrwmQ68YeB/FlubpNrj+AHLkWmNmUQE+8f3H1X9Agkb4/L17KEJAvLF+JEAdIwHBHK/1yidLpIIPoPPBPtYuct+ODPVVSLPpJWqNXD95s35WrwSnpuVi/D3dfpf/iPd/vb7CZ7zsx+dlMcUCc4me5RXvEpgWIFsR1b566jFKKvethERYnzjPw58nnbnBg2gv/gDR6ZhAOjfqjPQd+flU4+ttsCysUANoC+bqag7UltgZVqgsP+7gg4LT6Sujn41Aece+4Iy0PsFieDcQCosSgKTaR3nRM5mBFzgCLl+sx35XkvSAGwEwB1AMJBVpN91U0qoCcx2IKA4rTqnBJPkbccgocdqR0w1rLuyhA20wyn04QzyOA73w9mnrDlnCeUAmuMaO3m97ADnytm3VfKAQTUHjWBXA6Ix84POaifTl04esrebzdTYvKW7rpidVfTX6gLOSDa4Hcdr9r583Dz/UXY+kAUYCJLHXwA2BR3M0I/BvfisW/aNQRZJwduJdgArgr8C1fNYm6m571/v9Itv4l//KDW2butkrTjj18A5nglnwqgmNtY6AWLI5knCsw1gm8EcBeocoOH8ikgQ1z/GoIyy9niWqGcdwQha2n4MNI1nycSxXEuQIKrkvdl/E1ccGLT8vQFZ94NOv4B9A+j4rtS/9qQrOuB14XMZTMg1yksWtwNguGQd6pgjQ3s6B66kckBShMF2k1OqWfGwgwH08bGByvTHhVKCfwiujYwk1k6EWRTkRAAug+d5HOUFqIAYSQN8FzTy+9KBLj/3vhkznfKc0b5+f+AzrCEQRBAYwxqiLD4CfQ7sIdsmZ/NjbeUjsoW6l/58pDk5bs0/67+XwKACrn73+D2D/sIOXP+W/FdwPayD5gFf3+nncDU1EAH084YEoH9umQPoqH0O6fdHH300HXbYYenee+/tuwTw/U9+8hNm0v/0pz/tqoX+wAMPpEMOOYTXopb6Zz7zmZ7tPPbYY2nffffld6961avSVVddtZqWXD3W3cQCrR//Xh6J97XzBc+roF7FHqsJOPfQW70A9Gjb2dZMJCfi77f/VpZr4t/qLgeq0ypJg66n249xbNKpfRuRZ6tbgcL7DIBw8S1Chrr/lkefyQB+/Nf7epxnKWLWlxapDqOAD1LUhnoYi0RkkfQajdT+1fbU+E9rO1LMXXsaXc99osnLYduD/jhr3P008K89Tc5qlgz0mAB8AOee01jzvHS3kdr/vjU1nrYhfxL9Wu6l5d+hX1SKkp9CrD+0jWtLaR81bp82EjN930g4NgmifCdQllOujGOrtXkfZv/BfiLOZd9MilSWPYmfytjmvyMDqYfO7HP6Klp4hQgbgGkrc5n4vH5trmdOv0bzhCFGIqf9La9P++Zey54D7YsbAJVNXLDvY19GZZBYriwQXaH+xH03/CZnkpuIbj+puu8v5GL5wZ5/Dr/y7LqvsncGhUUQh7+B/nKdw3eQapeBYKxpjA2qU1aCM+nY69hrkoQX+Q2hLNT4If8w8L92BUBHX0yOMQnafqpJIn6OvH4dQLJPbQDac+x4iufPKmZ8f4jEYzUA+7UkQ0xnVT37r1gLKsPVUQGR/811BiK0CDrtdprv3zz+nUC78bAv50x0guJ6B1gFA3MJwpB9ST2HjpeMb/z2wOdpd27QAPqLPnBkev5rDhzoUG/98wfSjTWAPlCb1o3VFqgt0NsCNYBer4zaArUFdtoCrftO7c4CmIWoP5+brUbwnL677dgvoGbjzfY9wFUAQp6DGXhNH6Y2gD2yoytZ2PL/i3R1AW0lS5YjFp066pHNXQXxcaodNDvDdtBicDAuErKfxVp21jQcMozLDpD7xPaVEVFA9+DkR0fZ94MjTBC9smgRELCzFvtjoJmZ7gLFqrE1nOOgE35mVr8luhVMYSaCAljFqXZtcgV8nAFgcM/Z9a5VbXCzgIGVGoF2CA2eOwvf81UNyHouHFSjU2uZszBIBnpECnDwDV8r67zIr9kJ9zjl+BKUhcxvczw1n/bl+bwS5jwHwSAEBRAQcnYvAywegwMFDB4oYIFMcUuvhzIEJVATZeYMoEeiANoBWIzvFFxpbN8eAFQB8Q7wMGAGeXuB0rimNZmDMbGWXgTH7fSb9e/ATgxGwTpebyUoHJ4FPaJd82IZ8q5sCmcrqybhurVZvp0ZJnjmpnO2OoKcIBrgmbF8Y1FFkE32WM9A6DCyz+NioJQ7xsLnS4FiZ7+gfyKXsBY9bMMguWT8+MxnedJS+qK60pRtzvqEuIfro3Pum4mBPHxGAD2TMrqCZgokcY3ETA7E3JBVjlru+nnORa4TeE14l5W6kQ5CWQqV864MEwcLXfcefZU04SCymebb993lvAigv3ZIAPr/s8wB9IceeqjIsV9wwQXpiiuu6Du9+P7KK6/k97jOUvH4/fOf/3w677zz+N0111yTzj333L7tHHrooen+++9P+++/f3rkkUd2l+VUj2OVWYAg+mwAenU/Wt0fV76fL5Cwu5m59aOTOzV3u/yGBTih2Dtt297xY7xvdZZolzMVHSvJGXOPJkerbJO1tygqA6E/nMvK+f3Ac/s0LCMUJNANQPebUO9TfVuTbE1uxvXMelYGbC8fp3zmWuDqJGW1RcarZsfjftvgf1Yz010HOZT88f7LNeG5p00pIfvT69vrHmC6gdhC4JWxPcZ4jYF/A6AYC/ao2HvHLNICAqsRk1g9dpcA85ogQTGArXEvaVtEcrnbwRid3WvSsX1tgqhBMSoCwlxG8CFBvu2oBAxCwh175+zrCORkKSmRRE0C9ZpD3y29DiKF1g6JqgaK7ffCP40kfPsQsC3mCARdl4YSMaGUkTJAG993iE3AzyqlL1JnL44MZ4O2tHsoS0a/SM9aAF3LOjIJoDyLYV4jESUqhSB7fkwluEhGEYDONSWFPWRei8RNUjXJyVD7UmJDUTKwfwhiyVhqs353XofNA4dHaG09qEx0r2mOVQu1vN68zvWFlb5sR5f9imXqvNbhVwAUj6p/VgBD8oNjH5g7tIdYgP10ZXZ3nlNluG/eynUzfviNvEvrx7+fFgpeM9nHzxmJISboOE4gcB4+04R+5npFRrxLkuU1tdB7725/dxc7HgPopw0BQL/tzx9I364B9MVOTX1dbYHaAguwQA2gL8BY9am1BWoLzG6B1k9O61KxXqi9Vitwbju1/vnk7OTD+cBhx7AXYN4PRN+ypSMPHs9xsIIBhVC33I5qFXAvjn/oB86FE23HqwCsCrDAwXcGbx5ARwKavyJIoBsVefXuGs1kK8NnQ6DDzlm5p2vq2dmrZI7bZgXE7xdEC46yQWpcS7mwcA2ANUnKdQFsXVkUfdQWyNBGJoGyUCFXBscR7ZvFbBtVHxRmM4QAlMkClEhXNqlqwxE8jOCp59kBpDjvMfAT1xd+joGifk60g01o08CugTwGv2IQKAzKgSUDiAgWKAuhud+1C31N9Dy/BIMo+5cBbWaWwzZmy9thdjY6soMtq2gJQcrPS9Y9BpFKBroCeSZzoN63MyoATiJIwvmYSgRtTVYgSAt2/RgZ7cxYF9u9SBiqzh+l7XCun5koe2dShwF03JNjRDAGspsIECqw6ywHPw/oDyTnYYfJqRwEcw1vj5XPQS4NwO/9DoJ8IgNGlg1XNo+zmxkcU0kBBoMkwdxuD6UGenURTDx0egkGEtSGvWHfmN3DgFnIOPLvJfDGB0/Z6M7aykoJOWi2I4PtkoCnfL+kBnk/ZOwjOIgaic7Wd+DPz8YCwfLZHo6Jn740z58JNA5gmijgsTqzCHOJYCQkQZvNoQbpBvJQL+NGqgD6Hvt26n4Potub/3VbWu4A+vXXX59e+tK8Bj/xiU+kt73tbX2Hju/f/va383tc95KXdMoRvOtd70of+9jH+N3dd9+djjrqqL7tnH322enaa69lBvtTTz2VNmxQ1uEgjF63UVtgF1igq4a3798PQI+fa+9uUGEXdH2X37L1gxMzec57JO/xDFLOp4cAzyNZthexAd+bDDtD0Us36QKh5EtEwqPBIQNIESQsPov2X/6d+68wCBMBvQc3oBXVi+inNVWPOYL7IZMzgoYebwHXuauoDCok6ZqgB5BpjfabzrLGnmMC81HJHsfvBpDtm9CXCQC+97HIOI8S100Rle2LVdG+KoCOnhucxr++r7PLTXzmfkkqRMXEobxWzJz2955Pk40NnPKeYZ7K3ArktxQ4fO+odmTCd/X66DNh70ifc3TgpMcWamKXdRvqddt/NUmdfqsUs0jOkIoR1oxJ1Cadk7gcwGH7u1iT8I9I3s2EABKeLWuP5WmfH53yHhq2AZmXRHmXypIfYqKISTAmH9vPxrUFzNdA7bvGsmTYy4MYTN/Qcyl/AfdADAJ9g3IXy3EJFDeAjmvg0+F71TKnDL39Kv9bCCdKACAZPStcsaSY9uzjv/n383lz7fQ5rfv/QO8JkY29vv2uso8U17ZrusPGtJceQL6LRLjGXNtnNkGZ8YtMsM4+uXxzS9eThC+/1wR9jrCdxn/zGzs9VjTA0pOFXKQYi+Mn6BNLmQX1DIyH0vyj+W8Mplkg/kA6tMoaiQD6sQPOQAeA/o9DBNAff/zxdMcdd/C/O++8k//98pe/5Ay++tWvTl/4whcWNJs33HADScVo74knnkhPf/rT0wte8IL0hje8IZ1++unzamtycjJ97nOfoxoXFMA2b96c9ttvv3Taaaelt7zlLemII46YVzv1SbUFagsszAI1gL4we9Vn1xaoLTCHBbIM+cKPVQ+ef/+EbDQ4Fdqol017CRzIrv3AczgAW7Z2jO/gCzOAJV8FxwTgnJ1HYnyVGnIFVFfwBs4jzofjgWvtOMGpgMPjbISStS0muJwf/uPa5vxZXTRwBYfKQBzlydVfnxdrVxW2dEn1mCnDN0OKMTjEMWhFOwowKzaV92yH14E41zPvyjyXV1mdj5iBjqGi/3AiEbiJNfAYfJET2hV9kd0daEIbsFXJtFfWLO4L585ZsTRJpT1nd0RAnnNgWUHJXMcMCNeF8zhwvgFjkwyKQ11h7cdHv9wjSKcRxMsBsUEGCSBtTSlAB84c4IJdHezAzwQTBTZb8s9BVwbWFERCMAV2iAECdFrZ2ABKCVwiGwNz6gCLQWvOeQ4WNRCgLQC6Mpedhb9tm0DdbBfWRkcWeyQm2OH3M+LAnwMSUU4PmcVrO30r9SgByiPwsXZtQp1BgOE5o6Mh2fmQVe7gLDKscU/Kl0/m4A6DWpNhPWq98T2gOnPoA2t/d2pGNp89GKLEbH9ZChDBTBqtBc9nCagKQEdDXrqefwczLY3u9w5sBlB+C8oEoGbfeCeBTBlNBMzbqtUI+5lw4PWhezT3v27hfxxnuQIgelEwMGEjvn9MymBArFNyAHM6ftDSBOkGOuBl0lgE0F9902lpGAD6F0/KNdARYLF0eb/hQxp9Z4+F1kBHxvmb3vQm3vYrX/lKevnLX963C6hZ/opXvILf4zpkpPtAxvmXvvQl/opA0j777NO3HdRQv/zyy/n9fffdl5CRXh+1BVayBWYA6P3AcwwygBWrPZDfuuu4bA8Atk3tNUwQNqDez1eKC8akY59bMs9DHeqtrZQ25DI9PCKgFNuKuHMBvgMxt8rptf8VAfPof1Xb9vkuLUMfQXsynMu9iTJhCxDoDsebW2I8gF8F7I83jRnXAQyOfmMcJ5WUpjPgS4K1ADaA4PQhBKxaQtq3sq/DPSZ8HfiaMjKIrmUeg9pZ6WbsVyVDHOfYP3UmsPd1zCa3r6rrou2xliKxwc+fweRCEPdicKa+95ieABCOQ335fv0uz7fOL6XF9PvISBq0tDdlvWPGPLOjgypTUSBTpwsRQOPB/ha+kGt8G4w0WZvAMoi6UjwwUQUS5yaUw3xFOS6TsLNEOqTdkbWdsn8FkFny39xvs2RTJtGzdJZJJCZI26f18i+EUhH5Y8krZEFX1QW4FrOkeB4j9tDZ35sh4W67UN0M8QwnCkjFzWpmhZQhAJ1rM8cFAKB3Xi6NJSG4IpOb84/1aWUJxxAieN6L7BPJALatSdf0Q9d05sW+IezQZR+8H0TG8LuM8R88e9ka4wcPBjyPb7XyN5fEKD2vjl+RyCM1DCovSMJd/uxq/7u7M3stA+invv/INGgA/XYA6Jfdm0488cR0880370w3e14L0m6/YyEAOkpfoVSVFbl6tQkQHX7SbPcEeH/mmWem22+/vWe31qxZkz796U+n173udQO3Rd1gbYHVboEaQF/tK6Aef22BAVqA4HnANefT9KoHzr97vKSkQnak2fxkUwvAwr/YyBcnu2JdOAIE4+R1RGk57Puw+bdDRIAnAkn9okEhYOfbRUfK94gAfGQlu75czGao3grnO+Pejjvai9navif7r/pX6I8dfwNgVSIAszUUwOkVlCznCzR18KCr1jHqGyZlnoZ6gQakDcwZWDa4ibbs0ANkA4CJoBKDN2Ej7hiLspZzdnoA1gsjO2cgMEDmYAXG5mAQQWEH94JsvSXtIhBsgobXlMFGrx0HQTznVb8hgvH+ORIQPAZlKbDuN+rGQZ2AwPngHWJnILMetgFer3nX5CvjkY1j4EiyglxTvN4sdCkGmDjiem3I4iUZJQcEGpCjczBOAbYiJW+JdgY2R7JUH4B4Z+TLcWcQCCAssuBLJrtBfGX3u5ZlfPydGU5CxZqSWY5nhaA3AwKSZmQwyNmy+XlqkHmvOoQly0STjkwR9McS7R5LYOSzK24ffWB9cEguZsn6/M5KaVBqA7P9XWGGAea9ZGS5BrquikEfZ8bgKwdKHVRjwGc8qwWwxmlKDQTaMX9aK86WARGEsuoOxDhIFog8g5Dc7DVu3JdrDwY2kYbzrUwa1a9n39AvBq9GSHCopdvns0PpfU4E0P9sSAD6/ysAfT69RFBmZ4+FAugf/ehH07vf/W7e9utf//qsWRP43lnnyDZ/xzveUbqLINDXvvY1/r5t27a0tuwHZo7oPe95T7rsssv4xV133ZWOOeaYnR12fX1tgV1mgZ7Z5/x7GYDNHr1bzUH81i3HZIAV+1iA51IP6srURmY0PtggJZx+M4y/ndjTaG/mvUre00yn9rbJ1ABw3gv4nm3V9EjgLm338oGc1e69iNdA3NPH+3nf72x2kjkFVrLWtwgFBKwNMAOUEoBN7rD2Rvw5DNDqVtXPTersGkhYpwSo5W9hfuyvuSRUIU4Tfc/zQ5KtiAr+1wC81YNQg9h+pX0928KKZc4yx1qggpSy2C3ZjutiJmxXrXH7khU7mDTsPpYyPCaGRp9Le8g4Lto92L4K0Nrm9gNiZqwzcOWjDCtOwgxkHN4zGvilDxxIqL0YI/Bh2E+V76pmnnMNKzObku9KDrCbCiKva58b7C4l3iB5LtKvwHcAsh35/Ty/BLWZ1a45cfawfM0i6d7Lh1VZKmaOr1FG/NRUlqQvKmTyA0EmxlpSzIA+Jvb3JPwrJgEg3D65n0/7XtVx8jkPMQycz/iOCNl64QxTyt2PUAsxKMyPyQrK8i/xhPje8XsjknNiWSwTHdaglBWIGBPFR6YfqfkBEQQ+O5eIle2oZqD3RqMxVIIvyyXa57Pvir7Zh/OYXe6tkG5SGj+irnu+2A2TAfRT3n9kOmbAGegA0G9aIgD9Oc95Tjr88MPTN76RY1kLAdDf+973pksuuYTXHX300fShDjrooPTggw/St4EKFw6c9+EPf7inqaemptIpp5ySbrrpJn7/spe9LJ1//vlp7733JqCO65AxPzo6SsWvF784lG1Y7OTV19UWqC1QLFAD6PViqC1QW2AgFlhM5vmwnMKBDGgJGikZFGbmW+LcgQtv8LGpB/ARA8u92JAIoiDb1Q6/nTR6KSGrmZ8rA8HjjKDogsYeokFmElv2LbtHai3UVve97DQLYM2Z8ZJKhCNVggi6R3SCC5hOY3UCFRxbIAd47A5ERdm2ftnUrFUmKUX0g4Ec16lzdgBAd9ckxHc5m7dk8+M7g3iWrkObrGMXMrJtazvs/t5BCXwOezlYgXu4xpwDZVQGUFZ1mU+x7OnwqxZcsbf6gN+dWR8l99BGAQP7BHK7gPMwMeifa/UBrNVYhw3WTTz6hwy4EDC2FBttobrX+NHy5lEmLhIe/Nw5sEIgPEsG8mCdc5EoMPXOPJ+YEGiPgIrq/BlERRueW4HLBGCd3eKgmdogAcDnI2CDQI3l0R3EIhAqWUv0i7JzWCcC2WF3BZtKHXg/EwBPKTmvWpTuu2XAvUZJYBnJ2erMxJDkngNTMdhXrc+ONhxIsWQfkqMGJNc/2+updfcJQYIxnInnx2QaPhMq/1Aljfh5F/jPTHr8h1r1eLcKfLZ0ewTGJ356lu6hjAqMeYiZ98geyoSRLB+f30cmTOh95fFY3h/zKnJD84Dh1Vlc0J+QFXhyBND/dEgA+v9c5gD6hz70ofSBD3yAs/etb32LAZ1+x4033phOPfVUfo3r3ve+95VT8Tm+x4HA0EgVJAmN4n64HgeyTJBtUh+1BVaqBWaVb+9DilnN4PnEPz4vNcasngQiMFSFsCeGMsxIau+YSo11IDbq8z336AaHqwsFe0WU+jHZLQJD1Z+1nczbSskVL4a3VJ1X/E323+9q/3yu94u+t4hwBXwu5a7yXoX7Q4LnsURW8I0MnsdsexOKDWhRGl/7TPuLkYAY+0pSb87Q5XxwX6pMcu6jQ5ap2zJwHNuhWpSucxvMXtfeNjIZ0I79o1jeCsArDtjVGbFlD6s+Yj9o4Mw2pY+svkewn35iIE8XZa5KbfgClmu8tn+v5zjaEfey32fFs5HhAogY8sRDZ9BMJNCirySSiAARiaRW7SqEYz0YtLmUBeyz+HmwLxVlu9GmM89x4+lp+TrK9PY6IVlCc+t9KyTcvaY5R3mCqNiFeuNW+vLewfW1Y2zAILvnu8QpNF8oPQW/yf0ywI37Sbrdzz6BYIwZ7w6Dyk5wkI+VSdUYB/bnIbGBvrOUI6K94DPxXmOZfJyWKAudSh49VDWKSkTlyyK4IJ/DRHWsczxvIBvg+cIIqKaWy2FRPUA+bPPAG8pTX4B0+VfD/vvGUpM4rIbgf028j+Q1lhuQv6weD7t/K3UvM59+RwD9eQMGyTjTjQAAIABJREFU0O8YMoD+wQ9+MKH/+O8Zz3hGWijhGPbZtGkTgXdIrx977LEEwNeVhIaUtm7dmk4++WSSg8fGxqiyBXC9ekAu/rWvfS0/vvDCC4sql8/DfUAufvLJJ9PBBx+c7rnnHrZXH7UFagsMxgI1gD4YO9at1BZY9RaoAfSFLwEyfx08sVNlB7BkAbczeA5H1fJ8vcBzO4VwKM3Aj11SVmxXbTh6OKptFs8tDlLIaO45vPlEjwIAC4fMgQsHF+BQRba9gw1FOl3OPQE7ycZFqXgC187arpAC3Gfby4EPyhkrA6Orrp7k3A2C2jE0qB/l6+xTEsgXsz5mwjsAgD5Y3ptECAWDDOQ72zyCpuW+IbCA60xQsPNHSTnLiktWG9+ZxY/vSl1DywB2anmx385a7ZpfkS1oc4DtAtrtaEbwvIAdMohAYtZZNoiMLg4ZrGMNdBMdnE3hLHL3g0695ssZBPwOmThZgrvLxrBJYaQryOLa4FbaQMAF8wAg2sHYasDMgUkB3wSwUyM1VH+RgSFLjiIIiTkZH0sNlGMwOF19lk1SwAJA+86EIHAO4oKeNZM4/PywRITeJSCKQJod//p6k3UQ8JD8N4NFnnurFaiWdiaXiPRiAokl+FiaATUBczCluf/fLfwlucArWv98cnfQGH0rQUkQTfQ+cXDMxBFLopKMAlWBsdRuSlIR8wM7IViGFYRAmZzRXgD5xP/5wzzeIYLnaD/Lb6pmX1xzDhaXOVcWDd4LJRssv5cHLQm6wOlasadHAP2/3fSitMczB1wD/bFt6X+dlLMblquEe52BvmKXb93xZWCBGjxf+CQQQB/PWYoEy9ePp9SaTttvfDStPWU/gugEz5GBbuAj7EN73lH7IP7dr+6zugBmZxmHEkje2+Wdwcw62IsmJod7Yb9B0FmfmcxbaqkLKCdQlZXG2v+2JRMJWEpHWZ2lLJX8pUhU6rdn5ee5JE0hEfeypwF0ZJ6TmKs9If2RDEBmzDPUHS4+nXwH3MpZ7G4H+2HI85fMZGWWw65UXoLEtsjWuA8+g//GzY2uM1CPsZgcbTBPpXeybxn6ETFDZre7/yKsVknaHJpIy9zLgxQhm0VyhIHzYnuVTzPQzL1qttMgS1z1WvdU7TLx2j61Jdvt6xrYN1mh+COBaBBBYD0G2b8WIQG2DqBw+Q6nWE3NczYj0x2KVrlmeixPVuqhm7QAG+N2kRBTBh1800heN1Ea/rkIwSRFi1RABbFAqjBoTmly9BtEDfhNaB7tFhIE/EkRAEzotiKCiTAG0GPMgGsW6l251jv85yXJQAfpuPioscyCif7ymfn+EWhenmskdqxJCWQByNtv2NAtsOfMe3xn/xu+UQDQ6ctACWF0ZKhZ57wPwHOrctin9Zp1DCbOCUnz+k+f1wD6wv9u+woD6L/3/o1p0AD6nX++Kd08xAz06qgXA6BfdNFFlFXHceutt6bjjjtuhjFvu+22dPzxx/NzlKz65Cc/OeOcjRs3EhTfa6+9EnzR9etnKu1ceuml6eKLL+a1KKF1zjnnLH7i6itrC9QW6LJADaDXC6K2QG2BnbZAkW7Pft+8jtWefc7N/N0vzA6awTg6V9rNw6GxLLedOGSu2gHsBaLzGtTDa+XAAg9nb4vB7yCMZ8lZ2D6VjmiFhd9rRrva6QOkVz+GM2IWJO5hyTADVZa4c9AK49m6TQQCg+QwnAIkHqMB9BgE8Tgc7IkyYUXeWYEdL1oDgHD6HNxx8MMOMGzh+9mZZNaFM75tdjnQ+BUOpgMEyuzlvKi+Wq7jKBnJmKEeMyTcn1iT3XJ1lsczWB7qh3Vlkrs+G+udSaKvZOTr4XXgyFLertlOogD+M9CsLACTGlhrXZnadji1bhGUGHoGugF0A+Rm/DNTKM9NkZArBBMFBgwswt7ou4MeZd13kwMIeDugpowBZCIQEPeaNAHGgclCOlD2uIN9BZBFgAhrOz+3bAsBGkvH40OsDfet4tQzgIF7rV+fwXMTH/z88x0jdQC/J3bsyAE01q2U0gHsRoAYNQoz2aPILDKIJoDfmT1+37i+toMsBtHXNKlCMOz591RlIlc1naKRA6deu/G9ZCIN1sj6tZ3nEJkyDNqprujkVCYaYL5kF/zc/I3r5/X3bhgntTa9qKMMUBQkRCLi+wokCpR80LPuciAicnAsI6NLEqQbxvh3ZZsRQP+/bnrxUAD0q07KNep/9rOfpUHUOJ/LXgsNCNU10OeyaP19bYHeFqil2xe/Mijhzr16Stv+/mdpcttU2uOM56S0uZUa/3ld3qcCgMVB5S5ldva7pUmkzCYVwMtNmMjFVUJxBNZMoI3+WNUHrvpBvTKSY9/i92iXALqAaO9dTcStngvw/JebU5psp8ae4ymt09hNGGSZIG2RVGKpgNME6aWmxS2UwF0D2/YvDCrxe/s7KaXtEwKtBWTTXzBALAJqJPUKtOwqPcUsUM0fvieALtIxbuUsaOx/uRfFnl1y/tiTU1Yb/ps6RmATQKD2hAbQTdo2MYB+tQnjYTLs22E/TZ/NMs82out365pIUPTaKOpA8kUMTuedvmyuDHevrdHRoQPorQdBwNR+0aCi/SZ0Df2s+pvcZ7qUWmWh28f2s2Mzgmw7Dn9CtcHLunL5KKlb4XwqKfSIJ9jfcf/YnvyTyUnuY1kizP6Mn4soCQ9b03dXvw2grkV2u+uWxzHlOSZY/qsnU3uvX88zZtCfvhhk7uWbySey1HtWEpMilFXEHGOwvcu4pJzm35XNPn5Q3gMO+2Dpq+phf4/vhTAnXKMi6nMOpBhAXwn1whtFeY7qWPZFRA5ZqjH1slkB0CORmsQnxc5iORBLytvf87PQbtcy7otckBFAP/rVBy6yld6X3XnlpnTLMgbQUWYL0u+PPvpoOuyww9K9997bd/z4/ic/+Qn9vp/+9KddtdAfeOCBdMghh/Ba1FL/zGc+07Odxx57LO2777787lWvelW66qqrBmrvurHaAqvZAjWAvppnvx57bYEBWKBknldZyHO0XQPoKbV+IPlROyfVQE0X+KZ66NjEA0ifLQudtdAFoEQptip4jjlCIMYZ1gtZD75/VzCo4vgaZIzZ1mB7W2Ib1zL4YWkvsZ/hlMKhxfcIaiEAhiPELPiz62vFfqM9BwUMdNJRRn0tgKMKEjkY5GtjVoXHhPOZTSEJthg4gFNlBzySGnit7GDHEWOeyNmrOSAhsJkSiQru0MFWwAJjd2aps1qcvWFJMZMk7MiWwBSCT7In2sP8OxBiiTisjyhlj3uQXS0iAMcQlAmchU1HOkyE2/X4ncHDwKMlF5Hl+s2FrKxFnTvx05fm+Wg0chZCWYr5BwRY8CHrrcG2cQ44PgQqYXdI7092ggJoyEG4EkBEdvYowec2s2tkb9YTn2QwhnLfVghg8LKS3eRRcj7HizwhwWocURadz6lqifs7qhEEKXfL+ls+D8A3niOMGjbRmm6j3h9lvacyMO5n0UC9g2CqyddG4BCShu3p3M5TmzslBWI/8TPujdp3Btn52XhWIgCIvv91i5rbhV7EbAZnyuNivwsdWLVqhQKvXBsmnUxP5Uxzr2EQCSSXSjlC2BNBIgVcl0KWvt/4Jx4+M0tY+j3k9xPedRFAx+euT+lgEAkSeU0tRZbLQudwuZ/fDaCfnvZ45hy1dhc4oM2PbU1XnZRlLpcrgH7dddels846i338xCc+kd72trf1HSW+f/vb387vUZPP9dDx+zvf+c708Y9/nN+h/t9RRx3Vt52zzz47XXvttQwoPfXUU2kDMp7qo7bACrJADZ7v3GRt/7/3TyOH75Uae4znbHNsXYvcsNqGjLgBXACwe8zxnmAWOvZezsD0nljtGUQywIk9jUGw+Q4n+mxVEN2gItpylqpJwJSV1r7Ve1X4DVu2p7SHlE/oD0xlEqBrQntPD9/AbeFfEDOrJEKXesH9UXPcewbv43E+2vdBv0P7fHxG1RsobClb0wTSKojuzHFfa7/GPoZl4JmBDmJxyEAvIC/k+lGzWX5ZBK1NMPX8uP/eo8d696yDbvUk7aelCkUDWbbd5zlj2TYwAcC/w2YGbCOh0fYxCYJ+pUp+WRXN/QxrZNiZrgTQ7VNHwjHHoy/sT3uPCZ/aig24pkjRa0HZ7hiH1bkwJyCkrlmTy0FNTwv8zmQVfFZ8FOy9uV/H3LgGe+UBo9+TM7VZwiiC5ibH4xKT9enDY55dYkBkCDzr8BMwBhB9x1CjW4kHzkKWgpVLkdG3tH8TCeV+bqnuBdKw+oX1vXlrqP8uO3ktYxxYy+gLydP63gD6b2YVoqU4Wj/43aC4FrLOPTY/Z4G8zXGi34WYgvhGVg2Azehj6t1GYsESqNHNZSvWP+d7An6/yCD2mbRW8yMgG5gsHp/3GkSfy8w9vzeAfvL7N6ZBA+h3LXMA/aGHHipy7BdccEECAbnfge+vvPJKfo3rDjjggHLq5z//+XTeeefx92uuuSade+65fds59NBD0/3335/233//9MgjjyxqzuqLagvUFphpgRpAr1dFbYHaAjtlgTr7fKfMl1rfP6F3AwZD8k4++1VwXLCZt5xZrytxHQBS1242o5uAYQB3eS1qayNDUaz/vkOpMsKdoe5sjQpzn5kNysjGv3auDITT8RYwTRAdIJ0ymwkUKlvAQRUHlAzs4ms6vshQUN30kuEb6pBb+pDBHQDJswS9HBhzoAm2gL3RBgM1Ysk7aBAzVWy3AsKFGuxwGtEObOCsbgfPJNXN4Bgcc7LjQ63AGMyxzdhelpTOAb8gHejsDK4PjdXycpHpjmzlrsxzscnpyOvwOAtTu8caKNm7QeoO9d/sKFdk2nbuSZn9aoCJXtKZlCD5emfpmyXPgNB0Dro4w9/BjLVrZVcECpWJjYAevrcUprLUc63yHFCk7J+l96KcuwNm/AxzFVhGlAzPa4NSgQg2UBYwAKKu050gjYdAKbIhIBuooASecddCdw1ztLVeWVgOemmtsr4fgxsIPE+nBkgdJAwgA11EC/zu5xdBJQS9ICXv+ogYQsx+cpCB5IlGJveYkKHntw27UsJ9aQB03KuLnOQgYQlMK/BnMBkEBgXbcnZJS7J9I6ldMuvbuda4AXRlsexKAN1PBGpZZiDdASGtbQcaMed8T0JlQKUwRMTB+ECOwJQNWzJ0mM//UrcdAfRX/dMZQwHQrz4516hfrgB6HRBa6lVX3293sEANoO/8LE5c/1tZrh1uxlrV9oS0uwFb38KA7mykY5zLesWh/rNJd/yDX8nAtNKV/RPfqyepuDLW6jkRSHc2p/0U7LlciscAu4FAZUO2f7E5y9iD/Il9Gc7buj0D6ey7/1fJup2A9LlqyBsMBxnBculox2AimohAuPtMf0bt+nsA31Tjkv9BoEr7XtdGJ4lZbt4MhStln5ugDFB+LcjUAultc4y/KJBpT8t5EjAWCQFW9UJXg6R0VnQS2bb4XeoYbaKscPsHBeDUmKsEYq8Zg+L2x8o8iHRQFAycDW2J+8aSltQpADr6V028joSOqDLgrGrYOYLNkWCCmr4gm7L0nJQgsBRBLKZiVS5Rxazx6alMbrb8OgF2kZbpp0bShhS0QGAlCJ/J5/SJqDAlvxvzir6tWZMJvfap2F+14cfSY+jyfZV8gHNQikwlt6jGZZlv3MsKejjPvoWIx1yvsEEXQSOoW9g/xYlQ4PNz5LiC7luVOt/5N2f/FlrfPa5TJiKeFmMrLpeAcbKu+VgG0F0LHr6e5waEa5NOVBc+v5PaQy/pNpedCKLjsPoI5suJEhgb179iCCWGolJrfv8pQWH8uf801+3q72UBA+gnvX9jOmrAGejfvXJT+s4yzkAHcfilL30pLbEzhON3vetd6WMf+xjbqQnH9aNVW2DXWKAG0HeN3eu71hbYLSywGPAcA1+KjNSVZOAC9si5KBJp3sS7tlqsBdYvAz22Aedl+7YeksZgZ8u5BSgUZdtmGM4BBUnNle+jVx3rvymY5cCIGduW93L/DCh21TBXjS2eoxvZscSv7ieAP3IBAqgLBzY62w5y2bGvZnygvX4BL4PHaBPBAALwAhkjsSH2rdeCw/WsW68A3WyL0kx/9ytmUHgMrnlo08fxuuYeM6mVIetxYN04s5pEAGdoS16RZAf3VXUUkaXCwJHqKxZGvkA6Z6grmEGJdAC3SwyU4n7IPie4ifhZyIhA1jXBaUx1CYJmKXfWtMZnsAUJHgB/UctNZIl8lcBiMgUYJGEwBax7BE+Yqa7PLK/u+8NumK8drdRwICg63pANJKCttQ6JcAZckMUzpiCTgoQAspvNfE8HgzxnOJeOPAgV6A9IH+O59AEOqQ5wzMqktgx8kWYvALqCnsgaxxpSFknjqS2d0hCF5CFCTiEGeCAKUKHPXHdSzlhiufNCTEJ/HVDjO0T9YbBTfUUwj8G+0byOKAs6mufH84prVQO9EAxQmmDfv9nlf2paD7yoI+3fRWjAH9ssqcg+Y72aHGB1ENrH8pojS1KnfpcbbAAdiAD6K//pJUMB0K85+Wvs6XIF0CFJCInBn//853NKEh5++OHpvvvuS/vttx/HgwxyH8iQQKYEjvlKEr7yla9MV1999QBmsm6itsDSWmAxAPqws1GX1gI7f7dt/+M5qTE2ktqT02n00L1SY2/tpQzWAkzHYcUmZ6POptxl4BP+RVWhCm1572NgJe7nevkXsw3TAKVJtVY68ucG3tBv7B9BACjy4RoXt6V6j/r+BqbjvfEZ/TCX9cLvACkNqsuPoo8mQjX3SsrOdFu+l7P77YNFMjH3VCJ2uuY67hXtE/tcJSKT4Ik97BoBkIGMXIiQUheogo6xXe/5PGfazhdJbZxLOW35kQQEXfNZhGJcY6C4WurKGeNlTy1yujKsOzWlpewVbe9MXvlq8FmWEij1dLIEkH3qaAsC/PJLWJYrKIqRWCEf0opOON/gtQma8AvtczlT3OoAuB6kVUl68zwD0OocQW/K+QcAHdcAoLUvCkUx+kQquebsePQZ5zLjXf6dwXOCofCZdCOTIooynnwoPAckg4tUjnYoE68+udRA6V/H72bmNfpAX00y704ocNY7FQ4CoM5YQVOEaCcopKHXBK++olp3HdcpXcH179IRyt73XCnGgPnIBAYo6KXsM3F+GqmxeTNjAiSr4zt9jmaXg+oV/w57TXpd418SgEJpRSrJKdmEBgulACanUg2gz//veQTQf3sIAPr/vuzehHv81V/91Zyd2tmyWHXJqzlNXJ9QW2C3tUANoO+2U1sPrLbA8C1QAPTIXp7jtjV43ttAHfmsinRWCdwI7In1pecK0MAZ2LylO/PV7bEuN+rhqdY6Pw8BhdK2gyuhxnX5Lkw8nFfXlYvsdUvIm9Vr6SzXy44Z564HFx3yAtwG6TNnr5Pd7P7JqbHT3i/b3EEWZwEwM1kgMYej3/E5s09FNHANZWZP6J4OovQKtqEps9EBWlma27ZzhgQDOQIjI4u/MNEV1HF2Q1fQLgSXHBFAv8FoZ2ZykKG0zB1rsoWghAFGO4jO6ohyhe6jA4zOPEf7YKGvXUOmeVsymc19/3r4Lx/dYeKh0xl4oOx2sY2DP5YY1Ml05DOYWDLIDTwLLCZI6vVbsnUFMGLNksySMuueYGTOUnEWeQHA0YaCn+ybnwMuMQRb1mSAGUAmAnexH4ituJ66smCyVJ4yLeLzZ4UD3M8qB/iemfJjqY1MJGcDMdvBEu5SZUB3kGFuRQesM/QH/TOAvHVrlr7Heb6fM0WscIFxuD4i7LF+fW5Hz1vzOX+3ZGvCN2JdPwTLEAyD7URoyOoXCiYzwK75hSy/M5SwRhys8/vIGSelXmJaHgA6JOsxtw6wO4jLtab6oFYqQGkB/w3gs5DLVLRBFOK6mdzl2SFLvlAWccMuAP0fX5o2DFjCfctjW9M1v5cVG5YrgI6+XXjhhaUG36233pqOO+64Gda87bbb0vHHH8/Pcf7ll18+45wjjjiCNQH33ntvjnc93h+V49JLL00XX3wxP/3yl7+cXvGKVyxi5upLagvsOgswaB/3x+5KBHZ7gLE1gD5zzn71/mekdb+7b2rsgYzBCqGWmZLKIHYWt/des00//nZyvxbA5giWFv9Ffpr9iMUA6FT6CeVjMIitO1LakLM4+bfZa6Xrb7r+bvcah5Ws8B37ZIBdtasN/Jl7DLC6ZP0KOOLeX9cXf0k3s/S6fZVefYC9WIJrWso38q9of/mZ3GvIl4prH+A89mTYe2K/GX2rmO1O/0wAZASB/ZlJqFYF872dyR4BW++DsDf2ebSfpekjkKv7eu7cD2ep+9/iX4LYLSJyUJQqKk6Sum7+RibLLdVB0iXHGHzfOOeFpB2yur2mTMZw/XleJ9+KhIW8pkHaLaWjKLUu+XOShFXKDPabmsrlhEwIgaw79uFWfvMaBtHTShKyJducbufs9h07ckY0yMPwQdatzX4aycRe8CJOuOyYSz1h7rG/V//b6wC+Z1JEBvpFyGlLvQznxjJRsA3acMkqLHXX/7aSBLugfTp+NDmXpN6UlcNGRgU05/7uEmIFSgsWkoKVH+TPglxksF9+QyYdq1wYiAj4/v5HUzp43yzZDyJ7ILIs9Vrv90xlAF1KG1SuCKXfrMJGQrLe9SZ8cO2IlAxy09hYGn/uTUv16K7o+xhAP/F9G9OgAfTvXbkp3frR/nXFq4YDAXhnjoUC6B/96EfTu9/9bt7y61//ejr99NP73h7fu8wVss3f8Y53lHPPPPPM9LWv5b8X27ZtS2ul8Nersfe85z3psssu41d33XVXOuaYY3ZmyPW1tQVqC8gCNYBeL4XaArUFFmWBxYDnuFENoPc2NzMmCc4apBMFvICWZiU3Utpzj/410M0cp682nZ1Ht2uwDsENbPzNrDXLvJoN4K7y8wjWxjFYMh3enrJAWGtc59sRM1DvdiJo7VrjMdjj7wF6OZPat3V9aAez8LlBXgeQYuApjqMwwYOcHvsq1n0B7/07stDXpATgyX3y2CxPZ5nwmAHBTAzVNysOvCYH+3aMGU6nQUYD154D/+sa6V1SdpU15HredlLFZM+M75Rl1CxXXgB0jQ/n2Om1lL+BULPNbdsCAgv0N6iPYAgcf2VwLJWDzLrnziT3Go9qCgUI17PDQIX6TgID5ARVG8/lETDPtqPAa/I6TADJFs31xRmMDG2WddjIQReSVJSBDoa+gzghwFTqqztY50xhZDts38YsCgRvKLdumcKoNgCbO7vKMut89jNYziwM1e6mSgACi8pKp+1wjTPf/e5QtjoJEVgTExO5pjvsFbPYvR5gc9bpRMBU9TXXrVWt8JG0K8Dz8qq495SijEAiA8ZQJeiABKI6d8xosZpCCSKFDH/aG9kpjdR8+lcX9bdzkBdRvt2lHBz4BvmiGshl+QzUFFXgz+QQScGSCOP32ehYnYk+xyR1A+hnDQlAz6ST5QygI3t848aNaXJyMh177LHppptuSutMxlCA56STTmLwZmxsLN1zzz3p4IMPnmHdWNfvoosuSp/61Ke6znnwwQfT8573vPTkk0+yjiCy2dFefdQWWCkW6Jt57n2DB1IJ7Nbgef8Zbt36/G4SrQHalkApXIq9+IZuQt+sawb7IeztvH/E30jsm7yP9v7K87aQQHz1XBJXm0U9a/qh/0gjhzxNexQRQP233GAO97Ai+FYVugi8g5CrERrYxK/4zqRjZ+pDsh17YPokISO97J0rsuO0p8jUBZ8Pfo1JyFTeaneIw/aZcH2UV6/6dgTnxvOelr6r9tfuj+0XVYV6TSb2qVWAMwL1Jovn7byUCgDihqxg292y+Vbdsg/hvXDVb/J5JoQbOC1kcJBURcyWzzS+lLWuAZ7Tjq7/HEjhXjeRrGB1BK931wDH2uJaUKyBBHqQJzLgXOqGt3ONc9iW/gZ8BM6FFL7sB6NUlaTUCXzjXO5ttS6lbEBfzLXm49xPTxWlsVzPHPfK/gvPj2QBEjw07vJ8aW1jr0zlAfkKAIohBQ+S+Egj32M79tEqoWY/uep3o/8sp6fSbLaTSSGOEVhG3AD86MguAc6Lz/Sjk3J8wgC/++e4EUm5UmwwyYdxF8xfVh5o/MevUnuvX8+xBzxSOO+Rx3iL5snf2+V/sguRjXEp+UuOIfi9Zul+kh5EqGIMTfvOSOJJjTS+8du7fFzLvQMRQP+tAWeg373MAfQPfehD6QMf+ACn6Fvf+lY65ZRT+k7XjTfemE49NZcZwHXve9/7yrn4HN/jmJqaSiOR7FJpEffD9ThuvvnmdOKJJy73JVL3r7bAirBADaCviGmqO1lbYPlZgAD6Ao8aPJ/dYK27UQ/dmdYheILLzA5nMGhDp8b4XHNg5wDnwWk1e7xI0gVgPmY38579QPOZSe3sn+tA+16xb8U5kdNqtq/Be7L/7dCrjp3qaZW6dswqz4zzLHMf+kdWcMimj/d2wMnZ1l0bTgWjnJFgkNyyx2xHAJod7pIVobni53K+bTcD07a52OwF2MI1dj4phad6eBHMZ1BL2ekxI6aMTdkQduTsqK9dk2s3EzxvdbI4nFXuetW0h7Jc3GZRClCAxW0i6IGf6Sgra9prC6x7ZdsvZU3oiYdfohprIp6UdaHfZxCM9YHH6DXEICZA0dFMlPBcxIAXxogAEO6BgIACdAz0ODDJRaD1hAxuZDSTuCBgOa5X2Hv9upz5qwAF5QEB3gLgxoFrx5RRgT4W+e1G7icO9Nn1+xiwVC1AZDMAMDbJAc8SAxkA0Mfz5zsmsv0AzPtZx30gL491gQwS1NNEv3BvBwzdN3yOzxgQG2GddkovYj1TBn4Nywk09/vbud5SS/L9xKNnk0wAG5AAofcqgWO/a6zKQSn8LH/efMZfpYmf/1GnTryyufH5cjiYTeTan34XRRAdneS8K8OKJQv0bsE8g/ziuqVaB80Dcv3t+uhtgQign/uSQ0FCAAAgAElEQVTtPxwKgP7//f61vPmwAPRbbrklbdq0qQzwF7/4RUJ9PRwnnHBCev3rX981+Ne85jU9jYGscGSH4zj66KMTsh4AcgP0/shHPsI6fThw3iWXXNKzDQSCTj755PSd73yH359zzjnp/PPPT3vttVe64447GAR6/PHHGSy67rrr0hlnnFEvzdoCK8oCc0q38+/RzKyoGkDvP807vnpEGnnmupxt7sMAiGttY3+8rpmJgzgikNqraQO/RWlJtXIjIDtXG6Uvc2S5ee/Ztb9TSSWD3/ad7LOZJKq/3cVvsv+D/WDJKhdR198ZOEf/AJhT9lqkx7j2mJXtbGvsEYJSlve4ZX+s/TbblOoQSYoAngSW2t8jwA9StFSwpOjEc3nPkUx28P7We/SYHe3nxOB6zC6m3aW05DZ6zTn7LoUuZ9TG588+njNTS5Y7AGLVSLa9sLcqfrsAf4wLe8wyPoxbwBvGjrGSuNlccjnrFhSLquCxCdr2UfwMefnaT7QPSEKDbEiAW/6sM7vl3zIz3H6NyAIE0e07+r6yB30Z2xq+VQHQ1SH4FchCxzHdzv4VMpz9LDgBAWA6rsd6wvqGz7NN/hBJNlKnQDsmQLCEgTLR8bmJxsimh88AEjPGh7VhRTqPHX6Qss8JsOPeOMf30rIsZdH8fIi4y/NhQ/iNzfFdCqBXX4kuEeUsf36v55wZ/4FYZCKyXrSd51zxleXiV5S/xU5Q4bpX/MgxKSnsFTl3Ej1c6sFlDhSz4iPQrjPR59iRGUA/4X0b0zAA9Ns+unwl3OsM9BW1Xa87W1tgVgvUAHq9QGoL1BZYlAXmBtBjKrRv0ZH8Hj/kHxZ139VwUevuFxYHsQskJotbQDUBsQVo5zNbe0oAmwMe1evnKWlUWOqqmUyHCpkDkO+yZKACIg5KmH3NwI5k4/yzHTIzue3IkAU9moFAg70MYimbwovBcnqs710B/cV8z5nHkj5EIKOAzpkJDwCYdcsMwio4RMccjGo4uDHIYEDcwRlnJQTH3BnZUUaR9cCcDQ5nHUoABtOdvWsA19n0nrtyz0B68JjE5icDnEc7g71o24E/Z587SOI+l8dTgDqJCkFBQCxynoY2IKX3a3tmqTy0jQCDJHebz86gz1IczL41oSJm6VSfi0heYMfiu0lBRAf+AKBijFzH4TwENpB9ILJDzoIQ6WFqKgPHMcBqaU7cDm3FDHTVUedcMZs8y+qjDdYeJxFFtffQptZLriXXyP2wIgL6ECTFS5Y67kHAPa91AvWRaIE+G2zH82WbYb1QXh7yi5IzZda21oazmyx17sCaruPDifZCQGm5BE28JieeeIUIAQqE4tlGUJSEA5MscqZB85kdkHzisZflJaEgYXOfryzFMp/zHhP/ckauMxjXnwPoJCUpqIvMGsyjVAVKyQ2/I7FOvPYmWkse2J1zoMvohAig/8m3/2goAPqXfv9vOOJhAegAxL/4xS/O26r9JA+np6cJdiOLvN9x3nnnpSuvvHLWbAkA+JAtvPPOO3s202w2mZmOe9VHbYGVZoE5AfQe4HkcYw2k95/xieuemxr7CFiL0uDOSCdJbk2nzNR8Fg/3y9MpATjz3pfbx3n6Xb3mkwBcd13nTgkfKPyMaX8R9p7+W27Q02pVJvliz2LVL5xjEJB7smYnc96EAm+BLVPM/WXw/Tw8g+be52JvwIzxXMaoZGJGEJS+jGS80W+rhRkIN6HTAKzrDZuoSxWcoGqGvqJr2LNYMhyfmdBL8ip8EMtsy+dBX72XrxIDCqipeu1VJZMC2mvvZAl2qpRZPj/Iz1tBDd+5j/65+FKSwPb84x7wo7DHPPCG+azGgZxT4jaRhC5ScHaNNPlec9jfY69vMknxHUXKNGBtskBUREAdcpBwqYzV5tqmKpruQfCbqlACL3F/rF2TLEoN9OC7o02VB6A8uJ4FArfwpdBP+m9ZJcEy6swgB4BOMD+UNuJzXVFs4tygtNNYlpbHmLhuUQNdhHT77ZE8oL5BSp42g23sV+I+jndgD47nEj4c+o33EojKBmzHx9JSKhLMZ2Fx3Ty1ObWf9rQO2VpJA0UBC4/E+Hhq/sb1pcmJbx+d0gH75vl/6F9T89Tvz+d2Qz+n9ePf6yQwOL4RY1P2m6If73dOlPT3WtqCUokpjb/gjqH3fSXfwAD6C9/33PTcPztooEP5/mcfSLd/9MfMska29bCPhUq4X3HFFelNb3oTu/WVr3wlvfzlL+/bxa9+9aulRBWuu+CCC8q55557bvrSl77E35944om0zz779G3nzW9+cymZBdWuQw89dNhmqduvLbAqLFAD6KtimutB1hYYvAX6A+jzDC6kdi3nPsu0EES3k+vzaFrUTxbIZuBjtukl4Afp3lZmAwfAbsGrwjEW98NZm5ZZhxNtBnrsW2G7O2s8BiZCFjeucbADDjBAPwSvHDCCUyvGOftuAJh1qMfyuVHC24QDZwHEDHQEquTIM1vb2RSOW3kZs3shWAIwPWTvA8zsKSc3m3FLbEzt6h5krmv8rNHNDA4BqGLx58z7nAVNW0tqjWA97Zcy8Ol+Ozu6gPAmGATJetsSNjQTu9hWQQmsG2cqM1AwxkyRxpObSzbD+KHfXPCSWuwFAA05VgPoJfsb9c3z5NF+JlUUskZ4P3XJFCJ4A1lFSFq6ppsmSkGzEoTBdbABgGwG11CDDwGX8VxPnPXuQ0CyyE2qPUir+14AsoNKBEF1PN+U1VTdStZKB/CZQW+sVwaGTLggeUWAu4kefo7WrcuyejjYjt4FU9M5o8JyjM4mUQAW1zBzBMEgBzdjRr6DapQ7THnMyPJAOwhcYoyW9F8B2cwT//Yn2Z4q5dD8z3+52KW5y66beOj0LC/p9wZ6UjIsELRHJp5AdEhL+h3B9a21gdqRCig3D6yz0PtNZjeA/sdDAtD/mrdf7gC6bYS6fADJAYADDEdgBwEzBH/mmzEOKfjPfvaz6eqrr2ZN9C1btqRnPetZlDN861vfSrn4+qgtsBItMCuAPgd47vHWIHr/maecO7Km+TcvAsJQGMqAWCEmzgaCW7LYe2YDp1aBin6K987u1lzzyCx49U0+0tRtj6bR335aSs6szbvXjt9hQBcfM9szkIQJ7oWxQj4boB/AOXwca5w7q5gAtyTg7R9Es5poaf8p+BldfpJltPu5/N5fYCwmuBpAt58464Pcg9Dtj4qdpf5F8rU6QjKhSjPZNpEQXPzAHh2nD4Z9k5TOrOTjObFNis8kBS/b1gC7/V8SpidLxnOb++V2JtxqDONHLI30c+snpxUudxcJ3aQFPxNWzTI5Ic5R1WT0Ew1cB2IIrvG68foF8B2JHioplVWwKkB6fD4F1MIvoi8zPZ39PnwOAH4d1L7k/9hnsv/qRw1zYCUw3I/y6VIEQP/og+lk2AMlqOwPGyTFU+kSSCar+l1AxaoM7tMHNPk9qjSwzwLnsQ42b+G+m8pdAPdNShlpLDsAvd9jChJ7KekAF3oF+HrVsZS/y44t+d1ixT6WJtA7LCo3OvZj/xgxzY3/uBK3JkvW5w6A/lvpuX/2mwO97/c/e3+6/aP/vGwBdChnnXXWWRzzJz7xifS2t72t7/jx/dvf/nZ+f/3115d66Pj9ne98Z/r4xz/O76DuddRRR/Vt5+yzz07XXnttajQa6amnnkoboF5aH7UFagvstAVqAH2nTVg3UFtgdVlg9szz+YPnsFot6d5/7bS+98JOzS6z2MnaBwiiTAs4pP0CQQZpHSBRpjCdSAaC5plt7i7G09EfSES7P9HhgFNhUA6OqLMWDOpaug/tlixJ3SS2pwxWgloEJAWY+150kF3PWlm8rkUezVrOkwyhs39d3yo4x5mZrjXsQEoEowWuZem6uY4cxCtZ7eo/Gfglu3mONsCgF0BbAlB24NBPZUFnVj8AY2W3OxCAMWAOKJmXs5xlyO4bO2DmTx180D2YnQ/nkgAjMk9GcyAD63BkZJfUgiaAzizryTxuHA7UeM4NHlq1oIy639rXemLWjgIuJhRQtlKZN8rCj/USGRhDtgUCIbBTzADOi7djcwSCJKdNkBr9RACJNcsFdgPgnprKIDSCrgycTibW8ca48S8OS1GG6ywJ30WuwDXoowF2XIf67MiocHa8gwQC+DOArvqBDrqir15LCBqtRUBLWeeWI5yeLrUMlzK7Zq4ncjV9P0PW3XNGCUIFDr32GNRWIBRZN5KoR3CvuX+uw10f3RaIAPp/vfFlacMzBxuY2PLYlvTlU7L6wbAA9HpOawvUFlgaC+xs9nnsZQ2i95+z1m2oia7vmY0qsinIfZZ5FrGvr+9ksNfKV2iHgOwsJa3mWkbYW5nI6Pawp9q8PU39+N/S6PHPznstHKwfHkpomRSHv8usC619fBU8xzXrJVOPfajtAFKBM8yjLDvlrXXPav8NChEQlty1VZe4nRUBt9+47T9569vPT52xFe9RlztkLXfdbgZZwWRsnVUy91USzABlr75EVQBc3uVLdTHHOz6iy4bZb+VeXFntLokDXyCWvSJBEeXGUmr+xtfmWjUD/b4LQOcYRTqwj+mSYhiPfZguv19kjdlA9Ej2YO9REmpNJtXiYF1tNQrFI3yHs1xuKhLk7W/jdPpBIOVmkjcJCHwe0X5TYPeUam5DScly+e1MBEa7fobR/6AUxw6ULHQRjR0n4FpRrAH+PNcUlPw0rwTQ1R4AdNReB4DuZAX7Z15PVgzDMw4AHfXVQUSnn5WJ/OMHf2Og8143Nj8LdNVFj8+/4ygmmRcyflhHJlv8anMaP/G787vhKjyrAOjv/e105MAB9J+kOz62fAH0hx56iKWtcIBUjMzyfge+BxkZB6474IADyqlQ+oKiF45rrrkmISO934GM8/vvvz/tv//+6ZFHHlmFK64ecm2B4VigBtCHY9e61doCu60FBpF5buPUAPrsyySD6IVy33F4DeDusWF2AN3NMxijrG84d3T457lEq8A5+6P6Z5bqs5PorAk7w7hFNchj0JsBGGTVAmzsONSFGW9A0PJvMesjgucO9DhrJPbX5+FfgJB0mpGhDDBdILizFhwEKTbr1FlnJjPH3Mi1xX1tOReMeGULU1YOAa5Qp92BNwPThbGc5fhnB9VnJzpQHs/3jnKJkQhQCBizzLmJBA78jAskB0C+/3XlwtamF/Pn8d/8+3kuoOGcxn54HTNjRPZ3sJEEEtmuBD616BGMCQGWEtzB3Bowh/oBDlxCG1s9IY7HsowjytqVHDxAahMaoiy4LmXABCAz68dNZjAcWex77NGRHZycyrL4liK0zLoDnkXFQH2EGoSPCOAzYAMpwrFMeFBdQWZ9YKwImlEeE1kyIqK4xqOzLJg5oufFNd9xLtYIniuA/cpUL4Er1HZcQjn/4ayyld8qyxyEMgClfAYCeFbhwPrGfyY8Icird1ydhd57DUQA/RU3njMUAP0rp2QVhBpAX/nPYT2C1WuB1j2/33uf3hdUnH3PVwPo/dcSJIMbG5SNSrKffB36HZLght8Uj9nmAXtIZHNXAdaFLGfvv3EN5b5FxjQwR7AZJEqBjAY2o19EAB7jgoKQspd9Htr1uVCYIcgewH60Hcfv7FsT6KpAtImi3PdZmUay7BW/sd1oUPUG/3K7bODc+3F+GMintpt9UtYYV8Y4aw8HYrX7VaTklelMIF/AJi+oPC/xV7dRHSOzkKWWZPtbxQr/Fqn1HhPNocoQJQtVBETsoURQjD4SwOulVOjqtTwJoMfDMYEq6F18HRgy+Ezc86vsEeYX6xCfOes7qgqYjGvb0rcw8UOEDEqxq355amSfCMprlkC3mdEN7VeZaY5yV/CH4IusXUPSMUusaZ8L/wpzyzJXuH8EzuNyEUhe1pyl+nE/jNNEcfSNGewC4f2+sCIFny+ogKEkG4B9qT2ZqMpnQapw+Ay10kfRP9VkxzNLED7bevygXetbL+TVtjueW/5ex+QTz7ljShi4iUyIJ0Q1g4lWGn/e/94dTbPTYzKAfjwB9IN3ur3YwA8+CwD9R8s2Ax0lsJ797Genn//85+mwww6jwla/4/DDD0+QXN9vv/3o/yGD3AcAcUuxv/GNb0yf+cxnejbz2GOPpX333ZffvfKVr6SqV33UFqgtMBgL1AD6YOxYt1JbYNVYoAOgG8GZLxJrE3W82xpAn3vZEET3EYM42FAhENSPnV/MreAJzO4a6EUyWgElg+LV7kTifQEXw0kOJBiEjlJ9BqTjv7jUsm5m8cZASwTD4TBHKeIIoHvM+MxAqAF03MN+P6XblalN0BQMcTHTvWzp6MsBxoWQ1LZ0F653/9FPOU8E4H094nNw1HEt+sWMA5yrfjCAIOfZ/Y4kAjvO6IPrS2MMuB5AlrN6w3wyK51AvYIGltA3gF4NCDrjhrapyFu6XWffew4YXBjd5UB5vyektelFkrLXRHg9laCQF0IcoIIUApRLXXsyzCWBb+lHZ5JYGr08P6oH6DXGbG0EcJSRgLYRSME68vyg/bgm0CVkK6xdlyUBsX4wDADclhnkOWsk9T6SGk9tUY1rsd4Jfo906rWTcGJbQC3AEvD6eGwstdG+SCskhVCq3qoEWr/T7ZwRAYlvr3krXTgYhWCS+waiATJCntpMQ7f9TkLMa98sQV0fu84CE4/+YX4/cS1O5UwezLvei20oiWCtYn2pNEHOhGlkotHYaBeBZteNZHndOQLoL//Wy4cCoH/11K9y0DWAvrzmvu5NbYGFWKBn9nk/0Dbu23vcpAbP57Y8s9AnplNaC9AugOi4FJLmVQCd++3gx0YyLX52Le1+Eu5zd2nmGQapsWerApjxbPsF9h0MOONz70GLdHk7pbXjUhkScYDlnpBFrvHx5+wLcVzR/7IdeE4AOUO5q7I8I2CODy37XR1p6b9JA7xJvjfsSdBRpYTi2i+gtva7hXwsKWUBqTkTPswdfcIMzM444v4V/QL5E/vcXof8r0ycCKXFyq2CPS2HH4C0XQ2W9xoSAfQCaIssHEv8mPzQRUjQvEWwOaqZYf5MwPX6AemCtdFFcoC/wPkwGK9yCljTmPtQhoxkT5N0He+wj441CcAc4PiW7J+099yQCaJF9W40+zkE4ic6tcijQejXT2fQ01n3XMPKLvfawXfMeh/P9+D60b1M9nDf4CfGLHYTK9CuywHgM6w5k5FxP/RxvcjPKBO/i4npi3mV7Y7XEER3+QUM0CR9/KwyDEWlAesY76PwGho/8p92R7Ps9JgMoB/3349KR/7ZITvdXmzgB39xX7rzYz9ctgA6+nrhhRcWwPvWW29Nxx133Awb3Hbbben444/n5zj/8ssvn3HOEUccQQB+7733pn+4HskWlePSSy9NF198MT/98pe/XGqqD9TodWO1BVapBWoAfZVOfD3s2gKLtUA3gL7QVrJTWwPnC7VbSjNqoiPwAaZ1lNWrNosNPeV5lT1aQFeBa3Yeu+S9e/Qt+L78thqbCGBy/l5BG/wcgWT0FUAfnGYGgMJ/ZvbCuaXkHWQHlZHgIInbLUA0HBcFyVzrmmxwOe7OOIFDD8CS2R8C1d1P/AsACYx224GOU6hXhgCSWexmmsMIDj4wWJCz2+lgu2Z7NYgTg2b+uQTPKkQUj3n9ulxXbevW3LbruVNiTPYR4FXAUNvSARGD43HM9vbibR1cY5Z9Iy33YC3fRbEepJUCnI3DtRdYIPjdtcdtC4LEej6cqRuDPcwowboRaSJKaKIN1N8bHe1IEBpAd91zBntUEy+SJQSY81qrQrirCl5S3pAZ6M5+b2Rg24ElZzxgnePnGEhkFnogb7Cva7NEvEkifA5NDNFzh+CjnwMHLTF+E2gQ+MTnCFKpX+2m6nrifq6fXUt/L/wlP6QrJh4+MxM1JK1PAo4DgVDk2HOP1PiPJ3NQ0QQaEUrGD/mHIfVqZTfbDaD/1yEB6F+mkWoAfWWvlbr3q9sCMwD0ucDzrn1ax3bLfT+23Ga59Z1jUxoHUKi9uu2+5/oMnM12dO2lRFR1Zqmvmw34XqgxYja5rzX4TDIxwD3VaqbPEjLrTS62TLv9JNeDtx8UQWj8nY9+Dn01gdEm+GJPScLzSMkwd9eYaQ6fqdQaV/+c+Y6a8yYUE2AKvlcsqYT9Jn2moEQWfUzsk00QcK1uPx9V/8pzhjatYLZ9e0e5LdoPbaBP6L+vK3MuckL0X00Wt53id1Ri6/iVyxk8a913ap53A8BUAhAhm76znhcTwvEZs6abUpPTPNN+YyLlY+4q5cFsE/s0ANENNisLu6O6AOLH2iJtj/O4X0VpKcYvRGBQJjoAcgLo9IdDPIIAO2qXYy5GMuiN7+GP47+4v8V3Au67HlWWi1OnMXaQ7kVOpsocAG+MJa4D28h11B1/MFBu8J02FHHBsQg/q+B91JnnC31rDu381h0vyEQrPxN854KYjpJrUymtRQlDrAeVUmP8J7/jxp9bg+f9JqYDoB89BAD93mUPoCN7fOPGjWlycjIde+yx6aabbkrrrHaYwOfalk466aR01113pbGxsXTPPfekgw+emakfZdwvuuii9KlPfarL5A8++GB63vOel5588knKxiObHe3VR22B2gKDsUANoA/GjnUrtQVWjQUygF4B++Y1+m7UtQbR52W0clLJRCfgJbY0ADYEC/ptjOxcerqc0R3raHfJ+M0uHcnOWHrPwJ4DCzFjoBpkcBaEssCzI2KAT2C7gxfOqi2y28jEjjXHgxSjs9lxb2df494lQ12BDcuRVwMlqGk2sSOz1M16L1LsAuLNvLeUPNp28K0rS0JMfdaqdjChMscOUNlm/F3yizOCQYrQIEMUATRK1gWblcnQeXbMPX5K5yvzvStrogIou65XzA6QvccP+9bCFukSn52DQSI6eG1y/MqygX1jvUhl4JY64yKWsL4715CyCyTX31VL08EW2BLtmCSB2pqwoWU1cR4k+oqkW8ryfwy86Nl1Xx1YdG15T836tblUAGUEFYhEqYNJSxe2SmCnsRWAOrIplFXkuXZGkEkeqLUHqUCt3VLfHNc6wMr7B4UCBqKk4OAAQpEt1GTjHGThFxnC/Kw2n/U3S7wa6tv1s8DEv5yRuMZNprJkKgJBUCbYY0MmcVjFw+9VkN2W+TtgV816BNDP+eafDAVA/8vTvsTh1QD6rprl+r61BXbeAgVAnw9w7ttV94N4Fx9+4853ZhW10LrpmJyFjsMZ2/h5XXMm4bCfXbiHhHyzSLX2RQi0RYLmAg3rtUAQXhu/ouSltkhIlGy2wV8SZ3W+s8pxOtqLZNJqWybVggjqfZ0lqU06jsRkZd/mslXd/j73ji77YuUn9MlrdkxkYvRrAgC1wOpIpra5sMelP4g9eIV8zXEFAD36TyZg9zK7yxXhOxKjg48Qz7dMu/0w+rQClHuSxEUwKP6b/GH6hBm0xTG+8dsLXAxLe3rrn0/u+KieM9uWBFr5NwCOVY4KwHQhMnDdG3gP9dJLrXDVJncWtpePiRNe084aR3vKzGbpIADo9LGk6mVyifta1MIEZHOdjJDMzPU6mktjudY82wLxGGNkySnUMAcRWMSOqPzGOIIGiPuxX6hbPxb8uEAWKJn2eseQrI4xKA6Bpgyse5r5PORkApdAwD3r7POlfQ7mulupiW7iB3xwk0+kzkW/3u81A+i/ffNcTa/a7zsA+vPSxj8dbAb6D//i3nTnx38wtAz0W265JW3atKnM3S9+8Yv0rne9i7+fcMIJ6fWvf33XvL7mNa/pOc/ICkd2OI6jjz46vec97yHIDdD7Ix/5SLr77rv5Hc675JJLerYxNTWVTj755PSd73yH359zzjnp/PPPT3vttVe644470oc+9KH0+OOPp5GRkXTdddelM844Y9WuuXrgtQWGYYEaQB+GVes2awvs5hZo3f8HixhhDaAvwmjlklwPPQKi2rVDOspZtdUAnR3XaHoGYBRQcBYi63WbFxEp3bp9vN5ZDK4Vbee1OMli9NvZJXtcgRFnyhYZtACe24FHewbpnKXNOmsK0PSqOee6Yw4OwR4cTyMDmQ4YcTgaH8BIjAHAUawZbYsXUFGDd5AL3zs4FQNf6L/rSZc2lD0cg6Fxjhy4wfm2EbsoYB2OuEkSrivPuXN9wypDQtc6yGcA3f2JQLp/LlkpIim4ft+h39yZ5bpk17Z+/HsdhQUGEack1T+agzD43YQHERsKgI7adpbEdJDDICOCPAYdnX0NmyHDYaSRMyC8FpzpH9cO1i6y0SknmSeYWRUIFPrw8xzXFMsMKOMd0ujMGoY8O65vBclDSHGrLp+fQdawDixj9Iv1211/L5YxaGdpbxNVTIgxyB/XBdq14oEzoTgGvTRw7domA1cICtXS7Uu2/Od1o4mfnlWydVhWwEokmD8E8SD/ZiUEKF00Gmn8uTfNq+3VelIE0P/4H16ZNjxzj4GaYstjm9Nf/8E1bLMG0Adq2rqx2gK7xAIk/M336AGg49IaRJ+vAVMqADrAXYPNyJCGjDt8BO6r5nkwUxmAMAC2kAk+z8u5L2NpKPkT2F8RoHPdZBF2q5no6PvapvZf2M/2AM8NBHfVexeIZ/IyAT1lgWOP3wKh07Watd+034D9J0BD+SrMNsdBsuVIStgjWIkpSsnjHIDn7iN+j2pQHrNtxpIxAtAxLyRrymfi/Wwr+Vy9svSr9rcfBoUk7HM8X1F+v9oP+57OqO7ys6slwXRDg7j8NUvejx+xvIHzaKrWj07qqFvBzJgLjN+qCvgMzwnXrGqC2y81eE7/StnraNzlrmBzErh5Ypa3xu8Gp3vFJQw8m5BLZbns/2TyMZ4/Sa9X5xzgPr4HcZmZ5628fjFHaAMkY3QFvjT9IT2Hnnf32+2aYGoiNXwyrFOoiVF1QXtoK5dVJe+7wHP46lbaq3Q8KoxRmbFWe5rv63TY5zHeBvKEHu9CKinxgnZqPzmRGvus19rMi6RJ/ZEAACAASURBVHr8+bcPu2srun0D6L/z349JG//00IGO5Yd/cU+66+PfHxqADkD8i1/84rz7jJrnvY7p6WmC3cgi73ecd9556corryQA3u8AgP+Sl7wk3XnnnT1PaTabzEzHveqjtkBtgcFaoAbQB2vPurXaAqvGAj1B9AgOztg8zNxM1Fno81sure/mejjd2unKMIaTiGABDjis0RnsgYUXEL6wuwWuBSmxAqpVp4xAmuWklVWA4AGCQAbFu+qWqU6zuw5nmAGT4HSXrAc58HBQLZtlR7vUKROwHKXYcT1AcIJ4yrCl5KDY5VUTI+scGcFwqhEwcC0+AJvF6e8zL1XgPJ7GrGfJIuLzIjeoDJNem+nZMpLs3CMDHW0hiBDrvPPermEvxr/7U+qxhQnsSiIxQM+ohQgYeaMOwkHzwBvmtzCXwVlUxCiy9gpsUj4QmUcihoRsnJzhoECMgW2Dws6mUe15BktoFNUwx88oBYBshBLUUaCo+qwpy4IA+tR0zgDmMxdk93mNmSshg8frHQQQPNsKBFF6Hfe17KUl/f1uEGjfLeWuYI/XH65FNjqCQS43EEsQVNcpnzVl8kflCpdOcCY+glcKSjf3+9tlsDLqLsACBM8Z9JlKRbo9AgBYC3jHQK5wosVM9DqQN/fa6QbQXzUkAP1qdqQG0Oeej/qM2gLL3QKDANCxX1hJQN2unBPK8Nov8h7QIDNBdGSsYq8+h5y7B4H9GwHF4DPhu1huqjpg7LcA2Jk4aWKuS6VgH0Uio/bqvbLQsa8qSmEhK9sEY9YUl1x1kTvXmOzX+X72TUy+RNtWOUJf6FN0D4JZsuifs3idocx7GigN5bri5fSLsH806Vck37IfFTka3wM8N4iNdg3eytXpkLx7kJg9D/gXYxpvSmK5UufdmfjOYLcP5rETXK2I7Pl3+xnMtIby0ngeKcDzQ1YG4bgs5XtP6UjXe1/vvX4hXcipcVm1+JyY0G6fiXGA8Q6p12Rm+vKj3aXWIqmC/pV6ZRAfqkhjIkBb3t8dN6Bkv6mQyZX5rfIDLH9lGXerynFDbIn2sMhZpgvE4+Avk2gqAoD9Kj8YlvHmulQ7XUkGeqfYbzQpm89gIBJ4DcmudQb6rvxr0bk3/24gTmUA3fNtsjnmesdkShtEyNieSfHjJ313eQxgGfeiG0A/bKA9/eFf/HhFAOge9Ne+9jWC5ADAAYbvs88+Cfa54IIL5p0xDin4z372s+nqq69mTfQtW7akZz3rWenUU09Nb33rWykXXx+1BWoLDN4CNYA+eJvWLdYWWBUW6ALQewGBXUBMDyaePhpXpmsJLum6OtMibOgLgF5ZWmaLq05dlgUTa7bfKoQDieCRM54NprgWsmuF9SJPxuwJBIboQMqJtMyh2+mSx7OcODJBAsgcA0AM4jSy5J6zX902gyqSv2aWgqWtcf5EDpToMzLFfRTn1jXt2hmAZDa3MpMNopsY4KANgj7OyDDoaak3O8QMHsVa5HKmu6QX9b0DDvN9O1gWENmhaDYC6J4b3i4it5K3d71uZ5iU06w00EMG3078wd+Ybw+XxXmtTS/uAOUO5mgtkUSBA7+XGn0CrdsgUiAQmuv+ZWA9ZJtITSBLCQqEtjQ81hius+Q7s7cVVMV3yoIpaxEAOrIiDNxbDpTzoj46EOTsGGeEJ4HohSQzlTPHsR5wxOcMfeJzkLOHmDlksoqfc0v989nQgAt5xhKV4d+Sma5sfpwLggFIM+PjHelvf4YM9P2uXRZro+5EShM/O4vrlGQJ/73gulMGnGpBtn9tz/y3Y3o6NZ/xl7Xp5rBAN4D+39L6AWegb2UG+v9iL2oAvV6OtQVWvgUoCes9aTULtjq8XoRL+0Yh05VyzDggAVzXXi1WLOC5P3GGs8Fc7tFEHkUm+mxkVuwBy/7Q2aQixhYJ8z5lm6rKVfSb8h6KWY3O4kU/sXe0PxEBf2fPxwxznE9gD6ClVIb8mbPNvZ81aO9yX6wvLSImywSN9hx/2UOij6j1C98RdoK9QEJ2Rr3l13E/yrd7bALF8XsgWmZJfNkvAuz4GSAsDu9b6ZdJqaz6jFSA/qIMhr0psp7RR+/d4/NkcrJ9VWet0zdFqZugEoB7GDhjnWOVNYLtrda1wsrcdL2HDC6bYIHxRv/EJAH7KRGMthqcGQfFFW0n1i+nzwNfIpDZMQ9RDcCqApgz3BeqXSIQc8+Kz2Bzz6PXAPobS6UVf0YvQ/gj8J3L0c6+CudSC8cEZvraUmfwy9RxgKi0x++86IIT7o/ch0i0L9nuIlzwWYBtBNCT7DFey7cvoz/v/NsBUjHWhRND4t8H+VLtza3U+LU1qb0l++LNk7+3jEaxPLtSAPSLj01H/OnhA+3kjz4HAP17Q8tAH2hn68ZqC9QWWNEWqAH0FT19dedrCyyNBVoPvCiNB2ANv8/rKE5rBY2Nv1YDRTWAPsO0zECPATf/bFCWoLJqICNIUg0GRYyVTqkCQrim1IiLYLiCQXYMYwBDgGPasD47grgfMhMikOxAzfbtAhPhiATZtJIVIRDP0u6u8V38VAG9CIYYoDTDfE0zA6AKglhirct4yjAGcEQpOJyPPpEkoKAMHCXWRFO9tQiWm+3uAAplvZVJYTDdkl5RXhA/g6QQ+8379wmy9XqYPKfr1uVvEcBy/a35AOiMI4B0AIDWdRaDVL9JCpDdDjXmmY0KZ3D/6+b1iO/KkyYePjMD05YURHDDAC9k1tvtrETAY2ZaScnIFSkD0uw54KN1Cfk/2l1ZKWhG2QjMkChS2KqLjssQEOG6yZk8BOa1/iDFDhl2SsDjucNzgmCmg0HMVtL6QkY3ZLX1fRvZUlh/O3bka32eA0/oG55tBVU5FvSiq3amn+tK5LGMWaZisFhZNvioBIlBEkBAK9flbI+MZgl81oFrZFB9dDQ1/8v1u3JZ1PcOFrB8ewHQ/U7z+0GZNu0N67gem8/5u9p+87BABND/6Bt/OhQA/W9e9D/ZkxpAn8eE1KfUFlhmFgApeFzgWusnp83sXS+Q3GfF77p+LidoW6PNYA2gd9m3C0A38Mz9G8r8CAQH2OtMw9kAdGYry9eg+o9qKJfSVyod1G/92TcwuZIlgSR/jbZ8oIZzIWVqj2b5+Sp4HonEzgznPk6kTEqMV+THI1BusmU14z2OwaRiy1ajb1baAojOvuI/kUcNhhtAx/X2Lew3FuJeBajm9wFAd/Yv5bSt2hT2rdWMX/ebbQBAh2S++hdrw3NTrD2+AXT5lNw7x+xo+tfyo/JuupN5Hs/F9SsIBC0Aut8r9oWDH5jNaaBZRHEThe13m8Bh3wprGuRtK1vZR0FTJctbNcEdu+CzoeeQazHfE/48yw3Rzw5EYPvR8Fs5f5p4q3aRLJ19bap3oT/w4+AnknQc/Bpc6jaYhBCUKFy+KxJg4rNRJW/QXCJY2K5+tqPCF/qH9YdYB8ZXA+jL7K92SgVAR8+q8R7Me/C/209sZ/+bZ/5w2Y1jOXbIAPoLLn7+EAD0f07frQH05TjtdZ9qC+x2FqgB9N1uSusB1RbYeQsQILcTEAILANF3GjyndxT62CdQVGegZxu17vydTo3nGVMb2PkIzsABJGPcIKAuqDp7dBrFrmV9MDPue6ydXnMFJ5ASaXIE4WxaBo7S2XBM9RmBONVBjw61QTrKbYsBDqfSR2HEu5afgDudS2AygkExWOSgB4BLOM04z05Pqa8uZrz7h3NwrqUWkfVhENsseLfLe8MxVxAoZi1grA6Ysf66sjEMtNPRlk1opyDd3eWg6/6QVobdAOSS/R9O4rxGdoSuMRDsAFDXRb7ecvi5thsDicjEdsbBmjUrIhO19eCLuX4pSe6gDIcou3RlACkYA7AXILZJFaqfx2ukLtAeHcmy6yRWKIBnU2PNona0wHmCyMgKx2G5dQfpUD8PAT3MG9cL6kgqqGNw3pLsa9ZkwJ3rRkoNOF/1ADFHrL0+OZnaa9emxrZtqSNViACRag9ifH4PwBImzTj7wlk0ZSmEgKIzrSzlCfuxdmHObmdgS/bmuvn/2XsTKFmzqzrzRmZGZL6hqiRkUANGCCHEIJAQGtBcGrCEwAhjDGLw6tW9unv16AEzmLa77WW37bbbNhhsPHS3jY3B7rYNSGhCM6ARSaW5kAAhY3f3shGgGt57OUXmi17fvmffOPG/yKpXqsiqHG6sVZWZEf9/h3PvH++es8/eh2cs2BSu3T55bAfQH/y/wqtrYf+3v6nuZSenPf50qUyszhKra2kRQP9PjwlA/ykNuAPoq1u33lK3wHFZQMCUz3fDTpYBtEcB6EexzxfykNO5MRJFx1/zK8c1tVPV7vSdTytlvFbKPnWRl8izm9UsAJ1a6E56vMlpmo19lOx6bsbXKKE2lIvYCxqD/SZKA0UZLLO5zTr3/c7/NIDN2RMfjLHkhFonPloKXYAhbPtg2NpnWgYAMm72nuWrqZNO//aZmu8SSamq375Xyu5B7PtIUDAgHcmstX51KHplYNwsdJ+rBUTHOM2E5sxq9QApp8VklUwchh7ORcAp/m2AlEc9i27LvqT81CFofsPN9Wzuc7p9V87cG+tl8tjX3eQmevguU/1zA7lObDCIzrDsp+fP7NtgdDPUmbvKhYWSl0Bs1gtlBZLU0xxZoygf1d41uzcB5/oMBS3UkIzfTw9q2SzGRUKxCQAa66z6Ylawi/fwyVQP3Qxy/DjGahUFldYKVS3G7z0bqgLaB2bA0+ZwjE48yXEyf8cL8I/zdvbn6j8OdV70iV8Vvve4n8kfvgfiiJ6nd76gfR90dZfVLM8cQH9G+co/vloG+sf+8cfKHT9yR2egr2apeivdAt0C92GBDqD37dEt0C2wAIrfNEi+BGBfMGULAh0t367rcxZ0aqAD6KVM73hmgLGZRRtOmL1Tg7uwnUO+ec6iXRJgyMuBAwd7YbhEQ5WyvLAETXBKYSDIGQ32bZNRR1IwnFzJrpvVG4kTzvpvDOjKZq3s7ISC22F1UIPrg8ExI/t82UuZ3qPK2hVzIWTScFL3I9A0BPFtTrGBCbgkRgjDEWsjmMdmHZjlQYDGAHgOdBH8UoAiwHIzWFo2cwT11F4wW4ZBU4Psly7VeTA2s/A99xsA9JiMANJo+4Z2PWEHu6I+HYxiBxNV6290KgB0m6JJueeEBAcqsKWDJA5yKJAYwULJpG/WYAvBH4JhyJObweLgJfeaVUAwBplAA+3LpCIVYCK44yBu3d+13QjeKGAUbBkF42oyifY4gUAF9FLCi6W4Q4Jea0abAOtmgigQWRniWm2zNlogMyQ7g7FUEwEWvwSq/HsK8NKQnx0SLW65PFfFkELFvB7g5A92CfeTdqzY/9TLNKTJ415/0oZ2KseTAfRvfeN/Vi4++vJK57H9O1fLq17yT9VmB9BXatreWLfAg7ZAA8vtw/hccV9M5tyrmbhLz7HLfKY501z/Vje/SQPQ3+Mnvf1Bz+u0NzB9z9Mrw5wz+FHsajPQDWpzlnsg64d/sbN3dPvZiE6mBVwUgB7Abz73C1iMs6XUscIVauzbOF/RrgF0+3v2QTxXJyZ7DC7v5cTmBd8h/vB2c5Kuz3nyOSJJO4PV7gsAn2uoB5zjAS5/pURe+64xp6aIlNq1vyn59/Bdml/JOTTWUwzpuNjJr8t814ucm/FLI/l1mOiQgU4dkIOVbJ/gqERl35cBdM7ZOTFiNiuTLzk9ZyyVzvMaL6h0uVRBsMfxn/BjlJgQzH72rZN/BWSHglYreRWAutv32tofyon3C/unqunhW2l55IsESO9yBfhHWZUuniEB7bt7BbUvvSKhXYA6il4kOls9a3t7kHxCYjRqd/FcGGB3IkiOZfg5WkYwyAknkdykPUZSgBQwQpbe/3aUUZdwP4H/8Lg8yvirf/kEju50DmkOoH99+co//lUrncTH/vFHyx0/8v4OoK/Uqr2xboFugWUW6AB63xfdAufcAguM8pyFfH92Uc3eAdM53yOHyLWnU+moiPc0h7s5EfObO3hebSHpdgPOvDEEf+104pAqoAKzdRHMaizqocPnTHEcyua83oCj1bv4HCY0ARax3JHvi7p7ZrObxYzTyWd2eIPRq+xvQENLphHQEVgYNfjsmHsPOughp3y2mGGuQc1pB5JnM1O4Mb0J0ESwin5g53rv8TMzPIJR3DLnCdj4GgegeCtnoZtNbLsqKz/d54x45mWGutfSDGA9IjHOvLaaXgR1sDvseWzq/ILGnIgAlQMJBtQdCMrPWgsQMg+zUYR61oz/R9w2D4wwLiTyr22fGsfeTHTtf9vbdcWHSQReMycwYAPLF7IOLoPgvWtWNu0QHLKk+9qoSPI+GEUj9oRZ4BmsjoAjMuwj1pEArF9WOcglDgDwadeyhDlIwzPO+zy39EedP2Td+dsJGsGmUFmDWHfJ1TOmqI+en582FNvL0p1OFrCqQvp+J+FAAWFkvz//5+/vX4v+ebfAmbJABtBf/sb//FgA9F94yU/KZh1AP1Nbp0/mDFigAejLGIh5fhlQX5a8OTybDBOKM9hnMMZn8nYuqL90Bnop03c9rVrfwLXP2JEs6LOazvKT8D10QzoDGijzOTyvJ+uDUpWUrZbQuN0vPw04cpnloa3U5T7tU9gf0xkTKewYDz/9t5nSAuNT3XLOk2o/xqPrwn/IstTDefgsakl5708nPjMW2N9N1jzqxuPn0P7Fzepj7aZEYt4HCBfjfMD+t/8CwGgbA76H+WvZK7P0XToowFOfe12OSmNLpZXaIbZE4ils5fD5FlSoBqWJvMZm5qudQd11+1VeT7P6UYxSAi2xkFmt3U2S4mkC0K2a4YQOKwQ0mw1o/pl5je8uQDiSzu032V/w3uZzJ7NjS+IH+dlRMnGofKlEVLTLGnMtP/GBI0F+QYYdH4iXShZQRmrOXG9l7+Sf1XiVgXQpZy0rX2cpdz/a9ucyM709R8kxU8JG7GXHYAbfz00hMJ6z8RPefAb+JexT6Ba4eQsYQH/6DwOgP/Hmb7yJKz/2Tz5SPtAB9JuwVL+kW6Bb4MFaoAPoD9aC/f5ugVNsgQae5yDQzcwnB32OYlwM2bJ2ksM/rX6qWRWLaeTnHUAXcJ4deYPKtnUOxJmZ7YxsPrNcn0G8ra05XmZg206lgLhwXr329Cfmd7yBM0ktbrMbJOnshYz665lRgwNJUEdBjgiWAKDbSTd4bvk7s7UziEjzSEZLahsAfSjFOJozhD0HO7mWnG5Ac7CxzTzRXkzBL8B0M+txmOUAR7BIQRH+dl3oAJ0FzidmiGTrXB89GOgAmAZ0M8jt+Xstsrx7fv64H7tbMnL4bBpkHwLogMGNEZ+eLQc0aHdrs8riXduuteKoab+7VwFevcI+MJu+7ORLPs9lquN7RTUrg0XhtTKz5yhA3fJ62hsRwIStEPX2pGxgaUIHgCKINgeqAdGRvkzPFHtnc1xmG+MA0LFxergM3jsQFQG6GWvCi73l/hw85RonChxMa6kCXpltA5OdtV7yEis922HI5BHrI2QI9SwE+4f3+Q8gnqSdybhMvuCVN/OvRr+mW+DMWCAD6N/yBgD0W1Y6t+3fuVJe/dIOoK/UqL2xboEVWEDg+dAHWuYH5feGZ44hCzafvTMAkwGrpQB6B89tOoHnAvQSQ19n5CTN7Yt5j+Rc/AqdecOPMYDI2SbXhM5gmJSqUrmpfObKAPp4vcx+b6eMPmer+hdKlo2GGmDbDqVz1Sz8CAHgwVrl/G9fKQPjnAWVWBv+jT+zH3SUb87Z79q12p99CM9HyZPJ/5kOzon28/i5FSWqdkLxC9sqKTqB1EPWbgCZ6lclkpLiF7L7Oq/GerVze9RSt818rjeAbv9Y/ZMYAYvYya7JR+NzbOQ90UozpfEuS2Dx9fjR2NzJE/hQKJ41pbPwmfen5bRIPk/vfOHct/DZXvs0/BcnfeuzwZdnzh9RbCDUBnim7HewRooFxL22fyoxpU/M9na/tEWbfm4Au/WK58VJKUryTb5UyL/j3yh2QLmpSGwY7U/n/hDbQmW3BjGoJs0/SI5xF/YrNZQMoMczk/0qJ0Bzrfd7Sh7osu0r+Me4N3GqLGAA/Wk//KzyFd/71Ssd+53/5MPlgz/63s5AX6lVe2PdAt0CyyzQAfS+L7oFzqEFbqhjvsCiOIKFnO10MwB6c4QW/Z7mAw2dj7jsvIPnmKEB6LbhkOmA32bH1sxvvWeGd9R6dkDD4LcdVQcccPybY5oW2EwBv4XzBxPajFQk2sn0pn66gzYGgwV0I0cYTF07jwbQHbCR9HTIV+f5GURcW6/1oHX/APCOcUm2Gmn2hUDIqBzJNndgLYP9BsoddIFNYfu4Djr9OXkgg9ZyolO9dwPokiKMIILXSmsZzn+rQR2Z8bkWou3l+wHbsbekHcNJz+vSgkoEw8JYBtCHQVsD6CETrmbESt6QJHdlMUe9QjOxuSYy60/Ls9nY6GIfhQqBg0OuSd9qQufN7qBJCtIQJKO+OkEzgkICxyNwwuV6/gDHYUDw3NWAo9gN2f6A2VubUYs69gxr6g1O22YwOKYqGcIIZiGbbuUAmDr6ojiM4F2UK8gJLw5cKgEgJZ9cv15GTlTR/o09tWxvWUEEW8X4m6oDn0WizuSLXp3/dei/dwuceQssAuj/xTEB6P9YduwM9DO/nfoET7gFBDTpHGgJ6SitEszHpcMfAujDRNjh+WwBYA0QMQPtGUAfMETPO/t8/21PKSMAXdvQ4LeVpnTWDXDYLGmA5nz2zn4B/o1BQK+TFYmkCHWwyKLNwLk3Q5T5qTWPgy0tvy38Hn6nHUtbG/T1OY5zHmPwNYD6PnOajW6fwudZsYDzbhwAgQDl+G0tWTPOsBqLWcAJQF/mM9lHob4849veW0zcxP/xy+tgP0Vncv0v6kA7EZmyQwlA5/4MoLf56tAayQb4uz6LR8KzEw28rox/AegNAN1+gJ/JNj5Ll4cNfI6WnxvMfyctoBLFed/n+JZ8ESXJZqhCnA755xtKUuSEkSabn8pOLfvCawD6Rk1Ocb1xK8/5HttSz2Ek+Bq0ly2dZFL3lxK9nUQiVYH0uZ4tJyuT2At7PRJKomRCLXMVMQSVvop9I586JzpH4jTjGiYe22dyAsgN80+JMS35IFQoFFvIMZtRGX/pG074v3h9eN0Cq7fAHEB/9jEA6B/qAPrql6y32C3QLbDEAh1A79uiW+AcWUDAeXYmmzMbzkXGj4Z2yZ8NwZbhtZl9PvDfdWnL5k1MASQIv/Kt52g1jp7qAgN9uGC2nZjma9Vh5HecSIPquWlAOIPaklGPgI1qhMOWdWZ33GTnM68xQQQywXPQyHXOcl/LWDUOTADiA3YrMDWvb7Zwe9SpbsD5sO3GDigVOLwKiyJYsvnaZUEfM+ppI/+uAAmbL+SucdCz5J8/t2Si2Qh2yLG5nWsD6AoMhEw610laP0khsm5ORnCQyH1qTZHJRx4x2BnMtYGtflYTK5g+uNbAq/ZEYhHkDH3Px/vKmfxa9wgKux0HjwKAPa3P5/6nvrHW0fPLDBsHOnKtu/y8iQERygkoE7gmpRlifJ6TjwC5YakAUvNcGbSPfmGUK3jjAI7WLQUQsXNmy3u8EXAFgNe+2RyX0TalF2AqBQspj4P7FEg06D4P+uu5yczyfJ9/N3PC66+9W9lKs0uX6ncBZRNClaFLuPd/ts6bBTKA/off8F8eC4D+mpf+nzJrB9DP2+7q8z0pFph+9PZ56Z4FH4h/e4MleYOvlMBv5+ct85mGALpAzSNqd+ssEc5T+v28A+c2vQD0S+MKAg/rn/ts7RJLBmpVjmfAVtd5PYBczjf4BRlMtLpWPo9zD2fDBuRFooXlyP25+ouEzny2i2Vd2EaMwT5EVupq9/lMFwCgywwd9eD4zLuzU4rOjj6UpgTK7PfQ7EZKFrHfI0Ut1yQPoHE/5sU9EioKn4qfTvZsZZCiX7O27TM5kdnX+zlowKMcrvmZmvv8HCipIPWlc3WA7J63r3EitH0w+wD2/1r5p+QvNBZ9CmaYHa15pDV1srfuWTs1APpw26g2et5rA1+mqcv5fYHn4Tg6cYSEbNkjAG35JbGG9lFaQn0kJ3vtlLxLBgIqERHjyD60fOOkGtHKdaWZSMEB5YdSpfa951hr+fnJ97Lf5+T2lnSckjVuoOBnqw0Da4mhTgyGvpwscHBQxl/xlpPyT1wfR7fAQ2YBA+hP/eHnlK/43q9Zab93/pMPlg/96K92BvpKrdob6xboFlhmgQ6g933RLXCOLNCY5znTvrEijjCEwTR/nPzKpXc4u/y+7JqB+wginVZwbtXb52jwnJ4iiGan35nNWU5QIF9y3hwYMHvVTGucyVz/XM0DLgco6H0hJxapvmBrEAQSgBagbmbZ5N/dnh3l224pZWdP9emQVlt8JRbwsI207xr4eHgo2fHRPffOmQh5XzowOQTK/T7BKUsVNtA5gqECxAcBEdrBrmZwu13GxtCVTJDkGR0MMrNE96/P2d2MQ5LvSb7QwQWcbAJnTogwc98S3erTQdYUQBgC6Fy3IG83cPZzkMsBOsmTDx5wS+mpxubpYFMc9UxOP/mSeeDQoHCWu1fwrNbK0wu7bE1K2R1KskesiHXCxmk7i8nPfthFHjAlO2hPxB4wU6UNNNbTQb28BN57o7UCk2J0gAzheF6/zwoUrnHu51b7dU3sjTrAWR0XvzoQtQw895iH+8BjJfmFPbm1WSaf97Or/vrr7XULnAoLZAD9m3/xvzoWAP213/h/yBYdQD8VW6IP8gxaoAHoBtEykG0wzv9mtrOq2avxRv73PB11b5AAPioxOSWONsbkKWK3Hve22Pu5J5a1R1+owDm25mdmhAuEDUCOcxG1z31m97mbn14n7ncZLAGzwWDlc9SgOA/ml2WphwC6y0LRlxmtJGLa58i+NW0YwPYcSDbmXM9Pg2837DUS6Ll1uAAAIABJREFUG5HPPsLKZv/ab+NcCnhoH2chsTQB4PlsaMDc4OdBGAqA3b6Q/JU0BicxWM7dQKoBVyV9G+SOfrP8e362/FyolnZinXu9rCiQ50Jb1I7PAHpmwythONXexr66J3ygZeffpogW8ybJmf2Q173tp3rN+MnvOO7tf6ztC0hv9k8qap6nE4KV3BBJRUocsXJZbHJ/7+WfHrn8rM2qHHcYJd+cQEFb6iOxzH2ffaoMrLc2Q2nA9+OzsLYuKRcJxtojOcE8/Cb5frzyHslJBJksYNLBcCW8h6wmh2IefvST336sa9Yb7xY4qRZoAPqffU758u990kqH+WsA6H/nPR1AX6lVe2PdAt0CyyzQAfS+L7oFzokFGvtc/kyK4gwB8hvskSM+S+Tdh6z1GxzPJYh7emv85W8+Jytw/9OcfuDZc3k7XT7IajYwbqBb4F8wm+3Q5VsIHlhiErAPdiwvSbcj153ly2J8OJxZco12cW5xEpH+M8ud9/Pvrd9wXDNQZweZQBB+9gJIPoj8pD8F+AnQNsMhMsaRk771cgXQM6tjuPdgMwisToA4wRYCaAaxzUpR0CySByz1mJfMASHGji1Zi8x+z33AcJEjDgs9Aaet5nkA6PnRoH3Xgs/BPTPSDahrW0QAx5KO3gNOrNCaJ2m7ljATCRiycaoFKAd/XNUIzKbwNWkbjr/2nfe/iU/wFZIptO3Y4zkoYxvl5CLmwhralmaX8L6erZDqdwIL+xKlheuHZUSyiFkU2NcBWcu0284ugeDnKa8h7Uk9olQJefas2on6kQLv44HJwXeY56ynkmHqgowITFkpIX9XOLAstlAKVg0ZVW3fzYH5yWNfe4JXuw+tW+D4LLAIoP/XxwSg/yNNoAPox7eOveVugfuywPTOF8RZaUkZofbvaD60Rms+2y0DNv3Z8Lx6VNJaBu0BYJ74S33RkgUEoH/+xXo+cnKsk3TzWZ7PkR3PUu6sT07oNfBucA7fx+uF6o7OdXFPvpY2BMbF62L4WqqXHu9nV9r9ug0nV3Jm43o+J3mTxGWAeL+OSjD254CYnGs5kzJfq5PZj+P8yVkSf0VJv8mQZo/jv8Dkt2/E+ypLFSz7fbPeg3kvXy8SiOWizcpI5+y4jy7Mps9gqAD0MC7+j/wmztvhmzhh3HWnPeYM7su/irr3mUGvpIaQ7bet7YvJf4vx+jzus7yl9vNa2b9yooTO7KEwxnWSFY9X9oPjrfFT331qn1d9/zne4KRjA8leJ8UBLNsf6wnrOtvFe8e2U8I3iUahtIC/wp71emj/kOxCO5GwokSY8HnMEOc6qwYYFLdanFnmXCMW+OGiklteFfc3lG6njWXfy46bMe68p5UI44cqNpGf2cmkjB/fpdtP7cPQB/6gLTAH0J97TAD6uzuA/qBXqTfQLdAtcH8W6AD6/Vmof94tcEYsIPalXsNU4Bs8v3hjiKwvAcKzbY4KCuma+b3jJ3TA/KgtJQBd5jrK1vE+ARLLLDfALdWdcwfOvhfrYlJBUpw5HNVr23PwzdfTJgEjMdMDdL7lcgVWxV4NZ9VjzMEcBZwUgamtCbwLeXN+v3QxpORjiu3eZRFGAENqm1vu2vLnsSfNiAcUNLCYpRi1zUPSTwGOxFog2IIst+UbDSCaxcBYxQ53kMjzSSxhA5MOKOQgHZcTbHIArs0zglnuz4EjP5Ka01pIyw3W3yAr43Kwz0B+lv02e5rxiR1NPbkEsN5QWmHAZCcohMx+Zm2074wavBg/9T2n9huxssmCreO1815ps4rAW2YJ8f0lRkLY0skhXGNWTgT+UEZQDfRhsomVHXKQz31q77luYgT26MNra3Cd612OgffMpsjfF5EgMCNZxS/iVAR/c+3z9ozGXqMN6g2urddr6ds1DBM4PwtJRG6fPO71p3Yv9IF3CzwYC2QA/Zte/98cC4D+upf9Qw2xA+gPZqX6vd0Cn50FGnjuM9oQMB+CmT7K3ldScj7a3QyAHteMv+ptn90kzvhd+7/45DK6Nfwa1zsH/AWM5eUa2JznBaRxrkmqQbleN9f7fOYzmc9RrC3y54DbXl+fxbkGnyorg9FWTkTO533vJ53Bwm+S0hP+FcB1+CuA8PhjQ3r5USA6/QM2GtAzgK5zI/5FTcbUNQajM0guQDT8F96fXq9guCTtkz39vnyc+A+7HcwWwXP6Mqvcalw+d0f/s8MA292OJdwNyDohoAH06QGSXxSqA06GtW0E+sZntrPmRjJrnOVtH1+L/+M1NBAq0HZQfsvAcWMxR9mvnMRqW5Lwcop9Jkw3/fDz5v68ykz4lfwhs7CblHtscvtHSsyH3R97wsn3TjInNoH9eMa0hrG29sdbJnAqj+H9LJ86ZYLYb2IIBum1nsO4Svqito8dCcv1uyM9i4Mp68/w//CJlODsfcE8MyvdShJlVMZPeNMZ/0bu0+sWONoCBtC/7oeeV778e5+8UlP92k9+oHz477yrA+grtWpvrFugW2CZBTqA3vdFt8A5ssBiDfTlwOWN5shIUoDv94WtHwn+lu483M9ea/LtOGJLs54DhDV46jp8lqbL7bNGbkOs60mVAnSA4dq1RQlxtRkgbs7oxku045nlzAwI5j5bAAYZ8o1Wh9m1khuWuxAAmkcdRzjVYke4VmAELoYAYQbQMwPd4zTjwLLrjHU3MskVDIpsd8buQJKDPQRwMoBuxgrvm9ltB19sBcYcEu4BXtYajCEJrwAZMtoEmALQFvsdRnA4/a65aFB/mbPu+vVmrHCNg2IOUpil7ix/g62qxx195X3RgjwJSBe7YFincU7HOBPBIG+5/Jx5TzqgkyVAzVxpQHoOroStnLRAnfCtzTIioGoGBBvf6gasmxIUUl6RGRNmXwyfqQG7/AbJSH8fxJhnCuxFsDQCgCp/YMBd+5REkYM5q8jAu+VKR2tlRDLFgux8KTMl4rCfS5l80avP0b+efardAnMLZAD9Za//b8vFR9+6UvNs/8695fUv+wdqswPoKzVtb6xb4KYtMP3I8+YsSe7KIPrwXLrs3Dbs6QYMJ72x5MzfS1vd91Ltv+Vry2hrowK812dltntYRpzXtuL8Y5lwn+8bmx9k0zLNAbZyjQFlzmecuXMiotjbJPbG+Y3r8QvMQuX85fJLfOZzG9LUR73sq/A5fS34DSlp88gGZiHJHj6IVbEEKsZNSsYkqRafbFSvt7y5cUe6MsPa/gvXOBkBH8UgdTDHG9PctdIzgI5tmf/mRmInBxjNM7Q7LbPwfwTQm0lu6XaPxf6KWc/4Wl4j+uVz/nb+uH0wJyq3ZzIlB9hvsp+jetpRq9vn32HCqQF8rnPShZNxtW9S6S9L3jv55bl33PT3zUm8cCEusRA2GgDoApQjcV7rEGpZTgDWT+IJltZnX6IGd72WdhuNKhDtPSz/NNrzs+Q9zf7AD7Fa2zKfzmvKJmm+XiKQWImA68LH0xj8HeEEfidKc90QhHei88Z6HT+xA5VLGNc4Am3gQ0UJhs5AP4k7vI/pobLAHEB/fnnCigH0j//kHR1Af6gWsvfTLXDOLdAB9HO+Afr0z58FKoh+s+C5ogApA95sxRvVxZslm6OTWec96/Zmdtq8/vkRVzv72xn0/O2AwfAWZ87ntb4tBfm5d3s7iwPUQNCFYK5aBtAy6rn9DOjlAIUDgDi2DjzlOuBxrSXc5aiKuRB15wygO1ji9obSfGLKb1W2tEHnHBjz+AjiOBPcDHWCH6rRFsDnfgSe5GwHm0JBmYgsAaBnqUZdExKAdvQJMpnFQEDNAKVl3AnGGDG1pKJZ65JuD+ZDZtEnBoMeQcZte9nhd710B95gPTtqRvAC9gpr7FroGTwfBpYs6W4GhueQBSuIF55yGXemraB4fi39PnQSRCSzSPUhJNF5a2trrpJg5oGZDxsbqhPeWOiNQZQimgqcRtRP0unBdsjrYpaG1jvKNQyf8wb8I9seSSt5PgcHZeRgomTja5BXTHNA/twukvFSq0CWcKb5zdbXixJblAByWAF09t14vUw+/5U387XWr+kWOHMWyAD6N77uvzsWAP0Xv+nvy24dQD9z26dP6JRZQOVfhmfdDKYvc6mWMdGHCYw+43bw/LPaEftve4rArxFnfZIXdw8qoJ7Z6AY0LfXtngJ0159mqFp9Sf5AKQUWuBJg40XpKycu85bPS/gwZtHye5Zz57rcbttH8Yv3CepFTqaUn3EEgC4/INS9aAIlLiVrxvky+0tuWxLswb7mWlj4GleVXNd0deYPX4NxMAfOpVbnkp1QySL5d1ZmkQg6ClvDJhdW6QQGJxpklr3Z8YezOYDOOthvcjI4Y8klluSPVln5xorX3FMCiiXovc6yU9iY98yu55xM/XLGpURmy4mXqpQGiGtlM/tcDciPfWH2u4HZg+tltsO6Y4MYU9tTpUy++SOf1f4+KTcpNpH9CvsvVj1rAHZI/csEYQuz0uU3hwy7fXT7QLQtvwMVBfzlTNxID4yTFuyH61nMmSLRb93R8VxnSfVoSyT3eMbW1uQP5WCIgHA/Zx6rk4nzouALYQPKZq2tVfCd8TMXfid5xn7Z4WHpJQtPyo7u43g4LGAA/Sk/9PzyhO/52pUOAQD9Iz/2zs5AX6lVe2PdAt0CyyzQAfS+L7oFzqEFpr/10kVcPPscN9hjSJkIxyTJsrdblrArumTVzW8wOalLpcaijQagU8cPOe+Qo17WxVCGm78BnXHmDPgRDLL0mAFg2uS6Bg4j3e7Ma2rWhRRiBtHdnoDgWQ1CNMm1GGN2vrlmF6lwWLEEckIiG3AOEDgHLbQ3c5AknF7AYQPuDhg5yOG5E5gxWO5gjLLBsV841Ti7rsXn4FHIEcqskwAUzd5VMCnqnlmOUBLxBJ74LEB7JQ4E+Kqs+wAvDZJ7SmbBmI3eYgUB5vN3rhOY2S846U6gcCDCbGfeVwY8jI2Qncz7xEkKepxz3foYWAbwabtJJJ4BSUID6NobYRTJZ7KOrueXgzI5mBORItXGDIUEAGmXR1AiQmXazAH0+M500MlKBpFUoeCNVQRsfo/FALeZHd7jYugEAE/g2OyitsaR+XBwWEZKFolnV895JGNEsLL2n5gjVo+QckHMnYQAAHXmGTU5J4/+uZv/cutXdgucIQssAuj//TEB6D8hi3UA/QxtnD6VU2uB6SdevFgPmJksU386aobLEhiXAOfc3pnnN79N9l/zNaVcquzq0WacpXy7a3mbrYy9SYrlldWchudsf86a3XopWov6xvhNkoQPNwdwVufBAAztp7XExQRsZz9IsuVWqooyW8PzuHqOjvBVBM6HQhd+jH0ZQG0nEbfE4xi2GOXhm7imtKXo41qB3/TEtdhH/kOcGyeRQGCQFH/n8HqZHcQ9tC03LVjvHE2H69DktqPN/cN6P9fKT7PflGTUrYQm0DUB7mFDA//VZAH+S21tYw7yOzlAALrrrOObBdBr+xoIxZ6ce7P8vveJk57NeDdLPmyhxAH8RWyxe1jK3mH7e/LCD978hj6hV04/+OyIOdhfjOdB+z2VvrIv473s+SjxNtTftBZj7cuaUF/30EjlBWIve22kyhAJzS1RKf522wa7W/3x+MD+k+MVOekJ4Nz7csHmszKymtuwPZ5X9oN9uHCPBJ4T08A34vm0+pufRca9vVPGT3r7CV3dPqxugeO3wBxAv7084XuestIOP/6T7y8f+bF3dAB9pVbtjXULdAsss0AH0Pu+6BY4hxYQgD58LcPJl4HkC9rDqZEl94+/7I3n0LoPbsqtDnp2DB2YaZntwUaV8zcE+o7o38G7yxEMMpv2ytXFG+gDANw1/cjUF9sBoBnAMNUay84oYwHQFUCf6rHL4Q3nml93kWgjCBTt2cmOGtJNvj2D4nmElvoLEK/JpBmY9n0tWBJscY+b+8wC188AFj0tA9oE3njl4I6z4hVsCam2fD9BGrPeHRjLTBHPFRlCO/QEq7KMfJir1VEX8OlM/lT/TWwKs90jANHk5KoUP2DnaHu3FAJ+YgPMY3EtKWFZANcyioyF8TupQ4GyGrgYP+WdD26jP8x3Tz/6/BrA8UuBs/35uogB43p/iYZvG0TwcgY7KQfRU4BUARX2eWMw1CBhBbuDXWTbZgab2Rut9mJIEDJWalI66KmgYQQeh3UyPa+DaWWQ6/kPwH/IJOEz2OdOyGFOnjLtKAALI2napAgnX9DZ5w/zFu7dP4wWWATQ/0S5sGIJ953fubf84jf9Xc2wA+gP40L3rrsFUK2584X1HJRfTl48ykI+b+nf7bgon7eWnL06cP7At9v+W59SAVsDxTRhVSif6wHQDYQC1g7Bc511Q8o9f8bZ+8KkstB99sN/ubY7L03O2nKO5/wEeIYv05SvAJtj8XPSsRny+ExSkkos95y8yrgsC40PZuDb5zIlgcJ6tYT7oiPeZNbtm3DmdSkuJxfoaDirzHFfxxxok5cAdZJog5EecuYCwCPxGgBbgHYkL7f18FnTPoVLV2UG+wKAHipPTk72dgBAB5Be9kwJz0312GGzR1mtlhhggF1M/EhudtJxyHe3dWOMnN3td3stlRBR1cnMtvexu9WJp5/pYQXQOauzL6PP8dN/9YFv7hN2RytpoaR3fGuUAaLmN3YlRsB7Xnft+QDAMZZq0FclhAycZ5EuEnW15xtoHqpvgO3eR/lZyjYicdn+DnuSpBqNKflQuNHZxx3ErpRwbAb6Mv84g/EBwKPcJaWvBT8ubdbrh2X8hDefsNXsw+kWeGgtYAD9a3/oBSsH0D/xk+/rAPpDu5y9t26Bc2uBDqCf26XvEz+vFgA8b3WecmBnwSB2+J0YPNQnTB7HUuC9NtYB9Ae2yxZqjeVbBaIGmGcnVQEUS/ylRQDwJgCgum6Bghk8l/R51EE36HdtO9jRientetvqA2CcrOrpnK2ufWOqbIB4qrE+mQeZhrJq3E9Qwsz2ZY6ps7sNGjcwfJAnoPpnBCaoM8a4AKQHNd8JWghERx7bUoQwHYK9T9sEo6JuH1MS+8JB0en1KhXI367x5zYNoDvQdi1qHFqKEOe8sdwj6KL5EuiKsdCha+kNpdvFZLDcXUQAxQaJTWH5OTGa05hday0CcrMLW2V075W5zbUvYhx+9ocJES1IbPZ89G8p/giMnHYAnelPf+Mbol4dz0QEKmWjQcBFZk9JIASENsdlpmdtWgM0EfRsTHQHkAhMTvfVgoBzy//nIOl9fIe2r4Flz8uyr5fWVnxnsIfNhFp6fUqIaZ/XZ1qS8OzjtbUu1/7Avsr71WfcAhlAf+lr/+SxAOhv+OYflxU7gH7GN1Of3om3QAPQm9JPGrKTzQy4tbP5gipwveG+APTZrIy/6m0n3hYnaYD7b/payY6PLo4r+JvBbwPElvR2Xq8Z6EwkXz/83fgXfo2Vu+xT4Tdl+XCzyfEpOCtzvZSsomZ3XnefDbfGVe1ruKeyu31tZ66MNFSp0rE0gGP7EMEKlxR7yLMvsLMp0YU/FLXEJcPu4y3+iBOAGQPX2NfSgbmCmrPtANZbQncasNsDuHQSMx/jr/GyktXuYZkdXp+zz/ncdmjJ0wFI0u8+oHUCZv0sxTlavpvBcQP5gO5KpIjje/bv7DtZ1n5cfdfZ5qTWsVYybfgBLmNGX+GbCszHJXC/9sNmszK7sq91EXgepQX4e/y095ykR+ezHsv0w88LRYNI3jBobia6W242iVAESSg8F3ruwrZR5kyAdkpQEhNd+zL8kKMShBdmcTOO1OA7ON8ficsC4Fvy8hIz3Zcv5v0U8ZoOmn/W26zfeAYt0AD0H3xB+bLv+bqVzvAT//R95aM/9vbOQF+pVXtj3QLdAsss0AH0vi+6Bc6RBfY/9Y2Nvbjc7xgA42YPL7VRAtmPsGEH0G9+cy0Fz53pbNDZDnzOnnZ0wIA49bAJ2hAAkAOagkQsGfc6IESgYgc5QmTaIwhCnzi5klYn8AFDIti5w+t8D+0BDgK8t1cKqtDmPffOM/rlQC9xdnNygAFxB7EyeNwA9HDGt5GDD6Y2bSuYFSA7Di1zEegccvJuk8AIUoKWLwwWxUJNv5iPGAaWQcQeYiIEGL6f2OoKqAQr2etl0JT+YOAT5DLTI7FAFPRycM/saLNFMoDOmBScCBZ6A86DJcwawyh+1CPL6K57Ghg6Yg0cwDIbuUnbz+XA2xLmeuhi1QT7/IzI0E0/dnvUrwt2EUkeXrO2V0LyXIG6+M/yfVZkGDLMJU2YAkJ5797fV8JRMaBhAPa+gji0oQAV6gFVraLWMY/XQh/xR0usiDkK8A+2yHijA+j3t27983NlgUUA/U8dE4D+Y7JpB9DP1dbqkz1hFlD98wXVlsHZ1v8WO9GVZNKs4NL+3c3+1eAf+mijA+gPbPEB0EeXAzzn1pSMCnjawGM+8xne/ocBO6v4OMGU91033aC66mZPqkPFWWoHYHsOPmvU9l9cF53rdkOByNPyPmIsTjrOeytPH+WoHZjQAfotOxsaCE4sd/wZ+TC5vrnZ+bDpeW2jBBZ1zPmbY58A6GAJy0YBWMt/Qa0r/t47qPLrwzOoz872rwCRBchbFn7OYjebfBSS5wLY7WMoESV8HH7u7Vf2eS5flew5IsGZPjYjQVWM6FFNADDTnURS1qX5hnHmZw2cnM74L16ofQPg4tdevbbIRmZs+IzsLebWarbHeF0LfWOtjC5EubRQLhg/9d0PbHOf0Ksl5a79HupurbxXJNNbtUzJHXGNk8pbbXF84FAbkF+9URN2h0D58Ht02ffqkXa6H0B9iQ+lZOgs1ea50cdRbPTcvxLu5yXies3zE7qJ+7AeFgvMAfQXHQOA/t7y0R/7lQ6gPywr2zvtFjhfFugA+vla7z7bc24BAHT5ykNHILMW6xVHWOoIh2TJ2x08v/nNdiTz3E0Q2PFrATyPN3FAYTLYWUUCjXp5Bs+5THXJBgA678NwMDBuILBJ1gUAl5k1GWinPwIOAmyTbLujh2J5AxoTbAoAzzJ42SE1G9eBKwdQ2pwJaMX9BrHp244614VkX2NdmE0hyXZFoOYs4swMgWlu6bW8j/0ISIHOtfMCXBQrN5jsBwdltnNQA3W3bEXgNOTizKiwhFyA52K2BzNhBiM91/DDjoz9QtShJxEig+eZ+d9YFK4RFyC3as1tlNkjblNg4tpoVC7t7pURcv0E/lgTr6mDUmICBIPHDA4FSZL8ntnnX/eum9/cJ/xKsSn8cpKFnxc/T05mODxUkEdgtOpQDiQlo4zBDPA9mNs3fpXeoNW+3EL3E/s50qx+dg4P6jhDUrPJFzi4KUA9jaUlDoxqnfN4TR772hO+gn143QIPvQUygP6S13zfsQDob/zDP6qJdQD9oV/f3mO3ABaY3vmCed3fZSbJvtQydno+5w5/z3/Hv/fjJ3YG+s3uvCbd7mTSLLEPwznO9TqbbwUDmsadvMsZ+fd3yuhR4cOQQMlrCIyzNpvjCqj6zMRZOidGNqUrL2RIoA/BcY5ctHUBoDaSM/OEeY+zpRS/Anx3P95rHN0kPpbqTsN8Toz4EYm4ybdpEuP4EvKhqsz5zL5F+H7ySzIwnA/HShhmfMEGv68zqtjZ0ZYTfZ2AGj4T9huxLiReA9Bb4tvrE0zgGQpfQ5+QccXxtfWDXe3DOFHZtdnDz9G12M7JFC61FbL7s4sX63gicUJJx3SED2v5fKuTOTnACQ6A9XuHIdseoH7syfEz3nuz2/rEXzd9/zMXQ0SOSTjpmHV2iarsdyux9zDKwIV9mK2VuUhgb0oPn4UZ7tdnOip+Fe/jl5tNv+zSHAcZDg+fKYH748e/4bOYQL+lW+BsW8AA+pN/8EXly777qSud7K//018tH/3xDqCv1Ki9sW6BboGlFugAet8Y3QLnxAL7n3pZzLR6Bgsg+gN1PHLgQEGgRSN28PyBbSoB6Dn4I4c8MUZzhrfZAO4CxrmYEfESuDetGfReGzt2xssMfPM5QQGCAw4CyKEN1vaQ9eo+DLQjBw/Qe9RL4C/BIJgAATZqTGnDGNAzwJ8DJQowBWidZLLLJBjy3JulE4N9IgaGg2YCpENOUYBzZL0zLtrfntZAW7wAy/Pfrp3W6gOK6YCzHEGCvb05gE4wyAEZvGnWhXUwizzV8RPzAqLFQbBkPADYFLAxSIig1iKJEAS5HCxTzfUYryQJIzlCIH+trT277dYapLhwoRxe2FK8aO13f7+Ua9eCqV/r8+llpo3mkwD0DKxzndZhrYzPEHhuk08/env6box1ZQ0ICJl5k6QpWz093mss/WBkEJQluSJYJ7VOvb5x7+NL4X6/gOfP8n19tYgBV5nnlUmRWETtuYvahYxbDJBZrQe4tlZGSrqpMpZOiJl84S/cV4/9s26Bc2mBDKD/IQHot63UDju/c095UwfQV2rT3li3wAO1wPTXXjhnny9jCi8kNA6Y6cOzbu58eAZG7KmD5ze9PPuvfVIZ3TIuxQxm+y8BfM6sCsWx1TW2s+oV57S7d8ro1s25rLSSIuPcZN/CCb8AriQL6wWgultrhNu34nOdv+ZgXL00KXvxO77LpYvRzOBM6D3BOJQEnUpTLau0YxA9gefye3hhF2TPh2WonFCJTWBLo4Clc2JN+pWfgx8jUDTm6uRKHBZ8mO1pvT6mii/TpNIXEkqqTyE2PIC+67wbQKe8UaoRrg7lnwW7HcCe/qgnPixDZH+IIdIH84FdT1kvrU34OOHTtPFxrRIInAxd1boKfjR1ufFpLTN+cFiTjlkL7wtstn99sY2wn1jyB7MyusiZP/ypqHV/lgB0P6TTDwQTXYnHwTZ3koSSsF3+i2T3kOM3iO7nKFTNpHRn1St+8V65r2+EZYxwNXJ/XyODC1DoctK4ZPuX+E1OjnKfrdxASNEz3kiI6eD5/dm/f35eLTAH0F9cHv/dT1upGQDQP/bjv9QZ6Cu1am+sW6BbYJkFOoCLxZrWAAAgAElEQVTe90W3wDmxwBxAn3sYDURf8CeMaNowA2fjCOekg+af/UaSI9rY4jk7IQCxxA6tLPPI1sf5hMkgmfKo3Qx4jiOnWlwbc3C8LWeA0pcvzzG9q9TzQ+Y8arwpKFGlnxdezjS3VDtBh4ix3DB7wHPqBPICqAOo59Vq7gX460CMAPQaoGlgsZzrkFMk0GPAODvJBpABKgk4EXCxJLtZBgRvnB2PoRXoIthBMGQO7EsKUGMstd6e5lavF6OD/wyM4mVjj2lioCPZpzFH3XjVP6SfKpGvwJODThujKoMY9lMAKEu745Bbup9rCDCwtqrph53C4rYJc7V0/OVLAt5nly6WK7dcLrfcdU8ZAZ4LzI/glMDf2AsOYuQMe0B72S8CWhGwGj/1bNTxW/a0Tj/x4vmzpD0WzxHJH3oWYm8OHlG1lQJBM54PB4COCvLc59dF+pLNz0O+xwHjrE7B8PzMJvn92fp6Gen5js1m5QbHeqNMw2hnt8x4VjcnpQPn97lA/cNzboEMoH/Dq7//WAD0N3/L35aVOwP9nG+2Pv2HxQJKrDNQ4gTXfEb2qIZM4gVw3EDj8qzj8RN/6WGZ22nvtEm3cx7miGrZcs7ZwapWMixnd87lC+D5epndu1dGjwAQJ2k0fADORYCnML+dTJp9jYubCyBwk1e3MTmLO9nV50ezva3Qhb921AufA4Uo/BgSOM149nnfgDn3G6iPuetkZ5n28FtuULdSbmUFMgW0c73B9+SHyQ+SPxFAsMYb/sLh9bmceiQ447vI57JPYd8EDP4S8vqpPBTPE0pO4QuJgW6VK/wmzqhW7GI9kEsnGSLXm2c46yOx1w2MKyGZ5IQGvkeZLM7hu/u1Dfs+AvVDzh2fLpSjNM3JuMy2UAe4XkbIt+PHKik9wHz2m+XuWVuvOX7nNdYtpNvz+uO+PfN9p/2RO3L8048+v342TLh3gnFOXFecI9WyZ83ZZ0i44zfdkKQ0TDw+IgB1Q0LSMI6Vhn/9sIwM6OtZCt/OPhVtCRDPCl5pfvE5vtKIZBd9b9QEgA6en9lt3ie2AgsYQH/SD3xDefx3P30FLc6b+I1/9p7ysR9/WwfQV2rV3li3QLfAMgt0AL3vi26Bc2CB/X8b7PMbgPLERE+fLXMCpp98SThJNxqsg+cPbhO1mmK5GdbDACoOvh1UO5iq1RaLRlCCLHk5rAFUSw5vo4Kuwxf3cT+f458CdKtOeALQuSY7s4zBdQCXSVVmP5e66rBZXVvO/TtIY+A2svMFCpvJEUGZmtEejbpOuMFi2cZzT1KGBDQAxcVED8V22nFAyKxxZ8czDuoAOuB2EWZ7jf4o4GL7BhtFIDqBrcZQCMYKDAleBIP4DBaEgP9gQeCg701r4KmtWbC9A5iGiSH5PwJXWxs1MQI7eg3MaLi2G+tMU1HrUEE65BKjbxz/9Y0yu+2W6tjv7ZfR3XfXNbGsd47nmuXvPeY6kkMAnc8hup9REF1yrdr3lrN3DXEjzWlPaMHTpt9YLzPUGLIaxFFfC0eB6lZFUM1yRXPKTOoJViBYwmL3tfn5WWABEbysz4Ok2fX9MJoD6gSceZ7H4zLa3atBrDIqk8e8+sF9qfW7uwXOsAUWAfQfOCYA/W/Jgh1AP8MbqU/txFpgDqAvkdoejHr8lW9dOo92ptDZavHIMP7qXz6xcz/pA9t/21MqezkzkeUXXFdiqqXIVV7JDHQmJTA3zlECyaO2tydsH8g+Cu8bGN6C4RyAK34VMuHZp04SzjpHOqlV53LqOydGus/a2dDbOxW8X5agyZlcUlJO8p0n0TY/wLag32CWq1Z3JBI0lbHmU9S65/J/EqNcjG4pWgULnfFnO9lHwl/BHwK8th+ltmJS3O7PnXTt5GUk9u2/bAGyR3IyZ1F8UQZLcvJ2+FguTe1jNwD65bHUvxj7SD7TJJKLg0WMqTiP7+6VsnNQk6KdkMr9qkMfzHjb9cKFeuaejMvonitz6XbuQ6L9MGqfM46U4C0b4gdurheB+TnpHWD1WWcTQJ9+6Dnh01qy3goC8ZiZzZ33uZPl8bNIWMDXNYu9XXdfil3+Mq0xEiUN537yd4KyHeadz1BZ0B70Jo1YQfP103e9gXQ/qwbU9T0SyR7hp/H3+Eu7bPtJ/3ejj+/htcAcQH9J+dKVA+jvLnf++Fs7gP7wLnHvvVvgXFigA+jnYpn7JM+7BYYA+uRxr7/BJNPfeqmckJvNoJ3+5ktKB85Xs7MaAz35hU0mOnch5mjUG8+MCrMD7o/takeSe5ERNDBP4CbXQXegxwxcB4B8fQ42aczBiJdUe8gP+pqFcUYQUSyBFEx0UMOOKg4wARGy04N1LalzAeiVkU7wRa8UbFKgjLfE0o6McRxmA+ghcV7Z9o7ElFJ29nWP7odlwhxgPrgdX0pgiaAdEteqeXi9Xmv2vh13xm0pdyURBMvlyl4N4jA2M1yy/KBrHiroNK7gtyXxCNwpoENiQszf9dmdWLBJkC5sxFpdulDEPgY4F7MlkinymngcIeetxATGSGCJV2agEwh62hlmoMOkMDPhKDUGJZ1YxSASP0ZrYvtrzR7Iy4Ec1QWcldGBa3Am6UOvvze7184BPz9Hy95PyRoCydm3BCcJKsLCiAQNAetKCpkn0Ey+4JUPZCb92m6Bc2WBDKC/+NU/eCwA+lu+5W/Kph1AP1dbq0/2BFhArEonXHLu+aoba5NPP/4iAYs3yyKffvi5mtn4ye84ATM8/UOYvvNpc0DZCb9WpDIAbonyzM72ubap9RzBatV1LmsUdbNJkmRfcC/npyEz2gnAqscd5/j7MjXXy8fB5wjJ9uH1wUBvcuzMJXwAmOSNVW5AWG0meXYD+7aJ/a+YXytbZYa2k3rFzo7SUE469nl0elhmV6eVCX45zr0kL4eMefPLNgKovpiY6JhbybwEHFBUgy0ealdW3pKfB+B9XWoB1S8JGfCQYBe7nTZIdsZ3g4HO/WYP4wMh5x4qYbp2Sl81kUJJAoDd9n24z1LuF7bK6O57oh59JD77jJ0SEJQkQTJA1JaXr+n25DdWp/vMAuio57ncnMuj5ThD81/xL0LWXc8Idt+oftMCeH5/wHnEG9gb+M4kR2g/z0sKyA9zTMKxkbQW7fHyfU5O54PsGyv5Pvxg+kPF7eLF2pfJB+xlVArGG2X8hDed/i/VPoNugWO0gAH0r/mBl5Qv/a5nrLSn3/xn7yp3/t0OoK/UqL2xboFugaUW6AB63xjdAufAAvu//U3NwVgGnp8DE5z4KR7JQndwwTXAhyA5zroCHwHMDmfq4In9UgPL1HozWxb2Ok5gBg3tcOJAUo97GetcDmcwOILZWpnwAQA64MF1OUaVAXRLtOuauIhxiO0d0oswPZA3DyXABYl2Aknh9LY65X4vglliGhBgMYBuFrYY6zU4Mrs2rbJ+lyY14EIgyMwT2wJsmnFdol5esPcJfjW57FQ30LKFrFuoANDHDSwQggAw13H6Na6oTQ2oSR9bkzIbrZURKgHYdnfaJOotwd4SC8zYd8IDbZLhTxIB9xNsyHb2vPIesxoANuNl2f5YmzMNoH/4eYONGg+NAjwhabmg8oB9qgTh7NbLi0/efcRk6zPjoBxs83heCKLmNTHzB4A+9tPI7JzWWwo4ORCkvlNAiSCr5RGdBMO1+/tlRD1KvgfW1srkC1914r8n+wC7BU6CBTKA/qJf+LPHAqC/9eV/Q1PtAPpJWPE+hvNkgSlngVSHt9cnP3mrP33HUyvAnQFN+SSpPJHPscOkUdZWZa/u66AWc/YlnK9bHfRSConHQwCd4xhnNfyro+o4S/mqlnVqpbf2EnjuZIDBWV0y9QL1SHSMROHw7+T7cGQnidYAupnl9utCSav6ALmcVgC9GUAHGDeA7vrOGlfcmwF0lLvoH9Aev8ly7vIfKpNdYLfBSN7XOTYx6lX/PGqgK8k4ZNOp4y7lrpDaDta3EpkNypNUzLhIWuB8C6OZ+/Fd7r1ay1nBVKcNSf6HDfG92B9OulDScgXgZxcvlBH3ktRqVQD7rU5e1Vm91BrtYqZHwsEAAx4/+/0n7+FZ4Yim7w0gTOXlrFwXmb2SyudZS0p4TjDHziSk2Oepv8xHlu2o9T9IbHOS4FErCAl132XA3slPii+EX+1+cgxF/m3q16A6b1kVgb5JxHAScoD3jml0IskKN1Nv6kxbYA6gv/SYAPS3dAb6md5BfXLdAifDAh1APxnr0EfRLXCsFgBAnzz2dcfaR2/8wVlgesezljewUIs85KWd8Y2D5zrkBHMc4KClLAOY/7bzSFBkIRi0RMZdAOxkXsNu6OACDNuBdd06sWoj0JKZ5RpDAvX424EIy6mBI1qm0GxqgEYCMzDQ5RSXVjvcjHOxMTKIriT1yEonSEINRDnEOPNRDx0DecyA0vuHFUC/vFkDLg4EZWcbIBrGwiVYKOFgCwiNueQEAGmdb9S64wCjgPQwNmwHB7VgQgCgX9is9iGwxDgUfBuV2aULctxH99xb29meVva9A2CWW3R9RLEqQpaQtRtTW25S2cbsEV6M2aBtBsij5mCtfR6BSQckYFo9/Vcf3CY/BXdPP3b7vPQAe8V7k7UkqYFACi+tH6wkEj3GdZ2Oeg2TRyLpYuRAIYEgJ0+Y8a/nI95X0HBdUuuqZe7nX+Nwfck0pgV5h7nag4ByB6gYt0ssdPD8FOzMPsSTZIEMoL/wF374WAD0t738r2vKHUA/SSvfx3IeLACAPn7y28/DVE/tHJFxV2Kt5bh1ZorpWA3KP/MssxKVk2mXWWGIrXPuvlzP4y3Z+Or2XBaczy3znsFzA/w++6uudiTo2l8ykC+md5oH90R+ZZM8xz8IqXoPW8xn+kEOnXMdClqZte25WPEKnylLmrsh7iFRwDYl8ZhzsNXPNJ4Kbgs45kQ5BNBZk+wLwUK/BT8yZK+TOtiC2bNP5T7ww1y/3EnU9pkA5XUm36tzx+aszdZmmY03is7X995byl4kRNMvbfCSvarfJr8Sv04lygD6KzN6hO+m8mYG3wcluJSAPQf4m3R7TlaHff6csw2gY87p+59Z11zy7FYuu17LmVn6XMm77C/k+lEMmGid6iZSlvJ8OwySELQWelYSGM5zEL519YMG8uv0IzUA1j2Sk92X/W/FICIrxGpe2uOxT6z4p2c3ydKXWRk//o2n9ruzD7xb4OGwgAH0r/7+byxf+l1fv9Ih/OZPvbP82t99cwfQV2rV3li3QLfAMgt0AL3vi26BboFugRNkAclC4nR/5VvL9BMvrqBnA3ETKi6gdFL/w/9T3fH9OhM7ejiVrvmXa6Hb0SX4YACegEXI3cnBlZwdbS/WEGumkqQZwSPXEotPBFwnNnl2Sj18z4dgD7KFYozE/QRvAM8ZG1nr9INEXkhrN8lCfFmCIIDFBIsIjFh+uwHz8whYrS0e9Quz4x0sDcm1E0y5ZavOiaDL/uE84MLw8NE3YFNEUgGZ9HLIA8ikXQdb/B4/cfKnwYLINQK5l+ASbApAedqzQy9pwwrSKxh075Xa9vZ+q9lelQdC/pBlskwl9jMTY3297D3qkWVy9z1ldPe98yCDAxEOPLF/vG5iaNS5nWXG+VGPveRbHYyzIgD2JPBp5jh/69mD3U2iQuyF+/ou2ZuWEcwjJ0qY7WC2hPsSwyFqCWp/1eCoajPyp8fgZ9MJHHy4IDM5KJUQgVwzzpnj5NE/e4K+/fpQugVOhwUWAfT/sVz4vNtWOvCdT99T3vby/1VtdgB9pabtjXULdAucIQvsv+HJOrtOvvHDmtUNzHSf0fmp5NQ4W5sB7iRJ+w2WE4d57JddCc55Yjtv1k84k++E34UvwvtOalxmY3ysZe0aOB/cY9l2l6ySH8OLhFwD1U7shYGtdsJncrmouF734eNEeSb5PJa412dzsF6/0y4+E31a6cyg+/VZTTLmMgHZG1W5C1CdNs3MN0h9SzDQrczl2tROLpAfGQz0OsEq806iwD1Raz6zx6k1fjnVpJcfFEx4rTPn9b2mMjC7gjJarWOun5Ek0EB0fLAMoIsZjXLXtQrQc8bfR0o+6qizV/APIcuHOpyTGM4DYD7c2tM7nhmy7JFsoeRjksGt9HBY/Vsnr5B0rBJl0dJRADo1zg2SO/G7sdwth+Bk9iip5We1JcxHyYWWNJ7R+Wgj+01uv/UT3xu0+/t3lfEzz2Y9+zP0T0Kfygm1QAbQH/ddz1zpKH/zp95RPt4B9JXatDfWLdAtsNwCHUDvO6NboFugW+CEW2D6oefMgzIwF3Dmce5cc5mADVnekhQkEBGo9UJwIk3SQB33wUL3dTiotAFwbnl335Z9TljSAPIAin7ZAR3WEMt/M2aCMQbZL0xKsTy7YkKzWoecawhgKIByuACgqzuzzRWwguV9UOuVu12B165dGN40rAXJuAd7BAef4BfgfLBAFJyCgU5w5NreHEDPbH7LEZpNQYa9X5K5J0seebnKGq4Z+dTeOyjlynaZwaYwC4Jx0h7BG+r3cT814ey4G0xnzWVzgnW7c3l5ydpRR7DK0AlAl3xksChuu1XreGVzUm45OCyj//g7tW0lJ9AmAcKoz24JSdsu5ODHz3jvCX86Vj+86SdfWsaPf8NCw7wn25GkwvqKQTEuM+QiszTlUcPB/gbgtYeJvAVjIqscsDZOdgEw93XIxAfzSZLvoe4wIyDqxBE/G2ujKtvvoCTvx/M7I6Al1nwF4yeP/rnVG7C32C1wxi2QAfQX/MKfKxc+7xErnfHOp+8uv/Tyv6Y2O4C+UtP2xroFugXOuAWm73paPYNzjuV81BJShQwvlrgZslM5d0klKA7+Q8a0WOZRwgk7ArByNjM4KNsOKLQGhHUGHFLb4xxv9nxinKulUN5aSGY+CAZ5vaAqYwnorsClJMstZW+2LdfiB+FvYAGXqZL/k4bcAMhIGMa/kBT7xqI/FuD76LZIKAbs3qaWu32xkPOGzX6Jc2dK6DaQnhSuqtmiFrrY3SQeH9Qa6PTFeLlvEnNQQnRlMus/zrtZwpuzupjs01J2rCoW/iPLE/XKJX9PW0oon9S63JHcKhb71au1jBnjSWf4BRszjvDBxs+944w/XTdOb0otdNzZr3tX+1CJyLzwl7Ajfkdjno8H5Rcakl7viUd05CQIJxvbp1nAwEOGPaks1CQJ76V4sOSXxx7LILnbztPyPtJ1ZtSXUu6+51wosZ27Ddwn/JBYwAD6E7//m8rjXrFaAP2TP/X28vG/96bOQH9IVrJ30i1wvi3QAfTzvf599t0C3QKn1ALT33xJzbDHaTSAJwnu/RoI4EWQ5+q1uUMqZzYFGWBLZImyoQO5zDb0sb09l7b2NQ7W5JqDQya62dIEoAhG4KQC3CInGCCigjswKQw+A6LvwLoOtrWDQWZH0BYMENeiS9LtGloeg+vniY1OrcBSGfAEo5CIJwgl8Jks+pnA/dm1VJvQgHcA1grKWdqbvmxLScaFsy45xf3KaCeoRaDKgTlLJQpAD9b/rZfL7JbLWrcRLJP19aK619gcW9heZkLQHoEgSzMyNpIPIlCh4BEsdhIlptSQOyzlyrW5jLvrFTqgYACdeFnPtJ8HgwDQCZ5p38azRbBNZRAWg6WTz//5sv8fvm1eR2F3b86iaOB3PLdK5EiS+mZuCOCmfl/UigyW+iw/6zBkLOeu4NE8MCt2Rw4M+XMFhiKQ2aXbT+m3fx/2w22BBQD9VX++bK0YQN8FQP/Wv6ppdgD94V7t3n+3QLfAabfA9IMV5HPyYZObdsKrzlgwixN47uut8mNAXiV7jpacvsFWgOzTkG3PHzp5MicW+z3lWEbdc3wTM+SjpFVWG5KMumXjuT/KUrVrQhZedboBi1HCItEy6nfLL/ErJ3TmeuGUwhKzm9JdwUAHHKdvKTBRBz1Y6ALwAexxJEa1Pjg4JqCm/KbwUW1C2TcSkGknSl9pfBpjyHfrfv6L0lych0liJekb3ybWhHPyaGevqqrh49lmTrRmrmqLseEzTWrdeoB4fCYntyoRG0b99VJ2dqrdmLv9XfzSsctdVQNObv/AaX9UVjb+6YefWxO/ebHmrPHFrZYMXLMOSpl88Wt1yf6//5Z67cG0+kxmjbMmVuqyOpc/s4qaE5uVdBxl1YKFvpBQnJW/8J9ygkwuz8DebEnIJDsTFzjU/h9/zS+vzEa9oW6B82KBRQD9iLKVn6UxKoD+xg6gf5b267d1C3QL3LwFOoB+87bqV3YLdAt0C5wYC0zvfEHIhUeWP47/sA76sLYXjqJr2QGeI0mHU0jgwCz0PMPhe4CHBJeyHHyWbM+S0nJSB+YySAtY7MCEZfAygA54qGz1cFgJhOxfb1J5CgoBGCMdyLxJJCBAorZSp3n8Zn0YtCYYpPp5sBRm8xrotIkTTd/7+5X9kOrmKRBkAJ0xElAy8wSZ7c3NMgrmRI0I7NWAE8EqpAhtLz6L4JSAe2wC84Ga5ZcuhRQjNiR4c1hGV66Wsh2seK1jJBQQMyJ2ZQAdRj7tWIkgZ+J7/S1FGPL1bZW8fozr+qyMn332a/c90Aea2qhaN9ZdAPrFhSYAz7XsAtBZp8MayHNQLsQh9BkKEpbSl6JBBHBZWjFqDutessTo+lrIuFfVABIsanmHXMOvBqSa3DtBqCEZCqg9GPR8OPnCVz1QM/TruwXOtQUWAfT/+ZgA9P9FNu4A+rnean3y3QLdAiuwgAB0g24NsOWwVs/nN7x8VrfCj5ODAWEvbdYzoF5L7s2NUbZnO5Kd/X4CyVV/2/5JTiYWWB8gOn3jI3AWxBfy+V/dB4i9kMAb6lqc8RPjXWA3PoLUlA4XVbYycJ5Vt8TkL2VE0jH3kvDJOGGbNwA9alkzHhKer9bEaAHom2tVNt7JuUoWDvlu+4Hc51rrzJHzMupgJB1YtcsJ2CrdtVbH45rzMMRjDVFZ0rkZv3B7p8y2AcQjeRxfzsnYoUgm/4v1VEmuOFMLzGdMIUEuJbT9RaUz5sdl9GvFM471L/rgCnbr2Wli+r6vjxr1NXFCql3eC/gfj63gOS8D6KPp/lyBLicCyzfneQm5dvaM9kXsL7tD7Bszx0O9S/6SnptUdi7HC9yGn62s3ODHPMozjL+s1z8/Ozu0z+ShssAcQP/m8iWvWD2A/om/94YOoD9Ui9n76RY4xxboAPo5Xvw+9W6BboHTaYHpb/yhYDXv1eANQK4A9O15/fPh1HLAhWx92LQZYL58aX7HMjAdIM7y1b4yO5+8Z2B32Ley+SPIYJlxrsXJNThInwQkALZV3z1qDBJ8MsgNE8EvmiOA4qDSwfVgU0RQwz5y1PJrILiCIqWM6MdMeIJBuzVLXmwE2hS4eVhmV/crA8IZ7dQs971ko4vNG1KCzrAX6MncCP5M5yC8M93NzJBNiLislRHS8ZLOH5cZwaWLF8rh1mZZv+vuMiKYRJDv6naZXYMdE0bICQrEflyfHbuGBHtjxcNCv/XWCsQzPv2XpBZpMtZo/Kxe4+3+vhlQgJiReGLWD8sY4Hm+V8+qavdp8ecSkzlY6edtY6OIOSP1glGRPLsB9rW1MnNNP5rhuXBgV4GeqIuJYoGkLCtIrvst396e6wDcY+yTx7zm/qbbP+8W6BZIFsgA+u2v+gvHAqD/8rf+ZfXYAfS+9boFugW6BR6cBVSnOdewdokcqwLl5n22to+DX5Al0SULj2JV+ClDn8kJxAJyA8B1+/ns13ypCpQPX/JH6EsAekr4nV6XFLtKV3EelAR5SGRLWQuWOEnFAaAHsFjLRQWbHb8HprgThHPS8/B8aj/FCcbTw+oziYGdymNxRuUzkoUZG3bhc8bHf/IzQgEpGMWHb/9/yvrtXzyv367SWgdldoUSXhX0ZJ6yhZJXQ3HrEZOavEwyOP4Nv3P+dc1sGOMw4u/ZrWMRiB42jiRs2QL/Syzm8FEZo2rdh2w9/h2+L+fynYOayL3AWh6VyQs66/z+ns7px26XvzRT6blgnn/J62+4Tep6UoHAPw1nl32VGef5ecMHJwaCn6Wf4ddKwn3Uko5HfA54rrWPBAlLtPs5byoI80p48vEN4ouhXj/rIPr9rXj/vFtg0QIG0L/qz/zh8iWvCEWYFRnpt/75r5RP/L1f7AD6iuzZm+kW6BY42gIdQO+7o1ugW6Bb4JRZQKDczm4FQfHkyOjGwQMUV+AjggSJnNqcT5xXsVKNqqWMe4JBjVURRpHMOhLkgPXp5QBEBtEdnMiEDAcaFDih1lzUb7fjCiCML3thUmZX92odb0msU1cvZAHpG0nCLL8H60K10qnLh6ze9VrTzyxqsQyCHEL7YO9JplyBpEuT6kjjmF9DJr7KGoqRcGGzlJ29CsrTlsFK1QJcK61ungIvwZYXe/xiUd02BbAq+C+mhsc+BPZhZxCUQnYdAB0WBOBnqus3unatlHuv1rYsA++lkMycaw0mNoRiQZWpUWXmN7RPNL7P3FUZHiGtdx7rnK/ykTfbfBmATj/TX3vhXBLStR8JxvHKgSAFFkN1QXs3IjVcR4AQNkzICgpkj/sFlEuKMNPbU91zsSZij3CPvh4SmD8aNQnFVdqlt9UtcFYtkAH057/yLx4LgP4rf+QvyXwdQD+ru6jPq1ugW+ChsMD0/c+sZ6chgC5QbQBcGyj3205G9TmbAdvvUbLt5MYpcB7DB0AVa3jO0/Fr3ucs/Cad1/XRov+m5FrGSYLudNrqm8tvQHnL4LLY3iGNjk8DCE3/9oVonARgfB+udeKwE3rt52R2PPc4CRm5c7PQeZ+yVJF4rOTdXFvdfpPOrlXGXfXXDd5LvQswEmcH/+p6BcKZJ/4m7HP8pkjybXM0IH8ZpS0SWGvdct0POMsZmiRUbI/fhA9HO6r3nnxAs5FZP4HysYRi24eN8JVDaUrtMy4pCczKeaxzvurndP9TLyuTx90IoLsfqX3hp1hWfaHGfSWXbC0AACAASURBVPjtrmnOXnK5K2cMR0IJZdDk7hykRJYGoA+SIdx5i2GEKpie+bg2ntPxE960apP09roFzrQFMoD+2Fc8Z6Vz/a1//svl1zuAvlKb9sa6BboFllugA+h9Z3QLdAt0C5wyCwiQs0NfQy6VCe0s7MjulkNpTI3Akesr5+xrg8pI4Lm+spxF6ujtztmzR0kcRvcahkH1oRSgghURiLm8NQfpJQUYASzAeyUFBMJsAJ1+AXoPDoJ9PQ885Xp+Ylpcn9UAzcZaBdPjPY0ty7EpCANQHnLsYhhEnTzaoM+L41K2Ae0ZYwTe3E7Iz4lVAWvBzI8LW5L0HpmpH0xvMSAYj6UDmYLZDmKfEwQKNjOy4NgCGXaSBP7jp0OKEDCfMVq2O+ZE4Md11VWbOzn8kUwgxjxsGzHcJz1z/oQ879Nf/4bFOuVmoJtB4/3GE+4yAcj5m80eqggKGlIr00HdzDjXHtCGbcyJOv3YRxEMmjz2dSfEKn0Y3QIn3wIZQH/eK//SsQDob/8jf1GG6AD6yd8PfYTdAt0CJ9cC0w88u561rAZkf8aS0KHa0wB2phIJtc2vWUg6hqUcrPA8bXwa/Rd+zDJmunyluAlwOilKL4Dn4ZsJmAYYtw9j9Sjk1Dnz5drqFynHFWDf4fUKtmc/yAB69pEAjFUXfVaTiBtrPXwtMcWjZjgJv8yb5GWAf9cW53hpQB9fh4ThnHjsNjbXa+Izfhqgp1SQoh8SEbAda0GiwF1Rmiv6kW8XAHq5sF5Gt4QvCdDN2ZkEA8ahhOuaxCwFMeYjBroTjeOA7PeYj8D8+Zo035LxhcrA+KvednI3+Dka2fTjL5rHDfz86pmKB6nJ76/PS1ktsM/ZJHMmub4T9H0QG6D5TKk6Q1OkmMdUxk948zmyep9qt8CDt0AD0L/vW8qxAOg/8frOQH/wy9Rb6BboFrgfC3QAvW+RboFugW6BU2YBZWaLfc4rPDuYyzj7rmvubGyTWLMEmefr9wgewGL3iwCEwO3KyF54Zca5ncpElF1gc/hzM9MJwhD0MWMDCXQzbZVBTtAlWAdiLVRJPsu5iUlA/24P8BlwGIk/AkX4uwRxaGsHpsagJnoeL0CzWROeIyx22Bz0TSBKNccjEGSpea7VfyGZ7tqEOO2uw4ddBYJiv7VS7t6eSyU2qbiQItxaL6pLeMvlqL2+Vma3XC7XNifl0s5uGd11jxIZZldqMGlBOlA13iIA53qNBMNum9RrGSfjJlEAmyCLv7ZWxk/sgaCT8sgrGOSXgoDs5+tVel2BnRrcE0guecrEopjNqqx7BGkl3c43ggNANwDnkU2TawDCPu/g+UnZDn0cp8QCCwD6zwOgP3KlI9/99F3l7d/WAfSVGrU31i3QLXAuLSAG+lCqXczj8DuwSvZtzEKXdHSUoNLhKpSt+B1fxopd+DKqT34/jPMMnKvP8OKGLPgkEe6kYPXFORAfST5I1H8W4Bz9MyaranE+lER7+DB0ZQA9FL8AlwXQA6Bb0t1s7ZyQaYlz1LcAst3P/kEof5WakIwvhg1I9MUHsQ8JSB2Jy1L5ok/mQFIvc0ddiXOpyhch24T0Ospn1EEPUB9fSeW9UO3aKOW28JmUOLxRyt33lNmjP7eMtndq8jeqZVdDBt4+I76d62+TYMD8ee8C/nPIeis3uSqNKTlakuOljJ/09nP57JzESUsO3v4Ne4JnQiUZSJJgP1RVOPwj+UX4UoDmTpQRyB6BC/lNPE/z9W/JFFnVi2dc3wH1x/jLO4B+EvdGH9PJtYAB9K/8vpeXx77iuSsd6Kf++S+VX/+J13UAfaVW7Y11C3QLLLNAB9D7vugW6BboFjhlFph+BAB9ID0Y0nWq1ZZfWUKd9zMjAsdRwGpIEIo57Rpho8WAktvMsvAEHJbV8yNgY7Da7HPuD9BZn0lusAK6VX6P4EsA6KoxF2wBAEV+57qrOzVY45p9BIMecaHet4MUPIGQSSk71Kub1WsdOJHjmxkVVbZPTANeyLnL0Q7gGTYDfxMg2kaKMd4n8JPk3BsbA+f98sVa75Dae34RUEKmPoD4WQSlFLQJifUmJ0+gZmurMtCpSX/PvWV09VqVDmROmIU5xUtAOe1niXjWE3Y94/yc26qMYav3V9d5/DW/csp2/NkfLkA6696Ac56HqIWurcveyMoRh9cl2y6wXIHUeObMpGhJLYlpnve/gpZ17/ca6Gd/f/UZrtYCGUB/7s//5WMB0N/xbX9Bg+4M9NWuXW+tW6Bb4HxZYPq+rw8GegBs8kfiDO7kWB20AkjPfgtAavYfOE8rKTXqLofM+PzMH75O8wFSoquVp5aodEnKPftN3I8KFYB1O8MbtM/leiJh1/25TBDnxb1pLUOF3xCKRSPkz1H4oiQUCcMkIfPaP6wJw/abGoAeoDP+HjYLlSuB2fhNu+F76TBZE0Dl87i+OqzzW8f1vbB5SwpQ4naUH5NPG5PAr7m31lGvScxIvK9XsFvM/yh9BTMfX21rs7KNURADPCcJ3HNnfQH0Xcf91s0yu4JPhQT9YW2T9YRdfysM/mo3vZjzxkYZP+Wd5+uBOQWznX709kg6jzIAuTRVJBxLvp09J4WuiG3Ec1Xx83iOBjyBtg/5JSfWhKRfl28/BRukD/HEWcAA+ld837eWx37nigH0n35b+Y0OoJ+4Ne8D6hY4ixboAPpZXNU+p26BboEzbYHph55Tgd72AmCOTHmCBy1wsyjT3CQAAd0sd0bwAbAOyTsHdcx0wPEUqB1gvZmt/G32hp1LA89NZjA5q/iph7MaqLDzyu9mILjGGfdK/jBquzMnjSHmcXWvCICOvlSPj3bMIiGAwvUwxy3hnpgcC2B/yJu3YNDlSQW0CQaZsU19PUB7AGqCWwDTAO63Xaw1xG0PMc2jFuL+XpnduxtjRl591iQUNfYIhEnGUIGbtSppSCAKkHRzs8wuXyr7F7bK5MrVMrpytfZFTT8YIgqE1SiTQPILm2V293Ype4D/sxrcMrueNklSMDtmfb2Mn9xZFCf1y2H6yZfOpQlzoguPDTXQlXARyS5mojsI7EBQBItqXcl5uYM2ZweKYu9Ovvi1J9UcfVzdAifWAhlAf87P/ZVjAdDf+Uf/J82/A+gndhv0gXULdAucEgtM3/P0OUAr9abwHWBvN7Us/JY0IfspvGX1K4O4AqhRmuLcHeDygu8Vf0hSPcBxq13Z1yIZ2DXGOd/j29iHMraHAhEAIO/T9xbJlCE5zTkQvwcfxCA7ADAv3qNOuSTVY4KAxIyVRGOrmAXLviUKA1jDXm+12WMeXMe4AbBhkcPaDqUujVl2ISt5rczu3ikFBTCpjq3XMlVDOXuxycM/uWenysfTJ+3sHNa/VUosWOlZ/h0mPMpdAOj4aQDo/9/vlfLY/6SMrm2Hkhm1zw8qg11tVdl9/Cax5OkDUB8lspCUF7Md2+CLwWQPifDx173rlOzy8zVMlbOTjx8JxNqj2vxNwU4gesQ8RlLmi4SQMJX8Kj+qVpgw25xrncyvn/XCLt9+vvZZn+1qLDAH0P9I+eLvfP5qGo1W/u1Pv7X8xk+8pjPQV2rV3li3QLfAMgt0AL3vi26BboFugVNkAdVNxgk0W7xGSuoMCJxInjmA7waIh1OZHUH/ThADEB3gGiCd9y0fbZDejHGxXXEoXRsMifCod5dtKFAvhiUp8QqgtyAVH6uu3ySkCEPeD7DesvLMxex0+miS9UWAdmVtbw2c25Asv7Jba5fneuE5KJbHSi095AsJmtiR9vwYAwC6a48TfEIiHoZ4S0KIIBxOOmPc2y+qeU5gzDXPY4kUEHJQjCAWwZzPvVhHQ81z2ghgfsbfBM1gn/MeP2WHYMnT/yNvKeXqtTK728oBqbY6bTJe10/cGBfanDzu9adot5+voU4/8eJ5woj3omM7AOjIXPKM8p6CmzlBJssRJqUJB5dsSgdXg80+ecyrz5eR+2y7BVZggUUA/a8eE4D+5zXSDqCvYMF6E90C3QLn1gLTO5Bwj/NSVqUCrPXfSHobNDZw3nyCOM8b7PbnBq1bInEqMYW1h+x1u2uA5VayymRyPndbueQU2B0+A76KWbVznelQxQpGvdoI2WraAoC/d6+C3rdu3XjG5PqDg8rKxmfBZ4MBji0M+DvxVyxus7W3FkFx+5SHh7UtgHsY9ADdt8E0xy8NAFMS9AH0S659pyYBB2gu19VJB8iwm3WOz/Q5m3UMqJNRNsvM4s2JSiDJZ6IBksldimz/uuyt+aPc9BlY6odKLpCKmP1agHSSmknOhqWPj8oy9vrnJ/K7Q2p8PA+5zIJHSmIJfhP7RP46z39Sx3M+v+IOdXsCpo8cA5F/NVd6d3ygs89P5FbogzoFFjCA/uV/+tuOAUB/S/nNv98B9FOwDfoQuwVOvQU6gH7ql7BPoFugW+A8WUAAOmxxnMPMNpcRUiTGNdB521nZQwDdwRpAOdfy4xrYzgbNM4s1B3RgGoh9EdbPtdYddCLIBJshXgoYSY4d9gJgIAyCqIFHzXAacx0z7iHAgsNrQJ8+rk0rG4P7YVcPpdeY9/b+nIGea98JxE+sXDPAxeQOJkOTdQsQEgD9atRXvzWYH4yRpAPGK+n8jWA8AHBPy+weasgHA9g2p756MDsUVIIlTn22RxKkQUIxDEhbgN0XtipQv7tbA0KsuQJFYQ/sA0sCmcIr0xr04hXyig2oZ24Xx/VapAh7IOjEfl3o2XZihgN6jFbsnrUyY8+5Jqcfd56PpoQwUJwwm8I1z3u98xO79n1gp8sCGUB/9s/+tWMB0N/17X9ORukA+unaG3203QLdAifHAq3+uf0SQNrhC8Y1Z2gnuHJtBr95fwi8G1zWWSwB7KntBsi7vraZ3QMF9sZydVtZPMiKU8iLiwEevocTpa2mpX7tEIWsO+dFWNg7B1U96/JmHZ3OjcEo535A76v71V8LsFmthaJRA7RdvxyQGWAcv2Log5VRmd2FKhZJ2WtFpaYkqR2J0kqODgZ9qGNJtUu111MCAsdZ1gQwXgA6oHltT0nPzEfAKT4YoP5mmU0mZWTFLWTcST7mGrPZL29FkjM+VQDogOb4k/bP6AMmOn5ZKD515a6T8zznkVQAHen+mqzRyiqwx9gf1EGPGvY1OT+SS/yoZDW+lvVfQu59Drb3eucnc/37qE6XBeYA+h8tj/nO21c6+N/+aQD0X+gM9JVatTfWLdAtsMwCHUDv+6JboFugW+CUWGB65wtqQCDL/NVoSBk/9T1lesezIjgCeBvAmiXQMyDnz/K8zTbPQaEhE8LBHbEHRhW0HQLnko6OhiEYcB31yA1cW1Kd4AwBkdsu1TlR45uMf7LFW6MRmdnf05wlpQ6LgGDULTApCMhEXwpghRMNExyGxz0wswNUBCznPgVTkrw1wROkAfnJ5641HUGlcu9urSFIP9RcF6MbqXgCQjjosEI2q9yfgmwA+Ht1UPjf9Iedgu3QchwioKb2Qtq+SSLS7oWtMnvkI0q5tl1G2Ida5gbjCRjwIoGCgBwBpqixLgCdwBYvJBSRnReAPk9YGH/FW07Jjj8/w2zgOftnYV/XRBLJEIpRsVaTNcze0XdBlXivey4x063mQK7Fl3TlgfOzm/pMj9sCGUB/1s/+9WMB0N/97T+saXQA/bhXs7ffLdAtcFYtMH3vMxbB7zTR8bPeV6bvfnqttR1+Vas5jh/DedrgeRCmm1IVPkZIq+unS1S5/fCDmvw6eDW+h32mbHCDe37PfpT7pK3wp1Snm0aUdIzSVJT1ae2Fihi1yFWHvMqXS0YdyXP65158LQOKUdJLkuZXSFIOcjnANWdO6qPjS4XEuhKYAdHlP8HwtS/GuEjqDTY7Y9parwnPBtGlLEaydNSQp7OdqMe+PqrJz/R5jRrlMOOnNeEYXwkAnzHBEKcN/Bvswfl3UlW2VOuateScTOIxL5+R8aWmh9WXRMId+9DmxY1ap/0zlDJDEWxj7uuNRmX81Hef1cfjVM9LADrrJX8okktYd/Yj+4F9hN9k30jqXTxMUfaO9xsrfRbs81rSoYPmp3pr9MGfQAvMAfRvL4/5jhUD6D/z5g6gn8A170PqFjiLFugA+llc1T6nboFugTNpgemdLyyFrPocZAkgbfyUd84BdEuh4zz6JbnAQT0vOY+Z6hAsi8woIMihgERcp6AFIF6SQhu2ISX2er2CSqNRZRLkceuztVIecbGU7d0KtMOsEDgfYKCc3utRAzxq2TmAY1a1Ookg10EkFxDYosYdzHGPTUGXaBew2WyHYJoQPFEgiDGYqULf+9NaJy+k6MWocDCIABSBG/5TwCsk6HeiBrpqLK6X2aevVfY5Y+JFwIbkAY+N6wjgiFVf2eICzwHzAdAFyu/UgBHvZTY6tkWOkLYxuWsDXiIYtKt1E1OEtm+5rO47gH5yvh5Uw6+B34lNboA8FBtmPBNi+5CQcrC458y8YAM0mdLYD5SZ7OD5yVnwPpIzYYEFAP3f/I3jAdD/2J+VrTqAfia2TJ9Et0C3wENsAYHnnIks8Wx3Z31Uxs94r0YzfcdTK7BtmXJAYVyj6fWarOtzemZ6u51cK9wAupOVAwRvADptOvk522FJcrE+DpxvgeG9HoCx/CLKY7n2+Hhe/sqlr3an1e+iT46W8gMoVTWv+lVLdh1W/05JygEq23fDn7iwUUti8Zn9OMZxYaOMHrlZgfD2ivOpWO/zM6j6NhhvxS4GooTpUlXP9iuYXY1fyvVP71SW+LVp9Y1YFyeEWxoecF5t49/cUmZra4k9HDLulo5P6l20ObuXPitYD+Oc/2bXDmoyshOrKdkFC/3wsPQ66A/xw3sf3U0//Lwb8uyb7+7sDxKPpegWDHXXPjdZwCXrnGhMEgy+VTzbHUA/OevdR3I2LGAA/Ql/GgD9BSud1G//zJvLJ//+qzoDfaVW7Y11C3QLLLNAB9D7vugW6BboFjglFph+9HZJhNfa4ME0BRB9yrvaDBQwcq3yCEQoYJIBdL0/AM4VsImadwRTHGQhgITD6ctVUy/VDXRbZkg48KMuUt1zj9D9xnhgD8yu7VegHUDdPzcCTFfd72CWX4OlEHX1GBdyhAu19KgVflCDIGZ+e9yXg1EAy52g1907NSAEox1A+w9EPXWY2i1rPYI5e3saI6+RwMqQd4T5ofrxZLxH3XkF6wIIlXzifpn9Lix2mCDYNwB0SatvlBKsehgVkiUkcIP03IQ6f+Myow/GSM172TaYFTBI+A+5RdpgzSK5QQGlrfXKrqCuoJnzWyQJTMr4q3/5lOz4sz9MMc+D/dMSN/T8htxlRBMVCFItv0iKcd1H2BNK4ojvBCdxxPfD5LGvO/tG7DPsFniILbAAoP/rYwLQv6MD6A/xsvbuugW6Bc6YBabvefoceI25jZ/5vjbL/bc+pfpDAKn4E7Cd8V/2ry/8HUexRQA6WOhN+t0gOgmygL/2gZyUbD9s6A8lQL4B5xlYN7BMPvKF9coGxy/g3AfIi49mVSzahmnNi/Ftk1iMSlEpo1smNan37p3K3JaSWIDmkjAv1W/A1wGcpm38ko2N8JkOKkMcAPvyuIweUf0TgehO+jTIvbtf1bvwrwz0O0nZDqXrT7s8FedcapRf3S+z3yMxOBKPVXd9vbLCQ27fNeHl79A+yQHcbxUxnFjKX5GUvLNXCj4UID1qA9sxj0hCEIBOooDY8Ic1OcEgOnXT8bO/bu5nn7FH5NRNZ/rh59Yxq6QBCQ4Hde1zMocA9Ch7Zd85S7izT7IfRdgDKXjW+vFvPHU26QPuFjjpFmgA+p/6Y+WLvuOFKx3uv/uZN5VP/oNXdgB9pVbtjXULdAsss0AH0Pu+6BboFugWOCUWEIBuR3G0VsZPfNvSkU/veOZcBt219oYAup3PZS1kAJ3PCUDsGsAlWBIMdEVZgtUuB3Xe2A3geQ4G+bKoZU5ARrJ8lkwExJYsYbAZuJ5A0b3bNXgTf49uCxl3MwymBxVMJmhjQNnZ6GYqXKxBI9XouzpVIEqEc2rewS4XKO96fYwpGPDUe/dLtdNxzIlIhWw7n0k2sAaMxNDfWFMwanb3fg3IMCbwTwJ1FwJAZ6yuEy+pw/VSAuiukt01MCUpOur7wUTf3q6gPWO6PqtMD9qGTYEdYYYQ9CIYxnsxlsacn4zL+ElvPyW7/mwPUwC6GRAt8SVKJLRna1Tr+LEHsnS7g7N6nwSOkO6UJGEtXdDZ52d7//TZPTwWyAD6MwWgf85KB7L76c+U93QAfaU27Y11C3QLnD8LSKI9lLCY/fjpv3qDEQSiw6rGtzEJOgPoPl8136X+Mju4rqTaprhl9SrAV8DrrNRln2uoBDYEz3VddCQWe6iHGYQPhjzXAGLj8whYJvG2qRkBctdyP7OroaAF6A3gTSImJbPw62hkZyrmufwH/AkSffG+aPPCegDta1VmHXl3FLn4XOzstXodkuqcTfGBJM0O8H0g/0Of45+Igc44N0J+Ps6rNGZm+7j6TEoMRk6dhOnw96TSJTB/VOXd8d1o0+9fCDa8VcGknhYlvEjEvrZdx6SSWlFWC3+RBGPmsgkTfVztxfus01bMj33z3DvO38NzQmcsAL0lboQfzv6zlDv7jwR0+9Dyz2MvKRZBokzsD0BzklHwm+Ka8RPefEJn3ofVLXB6LTAH0L/jGAD0N3YA/fRujT7yboFTZYEOoJ+q5eqD7RboFugWuDkLCER3ECYz0LmdAIaY0gCvCfV2zfPcRQbgXb8cwM6S5r5WNcNrhwv1zv35EEBvTPSo7RfXiSGxSY2/qE1mJi5A9rXdCqAT4CHocduFcILD8QVQpt665dmpO06QSRLpwaa4sFXB5wgG1dqHs5BPrwEUBXmUyU5yAPKHSZ49HPMG8DNOapEfHta6f/jo47kMoQI9BKYAyYPdoqDPLeMy2tqoY+UzGOq8YHUoIDWuY4gAwOzihTK690p18L12kh8cVYCeubo+PHOFsXHrxcrAgIHCC9l7hve099zcJupXHasFVJLBzCJ6cuIGv7ssQSSANAa6A0QODLLVVPIg9qjkOgNA77XPj3X9euPn1wILAPq/+t+OB0D/zh+SgbuE+/ndZ33m3QLdAg+NBabvelrtSGpSsyZXrjrfAtYjWTgn56ahiRVulSqd55Bsj7rhVvQyCG6gXX5OKHWF5Htrcgiie2z6GePkd3wh2OSWpG4ZALMKoAMG42fgCwGgqxRQqFnZZwLox6cDuMavAGwnmZdk3GBgk0TdEoJpywxtaqsbHMc3NOiPr8I8AcEBt33GZSBRo1w+E/4P9sXE+ER37dWSVyQfI6PvfqIUFf4TQL58NxIYQh5eNd7lu4WKGGdlbIhqmxnuThKgtjoS7iQwYz/uJYH6lqp8pnFNq9oa/tj4+R08f2iewvvvRbENxwW83vqbZ6ImjegFgM777Hdeej4joaIlv7MXa6k3ybdfn/USZ/e/BP2KboHPygIG0L/sT31H+aI/9qLPqo2jbvp3P/PG8lv/8Oc7A32lVu2NdQt0CyyzQAfQ+77oFugW6BY4gxaYvh8WeryGEu44lapDZ5m/AYhu0DwHcGiKv6MGeWNTuw8CIIB6ANhD6fajpNzdrfuBAUAghrZ4NecYRjc1+PYqowP2gOUIYaq7/ZDCnt2zW4NBGUBHwl1BJmoFXq8MdEu90yfzmsC2qDXIFYyyXDYgOv3zE7DS9tP7UTON4BLMBcbHvC6ua5wKXrkOYcytAeiXN8vsyl4dP3MiuEPASoEr5AhhaxDQiaCUHHwaj2gc9qbfewhCHdZEAGyHHCFBpcsX6/0A76qRPussihP0rAtA1z6P/R5JGzMkLxvzqNQgEPtOjKFYe5VVSNKZliJkj6jOZkx0fb1MHvOaEzTrPpRugdNvgRsA9M9dMQP9dz9T3tMB9NO/UfoMugW6BU6FBRqATlJjYo/rvM57JLVSNmmobsXshsnDnrEko8PBsRpWnM2sukXiawOdkyumJpy0nH/P5bIYFyzwWzYrUA3THt9OfYwkXS7GNUAxbcEovzyZlwETsx5fKQDlg+tN5QsfQoDypQC/r+1VBvpOZZWLXY6/ceu4tom/lBNCVV89Ejrtx0ixKxKeryPVTtktS6bj74wqsM179t+i/jnJzaPLGxVcl9w8flMofjFW10K3hDvnZp2Hq+8jfxI/jn5/d7sqd5G4zHkbAB3/71Fb1X6U3sKviqWbvORDp2IPn/VBNmKASwaYYW7wnHVmzSlTAFGgJcPHM4rPrjJstRRAVe06KCOea5fSIsn8K95y1k3Z59ct8JBbYA6gv6L8wZUD6G8on/qHP9cB9Id8VXuH3QLnzwIdQD9/a95n3C3QLXAOLFBl3JMUYAvoRLDGEumWLVPtuVkNEAEC51dji+NwAviNauY/wZobLh3UPc81AB0Muq8gEWwD2A9mYStTPAB02NQEeAh8wIBAdt019ez8wlLnc5gUMQ8x0KPGXbm4pfqAAq4B0EOmT3XvCKAQMEJCfkKQKYJLCsDU2uUCMmHuMy8D24wLxgqBH4JVAjbXKqjtGn70g/QgLAdkCAFNSRggOMQyOZAUbJcqJU/QLgI6CkbBvo8xC0C9Xmb3IOkedf1g5sOYcBuSdVwrhbrvBBj2p2X8jPeeg91/sqc4/fiL5okQYpCH9KCCjxS5DFaSpjGqADrBIAVGQxWBQCC/8z4v9j/vWaaQ90LuvQPoJ3s/9NGdPgssAOj/998sW8cBoL/iB2WYzkA/ffujj7hboFvg9FlAcu/DhF8nDePv4DfpHB4+Ab4CdbUFFHMkdzZw5DpKLSq/Z/R8ziAfbYwqyK1rk83Iq+TvBsDHuTDA+no8BAzkHMjPtQoiZwCdWuj4Ifg3+EOA7ShRkQzgc+Ph9SrLTs12/BXXBactfBQx0AGrD8rsrv3G+pZ0PNO5bVL9GPq9fr1c/8RnytpXf+5cPl3nXdKbVQAAIABJREFUW5ISYnL4dHt71Ze7Grajz2CvS5VLYzms6mGoaam81YbmqURo6qCbvR42kO+GjD0KZoD5AOn71H8PRrLrX5N0/Jm9KhXvkmCShg9/jD7oE98JMLX7TA/7gzz9tReWskuyOQ9ZJB1Pwke2/2TlrUiWUNkr+d0odAWAju8eZdHUUPhMAtDZfweHtVzW2nqZPPa1D/u8+wC6Bc6SBRqA/icB0F+80qn9u3/xix1AX6lFe2PdAt0CR1mgA+h9b3QLdAt0C5xRC4iFbiaC2A8pGCPwjbpvweCmrjYMhhTrUSDJL7PP429Y5tT+47VQ/w+yNEBu3EsgQmA7snsEZyLQtFDjL/fB7zACkBp0prhBQhxo1yJHZo/a6Uiuu6ZeSCzW2nkJQCcQgjQ6AaEtmBoHZfb7OzVQdJkAWNQnJ3CChHzUG5fBCEAxTWWsx0ABsXenFbxX/ekqoT37/WuVNQHbw4EzbMGcCQRxLXKBm+tl9MjNylph/MF4UECJuouMF9YHfV64UGZbm2V09dpcQtL1GH/v7mpT+gDAV90+ZBAjYKA1jmQEAkI9GHQinvTpJ17cno8W4IEVwz4oozJSgkZ9DgSe8772TyS5KMAbm1H3BIBOAEkAeoDwUW9y8rn/5kTMuw+iW+CsWGABQP+//lbZXDGAvgcD/bt+QObqAPpZ2TV9Ht0C3QIn3QJioltiPcur6zwdwKzZ1YDnUTO8la6SU5Sk2Tnn23dQZaiQguc6koU31spMflEA6OC2mcmNlLjOeGaqNyds3i7Xj9fE0BaIjE8khm0tDyVfaQvWeFUnEigOeAioTv+A0ZSbsloWYDxJxyQpXwSQLmX2me1SqJVOP7dNao1y8p2VdBz11xmHynnBDI/SQh47tmIOgPdRikhqYS5zxVjsywBu8wLUJhEY4P9W6rf770hcwO8hEZrP8XsAPynTpVrwJIRzHo4a8rR39VpNVlYSdtwriflITuWakG1XUsLaqExe+MGTvmXP/PgEoOOL6xWJLE5iZ7+pVAC+elVgmIXvU5+jeN7sM2lvOBklkmH0gNb1NsA++cJXnXm79gl2CzyUFmgA+p84JgD9H3UG+kO5nr2vboHzaoEOoJ/Xle/z7hboFjgXFph++LnVIRRLel4buQYnQuKM93E+FaAZmCUFkCQRbtCcetsAfAlk12ew0wnC2NUl+AEbm+tgQmSmBX3BllbQJskcci/vC0i/WIFn2mYOBGFgfeAXA6bTNp+7th1tEZAhGBPMEQJUCvIQJCFhgPneu6ugFdKHYqO7rjpShHa+Gcf+fnWsyWLHPpIgPCyzz+xWQP7ipILp4ZArmOOgDuwGsy4ygE4Q6BGbdY7Kpo8AAOuxux+MEhgd4zK7dEGMitHOTg0KRHb96J57S7l7uwbeIrlBgSFGy7joz1L89Lcx6vLtJ+CJn/76N8yVC1ggBRkjmDMalZlYOHqjjNhHSuagtmMA5a4r6cQY1/5TMBRWekzS8v/x5+QP/OsTMPs+hG6Bs2GBBQD9Xx4TgP7dHUA/G7ulz6JboFvgNFlgesezKgg8lGsXIzrAaQPQ4e4oqThUuZosu5KJk6KQgfGo2mO1rQZcYyQzyaMsk8YQwLJsqLN9es/S8FKsAviGYR5MeQ6E+ExcQ8IvPhPjwW8hkRigmr9J/CUBV2B/VdCyZLrKSfFCwh35dkpMkRh91171N1wD3SpmjAPb2F+xatK1aZlNDysDnhf+FInVd+NjxRiof6766NXXUjJxsMHx4QDRxUy3rD5n4524n+sYAwB6SyyNhFKDrf/h96tEPfPEjPfuVxb6xXFNFshlv1AMwxV92UdO09Y9c2OVYpf3i3wmfP71+nxqD+HvUkKOB7E6vZJvd+3z/PyxF+0zWbUrSbfrHtQLZrPSAfQzt5X6hB5mCxhAf/yf+K6VM9D/PQz0f/SzXcL9YV7j3n23wHmwQAfQz8Mq9zl2C3QLnGsLTD/yvCrNjnNpUJXogZjbSNxFBMisC0uTE3gJxxXwXK8hQ90BoWhXgQ1fI+n0rSqjJ3n4w3l9PQInsBkIklzcqKwASxW6xh9MbYIzSPI5KBOy1E3OnDFRr5AgCOwK1WG/rnrg/Gz1xm+7MGeSK2Eggkp2xEO2sEobRia6JbEtEaeA2vXKMCfwAiBPwEwscCQIg5HP5x6P5dwNkktuca0G2njx0zaERaE2CRoFEx5bbqyX2eVLNSBwbbuMrlytyQTIHJLIMFkTC2MBQKcP2PSwK8Ku4+feca6fg5Mw+eknX5IYSJVhPkKKP549geY8l3omo26jJCmjdiR7VzXTUyBIz0DUmjRj3UB6tDt51L86CdPvY+gWOBMWWADQ/8XfPh4G+vd8v2zVGehnYsv0SXQLdAucEgtMP/ic6htxrrdf5DMVZaCu7c6l3sW6Dt8I/8Bgt0HuIZtdZXs4v1VjjGCM4wfYx8IHcGIyvgz+xMFMAK9A7mCN6x6Y1JwZ8QOklIXaFkDw9crIpja6OgkJdfk94cpFnfFaB3pWfaYoQYUfMnr0xXl5IW5Bect+DjWmr+xV/4r/pMQVEwLQZn4kcdrvo13k2nltkTRd/bM2NiUdT6sPw3y5n7rmgNjBrheQzlRku7Cfkg2iJFZjtsd8AdHxmZjvNopjtL9f5xg+UQPQmS/z4Cf+KKA+blgHzx/2J1YAumMSkUBey6uFsoCVFuQ3VeUDybCbac6edxkBAejxILMv2NO8JOue3idx4gte+bDPvQ+gW+AsWWABQP/2b1jp1P79v3x9B9BXatHeWLdAt8BRFugAet8b3QLdAt0CZ9wC0ztfEAEDmAW1Tp0YAjiZu7v1MwU8qoS0WNdmLZjdHEx0BS8s227A2wEiBzdUdzukyXFcFYiqYPns3r0yItgiAD2Y27DIee1F/T1+5y0A5UddmjvCXidnnjtzHMf56m4NvkjODeY4gScc55FAeLELzKRgTDjOjJ8gD/eYvQsj2HWp790O2fU6PtUJjJrmAuwZdwSfFoJBkndMASLPT2pyMT7eY2wOEBlId5101xRUdv16md1yuWXXj+66u9rUzHc+YTxi3s8qQwQGCUz09VEZP/N9Z3yHn47p7f/bl82DjDxXAYZTf09S7X7xfJhpzjNJqQX2DntUz0YoMjjgkwO0sCcG4Dm3dAD9dOyRPsrTYYFFAP1HjglA/zMyRgfQT8ee6KPsFugWODsWmH7g2e2MpjMXQJzPWiqVE2cxzugkCZMgjH/Ai4RGLsE/0Nk+6pmb4S1VrbBVMJ2VPGvgj1rPkeQrv2M6K6NHXSiSPKcPZMYvj8vsStQ4D9UvQHNJnVMCyi8DjZw3rULGPGC4o5jlElMuA0UCgNoZ14TezWCM055tYMavAE3mEvOBpT4FvEdBi8Te8JlgdqMSZsUtJzur9nn4lFIPC5+Q9+wjOdnYrHN+4t8YSA8fSv6OJfYZD76dSpQFgL4Xdc/t7+5dnzPQ6cvy8AD8nJlf/KGzs5lP8Uxa0rGeqygNoHgEyS0Df4cEFtadBHQ/q/j6AORNtas5SM2nruYZkAQODsrkMa85xZbrQ+8WOFkWaAD6//Bd5Q8eB4D+v3cG+sla8T6aboGzaYEOoJ/Nde2z6hboFugWWLCAnFDXRhZ4B6ANW5z63BEcMrAOOJuYy6oVF3X+bgDQG6M9dUddP2XzT1rNcQHWOL8EUdaphx7BFGQPg8FgEFisBckMrpfRI7YSCyJYt7TlrHO6JYCFs3x1p9b624QdMZ1LEo7XytrnXajBLMYkgDLk7BWECSCctgxyY4sre5X9QYCG/nYiQNbquA/07mXXkEA0m4TgTyQMCFQXeThA9JC8l00NpJM0ELXSFUBy/esx9f8IAqyVcvXqnHFs2X1qvrNmBO0YH21vRiCo1/A7Ed8G+//vy2sA0+C4JC7jWaT2+cFBlWwfrZXRIb9H8NLguqUIW9AypP81uwgKOTbEta4Z2OUIT8T690GcHQtkAP3rf/pHjwVA/9U//n0yWAfQz86+6TPpFugWOB0WmH7s9vlAW01vcDZA32BXyxeJs5bO4LXGeJNd5yzu87wSa0nujeRb+xo+vm2MKvAMYE37B1UdTDLrnPAuTZSAbHa6fKOdafV1AKQpsYUvhdQ5Mu4CDc2Oj3ro9k/wK5Q4fCCQW+W4+MxKVuT3PvqiSnQJRMfHUo328C3amTTVF2c+u9MKSssvW6uS6ID0sM3tq6SyX0VCS7M6/lAPE6A+WVdteIPrjR2sUliVRazkY/tW2M7Xw1SXzLf9p7VSdvfquVulvWqSg/qEwY+Nw35KHKD7l3/sdGzSMz7KFrdg37FHte9GZeTkeeIYSLZvrJeRkuCRbyd5P/Z+Xem5leL+I81mn+n/b+9e4CSr6juBn5rp7mEewKCAIGAQ0fhKCIoIaDCYaECQEA1xE2M0MQoCa1yzYoguanQJvtYlrsqiMcbsLghGE3mo8REeRiaAQWMi+EAFBMQBec6ruqdrP/9z7q2pghmmR+r2VFd96/MxQHf1rXu/9/Run/O7//+pigOm9r14xIVdHoH5E6gD9Med/Dtp7xc9b6AffNN5n00/+PAFWrgPVNXBCBDYnIAA3bggQIDAGAlMf+/XN7WDrhdXcrVELCRsLBUG9RPb4RJVzj0BerdqIr7Xu2d63cKwak/Yir3EY5FmYjKljVWVQx0cxs9Gm79Yx6hampd92qdT5552tQ9ftC6v2vitWLJpH/Q451xFUU2McxV5tSd6Pfmt90O/t11C9LyX+lRZXIqW8vWegXU/+1zJGyF59UR7HDoM1lQ/H+0N6z3Vc3DfMyGv283nff56Ks97A/QIvuuAPD6pakXYPY9YE6irKSJAryovsk3dGjEq42PyHwtZ9QMEdSV+1Vo+t3Jsz5bFsHiQYNlEmnzWNWM0uof7Utu3HFudYNVGsK5Cz79Hs6m1oV3t3Vf2p8z3Pn4v6pbtcZ/zXo7x81VFRbf7Q0+AXm89UC0yTe1z4XDDODsCC0ygL0D/2//ZTID+stdlFQH6AhscTpcAgZERyPuh573Pq73A637tdUV3/L21Ybo84Bp7jMf0JOYL8dBxBOq5q1T191n9gGs996lD9PhzLvYGr/fxjr/r4m/A+m/8+Px6q6r83GTMjSZL1Xt03lo30+36nqvQd6z2Qa+rbmOu0523ROhdOmCVDloR6E+kdN/6fMzZG+/NX2+tjHlXq3SyikAy/hatAssSzFdznvqhzThWe6YE6PEwQd0Svncf+d5R0dsaPzqGxYMC4TVVVYPnKvbq4Pkh40XdouL8L3WYngP3alus+HrdUj7uU29r+fzwavXgdDxsHOcY9ym6d8X7eqrhp478xsiM34V8IXWA3qnn6PXvS90Rb8OGPC/O217V4zHWHHLXgmrs1GF7bxV6jVKPwfrB5nqOFQ8d7/e5hUzn3AkMnUBfgP6bAw7QPyFAH7ob7oQIjKiAAH1Eb6zLIkCAwOYEpr/7/LLw8IDi6fyFWLCJveJiMaj+fg6MU+rUVc31YkkcvA7Qeyur4+vVJDcH1sumyh7m0RY+XnlfslL5kNZtKJPeCIx32CGle9eU9n8RAN+9obw/9kHfZUlKS6vgOx+/Z2LcWwlRX3D9QMDa6XycWMgpi0CtXJ2ePy8fr2cPvt5j1otKsf/gnbHXYbx10wJU3r88qjLqqonqc/NiTLwvqtbDJK4/Pi8Woep9/OoHDWKRK/baWzdT9kOvQ/PaqH7YoK6kiGrk+NnFi9Pkk75scI+YQK5Oz78j0TlgsmrTXrcnrAZN/H7WD4vEGI+F26XRqrP+fagegMnVGlX3gb0/M2JSLofA9hfoDdAP/ngzAfpVvy9A3/532hkQIDDuAtNfPSilnXes/9Kvqpk3lu11Zjemzl1rN+1bHn/r1wF6hOgPDNCrfcbzHKt+sLbe0zse9o05QzzoWz8oHHOwvPVW9fddPCAc85XYJmvjxqrTVrR875R/j/lGPDAcAXsEi/UrpjD/9pO06PE7p7Ssp8V7/f34+zLqde9Yl4+Tq+Hjz8iYq/Q+fFy/P8+ZqhZk3YcJZvPDu90Hoet5YlxXBOPxE/XDxfEfVev5tH4mdaIaPCred4yHr0v4nT83z1c75eGCyWru2rv9Vcy5lsS5xkMH1f7p9cOnddDfWpQmf+GycR/GI3X97R8enVrx0PzsbAnQIzjP8+eeFu153aJad6jHaA7bqy2zYlxtaJeOX/mhjEVpat9LRsrJxRAYFoFugH7S76a9Bx6gX5J+8BEV6MNyr50HgVEWEKCP8t11bQQIEHiAwPQNv97/lboKNgLbeJo7KsPrFup5taNTFjzqffR6W0TX76tC9rzYUr9igSO3ca/CwAjIe1v3RYBeV27EMVcsS2nd+hwQd+5cWxaCIhqMxaAI0FdUrdy3dEd7jx3XFPvdRTV7LGTlyoJo51cqQfLC0IqlZWL9wJaEuY3gTGlFGJUJ1aJOWrq4tDLcYarsdxjV3tGaMF6xMBT76cX+fQ+sPFk+WcLz+KxYGKtbuMd1La8qWuJ7sRBXV8bXbb1z+8cI/Kvqj+qhhsmn/JNxPWIC7duOKw+uxIJkLJb2tt+sHziJ8RB7OsaYzQtHnZSWLysSvRUU1Z6W9u8bsUHicoZGoD9APyst2fURAz23DXf8NF31+3+cj6kCfaC0DkaAAIFtEpi+9rDyN3q36jr+kI+HjqP6u50666tW7vU8pLfSvKf7VGmDXh5KLnOqCNGriVO1lVPZyzv+7o+5UxUe1w805wePq4eR6xA82rBH9fY97aqFfMwtJsq8Ke8BXv1wnPvaDWUOkyvJq1fMW3aMwD4C+ngYYMOmlvAxt4nziwrvnZdsCh7z5KynAj2uJQL4qCSPfdmrNum5nXzMe+IVDyLHnKnegzy64N8fD22XyvO6y1e+/qUTm1q0dx8ySOVY8eBA/L1bVx3nrmT11lnVwwX5weN63+vy8ZO/eMU23XNvHn6B9g+Oyq3b89ZXebuz3oeO4wGNeOi46t6W1xxmysPzdYBerUPkIL56TT7+H4f/wp0hgQUosClAf2lDAfr5WrgvwHHhlAksNAEB+kK7Y86XAAECAxDoBul5oSP2vltbJpvxyhXY1YJEXX0eazB1EFwthnQXZuoFod7zioC43pMugvRYfMqLOZsKZvPnRWhfB4Ex+V2zJnXua5eFlWgjGG37dplKacdl3aravNATLaxzy/Wekvju4lVUgpe97XKgHZ+5fEm+vjh2bum+cwnkJw/4Sp/m9L/9cgnIN8xUbRhLlXpeuIkKiLiG+rNjEefe9SVoz6F9WZTK1epRJRKG4RA/myvt60rzMA7P6qNjz/gcrkcrwmoRIO+FXbVJjMn+5ETqTEykqf0+O4C77xDDKJBbvMeYmaoqKbrtN8sDIaU6faosAsUDL3mvv6rCKH4X4oGUGLupo4piGG+wcxoZgQcH6I8c6LVtuONOAfpARR2MAAECP7tA3hM9zzd6tnu69/5u6+9OtXVU/tu+fpC23qc7/pnnVHXnrmqO1Q2Tq5bkvZXXueI6Ol1Fe/hoS11NGNauL3Om6FQU3489vO9em9LajSVIj2nDiskqQI9K83gouKpej/A8XveuT2nHJdV8rKeDUT1nisr52Dorfjqq6/Me7DGHK3+bTh741S5kfrigbt8eBnVnrmq+kyvh679T46emozNYeQgg2s/HXuZ56hbnHkZhF53HHrlD+f4O1dyoDuzrCv+ojA+T+H7dqj3PmaqHG/Jm5uV6J5/4pZ/9xvvJoRVo33h0mef33f88Wjc9ZLxD1W2hqlZPS5duup6ZmdS6f035740b0+RTLh3aa3ViBBa6QDdAf81L014DrkC/+fyoQBegL/Qx4vwJLAQBAfpCuEvOkQABAgMSiD3QYz+xVv3EfoRx8e8R0EUIFyF2XsyoKtHrAD3PSXv2Wa7304v39lZd9J5nFB7ULQmj1XR+QrxagIp/RAiYq7lnU4pJbgTT69enzj3rU7p/urRej3ZsO02VwDu3aOvpPR8/n5847wnR6/3Ac7lDdW7xnggl29Opc9+GUhEei0cTE2nyFy7vk53+xi+XgDJ+pqrI6O4dGD+3bIeUJ+Bx3hH2x6JPLPjkCXh1qFhEio+Pf9YtHONb9T7n1dvytdX7nEdrwtoqV5+EVbUwlMP3ieTJ+AH9EgzxYdo/flE5u/p3sd5vM7ozxHjIFegxrjaW35m8WhpjL/Z3jN+nqjopxm+ModjLb69/GOIrdmoEFp5AX4D+N1GB3kCA/nIV6AtvZDhjAgRGSeCqA6bSgR85sHT7ydOPqktVHUrH1yLsjjbk+WHaeIA2/h6LbkJRCV39nR9fjzlCd+5UQvTuPCfeV+/tPdlKrZij1MHghnaavfn+tOjxj8wtp/NnRIV1/J0X/7tvbQ6lO7F9VPxVuPNUai1dXB48jjO+6e7UeszOmzpu1TcoriEqdOPvx3qv6Ho+F6F96pT5WLRWjwC9CsInn7aZAD130IqHPas5ZTUtyh264k/UVbenxYftUbqL1a3bY+4U86KoXI8HpuPZhAjX622t4gfrCvT6YYOYJ8X348HsOqSPB5DjYYJ6y6s4UN4uLKXJJ+vYNUq/jw+8lvZNx2yaB+Xfi9zmrcyF4ncz1h6qrnT9Dx2XrgmttWvL3CleU1Ops2Qq6d41yiPGtW0vgb4A/bjnD/Q0bj7/4vSDvxKgDxTVwQgQ2KyAAN3AIECAwBgJTH/v+WWRJleNV6Fb3W6vboO2LvZBb+cq6r727ZVTDqBjaaUOjvN/9LQlzCs4sYARe48tKvuP77ii/HRvq/W8aLNxU0u/HN53Urp/XercsT51InyONaVYDFoxWULw2Au8rqjI+4THAkvV6jBXQbSrz44K9bp1YbxnolxTLO7klulVW/T4157FoM0NhVyVXlWe9O6jN33VwSVArzP9ekvAbBF7oZf9ALsLZtXe8PWW1Tkwj6/F4ltUU+QK9FgUqtq21yFoq2Xf8zH6HW3f9ptlHC+JlpnVwyHx33lBM5WHN+LLsS1C/exIXV1RPwCTf8fLCqYAfYwGj0udF4HeAP0ZH/vLRgL0q1/x2nwtTbVwb/U+ePYQas95znPSpZc+dGXW5z73uXTOOeekq666Kq1evTrttttu6eCDD06vfvWr05FHHjkv98SHECBAYNACOUD/2DPK/CP/bR/zpniyuFPC3whq42sbonNWtC+PB5KrVuIR8ub5xqJSaR0tzOtuXvU8Ibcv75Q9wuvgON7/yGrOFBd0z5qUdl5eLi1C77x9VqeExtU+zjlAvyc6epU5U1qyKLV22qEEilGVXW/9E8fIe6M/4OHL+uv1vtH5IeLoTtZOnQjQ42GA2Gs8fvygVQ/JnKvSe151xfr01w5Nnah+rx8iyC3qq3lhZJh1K/doKx+vXFFe/bPe/iq+Hp29Yl5ZV+znAL16oKB+6CDO8wEPSA96bDje8AjkavQY43UHr+hwV2+BFv/e27Wrfug45k11V4m4lHhwOZYd2u00+YQvDM/FORMCIyDQDdBP/L2016AD9AsiQP+EFu4jME5cAoFhFxCgD/sdcn4ECBAYsED7+0eWCvSYRMZiTB1c58qJdkpr1pVFoKiOqCvVe6rPY+G9kxeQqhOrg+MIvHsD8twqfaos1vSGgXmi2rNnXV4QqYLx+HpUU6xeV/bzixbuKyZSbgMY7QOr8D6fd0yW6+ryOEZMkuuvRQhdf0ZcaPxcbhlfBeixGLSZaoptoZ7+xrPLnoJxnj3t2LNdXGLsSVi3kO/dhzAvDG36pG6b+rwHYlVRkh9mqNo2CtC35baM3Hvbt784pZmqjWcs+OSWhbGlwNSmcRQPh8QrL3pWi7vV79nUPheOnIkLIrA9BfoC9L9+fzMB+h/853yJwxygR7By4okn5vB8S68I0c8+++w018B+e95Xn02AAIEHCsTDsmnF8k3zm1xtXbUKj7nI2vVVx6nZlKajsjXmLhGuRyieUmtycXmYNuYG+aHfeMAxyrJLy/JOeza1qorq1o7xsHDVbajexim6YlUdhUq1bHXsPH8rc5u89dWdEU5XAXqEzLuUrarKQ9PRoah66DhaucfX4ufjeDHPqyu460r0QMgPBsS8qoTWJeBfvNUA/aFG0PSqZ5QHj/M8qArI69b20Xmsrt7P87eeuVKuzq+OHN3J8h7vi8te6WFQPxRdb39l3/Ox/UXO22Hl/c8nyhjLe5xXW2DVawjx/Rj/MV7y+CsP9Mee6vESoI/t8HHhDQnUAfp+EaD/xoAr0C+4OP3wowL0hm6dwxIg0CMgQDccCBAgMIYC09c9N7cqy2sYdZgeCyn1RPKe+0pbvVyNsCntzVXSMd+MRY54xYJK/EzeO69a8MgHrdq358rxiVItW09c4/t1JUeuxqhWRfICT7u0QrwzFqRKe79caRAtDZdOlIWeus3hDlVLwbxne7S1rhZl6gqEfNzq3PMi0WxZJMrVD6VKfPLpVz6su59bvuf22lX7xerBgrxAVC8ExcJYXFNP9/lwrFsW5gl8PCSQF81KaN7ZZWVqxb70VYtG7dsf1m1a0D/cDdBjsbVe3Ix2ovXvWfwzLxBVX6i3Hqi2S5h6jAB9QQ8AJz90An0B+kcbCtD/cH4C9Ne85jXppJNO2qLx8uXL02Mf+9jNfv9Nb3pTOuOMM/L3DjzwwHTqqaemxz3ucemGG25I73rXu9K1116bvxfve8c73jF099EJESBAYC4Cl+87mQ79zCH5Yd/OD+5Irb12TGkq2kNXDyzGg79RYR5BebziYdieeVBn7XSZI+UHZSO8rvb7jqrrCITzQ72tlJbGXGBRSsur8Du3We95wPiBD+PG3GZDbE81nTo/ja246nbrrdRauaQE+zmrbpX9wqNCN/495irrqwcv837hpeV5dz4W85XodhQPI8ccJQeMZRIz+Yx/mQvZFt8z/dWDNoXjMW2MBw7if/E9cL2/AAAgAElEQVTQdu2X/66t5pl1tX98rQrVy57si0pr+Tj/mM/GQw3d7Y9SmnzqZQ/rPP3wwhVo3/Ib1UP1G1NrdjZ1Yg0iB+rVNfUF6J3UWr+hbGVXP1j/pC8v3It35gSGUKAboJ8QAfqvD/QMb77govTDvxagDxTVwQgQ2KyAAN3AIECAwBgLdKvRI3SLmWVMIGPRZP10qZ6uA+vKKC/szHZKuB0VDlOLU2d9teddHaDn6oyoCGjlyosU1eOxX3O1J12XO7dgrxaZ4osxgV3fLosp906XhZTIpWOBJIL6ai+9snf4orIfebzq8LxuC5grKXr2wPvFK+blDkd7wlhkygtl9UMFoVpXn+Rq/mov9LxXYCyMlbaNuUV9WEWl/sTi1ImW9zMbU2vjTOosXpym9r1kXq7BhwyfQA7Q48GSvIAZv3cTKS3tad8ev2vxuxMLP3X1ed2hIVq473vx8F2UMyKwgAX6AvS/+l/NVKC/8pQs1HQF+lve8pb01re+dZvvxve+9730pCc9Kc3MzKSDDjooXX755Wnp0qXd46xduzZF+/drrrkmTUxMpOuvvz6H614ECBBYqAI3vWxF2vNPDyrzpQhrc4AdD/5uLHOW2Ie8DsnjIuMB2pizxJwppkY7LE6d6aj8Lg8e1xXXuZI6V6EvKvuX1wF6PKCbH5isunbVc4i6RXV8Px52js+PFu7RDn55PHQcc6SJUuWeH/CNudTUpj3P47zrgLqu4M4PJy9Kkwf+87zcnumrn1kMwi3PN6MrWrUver3VWDwAkOdzZd4XDyfnB7mXLS4PVUc1fcwHoyNTPR+s9z8Xgs7LfRy2D8kV6DHfnplJrVjPiHETv0O96w3x9Tyfii0Y4ndrNrXuubdcSjRkOGB+1g2Gzc75EGhKYFOA/rKGAvTztHBv6uY5LgECXQEBusFAgACBMRaY/u7zS/VqLADVe6BHiL623a1cqHlaK6ZK5Xgs1MzMlsrwaD8YizAxAY3Whb3VERGg7zhVKhpi/+Z6b7L6gHWLvrpiYO26KrivPiOquGMxZWpxyqF5LCrF4tJOS8u5VpUXdcv07t7mVSVC737l83WLp//1sFIJH4tocc6xZ2CcTwSguUq/quhfMpk6d64t4XmE6FEZEu/LAfpE6oRVPHSwaFGa2vWC+Tp9nzOEAjlAX7u2ancZi6zxsEVU3VQtMOOc6+qgutNCLD7G71f1kIoq9CG8sU5pwQr0BugHfeQDjQTo1/zRydlnWAP0k08+OX3wgx/M53jllVemQw455EH3c9WqVenQQw/NXz/llFPS+9///gV7z504AQIEupXoEcDV3bkWLUqde9alFHugx1wot26Pv/9LhXS3I1XdBj3mAb0BentjasU+3osXpdbKmDNVFeGxjVP+O6/q0pUD+9mU1m1IaWk1t7p/fZmDRYgenx8PNMfnxz7qccypOIeJEsjH3CQC66qFeq5Kz8F9zK2q/c3nKTyvR9L0NYek1J7ZNI+MrmMR9FdbcHXWls5i+cGDmFdFlX+c8w5VgF7NmbJZu502/vMt+f07nPQDg3VMBXKAvqGdWrGuEb+bMbeOCvQ8kLr/pyTlVZDemplJndai1Iq51saNafKAr4ypnssm0IxAX4B+7IAr0D8ZFejNBehz3YIqHhq+9NJLHxLwc5/7XN726qqrrkqrV69Ou+22Wzr44INTbHd15JFHNoPvqAQIDExAgD4wSgciQIDAwhKY/s6vpc7ERGpFcB1VDLEwE694WjuqqOuK7jzfbOUFjGgx3rkn9tnrpNbOS7p76uWFjfj5CNGrPcBjkSNXCOTwu6oOiFC4XnSKxZyYvMYkNqoHFi1Oac2aEsrHAs+6jaU6Iz4rqs93nEytR64oCyszG1Pnvg1lPly1lc9hda6kiEWnsuA03yF6rkKPxat45T2qq9bu8VBC755+UUWypuxXmCtFYgErHjRYukN5Uj4vwEUVyUSaeuT5C2tgOduBCrRv+83yAEaMmRgn0TY0FoEiHK8WPfMH1lsY5B0K4mGNatuCVitNPeaigZ6TgxEYZ4FxD9Bj7/N99tkn3XLLLemJT3xiuu6667Y4HOL73/72t9Pee++dbrrpJnuhj/MvjmsnsMAFpr/y9JR2Xp67/uR5UsxBJhaVB47vq0L1el/u2KN72UTKIXDMZeKh4gi1Zzp5C6f8tcUpde5ulzlWDoYnUmtZ/J0Xm6eXPcdTBOnxarfTd07913TnDbPp51+wPD3ixF8ofweu2ZA60QEs9hCPuVh8Xnz2TpMp76m+PFrNp9S5t5rnVXuP522jYv6RP2c7Beixv3yerE1sauueHwCtHgyN66v3qd4wUzqehVXMCePh4whH42ezz3Ra9/c3pJ3+/PYFPsqc/sMRaP/o2Py70up0Uifm1fFAesyn8y9r9/+U/663v4qtD2KeHlundTpp8pfmpwvDw7lOP0tgIQl0A/RXvyw9+tjBBsU/+uSF6YcfG+4APeZNJ554Yg7Pt/SKEP3ss882T1pIA9u5jp2AAH3sbrkLJkCAQDVv/M6vldA7wrm1G0pgHnvQ5f3JezbsjjlnLGDsWNqwd+5aU0LtnXYoizfxiqr0XDEe7QIj3KvauFcVF7kl4dJqz/K61d5995ewO+9VHj3TJksIGBPaqJL46YZyzDhWVGpHxXtUHUTb+FisiiqOqEivK72jFVu9F3mE8dsjQL/2WWXBK64lHPOea63UiaqKeBo+Qs243vURnvfs7xfvy+H/VOpEa/plS/Oi2fTiVlq+8jxDdowFcgV6jJX4vYjFwjy2o+NDLCRWD2rUG/vF+3JrzvI7lMdUXi+qHizpWTuaetSnxljVpRP42QX6AvQPf7CZCvRXlX3Jh7EC/fvf/363HfsJJ5yQF3y29Irv1wtG8XNb2k/9Z78bfpIAAQLzI7D+r/dPiw/Yvew7HvOT/EBvmTPFA79li6nSbjwC7BzurmunzrqqyjxC8nhFiB5h9/qZ0sI8wuCYNkUwHH+vLV1cwvYdYm4Qc6RW2njFj9JNX7g9PfIJy9Pyn985Lf6lXct2PvEAdPxVeHe7VKFHsB9zjtgaKoL0+GfM7eJ8qweO8/Za+QHnSPDjb8Wyz/nk01fND2T1KbmNez6n6sHj+Ho9N4qvVXue539uKA8s5L9t6yr/mDvllu2dNPPlm9LVZ92WHnPoZNr33LXzeh0+bHgEcoAev2axVULugFfm4Wlj6cDQXXeI98S4minzpdadPy1rH+vaKS1bkrvAdVYsL2NtQztN7fe54blIZ0JggQl0A/RX/f7gA/S/iwD93MZauNcV6K95zWvSSSeVudnmXsuXL9/iHOdNb3pTOuOMM/KPHXjggenUU0/N86gbbrghvetd70rXXntt/l687x3veMcCu7tOl8D4CAjQx+deu1ICBAg8SGD6O88rAXpUgufwenbT3uf1wkUs3eSFnKkczkXVRV4kiuqFdrXvX9WOMFdU57WemKz2hPCxGLTTktKivA6542zq/fzqfdNj4SSqyyOkj+qMCNKjmiLeV+8VnitsO2VhKKonoro9FlAiI5yn/c63NJSm/+2Xy7nEpL2qPp/8+S/2vX36279WvOM9OTSPfQqr4DPC91bVcm5yIm1cujQtnp1NU4/4hNE7xgLtn7y4+l2pFhnb0dEgFhrLwyVph6VlwSi+FuF5btW5cVPrzxhXsQhUt3aP3F2APsYjyqU/HIG+AP2cDzUToL/6NfkUmw7Qn/zkJ+d9zKM6PPYq32OPPdJhhx2WXvGKV6Qjjjhis0wXX3xxOuaYY/L33ve+96XXve51W+SM77/+9a/P34+fe8ELXvBw6P0sAQIEtqvA9DeeXW3VNFPmSxviAccyd8pzowjDo9p85Q5ljhQP/G7slCA7/nttdAeqHliO8DymTPGQcYTqcbz475hfrZgoW1bFK4Li2Wq/9fjvCM7rLbNi/jC7MXXuWFdauee92KtW7ssmSvewaPOeu10tSq1dlpWQelErTR741e1qGR+eO3dVVfH1yfSe1/TXn1W+nPeqjoet60A0/sbNPe7L37ezs+nKYzc9AHD4D6vAfbtfoROYb4Hcxj3GRf0QcX4IeWNqxfxoUSt1VqyoqtGr9YRo+R57oMd6SLXtXGenncrPx3wq5uEC9Pm+jT5vhAT6AvQXDrgCPQL0v2k+QH/LW96S3vrWt27zXfne976XnvSkJ+W51kEHHZQuv/zytHRp9f+3p9ilb22K9u/XXHNNnoddf/313YeUt/nD/AABAo0KCNAb5XVwAgQIDLdA+wdHpVZURUcFQlS0tqdT59511UJFmX/m6vNYgIlXVDHU+/UtXZInlp17N5SwO6owctV19f56b/RY9Mj72k2U0D0WP+r9yGqemNTWLftiIeieDZsmt/fPlAWoOEYcOya0EaZHa8JYEKpC+clfuHwosKf/44huWPnA8Lx7uRGixysq+uv9ztdvyJ657Vze5zq8ptLGxYvT0pXnDsW1OYntI5AD9HqhMKoletqz54XV2Boh/35WHSViwSe3fI+2hfH7Vz3UEe+pF13jdz610tSen94+F+VTCSxQgd4A/ekRoD/ykQO9kg133pm+VgXosU/ennvu+ZDHj/bo2/qay55+xx13XPrYxz6Wdt55577DR8V5VGLE64ILLki/9Vu/tcWP/+QnP5mOP/74/P34uahI9yJAgMBCFZj+9+dUf29FJ6yZ1Pnp+rL11PqN5aHeeOA4guqYN8XXYy5Ub4MV86kI0aPde/0AclSrR4Cew7pqHhXzpt527jFPqB/MjU/P87bqIeX4ey8+5/6qK1F0BFtTbcMV51PNm2JLrXxO8b+q7fnQBOhxTdX2XpNPe3Conx9aqK930aJ041uuTj93+tNKe/v4Wza27ZrdmNqfuSF97a/uyPdHgL5Qf8MGc97tW35j04MZ0zOlC1wUAcR8KcKrqhlEfqA9Wr7feVf5HVu5U3mQP6rPZ2fLz0UAX3V6mDzgisGcoKMQGCOBOkB/7B+9PD36mKMGeuU/+tRn0o0f/3+NV6D/rAH6ySefnD74wQ/ma77yyivTIYcc8qDrX7VqVTr00EPz10855ZT0/ve/f6BGDkaAwGAEBOiDcXQUAgQILFiB9vePSq1oKR6TxPXtUr0Qr5490XO79likiEWdaMEee4VFpURUTMT7o9q8qpzIleHRbj2yugi+Y1/0usJ8alFpUZjbo+1QPqduO51DwaoyIyaxsdB07/rS5rBaOMkLVNHGPVoP7hxVFKUCYSHuVxYPL8RkfernLn7Q2Gnfdlx5emFqKk09UvX5gv3lGvCJt296YQnEo7o81k7zdgHxoEXV2SF+h/N+6VGZXoXm8XuUn4TpaX1Zt8CMSvTd/27AZ+lwBEZboC9A/99nNxOgn3DinBHztivb+IpWg8cee2z61V/91byP+YoVK9Lq1avTZZddloPuO++8Mx8xqiK+8IUvpMl4qKt6vfvd787tB+P12c9+Nh155JarSeL7ddX5e97znvQnf/In23im3k6AAIHhEbj7tN3T8t99YukkFa3cIwyPuVHMh6ZnSxgeld8rpnJInpZE5XmEdLNl7/OYM0XFejx4HA8kx3wpwu2dp1LnvnaZM8V8KgLl3GFocQm+46HlKmTO87N67lRVs+f3xmfkPdc35gA+t3SPvwDjfHaeKmF6HVQfNL/t2h/uHWxf+rTU2nEqH+aBreanv3pQmv3xunTred/P33/s+ese7sf5+REQaP/w6LxO0MoPF5fuXJ2dd9wUoMffTmvXlfA8fp9W7pw68VBy/M7GpKmqPs/bK8R6Q2qlyf0/PwIyLoHA/AqMa4Ae87N99tkn3XLLLXmudd11120RPr7/7W9/O8VD0dEVbC4POs/vXfRpBAgI0I0BAgQIEEi5ajraQsdefbGoE686QK8rJ1bEE9utlKJSOqqk857n0Xq8zDPzolC8IkCPhZqI+tbO5IqMXIER/5tolXbwjygVbZNP/FL+5/R1zy0T1fhfvV/z2nUprZvJC04R4Hd+siYfo/WopZv2NYuJbhznKZe6iwRGXqB98wtLOF7/L644HmiJior4vYnfmfw7VLV6j4C93iqgbt9eb4GQ942MLgiLbBEw8iPHBQ5SoC9AP/t/NxOgnzj3Su2fJUC/++6708qVKzfLcvvtt6ejjjqquyffWWedlV772td23/v2t789nX766fm/v/SlL6XnPve5W+T98pe/nEP6eMXPvfnNbx7krXAsAgQIbBeB6a8dktK66dRZU3X8iYryaNVeVaG3dl9RQru8T3oVmMc8qGeOledIEaCvmMwt3nPgXbdbj3/mTkKt1Np1Rf5bru60lbeLyseNB5g3VnuZd1JnTTulCNBz2/b47+m8F3t+6Dj2Pa/nTAf+83Yx86EE5lug/f0jU2vdhjL+Y1u42P5gWdU+eePG1Lr73jJ3iorzCNiXL6s6NMSDx61ShX7f/amz44ryuxa/Q9MzaWrfBz/8Pt/X5vMILBSBboD+ylcMvgL90/8wtBXo3//+97vt2KMDVzygvKVXfP+cc87J346fe+xjH7tQbq/zJDA2AgL0sbnVLpQAAQIPLTD9zcNTum9tCbvzXnNVu+fI0yP4Xj5ZgroI0KNFYATs0Va91Uqd6Y0pxb5+EaRPLS7t2neIfeoWbapcj5+tWqClqck5VY1HVUGKp8XXrk2duzfk/dXzQlJ80MRiwblBPXYC7RuPLqF4/H7G71sE4XUL9/vuL79zEZznFu1pU/v2+Fr8XscrKi2iMj0fp5OmHnn+2Dm6YAI/q8B8BuhNtXDf2rXH4k3s2ddut9P++++fvvvd73Z/RAX61vR8nwCBURfIIfbda0uXrFwRXuZMOUSfWFS2mYqOWzGdiorw2Cs9/mzbcbJUra+fKeF7VIdHBXrMm5YvKX/b1YFfHC9C9qhmjyx9K1Xj7X86sITlsc/z3VHNPptaeyyr9oNelCYF56M+LF3fAwSmr//VHIh3WotSa8OG1FkyVcLyeK1vp9Ydd6YUX4vt4GI7ucnJ1MndHsqDyK1161InOuZFmJ4r0ss8SoBuqBGYu0BfgH70C+b+g3N4548iQP/b/9t4C/cnP/nJeR/zqA6Pvcr32GOPdNhhh6VXvOIV6YgjjtjsmV588cXpmGOOyd973/vel173utdt8Yri+69//evz9+Pn6u5dcyDwFgIE5klAgD5P0D6GAAECwy4w/Y1fTmlNVLDGAlDVPrCnNWCuHN9xaQnQo/I82rBHxXpUnsf/oqqiDtBXTKS046ZFmzTdTmmyav0eEIsXpckDvrJVkun/+JUyWY1J730bUiv2UN9hMk0+/cqt/qw3EBhFgdySMMLvqjKi28K93qMv2njGL2Ld1jneF+F57BUZLQgjNO9u/leEbBMwiiPFNTUl0BugP+1D5zRSgf6vr3l1Pv2bb745t/PbHq9Y9IlFnHhF+8FHP/rR+d/tgb497obPJEBgmARiX+7OXWtLxXjMiWIP88jQY6up+PdovR7V5XVr9U4ntZZFlXlsl7UxddZVc6fo2hUV6CunygORucJ1uvytFn/XxQPKk4u3Gp7XNtNff1aukO3ctaHs3rNiMk0eevUw0TkXAvMmkNcRdliSOosW5Vbundj2KqrQ47V+Q2rdvyZ1li0rW9lFhXl8v7WoVKJv7jU7m6b2/sy8nb8PIjAKAt0A/Q9fkfYccIB+y9+XAD0+41Of+tRWubZ1TjWXVurHHXdc+tjHPpZ23rl02Kxf5ktbvR3eQGBBCQjQF9TtcrIECBBoTmD66meWg0erwQ1lr/EcjkcwHvt/RXVEhOZTk+Up7KiQiPfeP1MWkOKVK2KrhaOdlpQnuuN/OcyLvZvL+yaf9OVtvpA4v8ln/Ms2/5wfIDBKAtGOMC2KRdbFJUSP/8XvWPzuxcMteX/L6netDs/j/Tk4r8rWY1G2WuxVfT5Ko8O1zIdAX4D+wQjQdx3ox2648470rydt/wA99jmPavN4RSV8LE7F66KLLkovfOEL87+rqBjorXcwAgQWkMD0Px+UOhGexzwpqs3jgd81M6UKPQL0FZMpLZsqX18/XTp8xffXxz7l1d9hi1tlj/MI0aPTVwR4dZehupNQzJt+advark9/5ellvvXsry0gUadKYPACeZu6HabK/ua9c6bpmRKax8Mtsa5x/9rU2WlFrkIvc6ZqXSPWLuKhlomJNLXPhYM/QUckMOIC3QD9D/6gmQD9//yfOQtu67ZXy5cvT8cee2zejir2KV+xYkVavXp1uuyyy/IDxXfeeWf+7Oc85znpC1/4QpqM//ejeunYNefb4o0EFoSAAH1B3CYnSYAAgfkRmL7mkFLxMNt5QFvC2Nt8Udm/PBZ32iU0z63bZzolQI/gPEK5qKaYiH9WT3nXf0jmYC+lySd8YX4uxqcQGEGB9g9fUAL0+HWqF3jqPdBzgF5ddOyNGb978XsXv7OxSBSveJglFnMnFqepXS8YQSGXRKBZgb4A/QMNBegnb/8A/Q1veEN6z3vekzF7A3R7+jU7vhydAIGFIxBbTeVgPALxeOg45k/x0HEE6tGefccdSreuCNqj8jxavsf7q7/VWrHd1dLFeQ6VW7XH33cxD4uHJKvXXDp2LRwxZ0pgfgWmv/v8PBfqRAFAzH+iZXt08srB+Ez53VuztuyRvnz5pt+9CNUXL0qtNdHGfUk+6anHXDS/J+/TCIyAQF+A/oKjB3pFt/zD36cbGwzQ77777rRy5crNnvPtt9+ejjrqqHTttdfm75911lnpta99bfe9b3/729Ppp5+e//tLX/pSeu5zn7vFa//yl7+cQ/p4xc+9+c1vHqiTgxEg8PAFBOgP39ARCBAgMFICed/xWACq9vHrLgzFYs/kotTaZXlKa9eX6vR4VQtGaSL2VE6ptXJp/rI26yM1LFzMEAm0f3RstfhaPbQS7dlzxVI8/BLtRGMRd7YE6PUibH4wpuoU0Ulpao+ttzkbokt2KgSGRqAvQP9fH26mAv2UV+Xr3Z4t3I8++uh0ySWX5POIa95rr73yv0f1RrRAvPXWW3M1xnXXXbfFexP7qF9//fX5Z+Na5tIKcWhutBMhQIDAVgSi0jvPh+Jh4twVqMyLOu3Z1IpgPCrRFy/qeyg574m+qFX2K984m1orl2izbqQRaEig/f2jyhypatAVWyLkrRLi9zUqy+NXd83aErDXHb3yFnYbU+v21d0tsSYPvqqhM3RYAqMtMB8BelMt3Ld2Z+Kh4pjrtNvttP/++6fvfve73R9Rgb41Pd8nsLAEBOgL6345WwIECMyLwPRVB5entOMJ7Vgwv6+dF4dyG/fJCMoXlZBuyVTq/OT+0hk6KtSj/eDMbJo87Jp5OU8fQmAcBdq3HlcWdOL3MLo+xL6ZVYeHHJJHVUX+flSfT1ZV51WAPttJU3t+ehzZXDOBgQj0BugHvv8jjQTo1/7nP8rnur0C9FgQinB8eno67bfffumGG27oszvppJPShz70ofy1K6+8Mh1yyCEPsl21alU69NBD89fj/R/4wAcG4u8gBAgQGCaB3Mo9tr6aWJQ6uV37xtS5f7p07VoeVehTKa2L1u6zKa2NcH1jauXtriZSio5dUdn63FLB5kWAwGAF2jceXc2JYjur1qbwPD5mtnSNyHOmdjulHar90eN7MzOptfqONPn0VYM9IUcjMGYCdYC+7yv+MO3ZQAX6Tf/3b9Ozn/3sdMUVV2wX2WOOOSZdfPHF+bNvueWW9OhHPzr/uz3Qt8vt8KEEGhMQoDdG68AECBBY2ALTXzu0tH2OhaBYGIqFoKiWWLQoV5dPf/PwlNatT527NuQFoNayeHJ7Mk0+7asL+8KdPYEhF2j/+EX9AXrdyj0WgXpfeZ/zek/0apEoFmpVnw/5HXZ6wyzQF6D/ZUMB+mubC9AvvPDC3HJwIiqwNvN6YEvC9773ven1r3993zu/853vpKc85SlpZmYmHXTQQenyyy9PS5eW7jPxWrduXTr88MPTNddckz/nW9/6Vnr84x8/zLfVuREgQOBnFphe9Yzcxj1vbbW+bIWVdpzMrdzrh4pz0H7Xhm6Hr9YuU/nzPHT8M7P7QQJbFWjfdExVgV517cpzpmpulDtzVf8eU6h4GDlvdRXbMkyn1n33p8kDtk8ot9UL8wYCC0RgU4D+yrTnUYNu4f7pdNP/274B+qmnnpqi2jxevVteXXTRRemFL3xh/vr73ve+9LrXvW6Ldyy+X8+1Iox/wQtesEDurtMkMD4CAvTxudeulAABAtskkAP06ZnSmjDaue9c9v+qn8TO+6VvmEmdtTOldXtUWMT3tTjbJmdvJrAtArn6PLcirBaC4ofr6vNY9ImFoAjO6wr1es/zaBORv5/S1O5/ty0f6b0ECPQI9Afof9VMBfprX5k/sYkK9H333TdXlr/4xS/OFeLx3xF+33HHHenSSy/NFRN33nln/vyo6PjiF7+YlkRb0we8TjvttHTmmWfmrx544IHpjW98Y3rc4x6Xq9Xf+c53dvcEjPedccYZxhABAgRGVmD6ymfkyvJ46Di3c4/K88lFafJZmzpytf/pwJTunyl/i01V7d13WJwmD7l6ZF1cGIHtLZAD9OioVwfn9VYLdeV5aQKRUqvaCiumSlVr96hQn9rvc9v7Enw+gQUt0A3QX95AgP6Z7R+gv+ENb0jvec978j3qDdCjm1fMi+J1wgkn5PnVll7x/XPOOSd/O37usY997IK+506ewCgKCNBH8a66JgIECAxAYPrfn5MD9DyJjEq1yYk0+QuXP+jIUXXRWb8xtVZMpTQ1mSZ/0ZPaA+B3CAKbFcgBet5aodprMy8EVf8rK0DV/ujVYlAE57kyvSdA3/UCugQI/IwCfQH6WR9tJkD/4z/MZ9dUgH7jjTdu9eojYP/IRz6SVq5cudn3zs7Ople96lXpox/96BaP9cpXvjIvCC2qu2Rs9VO9gY9pC9UAACAASURBVAABAgtToBuix59fSyc2+0BxnjPd085/l7V2qR5MFqAvzBvurIdeoP3Do1OaiG2uqjlR/c9qulQuoErQ4+HjmC9FW/e6oVdnNk3t9Zmhv04nSGCYBfoC9COPGeip3hIB+rkf364t3I8++uh0ySWX5OuKOeJee+2V/73T6aS999473XrrrXlbrOuuu26L1x77qF9//fX5Z2Pu16qLIwaq5WAECDwcAQH6w9HzswQIEBhhgZh0tu6/P4fi8Zp8whe3eLV5z/SJCe3bR3g8uLTtL9D+0bElPI8qid4K9Fx1HqF6tQ5U/Xus/0RzwvhO/c8lK8/d/hfiDAgsYIHeAP2XIkB/xK4DvZoNP70jfb3BAP2yyy5L8b/YuzyqHKLy/N57700rVqxI++yzTzrssMPSy1/+8u7+5Vu7uFg0ipD86quvzsfaddddUyyWRTVFtIr3IkCAwDgI5LnQ9GyuLp98xr885CW3P/uLqbV8Mk0e/rVxoHGNBOZdoP39o8rcKHftSiktroL0ulNX3bq97yHkmDBVLdyrM57a89Pzfu4+kMAoCdQB+s/9/h+lPQccoN964ae2a4Ae86gIx6Oz13777Ze7cPW+TjrppPShD30ofynmXYcccsiDbu2qVau6c654/wc+8IFRuv2uhcDICAjQR+ZWuhACBAgQIEBglAXat/3mA6rNe1q25wWgWChqpU6rlQPz2W4JRekouvMu540yj2sjMC8CfQH6//zrZgL01/1BvpYmKtDnBcmHECBAgAABAgS2k0D7hy+oHjauHjquz6MO1OuHkesW7r3nWW959ahPbaez97EERkegG6C/rKEA/bxmKtAvvPDC/CDwRHTi3Mzr9ttvz9+/9tpr83ff+973dvcxr9/+ne98Jz3lKU9JMzMz6aCDDkqXX3553jarfq1bty4dfvjh6Zprrsmf861vfSs9/vGPH52b70oIjJCAAH2EbqZLIUCAAAECBEZXoH3bcSUkj33M99i0qNP+8Yu6+/t1Fi/O4Xlp2t4phRQViQB9dMeGK5s/gb4A/X0fayZA/y+vyBckQJ+/++qTCBAgQIAAgdEQaP/gqO6DxXne9JiL8oW1b3phqUifnNy05VW9/VVu3V71b+/0z7VGQ8VVEJh/gU0B+qvSnr/+woGewK0X/V266by/aaSF+7777psry2NLq0MPPTTFf0f4Hd22Lr300ryn+Z133pmv59nPfnb64he/mJYsKVuz9L5OO+20dOaZZ+YvHXjggemNb3xj3hs9qtXf+c53dgP4eN8ZZ5wxUB8HI0BgcAIC9MFZOhIBAgQIECBAoDGB9i2/kY89tdc/9H1GDtB7QvX23b+TZnIXwojQBeiN3RAHHkuBvgD9fzQUoL9egD6Wg8tFEyBAgAABAg9bYPqGX8/HmHzc5x90rPZNZR/mCNVzd6+89VXV1Sv+KTx/2P4OQKAW6Abov/eqtEcDAfrNn2guQL/xxhu3eiMjYP/IRz6SVq5cudn3zs7Ople96lXpox/96BaP9cpXvjJvh7Wo3pJvq5/qDQQIzLeAAH2+xX0eAQIECBAgQKBBgfZd/6ns4RcVFtHaPf9LSlO72P+8QXaHHhOBvgD9vX+Tpga8B3o79kD/k5dnTRXoYzKoXCYBAgQIECAw7wI5QI/EPM+bWinV7dsf8LDyvJ+YDyQwIgJ9Afrzjx3oVd168d+lmz/xsUYq0C+77LIU/4u9y2Ov86g8v/fee9OKFSvSPvvskw477LD08pe/vLt/+dYu7JJLLskh+dVXX52Pteuuu6awOeGEE3IreC8CBIZbQIA+3PfH2REgQIAAAQIEtkmg/dOXlAWgahEofnhq1wu26RjeTIDA5gX6A/SPNxSg/37+cAG6UUiAAAECBAgQaEYgd/GanU1p48ZN4XnV7r2ZT3RUAuMl0A3QX/rqtMfAA/RPppvPbyZAH6+75GoJENiagAB9a0K+T4AAAQIECBAgQIAAgZRSX4D+noYC9P8qQDfYCBAgQIAAAQIECBBYuAJ9AfrzBlyBfokAfeGODGdOYGEJCNAX1v1ytgQIECBAgAABAgQIbCeBvgD93RGg7zbQM2n/dHX6+hsE6ANFdTACBAgQIECAAAECBOZVoA7QH/O7J6Q9Bhyg3xYB+gV/3UgL93lF8mEECAy9gAB96G+REyRAgAABAgQIECBAYBgEegP0A971t40E6N849WX5UrVwH4Y77hwIECBAgAABAgQIENhWgf4A/Te29ccf8v23XXKBAH2gog5GgMCWBAToxgYBAgQIECBAgAABAgTmINAXoL+zoQD9jQL0OdwKbyFAgAABAgQIECBAYEgFugH670QF+oAD9M8K0If0tjstAiMnIEAfuVvqgggQIECAAAECBAgQaEKgP0D/P2lqlwG3cL9rdfrGG38vn7oK9CbuoGMSIECAAAECBAgQINC0wKYA/cS0x681EKB/8qNauDd9Ex2fAIEkQDcICBAgQIAAAQIECBAgMAeB/gD9/zYUoL9UgD6He+EtBAgQIECAAAECBAgMp0A3QP9PJ6ZH/dpxAz3J2z57QfrR3/2VAH2gqg5GgMDmBAToxgUBAgQIECBAgAABAgTmINAboP/imc0E6P/2pwL0OdwKbyFAgAABAgQIECBAYEgF+gL0Xx1wgP45AfqQ3nanRWDkBAToI3dLXRABAgQIECBAgAABAk0I9AXof/H/GqlA/7fTfjefuhbuTdxBxyRAgAABAgQIECBAoGmBboD+ktekRw08QD8//ehTKtCbvoeOT4BA0sLdICBAgAABAgQIECBAgMBcBPoC9DPObSZA/7PfEaDP5WZ4DwECBAgQIECAAAECQynQH6D/5kDP8bbPRYD+ES3cB6rqYAQIbE5ABbpxQYAAAQIECBAgQIAAgTkI9AXo/72hAP1NAvQ53ApvIUCAAAECBAgQIEBgSAXqAH2fqEB/7oAD9M+fn24RoA/pnXdaBEZLQIA+WvfT1RAgQIAAAQIECBAg0JBAb4D+C+84r5EK9G+++T/ls9fCvaGb6LAECBAgQIAAAQIECDQq0A3Qjz958AH6P34i3fLpD6tAb/QOOjgBAiEgQDcOCBAgQIAAAQIECBAgMAeBvgD97Z9oJkD/by/JZyJAn8MN8RYCBAgQIECAAAECBIZOoC9AP2LAFegRoP+9AH3obroTIjCCAgL0EbypLokAAQIECBAgQIAAgcELCNAHb+qIBAgQIECAAAECBAiMlkA3QP+tk9PuR7xooBf34y+cJ0AfqKiDESCwJQEBurFBgAABAgQIECBAgACBOQj0Beh/3lAF+ukq0OdwK7yFAAECBAgQIECAAIEhFdgUoJ+Sdv+VBgL0fzhHC/chvfdOi8AoCQjQR+luuhYCBAgQIECAAAECBBoT6AvQ39ZQgP4WAXpjN9CBCRAgQIAAAQIECBBoXECA3jixDyBAYB4EBOjzgOwjCBAgQIAAAQIECBBY+AIC9IV/D10BAQIECBAgQIAAAQLNCnQD9Bc3UIH+xfPSLSrQm72Bjk6AQBYQoBsIBAgQIECAAAECBAgQmIPAgwL0lbvN4afm/pb23avTN1Wgzx3MOwkQIECAAAECBAgQGDqBvgD9OQNu4R4B+me0cB+6m+6ECIyggAB9BG+qSyJAgAABAgQIECBAYPACfQH6Wz+RppoI0N+qhfvg75wjEiBAgAABAgQIECAwXwIC9PmS9jkECDQpIEBvUtexCRAgQIAAAQIECBAYGYHeAP2pb2kmQP/3twnQR2bAuBACBAgQIECAAAECYyjQDdBfdErafdAV6F9SgT6GQ8olE9guAgL07cLuQwkQIECAAAECBAgQWGgCfQH66Q0F6H8uQF9o48L5EiBAgAABAgQIECCwSaAboP9mQwH6hVq4G28ECDQvIEBv3tgnECBAgAABAgQIECAwAgIC9BG4iS6BAAECBAgQIECAAIFGBQTojfI6OAEC8yQgQJ8naB9DgAABAgQIECBAgMDCFugL0P9bQxXob1eBvrBHibMnQIAAAQIECBAgMN4CdYC+d1SgH/6igWL8+EvnpVsvUoE+UFQHI0BgswICdAODAAECBAgQIECAAAECcxDoC9Df3FCA/g4B+hxuhbcQIECAAAECBAgQIDCkAt0A/bgGAvQvC9CH9LY7LQIjJyBAH7lb6oIIECBAgAABAgQIEGhCQIDehKpjEiBAgAABAgQIECAwSgIC9FG6m66FwPgKCNDH9967cgIECBAgQIAAAQIEtkGgL0B/U0MV6P9dBfo23BJvJUCAAAECBAgQIEBgyAS6AfpvnJJ2/+UBt3D/p/PSrRdr4T5kt9zpEBhJAQH6SN5WF0WAAAECBAgQIECAwKAF+gL0P2soQD9DgD7o++Z4BAgQIECAAAECBAjMn4AAff6sfRIBAs0JCNCbs3VkAgQIECBAgAABAgRGSECAPkI306UQIECAAAECBAgQINCIQF+A/uwGKtAvUYHeyI1zUAIE+gQE6AYEAQIECBAgQIAAAQIE5iDQF6Cf1lAF+l+oQJ/DrfAWAgQIECBAgAABAgSGVKAboB97Stp90AH6peelWwXoQ3rnnRaB0RIQoI/W/XQ1BAgQIECAAAECBAg0JPCgAH3n3Qb6Se17Vqd/F6AP1NTBCBAgQIAAAQIECBCYXwEB+vx6+zQCBJoREKA34+qoBAgQIECAAAECBAiMmIAAfcRuqMshQIAAAQIECBAgQGDgAn0B+rMG3MI9KtA/q4X7wG+aAxIg8CABAbpBQYAAAQIECBAgQIAAgTkI9AXof/qJNNVEBfqZWrjP4VZ4CwECBAgQIECAAAECQyrQDdBfeErafdAB+mUC9CG97U6LwMgJCNBH7pa6IAIECBAgQIAAAQIEmhDoC9Df2FCA/k4BehP3zjEJECBAgAABAgQIEJgfAQH6/Dj7FAIEmhUQoDfr6+gECBAgQIAAAQIECIyIQG+A/pSGAvT/EKCPyGhxGQQIECBAgAABAgTGU6AboB/TUAX657RwH8+R5aoJzK+AAH1+vX0aAQIECBAgQIAAAQILVKAvQD+1mQr0/3iXCvQFOjycNgECBAgQIECAAAECKaW+AP2wAe+Bfvl56VYBunFGgMA8CAjQ5wHZRxAgQIAAAQIECBAgsPAF+gL0NzQUoL9bgL7wR4orIECAAAECBAgQIDC+AnWAvldUoA84QL9dgD6+A8uVE5hnAQH6PIP7OAIECBAgQIAAAQIEFqaAAH1h3jdnTYAAAQIECBAgQIDA/Al0A/SjGwrQP6+F+/zdTZ9EYHwFBOjje+9dOQECBAgQIECAAAEC2yDQF6D/14Yq0N+jAn0bbom3EiBAgAABAgQIECAwZAJ9Afqhg23hfvsV56VbBehDdsedDoHRFBCgj+Z9dVUECBAgQIAAAQIECAxYoC9A/5OGAvT3CtAHfNscjgABAgQIECBAgACBeRQQoM8jto8iQKAxAQF6Y7QOTIAAAQIECBAgQIDAKAkI0EfpbroWAgQIECBAgAABAgSaEOgG6C84Je1+SAMV6F/Qwr2J++aYBAj0CwjQjQgCBAgQIECAAAECBAjMQaAvQH/9+Wlq593m8FNzf0v7ntXpP/7Hb+cfuPnmm9Pee+899x/2TgIECBAgQIAAAQIECAyBgAB9CG6CUyBA4GELCNAfNqEDECBAgAABAgQIECAwDgJ9Afp/aShAf58AfRzGkmskQIAAAQIECBAgMKoC3QD9qKhAf/FAL/P2r5ybblWBPlBTByNAYPMCAnQjgwABAgQIECBAgAABAnMQEKDPAclbCBAgQIAAAQIECBAYa4FugH5kQwH6F7VwH+sB5uIJzJOAAH2eoH0MAQIECBAgQIAAAQILW+BBAfpOA27hfu/q9B8q0Bf2IHH2BAgQIECAAAECBMZcQIA+5gPA5RMYEQEB+ojcSJdBgAABAgQIECBAgECzAn0B+uvOT1NNBOj/Uwv3Zu+ioxMgQIAAAQIECBAg0KRAX4D+zAG3cP/nc9OtKtCbvH2OTYBAJSBANxQIECBAgAABAgQIECAwBwEB+hyQvIUAAQIECBAgQIAAgbEW6Abov35K2r2JAP1LWriP9QBz8QTmSUCAPk/QPoYAAQIECBAgQIAAgYUt0Beg/3FDFehnqUBf2KPE2RMgQIAAAQIECBAYbwEB+njff1dPYFQEBOijciddBwECBAgQIECAAAECjQr0BuhPfm0zAfq3/lKA3uhNdHACBAgQIECAAAECBBoV6AvQDx5wC/evnptuVYHe6P1zcAIEioAA3UggQIAAAQIECBAgQIDAHAQE6HNA8hYCBAgQIECAAAECBMZaoBugP/+UtFsDAfptX9bCfawHmIsnME8CAvR5gvYxBAgQIECAAAECBAgsbIG+AP0/N1SB/n4V6At7lDh7AgQIECBAgAABAuMtIEAf7/vv6gmMioAAfVTupOsgQIAAAQIECBAgQKBRgb4A/ZSGAvT/JUBv9CY6OAECBAgQIECAAAECjQr0BejPGHAL9yvPTSrQG719Dk6AQCUgQDcUCBAgQIAAAQIECBAgMAcBAfockLyFAAECBAgQIECAAIGxFugG6M87Je3WRID+T1q4j/UAc/EE5klAgD5P0D6GAAECBAgQIECAAIGFLdAXoJ/cUAX6B1SgL+xR4uwJECBAgAABAgQIjLeAAH2877+rJzAqAgL0UbmTroMAAQIECBAgQIAAgUYF+gL0kxoK0D8oQG/0Jjo4AQIECBAgQIAAAQKNCnQD9F87Je120IBbuK86N912qQr0Rm+ggxMgkAUE6AYCAQIECBAgQIAAAQIE5iAgQJ8DkrcQIECAAAECBAgQIDDWAgL0sb79Lp7AyAgI0EfmVroQAgQIECBAgAABAgSaFOgL0F/TUAX6h1SgN3kPHZsAAQIECBAgQIAAgWYFugH6rzZQgf4vKtCbvXuOToBALSBANxYIECBAgAABAgQIECAwB4EHBeg77jaHn5r7W9r3rU7fEqDPHcw7CRAgQIAAAQIECBAYOoG+AP3pA27hHgH6ZVq4D91Nd0IERlBAgD6CN9UlESBAgAABAgQIECAweIFRC9Bvuumm9Jd/+Zfp4osvTvHvS5YsSfvvv3/67d/+7XTSSSelZcuWDR7REQkQIECAAAECBAgQGGmBUQnQzZdGepi6OAJbFRCgb5XIGwgQIECAAAECBAgQIJBSX4B+4vlpqokK9LPnp4V7hOYvfelL0z333LPZW/vzP//z6ZJLLkn77befW0+AAAECBAgQIECAAIE5C3QD9OeeknZrogL98uYr0M2X5ny7vZHAyAoI0Ef21rowAgQIECBAgAABAgQGKdAXoJ/QUID+v5sP0L/xjW+kww47LK1duzatWLEinXbaaemII45I69atS+edd1768Ic/nNme+MQnpquvvjq/x4sAAQIECBAgQIAAAQJzEegL0J824BbuV52bbms4QDdfmstd9h4Coy8gQB/9e+wKCRAgQIAAAQIECBAYgMCoBOgRll966aVpYmIiXX755enQQw/t03n3u9+dTj311Py1t73tben0008fgJ5DECBAgAABAgQIECAwDgILPUA3XxqHUeoaCWxdQIC+dSPvIECAAAECBAgQIECAQF8L9ye9upkK9OvOabYCPSrKDz744Hw3TzjhhHT22Wc/6M7Ozs6mpz71qem6665Lu+yyS7r99tvT5OSkEUCAAAECBAgQIECAAIGtCtQB+qOPOCXtNuAK9J9EBfoVzbVwN1/a6u31BgJjIyBAH5tb7UIJECBAgAABAgQIEHg4Ar0V6E96VUMB+oebDdDf9KY3pTPOOCMzrFq1Kj3zmc/cLMmZZ56ZW7vH6x//8R/T8573vIdD52cJECBAgAABAgQIEBgTgW6A/isNBOhXNxugmy+NySB1mQTmICBAnwOStxAgQIAAAQIECBAgQGAUAvTDDz88XXHFFWn58uXp7rvvzm3cN/e68sor8z7p8YoW7tHK3YsAAQIECBAgQIAAAQJbE1jIAbr50tburu8TGB8BAfr43GtXSoAAAQIECBAgQIDAwxDoC9D/qKEK9I80W4G+2267pTvuuCMdcMAB6etf//oWNe666670iEc8In//+OOPT+eff/7DkPOjBAgQIECAAAECBAiMi0BfgH7giwd62T+JCvSvNNfC3XxpoLfLwQgsaAEB+oK+fU6eAAECBAgQIECAAIH5EugN0Pd/yQfS5IrdBvrR0/evTt/7xMn5mFdddVXac889H/L4e++99zZ9/vr169PSpUvzzxx99NHpoosuesifX7FiRVqzZk065JBDUlSkexEgQIAAAQIECBAgQGBrAnWAvtvTX5J2/aXBBuirv3ZuuuPrn07xGZ/61Ke2dippW+ZM5ktb5fQGAmMlIEAfq9vtYgkQIECAAAECBAgQ+FkFegP0n/UYg/y5TqezTYdbvXp12n333fPPvOQlL0nnnXfeQ/78ox71qPSTn/wkPfWpT03f/OY3t+mzvJkAAQIECBAgQIAAgfEUqAP0Ybj6bZkzmS8Nwx1zDgSGR0CAPjz3wpkQIECAAAECBAgQIDDEAgs9QL/55pvTYx7zmCz8spe9LH384x9/SO14b/zM4x73uPS9731viO+MUyNAgAABAgQIECBAYFgEFmqAbr40LCPIeRAYDgEB+nDcB2dBgAABAgQIECBAgMCQC8zMzKQf//jHjZ/lbbfdlmLvvYmJiYf8rG1pRxgHUlHR+K3zAQQIECBAgAABAgTGXuD2229P09PTjTrccccdadddd93qZ2zLnMl8aauc3kBgrAQE6GN1u10sAQIECBAgQIAAAQLjKmBPv3G9866bAAECBAgQIECAAIGtCZgvbU3I9wmMl4AAfbzut6slQIAAAQIECBAgQGCMBaKyPao1DjjggPT1r399ixJ33XVXesQjHpG/f/zxx6fzzz9/jNVcOgECBAgQIECAAAEC4yBgvjQOd9k1EpibgAB9bk7eRYAAAQIECBAgQIAAgQUvcPjhh6crrrgiLV++PN19991bbBN/5ZVXpsMOOyxf7+mnn57e9ra3LfhrdwEECBAgQIAAAQIECBB4KAHzJeODAIFaQIBuLBAgQIAAAQIECBAgQGBMBP7sz/4s/cVf/EW+2lWrVqVnPvOZm73yM888M5122mn5e5///OfT85///DERcpkECBAgQIAAAQIECIyrgPnSuN55103gwQICdKOCAAECBAgQIECAAAECYyJw1VVXdUPzE044IZ199tkPuvLZ2dn01Kc+NV133XVp5cqV6Sc/+UmanJwcEyGXSYAAAQIECBAgQIDAuAqYL43rnXfdBAToxgABAgQIECBAgAABAgTGWqBuSzgxMZEuv/zydOihh/Z5vPvd706nnnpq/tpb3vKW9Na3vnWsvVw8AQIECBAgQIAAAQLjI2C+ND732pUSeCgBFejGBwECBAgQIECAAAECBMZI4Nprr03Petaz0rp169KKFStStCk84ogj8n+fd9556ZxzzskaT3jCE9I111yTdtxxxzHScakECBAgQIAAAQIECIyzgPnSON99105gk4AA3WggQIAAAQIECBAgQIDAmAlceOGF6fd+7/fSvffeu9krj/D84osvTvvvv/+YybhcAgQIECBAgAABAgTGXcB8adxHgOsnkJIA3SggQIAAAQIECBAgQIDAGArceOON6ayzzspB+Y9+9KM0NTWVA/Pjjz8+nXLKKWnZsmVjqOKSCRAgQIAAAQIECBAgkJL5klFAYLwFBOjjff9dPQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUAgJ0Q4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQt3A0CAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBVSgGwgECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECAbgwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEioALdSCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0Y4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBQBFehGAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKAbAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoAioQDcSCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAN0YIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECRUAFupFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6MYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoAirQjQQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBANwYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEARUIFuJBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6MUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIqACnQjgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNCNAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUARUoBsJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgG4MECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBIqAC3UggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdGOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUARXoRgIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABaO4E5QAAEVRJREFUAgQIEBCgGwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAIqEA3EggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgADdGCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkVABbqRQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOjGAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKAIq0I0EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQDcGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEVCBbiQQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBujFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSKgAp0I4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQjQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAEVKAbCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQIBuDBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSKgAt1IIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnRjgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFAEV6EYCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoBsDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgCKhANxIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAA3RggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJFQAW6kUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToxgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECgCKtCNBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEA3BggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBFQgW4kECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboxQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEioAKdCOBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0I0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQBFSgGwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECAbgwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEioALdSCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0Y4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBQBFehGAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKAbAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoAioQDcSCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAN0YIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECRUAFupFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6MYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoAirQjQQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBANwYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEARUIFuJBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6MUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIqACnQjgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNCNAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUARUoBsJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgG4MECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBIqAC3UggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdGOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUARXoRgIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgGwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAIqEA3EggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgADdGCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkVABbqRQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOjGAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKAIq0I0EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQDcGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEVCBbiQQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBujFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSKgAp0I4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQjQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAEVKAbCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQIBuDBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSKgAt1IIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnRjgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFAEV6EYCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoBsDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgCKhANxIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAA3RggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJFQAW6kUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToxgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECgCKtCNBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEA3BggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBFQgW4kECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboxQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEioAKdCOBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0I0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQBFSgGwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECAbgwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEioALdSCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0Y4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBQBFehGAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKAbAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoAioQDcSCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAN0YIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECRUAFupFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6MYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoAirQjQQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBANwYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEARUIFuJBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6MUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIqACnQjgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNCNAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUARUoBsJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgG4MECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBIqAC3UggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdGOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUARXoRgIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgGwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAIqEA3EggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgADdGCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkVABbqRQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOjGAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKAIq0I0EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQDcGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEVCBbiQQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBujFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSKgAp0I4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQjQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAEVKAbCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQIBuDBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSKgAt1IIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnRjgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFAEV6EYCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoBsDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgCKhANxIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAA3RggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJFQAW6kUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToxgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECgCKtCNBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEA3BggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBH4/4lWQjQzvPEBAAAAAElFTkSuQmCC\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Text(0.5,1,'Total uncertainties')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure(figsize = (10,7))\n",
"plt.subplot(131)\n",
"yearly_sum_pred.SIS.where(yearly_sum_m_unc.SIS > 0, drop = True).plot(x = 'x', y = 'y',vmin = 1000, vmax = 1600, cmap = cm)\n",
"plt.axis('off')\n",
"plt.axis('equal')\n",
"plt.title('Prediction')\n",
"\n",
"plt.subplot(132)\n",
"yearly_sum_m_unc.SIS.where(yearly_sum_m_unc.SIS > 0, drop = True).plot(x = 'x', y = 'y',vmin = 0, vmax = 60, cmap = cm)\n",
"plt.axis('off')\n",
"plt.axis('equal')\n",
"plt.title('Model uncertainties')\n",
"\n",
"plt.subplot(133)\n",
"yearly_sum_d_unc.SIS.where(yearly_sum_m_unc.SIS > 0, drop = True).plot(x = 'x', y = 'y',vmin = 0, vmax = 300, cmap = cm)\n",
"plt.axis('off')\n",
"plt.axis('equal')\n",
"plt.title('Data uncertainties')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute daily sum and load raw data to compute daily sum of MMH; compare the result"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: b'/Users/alinawalch/Documents/EPFL/data/meteo/raw_data/2001_mmh.nc'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36macquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 167\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\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/xarray/backends/lru_cache.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0mmove_to_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: [<function _open_netcdf4_group at 0x7fbe2b1f79d8>, ('/Users/alinawalch/Documents/EPFL/data/meteo/raw_data/2001_mmh.nc', CombinedLock([<SerializableLock: 720f2a64-a69f-4f85-be6f-940bba4799b5>, <SerializableLock: 5a28c1c1-3b9d-4721-888d-8bfd46aaf989>])), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('group', None), ('persist', False))]",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-8ff5c4859453>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myr_mmh_raw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/alinawalch/Documents/EPFL/data/meteo/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'raw_data'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'2001_mmh.nc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0myr_raw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/alinawalch/Documents/EPFL/data/meteo/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'raw_data'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'2001.nc'\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/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'netcdf4'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m store = backends.NetCDF4DataStore.open(\n\u001b[0;32m--> 321\u001b[0;31m filename_or_obj, group=group, lock=lock, **backend_kwargs)\n\u001b[0m\u001b[1;32m 322\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'scipy'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0mstore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScipyDataStore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mbackend_kwargs\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 353\u001b[0m kwargs=dict(group=group, clobber=clobber, diskless=diskless,\n\u001b[1;32m 354\u001b[0m persist=persist, format=format))\n\u001b[0;32m--> 355\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mautoclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautoclose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/py3/lib/python3.6/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, manager, lock, autoclose)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_remote\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_remote_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mds\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--> 359\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_manager\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[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_store_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\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/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36macquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mode'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;31m# ensure file doesn't get overriden when opened again\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/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m_open_netcdf4_group\u001b[0;34m(filename, lock, mode, group, **kwargs)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnetCDF4\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnc4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnc4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mclose_on_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: b'/Users/alinawalch/Documents/EPFL/data/meteo/raw_data/2001_mmh.nc'"
]
}
],
"source": [
"yr_mmh_raw = xr.open_dataset(os.path.join('/Users/alinawalch/Documents/EPFL/data/meteo/', 'raw_data','2001_mmh.nc'))\n",
"yr_raw = xr.open_dataset(os.path.join('/Users/alinawalch/Documents/EPFL/data/meteo/', 'raw_data','2001.nc'))"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prediction = xr.open_mfdataset( os.path.join(path, 'SISDIR_dhm25_200_CH_ELM50_1000_CPU_prediction.nc'), chunks = {'x':200, 'y':200})\n",
+ "data_uncertainty = xr.open_mfdataset( os.path.join(path, 'SISDIR_dhm25_200_CH_ELM50_1000_CPU_data_unc.nc' ), chunks = {'x':200, 'y':200})\n",
+ "model_uncertainty= xr.open_mfdataset( os.path.join(path, 'SISDIR_dhm25_200_CH_ELM50_1000_CPU_model_unc.nc'), chunks = {'x':200, 'y':200})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"daily_pred = prediction.sum( dim = ['hour'] )\n",
"daily_munc = model_uncertainty.sum( dim = ['hour'] )\n",
"daily_dunc = data_uncertainty.sum( dim = ['hour'] )\n",
"daily_mean_m_unc = daily_munc.where( daily_munc > 0).mean(dim = ['x', 'y'])\n",
"daily_mean_d_unc = daily_dunc.where( daily_dunc > 0).mean(dim = ['x', 'y'])\n",
"# daily_mean_t_unc = total_uncertainty.sum( dim = ['hour'] ).mean(dim = ['x', 'y'])\n",
"daily_mean_pred = daily_pred.where( daily_pred > 0).mean(dim = ['x', 'y'])"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]"
]
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"monthly_mean_SIS = daily_mean_pred.SISDIR.values/1000 * days_per_month "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"736.80462290356002"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(monthly_mean_SIS)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 22.1, 35.2, 62.3, 79.6, 87.6, 100.7, 107.1, 89.7,\n",
" 68.2, 43.1, 23.3, 17.8])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.round(monthly_mean_SIS,1) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"total_data_unc = (daily_mean_m_unc/1000 * days_per_month).mean()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Data variables:\n",
" SIS float64 dask.array<shape=(), chunksize=()>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_data_unc"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 12)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2001-01-15 2001-02-15 ... 2001-12-15\n",
"Data variables:\n",
" SIS (date) float64 dask.array<shape=(12,), chunksize=(12,)>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(daily_mean_m_unc/daily_mean_pred) * 100"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"monthly_mean_munc = daily_mean_m_unc.SISDIR.values/1000 * days_per_month \n",
"monthly_mean_dunc = daily_mean_d_unc.SISDIR.values/1000 * days_per_month "
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2.28, 1.6 , 1.55, 1.91, 1.91, 2.1 , 2.23, 1.93, 2.04,\n",
" 1.83, 1.82, 2.18])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.round(monthly_mean_munc/monthly_mean_SIS * 100,2) "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 30.1, 23. , 26.8, 26.2, 29.2, 23.2, 26. , 22.5, 32.9,\n",
" 26.3, 31.8, 26.2])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.round(monthly_mean_dunc/monthly_mean_SIS * 100,1) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9873417721518987"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy = (1.950/1.975)\n",
"accuracy"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"mon_S = pd.date_range('2001-01-01', '2001-12-31', freq = 'MS')\n",
"mon_E = pd.date_range('2001-01-01', '2001-12-31', freq = 'M')\n",
"\n",
"monthly_mean = np.zeros(12)\n",
"for start, end, i in zip(mon_S, mon_E, range(12)):\n",
" mon = yr_raw.sel(date = slice(start, end)).SIS\n",
" monthly_sum = mon.sum( dim = ['hour', 'date'] )\n",
" monthly_mean[i] = monthly_sum.where(monthly_sum > 0).mean(dim = ['lon', 'lat'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 44.8, 66. , 113.8, 144.8, 167.6, 180.1, 182.3, 150.5, 115. ,\n",
" 76.9, 45.4, 36.5])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.round(monthly_mean/1000, 1)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1323.669998834362"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(monthly_mean/1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"daily_pred = prediction.sum( dim = ['hour'] )"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [
{
"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-55-70e6201d8e54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdaily_mean_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36mwhere\u001b[0;34m(self, cond, other, drop)\u001b[0m\n\u001b[1;32m 724\u001b[0m \u001b[0;31m# clip the data corresponding to coordinate dims that are not used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 725\u001b[0m \u001b[0mnonzeros\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipcond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipcond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 726\u001b[0;31m \u001b[0mindexers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnonzeros\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 728\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mindexers\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/xarray/core/common.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 724\u001b[0m \u001b[0;31m# clip the data corresponding to coordinate dims that are not used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 725\u001b[0m \u001b[0mnonzeros\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipcond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipcond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 726\u001b[0;31m \u001b[0mindexers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnonzeros\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 728\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mindexers\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/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0mar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\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--> 223\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Invalid axis kwarg specified for unique'\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/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[0;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0mFind\u001b[0m \u001b[0mthe\u001b[0m \u001b[0munique\u001b[0m \u001b[0melements\u001b[0m \u001b[0mof\u001b[0m \u001b[0man\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignoring\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 260\u001b[0m \"\"\"\n\u001b[0;32m--> 261\u001b[0;31m \u001b[0mar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\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 262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[0moptional_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_index\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"daily_mean_pred.where(prediction, drop = True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 12, x: 1393, y: 882)\n",
"Coordinates:\n",
" * x (x) float64 4.855e+05 4.858e+05 4.86e+05 4.862e+05 4.865e+05 ...\n",
" * y (y) float64 7.55e+04 7.575e+04 7.6e+04 7.625e+04 7.65e+04 ...\n",
" * date (date) datetime64[ns] 2001-01-15 2001-02-15 2001-03-15 ...\n",
"Data variables:\n",
" SIS (date, x, y) float64 nan nan nan nan nan nan nan nan nan nan ..."
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_pred.where(daily_pred > 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make bar chart with training data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"prediction['SIS'] = np.maximum(prediction.SIS, 0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"pred_df = prediction.SIS.to_dataframe().dropna().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"yr_raw = xr.open_dataset(os.path.join('/Users/alinawalch/Documents/EPFL/data/meteo/', 'raw_data','2001.nc'))\n",
"yr_raw = xr.merge([yr_raw.SIS, yr_raw.hourmask])\n",
"yr_df = util.to_pandas(yr_raw, coords = 'SIS')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"nbins = 10\n",
"maxval = 1000"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
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" }\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 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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qFt6nX8nejgOSixAAJiYigPLKeB1AEr7X9rK2caVuBYBYCWUuQcCCCCAAAIINCRAAPhfnUr83aySAeDgwYNNO9YqCNQGHvq+X6ElswlIx44dw+q+fN/Fe/PNN22NNdYIly90BeADDzxgO+ywQzi3mE1AnnvuOdtggw3C+c8880zdf871rLq+wjuVzz//3PS6bqYUsgnISiutZB9++GGDm4AQAObubYWMOZkreJ1/F/rvG8c3LEAASA+pioDXAYgAsCrdJe9NK/GXzLiemNoggAACCCCAQGwChUzG0/53yUr83aySAeAFF1xgJ5xwQuhy+s/a6bfQkn0N7da7/fbb57zEYYcdZldddVX4rdAAUBuV6JuG2nhj9dVXt1dffdVatGiRuKo6fu211w7Ha7dj7XqcrzQUAGrn37XWWiucetBBB9V9l7D+tV5++WUbNGhQ+GN9w/HKK6+c7xC+Adhw0xUy5mSu5HX+nbgTc2AiAQLAREwcVG4BrwNQ2v/SVu52burrVeIvmU39DFwfAQQQQAABBHwLFDIZT/vfJSvxd7NKBoAzZsywPn362JQpU6x169amlXZbbrll3g6r1YJz58619dZbr+4Yrfrr1auXaXMOBVuvvPLKfBte6ECtwNtkk01MOwGrFBoA6pxjjjnGLrnkknD+4YcfbldccUXYYbd+0eYm3333nXXp0qXupy+//NL0TUQVhXHZG6vUP7+hAFDHDhw40F5//fUQQD722GO26aabzneJ6dOn2x/+8Ad77733rHnz5vbOO+9Y9jcTdTABYMNjYiFjTuZKXuffvv/bIb7aEwDG1yY1USOvA1Da/9LmrfNV4i+Z3kyoLwIIIIAAAghUVqCQyXja/y5Zib+bVTIAVE966qmnQuincE6B1bBhw8L/KRhU0W63en1X4eC7775rI0eODAFcdrn44ovt2GOPDX/UvXt30w64Csq00cjo0aPt0ksvDTvn6nuDkydPLioA1LlaxadgTUUrAbUKb/ME4jwAACAASURBVJVVVgm7GWv+9eKLL4aNSM4++2zT9/OyS8+ePcMxCitVn+WXX77udWXt0LvIIouEwxsLAN9+++2wCnD27NnWqlUrO+KII2zrrbe29u3b21tvvRV2/h03bly41vHHH1+3EUh2XQgACQArO4rXzt0IAGunraN6UgLAqJrDbWUq8ZdMtzhUHAEEEEAAAQQqIkAA+F/mSvzdrNIBoJ7u6aeftj322MO0W21j5dZbb7W99957gcOOOuqosCovV+nUqVNYLbfjjjva+PHjiwoAdV2tVFQ4+fzzzzdYzZtvvnmBAPDqq6/Ou/Iv+/jGAkDd+Mknn7SddtrJtIIyX9Erz/JQqFq/EAASADb27xm/FydAAFicG2eVKEAAWCIgpweBSvwlE2oEEEAAAQQQQKAhgWICQL2ZuXC79Ln+OMts3jyzTh1a2L3ndG+SB6xGAKgH0Qo7BWGPPPJIeG116tSpIbzq3Lmz9e/f39Zff/0QvmnlXL6i1X4KvfSKrK6n7/YNGTIkfFtQ/7mh4Cv7uRvbqEOrEbXST68ba0WhNi/RysMBAwbYzjvvbFtssUVYFVi/6BuF1157rWkV37Rp0+peSS40ANR1dd/LLrssrHDUir9ffvklvPqs138PPvhg0wYr+QoBIAFgkwweXNQIAOkEVREgAKwKe+puSgCYuiblgRBAAAEEEHAnUEwA6O4hC6xwUwaABVaFwxFInUAhY07m4b3Ov1PXeFV+IALAKjdArd7e6wCU9u+2eOuPBIDeWoz6IoAAAgggkD6BQibjB5/3rU2bMTd9CPWeqOOiLeyaE5dM/XPygAhUQ6CQMYcAsBotFO89CQDjbZtU14wAMNXNW7GHIwCsGDU3QgABBBBAAIE8AsVMxsFEAAEEihUoZszxOv8u1ojzcgsQANIzqiLgdQBiBWBVukvemxIAxtUe1AYBBBBAAIFaFChmMl6LTjwzAgiUR6CYMcfr/Ls8YlwlI0AASF+oioDXAYgAsCrdhQAwLnZqgwACCCCAAAJZAsVMxgFEAAEEihUoZszxOv8u1ojzcgsQANIzqiLgdQAiAKxKdyEAjIud2iCAAAIIIIAAASB9AAEEqiRAAFgl+BTclgAwBY3o8REIAD22Wnx15hXg+NqEGiGAAAIIIFBrAsVMxmvNiOdFAIHyCRQz5nidf5dPjStJIBUB4JdffmlXXHGFPfroo6b/3Lp1a1tuueVs5513tkMPPdQWXnjhsrT2448/btddd5299tprNnnyZOvcubMNHDjQDjzwQNtiiy0S3WPOnDl244032p133mkfffSRzZw507p3726bbLKJHXnkkbbiiis2eJ1PPvnERo8ebc8995y988479u2331qzZs2sa9euoS577723DRkyJPxZvvLss8/ahhtumKi+p59+up1xxhmJji3kIK8DECsAC2nlpj+WALDpjbkDAggggAACCDQsUMxkHFMEEECgWIFixhyv8+9ijTgvt4D7AFCh3x577GHTp0/P+YTLL798CMx69+5ddB+YN2+eHXzwwSH8y1cUAl5zzTUNBm9Tp061oUOH2quvvprzMgour7rqKhsxYkTO3/fZZx+77bbbGn2OzTff3EaNGmUdOnTIeSwBYKOEeQ8gACzerinOJABsClWuiQACCCCAAAKFCBQzGS/k+hyLAAIIZAsUM+YQANKHJOA6ANQKuEGDBtmPP/5o7du3t5NOOimsbPvpp59CAHb99deHVl5hhRXs9ddfD8cUU0455RQ755xzwqmrrrqqHX/88danTx8bO3asXXDBBfbWW2+F33Tc2WefnfMWc+fOtY022sief/758PsOO+xgBxxwgHXs2DEEgjpv0qRJ1qJFi7CSUSFe/aJVgmPGjAnn7LjjjrbBBhvYsssuay1btgx1uOSSS0wrBFUGDx4cVgk2b958getkB4A33XSTrbnmmnlZunTpYvq/chevAxABYLl7QmnXIwAszY+zEUAAAQQQQKB0gWIm46XflSsggECtChQz5nidf9dqGzfVc7sOABX2KcxSAKZgbZ111pnP6cILLwxhncqZZ55pf/7znwt2/Oyzz6x///6mV3fXWGONcJ+2bdvWXUfh4/rrr29vvPFGqMfHH38cwsH65ZZbbrHhw4eHP9ZryVdeeeV8h+g+q6++us2YMcP69u1rH374Ybhedtl3331D4KmVgFotWL+oLgoOX3zxxfCTVgvutddeCxyXHQA+88wzIUisdPE6ABEAVrqnNHw/AsC42oPaIIAAAgggUIsCxUzGa9GJZ0YAgfIIFDPmeJ1/l0eMq2QE3AaAWtGnb96pHHTQQeH12/rlt99+s5VXXjl8a2/xxRe3iRMnWqtWrQpq/cMOOyy8lqvy8ssv29prr73A+a+88kpd+Hj44YfbyJEjFzhmpZVWCqGe6qF/+XJ9l/C8884LqxhV7rvvPhs2bFhBddXB77//vq2yyirhvG222cYefPDBBa5BAFgwa90JBIDF2zXFmQSATaHKNRFAAAEEEECgEIFiJuOFXJ9jEUAAgWyBYsYcAkD6kATcBoDZr+UqgFtrrbVytmh2qPbkk0/apptumrjl9e2/nj172ldffRVeI1aQmK/od71+26NHj7ARSfYmHPoXtF+/fuFUfUvw6quvznkZbejRrVu38Nvuu+8eNgoppmhzkilTpphCRwWC9QsBYDGq/3sOAWDxdk1xJgFgU6hyTQQQQAABBBAoRKCYyXgh1+dYBBBAIFugmDGHAJA+JAG3AeB6661nL7zwgrVr186+//77BV6XzTSvVu3ptVkVvQKsV4GTlnHjxtW9zptvlWHmWvo9s0mIzuvVq1fdbfSdvf322y/8891332277rpr3ipo05JPP/3Ull56aRs/fnzSqs533GKLLRZeJdZKwHfffXeBaxAAFsUaTiIALN6uKc4kAGwKVa6JAAIIIIAAAoUIFDMZL+T6HIsAAghkCxQz5hAA0ock4DYAzKxyGzBggL399tt5W/O7774Lm2ao7LTTTnbvvfcmbnltxrHVVluF4y+99FI7+uij856r3//0pz+F33XekCFD6o497rjj7KKLLgr/rM06fv/73+e9zrbbbmsPPfRQWEH4ww8/hICzkKLrr7baag0+b3YAqO8masWiNiDRvbSpiL4JeMghh9StWizk/kmP9ToAEQAmbeHKHEcAWBln7oIAAggggAAC+QWKmYzjiQACCBQrUMyY43X+XawR5+UWcBkA/vzzz3UbcQwdOtQeeeSRBttXu//OmjUrfL9PKwKTFn1XUEGYyt///vew826+om/2KWBU0XlaEZgpWvF3zz33hH+cPHmyderUKe919A3BzAYh2lBEKwILKaqD6tJQnbMDwHzX1u7Bp512mp1++unzvc6ctC4aYBoq33zzTd03HCdMmBBenfZQCADjaiUCwLjag9oggAACCCBQiwLFTMZr0YlnRgCB8ggUM+YQAJbH3vtVXAaACtG6dOkS7HfZZRcbNWpUg+3QtWvXsMJNG4K89957idssexfhxx57zLbYYou85+r3zKo/rfY75phj6o5VSDl69Ojwzz/99JO1adMm73VOOOEEu+CCC8Lv2llYOwMnLffff39dSKnztFFK9rcIM9dRAKhvDO6www42ePBg6927d3iFWisBH374Ybv99tvt119/DYdrU5JzzjknaRXqjst133wXIQAsmJcT/k+AAJCugAACCCCAAALVFihmMl7tOnN/BBDwK1DMmEMA6Le9y1lzlwGgAiN9I09lr732sttuu61BEx2rc/r06WOfffZZYr+//OUv4buBKmPGjLGNNtoo77lPP/20bbzxxuF3nXfqqafWHas/1+8qc+fONa2uy1d0P52vom8cKqBLUrRaULsi67Xhtm3bhvBwxRVXzHmqVkMutNBCeXdEfu2112yzzTaz6dOnhwDx3//+d4OvLee6CQFgklbjmFIFCABLFeR8BBBAAAEEEChVoJjJeKn35HwEEKhdgWLGHALA2u0v2U/uMgBkBeD8nffrr7+2dddd17744osQ2Gn34N12262kHq5r7LnnnuEa+++/v11//fUFXY9XgAvi4uAiBQgAi4TjNAQQQAABBBAom0Axk/Gy3ZwLIYBAzQkUM+YQANZcN8n5wC4DQL4B+N+2nDZtmmlH5A8++CD84RVXXGFHHHFEyb17zpw54VuFWgXYt2/fsDNxOYvXAYhvAJazF5R+LQLA0g25AgIIIIAAAgiUJlDMZLy0O3I2AgjUskAxY47X+Xctt3NTPLvLAFAQldgFWJuLbL311sG9lF2Ajz32WLv44ovDdcq5C7Be99XrxfrWn0r9V49L7TB6pVjXXnjhhcMmKuUsXgcgAsBy9oLSr0UAWLohV0AAAQQQQACB0gQSTcYfOsJs0kel3cjb2V36m20z0luto67vBhtsYM8995ytv/76pu+61y+ZzzBpI8czzjhjvp+zN4J85plnTNdKS9l3333t1ltvtWWWWSa8FZf2kmjMqYfgdf6d9ras9PO5DQC16k3fyGvXrp19//33YROLXEW7/g4aNCj8pO/rnXnmmYmNx40bF74bqKJdfbW7b76i36+77rrws87r1atX3aE33XST7bfffuGf7777btOuwPmKdv3Vajt9t3D8+PF5j9NmIltuuWX4LwCV4447rm7zkMQP2MiBa665ZviWIAHgf6EIAMvVu8pzHQLA8jhyFQQQQAABBBAoXiDRZPyGTcz+53//R/uaKT3WNNv/qSZ7XC1Q0GeLHnzwQXvnnXdsypQpYU6ozSK1CeSAAQNCyKWwrFu3bk1Wj0pemAAwtzYBYOO9kACwcaNaOMJtAHjyySfbueeeG9rolVdesbXWWitne5133nlhJ1uVJ554ImxukbTMmzfPevToYfrG3gorrGAffZT/f7Xr37+/aSOO7t27hw1HsjfBUKCnYE/l4IMPtquvvjpnFb799tu6/3LSN/zuuuuunMdph95tt93WtPNwY9dM+qz1j9MrwEsssYTNmDHDlltuOdNfbMpZvA5ABIDl7AWlX4sAsHRDroAAAggggAACpQkQAObxa8IAUJsW7rLLLolWeykM1DyrKUpjgVy579nY/UpZAagVg5nFMpoHeyqVCAAbsq20VaIxp16lvM6/K22b9vu5DQA16GdCv3yr83777TdbeeWVQ3DXoUMHmzRpUt6db/M19KGHHloX2Gk14dprr73AoQog11lnnfDnOv7KK69c4BjtyKt6dOzYMQSEWlVXv2SHlffee6/ttNNOCxyjXYS1gvC+++4Lv2kXZC13LmTX3SSd+o477gjXVtHqxRtuuCHJaYmP8ToAEQAmbuKKHEgAWBFmboIAAggggAACDQgkmoyzArBsfeizzz6z1VdfPSxUUNlmm21sxx13tH79+tlCCy0UVgJqReA///lP06uumn8RADbOTwDYsBEBYON9iCPiF3AbAIo28xqwlno///zzdSFchv3CCy+0448/PvxjY99B2GeffeyWW25ZoMW0em+llVYyrYhbY401wn3atm1bd5xexVU99Kqs6vHhhx+GTTPql+zXgA877DD729/+Nt8hY8eOtdVWWy38F5leO9ZqwvqvNet/iVEYd/PNN4dzhw0bZvfcc4+1aNEicU/77rvvwn8hNvTNB4Wrm2++eXi1WgOd/lnPXs5CAFhOzdq9FgFg7bY9T44AAggggEAsAgSAeVqiiVYAauWfFkuoaI41fPjwvF1h8uTJ4VjNv5qiNLYir9z3bOx+pYRUBIANt1YptuXuB4nGnHo39Tr/LrddrV/PdQCoDTXWXXddUwjXvn1702vBG264YfjnUaNG1X2TT/9rkAK6RRZZZL72zv4Qar4AUCfoFWKtzlNZddVV7YQTTgghnUK7888/P2zskTnunHPOydmntHJP35/417/+FX5XeHfAAQfY4osvHgI2beChFYrNmzc3bT6i7/vVL8ccc4xdcskl4Y+1slEr//S/cjVUdFx20UdR9X3C3/3ud7bddtuF//VM38RQiPjll1/aww8/bLfffrvpNWOVpvi2oK7rdQBiBWBcQyYBYFztQW0QQAABBBCoRYFEk/FcKwBbtTPrumI6yCZ+aPZrvU0DmyAA1Jxq0UUXtR9//DEsUMhshlgtxMYCuXLXq7H7lRJSEQA23Fql2Ja7HyQac+rd1Ov8u9x2tX491wGgGk+B1Z577lm3BLx+gyr8e/TRR8N37OqXpAGgXiVWWKf/hSlf0co8bQKiAC9f0XL0IUOG5P0vKoV5Whmoe+Uqyy67bIMbg+Q6p/73GzIBYGMdX4HgaaedFjZOKffrxQSAjenze1IBAsCkUhyHAAIIIIAAAk0lkGgynisAbIKArKmesdHrVuj5sr+Zrs8iaYPFYsvs2bPDN+L1f6+++qrp1eKZM2eGgFFzRy3IOPzww61Tp04L3CLzzbmG7p1vR1qFl5o3avMSvT2mN7T0uarf//73pu/A77333nnf8ColAMy3C7DegmtoFWXmGT///HPTfDS7aK55//33h3bQohatuNTbcnojbuutt7YjjjgiPFupRU5aePP000+He3Tu3Nn+8Ic/2B//+EfTxpWNfQNQxv/v//0/GzNmjP373/8OC1/U/no9XJvFaHGOrpFrcU2SOXj9xUTffPONPfDAA6G+evtOewrojUL1JQXXu+++e/jcV0PZQUNmicacehcgACy1F6bjfPcBoJpBu+VefvnlIehTx9a/uBq09S+VBu1c39vTeUkDwExTjx49OgzW+l+aFObpX2ANOPoGYa4Ve7m6iP7Fv/7668MGH/omoHavWmqppWzjjTe2o446KrxunK8kGXzqn1s/ANRA99BDD5m+Z6hB+quvvgrP8vPPP9tiiy0WNivRf7Hsv//+Cwzw5ezyXgcgVgCWsxeUfi0CwNINuQICCCCAAAIIlCaQaDJeoYCstCcp4ewKPd+0adPCRoUqCm7efvvtoiudJMTTvRTU6a2z7JLk3FwBoOaR22+/fZiD5SsDBw4M8zVtXlK/xBQAKojTs2TecMv1PHoG+eXbsDNJ4+nNPgVsmsfWL/pk1rXXXhs+06W34/KFrknm0XrTT/P9JZdccr7bJDk3OwDUKlXlEVpE1FDZdNNN7R//+Ed4k7HQkmjMqXdRr/PvQm04vmGBVASANLI/Aa8DEAFgXH2NADCu9qA2CCCAAAII1KJAosl4hQKyqvlX8PmyAxl9pkmfLCpmJZXeItOiCIVYCt2WXnrp8A12LS556qmnwttfCp202uz999+3Ll261PEqwNOqMq2c06emtKor8532zEEKgfQ2Wqa899574Zv1WgCiax1yyCFhFZtCRn0KSqGfwiwtGFFg9sILLyywgWVTBID67rvmZldddVXd5peqa/2ihSKtWrUKf6xn0EIYLWjRc8pBb7r17Nkz/KZATp+umjp1avjklT6ZpXCu0KKVmYMHDw4mrVu3Div+dB/9Z/2mz2/pHtpwUyvt8gWAqlf37t1tq622Cp/0UjCpttWqRm1++fjjj4eq6ZNdWiSUXbQngI5dZZVVwh+r3bTxZ3bRM+r6Kpm6qq20SEjnqQ/98MMPNm7cuLAYSP1ORas9FVwWWhKNOfUu6nX+XagNxzcsQABID6mKgNcBiACwKt0l700JAONqD2qDAAIIIIBALQokmoxXMCCrShtU8PkuvvhiO/bYY+seU6GPXjdVuKbgTN9qT1L0PffevXvn/dyRQrBBgwaF14JPPfXU8M32+qWxQC5zvN7K0iu+7777bli5qIAx16vFCqKGDh0aVo/dcMMNYQPI7NLY/Rr6Tl2+V4Az1y/kG4B6tVefrtIbZHqWXBtGKkhVm+h1WIWt+s58oUXXffPNN0PwqPto883soiB27bXXDgGmSr4AUP+O5tqoM3MthbcjRowI/6j76O28+iXpNwDV1upbuT5BlrmmNig966yzQt/75JNPGqxbLrNEY069E73OvwvtMxzfsAABID2kKgJeByACwKp0l7w3JQCMqz2oDQIIIIAAArUokGgyXsGArCptUMHna+z77FrdpaBsjz32CCu+SvmeuVacXXbZZWEDxlyr4hoL5DJtoU0eFVKqaKWaNmTMVzK7HOu14xdffHG+wxq7XyUCQH0+Sivq9AkpfYbryCOPzPssV199dVgtpwBPKw3zfZor1wX0uarMq8P6rNfIkSNz3ke7PMtMJV8AmOTfidVWWy2sVMx3r6QBYJJ76TVhvWosy4suusi02WchJdGYQwBYCGnNHEsAWDNNHdeDEgDG1R5ea0MA6LXlqDcCCCCAAALpEUg0Ga9gQFYV2So8n1bL6TVTbeyQ73trWkGmb8glWRWoV3r1jUEFW5nvqGuDC62M0yvG+vPMK7AZ48YCucxx2uRRK/r0Gu3HH3/cYBNdeeWVIYTSq7XaMESbMya9XyUCwDvvvDOs6FPR6r7638zLfrgPPvgghKcqei1YrzwnLXq995RTTgmHaxWgArpc5ddffw2vVCtgTBIAqm0nTpwYNhHN/q7giSeeGPYUUB1V1/ql2ABQfVOb1+gVYNU1U/baa6/wDUv9/9tuuy0pSzgu0ZhT74pe598FwXBwowIEgI0ScUBTCHgdgFgB2BS9ofhrEgAWb8eZCCCAAAIIIFAegUST8SoEZOV5uoRXqeLzKbjTRhT6Fp+CIn07b/r06XUV79atW/hz/f/6Rav6Lr30UnvsscdCSNNQUWiU/R1AHZs0ANR354rZsETfBtT34zKlsftVIgDMrIpM2DPqDtNKPW3SmbRoR2SFtwpC9V1BfZ8xX9loo43smWeeaTAAVLinFYkK9xTG5Sv9+/cPuzPXL4UEgAoZFZTeeOON4VuFP/30U9776TuB2nykkJJozKl3Qa/z70JcOLZxAQLAxo04ogkEvA5ABIBN0BlKuCQBYAl4nIoAAggggAACZRFINBmvYkBWlods7CIRPd8vv/xid911V3itUuGgir6lpxV42UXhzMEHHxw2bUhSvvjiiwU2smgskMtcVxtEfP3110luM98x9e/Z2P0qEQDq1Wr5FlpuueWWsJtv0rLFFlvYE088EVYYaqVhQyUTFuZaAagwTisw1d5JijaZ0eYg9UvSAFArRXfYYYcQKicpalOFl4WURGNOvQt6nX8X4sKxjQsQADZuxBFNIOB1ACIAbILOUMIlCQBLwONUBBBAAAEEECiLQKLJeEQBWVkeuv5FInw+hUcKkVS0S6u+t5bZLViv4Wp3VoV/WtWnnYS1ikzhzyKLLFL3qq92As5sxKFQSL9nl8YCucyxCrG0glDf9bvmmmsSN0H2zrs6qbH7VSIA3HXXXe2ee+4JK/O0sjJp6dGjh3Xo0CHp4bb55pvbk08+GVZuNhaeZuqUKwBU8Lf//vuH+2ojlqOPPjp8W1ChrL5JmHnFWjvyaqOSfK8RJw0ATzvtNDv77LPD/bSr8GGHHRZeX1YfaNu2bV0f1IYmWq2aa+fhxpASjTn1LuJ1/t2YBb8XJkAAWJgXR5dJwOsARABYpg5QpssQAJYJkssggAACCCCAQNECiSbjEQZkRT9wrhMjfb6ll17aJkyYEGqc/Tqtvvd2/vnnh/BHrwHrtc9cJXvH4VICwJVWWim8VtqvX7+w62uxJYYAUIHWVVddFR5BwVyuV6uLfb7s87JfAa7/LcT612/oFWDtEqzXcPUdSLW1QrhcRZu0aLOWUgJArTZcaqmlwuvkgwcPtueee64u8Kt/TwXQ77//PgFgOToL10gsQACYmIoDyylAAFhOzdq9FgFg7bY9T44AAggggEAsAgSAZhZpAJgJf9RXtAJwiSWWCN0mE/ZoZVZDq9gyIZTOyRUAbrjhhvbss882GuJo0wx9E04l16vESftyUwaAZ555ZtjwRCWzCUqueulVar1Sq1Loa71Jn1PHJd0EJLOKU6975wrvtKJz5syZ9qc//ckU6OYqel6tUFSgWUoAOHXqVOvUqVO4xRVXXGFHHHFEzvupPh07dgybgrACsJBewbGlChAAlirI+UUJEAAWxcZJ9QQIAOkSCCCAAAIIIFBtAQLAOANArRrTa5fa8GHRRRcNu8RmXuPUxgvaRXiFFVawjz76KGcX0iqu3r17123gkCsAzFxHQePLL7+ctytqA4xddtkl/H7ooYeadvotpjRlAKgVkVoZqaLv2LVu3TpnFb/66ivr1atXCK8GDBgQNl9paIOOYp5T57z22mvhVV2VI4880i6//PKcl/r73/9uO++8c/gtV3inFX96Hn3vUZuA5CoPPvigbbfddnmvoR8y15HRueeem/M6es07syvyeeedZyeccELO4/QsehVZhQCw2B7CecUIEAAWo8Y5JQsQAJZMyAXMjACQboAAAggggAAC1RYgAKxcAKiVUxtvvLHpO2tDhgzJ+3rlb7/9Flap6Rt+KlqBp++7ZYoCpZEjR4bztYOwArzsovBwq622mm9zhlwB4IgRI+zmm28O3xFUYJgJGOv3yblz54ZvDips1DHXX3993bcFc/VfvRqq+2mlYnZpygDwtttuq9uk44MPPrAVV1wx779aBx10kF133XXhdxlce+21eUNAvXr90EMP1X2Hr5B/X1dffXX797//Hb7J+PTTT4fXarOLNgdR23355Zfhj3MFgL/73e/Cq7/65p/+v74HmV3Gjh1r+h5f5juD+VYAKgxWm2gnYwW6uYr6nVaZKmzW9wb16rG+lZhdXn/99fC9SfVlFQLAQnoEx5YqQABYqiDnFyVAAFgUGyfVEyAApEsggAACCCCAQLUFig4AW7Uz65o/ZKn2cxV0/4kfmv06a/5Teqxptv9TBV2msYMVmuiVThUFOlq1tc4664TgR3+u4OWtt94KwZ/CHpXFFlvM3n777fk28FAIM3DgwPC7AqHjjz/eBg0aZG3atAmvBF966aWmdtWmHQoIVXIFgNmvw2pFl4JGI+qmQwAAIABJREFU3U9FoZXqlSmqj+6RCX60ycXuu+9umY0+FJSp7voO3UsvvRR2Mb7ooovmI2nKAPCzzz6zvn37hvttttlmdsopp4Tv+2VCTW2Aklnpp2eQu4JKFYWFBx54oCmwa9++fWgHhYhPPfWUjR49OoSfWilYaFGAptBPr/mqbf74xz+G4FerE/WbXhPWq926/zvvvJMzAJShNnlR0YpPtbW+yahVgQoVL7vsMtPO0bqGwsZ8AWDmNW7dWyv41DdUJxWtMFUIrHL44YfXrfBUH1Odl1tuOZs+fXqw0PcTZaRXgD/99FMCwEI7BceXJEAAWBIfJxcrQABYrBznZQsQANIfEEAAAQQQQKDaAkUHgNWueFPfvwkCQIU2ev1Uq+2SFAVad999dwim6pezzjrLTj/99LyXUQC38sor2/Dhw8MxuQJABWF6DXbcuHELXCdXkPTuu+/ajjvuGMLFxoq+yffnP/95vsOaMgDUjfSacr7VbfWff9q0abbHHnuEV6kbK/pWosK2Yorab99997XZs2cvcLoCSb3W++KLL9qtt96aM7zTq8pazakdhXMVvdqrcx999NG819B5CpG12lBhYf2yzz77hO8hqijoUzvp+FxFwd8DDzwQ2labhLACsJhewTnFChAAFivHeSUJEACWxMfJ/ydAAEhXQAABBBBAAIFqCxAA5mmBJggAdSe9ZvnKK6+E1WX6/9pVV99eUzjYrl27sAurQrltt93Whg0btsArmNm11YosrebSisBZs2aFVVxataXvxW266aYh1GkoANS1dG99E04B0/jx402vD6vkW0mm1Wx33XVXCIG02nDy5MnhmfTqqFYDasXb9ttvb9qgpH5p6gBQYZlWxN13333BVd9PVN1UcgWg+nMFe3fccUcI4fRKrtpBK+K0664shw4dGlYUasflYot2UNY39caMGRNW/HXu3DmswNPGHvpOoALCfAGg7ilzBYV6zVnX0qYfWkG6ySab2FFHHRVWBjZ2DV1HKzQvvPDCsCpU7Z4JA7MDQB2nPnDJJZeEMFXjg4LKnj17BgvdTxuONNaWDVklGnPqXcDr/LvYPsN5uQUIAOkZVRHwOgDtfPJXNuX7udauvdnw/VpVxY6b/leAAJDegAACCCCAAALVFkg0Gc+1S261K97U92+iALCpq831EYhdINGYQwAYezNWpX4EgFVh56YEgPSBcggQAJZDkWsggAACCCCAQCkCiSbjBIClEHMuAghkCSQacwgA6TM5BAgA6RZVESAArAp76m5KAJi6JuWBEEAAAQQQcCeQaDL+0BFmkz5y92wlVbhLf7NtRpZ0CU5GAIEFBRKNOQSAdB0CQPpALAIEgLG0hO96EAD6bj9qjwACCCCAQBoEipmMp+G5eQYEEKiOQDFjjtf5d3WE03tXVgCmt22jfjKvAxDfAIyrWxEAxtUe1AYBBBBAAIFaFChmMl6LTjwzAgiUR6CYMcfr/Ls8YlwlI0AASF+oioDXAYgAsCrdJe9NCQDjag9qgwACCCCAQC0KFDMZr0UnnhkBBMojUMyY43X+XR4xrkIASB+oqoDXAYgAsKrdZoGbEwDG1R7UBgEEEEAAgVoUKGYyXotOPDMCCJRHoJgxx+v8uzxiXIUAkD5QVQGvAxABYFW7DQFgXPzUBgEEEEAAAQTMrJjJOHAIIIBAsQLFjDle59/FGnFebgFeAaZnVEXA6wBEAFiV7pL3pqwAjKs9qA0CCCCAAAK1KFDMZLwWnXhmBBAoj0AxY47X+Xd5xLhKRoAAkL5QFQGvAxABYFW6CwFgXOzUBgEEEEAAAQSyBIqZjAOIAAIIFCtQzJjjdf5drBHn5RYgAKRnVEXA6wBEAFiV7kIAGBc7tUEAAQQQQAABAkD6AAIIVEmAALBK8Cm4LQFgChrR4yMQAHpstfjqzCvA8bUJNUIAAQQQQKDWBIqZjNeaEc+LAALlEyhmzPE6/y6fGleSAAEg/aAqAl4HIFYAVqW75L0pAWBc7UFtEEAAAQQQqEWBYibjtejEMyOAQHkEihlzvM6/yyPGVTICBID0haoIeB2ACACr0l0IAONipzYIIIAAAgggkCUwbtw4++WXX6xZs2bWr18/a968OT4IIIBAkwj89ttv9umnn9q8efOsdevW1rt370T38Tr/TvRwHJRYgAAwMRUHllPA6wBEAFjOXlD6tVgBWLohV0AAAQQQQACB0gS+/vprmz59erhI9+7dbdFFFy3tgpyNAAII5BGYMWOGffXVV+HXDh06WLdu3RJZeZ1/J3o4DkosQACYmIoDyyngdQAiACxnLyj9WgSApRtyBQQQQAABBBAoTWDmzJk2YcKEcJEWLVrYkksuae3bt2clYGmsnI0AAlkCWvmnsebbb7+1uXPnhl969uwZxpokxev8O8mzcUxyAQLA5FYcWUYBrwMQAWAZO0EZLkUAWAZELoEAAggggAACJQnoVbzx48fbTz/9VHcdvQ6sMJCCAAIIlENAoZ/Gmkxp27atLbPMMuHTA0mK1/l3kmfjmOQCBIDJrTiyjAJeByACwDJ2gjJcigCwDIhcAgEEEEAAAQRKFtDqnC+//HK+ELDki3IBBBBAIIeAwr+ll166oFXGXuffdIDyChAAlteTqyUU8DoAEQAmbOAKHUYAWCFoboMAAggggAACjQpodc6sWbPshx9+CEFg5jW9Rk/kAAQQQKARAa0oVvC3yCKLWLt27RKv/Mtc1uv8m45RXgECwPJ6crWEAl4HIALAhA1cocMIACsEzW0QQAABBBBAAAEEEEDArYDX+bdb8EgrTgAYacOkvVpeByACwLh6JgFgXO1BbRBAAAEEEEAAAQQQQCA+Aa/z7/gkfdeIANB3+7mtvdcBiAAwri5HABhXe1AbBBBAAAEEEEAAAQQQiE/A6/w7PknfNSIA9N1+bmvvdQAiAIyryxEAxtUe1AYBBBBAAAEEEEAAAQTiE/A6/45P0neNCAB9t5/b2nsdgAgA4+pyBIBxtQe1QQABBBBAAAEEEEAAgfgEvM6/45P0XSMCQN/t57b2XgcgAsC4uhwBYFztQW0QQAABBBBAAAEEEEAgPgGv8+/4JH3XiADQd/u5rb3XAYgAMK4uRwAYV3tQGwQQQAABBBBAAAEEEIhPwOv8Oz5J3zUiAPTdfm5r73UAIgCMq8sRAMbVHtQGAQQQQAABBBBAAAEE4hPwOv+OT9J3jQgAfbef29p7HYAIAOPqcgSAcbUHtUEAAQQQQAABBBBAAIH4BLzOv+OT9F0jAkDf7ee29l4HIALAuLocAWBc7UFtEEAAAQQQQAABBBBAID4Br/Pv+CR914gA0Hf7ua291wGIADCuLkcAGFd7UBsEEEAAAQQQQAABBBCIT8Dr/Ds+Sd81IgD03X5ua+91ACIAjKvLEQDG1R7UBgEEEEAAAQQQQAABBOIT8Dr/jk/Sd40IAH23n9vaex2ACADj6nIEgHG1B7VBAAEEEEAAAQQQQACB+AS8zr/jk/RdIwJA3+3ntvZeByACwLi6HAFgXO1BbRBAAAEEEEAAAQQQQCA+Aa/z7/gkfdeIANB3+7mtvdcBiAAwri5HABhXe1AbBBBAAAEEEEAAAQQQiE/A6/w7PknfNSIA9N1+bmvvdQAiAIyryxEAxtUe1AYBBBBAAAEEEEAAAQTiE/A6/45P0neNCAB9t5/b2nsdgAgA4+pyBIBxtQe1QQABBBBAAAEEEEAAgfgEvM6/45P0XSMCQN/t57b2XgcgAsC4uhwBYFztQW0QQAABBBBAAAEEEEAgPgGv8+/4JH3XiADQd/u5rb3XAYgAMK4uRwAYV3tQGwQQQAABBBBAAAEEEIhPwOv8Oz5J3zUiAPTdfm5r73UAIgCMq8sRAMbVHtQGAQQQQAABBBBAAAEE4hPwOv+OT9J3jQgAfbef29p7HYAIAOPqcgSAcbUHtUEAAQQQQAABBBBAAIH4BLzOv+OT9F0jAkDf7ee29l4HIALAuLocAWBc7UFtEEAAAQQQQAABBBBAID4Br/Pv+CR914gA0Hf7ua291wGIADCuLkcAGFd7UBsEEEAAAQQQQAABBBCIT8Dr/Ds+Sd81IgD03X5ua+91ACIAjKvLEQDG1R7UBgEEEEAAAQQQQAABBOIT8Dr/jk/Sd40IAH23n9vaex2ACADj6nIEgHG1B7VBAAEEEEAAAQQQQACB+AS8zr/jk/RdIwJA3+3ntvZeByACwLi6HAFgXO1BbRBAAAEEEEAAAQQQQCA+Aa/z7/gkfdeIANB3+7mtvdcBiAAwri5HABhXe1AbBBBAAAEEEEAAAQQQiE/A6/w7PknfNSIA9N1+bmvvdQAiAIyryxEAxtUe1AYBBBBAAAEEEEAAAQTiE/A6/45P0neNCAB9t5/b2nsdgAgA4+pyBIBxtQe1QQABBBBAAAEEEEAAgfgEvM6/45P0XSMCQN/t57b2XgcgAsC4uhwBYFztQW0QQAABBBBAAAEEEEAgPgGv8+/4JH3XiADQd/u5rb3XAYgAMK4uRwAYV3tQGwQQQAABBBBAAAEEEIhPwOv8Oz5J3zUiAPTdfm5r73UAIgCMq8sRAMbVHtQGAQQQQAABBBBAAAEE4hPwOv+OT9J3jQgAfbef29p7HYAIAOPqcgSAcbUHtUEAAQQQQAABBBBAAIH4BLzOv+OT9F0jAkDf7ee29l4HIALAuLocAWBc7UFtEEAAAQQQQAABBBBAID4Br/Pv+CR914gA0Hf7ua291wGIADCuLkcAGFd7UBsEEEAAAQQQQAABBBCIT8Dr/Ds+Sd81IgD03X5ua+91ACIAjKvLEQDG1R7UBgEEEEAAAQQQQAABBOIT8Dr/jk/Sd40IAH23n9vaex2ACADj6nIEgHG1B7VBAAEEEEAAAQQQQACB+AS8zr/jk/RdIwJA3+3ntvZeByACwLi6HAFgXO1BbRBAAAEEEEAAAQQQQCA+Aa/z7/gkfdeIANB3+7mtvdcBiAAwri5HABhXe1AbBBBAAAEEEEAAAQQQiE/A6/w7PknfNSIA9N1+bmvvdQAiAIyryxEAxtUe1AYBBBBAAAEEEEAAAQTiE/A6/45P0neNCAB9t5/b2nsdgAgA4+pyBIBxtQe1QQABBBBAAAEEEEAAgfgEvM6/45P0XSMCQN/t57b2XgcgAsC4uhwBYFztQW0QQAABBBBAAAEEEEAgPgGv8+/4JH3XiADQd/u5rb3XAYgAMK4uRwAYV3tQGwQQQAABBBBAAAEEEIhPwOv8Oz5J3zUiAPTdfm5r73UAIgCMq8sRAMbVHtQGAQQQQAABBBBAAAEE4hPwOv+OT9J3jQgAfbef29p7HYAIAOPqcgSAcbUHtUEAAQQQQAABBBBAAIH4BLzOv+OT9F0jAkDf7ee29l4HIALAuLocAWBc7UFtEEAAAQQQQAABBBBAID4Br/Pv+CR914gA0Hf7ua29uwHooSPMJn1kn345236dO89aNDfr3KWZW/+0VHzypHk29zezVi2aWb81BphtMzItj8ZzIIAAAggggAACCCCAAAJlEXA3/y7LU3OR+gIEgPSJqgi4G4Bu2MTsf16vihU3TSjQY02z/Z9KeDCHIYAAAggggAACCCCAAAK1IeBu/l0bzVLxpyQArDg5N5SAuwGIADD+jksAGH8bUUMEEEAAAQQQQAABBBCouIC7+XfFhWrjhgSAtdHO0T2luwGIADC6PrRAhQgA428jaogAAggggAACCCCAAAIVF3A3/664UG3ckACwNto5uqd0NwARAEbXhwgA428SaogAAggggAACCCCAAALVF3A3/64+WSprQACYymaN/6HcDUA5AsA5LRe26Yv1jR87hTVcbPp/rOWcH+d/MlYAprCleSQEEEAAAQQQQAABBBAoVcDd/LvUB+b8nAIEgHSMqgi4G4ByBIBTlxhgz212T1X8av2m6z+5iy0x9R0CwFrvCDw/AggggAACCCCAAAIINCrgbv7d6BNxQDECBIDFqHFOyQLuBiACwJLbvJwXIAAspybXQgABBBBAAAEEEEAAgTQLuJt/p7kxqvhsBIBVxK/lW7sbgAgAo+quBIBRNQeVQQABBBBAAAEEEEAAgYgF3M2/I7b0XDUCQM+t57ju7gYgAsCoehsBYFTNQWUQQAABBBBAAAEEEEAgYgF38++ILT1XjQDQc+s5rru7AYgAMKreRgAYVXNQGQQQQAABBBBAAAEEEIhYwN38O2JLz1UjAPTceo7r7m4AIgCMqrcRAEbVHFQGAQQQQAABBBBAAAEEIhZwN/+O2NJz1QgAPbee47q7G4AIAKPqbQSAUTUHlUEAAQQQQAABBBBAAIGIBdzNvyO29Fw1AkDPree47u4GIALAqHobAWBUzUFlEEAAAQQQQAABBBBAIGIBd/PviC09V40A0HPrOa67uwGIADCq3kYAGFVzUBkEEEAAAQQQQAABBBCIWMDd/DtiS89VIwD03HqO6+5uACIAjKq3EQBG1RxUBgEEEEAAAQQQQAABBCIWcDf/jtjSc9UIAD23nuO6uxuACACj6m0EgFE1B5VBAAEEEEAAAQQQQACBiAXczb8jtvRcNQJAz63nuO7uBiACwKh6GwFgVM1BZRBAAAEEEEAAAQQQQCBiAXfz74gtPVeNANBz6zmuu7sBiAAwqt5GABhVc1AZBBBAAAEEEEAAAQQQiFjA3fw7YkvPVSMA9Nx6juvubgAiAIyqtxEARtUcVAYBBBBAAAEEEEAAAQQiFnA3/47Y0nPVCAA9t57jursbgAgAo+ptBIBRNQeVQQABBBBAAAEEEEAAgYgF3M2/I7b0XDUCQM+t57ju7gYgAsCoehsBYFTNQWUQQAABBBBAAAEEEEAgYgF38++ILT1XjQDQc+s5rru7AYgAMKreRgAYVXNQGQQQQAABBBBAAAEEEIhYwN38O2JLz1UjAPTceo7r7m4AIgCMqrcRAEbVHFQGAQQQQAABBBBAAAEEIhZwN/+O2NJz1QgAPbee47q7G4AIAKPqbQSAUTUHlUEAAQQQQAABBBBAAIGIBdzNvyO29Fw1AkDPree47u4GIALAqHobAWBUzUFlEEAAAQQQQAABBBBAIGIBd/PviC09V40A0HPrOa67uwGIADCq3kYAGFVzUBkEEEAAAQQQQAABBBCIWMDd/DtiS89VIwD03HqO6+5uACIAjKq3EQBG1RxUBgEEEEAAAQQQQAABBCIWcDf/jtjSc9UIAD23nuO6uxuACACj6m0EgFE1B5VBAAEEEEAAAQQQQACBiAXczb8jtvRcNQJAz63nuO7uBiACwKh6GwFgVM1BZRBAAAEEEEAAAQQQQCBiAXfz74gtPVeNANBz6zmuu7sBiAAwqt5GABhVc1AZBBBAAAEEEEAAAQQQiFjA3fw7YkvPVSMA9Nx6juvubgAiAIyqtxEARtUcVAYBBBBAAAEEEEAAAQQiFnA3/47Y0nPVCAA9t57jursbgAgAo+ptBIBRNQeVQQABBBBAAAEEEEAAgYgF3M2/I7b0XDUCQM+t57ju7gYgAsCoehsBYFTNQWUQQAABBBBAAAEEEEAgYgF38++ILT1XjQDQc+s5rru7AYgAMKreRgAYVXNQGQQQQAABBBBAAAEEEIhYwN38O2JLz1VLRQD45Zdf2hVXXGGPPvqo6T+3bt3alltuOdt5553t0EMPtYUXXrgsbfT444/bddddZ6+99ppNnjzZOnfubAMHDrQDDzzQtthii0T3mDNnjt14441255132kcffWQzZ8607t272yabbGJHHnmkrbjiig1e55NPPrHRo0fbc889Z++88459++231qxZM+vatWuoy957721DhgwJf5akvPzyy3bVVVfZCy+8EK61+OKL24ABA2zfffe1XXfdNcklijrG3QBEAFhUOzfVSQSATSXLdRFAAAEEEEAAAQQQQCBtAu7m32lrgEiex30AqNBvjz32sOnTp+ckXX755UNg1rt376LJ582bZwcffHAI//IVhYDXXHNNg8Hb1KlTbejQofbqq6/mvIyCS4VxI0aMyPn7PvvsY7fddlujz7H55pvbqFGjrEOHDg0ee9ZZZ9mZZ55pv/32W87jtt56a7v33nutTZs2jd6z0APcDUAEgIU2cZMeTwDYpLxcHAEEEEAAAQQQQAABBFIk4G7+nSL7mB7FdQCoFXCDBg2yH3/80dq3b28nnXSSbbjhhvbTTz+FAOz6668P1iussIK9/vrr4ZhiyimnnGLnnHNOOHXVVVe1448/3vr06WNjx461Cy64wN56663wm447++yzc95i7ty5ttFGG9nzzz8fft9hhx3sgAMOsI4dO4ZAUOdNmjTJWrRoEVYyKsSrX7RKcMyYMeGcHXfc0TbYYANbdtllrWXLlqEOl1xyiWmFoMrgwYPDKsHmzZvnrM8NN9wQ7q+iZzn55JNtlVVWsa+//touv/xye+aZZ8JvClfvuOOOYtgaPMfdAEQAWPY+UMoFCQBL0eNcBBBAAAEEEEAAAQQQqCUBd/PvWmqcCj6r6wBQYd+zzz4bAjAFa+uss858dBdeeGEI61S00u3Pf/5zwbSfffaZ9e/f3/Tq7hprrBHu07Zt27rrKHxcf/317Y033gj1+Pjjj0OgVr/ccsstNnz48PDHei35yiuvnO8Q3Wf11Ve3GTNmWN++fe3DDz8M18suei1XgadWAmq1YP2iuig4fPHFF8NPWi241157LXDc999/b7169TL9/6WXXtrefPNN69SpU91xCiu33357e/jhh8OfKUhcb731CrZr6AR3AxABYFnbv9SLEQCWKsj5CCCAAAIIIIAAAgggUCsC7ubftdIwFX5OtwGgVvTpm3cqBx10UHj9tn7Rq60rr7xy+Naevm03ceJEa9WqVUHEhx12WHgtV0Xfy1t77bUXOP+VV16pCx8PP/xwGzly5ALHrLTSSiHUUz30L1+u7xKed955YRWjyn333WfDhg0rqK46+P333w8r+VS22WYbe/DBBxe4RnYwevfdd+f81p/qqNWFCgO32mqrujCw4ArlOcHdAEQAWK6mL8t1CADLwshFEEAAAQQQQAABBBBAoAYE3M2/a6BNqvGIbgPA7NdyFcCttdZaOf2yQ7Unn3zSNt1008TO+vZfz5497auvvgqvEStIzFf0u16/7dGjR9iIJHsTjv/85z/Wr1+/cKq+JXj11VfnvIw24ejWrVv4bffddw8bhRRTtDnJlClTTKGjAsH6Zd1117WXXnrJFl100bCZyUILLZTzNtrY5IknngirDXW9Yl+hznVxdwMQAWAxXbHJziEAbDJaLowAAggggAACCCCAAAIpE3A3/06ZfyyP4zYA1Cup2rm2Xbt24VXW+q/LZoC1ak+vzaroFWC9Cpy0jBs3ru513nyrDDPX0u+ZTUJ0nl6xzZSbbrrJ9ttvv/CP+VbcZY7VpiWffvppeDV3/PjxSas633GLLbZYeJVYKwHffffd+X6bPXt2MNMrzXpdWDsb5yvnnntu+DagytNPPx2+r1iu4m4AIgAsV9OX5ToEgGVh5CIIIIAAAggggAACCCBQAwLu5t810CbVeES3AWBmlduAAQPs7bffzmv33XffhU0zVHbaaaewq23Sos049PqryqWXXmpHH3103lP1+5/+9Kfwu84bMmRI3bHHHXecXXTRReGftVnH73//+7zX2Xbbbe2hhx4KKwh/+OGHENYVUnT91VZbLe/zfvDBB+G1aJWjjjrKLrvssryXf+CBB8JmJSr6ZqG+XViu4m4AIgAsV9OX5ToEgGVh5CIIIIAAAggggAACCCBQAwLu5t810CbVeESXAeDPP/9ctxHH0KFD7ZFHHmnQTq+uzpo1K3y/TysCkxZ9V/CQQw4Jh//9738PO+/mK/pmnwJGFZ2nFYGZsuuuu9o999wT/lGv3GZvuFH/evqGYGaDEG0oohWBhRTVQXXJV2et+Ntyyy3D7/oW4LHHHpv38trYZM011wy/n3jiiaYVgUmLBpiGyjfffFP3DccJEyaEV6ejLgSAUTUPAWBUzUFlEEAAAQQQQAABBBBAIGIBAsCIG6eCVXMZACpE69KlS2DaZZddbNSoUQ2Sde3a1SZNmhRWvr333nuJebM3y3jsscdM38TLV/R7ZtWfVvsdc8wxdYcqpBw9enT4559++snatGmT9zonnHCCXXDBBeF3BXDaGThpuf/+++tCSp2njVKyv0Wo6yjI3HnnncMl9S1CfZMwX9E3D1dcccXwc77NTfKdW/++DT0DAWDSFua4jAABIH0BAQQQQAABBBBAAAEEEEgmQACYzCntR7kMABUY6Rt5KnvttZfddtttDbaTjtU5ffr0sc8++yxxm/7lL38J3w1UGTNmjG200UZ5z9U38jbeeOPwu8479dRT647Vn+t3Fe2q27x587zX0f10voq+cTh48OBE9dVqQe2KrNeG27ZtG8LDTHiXfYHbb7/d9t577/BHN954o40YMSLv9bO/gahvGN5www2J6qKDCAATU3FgEQIEgEWgcQoCCCCAAAIIIIAAAgjUpAABYE02+wIP7TIAZAXg/O349ddfm3b2/eKLL0Lwpt2Dd9ttt5w9vFIrAHkFmAGmKQUIAJtSl2sjgAACCCCAAAIIIIBAmgQIANPUmsU/i8sAkG8A/rfBp02bZtoRWZt7qFxxxRV2xBFH5O0RlfoGYGNd0t0AxDcAG2vSiv5OAFhRbm6GAAIIIIAAAggggAACjgXczb8dW8dcdZcBoEArsQuwNhfZeuutQ/uVsguwNtq4+OKLw3XKuQuwXvfV68X61p9K/VePc3W8999/31ZZZZXwE7sAF/CvJgFgAVhNfygBYNMbcwcEEEAAAQQQQAABBBBIhwABYDrasdSncBsAatWbvpHXrl07+/77761ly5Y5LbTr76BBg8Jv+r7emWeemdgs+xt42tVXu/vmK/r9uuuuCz/rvF69etUdetNNN5m+oadCmYCBAAAgAElEQVRy9913m3YFzle06++nn34avnE4fvz4vMdpMxHt5vvcc8+FY4477ri6zUMaesDZs2fbwgsvHL5FuPnmm5tWBOYr2vX35JNPDj/rG4YbbrhhYrvGDnQ3ABEANtakFf2dALCi3NwMAQQQQAABBBBAAAEEHAu4m387to656m4DQAVTCqhUXnnlFVtrrbVyOp933nl20kknhd+eeOIJ22yzzRK3x7x586xHjx6mb+ytsMIKpl1x85X+/fubNuLo3r172HAkexMMBXoK9lS06652381Vvv32W+vWrVv4Sd/wu+uuu3Ie9+uvv9q2225r2nm4sWvmuoACUQWjiy66qOl7igsttFDO+2jXY5m1bt06HLfIIosktmvsQHcDEAFgY01a0d8JACvKzc0QQAABBBBAAAEEEEDAsYC7+bdj65ir7jYAfO211+pCv3yr83777TdbeeWVQ3DXoUMHmzRpkrVq1aqg9jj00EPrAjuFZmuvvfYC5yuAXGeddcKf6/grr7xygWO0I6/q0bFjxxAQahVe/ZIdVt5777220047LXCMVu5pBeF9990XftMuyLfeemtBu+5ecMEFdsIJJ4Tz861I1ACx7LLLhpWCQ4YMsUcffbQgt8YOdjcAEQA21qQV/Z0AsKLc3AwBBBBAAAEEEEAAAQQcC7ibfzu2jrnqbgNAoWZeA9brv88//3xdCJcBv/DCC+34448P/3j66afbGWecMV9bPPvss3Wvte6zzz52yy23LNBWWr230kor2Zw5c2yNNdYI92nbtm3dcXoVV/V44403wmvIH374ofXt23eB62S/BnzYYYfZ3/72t/mOGTt2rK222mo2Y8YM69OnT1hNWP+1Zq1I1KvEN998czh32LBhds8991iLFi0K6mPaOKR37942ffp0W2aZZezNN9+0JZZYou4aCv223357e/jhh8Oflfv1X13T3QBEAFhQH2vqgwkAm1qY6yOAAAIIIIAAAggggEBaBNzNv9MCH9lzuA4AtaHGuuuuawrh2rdvH75Xp+/U6Z9HjRpV902+fv36hYCu/iusSQJAtZdeIdbqPJVVV101rJ5TSKfQ7vzzzw8be2SOO+ecc3I2sUK19ddf3/71r3/VhXcHHHCALb744qbVjNrAQysUmzdvbtp8RN/3q1+OOeYYu+SSS8Ifa2WjVv7le303c66Oy1Wuvfba8Dqyip7llFNOCZuD6HXnyy67zJ555pnwW0OvIpfSl90NQASApTR32c8lACw7KRdEAAEEEEAAAQQQQACBlAq4m3+ntB2q/ViuA0DhaZXannvuGVbO5SoK//T66nLLLbfAz0kDQL1KrLBOq/jyFa3M0yYgCvDylSlTpoTXaTO79tY/TmGeVgbqXrmKXsltaGOQXOdo1WC+olWRCh7zHaO63n///damTZuy91N3AxABYNn7QCkXJAAsRY9zEUAAAQQQQAABBBBAoJYE3M2/a6lxKvis7gNAWSkUu/zyy0PQp46tIE2Bn76hd/jhh+f83p7OSxoAZtpj9OjRIeRTgKcwr1OnTrbmmmuavkGYa8VernbUq8TXX3992OBD3wScNWuWLbXUUrbxxhvbUUcdFV43zlfKHQDqPi+99FL4ZqF2VJ44cWL4VuKAAQNs+PDhYfVfUxV3AxABYFN1haKuSwBYFBsnIYAAAggggAACCCCAQA0KuJt/12AbVeKRUxEAVgKKe5RXwN0ARABY3g5Q4tUIAEsE5HQEEEAAAQQQQAABBBCoGQF38++aaZnKPigBYGW9udv/CbgbgAgAo+q7BIBRNQeVQQABBBBAAAEEEEAAgYgF3M2/I7b0XDUCQM+t57ju7gYgAsCoehsBYFTNQWUQQAABBBBAAAEEEEAgYgF38++ILT1XjQDQc+s5rru7AYgAMKreRgAYVXNQGQQQQAABBBBAAAEEEIhYwN38O2JLz1UjAPTceo7r7m4AIgCMqrcRAEbVHFQGAQQQQAABBBBAAAEEIhZwN/+O2NJz1QgAPbee47q7G4AIAKPqbQSAUTUHlUEAAQQQQAABBBBAAIGIBdzNvyO29Fw1AkDPree47u4GIALAqHobAWBUzUFlEEAAAQQQQAABBBBAIGIBd/PviC09Vy1RAPjLL79Y69atPT8ndY9MwN0ARAAYVQ8iAIyqOagMAggggAACCCCAAAIIRCzgbv4dsaXnqiUKADt27Gi77babDR8+3NZYYw3Pz0vdIxFwNwARAEbSc/63GgSAUTUHlUEAAQQQQAABBBBAAIGIBdzNvyO29Fy1RAFg8+bNrVmzZuE5V155Zdtvv/1sjz32sCWWWMLzs1P3Kgq4G4AIAKvYWxa8NQFgVM1BZRBAAAEEEEAAAQQQQCBiAXfz74gtPVctUQA4bNgwe/TRR2327NnhWRUGtmrVyrbZZhsbMWKEbb755nUBoWcM6l45AXcDEAFg5TpHgjsRACZA4hAEEEAAAQQQQAABBBBAwMzczb9ptSYRSBQA6s5Tp061O+64w2655RZ755136oJA/YelllrK9t133/B/ffr0aZKKctF0CbgbgAgAo+qABIBRNQeVQQABBBBAAAEEEEAAgYgF3M2/I7b0XLXEAWD2Q7711lt200032d13323Tpk2bLwxcb731wivCO+64o7Vp08azDXVvQgF3AxABYBP2hsIvTQBYuBlnIIAAAggggAACCCCAQG0KuJt/12YzNflTFxUAZmqlV4IffPBBu/nmm+2f//ynzZ07ty4MXGSRReo2Dhk4cGCTPwg38CXgbgAiAIyqgxEARtUcVAYBBBBAAAEEEEAAAQQiFnA3/47Y0nPVSgoAsx/866+/Dq8H33rrrfaf//ynLgjUf+jfv7/tv//+tvfee5t2FKYg4G4AIgCMqtMSAEbVHFQGAQQQQAABBBBAAAEEIhZwN/+O2NJz1coWAArhxx9/tFGjRtmpp55qEydODC7z5s2rCwP1SvABBxxgZ5xxhnXo0MGzG3UvUcDdAEQAWGKLl/d0AsDyenI1BBBAAAEEEEAAAQQQSK+Au/l3epuiqk9WlgDwhRdeCK8B33fffTZr1qy64K9Tp06200472XvvvWcvvvhiXRDYs2fP8M89evSo6sNz8+oJuBuACACr11ly3JkAMKrmoDIIIIAAAggggAACCCAQsYC7+XfElp6rVnQAqA6k13312u+4cePqQr9mzZrZxhtvHF753X777a1Vq1bhN70WfOGFF4bNQ7QqcK+99grnUmpTwN0ARAAYVUclAIyqOagMAggggAACCCCAAAIIRCzgbv4dsaXnqhUUAP7yyy/2wAMPhNV+Y8aMCUFe5hXf7t2727777ht2AF522WXzmvz1r3+10047zXT8hAkTPNtR9xIE3A1ABIAltHb5TyUALL8pV0QAAQQQQAABBBBAAIF0Cribf6ezGar+VIkCwNdffz2Efvq+3/Tp00OlFfy1bNnShg4dGlb7bbnllta8efNGH0ivAw8YMCAcO2fOnEaP54B0CrgbgAgAo+qIBIBRNQeVQQABBBBAAAEEEEAAgYgF3M2/I7b0XLVEAaDCOr3am1ntt9xyy9mIESNs+PDh1rVr14Kef+zYsda3b99wvblz5xZ0LgenR8DdAEQAGFXnIwCMqjmoDAIIIIAAAggggAACCEQs4G7+HbGl56olDgC1g6++6addfDfYYIOin/nnn3+2V199NZy//vrrF30dTvQt4G4AIgCMqsMRAEbVHFQGAQQQQAABBBBAAAEEIhZwN/+O2NJz1RIFgCNHjgybdnTo0MHzs1L3iATcDUAEgBH1HjMCwKiag8oggAACCCCAAAIIIIBAxALu5t8RW3quWqIA0PMDUvc4BdwNQASAUXUkAsComoPKIIAAAggggAACCCCAQMQC7ubfEVt6rhoBoOfWc1x3dwMQAWBUvY0AMKrm+P/snQeUFVX2rzfdkkEQEBQQBAURCSJBRxQEcxwVcwDFDDg6KmZU1GEcszJmQcWEqOOMioFBBcwgAoNiAuNSAUFABRTo7rf2ea/7dcPt7rr3VtXd59ZXa7lGuFWn9vl+x/Nf5/tX1aEYCEAAAhCAAAQgAAEIQMAwAe/W34ZZ+lxaIAH43XffyZAhQ9zGHY8++qi0bNmyyj5///33MnjwYHfOk08+Kc2bN/eZEbVHQMC7CQgBGMEoyLxJBGDm7LgSAhCAAAQgAAEIQAACEEgWAe/W38mKJ7beBhKAt956q1x00UWyxx57yIwZMwIVpxt8vPXWW3LHHXfIiBEjAl3DSckh4N0EhAA0NTgRgKbioBgIQAACEIAABCAAAQhAwDAB79bfhln6XFogAThw4ECZPn263HzzzfLXv/41UH9V/Om5++yzj0yZMiXQNZyUHALeTUAIQFODEwFoKg6KgQAEIAABCEAAAhCAAAQME/Bu/W2Ypc+lBRKA7du3l2+++UZef/110Sf7ghwqDAcMGCDbbbedfPHFF0Eu4ZwEEfBuAkIAmhqdCEBTcVAMBCAAAQhAAAIQgAAEIGCYgHfrb8MsfS4tkACsW7eurFu3Tj788EPp3r17oP7OmzdPevToIXrt6tWrA13DSckh4N0EhAA0NTgRgKbioBgIQAACEIAABCAAAQhAwDAB79bfhln6XFogAdisWTNZsWKF/Pe//xV9HTjIoU8L6uu/jRo1ctdyQKA8Ae8mIASgqQGMADQVB8VAAAIQgAAEIAABCEAAAoYJeLf+NszS59ICCcBevXrJnDlz5IorrpBrr702UH9HjRolf/vb36Rr166iTwNyQAAByBgIiwACMCyStAMBCEAAAhCAAAQgAAEI5DsBBGC+Jxysf4EE4KWXXio33nijbLHFFvLRRx/J1ltvXWXr33//vRN/q1atkvPPP19uueWWYNVwVmIIeDcB8QSgqbGJADQVB8VAAAIQgAAEIAABCEAAAoYJeLf+NszS59ICCUDdAKRjx46yYcMG2WGHHWTixInSrVu3lP3Wp/2OO+44+eyzz6RmzZqyYMECtxEIBwTKE/BuAkIAmhrACEBTcVAMBCAAAQhAAAIQgAAEIGCYgHfrb8MsfS4tkADUDt58881y8cUXS40aNdw/uhtwv3793NOA+ucffvhBZsyYIbr7b0lJiWMyZswYueSSS3zmQ+0REfBuAkIARjQSMmsWAZgZN66CAAQgAAEIQAACEIAABJJHwLv1d/IiiqXHgQWgVnPdddfJ6NGjpbi42Em/VIfKv4KCAneefjOQAwKpCHg3ASEATQ1kBKCpOCgGAhCAAAQgAAEIQAACEDBMwLv1t2GWPpeWlgDUjupmIPo9wFdffVVWrlxZoe/6jcCDDjpILrroIunevbvPXKg9YgLeTUAIwIhHRHrNIwDT48XZEIAABCAAAQhAAAIQgEByCXi3/k5uVJH2PG0BWFqNPun31VdfybJly9xfNWvWTNq1a1fpk4GR9oLGvSPg3QSEADQ1xhCApuKgGAhAAAIQgAAEIAABCEDAMAHv1t+GWfpcWsYC0OdOU3vuCXg3ASEAcz9oylWAADQVB8VAAAIQgAAEIAABCEAAAoYJeLf+NszS59IQgD6n53Ht3k1ACEBTow0BaCoOioEABCAAAQhAAAIQgAAEDBPwbv1tmKXPpSEAfU7P49q9m4AQgKZGGwLQVBwUAwEIQAACEIAABCAAAQgYJuDd+tswS59LS0sALl++XB577DF588035csvv5Rff/1VioqKquy/7ha8aNEinxlRewQEvJuAEIARjILMm0QAZs6OKyEAAQhAAAIQgAAEIACBZBHwbv2drHhi621gAfj000/LmWeeKb/88osrTjcBCXKoAKxOEgZph3Pyi4B3ExAC0NQARACaioNiIAABCEAAAhCAAAQgAAHDBLxbfxtm6XNpgQTg+++/L3vssYcUFxc78deyZUvp0aOHNGnSRAoKCqrt/0MPPVTtOZyQLALeTUAIQFMDFAFoKg6KgQAEIAABCEAAAhCAAAQME/Bu/W2Ypc+lBRKARx55pPz73/+WunXrygMPPCAnnHCCz32mdgMEvJuAEIAGRs3/LwEBaCoOioEABCAAAQhAAAIQgAAEDBPwbv1tmKXPpQUSgFtttZX89NNPcs0118ioUaN87i+1GyHg3QSEADQycv5vGQhAU3FQDAQgAAEIQAACEIAABCBgmIB362/DLH0uLZAArFOnjqxfv17effdd6dOnj8/9pXYjBLybgBCARkYOAtBUEBQDAQhAAAIQgAAEIAABCJgn4N362zxRPwsMJADbtWsn3377rbz33nvSu3dvP3tK1aYIeDcBIQBNjR+eADQVB8VAAAIQgAAEIAABCEAAAoYJeLf+NszS59ICCcAzzjhDxo8fL3fddZecffbZPveX2o0Q8G4CQgAaGTn/twwEoKk4KAYCEIAABCAAAQhAAAIQMEzAu/W3YZY+lxZIAH766afSq1cv0W8BzpkzRxo2bOhzn6ndAAHvJiAEoIFR8/9LQACaioNiIAABCEAAAhCAAAQgAAHDBLxbfxtm6XNpgQSgdvC5556TE088Ubp27eqeBtxpp5187je155iAdxMQAjDHI6bi7RGApuKgGAhAAAIQgAAEIAABCEDAMAHv1t+GWfpcWiABOHToUNfHefPmuScAa9So4URgp06dpF69elX2X88dN26cz4yoPQIC3k1ACMAIRkHmTSIAM2fHlRCAAAQgAAEIQAACEIBAsgh4t/5OVjyx9TaQACwoKHDSr/QoKSmp8OfKqi09r6ioKLYOcSM/CHg3ASEATQ0sBKCpOCgGAhCAAAQgAAEIQAACEDBMwLv1t2GWPpcWSABuu+22gYRfZSC++uornxlRewQEvJuAEIARjILMm0QAZs6OKyEAAQhAAAIQgAAEIACBZBHwbv2drHhi620gARhbNdwoMQS8m4AQgKbGJgLQVBwUAwEIQAACEIAABCAAAQgYJuDd+tswS59LQwD6nJ7HtXs3ASEATY02BKCpOCgGAhCAAAQgAAEIQAACEDBMwLv1t2GWPpeGAPQ5PY9r924CQgCaGm0IQFNxUAwEIAABCEAAAhCAAAQgYJiAd+tvwyx9Li1jAagbfPz888+yZs0aadmypRQWFvrMgdpjJuDdBIQAjHmEVH07BKCpOCgGAhCAAAQgAAEIQAACEDBMwLv1t2GWPpeWlgDU3XwnTJggDz30kMyaNUvWrVvnNgf53//+J507dy7j8OKLL8qMGTOkUaNGcsUVV/jMh9ojIuDdBIQAjGgkZNYsAjAzblwFAQhAAAIQgAAEIAABCCSPgHfr7+RFFEuPAwvApUuXyuGHHy7vv/++6NN/pYcKwPnz51cQgB9//LF07drVycHZs2fLzjvvHEtnuIk/BLybgBCApgYXAtBUHBQDAQhAAAIQgAAEIAABCBgm4N362zBLn0sLJACLi4tl9913l5kzZ0pBQYEcddRR0q9fPxkxYoSTfBsLQAXSt29fee+99+TKK6+U0aNH+8yI2iMg4N0EhACMYBRk3iQCMHN2XAkBCEAAAhCAAAQgAAEIJIuAd+vvZMUTW28DCcCHH35Yhg4dKjVr1pTnn39e9t9/f1egysDKBOANN9wgl19+uQwYMEBee+212DrEjfwg4N0EhAA0NbAQgKbioBgIQAACEIAABCAAAQhAwDAB79bfhln6XFogAajCb+rUqTJ8+HC58847y/pblQB89dVX5cADD3QbhOhg44BAeQLeTUAIQFMDGAFoKg6KgQAEIAABCEAAAhCAAAQME/Bu/W2Ypc+lBRKALVq0kGXLlolKvX322SeQAJwzZ4707NlTateuLWvXrvWZEbVHQMC7CQgBGMEoyLxJBGDm7LgSAhCAAAQgAAEIQAACEEgWAe/W38mKJ7beBhKAKvE2bNggKvW6desWSADq9wJ32203qVu3rqxevTq2DnEjPwh4NwEhAE0NLASgqTgoBgIQgAAEIAABCEAAAhAwTMC79bdhlj6XFkgAbrXVVvLTTz+514D1m36lR1WvAD/66KMyZMgQadOmjXz99dc+M6L2CAh4NwEhACMYBZk3iQDMnB1XQgACEIAABCAAAQhAAALJIuDd+jtZ8cTW20ACcODAgTJ9+nS55pprZNSoUYEEoH7/b8qUKXL44YfLs88+G1uHuJEfBLybgBCApgYWAtBUHBQDAQhAAAIQgAAEIAABCBgm4N362zBLn0sLJABvv/12ueCCC0S/Bfjxxx9LkyZNXJ8rewLwoYcektNOO83tEPzII4/ISSed5DMjao+AgHcTEAIwglGQeZMpBWDN+iItOmfeKFeGS6D5jiKHjQ23TVqDAAQgAAEIQAACEIAABNIm4N36O+0eckEQAoEEoG7i0aFDB/nxxx9l5513lgkTJshOO+20iQD87rvv5MYbb5R77rlHSkpK3DULFixw53FAoDwB7yYgBKCpAZxSAJqqkGKkdW+R06cCAgIQgAAEIAABCEAAAhDIMQHv1t855pWvtw8kALXzs2bNEn0VeM2aNY7FDjvsIJ9++ql7yq9v377uG4Gff/65+03lX8OGDeXtt9+WLl265Cs7+pUFAe8mIARgFmmHfykCMHymobeIAAwdKQ1CAAIQgAAEIAABCEAgEwLerb8z6STXVEsgsADUlubPn+9e59X/LT1UAJZKv9K/23HHHeWpp55C/lWLP7kneDcBIQBNDVYEoKk4UheDAPQgJEqEAAQgAAEIQAACEEgCAe/W30kIJQd9TEsAltY3efJk+c9//iMffPCBLF26VIqKiqRp06bSo0cPOeyww2TQoEG89puDMH26pXcTEALQ1PBCAJqKAwHoQRyUCAEIQAACEIAABCCQXALerb+TG1WkPc9IAEZaEY0ngoB3ExAC0NS47DFzlGy+8nP5aWmJFBWL1CysIR3b1DJVY6KKWbJAZP3qil3mCcBEDQE6CwEIQAACEIAABCBgl4B362+7KL2uDAHodXz+Fu/dBIQANDnYHhq3Xlb/JtKscaFMGtPKZI2JKCrFfx9sApKI5OkkBCAAAQhAAAIQgIAHBLxbf3vA1McSEYA+ppYHNXs3ASEATY46BKCRWBCARoKgDAhAAAIQgAAEIAABCGxKwLv1NyFGQgABGAlWGq2OgHcTEAKwukhz8jsCMCfYN70pAtBIEJQBAQhAAAIQgAAEIAABBCBjIDWBQAKwsLAwY366S/CGDRsyvp4L85MAAjA/c427VwjAuIlXcj8EoJEgKAMCEIAABCAAAQhAAAIIQMZAFgKwoKAgY34qAHWXYA4IlCeAAGQ8hEEAARgGxRDaQACGAJEmIAABCEAAAhCAAAQgEA0B79bf0WBIfKuBngAcPXp0taBWr14tn332mUydOlV+//132XXXXWX//fd311199dXVXs8JySLg3QTEK8AmBygC0EgsCEAjQVAGBCAAAQhAAAIQgAAENiXg3fqbECMhEEgApnPnZcuWydChQ+Wll16S2267Tc4999x0LufchBDwbgJCAJocmQhAI7EgAI0EQRkQgAAEIAABCEAAAhBAADIGUhMIXQDqbfSbf3369JGPPvpI3nzzTfc0IAcEyhNAADIewiCAAAyDYghtIABDgEgTEIAABCAAAQhAAAIQiIaAd+vvaDAkvtVIBKBSffjhh92TgEcffbQ89dRTiQcNgIoEvJuAeALQ5BBGABqJBQFoJAjKgAAEIAABCEAAAhCAwKYEvFt/E2IkBCITgLNnz5bevXtLy5YtRQcbBwTKE/BuAkIAmhzACEAjsSAAjQRBGRCAAAQgAAEIQAACEEAAMgZSE4hMAL799tuy5557Sq1atdymIBwQQAAyBsImgAAMm2iG7SEAMwTHZRCAAAQgAAEIQAACEIiegHcP4ESPJJF3iEwAXnjhhW4TkFatWsl3332XSLh0unIC3k1APAFocjgjAI3EggA0EgRlQAACEIAABCAAAQhAYFMC3q2/CTESAqELwNWrV8vYsWPlyiuvlJKSEjn55JPd9wA5IFCegHcTEALQ5ABGABqJBQFoJAjKgAAEIAABCEAAAhCAAAKQMZCaQCABOHDgwGr5FRcXy4oVK+Tzzz+XdevWOfnXoEED0W8BdujQodrrOSFZBBCAyco7qt4iAKMim2a7CMA0gXE6BCAAAQhAAAIQgAAE4iPg3fo7PjSJulMgAVhQUCA1atRwUi/o0bZtW3nsscekb9++QS/hvAQR8G4C4glAk6MTAWgkFgSgkSAoAwIQgAAEIAABCEAAApsS8G79TYiREAgkAPfaay8nAKs6VBI2bNhQ2rVrJ/3795eDDz5YatasGUnRNOo/Ae8mIASgyUGHADQSCwLQSBCUAQEIQAACEIAABCAAAQQgYyA1gUACEHgQCJsAAjBsoslsDwFoJHcEoJEgKAMCEIAABCAAAQhAAAIIQMYAApAxYIgAAtBQGB6XggA0Eh4C0EgQlAEBCEAAAhCAAAQgAAEEIGMAAcgYMEQAAWgoDI9LQQAaCQ8BaCQIyoAABCAAAQhAAAIQgAACkDGAAGQMGCKAADQUhselIACNhIcANBIEZUAAAhCAAAQgAAEIQAAByBjIQgBOmDAhEn6DBw+OpF0atU8AAWg/Ix8qRAAaSQkBaCQIyoAABCAAAQhAAAIQgAACkDGQhQDUHX6r2wU4XcDa3oYNG9K9jPPzhAACME+CzHE3EIA5DqD09ghAI0FQBgQgAAEIQAACEIAABBCAjIEsBWDYAFUAFhUVhd0s7XlCAAHoSVDGy0QAGgkIAWgkCMqAAAQgAAEIQAACEIAAApAxkIUA/Oabb2TFihVy1llnyaxZs6RLly4yZMgQ6dOnj7Ro0UJKSkpk6dKl7rdHHnlE5s+f73677777pHHjxpWyb9u2LbkklAACMKHBh9xtBGDIQDNtDgGYKTmugwAEIAABCEAAAhCAQOQEvFt/R04kmTeoUaL2rppj3bp10rdvX/nwww9l9LrEUCgAACAASURBVOjRcsUVV1T6SrA2N2bMGBk1apT06tVL3nrrLalVq1Z1t+D3hBHwbgJKITiWN+0u0/d7KmHJ2eouAtBIHghAI0FQBgQgAAEIQAACEIAABDYl4N36mxAjIRBIAN5yyy0ycuRIOeaYY2TixImBCjnuuOPk6aeflhtuuMFdywGB8gS8m4AQgCYHMALQSCwIQCNBUAYEIAABCEAAAhCAAAQQgIyB1AQCCcCePXvK3LlzZfLkyXLAAQcEYvnqq6/KgQceKD169JDZs2cHuoaTkkMAAZicrKPsKQIwSrpptI0ATAMWp0IAAhCAAAQgAAEIQCBeAt6tv+PFk5i7BRKA+h2/X3/9VT744AMn9IIcc+bMERWHm2++uaxcuTLIJZyTIALeTUA8AWhydN49dr0UF4sU1BBp0qjQZI1JKOr6zY6XjgXzKna1dW+R06cmofv0EQIQgAAEIAABCEAAAqYJeLf+Nk3T3+ICCcBGjRrJb7/9Jk8++aR7DTjIMWnSJNHXgBs2bCirVq0KcgnnJIiAdxMQAtDk6LzrzvVS/VdMTZaeV0WN3eJE2anm/xCAeZUqnYEABCAAAQhAAAIQyBcC3q2/8wW8sX4EEoC77bab2+F31113dZt6FBQUVNmN4uJit2nI+++/73YDfu+994x1m3JyTcC7CQgBmOshk/L+pQKwRg2RevVNlpiIom6qfaJ0KkQAJiJsOgkBCEAAAhCAAAQg4B0B79bf3hH2o+BAAvCuu+6Sc8891+38e/DBB8v9998vW221VcoeLlmyRM466yx5/vnn3fljx46VYcOG+UGDKmMj4N0EhACMbWykc6NSAVi/gcipp9VM51LODZFA/ynHStPlvAIcIlKaggAEIAABCEAAAhCAQGgEvFt/h9ZzGipPIJAA1Cf6+vXrJ++8846TerVr15b99ttPevfuLc2bN3d/p+JPnxKcMmWK/PHHH1JSUiJ77LGHTJs2rdonBokkeQS8m4AQgCYHKQLQRiwIQBs5UAUEIAABCEAAAhCAAARSEfBu/U2MkRAIJAD1zvoNwBNOOEFefPFFV4hKv1SHij89Dj30UHn88celQYMGkRROo34T8G4CQgCaHHAIQBuxIABt5EAVEIAABCAAAQhAAAIQQAAyBiojEFgAljbwwgsvyL333ivTp0+XNWvWVGi3Xr160r9/fznnnHPkkEMOgToEKiWAAGRwhEEAARgGxezbQABmz5AWIAABCEAAAhCAAAQgEBUB79bfUYFIeLtpC8BSXkVFRbJo0SJZsWKFe923SZMmst1220lhYWHCkdL9IAS8m4B4AjBIrLGfgwCMHXnKGyIAbeRAFRCAAAQgAAEIQAACEEhFwLv1NzFGQiBjARhJNTSaGALeTUAIQJNjEwFoIxYEoI0cqAICEIAABCAAAQhAAAIIQMZAZQQQgIyNnBBAAOYEe97dFAFoI1IEoI0cqAICEIAABCAAAQhAAAIIQMZAaALwl19+kWeeeUbeffddWbx4sfsO4Pjx46Vt27Zl9/jhhx9k5cqVUqdOHWnfvj30IbAJAQQggyIMAgjAMChm3wYCMHuGtAABCEAAAhCAAAQgAIGoCHi3/o4KRMLbTesJwLvuukuuuOIK+fXXXx02/faf7gY8f/586dy5cxnKJ598Uk488UQnAHWg6fcBOSBQnoB3ExCvAJscwAhAG7GkFIA164u0+P//d8FGpQmvovmOIoeNTTgEug8BCEAAAhCAAASSR8C79XfyIoqlx4EF4DXXXCPXXXedk361a9eWrl27ygcffJBSABYXF8s222zjnhDUHYPPOOOMWDrDTfwh4N0EhAA0ObgQgDZiSSkAbZRGFeUJtO4tcvpUmEAAAhCAAAQgAAEIJIyAd+vvhOUTV3cDCcA5c+ZIr169XE36ZN/YsWOlUaNGUlBQkFIA6nnnn3++3HnnnXLUUUfJpEmT4uoP9/GEgHcTEALQ5MhCANqIBQFoI4dqq0AAVouIEyAAAQhAAAIQgEA+EvBu/Z2PIRjoUyABOHToUHn44Ydl9913l7feequs7KoE4FNPPSXHH3+87LDDDvLJJ59E2tVvv/3WycbJkyeL/rs+obj99tvLMcccI8OGDZN69eqFcv9XXnlF7r//fpk5c6b89NNPsuWWW0qfPn3kzDPPlAMOOCDQPTZs2CDjxo2Txx9/3HH57bffpFWrVrLPPvvIX/7ylwqvUqdqUL+tOGvWLFdD6f/++OOP7tT+/fvLtGnTqq1DzxkwYEC15+kJV199tejTn2Ef3k1ACMCwh0Ao7SEAQ8GYdSMIwKwRxtMAAjAeztwFAhCAAAQgAAEIGCPg3frbGL98KSeQANxuu+3k66+/dtLquOOOCyQA33//ffnTn/4kDRo0EN04JKpDpZ8+lbhq1aqUt1AB+dJLL2W1GYm+9nz22Wc7+VfZoRJQX3fWbyJWdixfvlwOPvhgUTapDhWXd999t6hwrexo166dyyLVgQCMapSJCAIwQriZN40AzJxdmFciAMOkGWFbCMAI4dI0BCAAAQhAAAIQsEsAAWg3mzgrCyQA9Qm6P/74w33zr0ePHmX1VfUE4Ny5c2WXXXaRmjVrumujOObNm+eeStSdiFU0XnbZZe7JtrVr18rEiRPlgQcecLft1KmTe1pOz8nk0I1PxowZ4y7V/l988cWiUnTRokVy4403ir4irYeed/3116e8RVFRkQwcOFBmzJjhfj/yyCPdtxF1gxQVgnrd0qVLpbCw0D3JuP/++6dsZ9ttt5VvvvnG/daiRQvp3bu3vPjii+7PmQhA3cFZ26jsaN68ueg/YR/eTUAIwLCHQCjtIQBDwZh1Iz1mjpLNV34uPy0tkaJikZqFNaRjm1pZt0sDWRBYskBk/eqKDSAAswDKpRCAAAQgAAEIQMBfAt6tv/1FbbryQAKwcePGbuffd999173yWnpUJQBffvll97Sbvia7ZMmSSCCo7NPXWTfbbDMn1vSJw/LHTTfd5GSdHqNHj5arrroq7ToWLlwoO+64o+iru/odRL1P3bp1y9pR+ajiTeWo1vHpp586Objxoa9Qn3rqqe6v9bVk3VG5/KH36dmzp3taskOHDrJgwQLX3sbHzTffLPoUoEq7Nm3auJ9LnzrMRAC+8cYbstdee6XNJdsLvJuAEIDZRh7J9QjASLBm3OhD49bL6t9EmjUulEljWmXcDheGQCDFnCUIwBDA0gQEIAABCEAAAhDwj4B362//EHtRcSAB2KVLF/e9ugcffLBMYmnvqhKAf/3rX+WOO+5wUu7tt98OHYY+0VcqI8866yz3+u3Gh+5GXFr7Flts4USkPpGYzjF8+HD3Wq4eKkB32223TS5/7733yuTjiBEj3CYpGx877bSTk3pah/7Hl+q7hDfccIN7ilGPZ555RgYNGhSoVARgIEzZnYQAzI5fRFcjACMCm2GzCMAMwUVxGQIwCqq0CQEIQAACEIAABLwkgAD0MrbQiw4kAHVzin/+85+yxx57lL3CqpVUJgC//PJL2XnnnWX16tUyatSoSDaRKP9argq4XXfdNSWc8lJtypQpsu+++waGqN/+22abbeT77793rxFXtZmJ/v7ZZ59J69at3UYk5b8F+MUXX0jHjh3dffVbgvfcc0/KGhYvXixbb721++2EE05w31wMciAAg1DK8hwEYJYAo7kcARgN10xbRQBmSi6C6xCAEUClSQhAAAIQgAAEIOAnAQSgn7mFXXUgAfj555+7J+n0O3blhV4qAaivwupGISoB69Sp476TVyq1wiy+X79+8uabb0r9+vVFd8ZN9bqs3k+f2tPvBOqhrwDrq8BBD+1D6eu8lT1lWNqW/l66SYhep6/plh76nb3TTjvN/fHJJ5+ssJHKxrXopiXKW1/vLf3WX3X1IgCrIxTC7wjAECCG3wQCMHym2bSIAMyGXsjXIgBDBkpzEIAABCAAAQhAwF8CCEB/swuz8kACUG943XXXydVXX+2ebNNv4enrqZdeeqn7s35rb/369aJP2Ok3+UqP2267TfTpwSgO/bbgsmXLpHv37qIbjlR2rFixwm20ocfRRx8tkyZNClyObsZxyCGHuPO1L+eff36l1+rvF1xwgftdrzvooIPKzh05cqTot/v00A1D9OnIyo4///nP8vzzzzuu+t1FFZzVHdkIQH1FW59Y1A1I9F66yYh+E/Ccc84pe2qxuvtn8rt3ExACMJOYI78GARg54rRugABMC1e0JyMAo+VL6xCAAAQgAAEIQMAjAt6tvz1i61OpgQWgdkqfoNPdcPXbeuVfcS3fYX1tVn/Tc1UYRnH8/vvvZRtx6EYjpbvgVnYv3f1XX0fW7/fpE4FBD/2uoIowPZ5++mk56qijKr1Uv9mnglEPvU6fCCw99InIp556yv3xp59+kmbNmlXajn5DsHSDEN1QRJ8IrO7IRgBW1rY+3alPe5ZK3+pq2Ph3nWCqOn788ceybzh+99137tVp0wcC0GQ8CEBbsSAADeWBADQUBqVAAAIQgAAEIACB3BJAAOaWv5W7pyUAtWh9xVe/q/fKK6+I7oBb/qhVq5bsvffeot/nK33tNoqOqkRr3ry5a/rYY4+ViRMnVnmbFi1auCfc9DXm+fPnBy6p/C7CuqvxAQccUOm1+nvpU3/6tN+FF15Ydq5Kypdeesn9ee3ate7V6MqOSy65RG688Ub3s7LWnYGrOzIRgPqNwSOPPNJ917F9+/buFWp9EvCFF16QRx991D3RqYduSqLSN92jMkGcqh0EYLp0Ob+UAALQ1lhAABrKAwFoKAxKgQAEIAABCEAAArklgADMLX8rd09bAJYWvmHDBrerrYo1/TZg06ZNRXe6rVu3buR9U2Gk38jT4+STT5YJEyZUeU89V6/R7/ktXLgwcH362rM+yajHa6+9JgMHDqz02tdff93JTz30uiuvvLLsXP17/V0PZaVP11V26P30ej30G4cq6Ko70hWA+jSkytrKdkSeOXOm7LfffrJq1Sr3NOeHH35Y5WvLqepDAFaXGr+HQQABGAbF8NpAAIbHMuuWEIBZI6QBCEAAAhCAAAQgkC8EEID5kmR2/chYAGZ32+yu5gnAivzSFYBB6OsOxCeddJI79fTTT5cHHnggyGVl5/AKcFq4ODlDAgjADMFFdBkCMCKwmTSLAMyEGtdAAAIQgAAEIACBvCSAAMzLWNPuVCABqE+s6T/6KujFF1+c9k3CvoBvAEYvAPUJT/1WoT4F2KFDB7czcZiHdxMQ3wAMM/7Q2kIAhoYylIYQgKFgDKcRBGA4HGkFAhCAAAQgAAEI5AEB79bfecDcYhcCCUD9Zp1+E+6tt94S3TXWwhHHLsC6ucihhx7qupvNLsAXXXSR3HLLLa4dC7sAB82vT58+MmvWLKlXr57bRCXMw7sJCAEYZvyhtYUADA1lKA0hAEPBGE4jCMBwONIKBCAAAQhAAAIQyAMC3q2/84C5xS4EEoC6ScQ333zjdtBVKWTh6Nevn/tGXv369WXlypVuE4tUh9ZcuiGJfl9v9OjRgcv/8ssv3XcD9dBdfXV338oO/f3+++93P+t17dq1Kzt1/Pjxctppp7k/P/nkk6K7Ald26K6/+rSdfrdQmQc5ongFWO/bu3dvtxEJAlBEEIBBhmLs5yAAY0de5Q0RgIbyQAAaCoNSIAABCEAAAhCAQG4JIABzy9/K3QMJwFNOOcXtDHvXXXfJ2WefbaL2yy+/XP7+97+7Wt577z3ZddddU9alOxbrTrZ6vPrqq25zi6BHSUmJtG7dWn744Qfp1KmTfPLJJ5VeuuOOO8qnn34qrVq1chuOlN8EQ4Weij09lN8999yTsp3FixfL1ltv7X47/vjj5YknnghUahQCUF8B1o1dfvnlF9l+++3liy++CFRL0JO8m4AQgEGjjfU8BGCsuKu9GQKwWkTxnYAAjI81d4IABCAAAQhAAALGCXi3/jbO09fyAgnA2bNnu1d/9ak03RF28803z3l/dafaUulX2dN5xcXF0qVLFyfuGjdu7HYsrmzn28o6NGzYsDJhp08T7rbbbpucqgKy9NVoPV9F6cZH586dXR1NmjRxglCfqtv4KC8rJ02aJEcffXQgzlEIwMcee8ztsKyHPr344IMPBqol6EneTUAIwKDRxnoeAjBW3NXeDAFYLaL4TkAAxseaO0EAAhCAAAQgAAHjBLxbfxvn6Wt5gQSgdu6+++6TESNGOKGmgqv0tdpcdrz0NWB9/XfGjBmbfJ/wpptuKtu05Oqrr5ZrrrmmQrnTpk2TAQMGuL8bMmSIPPzww5t0R5/e22mnnUSfiOvVq5e7T926dcvOW7t2rWgd+qqs1rFgwQK3acbGR/nXgIcPHy7//Oc/K5yyaNEi2WWXXdwTd/rasT5NWNlrzRu3nY4AXLFihcybN0/22muvSqNTubr//vu7V6u1bf2z9j3Mw7sJCAEYZvyhtYUADA1lKA0hAEPBGE4jCMBwONIKBCAAAQhAAAIQyAMC3q2/84C5xS4EEoBDhw51tasIUsGlUmibbbaRbt26yRZbbCGFhYWV9k3PHTduXCR91w01+vbtKyrhGjRoIPpasAo9/fPEiRPLvsnXsWNHJ+gaNmxYoY4gAlAv0FeI9ek8PXr06CGXXHKJk3Qq7f7xj3+4jT1Kz9OdklMdRUVF0r9/f3n77bfdz4MGDZIzzjjD8VOu1113nXtCUXdb1s1HDjzwwJTtzJ07V/Sf8sepp57q/qivGV966aUVfjvqqKMcm9Lj66+/dt8n1OwOP/xw6dmzp3vtWDP89ttv5YUXXnCve+umL3qMHDlSbrzxxtDz824CQgCGPgbCaBABGAbF8NpAAIbHMuuWEIBZI6QBCEAAAhCAAAQgkC8EvFt/5wt4Y/0IJABVSpX/pp1+G0+P8n+Xql96np6j8iuqQ4XVSSed5J6cS3Wo/Js8ebL7jt3GR1ABqK8Sq6zTp/gqO/Q1Wd0ERFlVdixbtkwOOuggt7NuqqNWrVruyUC9V2WHPsWYzkYmX331lWy77bZlzZUKwOryUCE4atQo0Y1Tqsu5urZS/e7dBIQAzCTmyK9BAEaOOK0bIADTwhXtyQjAaPnSOgQgAAEIQAACEPCIgHfrb4/Y+lRqIAGoAikbCaQSKspDd8u94447nOjTga0iTYWffkNPX1tO9b09rSeoACyt/aWXXnKSTwWeyrxmzZq5nXL1G4SVPbG3cb/1VeIHHnjAbfCh3wRcvXq1tGzZUvbee28577zz3OvGVR3ZCsB169bJ888/73Z01icPv//+e9eX33//XRo1auSeItTXg08//fQK4jDs/LybgBCAYQ+BUNpDAIaCMbRGEIChocy+IQRg9gxpAQIQgAAEIAABCOQJAe/W33nC3Vo3AglAa0VTj/8EvJuAEIAmBx0C0FYsCEBDeSAADYVBKRCAAAQgAAEIQCC3BLxbf+cWV97evYIA/N///uc62qlTJ/cUHQcEoiLg3QSEAIxqKGTVLgIwK3yhX4wADB1p5g0iADNnx5UQgAAEIAABCEAgzwh4t/7OM/5WulNBAOr36/QfFYGdO3cuq/Haa691/z5s2DD32isHBLIl4N0EhADMNvJIrkcARoI140YRgBmjC/9CBGD4TGkRAhCAAAQgAAEIeErAu/W3p5ytl72JANRv/c2fP7+CACzdBGTjv7feOeqzS8C7CQgBaHIwIQBtxYIANJQHAtBQGJQCAQhAAAIQgAAEckvAu/V3bnHl7d0rCMDatWuLblKhm0P07NmzrNMIwLzNP2cd824CQgDmbKxUdWMEoK1YEICG8kAAGgqDUiAAAQhAAAIQgEBuCXi3/s4trry9ewUB2KZNG7cr7D333CNnnnkmAjBvY899x7ybgBCAuR80KSpAANqKBQFoKA8EoKEwKAUCEIAABCAAAQjkloB36+/c4srbu1cQgCeddJI88cQT0qhRIycAO3bsKDVr1pRTTjlF9NXg66+/Xlq1apU2jMGDB6d9DRfkNwHvJiAEoMkBiQC0FcvdY9dLcbFIQQ2RJo0KbRWXsGqu3+x46Vgwr2KvW/cWOX1qwkjQXQhAAAIQgAAEIAAB79bfRBYJgQoC8OOPP5Y+ffrI2rVrnfArPUpKSty/lv+7oNXoNfpaMQcEyhPwbgJCAJocwAhAW7GU5mGrqmRWM3aLE2Wnmv9DACYzfnoNAQhAAAIQgAAEKhDwbv1NfpEQqCAA9Q6zZ8+Wyy67TGbMmCHr1q3L+qYqAIuKirJuhwbyi4B3ExAC0OQARADaiqU0D/3/H9Wrb6u2pFVzU+0TpVMhAjBpudNfCEAAAhCAAAQgkIqAd+tvYoyEwCYCsPQu+tTesmXL5Pfff5f27du7p/9effVV6dChQ9qFtG3bNu1ruCC/CXg3ASEATQ5IBKCtWMjDTh79pxwrTZfzCrCdRKgEAhCAAAQgAAEI5I6Ad+vv3KHK6ztXKgDL95pdgPN6DOSkc95NQAjAnIyT6m6KcKqOULy/k0e8vKu6GwLQThZUAgEIQAACEIAABHJNwLv1d66B5en9AwnA0aNHu+4PHz5cmjVrlqco6FacBLybgBCAcQ6PwPdCOAVGFcuJ5BEL5kA3QQAGwsRJEIAABCAAAQhAIBEEvFt/JyKV+DsZSADGXxZ3zHcC3k1ACECTQxLhZCsW8rCTBwLQThZUAgEIQAACEIAABHJNwLv1d66B5en9EYB5Gqz1bnk3ASEATQ4phJOtWMjDTh4IQDtZUAkEIAABCEAAAhDINQHv1t+5Bpan90cA5mmw1rvl3QSEADQ5pBBOtmIhDzt5IADtZEElEIAABCAAAQhAINcEvFt/5xpYnt4fAZinwVrvlncTEALQ5JBCONmKhTzs5IEAtJMFlUAAAhCAAAQgAIFcE/Bu/Z1rYHl6fwRgngZrvVveTUAIQJNDCuFkKxbysJMHAtBOFlQCAQhAAAIQgAAEck3Au/V3roHl6f0RgHkarPVueTcBIQBNDimEk61YyMNOHghAO1lQCQQgAAEIQAACEMg1Ae/W37kGlqf3RwDmabDWu+XdBIQANDmkEE62YiEPO3kgAO1kQSUQgAAEIAABCEAg1wS8W3/nGlie3h8BmKfBWu+WdxMQAtDkkEI42YqFPOzkgQC0kwWVQAACEIAABCAAgVwT8G79nWtgeXp/BGCeBmu9W95NQAhAk0MK4WQrFvKwkwcC0E4WVAIBCEAAAhCAAARyTcC79XeugeXp/RGAeRqs9W55NwEhAE0OKYSTrVjIw04eCEA7WVAJBCAAAQhAAAIQyDUB79bfuQaWp/dHAOZpsNa75d0EhAA0OaQQTrZiIQ87eSAA7WRBJRCAAAQgAAEIQCDXBLxbf+caWJ7ev4IAvPbaayPp5lVXXRVJuzTqLwHvJiAEoMnBhnCyFQt52MkDAWgnCyqBAAQgAAEIQAACuSbg3fo718Dy9P4VBGBBQYHUqFEj9K4WFRWF3iYN+k3AuwkIAWhywCGcbMVCHnbyQADayYJKIAABCEAAAhCAQK4JeLf+zjWwPL3/JgKwun6qICwpKanytI3PKS4urq5Zfk8YAe8mIASgyRGKcLIVC3nYyQMBaCcLKoEABCAAAQhAAAK5JuDd+jvXwPL0/oG/Afj111/LscceK7NmzZIDDzxQhg4dKn369JEWLVo4NEuWLHG/jRs3Tl5++WXp3bu3TJo0Sdq2bZun6OhWNgS8m4AQgNnEHdm1CKfI0GbUMHlkhC2SixCAkWClUQhAAAIQgAAEIOAlAe/W315Stl90IAG4atUqJ/S++uoreeihh+Skk06qsmePP/64DBkyRNq1aycffPCBNGrUyD4JKoyVgHcTEAIw1vER9GYIp6Ck4jmPPOLhHOQuCMAglDgHAhCAAAQgAAEIJIOAd+vvZMQSey8DCcBrrrlGdIOQs88+W+6+++5ARZ5zzjly3333yZVXXumu5YBAeQLeTUAIQJMDGOFkKxbysJMHAtBOFlQCAQhAAAIQgAAEck3Au/V3roHl6f0DCcCuXbvKggULZOrUqTJgwIBAKN544w3Ze++9pXPnzvLRRx8FuoaTkkPAuwkIAWhycCKcbMVCHnbyQADayYJKIAABCEAAAhCAQK4JeLf+zjWwPL1/IAHYsGFDWbNmjfvG3y677BIIxYcffii9evWS+vXry6+//hroGk5KDgHvJiAEoMnBiXCyFQt52MkDAWgnCyqBAAQgAAEIQAACuSbg3fo718Dy9P6BBGCTJk1EvwM4fvx4922/IMcjjzwip556qjRu3Fh+/vnnIJdwToIIeDcBIQBNjk6Ek61YyMNOHghAO1lQCQQgAAEIQAACEMg1Ae/W37kGlqf3DyQA9bXf6dOnS6dOndymHvXq1asShz4tqE//ffbZZ7LnnnvKtGnT8hQf3cqUgHcTEAIw06gjvQ7hFCnetBsnj7SRRXYBAjAytDQMAQhAAAIQgAAEvCPg3frbO8J+FBxIAOquvieffLLUqFFDevbsKffff7/svPPOKXs4b948OfPMM93rwnr+o48+KieccIIfNKgyNgLeTUAIwNjGRjo3QjilQyv6c8kjesZB74AADEqK8yAAAQhAAAIQgED+E/Bu/Z3/keSkh4EEoFZ21FFHyb/+9S8n9fTQjUF69+4tzZs3d3+3ZMkSJ/3mz5/vfi8pKZFBgwbJ008/nZOOcVPbBLybgBCAJgcUwslWLORhJw8EoJ0sqAQCEIAABCAAAQjkmoB36+9cA8vT+wcWgEVFRXL++efLPffcI8XFxQ5HqQwsz0bFn/798OHD5dZbb5XNNtssT9HRrWwIeDcBIQCziTuyaxFOkaHNqGHyyAhbJBchACPBSqMQgAAEIAABCEDASwLerb+9pGy/6MACsLQr+oTfvffeK1OnTpWFCxe6J/1Kjw4dOsg+++wjZ511lnTr1s1+76kwZwS8m4AQgDkbK1XdGOFkKxbyrdkPCwAAIABJREFUsJMHAtBOFlQCAQhAAAIQgAAEck3Au/V3roHl6f3TFoDlOfzxxx+ycuVKJwG32GILqV27dp5iolthE/BuAkIAhj0EQmkP4RQKxtAaIY/QUGbdEAIwa4Q0AAEIQAACEIAABPKGgHfr77whb6sjWQlAW12hGp8IeDcBIQBNDi+Ek61YyMNOHghAO1lQCQQgAAEIQAACEMg1Ae/W37kGlqf3RwDmabDWu+XdBIQANDmkEE62YiEPO3kgAO1kQSUQgAAEIAABCEAg1wS8W3/nGlie3j9tAajf/ZswYYK8++67snjxYlm7dq288sorsv3225ch+uijj+Tbb7+V+vXrS//+/fMUHd3KhoB3ExACMJu4I7sW4RQZ2owaJo+MsEVyEQIwEqw0CgEIQAACEIAABLwk4N3620vK9osOLAB1599LLrlEbr/9drcLcOnmH7rjr24M0rlz57Levvzyy3LwwQe7HYC/+uoradWqlX0SVBgrAe8mIARgrOMj6M0QTkFJxXMeecTDOchdEIBBKHEOBCAAAQhAAAIQSAYB79bfyYgl9l4GFoBnnHGGjB8/3ok/FXp/+tOf5JlnnpFUAlB7oU8Eqvy79dZb5bzzzou9Y9zQNgHvJiAEoMkBhXCyFQt52MkDAWgnCyqBAAQgAAEIQAACuSbg3fo718Dy9P6BBOC0adNk4MCBTvZddtllMnr0aCksLJSCgoJKBaCe949//EMOO+ww+fe//52n+OhWpgS8m4AQgJlGHel1CKdI8abdOHmkjSyyCxCAkaGlYQhAAAIQgAAEIOAdAe/W394R9qPgQALwuOOOk0mTJrnXel944YWynlUlAJ977jkZNGiQbLfddvLFF1/4QYMqYyPg3QSEAIxtbKRzI4RTOrSiP5c8omcc9A4IwKCkOA8CEIAABCAAAQjkPwHv1t/5H0lOehhIALZt21Z0wDz77LNy+OGHBxKAs2bNkl133dVtBPLrr7/mpHPc1C4B7yYgBKDJwYRwshULedjJAwFoJwsqgQAEIAABCEAAArkm4N36O9fA8vT+gQRg3bp1Zd26dfLhhx9K9+7dAwnAOXPmSM+ePaVWrVry+++/5yk+upUpAe8mIARgplFHeh3CKVK8aTdOHmkji+wCBGBkaGkYAhCAAAQgAAEIeEfAu/W3d4T9KDiQAGzSpImsWrVK3nrrLbf5R+lR1SvA//nPf+SII46QFi1ayI8//ugHDaqMjYB3ExACMLaxkc6NEE7p0Ir+XPKInnHQOyAAg5LiPAhAAAIQgAAEIJD/BLxbf+d/JDnpYSABqE/yzZ07V8aOHSvDhg0LJAB11+Bx48bJgAED5LXXXstJ57ipXQLeTUAIQJODCeFkKxbysJMHAtBOFlQCAQhAAAIQgAAEck3Au/V3roHl6f0DCcDSHX27devmXgPWJ//0qOwJwNmzZ0vfvn1l/fr1csMNN8jIkSPzFB/dypSAdxMQAjDTqCO9DuEUKd60GyePtJFFdgECMDK0NAwBCEAAAhCAAAS8I+Dd+ts7wn4UHEgAfv/999KxY0f3Lb9TTjlF7r33XqlZs2ZKAagbhZx99tmyfPlyadSokXz99dfufzkgUJ6AdxMQAtDkAEY42YqFPOzkgQC0kwWVQAACEIAABCAAgVwT8G79nWtgeXr/QAJQ+66v8+prvTVq1JCWLVvKoYce6kSg/vn888+XNWvWyNSpU+XLL7+UkpIS9/cTJ06Uo48+Ok/R0a1sCHg3ASEAs4k7smsRTpGhzahh8sgIWyQXIQAjwUqjEIAABCAAAQhAwEsC3q2/vaRsv+jAAlC7Mn78ePnLX/7iZJ8Kvo0PFX961K5d28nBIUOG2CdAhTkh4N0EhADMyTip7qYIp+oIxfs7ecTLu6q7IQDtZEElEIAABCAAAQhAINcEvFt/5xpYnt4/LQGoDHTg3H777fL888/LwoULK2Bp1aqVHHbYYe6bf9tuu22eIqNbYRDwbgJCAIYRe+htIJxCR5pVg+SRFb5QL04pAGvWF2nROdT70FgWBJrvKHLY2Cwa4FIIQAACEIAABCAQjIB36+9g3eKsNAmkLQDLt//LL7/I0qVLpaioSJo2bSrNmjVL8/acnlQC3k1ACECTQxXhZCsW8rCTR0oBaKc8KlECrXuLnD4VFhCAAAQgAAEIQCByAt6tvyMnkswbZCUAk4mMXodBwLsJCAEYRuyht4FwCh1pVg2SR1b4Qr0YARgqzmgaQwBGw5VWIQABCEAAAhDYhIB3628yjIQAAjASrDRaHQHvJiAEYHWR5uR3hFNOsFd6U/KwkwcC0E4WlVaCAPQgJEqEAAQgAAEI5AcB79bf+YHdXC8CC0Dd+EOPevXqpezE2LFjZdKkSbJs2TJp166dDBs2TA455BBzHaYgGwS8m4AQgDYGzkZVIJxsxUIedvJAANrJAgHoQRaUCAEIQAACEMhzAt6tv/M8j1x1L5AAfOGFF+Twww+Xhg0bynfffef+t/wxdOhQeeSRR9xf6U7ApTsE//3vf5eLL744V33jvoYJeDcBIQBNjiaEk61YyMNOHj1mjpLNV34uPy0tkaJikZqFNaRjm1p2CkxaJUsWiKxfXbHXPAGYtFFAfyEAAQhAAAI5I+Dd+jtnpPL7xoEE4IgRI+Tuu++WwYMHy8MPP1yByFtvvSX9+vVz0k+fDuzYsaN8+umnsnbtWtlss81kzpw5stNOO+U3RXqXNgHvJiAEYNoZx3EBwikOysHvQR7BWcV15kPj1svq30SaNS6USWNaxXVb7rMxgRT/N4RNQBgmEIAABCAAAQjERcC79XdcYBJ2n0ACsFevXk7kjR8/XoYMGVIBkUrBxx57TFq1aiXvvvuutG7d2j0luMcee4gOsuHDh8udd96ZMKx0tzoC3k1ACMDqIs3J7winnGCv9KbkYSsPrQYBaCQTBKCRICgDAhCAAAQgkEwC3q2/kxlT5L0OJADbtGkj33//vbz55puy++67VyiqefPmsnz5ctn4dd+bb77Zvf7btWtXmTdvXuQd4QZ+EfBuAkIAmhxgCCdbsZCHrTwQgIbyQAAaCoNSIAABCEAAAskj4N36O3kRxdLjQAKwbt26sm7dOvcUYLdu3coKW7BggXTp0sW9/vvxxx9Lp06dyn6bNm2aDBw40H0vcNWqVbF0hpv4Q8C7CQgBaHJwIZxsxUIetvJAABrKAwFoKAxKgQAEIAABCCSPgHfr7+RFFEuPAwnABg0auG/6vfbaa7LXXnuVFXbffffJOeecI/oU4OLFiysUrE/99ejRQ2rWrCl//PFHLJ3hJv4Q8G4CQgCaHFwIJ1uxkIetPBCAhvJAABoKg1IgAAEIQAACySPg3fo7eRHF0uNAAnCHHXaQhQsXyi233CLnn39+WWGHHXaYTJ48WY488kh5+umnKxQ8Y8YMJwu33npr9/owBwTKE/BuAkIAmhzACCdbsZCHrTwQgIbyQAAaCoNSIAABCEAAAskj4N36O3kRxdLjQALw9NNPdxuAtG/fXt577z1p1qyZzJo1S/r27StFRUWiTwLqOeWP0qcD9SnA2bNnx9IZbuIPAe8mIASgycGFcLIVC3nYygMBaCgPBKChMCgFAhCAAAQgkDwC3q2/kxdRLD0OJAA//PBD6dOnj5SUlEj9+vWlY8eOot//+/3336VJkyby1VdfuW/9lT+OPfZYeeaZZ+SUU06RcePGxdIZbuIPAe8mIASgycGFcLIVC3nYygMBaCgPBKChMCgFAhCAAAQgkDwC3q2/kxdRLD0OJAC1kttuu01GjhwpxcXFZYXp9/0mTpwoRxxxRIViddOPVq1aue8GPv7443LcccfF0hlu4g8B7yYgBKDJwYVwshULedjKAwFoKA8EoKEwKAUCEIAABCCQPALerb+TF1EsPQ4sALWa+fPnu6f6dMMP/bbf8ccfL/p9wI2P//znP3L77be7v9ZvA+orwxwQKE/AuwkIAWhyACOcbMVCHrbyQAAaygMBaCgMSoEABCAAAQgkj4B36+/kRRRLj9MSgLFUxE0SQcC7CQgBaHJcIpxsxUIetvJAABrKAwFoKAxKgQAEIAABCCSPgHfr7+RFFEuPEYCxYOYmGxPwbgJCAJocxAgnW7GQh608EICG8kAAGgqDUiAAAQhAAALJI+Dd+jt5EcXSYwRgLJi5CQKQMRAFAYRTFFQzb5M8MmcX1ZUPjVsvq38Tada4UCaNaRXVbWi3OgIIwOoI8TsEIAABCEAAAhESQABGCNejpisIwG+//bas9DZt2pT9e/m/z6Rv5dvK5HquyT8C3k1APAFochAinGzFQh628tBqEIBGMkEAGgmCMiAAAQhAAALJJODd+juZMUXe6woCsLCw0N2wRo0asmHDhrKbl/59JtVs3FYmbXBN/hHwbgJCAJochAgnW7GQh608EICG8kAAGgqDUiAAAQhAAALJI+Dd+jt5EcXS4woCsKCgoEwAFhUVlRVQ+veZVKQCsHxbmbTBNflHwLsJCAFochAinGzFQh628kAAGsoDAWgoDEqBAAQgAAEIJI+Ad+vv5EUUS48rCMBHHnmk7KZDhgwp+/fyf59JVeXbyuR6rsk/At5NQAhAk4MQ4WQrFvKwlQcC0FAeCEBDYVAKBCAAAQhAIHkEvFt/Jy+iWHrMJiCxYOYmGxPwbgJCAJocxAgnW7GQh608EICG8kAAGgqDUiAAAQhAAALJI+Dd+jt5EcXSYwRgLJi5CQKQMRAFAYRTFFQzb5M8MmcX1ZVsAhIV2TTbRQCmCYzTIQABCEAAAhAIkwACMEya/raFAPQ3O68r924C4glAk+MN4WQrFvKwlYdWgwA0kgkC0EgQlAEBCEAAAhBIJgHv1t/JjCnyXiMAI0fMDVIR8G4CQgCaHMgIJ1uxkIetPBCAhvJAABoKg1IgAAEIQAACySPg3fo7eRHF0uMKAnDChAmR3HTw4MGRtEuj/hLwbgJCAJocbAgnW7GQh608EICG8kAAGgqDUiAAAQhAAALJI+Dd+jt5EcXS4woCsKCgQGrUqBHqjbW9DRs2hNomjflPwLsJCAFoctAhnGzFQh628kAAGsoDAWgoDEqBAAQgAAEIJI+Ad+vv5EUUS483EYBh31UFYFFRUdjN0p7nBLybgBCAJkccwslWLORhKw8EoKE8EICGwqAUCEAAAhCAQPIIeLf+Tl5EsfS4ggD85ptvIrlp27ZtI2mXRv0l4N0EhAA0OdgQTrZiIQ9beSAADeWBADQUBqVAAAIQgAAEkkfAu/V38iKKpcdsAhILZm6yMQHvJiAEoMlBjHCyFQt52MoDAWgoDwSgoTAoBQIQgAAEIJA8At6tv5MXUSw9RgDGgpmbIAAZA1EQQDhFQTXzNskjc3ZRXfnQuPWy+jeRZo0LZdKYVlHdhnarI4AArI4Qv0MAAhCAAAQgECEBBGCEcD1qGgHoUVj5VKp3ExBPAJocfggnW7GQh608tBoEoJFMEIBGgqAMCEAAAhCAQDIJeLf+TmZMkfcaARg5Ym6QioB3ExAC0ORARjjZioU8bOWh1dw9dr0UF4sU1BBp0qjQXoEJqej6zY6XjgXzKva2Zn2RFp0TQsCDbjbfUeSwsR4USokQgAAEIACB9Al4t/5Ov4tcEYBA2gJw0aJF8vzzz8u8efNk2bJlsnbtWikpKan0VroL8GuvvRagFE5JEgHvJiAEoMnhiXCyFQt52MpDqynNxF5lyapo7BYnyk41/5esTvvW29a9RU6f6lvV1AsBCEAAAhAIRMC79XegXnFSugQCC8A1a9bI8OHD5dFHH91E+KkAVNFX/iiVgvr3RUVF6dbF+XlOwLsJCAFockQinGzFQh628igvAPX/RNerb6++pFR0U+0TpVMhAtB03ghA0/FQHAQgAAEIZEfAu/V3dt3l6koIBBKAKvMOOOAAmTp1qpN/zZo1k9atW8vcuXOd+Ntjjz1kxYoV8tlnn8n69evd33Xs2FG22mord9s33niDACBQgYB3ExAC0OQIRjjZioU8bOVRXgDWbyBy6mk17RWYkIr6TzlWmi7f6BXghPTdm24iAL2JikIhAAEIQCB9At6tv9PvIlcEIBBIAE6aNEmOO+44J/auuuoqGTVqlCxYsEC6devm/q70Cb/Vq1fLgw8+6H6vW7euPPvss04OckBgYwLeTUAIQJODGOFkKxbysJUHAtBOHghAO1lUWgkC0IOQKBECEIAABDIl4N36O9OOcl2VBAIJwMMPP9x992/33XeXt956yzX48ccfS9euXSsIwNI7zZo1S/r37y8NGjRwTwm2bNmSGCBQgYB3ExAC0OQIRjjZioU8bOWBALSTR4+Zo2TzlZ/LT0tLpKhYpGZhDenYppadApNWyZIFIutXV+w1AjBpo4D+QgACEEgUAe/W34lKJ77OBhKA+rrvjz/+KOPHj5chQ4ZUKwD1hAsvvFBuu+02ufjii+WGG26Ir0fcyQsC3k1ACECT4wrhZCsW8rCVBwLQXh7symwjk5S7MiMAbYRDFRCAAAQgEAkB79bfkVCg0UACsE6dOu7bftOmTZM999zTUfv888+lU6dO7gnA3377zb3yW/54/fXXZZ999pHOnTvLRx99BGkIVCDg3QSEADQ5ghFOtmIhD1t5IADt5mGvsmRVlHJXZgRgsgYBvYUABCCQMALerb8Tlk9c3Q0kAPVV3rVr18rs2bNl5513drXpE4GtWrVyAnDhwoXSrl27CjXrub1795aGDRvKqlWr4uoP9/GEgHcTEALQ5MhCONmKhTxs5YEAtJsHuzLnNpuUuzIjAHMbCneHAAQgAIFICXi3/o6URnIbDyQAdUffRYsWySuvvCL77ruvo6W7AasY/P3330U3CRk0aFAFik899ZQcf/zx7slA3RyEAwLlCXg3ASEATQ5ghJOtWMjDVh4IQPKwR8BGRSk3ZUEA2giHKiAAAQhAIBIC3q2/I6FAo4EE4NFHHy3/+te/5KabbpILLrigjNrAgQNl+vTpstdee8lrr71W9vcbNmxwu//OnDnT7RSsG4FwQAAByBgImwDCKWyi2bVHHtnxi+JqMomCauZtkkfm7MK8EgEYJk3aggAEIAABHwggAH1IKfoaAwnAe+65R4YPH+6+6TdlypSyqh577DEZPHiwew1Yhd8xxxwja9askYkTJ8qcOXPc31933XVy+eWXR98T7uAVAe8mIJ4ANDm+WEzbioU8bOWh1ZCJrUzIw0YeCEAbOVAFBCAAAQjER8C79Xd8aBJ1p0ACcPHixe57fwUFBfLZZ59J+/btyyAddNBB7tVglX3lD31FuEePHvL222+LbiLCAYHyBLybgBCAJgcwi2lbsZCHrTwQgORhj4CNihCANnKgCghAAAIQiI+Ad+vv+NAk6k6BBKASKS4udt/9KywsrADojz/+kOuvv17GjRsnKgr1aNy4sZx44onyt7/9TTbffPNEAaWzwQh4NwEhAIMFG/NZCKeYgVdzO/KwlQcCkDzsEbBREQLQRg5UAQEIQAAC8RHwbv0dH5pE3SmwAAxC5eeffxb9/t+WW265yROBQa7nnOQQ8G4CQgCaHJwIJ1uxkIetPBCA5GGPgI2KEIA2cqAKCEAAAhCIj4B36+/40CTqTqEKwESRo7NZEfBuAkIAZpV3VBcjnKIim1m75JEZtyivIpMo6abfNnmkzyyKKxCAUVClTQhAAAIQsEzAu/W3ZZge14YA9Dg8n0v3bgJCAJocbiymbcVCHrby0GrIxFYm5GEjDwSgjRyoAgIQgAAE4iPg3fo7PjSJuhMCMFFx2+msdxMQAtDO4ClXCYtpW7GQh608EIDkYY+AjYoQgDZyoAoIQAACEIiPgHfr7/jQJOpOaQnAZcuWyeOPPy5vvvmmfPnll/Lrr79KUVFRlcB0d+BFixYlCiqdrZ6AdxMQArD6UHNwBsIpB9CruCV52MoDAUge9gjYqAgBaCMHqoAABCAAgfgIeLf+jg9Nou4UWAA+8cQTMmzYMCf99NAdgYMcKgCrk4RB2uGc/CLg3QSEADQ5ABFOtmIhD1t5IADJwx4BGxUhAG3kQBUQgAAEIBAfAe/W3/GhSdSdAgnA119/Xfbdd98y6de2bVvp1q2bNG7cWAoKCqoF9tBDD1V7Dicki4B3ExAC0OQARTjZioU8bOWBACQPewRsVIQAtJEDVUAAAhCAQHwEvFt/x4cmUXcKJAD3228/mTp1qhN++grwgQcemChIdDZ8At5NQAjA8AdBCC0inEKAGGIT5BEizJCaIpOQQIbUDHmEBDLLZhCAWQLkcghAAAIQ8I6Ad+tv7wj7UXAgAbjFFlvIL7/8Irfffruce+65fvSMKk0T8G4CQgCaHE8spm3FQh628tBqyMRWJuRhIw8EoI0cqAICEIAABOIj4N36Oz40ibpTIAHYqFEj+e2332TWrFmyyy67JAoQnY2GgHcTEAIwmoGQZassprMEGPLl5BEy0BCaI5MQIIbYBHmECDOLphCAWcDjUghAAAIQ8JKAd+tvLynbLzqQAFTpN2/ePJk2bZrsueee9ntFheYJeDcBIQBNjikW07ZiIQ9beWg1ZGIrE/KwkQcC0EYOVAEBCEAAAvER8G79HR+aRN0pkAC88cYb5dJLL5XLL79crr/++kQBorPREPBuAkIARjMQsmyVxXSWAEO+nDxCBhpCc2QSAsQQmyCPEGFm0RQCMAt4XAoBCEAAAl4S8G797SVl+0UHEoD6+u9uu+0mX3/9tXsKsFevXvZ7RoWmCXg3ASEATY4nFtO2YiEPW3loNWRiKxPysJEHAtBGDlQBAQhAAALxEfBu/R0fmkTdKZAAVCLffvutHHnkkbJgwQK54IIL5JhjjpGOHTtKnTp1cg5Ma7vzzjtl8uTJrs7atWvL9ttv72ocNmyY1KtXL5QaX3nlFbn//vtl5syZ8tNPP8mWW24pffr0kTPPPFMOOOCAQPfYsGGDjBs3zu2m/Mknn7hvK7Zq1Ur22Wcf+ctf/iKdO3eusp2VK1e6bzFqDaX/++OPP7pr+vfv7wRtOse7774rd999t7z55puyePFi0Q1funfvLqeccoocd9xx6TSV1rneTUAIwLTyjetkFtNxkQ52H/IIxinOs8gkTtrV34s8qmcUxxkIwDgocw8IQAACELBEwLv1tyV4eVRLYAGoff74449l4MCBsmzZssAIatSoISq9ojpU+p144omyatWqlLfYYYcd5KWXXpL27dtnXEJJSYmcffbZTv5VdqgEvPfee0X7W9mxfPlyOfjgg+X9999PeYqKS5VxQ4cOrbSNdu3auScxUx3pCsBrr71WRo8eLcXFxSnbO/TQQ2XSpEmRSF7vJiAEYMb//UR5IYvpKOmm3zZ5pM8s6ivIJGrC6bVPHunxiupsBGBUZGkXAhCAAASsEvBu/W0VpOd1BRaAt99+u4wcOdLJIhViQQ8VYkVFRUFPT+s83Zhk9913lzVr1kiDBg3ksssukwEDBsjatWtl4sSJ8sADD7j2OnXq5J6W03MyOa644goZM2aMu7RHjx5y8cUXy3bbbSeLFi0S/T7inDlz3G96XmXfSFQGKk9nzJjhztWnKc844wxp0qSJE4J63dKlS6WwsNA9ybj//vunLHXbbbeVb775xv3WokUL6d27t7z44ovuz+kIwAcffNDdXw/ti37fsWvXrvLDDz/IHXfcIW+88Yb7TeXqY489lgm2Kq/xbgJCAIY+BsJokMV0GBTDa4M8wmMZVktkEhbJcNohj3A4ZtsKAjBbglwPAQhAAAK+EfBu/e0bYE/qDSQA9Qm6Qw45xHVJBdUee+wh3bp1k8aNG0tBQUG1Xb366qurPSeTE1T26Suvm222mRNrf/rTnyo0c9NNNzlZp4c+6XbVVVelfZuFCxfKjjvu6J5i1G8f6n3q1q1b1o7KRxVvH3zwgavj008/dUJt4+Phhx+WU0891f21vpZ81113VThF79OzZ0/55ZdfpEOHDu5Va21v4+Pmm28WfQpQxV+bNm3cz6VPHQYVgPoasbah/6ttzJ49W5o1a1Z2K5WVRxxxhLzwwgvu76ZPny79+vVLm11VF3g3ASEAQ80/rMZYTIdFMpx2yCMcjmG2QiZh0sy+LfLInmEYLSAAw6BIGxCAAAQg4BMB79bfPsH1qNZAAlBFm0qgli1bin4Hr0uXLjnvoj7Rp9/f0+Oss85yr99ufOjTilqrfmtPv223ZMkSqVmzZlq1Dx8+3L2Wq4d+L083Q9n4eO+998rk44gRI2Ts2LGbnLPTTjs5qad16H98qb5LeMMNN7inGPV45plnZNCgQYFqTVcAlhejTz75ZMpv/WmN+rShykCVv6UyMFBBAU7ybgJCAAZINf5TWEzHz7yqO5KHrTy0GjKxlQl52MgDAWgjB6qAAAQgAIH4CHi3/o4PTaLuFEgANm3a1D0tpq+Nlj7FlmtK5V/LVQG36667piypvFSbMmWK7LvvvoFL11edt9lmG/n+++/da8QqEis79PfPPvtMWrdu7TYiKf8twC+++MJtmKKHfkvwnnvuSdmMbsKx9dZbu99OOOEEt1FIkCNdAdi3b1955513ZPPNN3ebmdSqVSvlbXRjk1dffdVtqqLffcz0FepUjXs3ASEAgwzF2M9hMR078ipvSB628kAAkoc9AjYqQgDayIEqIAABCEAgPgLerb/jQ5OoOwUSgPqq76+//upec9Vv4Fk49JVU3bm2fv36Tk6mel1W69Sn9vQ7gXroK8D6KnDQ48svvyx7nbeypwxL29LfSzcJ0ev0FdvSY/z48XLaaae5P1b2xF3pubppyeeff+5ezS391l919aYjANetW+eY6SvN+p1BfaKzsuPvf/+7+zagHq+//rr7vmJYh3cTEAIwrOhDbQfhFCrOrBsjj6wRht4AmYSONKsGySMrfKFdjAAMDSUNQQACEICAJwS8W397wtW3MgMJQP3e3Ifm6CnzAAAgAElEQVQffij//e9/3UYWFo4tt9zSPZXWvXt3mTt3bqUlrVixwm20ocfRRx/tdrUNeuhmHKXfPrztttvk/PPPr/RS/f2CCy5wv+t1Bx10UNm5unmKfrtPD90wZOedd660nT//+c/y/PPPuycIVbqqrKvuSEcA6k7Opa9wn3feeaKbu1R2PPfcc26zEj30m4X67cKwDu8mIARgWNGH2g6L6VBxZt0YeWSNMPQGyCR0pFk1SB5Z4QvtYgRgaChpCAIQgAAEPCHg3frbE66+lRlIAN56661y0UUXiX7f7s4778x5H3///feyjTgOPvjgsl1wKytMX11dvXq1+36fPhEY9NDvCp5zzjnu9KefflqOOuqoSi/Vb/apYNRDr9MnAkuP4447Tp566in3R33ltvyGGxs3qIxLNwjRDUX0icDqjnQEoD7xd+CBB7om9VuAmmtlhz7xqfJXj0svvVT0icCgh04wVR0//vhj2Tccv/vuO/fqtOkDAWgyHhbTtmIhD1t5aDVkYisT8rCRBwLQRg5UAQEIQAAC8RFAAMbH2vKdAgnA9evXy5577umeXlPRdeihh+a0TyrRmjdv7mo49thjZeLEiVXW06JFC1m6dKl78m3+/PmBay+/WcbLL78s+k28yg79vfSpP33a78ILLyw7VSWl7qSsx9q1a6VOnTqVtnPJJZfIjTfe6H5XAac7A1d3pCMAVWQec8wxrkn9FqF+k7CyQ7952LlzZ/dzZZubVHZt+W8gVlc/ArA6QvxeGQEW07bGBnnYykOrIRNbmZCHjTwQgDZyoAoIQAACEIiPAAIwPtaW7xRIAOqmFqtWrZIzzzxTdPddlW76j25skWo32407rN+zC/NQYVTa5sknnywTJkyosnk9V6/ZbrvtZOHChYFLue6669x3A/V47bXXqnz9Wb+Rt/fee7tz9borr7yy7D769/q7HrqrbkFBQaU16P30ej30G4d77LFHtfWmIwAfffRRGTx4sGtz3LhxMnTo0ErbL/8NRP2GoW4CE/RAAAYlxXnZEGAxnQ298K8lj/CZZtsimWRLMNzrySNcnpm2hgDMlBzXQQACEICArwQQgL4mF27dgQSgCqtSoaM746Yjd/Rc3XAizIMnACvSTEcAxvUEIK8AhzniaasyAiymbY0N8rCVh1ZDJrYyIQ8beSAAbeRAFRCAAAQgEB8BBGB8rC3fKbAAzLQTKqf0qbcwD74BmLkAjOsbgNXl7d0ExDcAq4s0J7+zmM4J9kpvSh628kAAkoc9AjYqQgDayIEqIAABCEAgPgLerb/jQ5OoOwUSgI888khWUIYMGZLV9akujmMX4BdffLHse4fZ7AKsG23ccsstrhu53gX4o48+kq5du7pa2AU4jWGJAEwDVnynIpziYx3kTuQRhFK855BJvLyruxt5VEcont8RgPFw5i4QgAAEIGCHAALQTha5rCSQAMxlgZXdu1+/fu4befXr15eVK1fKZpttlvJU3fV39913d7/p9/VGjx4duDvlv4Gnu/rq7r6VHfr7/fff737W69q1a1d26vjx40W/oafHk08+KborcGWH7vr7+eefu28cfvPNN4FqTecV4HXr1rnvNupTmfvvv7/oE4GVHbrr7+WXX+5+1m8YDhgwIFA9QU7ybgJCAAaJNfZzWEzHjrzKG5KHrTy0GjKxlQl52MgDAWgjB6qAAAQgAIH4CHi3/o4PTaLu5K0AVDGlgkqP9957T3bdddeUwd1www1y2WWXud9effVV2W+//QIHrN87bN26tfzwww/SqVMn0V1xKzt23HFH+fTTT6VVq1Zuw5Hy30lUoadiTw/ddVd33011LF68WLbeemv30/HHHy9PPPFEoFrTEYDaoApRFaObb7656PcUa9WqlfI+uuuxMqtdu7Y7r2HDhoHqCXKSdxMQAjBIrLGfw2I6duRV3pA8bOWh1ZCJrUzIw0YeCEAbOVAFBCAAAQjER8C79Xd8aBJ1p0ACUDcB0X/GjBkjF198sQlAM2fOLJN+lT2dV1xcLF26dHHirnHjxrJ06VKpWbNmWvUPGzasTNipNNttt902uV4F5J/+9Cf393r+XXfdtck5nTt3dnU0adLECcJUuyeXl5WTJk2So48+OlCt6QrAG2+8US655BLXdmVPJOoEse2227onBQ866CCZPHlyoFqCnuTdBIQADBptrOexmI4Vd7U3I49qEcV+ApnEjhxJbgt5ymoQgB6ERIkQgAAEIBAqAe/W36H2nsZKCQQSgHXq1JH169fLW2+9VSa6LCAsfQ1YX/+dMWPGJrXddNNNZcLy6quvlmuuuaZC2dOmTSt7rVW/U/jwww9v0i19em+nnXZyOxn36tXL3adu3bpl561du1a0jg8++MC9hrxgwQLp0KHDJu2Ufw14+PDh8s9//rPCOYsWLZJddtlFfvnlF9luu+3c04SVvda8cePpCsCff/5Z2rdvL6tWrZK2bdvK7NmzpWnTpmXNqvQ74ogj5IUXXnB/F/brv9qmdxMQAtDCf/Kb1IDcsBULedjKQ6shE1uZkIeNPBCANnKgCghAAAIQiI+Ad+vv+NAk6k6BBKDKIv0enT4B16dPHzOAdEONvn37ikq4Bg0auO/V6Xfq9M8TJ04s+yZfx44dnaDb+BXWIAJQO6uvEOvTeXr06NHDPT2nkk6l3T/+8Q+3sUfpefqUZKpDpVr//v3l7bffdj8PGjRIzjjjDNliiy1En2a87rrr3BOK+qSlbj5y4IEHpmxn7ty5ov+UP0499VT3R33N+NJLL63w21FHHeXYbHzcd9997nVkPbQvV1xxhdscRF93vv322+WNN95wv6XzKnI6A8O7CQgBmE68sZ3LYjo21IFuRB6BMMV6EpnEirvam5FHtYhiOQEBGAtmbgIBCEAAAoYIeLf+NsQun0oJJABPOeUUefTRR92rraXSyAoEfUrtpJNOck/OpTpU/unrq9tvv/0mPwcVgPoqsco6fYqvskM3+dBNQFTgVXYsW7bMvU47a9aslKfot/j0yUC9V2WHPsWYzkYmX331lXuVN9WhT0WqeNRvHaY6tNZnn31W9AnQsA/vJiAEYNhDIJT2WEyHgjG0RsgjNJShNUQmoaEMpSHyCAVj1o0gALNGSAMQgAAEIOAZAe/W357x9aXcQAJQXxHVb9zpzrQffvih2zzC0qFPJ95xxx1O9OnAVpGmwk+/oTdixIiU39vT+oMKwNK+vvTSS07yqcBTmdesWTPp3bu36DcIK3tib2NO+irxAw884Db40G8Crl69Wlq2bCl77723nHfeee5146qOMAWg3uedd95xYld3VF6yZIn7VmL37t1FnyrUp/+iOrybgBCAUQ2FrNplMZ0VvtAvJo/QkWbdIJlkjTDUBsgjVJwZN4YAzBgdF0IAAhCAgKcEvFt/e8rZetmBBKB2Ql8ZVZmmm2qoMNKdZDkgkCkB7yYgBGCmUUd6HYvpSPGm3Th5pI0s8gvIJHLEad2APNLCFdnJCMDI0NIwBCAAAQgYJeDd+tsoR9/LCiQAhw4d6vqp36rTTS5004ltttlGunXr5r5hV1hYWCkHPXfcuHG+c6L+kAl4NwEhAEMeAeE0x2I6HI5htUIeYZEMrx0yCY9lGC2RRxgUs28DAZg9Q1qAAAQgAAG/CHi3/vYLrzfVBhKA+l270p1mtWel34wr/3epeqzn6Tm6AQYHBMoT8G4CQgCaHMAspm3FQh628tBqyMRWJuRhIw8EoI0cqAICEIAABOIj4N36Oz40ibpTIAGom0hUJ/uqoqYbUXBAAAHIGAibAIvpsIlm1x55ZMcviqvJJAqqmbdJHpmzC/NKBGCYNGkLAhCAAAR8IIAA9CGl6GsMJACjL4M7JI2AdxMQTwCaHKIspm3FQh628tBqyMRWJuRhIw8EoI0cqAICEIAABOIj4N36Oz40iboTAjBRcdvprHcTEALQzuApVwmLaVuxkIetPBCA5GGPgI2KEIA2cqAKCEAAAhCIj4B36+/40CTqTgjARMVtp7PeTUAIQDuDBwFoMgtkk81YkLK2ciEPG3kgAG3kQBUQgAAEIBAfAe/W3/GhSdSdshKAGzZskBUrVjhguhvwZpttlih4dDZzAt5NQAjAzMOO8EoW0xHCzaBp8sgAWsSXkEnEgNNsnjzSBBbR6akE4OfF3eXKDU9GdEeaTYdAk80L5d5Lt0rnEs6FAAQgAIFqCHi3/ibRSAikLQA/+eQTufvuu2Xq1KnyxRdfVNgRuEOHDrLvvvvK2WefLZ07d46kYBrNDwLeTUAIQJMDj8W0rVjIw1YeWg2Z2MqEPGzkkUoAfry+m5y74nEbBSa8imaNC2XSmFYJp0D3IQABCIRLwLv1d7jdp7X/RyAtAXjZZZfJzTffLMXFxWXib2OSultwQUGBjBw5UsaMGQNoCKQk4N0EhAA0OZJZTNuKhTxs5YEAJA97BGxUlEoAflrUTUb+gQDMZUJrVouUlIggAHOZAveGAATylYB36+98DSLH/QosAM8991z35F+J/l9mEdlxxx1l1113la222sr93ZIlS2TmzJmyYMEC97uKwBEjRsgdd9yR4y5ye4sEvJuAEIAWhxFPNxlLBQFoLBCeADQXCP+N2IgklQBc3rS7TN/vKRsFJrSKh8atl9W/IQATGj/dhgAEIibg3fo7Yh5JbT6QAHz77bdlzz33dFJPxd/9998vu+++e0pm7777rnsFeP78+e78N998s9Jzkwqdfot4NwEhAE0OWxbTtmIhD1t5aDVkYisT8rCRBwLQRg4bV4EAtJkLVUEAAvlBwLv1d35gN9eLQAJw8ODB8thjj0n79u1l9uzZ0qhRoyo7smrVKunZs6d89dVXcuKJJ8qECRPMdZyCckvAuwkIAZjbAVPJ3VlM24qFPGzlgQAkD3sEbFSEALSRAwLQZg5UBQEI5CcB79bf+RlDznsVSAC2a9dOvv32W7nvvvvk9NNPD1T0Aw88IGeddZa0bdvWiUAOCJQn4N0EhAA0OYARTrZiIQ9beSAAycMeARsVIQBt5IAAtJkDVUEAAvlJwLv1d37GkPNeBRKAdevWlXXr1rlv/OmTfUEOfVKwd+/eUqdOHVmzZk2QSzgnQQS8m4AQgCZHJ8LJVizkYSsPBCB52CNgoyIEoI0cEIA2c6AqCEAgPwl4t/7Ozxhy3qtAAnCLLbaQX375Rd544w3p169foKKnT58uAwYMcK8Lr1ixItA1nJQcAt5NQAhAk4MT4WQrFvKwlQcCkDzsEbBREQLQRg4IQJs5UBUEIJCfBLxbf+dnDDnvVSABuMsuu8i8efPkr3/9q9x8882Bir7ooovk1ltvlR49erjvBnJAoDwB7yYgBKDJAYxwshULedjKAwFIHvYI2KgIAWgjBwSgzRyoCgIQyE8C3q2/8zOGnPcqkAC88sorZcyYMVKrVi2ZPHmy7L333lUW/tprr8lBBx0kGzZskMsvv1yuu+66nHeUAmwR8G4CQgDaGkD/rxqEk61YyMNWHghA8rBHwEZFCEAbOSAAbeZAVRCAQH4S8G79nZ8x5LxXgQTgsmXLZPvtt5dff/1VCgsL5YwzzpChQ4e6p/sKCgpcJ4qLi2XOnDkybtw4efDBB53809d/Fy5cKE2bNs15RynAFgHvJiAEoK0BhAAkD5ME7BWFlLWVCXnYyAMBaCMHBKDNHKgKAhDITwLerb/zM4ac9yqQANQqp0yZIocddpjbDKRGjRqucH0isEmTJu7Py5cvd7/pUVJS4n578cUXZZ999sl5JynAHgHvJiAEoL1BJCIspm3FQh628tBqyMRWJuRhIw8EoI0cEIA2c6AqCEAgPwl4t/7Ozxhy3qvAAlArnTt3rpx55pnywQcfVFm47v57//33S/fu3XPeQQqwScC7CQgBaHIgsZi2FQt52MoDAUge9gjYqAgBaCMHBKDNHKgKAhDITwLerb/zM4ac9yotAVha7axZs2Tq1Kny0Ucfyc8//+z+Wp8E7NKli3viTwUgBwSqIuDdBIQANDmgEU62YiEPW3kgAMnDHgEbFSEAbeSAALSZA1VBAAL5ScC79Xd+xpDzXmUkAHNeNQV4T8C7CQgBaHLMIZxsxUIetvJAAJKHPQI2KkIA2sgBAWgzB6qCAATyk4B36+/8jCHnvUIA5jyCZBbg3QSEADQ5UBFOtmIhD1t5IADJwx4BGxUhAG3kgAC0mQNVQQAC+UnAu/V3fsaQ814hAHMeQTIL8G4CQgCaHKgIJ1uxkIetPBCA5GGPgI2KEIA2ckAA2syBqiAAgfwk4N36Oz9jyHmvEIA5jyCZBXg3ASEATQ5UhJOtWMjDVh4IQPKwR8BGRQhAGzkgAG3mQFUQgEB+EvBu/Z2fMeS8V5sIwMLCwlCLqlGjhmzYsCHUNmnMfwLeTUAIQJODDuFkKxbysJUHApA87BGwUVEqAbhhs3qyqlEHGwUmtIqflpZIUbFIzcIa0rFXd5HDxiaUBN2GAAQgED4B79bf4SOgRRHZRAAWFBSECkYFYFFRUaht0pj/BLybgBCAJgcdwslWLORhKw8EIHnYI2CjolQC0EZlVFFGoHVvkdOnAgQCEIAABEIi4N36O6R+00xFApsIwFNPPTUrRiUlJfLSSy/J8uXLRf8dAZgVzry92LsJCAFociwinGzFQh628kAAkoc9AjYqQgDayKHKKhCAHoREiRCAgE8EvFt/+wTXo1pD/Qbgv//9b7n66qvlo48+cghUALZp00a+/vprj5BQahwEvJuAEIBxDIu074FwShtZpBeQR6R4M2qcTDLCFtlF5BEZ2rQaRgCmhSs3JyMAc8Odu0IAAnlLwLv1d94mkduOhSIA9Ym/q666SubMmVMm/rbeemu5/PLL5YwzzpBatWrltpfc3RwB7yYgBKC5MaQFsZi2FQt52MqD/0bIwx4BGxUhAG3kUGUVCEAPQqJECEDAJwLerb99gutRrVkJwP/+979O/M2cObNM/DVv3lwuueQSOeecc6ROnToeoaDUOAl4NwEhAOMcHoHvhXAKjCqWE8kjFsxp3YRM0sIV+cnkETniQDfoMXOUbL7yc1myuERKRKSwQGTL5jUCXctJ4RNotOoL2WzDmooNIwDDB02LEIBAogl4t/5OdFrRdT4jATh9+nQZNWqUvP3222Xir2nTpjJy5EgZMWKE1KtXL7qKaTkvCHg3ASEATY47FtO2YiEPW3loNWRiKxPyIA9bBGxUk/KJTASgjXCoAgIQyBsC3q2/84a8rY6kJQDfeecd98TfG2+8USb+GjduLBdccIGcf/750qBBA1u9oxqzBLybgBCAJscSi2lbsZCHrTwQgORhj4CtipizbOSBALSRA1VAAAL5TcC79Xd+x5Gz3gUSgLNmzXLib8qUKWXir2HDhk76qfxr1KhRzjrAjf0k4N0EhAA0OdBYvNmKhTxs5YEAJA97BGxVxJxlIw8EoI0cqAICEMhvAt6tv/M7jpz1rkoBOHfuXLer74svvlgm/urXr+9e89XXfZs0aZKzwrmx3wS8m4AQgCYHHIs3W7GQh608EIDkYY+ArYqYs2zkgQC0kQNVQAAC+U3Au/V3fseRs96lFIAff/yxE3/PPfdcmfirW7eu29jj0ksvlWbNmuWsYG6cHwS8m4AQgCYHHos3W7GQh608EIDkYY+ArYqYs2zkgQC0kQNVQAAC+U3Au/V3fseRs95tIgBPOOEEmTRpkpSUlLh/ateuLWeddZYTf1tttVXOCuXG+UXAuwkIAWhyALJ4sxULedjKAwFIHvYI2KqIOctGHghAGzlQBQQgkN8EvFt/53ccOevdJgKwoKCgrJgtt9xSzjvvPGndunVWBQ4ePDir67k4/wh4NwEhAE0OQhZvtmIhD1t5IADJwx4BWxUxZ9nIAwFoIweqgAAE8puAd+vv/I4jZ71LKQBr1KgRWkHa1v9h7z2g5CjO7+0iiBxFEEFggkgCDAJEFkLkaGRyzhkMGJFzMhlsMogMJgeLJAMmWYCNRDYZJEywSCIbEAYB/3Pr9/V+s6uZ7Z6d3u63qp86x8dou7rqrefW1k7dqTBhwoTcyqOgOAgENwBhAJrseEzebMmCHrb0wABED3sEbEXEmGVDDwxAGzoQBQQgEDeB4ObfcctRWus6XQGYR1QyAH/66ac8iqKMiAgENwBhAJrsfUzebMmCHrb0wABED3sEbEXEmGVDDwxAGzoQBQQgEDeB4ObfcctRWusmMgD//ve/5x7MwIEDcy+TAsMmENwAhAFossMxebMlC3rY0gMDED3sEbAVEWOWDT0wAG3oQBQQgEDcBIKbf8ctR2mtq3sLcGnRUHFlCAQ3AGEAmuybTN5syYIetvTAAEQPewRsRcSYZUMPDEAbOhAFBCAQN4Hg5t9xy1Fa6zAAS0Nf7YqDG4AwAE12WCZvtmRBD1t6YACihz0CtiJizLKhBwagDR2IAgIQiJtAcPPvuOUorXUYgKWhr3bFwQ1AGIAmOyyTN1uyoIctPTAA0cMeAVsRMWbZ0AMD0IYORAEBCMRNILj5d9xylNY6DMDS0Fe74uAGIAxAkx2WyZstWdDDlh4YgOhhj4CtiBizbOiBAWhDB6KAAATiJhDc/DtuOUprHQZgaeirXXFwAxAGoMkOy+TNlizoYUsPDED0sEfAVkSMWTb0wAC0oQNRQAACcRMIbv4dtxyltQ4DsDT01a44uAEIA9Bkh2XyZksW9LClBwYgetgjYCsixiwbemAA2tCBKCAAgbgJBDf/jluO0lqHAVga+mpXHNwAhAFossMyebMlC3rY0gMDED3sEbAVEWOWDT0wAG3oQBQQgEDcBIKbf8ctR2mtwwAsDX21Kw5uAMIANNlhmbzZkgU9bOmBAYge9gjYiogxy4YeGIA2dCAKCEAgbgLBzb/jlqO01mEAloa+2hUHNwBhAJrssEzebMmCHrb0wABED3sEbEXEmGVDDwxAGzoQBQQgEDeB4ObfcctRWuswAEtDX+2KgxuAMABNdlgmb7ZkQQ9bemAAooc9ArYiYsyyoQcGoA0diAICEIibQHDz77jlKK11GICloa92xcENQBiAJjsskzdbsqCHLT0wANHDHgFbETFm2dADA9CGDkQBAQjETSC4+XfccpTWOgzA0tBXu+LgBiAMQJMdlsmbLVnQw5YeGIDoYY+ArYgYs2zogQFoQweigAAE4iYQ3Pw7bjlKax0GYGnoq11xcAMQBqDJDsvkzZYs6GFLDwxA9LBHwFZEjFk29MAAtKEDUUAAAnETCG7+HbccpbUOA7A09NWuOLgBCAPQZIdl8mZLFvSwpQcGIHrYI2ArIsYsG3pgANrQgSggAIG4CQQ3/45bjtJahwFYGvpqVxzcAIQBaLLDMnmzJQt62NIDAxA97BGwFRFjlg09MABt6EAUEIBA3ASCm3/HLUdprcMALA19tSsObgDCADTZYZm82ZIFPWzpgQGIHvYI2IqIMcuGHhiANnQgCghAIG4Cwc2/45ajtNZhAJaGvtoVBzcAYQCa7LBM3mzJgh629MAARA97BGxFxJhlQw8MQBs6EAUEIBA3geDm33HLUVrrMABLQ1/tioMbgDAATXZYJm+2ZEEPW3pgAKKHPQK2ImLMsqEHBqANHYgCAhCIm0Bw8++45SitdRiApaGvdsXBDUAYgCY7LJM3W7Kghy09MADRwx4BWxExZtnQAwPQhg5EAQEIxE0guPl33HKU1joMwNLQV7vi4AYgDECTHZbJmy1Z0MOWHhiA6GGPgK2IGLNs6IEBaEMHooAABOImENz8O245SmsdBmBp6KtdcXADEAagyQ7L5M2WLOhhSw8MQPSwR8BWRIxZNvTAALShA1FAAAJxEwhu/h23HKW1DgOwNPTVrji4AQgD0GSHZfJmSxb0sKUHBiB62CNgKyLGLBt6YADa0IEoIACBuAkEN/+OW47SWocBWBr6alcc3ACEAWiywzJ5syULetjSAwMQPewRsBURY5YNPTAAbehAFBCAQNwEgpt/xy1Haa3DACwNfbUrDm4AwgA02WGZvNmSBT1s6YEBiB72CNiKiDHLhh4YgDZ0IAoIQCBuAsHNv+OWo7TWYQCWhr7aFQc3AGEAmuywTN5syYIetvTAAEQPewRsRcSYZUMPDEAbOhAFBCAQN4Hg5t9xy1Fa6zAAS0Nf7YqDG4AwAE12WCZvtmRBD1t6YACihz0CtiJizLKhBwagDR2IAgIQiJtAcPPvuOUorXUYgKWhr3bFwQ1AGIAmOyyTN1uyoIctPTAA0cMeAVsRMWbZ0AMD0IYORAEBCMRNILj5d9xylNY6DMDS0Fe74uAGIAxAkx2WyZstWdDDlh4YgOhhj4CtiBizbOiBAWhDB6KAAATiJhDc/DtuOUprHQZgaeirXXFwAxAGoMkOy+TNlizoYUsPDED0sEfAVkSMWTb0wAC0oQNRQAACcRMIbv4dtxyltQ4DsDT01a44uAEIA9Bkh2XyZksW9LClBwYgetgjYCsixiwbemAA2tCBKCAAgbgJBDf/jluO0lqHAVga+mpXHNwAhAFossMyebMlC3rY0gMDED3sEbAVEWOWDT0wAG3oQBQQgEDcBIKbf8ctR2mtwwAsDX21Kw5uAMIANNlhmbzZkgU9bOmBAYge9gjYiogxy4YeGIA2dCAKCEAgbgLBzb/jlqO01mEAloa+2hUHNwBhAJrssEzebMmCHrb0wABED3sEbEXEmGVDDwxAGzoQBQQgEDeB4ObfcctRWuswAEtDX+2KgxuAMABNdlgmb7ZkQQ9bemAAooc9ArYiYsyyoQcGoA0diAICEIibQHDz77jlKK11GICloa92xcENQBiAJjsskzdbsqCHLT0wANHDHgFbETFm2dADA9CGDkQBAQjETSC4+XfccpTWOgzA0tBXu+LgBiAMQJMdlsmbLVnQw5YeGIDoYY+ArYgYs2zogQFoQweigAAE4iYQ3Pw7bhDikPwAACAASURBVDlKax0GYGnoq11xcAMQBqDJDsvkzZYs6GFLDwxA9LBHwFZEjFk29MAAtKEDUUAAAnETCG7+HbccpbUOA7A09NWuOLgBCAPQZIdl8mZLFvSwpQcGIHrYI2ArIsYsG3pgANrQgSggAIG4CQQ3/45bjtJahwFYGvpqVxzcAIQBaLLDMnmzJQt62NIDAxA97BGwFRFjlg09MABt6EAUEIBA3ASCm3/HLUdprcMALA19tSsObgDCADTZYZm82ZIFPWzpgQGIHvYI2IqIMcuGHhiANnQgCghAIG4Cwc2/45ajtNZhAJaGvtoVBzcAYQCa7LBM3mzJgh629MAARA97BGxFxJhlQw8MQBs6EAUEIBA3geDm33HLUVrrMABLQ1/tioMbgDAATXZYJm+2ZEEPW3pgAKKHPQK2ImLMsqEHBqANHYgCAhCIm0Bw8++45SitdRiApaGvdsXBDUAYgCY7LJM3W7Kghy09MADRwx4BWxExZtnQAwPQhg5EAQEIxE0guPl33HKU1joMwNLQV7vi4AYgDECTHZbJmy1Z0MOWHhiA6GGPgK2IGLNs6IEBaEMHooAABOImENz8O245SmsdBmBp6KtdcXADEAagyQ7L5M2WLOhhSw8MQPSwR8BWRIxZNvTAALShA1FAAAJxEwhu/h23HKW1DgOwNPTVrji4AQgD0GSHZfJmSxb0sKUHBiB62CNgKyLGLBt6YADa0IEoIACBuAkEN/+OW47SWocBWBr6alcc3ACEAWiywzJ5syULetjSAwMQPewRsBURY5YNPTAAbehAFBCAQNwEgpt/xy1Haa3DACwNfbUrDm4AwgA02WGZvNmSBT1s6YEBiB72CNiKiDHLhh4YgDZ0IAoIQCBuAsHNv+OWo7TWYQCWhr7aFQc3AGEAmuywTN5syYIetvTAAEQPewRsRcSYZUMPDEAbOhAFBCAQN4Hg5t9xy1Fa6zAAS0Nf7YqDG4AwAE12WCZvtmRBD1t6YACihz0CtiJizLKhBwagDR2IAgIQiJtAcPPvuOUorXUYgKWhr3bFwQ1AGIAmOyyTN1uyoIctPTAA0cMeAVsRMWbZ0AMD0IYORAEBCMRNILj5d9xylNY6DMDS0Fe74uAGIAxAkx2WyZstWdDDlh4YgOhhj4CtiBizbOiBAWhDB6KAAATiJhDc/DtuOUprHQZgaeirXXFwAxAGoMkOy+TNlizoYUsPDED0sEfAVkSMWTb0wAC0oQNRQAACcRMIbv4dtxyltQ4DsDT01a44uAEIA9Bkh2XyZksW9LClBwYgetgjYCsixiwbemAA2tCBKCAAgbgJBDf/jluO0lqHAVga+mpXHNwAhAFossMyebMlC3rY0gMDED3sEbAVEWOWDT0wAG3oQBQQgEDcBIKbf8ctR2mtwwAsDX21Kw5uAMIANNlhmbzZkgU9bOmBAYge9gjYiogxy4YeGIA2dCAKCEAgbgLBzb/jlqO01mEAloa+2hUHNwBhAJrssEzebMmCHrb0wABED3sEbEXEmGVDDwxAGzoQBQQgEDeB4ObfcctRWuswAEtDX+2KgxuAMABNdlgmb7ZkQQ9bemAAooc9ArYiYsyyoQcGoA0diAICEIibQHDz77jlKK11GICloa92xcENQBiAJjsskzdbsqCHLT0wANHDHgFbETFm2dADA9CGDkQBAQjETSC4+XfccpTWOgzA0tBXu+LgBiAMQJMdlsmbLVnQw5YeGIDoYY+ArYgYs2zogQFoQweigAAE4iYQ3Pw7bjlKax0GYGnoq11xcAMQBqDJDsvkzZYs6GFLDwxA9LBHwFZEjFk29MAAtKEDUUAAAnETCG7+HbccpbUOA7A09NWuOLgBCAPQZIdl8mZLFvSwpQcGIHrYI2ArIsYsG3pgANrQgSggAIG4CQQ3/45bjtJahwFYGvpqVxzcAIQBaLLDMnmzJQt62NIDAxA97BGwFRFjlg09MABt6EAUEIBA3ASCm3/HLUdprcMALA19tSsObgDCADTZYZm82ZIFPWzpgQGIHvYI2IqIMcuGHhiANnQgCghAIG4Cwc2/45ajtNZhAJaGvtoVBzcAYQCa7LBM3mzJgh629MAARA97BGxFxJhlQw8MQBs6EAUEIBA3geDm33HLUVrrMABLQ1/tioMbgDAATXZYJm+2ZEEPW3pgAKKHPQK2ImLMsqEHBqANHYgCAhCIm0Bw8++45SitdRiApaGvdsXBDUAYgCY7LJM3W7Kghy09MADRwx4BWxExZtnQAwPQhg5EAQEIxE0guPl33HKU1joMwNLQV7vi4AYgDECTHZbJmy1Z0MOWHhiA6GGPgK2IGLNs6IEBaEMHooAABOImENz8O245SmsdBmBp6KtdcXADEAagyQ7L5M2WLOhhSw8MQPSwR8BWRIxZNvTAALShA1FAAAJxEwhu/h23HKW1DgOwNPTVrji4AQgD0GSHZfJmSxb0sKUHBiB62CNgKyLGLBt6YADa0IEoIACBuAkEN/+OW47SWocBWBr6alcc3ACEAWiywzJ5syULetjSAwMQPewRsBURY5YNPTAAbehAFBCAQNwEgpt/xy1Haa3DACwNfbUrDm4AwgA02WGZvNmSBT1s6YEBiB72CNiKiDHLhh4YgDZ0IAoIQCBuAsHNv+OWo7TWRWEAvvfee+7888939913n9N/TznllK5Pnz5uyy23dPvuu6+bZpppcgF8//33u6FDh7pRo0a5cePGudlmm80tv/zybs8993TrrbdepjomTJjgrrzySnfDDTe41157zX3zzTdu7rnndmuttZY74IADXN++fTOV89lnn/k2Dxs2zL3zzjvul19+cfPPP78bPHiwL2eWWWZpWI7yK2+WtNNOO7lrrrkmS9am8gQ3AGEANqVvUZmZvBVFOls96JGNU5G50KRI2ul1oUc6oyJzoEeRtBvXhQFoQweigAAE4iYQ3Pw7bjlKa13wBqBMv+2228599dVXdSEussgibvjw4W6BBRboMmSZa3vvvbc3/xolmYCXXnqpm2SSSRrmkWm34YYbupEjR9bNI+Py4osvdrvuumunsT799NNuk002cR9++GHdfHPNNZe766673HLLLVf3OQZgF7oCBmAXoHX/K0zeup9xMzWgRzO0ismLJsVwzloLemQlVUw+9CiGc1otGIBphHgOAQhAoHUCGICtM4yhhKANwBdffNGtvPLK7rvvvnPTTTedO/LII92gQYPc+PHj3c033+wuv/xyr9Giiy7qZJopT1fS0Ucf7U499VT/ar9+/dxhhx3mFlxwQTdmzBh35plnuueff94/U75TTjmlbhU//fSTW2ONNdyIESP880033dTtsccermfPnt4Q1HuffPKJm2yyyfxKxnXXXbduOWPHjnXLLrus+/jjj93kk0/uDj74YLfRRhv5vPfee68799xznVYZ9urVyz377LN+dWHHVGsAql6ZiY3SzDPPXLeMrnCsfSe4AQgDsFXJu+V9Jm/dgrXLhaJHl9F124to0m1ou1QwenQJW7e9hB7dhrapgjEAm8JFZghAAAJdIhDc/LtLreSlNAJBG4Ay+x577DFvhMlYW2mlldq196yzzvJmndKJJ57ojjvuuDQeEz0fPXq0W2yxxbypphV1qmfqqaduyyfzceDAge6ZZ57xcbz++uveHOyYtI12l1128T/WtuSLLrqoXRbVI2Pv66+/dgsttJB79dVXfXkd08477+yuvfZa/+Nbb73VbbHFFu2y3HbbbX7rs5Lqu+qqqyYqo9YAvPrqq53KLDoFNwBhABbdRTLVx+QtE6bCMqFHYagzV4QmmVEVkhE9CsGcuRL0yIyqWzNiAHYrXgqHAAQg4AkEN/9Gt24hEKwBqBV9On9Paa+99vLbbzumn3/+2S2xxBL+rD2tZNOquR49ejQFcr/99vPbcpX++c9/uhVXXHGi95966qk283H//fd3F1xwwUR5Fl98cW/qKQ798tU7l/D000/3qxiVbr/9drfZZpu1K0fxa0WfVhNqhaDOJKyXdB7hAw884FcTasWgVgPWJgzAprrA/2XGAOwCtO5/hclb9zNupgb0aIZWMXnRpBjOWWtBj6ykismHHsVwTqsFAzCNEM8hAAEItE4AA7B1hjGUEKwBWLstVwbcCiusUFePWlPtwQcfdGuvvXZm3XT23zzzzONNNG0jlpHYKOn5G2+84Xr37u0vIqk9C/Ctt95yCy+8sH9VZwlecskldYv56KOP3Jxzzumfbbvttv6ikNqkLc06a1BJW5y32mqruuXo2TbbbOOf6dxCbTWuTRiAmbvA/58RA7AL0Lr/FSZv3c+4mRrQoxlaxeRFk2I4Z60FPbKSKiYfehTDOa0WDMA0QjyHAAQg0DoBDMDWGcZQQrAG4GqrreYef/xxN+2007ovv/yy7nZZCaRVezonUElbgLUVOGt6++2327bzNlplmJSl58klIXqv9pZdbcPdbbfdfNabbrrJbb311g1D0KUlb775ppt33nndu+++2y7fjjvu6K6//nr/M10AMsccc9QtR890EYiS3km2DCeZMQCz9oCafBiAXYDW/a8weet+xs3UgB7N0ComL5oUwzlrLeiRlVQx+dCjGM5ptWAAphHiOQQgAIHWCWAAts4whhKCNQBnm2029+mnn7qlllrKvfDCCw21+OKLL/xFG0o6L0/n5mVNuowjuWDjj3/8ozvooIMavqrnupBDSe9tsMEGbXkPPfRQd/bZZ/t/68KQpZdeumE5upDj7rvv9isI//vf/3qDM0n9+/f3Zw3OOOOM3vTsLCmPzhPUO6NGjWqXtdYAXGaZZdznn3/uPvjgAzfVVFP5FYwDBgzwKw31rLtScAMQBmB3dYWWymXy1hK+3F9Gj9yRtlwgmrSMMNcC0CNXnC0Xhh4tI8ylAAzAXDBSCAQgAIFOCQQ3/0bPbiEQpAH4/ffft13EseGGG/rbbztLuv3322+/9ef3aUVg1qRzBffZZx+fXZdrbL755g1f1Zl9yYUcek8rApOkFX+33HKL/+e4cePcrLPO2rAcnSGYXBCiC0W0IjBJWvGncwB1nuDLL7/caTN09uErr7ziVwlqRWBtqjUAOytEbTjvvPPclFNOmRVZWz4NMJ0lxZSc4fj+++9749F0wgA0KQ+TN1uyoIctPRQNmtjSBD3QwxYBG9FgANrQgSggAIG4CWAAxq1v1tYFaQDKRJt99tl9G3UOns686yzpEoxPPvnEXwjy0ksvZWXjam8R/utf/+p0uUajpOfJqj+t9hsyZEhbVpmUw4cP9/8eP368X2nXKB1++OHuzDPP9I+12k83AydJqwF167DOO9S5h50l5dHKP5mfWklYm2QA9uvXz/32t791q6++ur91WDHJlNM5iVdeeaX75ptv/Cv1ziLMArD2DMS0/BiAaYR43ogAk2lbfQM9bOmhaNDElibogR62CNiIBgPQhg5EAQEIxE0AAzBufbO2LkgDUIaRzshT2mGHHdx1113XaXuVV+8suOCCbvTo0VnZuJNPPtmfG6j08MMPuzXWWKPhu4888ohbc801/XO9d8wxx7Tl1c/1XEk3+E466aQNy1F9el9JZxyuuuqqbXl1q69uNtYW3REjRnTajuSMRL0zYcKEdnl/+OEH/7N6NxEroy4tWWuttfxlJkp33XWX+81vfpOZmzJiADaFi8xdJMBkuovguuk19OgmsC0UiyYtwOuGV9GjG6C2UCR6tAAvx1cxAHOESVEQgAAEGhDAAKRreJ/mF111G1hiBWBrKwCzyP3EE094o1FJZuDf/va3LK+15WELcFO4yNxFAkzeugium15Dj24C20KxaNICvG54FT26AWoLRaJHC/ByfBUDMEeYFAUBCEAAA5A+0AmBIA1AzgBs7QzArL8RyTmC2h6sMxQ7W7mYtcwkX3DfQHAGYLMSF5KfyVshmDNXgh6ZURWWEU0KQ52pIvTIhKmwTOhRGOpOK8IAtKEDUUAAAnETCG7+HbccpbUuSANQtIq4BViXi2y88cZenFZuAT7kkEPcOeec48tp5Rbg5ZZbzj377LMt3wKctbdtueWW/vITJZ2hKOZ5peAGIAzAvKTPtRwmb7nibLkw9GgZYe4FoEnuSFsqED1awpf7y+iRO9IuFYgB2CVsvAQBCECgKQLBzb+bah2ZsxII1gBMzrjTxRhffvmlm3zyyeu2Wbf+rrzyyv6Zztc78cQTs7Jxb7/9tj83UEk34up230ZJz4cOHeof673555+/LetVV13ldtttN//vm266yelW4EZJt/6++eab/ozDd999t122HXfc0V1//fX+Z7qwQzf81kt6Ntdcc/lHeufaa6/N3ObajLrVWLcbK2EAruXcf55ux/GzWZZyf1/n/253JpVDgMlbOdwb1YoetvRQNGhiSxP0QA9bBGxEgwFoQweigAAE4iaAARi3vllbF6wBeNRRR7nTTjvNt1M34urW23rp9NNPd0ceeaR/9MADD7h11lknKxun4xF79+7tPvjgA7fooou61157reG7iy22mHv99dfd3HPP7S8cqb0EQ4aejD2lvffe211yySV1y/noo4/cnHPO6Z9ts8027sYbb2yXTwajjEYl3XysG5DrJT3T+0qXXXaZ23PPPTO3uTbj4osv7l599VU35ZRT+tuH2QKMAdiljtSNLzGZ7ka4XSgaPboArZtfQZNuBtxk8ejRJLBuzo4e3Qw4Y/EYgBlBkQ0CEIBACwQwAFuAF9GrwRqAo0aNajP9Gq3O0425OsdOxt1MM83kV7H16NGjKfn23XffNsNOqwlXXHHFid6XAbnSSiv5nyv/RRddNFGevn37+jh69uzpDcJ6N/DWmpW33nqr0wq82iSDUAaj2rXuuuu6+++/v25b1ltvPW92yrAbO3Zsw5WCnYGovQREtxg/9NBDTXFLyxzcAMQW4DRJS3nO5K0U7A0rRQ9beigaNLGlCXqghy0CNqLBALShA1FAAAJxEwhu/h23HKW1LlgDUMSSbcDa/jtixIg2Ey6hedZZZ7nDDjvM//P44493J5xwQjvQjz32mBs0aJD/2U477eSuueaaiYTQ6j2thJswYYLTGXyqZ+qpp27LN378eB/HM88847cha8XcQgstNFE5tduA99tvP3fhhRe2yzNmzBi3zDLLuK+//tpvO9Zqwnrbmmu3Aet8vs0337xdOfqZzu7rrE3Dhg1zm2yySbtVirWFjB492sn0e++99/yP77jjDrfpppvm2kmDG4AwAHPVP6/CmEznRTKfctAjH455loImedJsvSz0aJ1hniWgR540u14WBmDX2fEmBCAAgawEgpt/Z20Y+ZoiELQBqAs1VlllFScTbrrppnPaFixDT//WNtjkTL6FF17YG3TTTz99OzhZDEC9oC3EWp2n1K9fP3f44Yd7k06m3RlnnOEv9kjynXrqqXUF+Omnn9zAgQPdk08+6Z9vttlmbo899nAzzzyz02rGk08+2a9Q1Ko9XT6y/vrr1y1HqweXXXZZN27cOG8QDhkyxG200UY+r97TZSMyK3Vhx3PPPee3MHdM2p7cp08fb+otv/zyPo+2+Wqr84MPPuiuuOIKf+uvkszEW27J/5y74AYgDMCmBpaiMjN5K4p0tnrQIxunInOhSZG00+tCj3RGReZAjyJpN64LA9CGDkQBAQjETSC4+XfccpTWuqANQFG755573Pbbb+9XztVLMv/uu+8+b3h1TFkNQG25lVmnVXyNki75kOHY2Tl5n376qdtggw3c00+3P0suKXOKKabwKwNVV2dp5MiRbvDgwU5bguslXQ6iVX6NzkWsPZ+ws3r22Wcff/uxzMG8U3ADEAZg3l0gl/KYvOWCMbdC0CM3lLkVhCa5ocylIPTIBWNuhaBHbihbKggDsCV8vAwBCEAgE4Hg5t+ZWkWmZgkEbwCqwbot97zzzvNGnzq2jDQZfjpDb//996973p7ey2oAJlCHDx/uTT4ZeDLzZp11Vte/f39/MUejFXsdBdHqvMsvv9xf8KEzAbXSTjf2asvtgQce6LcbZ0mqX22W0ffOO+/4V3TzsLb2HnTQQW6WWWZpWIxMU51nKCNR7FSW4phhhhncAgss4AYMGOB23XVXf35id6XgBiAMwO7qCi2Vy+StJXy5v4weuSNtuUA0aRlhrgWgR644Wy4MPVpGmEsBGIC5YKQQCEAAAp0SCG7+jZ7dQiAKA7BbyFBotxIIbgDCAOzW/tDVwpm8dZVc97yHHt3DtZVS0aQVevm/ix75M22lRPRohV5+72IA5seSkiAAAQg0IhDc/Bspu4UABmC3YKXQNALBDUAYgGmSlvKcyVsp2BtWih629FA0aGJLE/RAD1sEbESDAWhDB6KAAATiJhDc/DtuOUprHQZgaeirXXFwAxAGoMkOy2TalizoYUsPDED0sEfAVkSMWTb0wAC0oQNRQAACcRMIbv4dtxyltQ4DsDT01a44uAEIA9Bkh2XyZksW9LClBwYgetgjYCsixiwbemAA2tCBKCAAgbgJBDf/jluO0lqHAVga+mpXHNwAhAFossMyebMlC3rY0gMDED3sEbAVEWOWDT0wAG3oQBQQgEDcBIKbf8ctR2mtwwAsDX21Kw5uAMIANNlhmbzZkgU9bOmBAYge9gjYiogxy4YeGIA2dCAKCEAgbgLBzb/jlqO01mEAloa+2hUHNwBhAJrssEzebMmCHrb0wABED3sEbEXEmGVDDwxAGzoQBQQgEDeB4ObfcctRWuswAEtDX+2KgxuAMABNdlgmb7ZkQQ9bemAAooc9ArYiYsyyoQcGoA0diAICEIibQHDz77jlKK11GICloa92xcENQBiAJjsskzdbsqCHLT0wANHDHgFbETFm2dADA9CGDkQBAQjETSC4+XfccpTWOgzA0tBXu+LgBiAMQJMdlsmbLVnQw5YeGIDoYY+ArYgYs2zogQFoQweigAAE4iYQ3Pw7bjlKax0GYGnoq11xcAMQBqDJDsvkzZYs6GFLDwxA9LBHwFZEjFk29MAAtKEDUUAAAnETCG7+HbccpbUOA7A09NWuOLgBCAPQZIdl8mZLFvSwpQcGIHrYI2ArIsYsG3pgANrQgSggAIG4CQQ3/45bjtJahwFYGvpqVxzcAIQBaLLDMnmzJQt62NIDAxA97BGwFRFjlg09MABt6EAUEIBA3ASCm3/HLUdprcMALA19tSsObgDCADTZYZm82ZIFPWzpgQGIHvYI2IqIMcuGHhiANnQgCghAIG4Cwc2/45ajtNZhAJaGvtoVBzcAYQCa7LBM3mzJgh629MAARA97BGxFxJhlQw8MQBs6EAUEIBA3geDm33HLUVrrMABLQ1/tioMbgDAATXZYJm+2ZEEPW3pgAKKHPQK2ImLMsqEHBqANHYgCAhCIm0Bw8++45SitdRiApaGvdsXBDUAYgCY7LJM3W7Kghy09MADRwx4BWxExZtnQAwPQhg5EAQEIxE0guPl33HKU1joMwNLQV7vi4AYgDECTHZbJmy1Z0MOWHhiA6GGPgK2IGLNs6IEBaEMHooAABOImENz8O245SmsdBmBp6KtdcXADEAagyQ7L5M2WLOhhSw8MQPSwR8BWRIxZNvTAALShA1FAAAJxEwhu/h23HKW1DgOwNPTVrji4AQgD0GSHZfJmSxb0sKUHBiB62CNgKyLGLBt6YADa0IEoIACBuAkEN/+OW47SWocBWBr6alcc3ACEAWiywzJ5syULetjSAwMQPewRsBURY5YNPTAAbehAFBCAQNwEgpt/xy1Haa3DACwNfbUrDm4AwgA02WGZvNmSBT1s6YEBiB72CNiKiDHLhh4YgDZ0IAoIQCBuAsHNv+OWo7TWYQCWhr7aFQc3AGEAmuywTN5syYIetvTAAEQPewRsRcSYZUMPDEAbOhAFBCAQN4Hg5t9xy1Fa6zAAS0Nf7YqDG4AwAE12WCZvtmRBD1t6YACihz0CtiJizLKhBwagDR2IAgIQiJtAcPPvuOUorXUYgKWhr3bFwQ1AGIAmOyyTN1uyoIctPTAA0cMeAVsRMWbZ0AMD0IYORAEBCMRNILj5d9xylNY6DMDS0Fe74uAGIAxAkx2WyZstWdDDlh4YgOhhj4CtiBizbOiBAWhDB6KAAATiJhDc/DtuOUprHQZgaeirXXFwAxAGoMkOy+TNlizoYUsPDED0sEfAVkSMWTb0wAC0oQNRQAACcRMIbv4dtxyltQ4DsDT01a44uAEIA9Bkh2XyZksW9LClBwYgetgjYCsixiwbemAA2tCBKCAAgbgJBDf/jluO0lqHAVga+mpXHNwAhAFossMyebMlC3rY0gMDED3sEbAVEWOWDT0wAG3oQBQQgEDcBIKbf8ctR2mtwwAsDX21Kw5uAMIANNlhmbzZkgU9bOmBAYge9gjYiogxy4YeGIA2dCAKCEAgbgLBzb/jlqO01mEAloa+2hUHNwBhAJrssEzebMmCHrb0wABED3sEbEXEmGVDDwxAGzoQBQQgEDeB4ObfcctRWuswAEtDX+2KgxuAMABNdlgmb7ZkQQ9bemAAooc9ArYiYsyyoQcGoA0diAICEIibQHDz77jlKK11GICloa92xcENQBiAJjsskzdbsqCHLT0wANHDHgFbETFm2dADA9CGDkQBAQjETSC4+XfccpTWOgzA0tBXu+LgBiAMQJMdlsmbLVnQw5YeGIDoYY+ArYgYs2zogQFoQweigAAE4iYQ3Pw7bjlKax0GYGnoq11xcAMQBqDJDsvkzZYs6GFLDwxA9LBHwFZEjFk29MAAtKEDUUAAAnETCG7+HbccpbUOA7A09NWuOLgBCAPQZIdl8mZLFvSwpQcGIHrYI2ArIsYsG3rUNQB7TOtcr742AiSK/yMw+2LO/eYCaEAAAoESCG7+HShn62FjAFpXKNL4ghuAMABN9kQmb7ZkQQ9bemAAooc9ArYiYsyyoUddA9BGaERRS6B35OIBRQAAIABJREFUf+d2fwgmEIBAoASCm38Hytl62BiA1hWKNL7gBiAMQJM9kcmbLVnQw5YeGIDoYY+ArYgYs2zogQFoQ4fUKDAAUxGRAQKWCQQ3/7YMM+DYMAADFi/k0IMbgDAATXY3Jm+2ZEEPW3pgAKKHPQK2ImLMsqEHBqANHVKjwABMRUQGCFgmENz82zLMgGPDAAxYvJBDD24AwgA02d2YvNmSBT1s6YEBiB72CNiKiDHLhh4YgDZ0SI0CAzAVERkgYJlAcPNvyzADjg0DMGDxQg49uAEIA9Bkd2PyZksW9LClBwYgetgjYCsixiwbevQbdayb4cs33bhPfnE//excj8kmcQvPO4WN4KoaxcevOvfjt+1bjwFY1d5AuyMhENz8OxLu1pqBAWhNkYrEE9wAhAFosmcyebMlC3rY0gMDED3sEbAVEWOWLT2uvvJH9+03zs0602Tu1lPnthVc1aKp87nXYQBWrRfQ3sgIBDf/joy/leZgAFpRomJxBDcAYQCa7KFM3mzJgh629MAARA97BGxFxJhlSw8MQEN6YAAaEoNQIJAPgeDm3/k0m1I6EMAApEuUQiC4AQgDsJR+klYpk7c0QsU+R49ieWepDU2yUCouD3oUxzpLTeiRhVJxeTAAi2OdWhMGYCoiMkAgNALBzb9DAxxIvBiAgQgVW5jBDUAYgCa7IJM3W7Kghy09FA2a2NIEPdDDFgFb0WAAGtIDA9CQGIQCgXwIBDf/zqfZlNKBAAYgXaIUAsENQBiApfSTtEqZTKcRKvY5ehTLO0ttaJKFUnF50KM41llqQo8slIrLgwFYHOvUmjAAUxGRAQKhEQhu/h0a4EDixQAMRKjYwgxuAMIANNkFmbzZkgU9bOmhaNDElibogR62CNiKBgPQkB4YgIbEIBQI5EMguPl3Ps2mlA4EMADpEqUQCG4AwgAspZ+kVcpkOo1Qsc/Ro1jeWWpDkyyUisuDHsWxzlITemShVFweDMDiWKfWhAGYiogMEAiNQHDz79AABxIvBmAgQsUWZnADEAagyS7I5M2WLOhhSw9Fgya2NEEP9LBFwFY0GICG9MAANCQGoUAgHwLBzb/zaTaldCCAAUiXKIVAcAMQBmAp/SStUibTaYSKfY4exfLOUhuaZKFUXB70KI51lprQIwul4vJgABbHOrUmDMBURGSAQGgEgpt/hwY4kHgxAAMRKrYwgxuAMABNdkEmb7ZkQQ9beigaNLGlCXqghy0CtqLBADSkBwagITEIBQL5EAhu/p1PsymlAwEMQLpEKQSCG4AwAEvpJ2mVMplOI1Tsc/QolneW2tAkC6Xi8qBHcayz1IQeWSgVlwcDsDjWqTVhAKYiIgMEQiMQ3Pw7NMCBxIsBGIhQsYUZ3ACEAWiyCzJ5syULetjSQ9GgiS1N0AM9bBGwFQ0GoCE9MAANiUEoEMiHQHDz73yaTSkdCGAA0iVKIRDcAIQBWEo/SauUyXQaoWKfo0exvLPUhiZZKBWXBz2KY52lJvTIQqm4PBiAxbFOrQkDMBURGSAQGoHg5t+hAQ4kXgzAQISKLczgBiAMQJNdkMmbLVnQw5YeigZNbGmCHuhhi4CtaDAADemBAWhIDEKBQD4Egpt/59NsSulAAAOQLlEKgeAGIAzAUvpJWqVMptMIFfscPYrlnaU2NMlCqbg86FEc6yw1oUcWSsXlwQAsjnVqTRiAqYjIAIHQCAQ3/w4NcCDxYgAGIlRsYQY3AGEAmuyCTN5syYIetvRQNGhiSxP0QA9bBGxFgwFoSA8MQENiEAoE8iEQ3Pw7n2ZTSgcCGIB0iVIIBDcAYQCW0k/SKmUynUao2OfoUSzvLLWhSRZKxeVBj+JYZ6kJPbJQKi4PBmBxrFNrwgBMRUQGCIRGILj5d2iAA4kXAzAQoWILM7gBCAPQZBdk8mZLFvSwpYeiQRNbmqAHetgiYCsaDEBDemAAGhKDUCCQD4Hg5t/5NJtSOhDAAKRLlEIguAEIA7CUfpJWKZPpNELFPkePYnlnqQ1NslAqLg96FMc6S03okYVScXkwAItjnVoTBmAqIjJAIDQCwc2/QwMcSLwYgIEIFVuYwQ1AGIAmuyCTN1uyoIctPRQNmtjSBD3QwxYBW9FgABrSAwPQkBiEAoF8CAQ3/86n2ZTSgQAGIF2iFALBDUAYgKX0k7RKmUynESr2OXoUyztLbWiShVJxedCjONZZakKPLJSKy4MBWBzr1JowAFMRkQECoREIbv4dGuBA4sUADESo2MIMbgDCADTZBZm82ZIFPWzpoWjQxJYm6IEetgjYigYD0JAeGICGxCAUCORDILj5dz7NppQOBDAA6RKlEAhuAMIALKWfpFXKZDqNULHP0aNY3llqQ5MslIrLgx7Fsc5SE3pkoVRcHgzA4lin1oQBmIqIDBAIjUBw8+/QAAcSLwZgIELFFmZwAxAGoMkuyOTNlizoYUsPRYMmtjRBD/SwRcBWNBiAhvTAADQkBqFAIB8Cwc2/82k2pXQggAFIlyiFQHADEAZgKf0krVIm02mEin2OHsXyzlIbmmShVFwe9CiOdZaa0CMLpeLyYAAWxzq1JgzAVERkgEBoBIKbf4cGOJB4MQADESq2MIMbgDAATXZBJm+2ZEEPW3ooGjSxpQl6oIctAraiwQA0pAcGoCExCAUC+RAIbv6dT7MppQMBDEC6RCkEghuAMABL6SdplTKZTiNU7HP0KJZ3ltrQJAul4vKgR3Gss9SEHlkoFZcHA7A41qk1YQCmIiIDBEIjENz8OzTAgcSLARiIULGFGdwAhAFosgsyebMlC3rY0kPRoIktTdADPWwRsBUNBqAhPTAADYlBKBDIh0Bw8+98mk0pHQhgANIlSiEQ3ACEAVhKP0mrlMl0GqFin6NHsbyz1IYmWSgVlwc9imOdpSb0yEKpuDwYgMWxTq0JAzAVERkgEBqB4ObfoQEOJF4MwECEii3M4AYgDECTXZDJmy1Z0MOWHooGTWxpgh7oYYuArWgwAA3pgQFoSAxCgUA+BIKbf+fTbErpQAADkC5RCoHgBiAMwFL6SVqlTKbTCBX7HD2K5Z2lNjTJQqm4POhRHOssNaFHFkrF5bn4gh/dzz87N+kkzvWccbLiKqamiQicMvk2buFJX2z/8979ndv9IWhBAAKBEghu/h0oZ+thYwBaVyjS+IIbgDAATfZEJm+2ZEEPW3ooGjSxpQl6oIctAraiSX4/bEVVzWgumHk7t3iPf2EAVlN+Wh0pgeDm35HqUHazMADLVqCi9Qc3AGEAmuypTKZtyYIetvTAAEQPewRsRcSYZVOPSSZxbpppbcVWtWjOmnI7t+hkGIBV0532xk0guPl33HKU1joMwNLQV7vi4AYgDECTHZbJmy1Z0MOWHhiA6GGPgK2IGLPQwxYBO9EMfHArN8tnbAG2owiRQKB1AsHNv1tvMiXUIYABSLcohUBwAxAGYCn9JK1SJm9phIp9jh7F8s5SG5pkoVRcHvQojnWWmtAjC6Xi8qBHcazTasIATCPEcwiERyC4+Xd4iIOIGAMwCJniCzK4AQgD0GQnZLJgSxb0sKWHokETW5qgB3rYImArGn4/7OiBAWhHCyKBQF4Egpt/59VwymlHAAOQDlEKgeAGIAzAUvpJWqVMFtIIFfscPYrlnaU2NMlCqbg86FEc6yw1oUcWSsXlQY/iWKfVhAGYRojnEAiPQHDz7/AQBxExBmAQMsUXZHADEAagyU7IZMGWLOhhSw9Fgya2NEEP9LBFwFY0/H7Y0QMD0I4WRAKBvAgEN//Oq+GU044ABiAdohQCwQ1AGICl9JO0SpkspBEq9jl6FMs7S21okoVScXnQozjWWWpCjyyUisuDHsWxTqsJAzCNEM8hEB6B4Obf4SEOImIMwCBkii/I4AYgDECTnZDJgi1Z0MOWHooGTWxpgh7oYYuArWj4/bCjBwagHS2IBAJ5EQhu/p1XwymnHQEMQDpEKQSCG4AwAEvpJ2mVMllII1Tsc/QolneW2tAkC6Xi8qBHcayz1IQeWSgVlwc9imOdVhMGYBohnkMgPALBzb/DQxxExBiAQcgUX5DBDUAYgCY7IZMFW7Kghy09FA2a2NIEPdDDFgFb0fD7YUcPDEA7WhAJBPIiENz8O6+GU047AhiAdIhSCAQ3AGEAltJP0iplspBGqNjn6FEs7yy1oUkWSsXlQY/iWGepCT2yUCouD3oUxzqtJgzANEI8h0B4BIKbf4eHOIiIMQCDkCm+IIMbgDAATXZCJgu2ZEEPW3ooGjSxpQl6oIctArai4ffDjh4YgHa0IBII5EUguPl3Xg2nnHYEMADpEKUQCG4AwgAspZ+kVcpkIY1Qsc/Ro1jeWWpDkyyUisuDHsWxzlITemShVFwe9CiOdVpNGIBphHgOgfAIBDf/Dg9xEBFjAAYhU3xBBjcAYQCa7IRMFmzJgh629FA0aGJLE/RAD1sEbEXD74cdPTAA7WhBJBDIi0Bw8++8Gk457QhgANIhSiEQ3ACEAVhKP0mrlMlCGqFin6NHsbyz1IYmWSgVlwc9imOdpSb0yEKpuDzoURzrtJowANMI8RwC4REIbv4dHuIgIsYADEKm+IIMbgDCADTZCZks2JIFPWzpoWjQxJYm6IEetgjYiobfDzt6YADa0YJIIJAXgeDm33k1nHLaEcAApEOUQiC4AQgDsJR+klYpk4U0QsU+R49ieWepDU2yUCouD3oUxzpLTeiRhVJxedCjONZpNWEAphHiOQTCIxDc/Ds8xEFEjAEYhEzxBRncAIQBaLITMlmwJQt62NJD0aCJLU3QAz1sEbAVDb8fdvTAALSjBZFAIC8Cwc2/82o45bQjgAFIhyiFQHADEAZgKf0krVImC2mEin2OHsXyzlIbmmShVFwe9CiOdZaa0CMLpeLyoEdxrNNqwgBMI8RzCIRHILj5d3iIg4gYAzAImeILMrgBCAPQZCdksmBLFvSwpYeiQRNbmqAHetgiYCsafj/s6IEBaEcLIoFAXgSCm3/n1XDKaUcAA5AOUQqB4AYgDMBS+klapUwW0ggV+xw9iuWdpTY0yUKpuDzoURzrLDWhRxZKxeVBj+JYp9WEAZhGiOcQCI9AcPPv8BAHETEGYBAyxRdkcAMQBqDJTshkwZYs6GFLD0WDJrY0QQ/0sEXAVjT8ftjRAwPQjhZEAoG8CAQ3/86r4ZTTjgAGIB2iFALBDUAYgKX0k7RKmSykESr2OXoUyztLbWiShVJxedCjONZZakKPLJSKy4MexbFOqwkDMI0QzyEQHoHg5t/hIQ4iYgzAIGSKL8jgBiAMQJOdkMmCLVnQw5YeigZNbGmCHuhhi4CtaPj9sKMHBqAdLYgEAnkRCG7+nVfDKacdAQxAOkQpBIIbgDAAS+knaZUyWUgjVOxz9CiWd5ba0CQLpeLyoEdxrLPUhB5ZKBWXBz2KY51WEwZgGiGeQyA8AsHNv8NDHETEGIBByBRfkMENQBiAJjshkwVbsqCHLT0UDZrY0gQ90MMWAVvR8PthRw8MQDtaEAkE8iIQ3Pw7r4ZTTjsCGIB0iFIIBDcAYQCW0k/SKmWykEao2OfoUSzvLLWhSRZKxeVBj+JYZ6kJPbJQKi4PehTHOq0mDMA0QjyHQHgEgpt/h4c4iIgxAIOQKb4ggxuAMABNdkImC7ZkQQ9beigaNLGlCXqghy0CtqLh98OOHhiAdrQgEgjkRSC4+XdeDaecdgQwAOkQpRAIbgDCACyln6RVymQhjVCxz9GjWN5ZakOTLJSKy4MexbHOUhN6ZKFUXB70KI51Wk0YgGmEeA6B8AgEN/8OD3EQEWMABiFTfEEGNwBhAJrshEwWbMmCHrb0UDRoYksT9EAPWwRsRcPvhx09MADtaEEkEMiLQHDz77waTjntCGAA0iFKIRDcAIQBWEo/SauUyUIaoWKfo0exvLPUhiZZKBWXBz2KY52lJvTIQqm4POhRHOu0mjAA0wjxHALhEQhu/h0e4iAixgAMQqb4ggxuAMIANNkJmSzYkgU9bOmhaNDElibogR62CNiKht8PO3pgANrRgkggkBeB4ObfeTWcctoRwACkQ5RCILgBCAOwlH6SVimThTRCxT5Hj2J5Z6kNTbJQKi4PehTHOktN6JGFUnF50KM41mk1YQCmEeI5BMIjENz8OzzEQUSMARiETPEFGdwAhAFoshMyWbAlC3rY0kPRoIktTdADPWwRsBUNvx929MAAtKMFkUAgLwLBzb/zajjltCOAAUiHKIVAcAMQBmAp/SStUiYLaYSKfY4exfLOUhuaZKFUXB70KI51lprQIwul4vKgR3Gs02rCAEwjxHMIhEcguPl3eIiDiBgDMAiZ4gsyuAEIA9BkJ2SyYEsW9LClh6JBE1uaoAd62CJgKxp+P+zogQFoRwsigUBeBIKbf+fVcMppRwADkA5RCoHgBiAMwFL6SVqlTBbSCBX7HD2K5Z2lNjTJQqm4POhRHOssNaFHFkrF5UGP4lin1VTXAOwxrXO9+qa9yvOiCMy+mHO/uaCo2qgnAgLBzb8jYG6xCRiAFlWpQEzBDUAYgCZ7JZMFW7Kghy09FA2a2NIEPdDDFgFb0fD7YUePugagnfCIRAR693du94dgAYHMBIKbf2duGRmbIYAB2Awt8uZGILgBCAMwN+3zLIjJQp40Wy8LPVpnmHcJaJI30dbKQ4/W+OX9NnrkTbS18tCjNX55vo0BmCfNbioLA7CbwMZbbHDz73ilKLVlGICl4q9u5cENQBiAJjsrkwVbsqCHLT0UDZrY0gQ90MMWAVvR8PthRw8MQDtaNIwEAzAAkWyFGNz82xa+aKLBAIxGyrAaEtwAhAFosoMxWbAlC3rY0gMDED3sEbAVEWMWetgiYCcaDEA7WmAABqBFICEGN/8OhGtoYUZhAL733nvu/PPPd/fdd5/Tf0855ZSuT58+bsstt3T77ruvm2aaaXLR5f7773dDhw51o0aNcuPGjXOzzTabW3755d2ee+7p1ltvvUx1TJgwwV155ZXuhhtucK+99pr75ptv3Nxzz+3WWmstd8ABB7i+fbMdrvvZZ5/5Ng8bNsy988477pdffnHzzz+/Gzx4sC9nlllmyRTPK6+84i644AL30EMPubFjx7rpppvOLbbYYm677bZzu+22m5t88skzldNspuAGIAzAZiUuJD+Tt0IwZ64EPTKjKiwjmhSGOlNF6JEJU2GZ0KMw1JkqQo9MmArJ1G/UsW6GL9904z75xf30s3M9JpvELTzvFIXUTSV1CHz8qnM/ftv+ASsA6SpNEghu/t1k+8iejUDwBqBMP5lVX331Vd0WL7LIIm748OFugQUWyEakTi6Za3vvvbc3/xolmYCXXnqpm2SSSRrmkWm34YYbupEjR9bNI+Py4osvdrvuumunsT799NNuk002cR9++GHdfHPNNZe766673HLLLddpOTIi99tvP/e///2vbr4VV1zR3XvvvZnNxGYABzcAYQA2I29heZksFIY6U0XokQlToZnQpFDcqZWhRyqiQjOgR6G4UytDj1REhWe4+sof3bffODfrTJO5W0+du/D6qfD/I1BnHsIlIPSOZgkEN/9utoHkz0QgaAPwxRdfdCuvvLL77rvv/Mq1I4880g0aNMiNHz/e3Xzzze7yyy/3EBZddFEn00x5upKOPvpod+qpp/pX+/Xr5w477DC34IILujFjxrgzzzzTPf/88/6Z8p1yyil1q/jpp5/cGmus4UaMGOGfb7rppm6PPfZwPXv29Iag3vvkk0/cZJNN5lcyrrvuunXL0Sq9ZZdd1n388cd+dd7BBx/sNtpoI59XZt25557rtMqwV69e7tlnn/WrC+ulBx54wG2wwQbu559/9nkV+worrOA+//xzz+3OO+/0r6222mru0UcfdZNOOmlX0DV8J7gBCAMwV/3zKozJQl4k8ykHPfLhmGcpaJInzdbLQo/WGeZZAnrkSbP1stCjdYZ5l4ABmDfRLpaHAdhFcLxWSyC4+TfydQuBoA1AmX2PPfaYN8JkrK200krtIJ111lnerFM68cQT3XHHHdc0xNGjR/stsTLVtKJO9Uw99dRt5ch8HDhwoHvmmWd8HK+//ro3Bzuma665xu2yyy7+x9qWfNFFF7XLonpk7H399dduoYUWcq+++mrd7bc777yzu/baa/27t956q9tiiy3alXPbbbf5rc9Kqu+qq66aKBa1RW1SnTPMMIN77rnnJopZKwO1GlFJ9e24445Ns+vsheAGIAzAXPXPqzAmC3mRzKcc9MiHY56loEmeNFsvCz1aZ5hnCeiRJ83Wy0KP1hnmXQIGYN5Eu1geBmAXwfFaLYHg5t/I1y0EgjUAtaJP5+8p7bXXXn77bcek1W1LLLGEP2tv5pln9qvmevTo0RTIWiPsn//8p9O22I7pqaeeajMf999/f3+mXse0+OKLe1NPceiXr965hKeffrpfxah0++23u80226xdMYpfK/q0mlArBHUmYb2k8wi1wk+rCbViUCv8alOtSXjaaae5I444YqJiZGz27t3bffHFF57hSy+91BS3tMzBDUAYgGmSlvKcyUIp2BtWih629FA0aGJLE/RAD1sEbEXD74ctPRQNBqARTTAAjQgRdhjBzb/Dxm02+mANwNptuTLgtH21Xqo11R588EG39tprZxZDZ//NM8883kTTNmIZiY2Snr/xxhveNNNFJLVnAb711ltu4YUX9q/qLMFLLrmkbjEfffSRm3POOf2zbbfd1l8UUpu0NVdnDSppi/NWW21Vtxw922abbfwznVuorca1SWcm3njjjf5HOkdwjjnmqFuOYr3sssv8szfffNOvTMwrBTcAYQDmJX2u5TBZyBVny4WhR8sIcy8ATXJH2lKB6NESvtxfRo/ckbZUIHq0hK9bXsYA7BaszReKAdg8M96YiEBw82807BYCwRqAOpvu8ccfd9NOO6378ssvG95Wq1V7OidQSVuAtRU4a3r77bfbtsY2WmWYlKXnySUhek838iZJ23B1o67STTfd5LbeeuuGIejSEplt8847r3v33Xfb5dM23Ouvv97/rDPjTs90EYiS3km2DCeFqez333/fqS5tWW6UFKuMSCW1IdnCnJVfZ/mCG4AwAPOQPfcymCzkjrSlAtGjJXzd8jKadAvWLheKHl1G1y0voke3YO1yoejRZXTd9iIGYLehba5gDMDmeJG7LoHg5t/o2C0EgjUAZ5ttNvfpp5+6pZZayr3wwgsN4WgLqy7aUNJ5eTo3L2vSZRzJBRt//OMf3UEHHdTwVT3XhRxKek8XbCTp0EMPdWeffbb/py4MWXrppRuWo9t97777br+C8L///a83OJPUv39/f9bgjDPO6E3PzpLy6DxBvTNq1Ki2rN98840/90+rG1XXsGHDGhajWJdZZhn/XG3QhSd5peAGIAzAvKTPtRwmC7nibLkw9GgZYe4FoEnuSFsqED1awpf7y+iRO9KWCkSPlvB1y8sYgN2CtflC6xmAPaZ1rlff5svijXwJ7P5QvuV1Y2nBzb+7kUWViw7SAPz+++/bLuLYcMMN/e23nSXd/vvtt9/68/u0IjBr0rmC++yzj8+uc/M233zzhq/qzL7kQg69pxWBSdKKv1tuucX/c9y4cW7WWWdtWI7OEEwuCNHqPK3SS5K26uocQJ0n+PLLL3faDJ3b98orr/jtvVoRmCSVqQtAlHS+4YUXXtiwHBmsMlqV1AatCMyaNMB0lrQCMVmZKYMy2fqctfzC8924tXMfvdiu2i9m6uueGvh/F6WQyiFwzVUT3C+/ODfNtM5ttfXk5QRBrW0E0MNeZ0ATW5qgB3rYImArGn4/bOmhaG65eYL77lvnes4wqbv0iP87pohUAoE685ASoqDKegQObnxEmDVg8gSSOxT+/e9/u/nmm89aiMRTAIEgDUCZaLPPPrvHo3PwdOZdZ0mXYHzyySdNX2ZRe4vwX//6V6fLNRolPU9W/Wm135AhQ9qyyqQcPny4//f48ePdVFNN1bCcww8/vG2lnVb76WbgJGk1oC7n0HmHOvews6Q8MtZkfmolYZJqL09RXTojsVFSrMllJVoJec8992TukrVnIGZ+iYwQgAAEIAABCEAAAhCAAAQgAAEIdBsB+QTaKUiqHoEgDUCtHtM5dko77LCDu+666zpVLjnzbsEFF3SjR4/OrPLJJ5/szw1Uevjhh90aa6zR8N1HHnnErbnmmv653jvmmGPa8urneq6kG3wnnXTShuWoPr2vpDMOV1111ba8utVXNxsPGDDAjRgxotN2JGck6p0JEya05VWZeqZ07LHHupNOOqlhOapL7yupDQ89lH2JMwZg5m5GRghAAAIQgAAEIAABCEAAAhCAQCEEMAALwWyykiANQFYA2l8BmLYFWNu4tR1ZqzO1zXjyye1v36xdNh3EtmWTQw5BWSFAf7aiBHHkRYA+nRdJyrFAgP5sQQViyJMAfTpPmpRVNoEQ+7MWBclHUVpyySU73ZVYNl/q7z4CQRqAnAFo/wzA7uuy5ZXMwanlsafm/AnQn/NnSonlEqBPl8uf2vMlQH/OlyellU+APl2+BkSQHwH6c34sKalYAkEagEJUxC3Aulxk44039oq0cgvwIYcc4s455xxfTiu3AC+33HLu2WefbfkW4Omnn97HUuYtwMV283xqY6DPhyOl2CBAf7ahA1HkR4A+nR9LSiqfAP25fA2IIF8C9Ol8eVJauQToz+Xyp/auEwjWAEzOuNPFGF9++WXDLaS69Te5bVbn65144omZab399ttO5wYq6VZf3e7bKOn50KFD/WO9N//887dlveqqq9xuu+3m/62bdHWjbqOkW3/ffPNNf8bhu+++2y7bjjvu6K6//nr/My071g2/9ZKezTXXXP6R3rn22mvbZUvORFRd2obbKCnWbbfd1j9WG3bZZZfM7GLMyEAfo6rVbRP9ubrax9py+nSCnXkeAAAgAElEQVSsylazXfTnauoec6vp0zGrW7220Z+rp3ksLQ7WADzqqKPcaaed5nXQjbi69bZe0i23Rx55pH/0wAMPuHXWWSezdr/88ovr3bu3++CDD9yiiy7qXnut8TXfiy22mDfT5p57bqdLSmovwZChJ7NNae+993aXXHJJ3Rg++ugjN+ecc/pn22yzjbvxxhvb5ZPBKKNRSTcf6wbkeknP9L7SZZdd5vbcc8922WTqydxT6sxIVKx6X+mNN95wCy+8cGZ2MWZkoI9R1eq2if5cXe1jbTl9OlZlq9ku+nM1dY+51fTpmNWtXtvoz9XTPJYWB2sA6hKGxPRrtDpPt9guscQS3ribaaaZ3CeffOJ69OjRlHb77rtvm2Gn1YQrrrjiRO/LgFxppZX8z5X/oosumihP3759fRw9e/b0BuE000wzUZ5as/LWW291W2yxRbs8MghlMKpd6667rrv//vvrtmW99dbzZqduGx47duxEKwVVdmIeykQ94ogjJirnu+++8+bnF1984RT7K6+80hS3GDMz0MeoanXbRH+urvaxtpw+Hauy1WwX/bmausfcavp0zOpWr2305+ppHkuLgzUAJUCyDVg3yI4YMaLNhEvEOeuss9xhhx3m/3n88ce7E044oZ1ujz32mBs0aJD/2U477eSuueaaiXTV6r3FF1/c6dYcncGneqaeeuq2fOPHj/dxPPPMM34b8quvvuoWWmihicqp3Qa83377uQsvvLBdnjFjxrhlllnGff31137bsVYT1rsZt3Yb8G233eY233zzduXoZ1tuuWWnbfrxxx+dViyqzhlmmME999xzbVudk8IU48UXX+z/efXVV7udd945lj7f5XYw0HcZHS8aJEB/NigKIbVEgD7dEj5eNkaA/mxMEMJpmQB9umWEFGCIAP3ZkBiE0hSBoA1AXaixyiqrOJlw0003ndO2YBl6+re2wSZn8mnrqgy65PKLhFAWA1B5tYVYq/OU+vXr5w4//HBvmMlAO+OMM/zFHkm+U089ta4AP/30kxs4cKB78skn/fPNNtvM7bHHHm7mmWd2Ws148skn+xWKWrWny0fWX3/9uuVo9eCyyy7rr/CWQThkyBC30UYb+bx6T5eNyKzUJSky9rSKr14aPny4v+BEqwl79erljjnmGLf88sv7FX+XX365u+OOO/xrq666qhOnySabrKmOFWNmBvoYVa1um+jP1dU+1pbTp2NVtprtoj9XU/eYW02fjlnd6rWN/lw9zWNpcdAGoES455573Pbbb+9XztVLMv/uu+8+16dPn4keZzUAZZLJrNMqvkZJl3zIcJSB1yh9+umnboMNNnBPP/103SxTTDGFXxmoujpLI0eOdIMHD3baElwv6XKQYcOGNTwXMXlHRt/+++/vfvjhh7rlyBAUu1lnnTWW/t5SOxjoW8LHy8YI0J+NCUI4LROgT7eMkAIMEaA/GxKDUHIhQJ/OBSOFGCFAfzYiBGE0TSB4A1At1m255513njer9MsoI02Gn87Qk8FV77w9vZfVAEyoatWcTD4ZeDLzZIz179/fX8zRaMVeR0W0Ok/Gmy740JmA3377rb+xd80113QHHnig326cJal+tVlG3zvvvONf0c3Dm2yyiTvooIPcLLPMkqUY9/LLL7vzzz/fPfzww/6yE92qrO3B2223ndt9990b3q6cqXAyQQACEIAABCAAAQhAAAIQgAAEIAABCJROIAoDsHSKBAABCEAAAhCAAAQgAAEIQAACEIAABCAAAaMEMACNCkNYEIAABCAAAQhAAAIQgAAEIAABCEAAAhDIgwAGYB4UKQMCEIAABCAAAQhAAAIQgAAEIAABCEAAAkYJYAAaFYawIAABCEAAAhCAAAQgAAEIQAACEIAABCCQBwEMwDwoUgYEIAABCEAAAhCAAAQgAAEIQAACEIAABIwSwAA0KgxhQQACEIAABCAAAQhAAAIQgAAEIAABCEAgDwIYgHlQpAwIQAACEIAABCAAAQhAAAIQgAAEIAABCBglgAFoVBjCggAEIAABCEAAAhCAAAQgAAEIQAACEIBAHgQwAPOgSBkQgAAEIAABCEAAAhCAAAQgAAEIQAACEDBKAAPQqDCEBQEIQAACEIAABCAAAQhAAAIQgAAEIACBPAhgAOZBkTKiJ/Dee++5888/3913331O/z3llFO6Pn36uC233NLtu+++bppppomeAQ0sj8Bzzz3n7r//fvf444+7l19+2X3yySeuR48ebq655nIrr7yy22233dyAAQMyB6iyhg4d6kaNGuXGjRvnZpttNrf88su7Pffc06233nqZypkwYYK78sor3Q033OBee+01980337i5557brbXWWu6AAw5wffv2zVQOmSBQS+Cwww5zZ511VtuPHn30Ubf66qt3Con+TB+yRODTTz91V111lbvrrrvcmDFj3BdffOFmmWUWN88887jVVlvNbbrppm6llVbqNOR//vOf7uKLL/Zj/kcffeRmnnlmt9RSS7mdd97Zbb311pmbe/PNN7urr77a/etf//JxzDHHHP5vxX777edWXHHFzOWQsZoEfvjhB3f99de72267zb344ovu888/95899Ld+lVVW8Z8ZsvQjxuhq9p8iWq3Pw/osq/89/fTT/n+fffaZr3qnnXZy11xzTVNhWOqraofmnsOGDXPvvPOO++WXX9z888/vBg8e7D9n6+8KCQJdIYAB2BVqvFMpAjL9tttuO/fVV1/Vbfciiyzihg8f7hZYYIFKcaGxxRAYOHCgGzFiRGplO+ywg7viiivcFFNM0TCvPjzsvffe3vxrlPSB/tJLL3WTTDJJwzz6ULLhhhu6kSNH1s0jg1yT11133TU1bjJAICGgCeZyyy3nZC4nqTMDkP5M37FGQEbJPvvs0zYBrRffJpts4id0jdJJJ53kTjzxRPfzzz/XzbLxxhu7W2+91U011VQNy/j+++/dFlts4e699966eSaddFJ3wgknuGOPPdYaQuIxQuD999/3f+dfeumlTiP6/e9/784555y6nxkYo42IGXEYnX1WbcYAtNZXZWTqb8WHH35YVz0tANCXTPrMRIJAswQwAJslRv5KEdCEVCusvvvuOzfddNO5I4880g0aNMiNHz/e6Zv1yy+/3PNYdNFF/bdOykOCQJ4EtNJUq0j0x14TOq3emHfeed1PP/3ktEpEH7zHjh3rq9xmm23cjTfe2LD6o48+2p166qn+eb9+/ZxWWy244IK+/DPPPNM9//zz/pnynXLKKXXLUb1rrLFGmymp1Sx77LGH69mzpzcE9Z6+kZ1sssn8itl11103TxyUFSkBmR1aSaJxdPbZZ/d9SKkzA5D+HGlnCLRZ1113ndtll128cac+LCNw1VVX9WOjVvFpnL3nnnvcjDPO6FdU1Uv6EkfjqZLG5qOOOsotueSS7oMPPnDnnXee/31Q0peSf/7znxuS0vPkb4E+sxx44IH+b4jMHP0NUCxK+gyz++67B0qcsLuLgL6EWWaZZdrMv1//+tfu4IMPdvrC+7///a974okn/GePb7/91oegzw+HHnroROEwRneXQpSbEKg1ALXKerHFFnMPPvigf9yMAWipr+oz/bLLLus+/vhjN/nkk/vfvY022si3SV/qnHvuuf6L0l69erlnn33Wr8glQaAZAhiAzdAib+UI6IPzY4895gdgrcLquG1HW9VkoijpG/vjjjuucoxocPcS0B/9HXfc0W222WbeVOuYtN1MW3HefPNN/0j9tN524NGjR/sPRvrQoG8MlW/qqaduK04mt1YbPvPMM76/v/76634C2jFpO4UmuUra/n7RRRe1y6J69MHl66+/dgsttJB79dVXfXkkCHRG4E9/+pPTShJ9mfLb3/7WnXbaaT57IwOQ/kx/skRAxyDoS5X//e9/fvxNjL56MWpbZb2V2l9++aXf3qX/15c8mtjNOuusbUXoyxf9bqhspb///e9+S3HHpJ8n2+a1WvAvf/lLu78d+puhMVrHmWhr8dtvv+1mmmkmSziJpWQCd9xxh9t88819FPrcq63oHT9/qH/q2Y8//uj7kb60qf1bzxhdsogVqf744493/fv39/+TIaatshpHlbIagNb6qo56uPbaa30btNpbX/7XJn2BpCOolPR5XEdOkCDQDAEMwGZokbdSBLQSReeiKe21115+W2THpG/6l1hiCX8Gmj4A6dsanY9CgkCRBPSNoCZ6SjoXRCtFOiad+aRtuUpaOVjv3J6nnnqqzeTef//93QUXXDBROYsvvrg39dTf//Of/9Q9//L000/3q2WVbr/9dm9ekiDQiIC2munMSJ0jKcNPX7roCxWlRgYg/Zn+ZImAzj59+OGHvWGnzwO1xl3WOGu/ULzpppvqnvWnMXe++ebzK8D15VBiBtbWoW2bOpZEho0mw717954oBO1g0IpxpbPPPtsNGTIka5jkqwABrTj64x//6Ft69913t32+6Nh07QCQwayk1aX6PJwkxugKdBSDTeyKAWipr2oeqRV9GuO1g0ZnEtZLOq/7gQce8OO8VgzK/CRBICsBDMCspMhXOQK1y8FljKywwgp1GdSaHVp2vvbaa1eOFQ0ul4CMk+mnn94Hoclfx3OfdLaJtkboQ4JWWGmC2ijp+RtvvOEnjVohUru94q233nILL7ywf1VnCV5yySV1i9F2tznnnNM/23bbbf1FISQINCIg81p9Nvm2XmeTdWYA0p/pS5YIaLW0Vlcrqe9qRUpXklZy/+Mf/3AzzDCDv5yp0XmuycRPZ61qNV/t0SP6WyDzUSsRle+vf/1r3VC0ClGXP2mlto45efLJJ7sSMu9ESkBfACar+3XxmL74q5e07VcGspJ2D2hlqRJjdKQdI4BmNWsAWuurOpZBZ3Er6Yuarbbaqi712i9xdK53cnREABIRogECGIAGRCAEmwS0tUbbHqaddlq/JafRNkatptIHaCVtAU4mrjZbRVQxEtDNfMltYDJT9I19bdIWr2Q7b6PVrEl+PU8uCdF7yVYKPdc2A904rNRohUpSjs4K0rZkbWV79913Y8ROm3IgoO0t+oCrc9JkpMiUSDMA6c85gKeI3AicfPLJbcd/vPLKK203oOvWXRl06ttptzXKkNNnDR3R0NmqDwWt7fE6G1DpkUce8ecSJ0n/XnPNNf0/le+II45o2E7Voy8t9dlGR0CweyG3LhF8Qbp5VOdGKmVZAagvCvU5Wea1EmN08F0g2AY0awBa66s68kc3byvpAhDd3F4v6ZnOdVXSO8mW4WCFI/BCCWAAFoqbykIioImoPrwvtdRS7oUXXmgYuj7k6wO+ks5p0ISWBIEiCWgLjrbiKOkbeR3IXZt0GUdygLC29Rx00EENw9Nzbf9R0nsbbLBBW97ab/t1YcjSSy/dsBzdXqaJgyYGOjRck1sSBGoJaMKolVNaMVp7GUGaAUh/ph9ZIpBsudXlHvo8oMs3NAb/61//agtTX6Rohau22ta7LEzGYbJ9UsaLzsRslGrHe63S0lmsSdK/tXpLSfkGDx7csBzVI6NHqda4tMSWWMohoBWouoBMK0S1MlXnSnY8A1CfAXSUiMzrjheQMUaXoxu1uqbPALTWV3WWoVbT6u+JPiN1lpRHv6N6Z9SoUcgPgcwEMAAzoyJjlQh8//33bRck1NtS2ZGFPtDrNjR9GNKKQBIEiiKgcyh1EHfyx19nV+qSj9qk8yt1I6WSDg9ODveuF6PO7EsOHNZ7WhGYpK233trdcsst/p+aIHR2zlXtFiKt7NKKQBIEaglom4uMP62g1q2SyXbzNAOQ/kw/skRA5p5WnejLQt362/FipNpYZfLp3KZk5UbyTOc8rb/++v6fOgvwkEMOadhETQ414VPSCr/kwpzk32eccYZ/Vu9vQW2h2rqZ3Nyq+rmx3VKvKj8WGci6TXr8+PH+ght9cagjQLTNXFvGdQuwvtzTF4Haal67Uokxunz9qhpBsysArfVV/R7pHEBtu9f2+86S/p7oyxu9oxWBJAhkJYABmJUU+SpFQObG7LPP7tus7Wk6a6GzpMNXdQOaBmMdhEyCQFEE9CE8mSzqhsg777xzoqprD5fXB3WdDdUo6Xmy6q/j4fDJShe9q0nBVFNN1bCcww8/vG0lYu3ZQEVxoR7bBGT46ZgFrSp57rnn3JJLLtkWcJoBSH+2rW3VoktWYehMPp29pxt1dTawVmVrS6Q+E+h4kOQ8PhneOl5k0kknbUNVe6ujzlbVGauNks5w1aU5Sh0va6o9zF75dKZro6R6ktWDXNZUtV6brb268Ovcc8/1x3/orLTapM+9+juvL3I6rvBnjM7Gl1z5E2jWALTWV/W7pCMZdO68zp/vLCmPvvzXIhSZ8SQIZCWAAZiVFPkqRUC3UursMqUddtjBXXfddZ22X3n1js5Z03XyJAgUQUDbcnT7pM6NkmGtLWf1bgKrPaNKN1WuscYaDcOrPUNK7x1zzDFteXW2lJ4r6Yay2glsxwI14dX7SprsamUMCQIioC1jWjUig6LelvU0A5D+TD+yREBn6Gk8VJKhLXO74y3rWqmtYxgSE7DjSmyd+aRznJSuvPJKt+uuuzZsYu2ZVTqT9YorrmjLq3/LrFEaM2aMW2CBBRqWU3umq+rffvvtLWEllpIJ/Pjjj/5Ma63S1hfc9ZJWourSG305WJsYo0sWr8LVN2sAWuur+huivxcDBgxwI0aM6FTJ5Kx6vaN5AAkCWQlgAGYlRb5KEWAFYKXkDrKxWvavDwg6c0orT7StbODAgXXbYu0bziCBE3RuBBKDT1+caIVJx9UjaQYg/Tk3KSgoBwLJESAqSsck6IKkeqn2nD+tDrzjjjvasrECMAchKCI3AjrSRjsBZEDIXNDZlbvssos3lHVEzsiRI91JJ53UdnSDzg5OLg1REIzRuUlBQU0SaNYAtNZXWQHYpOBk7xIBDMAuYeOl2AlwBmDsCofdvn//+99+Rd0HH3zgP5xr8qjtv42StTNOwqZP9K0Q0HmQOitNqwDvuusu95vf/Gai4tIMQPpzKwrwbt4E5pxzTn+RjZJuYkxW8tWrp3fv3m7s2LFunnnmce+9915bFs4AzFsVymuFgI4V0fEiStdcc42/wKZj0oqjddZZxz366KN+N4AuBfn1r3/tszFGt0Kfd1sh0KwBaK2vcgZgK+rzblYCGIBZSZGvcgS4BbhykgfRYJl+WvmnbWC6NEEfzjubcKpR9957r9t44419+1q5Bbh2UsAtwEF0F3NB6lKZoUOH+pUkf/jDH+rGp/PIktVRxx57bNt5Z+rD+nac/mxO1koHtPzyy/sLN5TSjljQhU0610mrtvVFY5J02HtyDmYrtwBfeOGF7ne/+50vlluAK90tu9x4nfWnC74+//xzf+nHG2+80bAsXQaSHO+hS0L0+YLPHF1Gz4s5EGjWALT2eUKX+D377LPcApxDX6CIxgQwAOkdEGhAIDlbQRNOXcWuc37qJd36q0O9lXTumc5MIUGgOwh8+umnfpuvtk0qabKnQ9/TUu2ZUTJg9I1no5QYNHqu93TDZZJqz4zSNjdtd2uUdOvvm2++6c/SfPfdd9NC5HlFCOy8885+lVRXkla+zjfffL5f6rxVJfpzV0jyTp4EtDVSX8QoPfjgg27ttdduWHxiFupzhW5TTZJWxE4zzTT+LEHdxqsVgY2Sbv096qij/GOdyTpo0KC2rLVnuCqfbglulFSP4tVnG235nGKKKfLEQlmBEtBqVq1qVUq7BK92t4wuF0vOuGSMDlT8CMJu1gC01lf1hb7OZFXSzb61t2vXyqNnyW3yeqern6sikJwmdIEABmAXoPFKNQjoA7Y+QCvpG3vdtlQv6ba/I4880j/SOWzaEkGCQN4EvvrqK395h25MVVK/0w18WZK+0dfWM60e1K2QunyhUVpsscWctmnOPffc/mIbrTJMkgw9GXtKuqVSt0jWS7UTiG222cbdeOONWcIkTwUI5GEA0p8r0FECauLVV1/ddmlH2g2+Wln12Wef1V1ZpS8S9YWibg7WOcSNDDkZLfqsoVWEyjf99NO30dJNkKpDhmKtIdMRp55rl8PXX3/ttCrxH//4R0DECbU7CeiLRvUNpc0228xpRXajpP6m/qqkS27uuece/9+M0d2pEGV3RqBZA9BaX9UOCX2xqXTzzTd7E75e0jN9vla67LLL/G3cJAhkJYABmJUU+SpHQFerJ6Zfo1UmuqlpiSWW8IbKTDPN5G9K69GjR+VY0eDuJfDdd995Y1nbbZSOPvpod8oppzRV6b777ttm2GmS2fGWShUmo1uTQSXlv+iiiyaqo2/fvr6/9+zZ0xuEWrXSMdWa4rfeeqvbYostmoqVzNUmkHYGYNI/EwOa/lzt/lJ262XoacWUbk3V6j+tqquXdGv76quv7h91vL1XPzvzzDPbvtRptML6P//5j18Fq5WCuqThvvvum6gq/VwrsbSyT6tm9eVPx1Q7eVS9uo2bBAER0OfamWee2ZvDWmGkFfyNdsDUbp/U1vPzzz+/DSKfOehPZRBo1gC09nlCX6DrC3j9Hna2Gjz5Ikjnb+pc2UYrBcvQgDrtE8AAtK8REZZIINkGrA8/ug0tMUeSkGpvjzr++OOdJq4kCORJQCs1dPZZMqlMOx+qUd1avbf44os7HdytM0bUn6eeeuq27OPHj3fq788884z/sK9txgsttNBExdVuA9b2Y21Drk1jxoxxyyyzjJ88aJumVhM2mjzkyYmy4iGQxQCkP8ejdwwtqTU76pl3Wiml8fWFF17wzdUXjP3792/XdJ25prMxtdr7V7/6lT8HapZZZmnLI9NPlz0lq6w6bv9NMtZuA9YlO3feeae/LCpJWuG17LLL+ktI9MWltsDJ8CFBICGw7bbbtt1mrfFYn287pi+++MKf/5ccSdJxBwxjNP2pDAJdMQCt9dXabcC65G/zzTdvh7L21nhd0JMcQVEGb+oMkwAGYJi6EXVBBHTRwSqrrOJkjkw33XT+3B2dt6N/6xt0LdVW0kHJMk5qt+IUFCLVRE5AW3A0gVPSFuA//elP7bbldmy+to2pP9ZL2qqu1XlK/fr186tNZNLJtDvjjDP8LX5KynfqqafWLUOTUJ1DmKxGVHx77LGHn0BqUnvyySf7lbD6VlKrA9Zff/3IFaJ5eRPIYgAm/ZT+nDd9yusKAW3F1RcrMtX0hYeOSNh000399siXXnrJj6/6MkRpn332cRdffHHdarSVS+8qaWzWam9dDqLjGzT268ZVpbSjFfRcn1GU9JlFFzRoNZdi0eU7GvOVdB5sst2sK+3mnTgJqK/KJNbuAyV9CSmjQQa1zv3TbgH1x+Qm6zXXXNM99NBDE8HgM0ec/cNSq5544gk3evTotpD0BUeyolnzt913371duDqGpF6y1Fe1u0a/f/q7or8nQ4YM8VvslfS5Wjd068t8bdXXsUD1Vnlb0ohY7BHAALSnCREZI6Bv27fffnu/oqlektmibTh9+vQxFjnhxECg9gy+LO3RyhF9A1ovaUuBzDqt4muUtDVNxrYMvEZJH7C0zSy5+bJjPpmQWhmoukgQaJZAVgOQ/twsWfJ3JwEdjaAVd7WT0Y717brrrt506+yoEK220hcpOpuqXtLYq1uyp5pqqobN0ZeUWjUyfPjwunk0vuuGbXYtdGePCLtsGXoykvX3vrOkLyZ1TmC9VaSM0WH3gRCib/Zc4UbjqrW+OnLkSDd48GCnLcH1krb8Dhs2rOH59CFoR4zlEcAALI89NQdEQGegnHfeed7o0xk8Mjhk+Olss/3337/uOWgBNY9QDRPI0wBMmqlJoUw+GXj6cK9D47UdTStBsq7Y07ePl19+ub/gQxNf3SKpFSZaCaBtytpuTIJAVwhkNQDpz12hyzvdSUDjoM6mlCHy1ltv+Zt+Z599dr+TQONr7Y29ncWhSzl0Buvjjz/uPv74Y79Vd6mllnK6cTg5+D1LOzQ+a3vYiy++6L788kvXq1cvN2DAAP+5peORJlnKI0+1COh8yyuvvNKfKfnKK6/4PqQVSTIf9JlBW4Vleqd9TuEzR7X6TZGtzcsAtPh5Qp/PNfeU0Zd8sT///PO7TTbZxK/qrj0iokjm1BU+AQzA8DWkBRCAAAQgAAEIQAACEIAABCAAAQhAAAIQaEgAA5DOAQEIQAACEIAABCAAAQhAAAIQgAAEIACBiAlgAEYsLk2DAAQgAAEIQAACEIAABCAAAQhAAAIQgAAGIH0AAhCAAAQgAAEIQAACEIAABCAAAQhAAAIRE8AAjFhcmgYBCEAAAhCAAAQgAAEIQAACEIAABCAAAQxA+gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCImgAEYsbg0DQIQgAAEIAABCEAAAhCAAAQgAAEIQAACGID0AQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIREwAAzBicWkaBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQwAOkDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCImAAGYMTi0jQIQAACEIAABCAAAQhAAAIQgAAEIAABCGAA0gcgAAEIQAACEIAABCAAAQhAAAIQgAAEIBAxAQzAiMWlaRCAAAQgAAEIQAACEIAABCAAAQhAAAIQwACkD0AAAhCAAAQgAAEIQAACEIAABCAAAQhAIGICGIARi0vTIAABCEAAAhCAAAQgAAEIQAACEIAABCCAAUgfgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAxAQwACMWl6ZBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAADkD4AAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGICWAARiwuTYMABCAAAQhAAAIQgEBMBMaOHetuu+02N3z4cPf666+7jz76yPXs2dOtssoq7rDDDnMrrLBCTM2lLRCAAAQgAIHcCGAA5oaSgiAAAQhAAAIQgNHFjV4AABuqSURBVAAEIACB7iRwxBFHuDPOOMMtuOCCbuDAgW722Wd3b731lhs2bJj75Zdf3E033eS23HLL7gyBsiEAAQhAAAJBEsAADFI2goYABCAAAQhAAAIQgED1CNx5551uttlmcwMGDGjX+Mcff9ytueaabvrpp3cffPCBm3LKKasHhxZDAAIQgAAEOiGAAUj3gAAEIAABCFSQwDXXXON22WUX3/J///vfbr755jNPIcSYuwK1s3ZWhUFXuLX6zo8//uiWWGIJ9+abb7qbb77ZbbXVVu2KhH2rhLv//XXXXdc9+OCD7umnn3bLLbdcuwr33Xdfd8kll7gdd9zRXXvttd0fDDVAAAIQgAAEjBHAADQmCOFAAAIQgEA1CHz77bfuhhtucHfddZd78cUX3aeffuomn3xyv52tV69ebqmllnKrr7663+I255xz5g4lRDMjxJi7IhwGYFeotf7Oueee64YMGeIWW2wx9/LLL7tJJ53UpAF4zz33uN/85jc+No0h2267bcPGP/DAA2699dZre/799993ujJuiy22cLfffrtv+9tvv+1+9atftStb5+/17t3b/2zUqFGuf//+rYPPsYSNNtrI3Xfffe755593Sy+9dLuS33//fdenTx8no3fkyJHmYs8RA0VBAAIQgAAE6hLAAKRjQAACEIAABAomoImzVhe98847qTXLDNQh93mnEM20EGPuim4YgF2h1to733zzjZt//vm9Ea8z5LbeeuuJCrTS/7TVdbXVVvPxXXzxxW6fffZp2PjEEEsyfPLJJ377bL0kc08rgSdMmOAGDx7s/vKXv0yU7bLLLnN77723m2OOOfw220kmmaQ18Dm+/d5777mFF17YzTzzzO4///mPm2yyySYqfc8993SXX365W2eddZzMURIEIAABCECgSgQwAKukNm2FAAQgAIHSCYwePdotu+yy7uuvv/axaCXP5ptv7ieuU0wxhTcgtCLwb3/7m3v00Uf97ZYYgKXLVmgAVoymQhtdcmVnnXWWv0F2nnnm8cZ8x9V/JYfXrvp//etffoWw0umnn+4OP/zwuuGNGTPGjys///xz23ONP7o8o1469thj3SmnnOIfPfLII27QoEETZUsMxd13390baVaSVvWttdZabsSIEe66665zO+ywQ93Q3njjDbfooov6ZxZXMFrhSRwQgAAEIBAnAQzAOHWlVRCAAAQgYJSAVv7deuutPrqrrrqq7Ry+euGOGzfO591vv/1ybw0mU+5IcysQbXJDmamgn376yS2wwAJOK8hkAuqGWcvp3XffbTuz86ijjnJ/+MMf6ob7+9//3v3pT39yM800k/vyyy99nmeffdYts8wyE+X/4Ycf3Lzzzus+/vhjfw7iSy+9NFGe7777zs0666xu/Pjx/uiCZBty2axkcO60007uz3/+s9tjjz3c0KFDOw1JX8A899xzbrvttvPvkCAAAQhAAAJVIYABWBWlaScEIAABCJROQEbDDDPM4DSR1gH1Oqi+rITJVBb59HrRJp1Rnjnuv/9+t/766/sitbpuySWXzLP43MuSmadtrkr777+/u+CCCyaqQ2eMzj333O6rr75yxxxzjDv11FP9SkCtKtbZoh2TzhLcfvvt/Y+1zVdbZTumu+++222yySZuqqmmcp999pmbZpppcm9bswX+8ssvTqsR9WWK4tflHmmrN5OzHtUOra6eccYZm62W/BCAAAQgAIEgCWAABikbQUMAAhCAQIgENNlMLvTQGWM6a6yVpFU7V1xxhbvtttv8pQWa7GvLsFb46GIA/a/RZDiLyaQyhw0b5nTm2CuvvOK0IrFHjx6+DSuvvLI/e2zFFVds2IQTTjjBnXjiif65JuqK7/zzz3d33nmnv3lY/7766qvdzjvvnAlDWszN1NdM3iS4Vnkk5XzxxRd+lZnYajXX9NNP737961+7vfbay+kShlbPAGw1zo5sdHGETCb117feess3Qxdl6DZVnQeny2vS0pNPPum1Vl/68MMPvRmlyyS0HXPTTTf1Z87JHK+XtFVT203//ve/+3Pn1Je0VXfNNdd0Bx10kFtooYXSqu/0+a677upjUzm6AbhR6kyX7mDWKA6xE3NxaHSjrW671a23+n1VHxNnHTvQaOXeSiut5J566qm28/PqmXvJ+XkbbLCBv2ijY9L24kSLG2+80W2zzTZ+BbPMOV3KIVNSz3/3u9/5lc/J+YH/+9///Eo8mXevvvqqz6ffB61ulOHYGQeZf9JOdV1//fV1z/3r+H7tCkrVKYYkCEAAAhCAQBUIYABWQWXaCAEIQAACJgh8/vnnbpZZZvGx6AyvF154octxaRKrVUuvvfZawzJWXXVVP+GXKdgxpZlpjz32WN0zwDqWc8QRR7jTTjutbgy1poiMFR283/Hik+4yANPqaza2PHgIkgwOnVUmE6xekhk1YMCAtq3hMkp1MUOSitZNpvW6667rz6WslzbeeGNvZDYymrVddLfddks1u48//ngnTWqTLqM44IADnMysRkkG10UXXeS3fnY16fIP9UudG6fz4xqlrAZgq8yytEOr1mToNbqsY/HFF/d9LdnmKsNUF2PUOx9P24K1Ilnp0EMPdWeeeeZEIchslGErA1Z6yPjtmHR7sAxsJZ3Fp63JjS7a0BcDxx13nDdcdSxCo7FQBmJSZm19MkET80/vawVjvUs/GrGca665/O+gjEgZlCQIQAACEIBAFQhgAFZBZdoIAQhAAAJmCMjMkXmnpAP8NeFO27LWMXjdWCoD8e233/aPZALIONKkVobRhRde6FdLKWllj1ZddZwcpxlJDz30kC93ww03dGussYZfQaQVWrpFVKsBtZIvaUejswxrTTat6JFZKeNAZ4dpC6NWk/3qV7/yMWZJaTE3U18zeRVbHjy04lHnq8mIUZJxobPLZp99dm+EaGviM8884/r379+2PbxZAzCPOGvZaKWnYpJuMvtkJusihZNPPrnNfL700kv96sWOSSbNeuut5y+0UdLqL61Kk9mkFWYyYP7xj3/4VWIyqjoagGKTGHIyu5VHl1po5ZgMI51vp76opO2piq/ZJC1kjimpT2t1WqOU1QBshVnW+HVe3/vvv+9/Nx9++OF2r6kPrL322v5nI0eOdMsvv7xLDEGNDR3PFNUK3GTrrMYU/U52TEm/1M9Vr8zAjqn2EhHVqe3USb+R3g8++KA3BbVyecopp/Tn8GkVp/6tlZxqi45JuOWWW/ztxkqLLLKIe/3119tVpX4lU1l6yBzUasMsq1BrC9HKQvWZPn36tK1qzcqefBCAAAQgAIFQCWAAhqoccUMAAhCAQJAEzjnnHHfIIYe0xa7JtowLmWArrLBCwxs6axsr0/Dss8/2P9L5XjJjapNW62g1k1bFKGkyre26tSnNTNNtxJpU6wKBekmTdt0IKnNHbdCNox1NxlojSSanzlpLjImuiJcWczP1NZNXsebBY8iQId7kU9KZbEceeWQ7DLrJVExllCSpWQMwjzhr2WiFneLpeG6cVrP27dvXXxohc7feCsHzzjvPGztKv/3tb/0qQBk/HZMMHa2ak4GdpDvuuMPfjq2k7b9a7dUxaWuyDGrdWCtjXYZys0aQzEcZsUoyyrVqtlHKagC2wizr74XOKdRW73pniSbmlsYUGaxK2qovM7Bjv9NZfjLzxLLRakK9rxWaJ510klt66aX9dt56SePYvffe6x/JKJYxqfy16eijj/YxKGlsmWOOOby5rvMKa5O2hf/lL3/xP9LRA7p8JElJ/5xuuuncgQceWFdztaVj3bXlqy1qk5L6sEx4EgQgAAEIQCB2AhiAsStM+yAAAQhAwBQBmR3artho21mvXr282aLVTjKDknOykkbovCxNmnURgAwYrbKpt/VN2wN1s6km+MqXrJRKykkz07JAk+mTTLK1Qki3a9amWiNJKxSvvPLKLMU2zJMWczP1NZM3a9Cd8ZBuOjtR5//JMJOJUm/lp1akSTeZgUrNGoBZYm1Gt4MPPtjJtK6XZGBqFauS+mPtZQrq51qlNnbsWG/uaBWXDJusScaWtqbKONSZkY2SVpWqfyvJjNb26mZSciGE3tHKRq0wbJSyGoBdZdZM3Nom/sQTT/h4FXeS1F+0qk38b7755jZzU9vvxefwww9v00zvSL/EiJaROmjQoLphJDfnapWfzLN6KVmVqGfSTNp1TKpDq/6UdAmHznesd+mKvrRIVirqbMEFF1ywrahkxWJnvNKOFtCq1eRLEa1E7NevXzP4yQsBCEAAAhAIkgAGYJCyETQEIAABCIROQKvhZD5olYwm6/WSTBBN4msnv1rRs8oqq/jsOqtLqwEbJW23TM5P09ldyQUkyp9mpnUsUwaWVspo+3ESr85oSybOMvdk8tWmWpNN7dQWv1ZSWszN1NdM3noxN8ujVjcZajKJGiUZv8klC60agM3GqZhq2ciE06Uy9VLtmW8yNGtXXMlUSQxhlZestsqiv0zDZIupVg3qwpzO0myzzeZXaMqYkkHVTKo1MbW9XWU1SlkNwK4yaybupI/oCwOtnkySVherf8l01bmGyYpIrabUqkqZXsn2Wm231diirfzamv7SSy/VDaFWDxl22qLeMdWeb6ozIzW+1Us6miBZTaoVsclK5o55tVU52Y7dcQVgM5wa5a1d+alzCmWQkiAAAQhAAAKxE8AAjF1h2gcBCEAAAqYJaEWYbkjVCjoZB9qGqLPikiTTTj9PzLuhQ4e2nbemyfRqq63WsH061yu5YVfbOGu336aZaSpUt3HqXDSZkFpBKMOgUdKttocddli7x7VGUh6T+LSYm6mvmbxJo1rhcdlll7VdnKALErSCq1Gq3Z7YFQOwlTgVUy0blVXvRljl+3/tnUuoTt8bx/eEksTITLnLpSQJRSYyUBgoIZdiwOCEzDDFKXeZKrmkTsrEwIA6EwOXiYEMiAEjKYVcMvr3WbXedvu/9/u++5zt2L93fZ76dTrH3muv9Vn73b/2932e55vP5iquCUE4lu12yywrY4BxDSWcdQPBG0OQOkHvQj5TBFmX3UqI+xUAx8qszrzJEKb33ZQpU7KfP3+GU/mJcMozpVjqi9kF84+mIByPeUvM0uP+xOW3LOK9S+YxXyQUs5I5Z3R0tCPwV/UE5Thcy6NhS5WYyHGUjlNCTt/R/POwDqNux5INGUW/6Fjc1NiOIwEJSEACEmgrAQXAtu6M85KABCQggSQJkLHFCynZMbzIEzS858WZyJfsUVZJk/yqILMFEwYiXw7I773ENLKHyNhDgOonyrK88kIS2YJ1XDrLrtlrznWuV+dY5jJeHnX2LS8W1hUAxztP1ppnQz/Jqsg7IyMA5fsEIgjjEE1QpouJTL9Bz78qMarbGIjdlH7WCXrIIXITlM1Pmzat8vR+BcCxMqsz73x2bxQu45cDlNZSSh4dxxk3rpPMwQcPHoRLUYqLOIshD8dXCb0x2zD/HCrO9fLly52sVjISyUwsC7L6yO4j05KMy6qgFJn7i56MfCnSdGAAQq9Egl6DYxGcm56T40lAAhKQgAT+NgEFwL9N2PElIAEJSEACYyCQF+94QafEkZ5xeSGpV88yyvBwTyXqCoCxxxjZPmQPUYa5ePHi8OIejRwoBY6iHiWeRRfXfoWkfvHUEQC7iTBcr+7cxstjeHg4O3nyZFhqr33L9yerKwCOd5512PQrAPYSqov7n18/Rjb0TOwn+JwUzSR6nXf69OlO2TDiaZkDbhyjTQIg9xL3FEGfT0w34EQZL5mXiKj5iA69ZAyTOZzvnUgbAdoJlMWvX7+CkMhPMgajaFY8Nvblw8iFkuGqQNAj45ls5LzZTfF41sMXIENDQ9m1a9d6bWPtf8/vJb0UY1uF2gN5ggQkIAEJSOA/REAB8D+0WU5VAhKQgATSIpBvqh/7k01ECTCCDWIfgdBw5syZUvAID9Gdc5AFwCZ4TEQJcBPzZKP7FUe7CYDjKQG+d+9etmPHjnDP/e3yzPy+FPsYFm/6NgmA+QxLRGL+iz02MQYqGmucP38+lOhHF18MNugFyJcK79+/rxQ+yRbcunVrMOzgS4ipU6eWPgsYF3MZXJmjE3DxQER5SnrpI9pNdPzw4UNnPmQ+k3nYdOQzFnsJ8k1f2/EkIAEJSEAC/4qAAuC/Iu91JSABCUhAAj0IrFmzJnv27Fk4ipdvMnHqmIDEl3zOr2MCglkApgFEmZgQp53PUhxkAbAJHnVMQLZs2dIRUepkADYxz6YEQMS0aB5S1wQE19cFCxaE22wsZb11Hiz5fRkZGekIj2VjtEkAzGdJIrzBmFJWREAMd4oRj58zZ04Q6siU/P79eyh95byqoBSbbMLNmzd3jGmKx1KCjMPznz9/slOnTmVkVZbF27dvOy7L3YTdfHlumbt4nf2tOhYzFJggaOJg3a33YxPXcwwJSEACEpBAGwgoALZhF5yDBCQgAQlIoECAhv403eclnawZXlIpx6VHIH/n96VLl4aX+bLeepw3d+7cIBwuWbIkmHjko5uYgRASnVefPn2arV69unR/MBCgLJAYZAGwCR7sG0YulDWSLYVLbpmZAuWT7BtiClFHAGxinlyziQxAysNnz56dffz4MRhTUHKKSNRvcG+/fv06ZJ6RoUU27N8IOM+YMSOUuOKgS6ZcVbRJAMQdeffu3WGqd+7cyfbv3x9MejBQIWOvGAhuGIDwJQKf1SNHjoRDuhm0kLHH3vHlAW7ihw8fLkXDMyg6QOMMvX379tLj8s677G3MMi4eHE1wEOXIFowtB5rcfxzWMVeiD+Ljx4+bHNqxJCABCUhAAq0loADY2q1xYhKQgAQkMGgEeJnlhZN+XGTUUH5XFognOGXipkns2bMnu337dudQyucuXLgQfmcsXpjzwYs7mVO3bt0Kf6bUj4yXfHQTMxCnVq5cGQ7nPM4vBoIARgQxBlkAbIoHxi6XLl0KyMpckzFKQbx5+PBhh2sdAbCpeTYhALIAerdFoQmxmD6UkydP/r97ifsd4wj6x8XIC1zcizCh/2RZIK5ScnzgwIEgGNYNzEuiozY/q6JNAiA8eIYQlPvS+w/hmCy7sucKZblklk6aNCkch6i6bNmycF5VkH23atWq8M9RyC07lucMAiTx7t27MH5ZxL6FOBfzBUWVKRAC4v3793vOr+4+x+O5XzB7IXOxbnbqWK/peRKQgAQkIIE2EFAAbMMuOAcJSEACEkiCAAJgdBmlBI/yu7Vr14Z+V/ydrD5KJxH+4ov59OnTs5cvX4Zsqhi8PJNxQ+8uAnEF8QMBBcEIl036sxGMj4tm8WW7m5iBgIihwKtXr8IYu3btCtlDZLAhBJBxRKYPjfNp6E8MsgDYFI+vX78GUQPH1ch137592cyZM7M3b94EcfDFixdBdOEnUUcAbGqeTQmACHu4UD969CisZeHChUE0JvsKx1lEPzJMo9hXNJGJxhKcS6/JQ4cOZRs2bAhC4I8fP4LYxL2NWPTly5cgKtXJMoyfp9gfD/GQXptVTsBtEgDzpctxHfS1O3bsWOmzFGEz79LMQfQ/7Oa2HO+D2Dew6iF9/PjxjGvzrCLDtSyzlXMxJMKYiIxi9r0q5s2bF55txS8+mvqfBPfjpk2bwnB8zrgfDQlIQAISkEAKBBQAU9hl1ygBCUhAAq0g8Pv374weXAgf/QR90BBHYjZe/hwcS3mhxvihKhDo6KeFo2YxejnqIjrST4wX+rIg64gegDFra5AFQNbfBA/GoRR748aNlfcAjss4tfKTqCMANjXPpgRA5kMpO9lhCMbdouz+oaSVrLGLFy+G8tZuQS+3z58/Z2SX1Q3KrhHhucbNmzczRNmyaJMAyH2EmBwD4ZN10C6gLPI9Gfl3HJMRohFiq4LnDlmlZVnG+XPIaqaUGHE2fvFQNiZfIPDsQ8il/15ZfPv2LZRkI2aT5UzWbNPBZ4u9XLRoUdfnZ9PXdTwJSEACEpDAvyagAPivd8DrS0ACEpBAUgTIiiL7hb5T/KQU79OnTxniICIGgtry5cuzbdu2hV5aZSWTERj9y2jQj2sq2Xq8PCP2rVixImTs0SOsqsy4lwDINXDjHB4eDuWX9AEjM2r+/PnBKAGDETKmYrbPoAuATfCI+0a2GiXAmC/AGK4IqpR9k23ZbW/atG/dXICLH+rR0dHsxo0b2ZMnT4IIRH83smDpT4nhDKXPVQ6zZEfifo3IhPDNfY5wRV9AstPI5iILtipzr58HTCw7ZSyE7bJokwCIeDdr1qzONIeGhkLJdVWQLclnN0Y3F16OQUxkfIS458+fd0qBy8YnOxNH8KNHj2ZXrlwpnQLPOHqXEt36CXJ/rF+/PhzHMxJxscngOcs8yMa9evVqp0S9yWs4lgQkIAEJSKCtBBQA27ozzksCEpCABCQgAQlIYEIIIMZTLk+pPC7E+ZL7CZlAyy5CeTCmH4hliP9VZb0tm3bP6dC+YO/eveGLEsTk8YjGPS/mARKQgAQkIIGWEVAAbNmGOB0JSEACEpCABCQggYkngKkG2a70xUMASzkwDME45ODBg9n169cHAgXZ15RN40h99uzZ7MSJEwOxLhchAQlIQAIS6JeAAmC/pDxOAhKQgAQkIAEJSGBgCWC8Q/k8ZfNkAVJinGqcO3cu9G+kFQFMBiFGRkaynTt3htJmWi+MpV/kIHBwDRKQgAQkkC4BBcB0996VS0ACEpCABCQgAQnkCFAiiviHUcu6detkM0AE7t69G9y2MTfCaMeQgAQkIAEJpEZAATC1HXe9EpCABCQgAQlIQAISkIAEJCABCUhAAkkRUABMartdrAQkIAEJSEACEpCABCQgAQlIQAISkEBqBBQAU9tx1ysBCUhAAhKQgAQkIAEJSEACEpCABCSQFAEFwKS228VKQAISkIAEJCABCUhAAhKQgAQkIAEJpEZAATC1HXe9EpCABCQgAQlIQAISkIAEJCABCUhAAkkRUABMartdrAQkIAEJSEACEpCABCQgAQlIQAISkEBqBBQAU9tx1ysBCUhAAhKQgAQkIAEJSEACEpCABCSQFAEFwKS228VKQAISkIAEJCABCUhAAhKQgAQkIAEJpEZAATC1HXe9EpCABCQgAQlIQAISkIAEJCABCUhAAkkRUABMartdrAQkIAEJSEACEpCABCQgAQlIQAISkEBqBBQAU9tx1ysBCUhAAhKQgAQkIAEJSEACEpCABCSQFAEFwKS228VKQAISkIAEJCABCUhAAhKQgAQkIAEJpEZAATC1HXe9EpCABCQgAQlIQAISkIAEJCABCUhAAkkRUABMartdrAQkIAEJSEACEpCABCQgAQlIQAISkEBqBBQAU9tx1ysBCUhAAhKQgAQkIAEJSEACEpCABCSQFAEFwKS228VKQAISkIAEJCABCUhAAhKQgAQkIAEJpEZAATC1HXe9EpCABCQgAQlIQAISkIAEJCABCUhAAkkR+B+J07EPg0PrcQAAAABJRU5ErkJggg==\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x189f0c080>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bins = np.linspace(0, maxval, nbins)\n",
"\n",
"plt.figure()\n",
"plt.hist( pred_df.SIS, bins = bins, density = True, histtype = 'bar', label = 'Prediction', facecolor = 'lightblue', edgecolor = 'royalblue')\n",
"plt.hist( yr_df, bins = bins, density = True, histtype = 'step', label = 'Satellite data', LineWidth = 2 )\n",
"plt.xlabel('Solar irradiance (in $W/m^2$)')\n",
"plt.ylabel('Normalised frequency')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 12, hour: 17, x: 1393, y: 882)\n",
"Coordinates:\n",
" * hour (hour) float64 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 ...\n",
" * x (x) float64 4.855e+05 4.858e+05 4.86e+05 4.862e+05 4.865e+05 ...\n",
" * y (y) float64 7.55e+04 7.575e+04 7.6e+04 7.625e+04 7.65e+04 ...\n",
" * date (date) datetime64[ns] 2001-01-15 2001-02-15 2001-03-15 ...\n",
"Data variables:\n",
" SIS (date, x, hour, y) float64 nan nan nan nan nan nan nan nan nan ..."
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
diff --git a/Visualisation/Visualise_sensitivity.ipynb b/Visualisation/Visualise_sensitivity.ipynb
index 2954aeb..1261a92 100644
--- a/Visualisation/Visualise_sensitivity.ipynb
+++ b/Visualisation/Visualise_sensitivity.ipynb
@@ -1,3775 +1,3802 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"import numpy as np\n",
"import xarray as xr\n",
"import pandas as pd\n",
"import os\n",
"import util\n",
"\n",
"%matplotlib notebook\n",
- "import matplotlib.pyplot as plt"
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.colors import LinearSegmentedColormap\n",
+ "from matplotlib import rc\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "colorsRGB = [(47,111,191),\n",
+ " (51,148,190),\n",
+ " (65,195,153),\n",
+ " (105,219,77),\n",
+ " (183,233,81),\n",
+ " (249,239,88),\n",
+ " (250,201,53),\n",
+ " (246,135,26),\n",
+ " (220,82,7),\n",
+ " (193,31,4)]\n",
+ "colors = []\n",
+ "for row in colorsRGB:\n",
+ " colors.append(tuple([i/255.0 for i in row]))\n",
+ " \n",
+ "cm_PV = LinearSegmentedColormap.from_list('radiation', colors, N=60)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sensitivity vars"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"col_names = {0 : '$G_h$',\n",
" 3 : '$(1-S_{sh})$',\n",
" 2 : '$SVF$',\n",
" 5 : r'$\\rho$',\n",
" 4 : '$C_{sh}, C_{pv}$',\n",
" 1 : '$T_{amb}$'}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"FILEPATH = '/scratch/walch/workspace_tilted_irrad/'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"fp = '/work/hyenergy/output/solar_potential/technical_potential/sensitivity/sensitivity_lin_no_thresh_withEff.csv'\n",
"sensitivity_linear = pd.read_csv( fp, index_col = 0 )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"sensitivity_linear = sensitivity_linear - 1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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 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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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb0734fe4e0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sensitivity_linear.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"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 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",
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" }\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='1d5c171d-3e96-4d02-a028-9d740c35e36c'></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"PV_pot = 24.58\n",
"\n",
"plt.rcParams['axes.axisbelow'] = True\n",
"\n",
"plt.figure()\n",
"ax = plt.gca()\n",
"\n",
- "plt.rcParams.update({'font.size': 12})\n",
+ "plt.rcParams.update({'font.size': 15}) # , 'family':'serif', 'font.serif' : 'Times New Roman'\n",
+ "rc('font',**{'family':'serif','serif':['Times New Roman']})\n",
+ "\n",
"\n",
"for color_idx, name in col_names.items():\n",
" sensitivity_linear.iloc[:, color_idx].plot(ax = ax, label = name, lw = 1.5) # , c = 'C%d' %color_idx\n",
" # ax.plot([-0., 0.], [-0., 0.], label = name, c = 'C%d' %color_idx)\n",
"\n",
"ax.legend(loc = 2)\n",
"ax.grid()\n",
"ax.set_ylabel('Change in PV potential ($\\Delta E_{PV} / E_{PV0}$)')\n",
"ax.set_ylim(-0.55, 0.55)\n",
"\n",
"ax2 = ax.twinx() # instantiate a second axes that shares the same x-axis\n",
"ax2.set_ylim((PV_pot * (1-0.55), PV_pot * (1+0.55)))\n",
"ax2.set_ylabel('Technical PV potential (in TWh)')\n",
"\n",
"plt.xlim(-0.55, 0.55)\n",
"ax.set_xlabel('Change in variable ($\\\\Delta X / X_{0}$)')\n",
"plt.tight_layout()\n",
- "plt.savefig('sensitivity_vars_Tamb.pdf', dpi = 300)\n",
+ "# plt.savefig('sensitivity_vars_Tamb.pdf', dpi = 300)\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Uncertaintyy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fp = '/Users/alinawalch/Documents/EPFL/data/sensitivity_uncertainty_cstThresh_10000.csv'\n",
"eval_unc = pd.read_csv( fp, index_col = 0 )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>G</th>\n",
" <th>Ssh</th>\n",
" <th>SVF</th>\n",
" <th>Csh</th>\n",
" <th>CPV</th>\n",
" <th>Gt</th>\n",
" <th>APV</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sigma_p_nosel</th>\n",
" <td>1.171855</td>\n",
" <td>1.036589</td>\n",
" <td>1.063071</td>\n",
" <td>1.083450</td>\n",
" <td>1.110249</td>\n",
" <td>1.237720</td>\n",
" <td>1.263486</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sigma_m_nosel</th>\n",
" <td>0.826949</td>\n",
" <td>0.887039</td>\n",
" <td>0.936820</td>\n",
" <td>0.916550</td>\n",
" <td>0.889751</td>\n",
" <td>0.764407</td>\n",
" <td>0.737880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sigma_p_sel</th>\n",
" <td>1.179539</td>\n",
" <td>1.034077</td>\n",
" <td>1.058291</td>\n",
" <td>1.067687</td>\n",
" <td>1.101791</td>\n",
" <td>1.246819</td>\n",
" <td>1.231142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sigma_m_sel</th>\n",
" <td>0.816013</td>\n",
" <td>0.881200</td>\n",
" <td>0.941599</td>\n",
" <td>0.932313</td>\n",
" <td>0.898209</td>\n",
" <td>0.754698</td>\n",
" <td>0.768906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>perc_change</th>\n",
" <td>0.172636</td>\n",
" <td>0.126869</td>\n",
" <td>0.174837</td>\n",
" <td>0.088883</td>\n",
" <td>0.113370</td>\n",
" <td>0.240324</td>\n",
" <td>0.273387</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" G Ssh SVF Csh CPV Gt \\\n",
"sigma_p_nosel 1.171855 1.036589 1.063071 1.083450 1.110249 1.237720 \n",
"sigma_m_nosel 0.826949 0.887039 0.936820 0.916550 0.889751 0.764407 \n",
"sigma_p_sel 1.179539 1.034077 1.058291 1.067687 1.101791 1.246819 \n",
"sigma_m_sel 0.816013 0.881200 0.941599 0.932313 0.898209 0.754698 \n",
"perc_change 0.172636 0.126869 0.174837 0.088883 0.113370 0.240324 \n",
"\n",
" APV \n",
"sigma_p_nosel 1.263486 \n",
"sigma_m_nosel 0.737880 \n",
"sigma_p_sel 1.231142 \n",
"sigma_m_sel 0.768906 \n",
"perc_change 0.273387 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eval_unc"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"eval_unc[:] = eval_unc.values.astype(float)\n",
"eval_unc.iloc[0:4,:] = eval_unc.iloc[0:4,:]-1\n",
"eval_unc = eval_unc.round(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get data for bounds"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"GT_THRESH = 1000\n",
"ASP_THRESH = 90\n",
"A_THRESH = 10"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def sel_roofs(ds, Gt_thresh = GT_THRESH, asp_thresh = ASP_THRESH, A_thresh = A_THRESH):\n",
" yearly_Gt = util.get_yearly_sum(ds, 'Gt' ) / 1000\n",
" yearly_E_PV = util.get_yearly_sum(ds, 'E_PV') / 1000\n",
" \n",
" return yearly_E_PV.where(((yearly_Gt >= Gt_thresh) \n",
" & (abs(data.roof_aspect) <= asp_thresh) \n",
" & (ds.A_PV >= A_thresh)), drop = True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda2/envs/py3_new/lib/python3.7/site-packages/xarray/backends/api.py:783: FutureWarning: In xarray version 0.13 `auto_combine` will be deprecated.\n",
" coords=coords)\n",
"/Applications/anaconda2/envs/py3_new/lib/python3.7/site-packages/xarray/backends/api.py:783: FutureWarning: The datasets supplied have global dimension coordinates. You may want\n",
"to use the new `combine_by_coords` function (or the\n",
"`combine='by_coords'` option to `open_mfdataset` to order the datasets\n",
"before concatenation. Alternatively, to continue concatenating based\n",
"on the order the datasets are supplied in in future, please use the\n",
"new `combine_nested` function (or the `combine='nested'` option to\n",
"open_mfdataset).\n",
" coords=coords)\n"
]
}
],
"source": [
"path = '/Volumes/Seagate Backup Plus Drive/DOKUMENTE/EPFL/data/Results/PV_w_uncertainty'\n",
"unc_PV = xr.open_mfdataset( os.path.join(path, 'unc_pv_potential/*'), \n",
" chunks = {'DF_UID' : 10000})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 143)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 5 ... 9890020 9890021 9890022 9890023\n",
"Data variables:\n",
" sigma_PV (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(10000, 143)>\n",
" var_PV (DF_UID, timestamp) float64 dask.array<shape=(9639231, 143), chunksize=(10000, 143)>\n",
" yearly_std_PV (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(10000,)>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unc_PV"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"fp = '/Users/alinawalch/Documents/EPFL/data/random_sample_data_10000.nc'\n",
"data = xr.open_dataset( fp )"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"ROOFS_FP = 'CH_ROOFS_all_replaced.csv'\n",
"roofs = pd.read_csv( os.path.join( path, ROOFS_FP ), usecols = ['DF_UID', 'FLAECHE'])\n",
"\n",
"data['At'] = roofs.set_index('DF_UID').FLAECHE.to_xarray()\n",
"\n",
"data['GRt'] = data.poa_ground_diffuse\n",
"data['GDt'] = data.poa_sky_diffuse * data.SVF * (1 - data.SVF_flat * data.is_flat)\n",
"data['GBt'] = data.poa_direct * data.S_sh * (1 - data.S_sh_flat * data.is_flat)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"suitable_roofs = sel_roofs(data)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray (DF_UID: 2343)>\n",
"array([ 9469.109199, 2717.181821, 3374.035695, ..., 6533.786002,\n",
" 11435.768407, 10641.497259])\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 2872 4995 11459 14827 ... 9858071 9870410 9888630"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"suitable_roofs"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"sigma_PV0 = unc_PV.yearly_std_PV.sel(DF_UID = suitable_roofs.DF_UID).compute()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Maximum and minimum bounds:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"max_sel = (suitable_roofs + sigma_PV0 ).sum() / suitable_roofs.sum()\n",
"min_sel = np.maximum(suitable_roofs - sigma_PV0 , 0).sum() / suitable_roofs.sum()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"col_labels = eval_unc.rename(columns = {'APV' : '$A_{PV}$', \n",
" 'Gt' : '$G_t$', \n",
" 'CPV' : '$C_{pv}$', \n",
" 'Csh' : '$C_{sh}$',\n",
" 'SVF' : '$SVF$',\n",
" 'Ssh' : '$S_{sh}$',\n",
" 'G' : '$G_h$'}).columns"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"mpl.get_websocket_type = function() {\n",
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
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" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" 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": [
"PV_pot = 24.58\n",
"\n",
"plt.rcParams['axes.axisbelow'] = True\n",
"plt.rcParams.update({'font.size': 12})\n",
"\n",
"bar_names = col_labels\n",
"plt_df = eval_unc.T.reset_index().drop(columns = 'index') \n",
"plt_df['zeros'] = 0\n",
"\n",
"plt.figure()\n",
"ax = plt.gca()\n",
"\n",
"ax.bar(plt_df.index, plt_df.sigma_p_nosel - plt_df.sigma_m_nosel, bottom = plt_df.sigma_m_nosel, \n",
" alpha = 0.4, label = 'all roofs')\n",
"ax.errorbar(plt_df.index, plt_df.zeros, yerr=abs(plt_df[['sigma_m_sel', 'sigma_p_sel']].T.values), \n",
" fmt = 'o', label= 'suitable_roofs')\n",
"ax.plot([-1, 7], [(max_sel-1), (max_sel-1)], 'C1', ls = '--', label = '$\\sigma_{PV}$')\n",
"ax.plot([-1, 7], [(min_sel-1), (min_sel-1)], 'C1', ls = '--', label = '')\n",
"\n",
"\n",
"ax.legend(loc = 0)\n",
"ax.yaxis.grid(ls = '--')\n",
"ax.set_ylabel('Change in PV potential ($\\Delta E_{PV} / E_{PV0}$)')\n",
"ax.set_ylim(-0.4, 0.4)\n",
"\n",
"ax2 = ax.twinx() # instantiate a second axes that shares the same x-axis\n",
"ax2.set_ylim((PV_pot * (1-0.4), PV_pot * (1+0.4)))\n",
"ax2.set_ylabel('Technical PV potential (in TWh)')\n",
"\n",
"plt.xticks(range(len(bar_names)), bar_names)\n",
"# plt.ylim((-0.3, 0.3))\n",
"plt.xlim(-0.7, 6.7)\n",
"plt.tight_layout()\n",
"plt.savefig('sensitivity_unc.pdf')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Threshold"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"col_names = {0 : '$th_{Gt}$ ($G_{t0} = 1000 kWh/m^2$)',\n",
" 1 : '$th_{\\gamma}$ ($\\gamma_0 = 90$° E/W)',\n",
" 2 : '$th_{A}$ ($A_{PV0} = 20m^2$)',\n",
" 3 : '$E_{PV, max}$'}"
]
},
{
"cell_type": "code",
"execution_count": 75,
"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": [
"PV_pot = 24.58\n",
"\n",
"plt.rcParams['axes.axisbelow'] = True\n",
"\n",
"plt.figure()\n",
"ax = plt.gca()\n",
"\n",
"for color_idx, name in col_names.items():\n",
" ax.plot([-0., 0.], [0., 0.], label = name, c = 'C%d' %color_idx)\n",
"ax.scatter([0.0], [0.5], color = 'k', marker = 'x', label = 'Selected threshold')\n",
"\n",
"ax.legend(loc = 4)\n",
"ax.grid()\n",
"ax.set_ylabel('$E_{PV, suitable}/E_{PV, max}$')\n",
"ax.set_ylim(-0.05, 1.05)\n",
"\n",
"#ax2 = ax.twinx() # instantiate a second axes that shares the same x-axis\n",
"#ax2.set_ylim((PV_pot * (1-0.55), PV_pot * (1+0.55)))\n",
"#ax2.set_ylabel('Technical PV potential (in TWh)')\n",
"\n",
"plt.xlim(-1.1, 1.1)\n",
"ax.set_xlabel('Change in threshold value ($\\\\Delta X / X_{0}$)')\n",
"plt.tight_layout()\n",
"plt.savefig('sensitivity_thresh_frame2.png', dpi = 300)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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